Capital constraints hinder enterprises’ carbon reduction efforts and affect the sustainability of the supply chain. To alleviate this limitation, the core enterprise considers offering two financial-based carbon reduction incentive mechanisms: cost-sharing mechanism (CS) and preferential financing mechanism (PF). In a supply chain with the dual sensitivity of market demand to price and carbon reduction, we model each incentive mechanism, discussing their impact, value, and selection strategies. The results show that neither party under CS pursues an excessively high share ratio. Only a below-threshold sharing ratio can promote the supplier’s carbon reduction behavior and improve efficiency for both parties. Conversely, PF has a stable incentive effect on the supplier’s carbon reduction behavior and can effectively increase the retailer’s profits. However, a reasonable carbon reduction standard is needed to attract the supplier. In addition, as market demand becomes more sensitive to carbon reduction, the feasible range of CS narrows and that of PF expands. We compare players’ preferences of PF and CS and find a Pareto region in which all players prefer PF to CS. Finally, we test the robustness of our findings by an extending model. Our study provides guidance for supply chain decisions facing dual pressures of financial constraints and carbon reduction. As environmental policies and market greening drive, enterprise management and environmental performance become inseparable. The PwC survey found that two-thirds of executives believe that low carbon and environmental protection will play an increasingly important role in global supply chain management. However, in practice, the carbon reduction effect of enterprises is poor, especially for SMEs. In the Sustainability Disclosure Database, SMEs provide only 12 percent of sustainability reports. In China, only 25% of SMEs in the manufacturing sector in Jiangsu province have set up energy management groups to work on low carbon; 176,000 SMEs in Beijing, Tianjin, and Hebei and its surrounding areas were once investigated for pollution. In the era of supply chain competition, this is bound to affect the sustainability of upstream and downstream stakeholders and even the entire supply chain. A clear case exists in the Xiaomi supply chain. As its suppliers were repeatedly disclosed with pollution problems, it nearly caused Xiaomi’s IPO to be blocked. Therefore, in the face of the greening requirements of the supply chain, the carbon emission reduction level of SMEs in the supply chain has gradually become one of the key points of concern for core enterprises.Some studies have found that financial constraints are one of the major reasons that discourage SMEs from reducing emissions . For exaTo alleviate the dual pressures of capital constraints and carbon emission reduction in enterprises, large companies in a central position are considering breaking the status quo of small and medium-sized enterprises (SMEs) who are fighting alone in the process of carbon reduction. In recent years, cost-sharing mechanisms (CS) have gained widespread attention from the industry as a funding gap-filling solution to relieve the financial pressure on companies to reduce carbon emissions. Nestle plans to invest 1.2 billion Swiss francs by 2025 to work with suppliers to promote regenerative agriculture. Wal-Mart is also actively involved in green product production and process transformation through CS, including sharing the cost of organic cotton cultivation with suppliers to facilitate the transformation of land from conventional to organic farming  ∪ , PF is better for both the retailer and the supplier than CS.s. It is not difficult to find that the requirement of s in the Pareto region cannot be too low. For visual display, s higher than the threshold. With the increase of Proposition 7 shows that there is a Pareto region where players can obtain relatively higher profits under PF in the preferential margin of interest rate Regarding PF with exogenous carbon reduction standards, we have demonstrated its value in terms of carbon reduction incentives and profit improvement, as well as its advantages compared to CS. We have provided insights into the trade-off between the preferential margin of the interest rate and carbon reduction standards. However, the retailer may also set specific carbon reduction targets based on the operational situation of the supplier, rather than directly adopting existing green enterprise selection criteria. Therefore, in this section, we discuss carbon reduction standards as a decision variable for the retailer to validate the robustness of the model.Proposition 8. When Proposition 8 indicates that the retailer’s determination of the optimal carbon reduction standards is contingent upon the preferential margin of interest rate offered by financial institutions. When the preferential margin of interest rate is either low or high  and preferential financing mechanism (PF) into a capital-constrained supply chain to stimulate enterprises’ carbon emission reduction investment. Taking no carbon emission reduction incentive as a benchmark model, we investigate the incentive effect on carbon emission reduction level and economic value of CS and PF, showing when and what incentive schemes could be adopted. To our knowledge, this is the first analysis of PF with a carbon emission reduction standard from a theoretical perspective, and a discussion of the setting of the carbon emission reduction standard line and compare PF with CS. Our analysis yields several findings that may help participants understand the role of different carbon emission reduction incentive mechanisms in low-carbon transformation, and also provides theoretical support for the application of PF in low-carbon supply chain practices.We mainly get the following insights: (1) CS has a certain incentive effect on carbon reduction, but the supplier’s carbon emission reduction level does not always increase with the sharing ratio. Also from an economic perspective, both parties will only reach a consensus based on CS if the sharing ratio is controlled within a specific range. This is because if the cost-sharing ratio is too high, the retailer as the core enterprise will transfer the cost to the supplier again through the pricing first-mover advantage, which again inhibits the supplier’s willingness to reduce carbon emissions and negatively affects the profits of both parties, and the CS fails. In addition, examining the applicability of CS finds that CS is more likely to be economically valuable in supply chains with high financing costs or in consumer markets that are relatively insensitive to carbon reduction information. (2) Compared to CS, PF has a more stable carbon reduction incentive effect. It can always increase the reduction level of the supplier. From the economic perspective, the feasibility of PF depends on the trade-off between the carbon emission reduction standard and the preferential margin of interest rate. For a given preferential margin of interest rate, the retailer setting a high carbon reduction standard can indeed increase her profits and force the supplier to improve the carbon emission reduction level. However, the supplier only accepts the carbon emission reduction standard below the threshold. The retailer needs to rationalize a carbon reduction standard to attract the supplier. In addition, the higher preferential margin of interest rate or carbon sensitivity in the consumer market can relieve the constraints of carbon reduction standard and expand the feasible region of PF. (3) Comparing the equilibrium decisions and profits of participants of CS and PF, it is found that PF can effectively alleviate the profit conflict between the retailer and the supplier if a reasonable relationship between the preferential margin of interest rate and the carbon emission reduction standard is given. There is a Pareto region where both the retailer and the supplier prefer PF over CS. (4) The extended model of PF, incorporating carbon reduction standards as endogenous parameters for the retailer, is introduced to assess the robustness of the results and find that the main results still hold in the extension.The research conducted in this paper sheds light on the influence of financial support from core enterprises on decision-making and economic benefits in low-carbon supply chains, offering valuable insights for enterprise management. To implement CS effectively, both parties need to reasonably control the cost-sharing ratio to prevent potential backlash of excessive cost-sharing through the pricing channel. Furthermore, the retailer can utilize PF to incentivize the supplier’s carbon reduction efforts and enhance her own profits, but it is crucial to set reasonable carbon emission reduction standards to attract supplier participation in PF. Additionally, enterprises are better positioned to employ CS in supply chains with higher financing costs or lower carbon sensitivity. The feasibility scope of PF can be expanded by increasing the preferential margin of interest rate or by raising consumer awareness of carbon emission reduction. Finally, enterprises can carefully manage the relationship between the preferential margin of interest rate and carbon reduction standards to maintain consistency in mechanism choice between the two parties.Although our analysis has achieved some results and management enlightenment, the premise of relevant conclusions for practice is that the retailer has a comprehensive grasp of the upstream supplier’s carbon emission reduction efficiency, production cost, and market information, as well as the sharing of market information such as initial market size, market sensitivity to prices and emission reductions. However, many enterprises in the real world are unwilling to share their information because of the consideration of private information disclosure. The following research can be extended to the study of carbon emission reduction incentives with information asymmetry.S1 Appendix(DOCX)Click here for additional data file.S1 File(DOCX)Click here for additional data file.


Peste des petits ruminants (PPR) is an acute viral disease of sheep, goats, camels and some wild ruminant species. It is widespread in Africa, the Middle East and Asia, where it causes heavy economic losses. The disease is currently targeted by the Global Eradication Program (PPR GEP), aimed at eradicating the disease by 2030. The United Arab Emirates (UAE) national animal health plan adopts mass vaccination to control and eradicate PPR from the country. In this study, using the historical and real-time animals’ identification and registration data together with different PPR occurrence risk factors, a mathematical algorithm was developed to intellectually design and implement a PPR vaccination campaign in the Emirate of Abu Dhabi. The algorithm was capable of prioritizing animal holdings at risk of PPR infection to be targeted by vaccination, facilitated the mobilization of vaccination teams in the field and resulted in an increased vaccination coverage of the targeted livestock population in a short time. Such achievements will enhance the UAE national PPR eradication plan to be fulfilled.Peste des petits ruminants (PPR) is a contagious and economically important transboundary viral disease of small ruminants. The United Arab Emirates (UAE) national animal health plan aimed to control and eradicate PPR from the country by following the global PPR control and eradication strategy which adopts small ruminants’ mass vaccination to eradicate the disease from the globe by 2030. A smart vaccination approach, which is less expensive and has longer-term sustainable benefits, is needed to accelerate the eradication of PPR. In this study, a mathematical algorithm was developed based on animals’ identification and registration data, belonging to the Abu Dhabi Agriculture and Food Safety Authority (ADAFSA), and other different parameters related to PPR risk occurrence. The latter included animal holding vaccination history, the number of animals per holding, forecasting of the number of animals and newborns per holding, the proximity of an animal holding to a PPR outbreak and the historical animal holding owner vaccination rejection attitude. The developed algorithm successfully prioritized animal holdings at risk of PPR infection within Abu Dhabi Emirate to be targeted by vaccination. This in turn facilitated the mobilization of field vaccination teams to target specific sheep and goat holdings to ensure the generation of immunity against the disease on a risk-based approach. The vaccination coverage of the targeted livestock population was increased to 86% and the vaccination rejection attitude was reduced by 35%. The duration of the vaccination campaign was reduced to 30 compared to 70 working days and hence can alleviate the depletion of human and logistic resources commonly used in classical mass vaccination campaigns. The results obtained from implementing the algorithm-based PPR vaccination campaign will reduce the negative impact of PPR on the UAE livestock sector and accelerate the achievement of the national PPR eradication plan requirements. Peste des petits ruminants (PPR) is an acute infectious viral disease affecting sheep, goats and some wild ruminant species . It is cDisease eradication campaigns have been characterized by large-scale mobilization and mass vaccination with long-term campaigns. The cost of eradication programs increases as the duration of a program is prolonged, resulting in a loss of stakeholder support, field teams fatiguing and failure or major setbacks. Therefore, a PPR eradication strategy should be focused on rapidly eliminating the disease from livestock populations by achieving very high levels of vaccination coverage in a short period of time .In the United Arab Emirates (UAE), the disease was first reported in wildlife in 1986 [The PPR vaccination model building framework adopted here follows the Cross Industry Standard Process for Data Mining (CRISP-DM), a framework which starts with business problem understanding, data understanding, data preparation, modeling, assessment, deployment and monitoring . The modThe Animal Identification and Registration system (AIRS) was implemented in ADAFSA at the beginning of 2009. The system aims to not only record and identify animals’ species and date of birth, but it also includes technical modules and functions to schedule and monitor vaccination campaigns. The system keeps records  of all animals’ movements (in and out of the holding), vaccination history, ownership, registration date, number of animals, tagging date, treatments and vaccination campaign implementation dates.ADAFSA implemented an Early Warning and Response System (EWRS) across all its core operational divisions, to which certain users were given an access to notify disease outbreak incidences and grant the privilege to the rest of the concerned departments to track and follow-up on these incidents. Most importantly, PPR outbreak notification data include outbreak starting date, geographic coordinates of the infected holding, animal species and the number of infected animals, morbidity, mortalities and the last PPR vaccination history date.ADAFSA implemented a geospatial system (GIS) that keeps track of and records all geospatial data of animal farms and holdings. The system incorporates object coordinates, shapes, dimensions and location and is integrated with other systems through the animal holding number.The framework, CRISP-DM, includes seven elements, as stated in ADAFSA is looking to optimize its operation costs in a way that maximizes its return on investment. An investment in animal health is a major component in the gross domestic product on the UAE level. ADAFSA used to randomly cover around 70% of the number of eligible animal holdings in each vaccination campaign, aligning with the global PPR control strategy for 2020–2030 and the regional PPR eradication roadmap in the Middle East . HoweverTo develop a data-driven solution for ADAFSA’s vaccination campaign optimization, a thorough understanding of the available data is crucial. The data elements utilized by ADAFSA, the integration of ADAFSA systems and the frequency of data refreshments need to be comprehensively examined. Additionally, it is essential to assess the quality of the data.To ensure data quality, a collaboration between subject matter experts, including veterinary epidemiologists, statisticians and data scientists, along with information technology and technical systems experts, was established. These experts carefully scrutinized the data elements, identified any potential limitations or inconsistencies and implemented data quality constraints.Data extraction was performed to retrieve relevant information from ADAFSA’s systems. This included extracting data points related to animal holdings, vaccination transactions and historical rejections.Data integration was carried out to combine data from different sources within ADAFSA’s systems. By integrating these disparate data sources, a unified and comprehensive dataset was created for analysis.Data mapping, filtering and cleansing techniques were employed to enhance the quality of the data. This involved handling missing data points, ensuring data consistency and addressing any data anomalies or errors.Data transformation techniques were applied to facilitate further analysis. This included feature extraction, standardization and normalization, which are crucial for ensuring that the data are in a suitable format for subsequent modeling.The data preparation phase was a crucial step in the vaccination campaign optimization process and demands meticulous attention. In this phase, various steps were undertaken to ensure that the data were in a suitable format and quality for subsequent analysis. Data preparation comprised the following steps: It is worth mentioning that the data preparation phase typically accounts for a significant portion, approximately 70%, of the overall data science project timeline. This highlights the importance of thorough and rigorous data preparation to ensure accurate and reliable results.The PPR model utilized in this study relies on specific attributes extracted from ADAFSA’s systems. These attributes were carefully selected to capture relevant information related to the vaccination campaign optimization and PPR control strategies. These included the following attributes.On average, 70% of holdings were visited at least once every year. These visits might have included the tagging of new moved-in animals, newborns, as well as vaccinations and treatments. However, the model focuses on the last date of PPR vaccination visit and thus extracts it from vaccination transactions on both the animal and holding level. Later, the model extracts how long the time of the last visit is from the day of executing it to plan for the new vaccination campaign. In this attribute, the outcome indicated the number of days could be from 1 day to more than 800 days.The second attribute in the model is the real-time count of animals at the holding level, which includes the new added ones. Greater numbers of animals in a holding with the longest period of days without being vaccinated are more exposed to risk of outbreak and greater losses in animals. The model extracts from the AIRS system the real-time number of current sheep and goats on every holding and maps it to the mentioned parameter. Authors extracted 36 months of tagged animals’ data at the holding level and performed multiple forecasting algorithms to produce the most accurate one. Then, the model incorporated the outcome of the forecasting exercise along with the previous attributes , as well as the upcoming for the scoring method. The forecasting exercise took place on a hierarchical level (bottom-up) and is further explained under the modeling section.The PPR virus could be transferred up to 200 m from an infected holding. Thus, we assumed that a 5 km radius distance from an outbreak focus represented a sufficient max distance for virus spread . The modThe model extracts all historical rejections at the animal owner level and aggregates them to the holding level where it ends up with a rejection pattern that needs to be considered when assigning weights and scores to the outcome. Level: the baseline value for the data series if it were a straight line.Trend: The optional and often linear increasing or decreasing behavior of the series over time. It also describes the movement along the term.Seasonality: the optional repeating patterns or cycles of behavior over time which represent seasonal changes.Noise: the optional variability in the observations that cannot be explained by the model.Arbitrarily, the rotation of sheep and goats’ herds turnover was estimated to be 30% on average. This included newborns and sold and slaughtered animals . ConsideVolume: the volume of the time series.Volatility: The volatility of the time series. Usually, series with high volatility are harder to automatically forecast than series with low volatility.Seasonal: Indicates whether the time series is seasonal. Seasonality is determined by a significance probability of 0.01 or less. If seasonal, a seasonal autoregressive integrated moving average (SARIMA) model is selected.Intermittent: Indicates whether the time series is intermittent. The time series regression is selected by default unless another algorithm exceeds its accuracy.Retired: Time series that are retired or are no longer active. The retired series model is selected for this segment.Demand span: The length of the time series included the following aspects.YEAR_ROUND: for time series with values that spread throughout the year.IN SEASON: for time series that occur only during certain seasons.ND (NOT DETERMINED).All of the time series have a level, most have noise and the trend and seasonality are optional. The above four components were studied programmatically on every holding and species level. That is, having 24,000 holdings with two types of animal species inside will result in 48,000 time series levels for a period of six to sixty-three months after conducting quality checks of outliers, missing values  and aggregation of time series data. Every time series was assessed in terms of the following:Based on the outcome of the above so-called demand classification attribute, which have been detected in the time series, the fit algorithm will be chosen as champion for that time series. Several algorithms were tested with the SAS VIYA platform, which supports different libraries of time series forecasting implying various accuracy metrices. The algorithms used included auto-forecasting, a non-seasonal model, regression for time series, a naive model and a hierarchical forecasting and seasonal model, as shown in To avoid biased evaluations, we must ensure that the training sets contain observations that occurred prior to the ones in the validation sets. A possible way to overcome this problem is to use a sliding window. This procedure is called time series cross validation, where we divide the time series observations into two parts, training and validation. We train the algorithm on the training dataset to forecast the validation dataset before we compare the results together. The comparison is based on Mean Absolute Percent of Error (MAPE). Since the classes are not balanced, a weighted MAPE (WMAPE) is introduced across all algorithms. ADAFSA has tried five different algorithms mentioned above, tuned their parameters and allowed for an input of an auto-forecasting package. This package studies the pattern of the data at a certain acceptable accuracy with the tradeoff model performance. The selected algorithm  is used to forecast October 2020 data and is represented in a graphical way as follows : The historical data of the number of animals, represented as “dots”, where they are the actual data on which different algorithms were trained.The statistical forecast of the number of animals, which stands for the result of the trained algorithm, represented as line. It is very clear that both the actual  and the statistical (forecasted by the model) follow the same pattern and trend, and they are almost close to each other.The final forecast, the line in the shaded area that refers to the forecasted value of the upcoming month, October 2020 in this case, where the trained and champion algorithm is used to forecast this data point which did not exist in the dataset before.The statistical confidence limit  at a significance level of 0.05 and 95% confidence level. The confidence interval is often computed assuming a normal distribution, but other distributions could be used.The model attributes have been successfully integrated into a unified dataset, aggregated at the holding level to facilitate the scoring task. These attributes were named as “period since last PPR vaccination visit”, “number of current animals”, “newborn forecasting of sheep and goats”, “distance from nearest PPR outbreak” and lastly, “vaccination rejection attitude”. The null hypotheses (H0) were run for all variables to determine the significance of each one. The below procedure is a normality test hypothesis generated for one variable, as an example, where the rest follow the same. The statement is SAS code and the H0 assumes that the variable is normally distributed at a significance level of 0.05.proc univariate data = mycas.ppr_output_final_dataset normal;var NO_OF_ANIMALS;run;p-value was less than 0.05, we rejected the null hypothesis of having a normal distribution pattern. So, the variable  was not normally distributed. Following this, all these attributes were scaled into a scale of 1–100 based on the distribution of the attributes. The scaling allowed us to assign weight based on the significance of the variable and multiply the scaled score with that weight, which will later be summed up into a final score. Common normalization methods include feature scaling , which is calculated as The Kolmogorov–Smirnov test was used to test the normality of the distribution. When the HIGH: assigns the largest of the corresponding ranks .LOW: assigns the smallest of the corresponding ranks .MEAN: assigns the mean of the corresponding rank .DENSE: computes scores and ranks by treating tied values as a single-order statistic. For the default method, ranks are consecutive integers that begin with the number one and end with the number of unique, non-missing values of the variable that is being ranked. Tied values are assigned the same rank.proc rank data = mycas.ppr_output_final_dataset = &ds_ranks groups = 10 descending ties = dense;VAR Distance_from_Outbreak;RANKS Distance_ranks;RUN;Every variable was ranked based on the best ranking method and again, considering the variable distribution. Subject Matter Expert (SME): analyzing the current data and involving SMEs in the decision-making process.Defining the threshold of targeted visits and randomly optimizing the result.Data driven weights—mainly for classification models.The model aims to prioritize the PPR vaccination visits of veterinarians to be more targeted instead of being randomized. Defining the right weight criteria is considered to be challenging, specifically when we want to assess the proposed prioritization matrix. It could be much easier if we were building a classification model of an event, where the algorithm would calculate the right weights for each variable. However, the model here is a combination of variables, and the target variable is to rank the holdings based on their top priorities. Weights of variables were agreed from three sources. These included:Later, ADAFSA implemented different weight iterations and assessed the outcome to make sure that the results follow the right distribution, normal distribution when all variables are already ranked. The five variables had different weights as follows: period since last PPR vaccination visit (40%), newborn forecasting of sheep and goat (20%), number of current animals (20%), distance from nearest PPR outbreak (10%) and vaccination rejection attitude (10%).When a champion algorithm was implemented on one holding for six months to forecast the number of animals expected in the same holding in October 2020, the compared actual versus the predicted values of animal counts were almost the same .Variables were ranked based on the SAS RANK procedure; considering the variable distribution, the five variables with different weights to prioritize targeting of holding by vaccination were within the right skewness measure as shown in In order to assess the impact of the developed PPR model on vaccination campaign outcomes, ADAFSA constructed a new PPR vaccination campaign using the data extracted from the model’s five variables and ran it at the same period in the year, with the same number (82) of veterinarians involved in the previous non-model-based campaign. Comparing results revealed that a total of 310,629 animals were vaccinated and tagged for the first time in the new model-based campaign, compared to 163,143 animals in the previous one. Thus, a total increase of 48% in number of animal tags in the model-based campaign was observed, although the holdings targeted were 35% less than the previous campaign. The average tagged animals per holding (63) was increased in the model-based campaign when compared (37) to the old non-model-based campaign. An increase in the average number of tagged animals per holding per region  was also observed in the model-based campaign compared to the non-model-based campaign . Similarly, the daily average tagged animals increased in the new PPR model-based campaign compared to the non-model-based designed one . AccordiForecasting the number of animals and newborns in animal holdings targeted by vaccinations resulted in accurate estimates of animal tags and vaccine doses needed. This also led to an improved fodder distribution plan to animal owners and reduced wastes.The model-based campaign was able to specify in advance animal holdings where owners used to decline vaccination, with a result that these holdings were targeted to be vaccinated. In total, a 35% reduction in the percentage of vaccination decline by animal owners was obtained in the PPR model-based vaccination campaign compared to the previous non-model based campaign.When the PPR model-based vaccination campaign was implemented blindly without the previous knowledge of field veterinarians, the campaign took less time to be completed. The same number of vets finished the campaign by reaching the targeted holdings in a duration of 30 working days compared to 70 working days in the previous non-model-based campaign. PPR is an endemic in Africa, Asia and the Middle East, including the UAE. In naïve populations of sheep and goats, morbidity and mortality can be greater than 90% . The UAESimulating the PPRV spread dynamic in Ethiopia using a metapopulation model estimated the level of viral transmission and the vaccination coverage required for its elimination. In an endemic setting, viral spread could be prevented if the proportion of immune small ruminants is kept permanently above 37% in at least 71% of pastoral village populations . In the In this study, when the PPR vaccination campaign based on the developed algorithm was deployed and compared to the preceding (non-model-based) campaign, considering the same time of year and the same number of veterinarians to carry out the vaccination activity, a total increase of 48% in the number of animals vaccinated and tagged for the first time was observed. The overall average of vaccinated sheep and goats per animal holding increased by 41%. Also, an increase in the averages  of the vaccinated and tagged animals per holding, respectively, was obtained in the three  regions of Abu Dhabi Emirate. This has collectively resulted in increasing the percentage of PPR-immunized holdings to 86%, which fulfills the requirements of the global control and eradication strategy [Challenges to eradicate PPR include the widespread distribution, high population turnover in small ruminants, low value of individual animals and clinical disease that varies by species and breed ,17. It iImmunity in PPR-vaccinated sheep and goats is likely to be effectively life-long, however the rapid population turnover in these animal species leads to the fact that coverage of the vaccinated animals may fall by as much as 25% per year, or even faster, if susceptible animals including newborns are introduced into the animal holding ,21. UtilAwareness of community accompanied with a good communication between veterinary services and livestock owners enhances the success of the PPR vaccination campaign ,27,28,29The implementation of mass PPR vaccination is used to control the disease in many countries, but it is difficult to be achieved and is expensive as it requires the mobilization of significant human, material and financial resources for a long period of time ,31. In tThe obtained benefits of implementing the PPR-based model included accuracy in the planning of vaccine doses and animal tags needed, a reduction of PPR-produced mortalities by assuring vaccination of at-risk susceptible animals and the optimization of human and logistic resources to carry out the PPR vaccination campaign in Abu Dhabi Emirate, which has turned over AED 24 million per year as both a direct saving and cost optimization The limitations of the model developed here to optimize the efficiency of vaccination and its coverage include that it should be combined with other control measures such as movement restriction, ring vaccination and stamping out policies to achieve the eradication goal of PPR.Using mathematical modeling, ADAFSA animals’ data from AIRS was used to develop an algorithm to formulate the PPR vaccination campaign and mobilize vaccination teams to target specific animal holdings that are at risk of PPR infection. Increasing the vaccination coverage of sheep and goats in a shorter time was observed when the algorithm-based vaccination campaign was deployed. Protecting small ruminants against PPR by vaccination will reduce its negative impact on the UAE livestock sector and actively accelerate the implementation of the national PPR eradication strategy requirements with a resultant of enhancing both bio and food securities in the country.


Environmental wind tunnels play a crucial role in the research and development of high-speed railways. However, constructing and operating these wind tunnels requires significant resources, especially with respect to the cooling system, which serves as a vital subsystem. The cooling system utilizes an air compression refrigeration cycle and consists of multiple components. The efficient operation of these components, along with the adoption of appropriate strategies, greatly enhances the efficiency of the wind tunnel refrigeration system. Despite this, the existing methods for evaluating the refrigeration system do not fully capture the energy consumption of an air compression refrigeration system during practical use. To address this issue and effectively evaluate the wind tunnel refrigeration system, we propose using an exergoeconomic evaluation coefficient with experimental cycles to establish the system. This method incorporates the use of frequency coefficients and related parameters. By employing the newly developed evaluation coefficient as an objective function, we utilize the adaptive value-sharing congestion genetic algorithm to optimize the wind tunnel for high-speed trains. Furthermore, we compare the advantages and disadvantages of different optimization schemes. Traditional optimization methods prove inefficient because of the system’s numerous variables and the presence of multiple peaks in the objective function. Inspired by the biogenetic breeding method, we introduce an optimization approach based on a specific gene mutation. This innovative method significantly reduces optimization time and improves efficiency by approximately 17%. High-speed railways play an increasingly important role in economic transport. As an essential experimental facility for the research and development of high-speed railways, the high-speed railway environmental wind tunnel has received significant attention. Nowadays, some of the world’s famous environmental wind tunnels for trains include the Vienna Climatic Wind Tunnel, the CIRA Icing Wind Tunnel, the McKinley Climatic Laboratory, and the Jules Verne climatic wind tunnel. They have played a significant role in advancing high-speed train technology. One notable example is the Climatic Wind Tunnel in Vienna, which boasts an impeccable weather simulation system. China, the world’s foremost high-speed rail nation, is yet to possess its own high-speed rail wind tunnels. Since the foundation of The National Innovation Centre of High-Speed Train in 2019, China has sought to construct its own environmental wind tunnels. However, the construction and operation of high-speed railway wind tunnels for environmental testing, especially for full-scale models, incurs considerable costs. This is attributed to the vast dimensions and intricacy of the wind tunnels. The wind tunnel for high-speed trains is a large-scale experimental facility consisting of several systems, including power, refrigeration, control, measurement, data acquisition systems, and others. The refrigeration system, a crucial subsystem for wind tunnel operations, not only provides the cooling capacity to simulate a low-temperature environment in the experimental section of the wind tunnel but is also responsible for dissipating heat from other components in the tunnel. Because of the substantial demand for cooling, the system consumes a significant amount of energy. The refrigeration system comprises several components, including suction compressors, turbo-compressors, water coolers, return coolers, and turbo-expanders, resulting in a complex structure. The coordination of these components and the appropriate strategy for their use determine the advantages and disadvantages of the refrigeration system. Therefore, optimizing the design of the refrigeration system can effectively enhance its operating efficiency and reduce costs.Before optimizing the wind tunnel refrigeration system, it is necessary to select or establish a suitable method for its evaluation. The methods of evaluating refrigeration systems have undergone many developments. Initially, most of them used the system energy analysis method, focusing on the cooling efficiency coefficient of performance (COP) and heat dissipation. These evaluations primarily studied the system’s energy use efficiency and consumption . Wang etThe environmental wind tunnel for high-speed trains must be tested under various experimental conditions, and a system model should be developed to analyze the operational mode. An in-depth analysis of the refrigeration system and wind tunnel operation under different operating conditions is crucial. Broniszewski  researchIn the process of optimizing high-speed train environmental wind tunnels, the system involves numerous variables, and the objective function has many peaks. The existing optimization methods require substantial lengths of time for optimization. To address these issues, a specific gene-mutation-based optimization method is proposed in addition to the improved original genetic algorithm. This method significantly reduces optimization time and improves efficiency.The cooling system of a high-speed train environmental wind tunnel is based on the Brayton inverse cycle and comprises specific components, as shown in The procedure begins with the compression of external air by the inlet compressor. The compressed air is then dried and purified, resulting in a high-temperature, high-pressure dry clean gas. After further compression via the turbo-compressor, the air pressure and temperature increase. Through the cooling system, the temperature of the compressed air is reduced by passing it through the water cooler and the re-cooler. The cooled, compressed air then enters the turbo-expander, where expansion produces low-temperature gases at atmospheric pressure. This cooled air is then sent into the high-speed train environmental wind tunnel via a pump serving as the cold source. The power output from the expander can be utilized to drive the co-axial compressor, enabling the recovery of the compressed air’s energy and increasing the energy efficiency of the system.Compared with traditional refrigeration systems, the refrigeration of a high-speed train environmental wind tunnel possesses two distinct characteristics.First, since the high-speed train environmental wind tunnel needs to simulate various environmental conditions, the cooling capacity and temperature of the cooling source provided by the refrigeration system vary accordingly. Additionally, the refrigeration system operates in different states. Existing thermodynamic evaluation systems often focus on assessing the performance of a single operating state, selecting one or several specific states for evaluation. However, in an air compression refrigeration system, each state is equally important, and evaluating only a few results in an incomplete evaluation of the train environment system. Moreover, the same set of equipment is used for the different states in the refrigeration system, so the design of the system components should consider the most severe environment to ensure that the cooling needs are met for all experimental sections of the high-speed train environmental wind tunnel.Second, since the air compression refrigeration system extracts a certain amount of air from the environmental wind tunnel and returns it to the cooler as the main cold source, the airflow in the latter half of the wind tunnel becomes smaller. The airflow slows down as a result of the constant air density, leading to a reduction in flow loss in the wind tunnel. In the environmental wind tunnel, this pumping method has a minor impact on the experimental section with higher wind speeds but a more significant impact on the experimental section with lower wind speeds. The original methods of evaluating refrigeration systems paid little attention to the energy flow coupling between the refrigeration and other systems and failed to accurately represent the influence of changes in the refrigeration subsystems on the operational efficiency and cost of the complete external system.To address these two problems, improvements based on the original economic analysis and evaluation can be made. For instance, environmental wind tunnels for high-speed trains primarily serve various environmental experiments. Our research found that testing a high-speed train, from the pre-research stage and initial testing to the final validation experiments, product finalization, and mass production, requires undertaking a series of experimental procedures. This entire experimental process follows a well-established set of validation standards. It can be assumed that there exists a complete experimental cycle for train experiments applicable to most environmental conditions and trains. Therefore, the weight coefficient of the system usage frequency Alternatively, Equation (2) can be obtained from Equation (1):OsV, USD/h. OsV and the previous sysF comprise the final evaluation factor for the refrigeration system known as sysG, USD/h. Equation (4) demonstrates how sysG can be rewritten to determine its final formThe impact of the refrigeration system’s energy consumption on the other systems in the wind tunnel can be addressed by conducting an exergy analysis on the entire experimental system. While this method accurately analyzes the influence of changes in the refrigeration system on other systems, it also significantly increases the number of analysis systems, thereby increasing the analysis cost. Moreover, it may fail to highlight the energy flow characteristics of the refrigeration system as a result of excessive redundant data, thus complicating the discovery of the refrigeration system’s energy flow coupling law. To address these related problems, a detailed energy flow analysis of the complete system of the high-speed train environmental wind tunnel is conducted first to clarify the energy flow coupling between the refrigeration system and the other systems in a high-speed-train environmental wind tunnel. Subsequently, we analyze and quantify the exergoeconomic cost of the refrigeration system in relation to the other systems present in the wind tunnel This evaluation index enables a more effective comparison of the advantages and disadvantages of different environmental cooling systems for high-speed trains. Consequently, it serves as the main objective function for the subsequent optimization of environmental cooling.By considering the principles of refrigeration, exergoeconomic analyses, and other methods, we developed an evaluation coefficient The modeling and sensitivity analyses of design parameters for air compression refrigeration systems have been discussed in . OptimizThe operating parameter indexes are summarized in The refrigeration system consists of six operating states, each corresponding to three operating parameter indicators. Therefore, a total of 18 operating parameter indicators must be optimized. Additionally, three other constraints need to be considered for optimizing the system:The compressor’s compression ratio in the system must exceed the minimum compression ratio and be less than the specified maximum. Simultaneously, the mass flow rate of the system must fall within the authorized range of each component’s mass flow rate. Ultimately, the system should deliver refrigeration capacity that meets or surpasses its requirements.We modeled the operation of the refrigeration system in order to calculate its performance in various states. In the refrigeration system model, the turbine and compressor are analyzed using one-dimensional flow characteristic analysis and the principle of similarity. The specific modeling process is shown in The re-cooler is modeled using the effective number of heat transfer units and the mean temperature-difference design method classical approach. The specific modeling process is shown in The key system components and operating parameters are then optimized in turn to obtain the exergoeconomic cost. The objective function is obtained by multiplying the result with the frequency coefficients of the wind tunnel and adding the effect on the other parameters.This article employs two primary optimization algorithms: the fitness-sharing crowding genetic algorithm and the gene-directed change genetic algorithm. The latter is an improvement of our existing algorithm based on the thermodynamic laws of the system. A detailed description of the gene-directed change genetic algorithm will be provided in later sections. The fitness-sharing crowding genetic algorithm has gained popularity as a reliable method for system optimization in recent years. It serves as the primary approach to addressing the issue of “genetic drift” in multipeak functions. The fundamental concept behind the adaptive value-sharing crowding genetic algorithm involves combining the ideas of adaptive value sharing and crowding. Specifically, the adaptive value-sharing genetic algorithm incorporates the sharing concept before the selection process, while the replacement process completely replaces all old individuals with new ones. On the other hand, the crowding algorithm employs non-dominated sorting to classify the population and calculate the crowding distances of individuals to maintain population diversity. The adaptive value-sharing and crowding genetic algorithm adjusts the adaptive values of individuals during the genetic algorithm’s selection process and introduces competitive comparison in the replacement process. Combined, these two algorithms complement each other, enhance the algorithm’s search capability, and resolve issues associated with “genetic drift” and similar problems. The general optimization steps are illustrated below in The implementation of a fast non-dominated sorting algorithm significantly reduces the complexity of the optimization algorithm;The introduction of an elite conservation mechanism ensures that good individuals are not lost during the evolutionary process by allowing the parent population to compete with their offspring to produce the next generation;Using crowding and crowding comparison operators promotes population homogeneity and diversity preservation.Firstly, an initial parent population Gen = 1, size N is randomly generated. Then, the parent population generates an offspring population of the same size. The parent and offspring populations are merged to form a population size 2N. This newly generated population undergoes non-dominated fast sorting. The crowding degree of all individuals in the 2N-sized population is computed, and appropriate individuals are selected using the crowding degree relationship and individual crowding degrees to form a new size N population. Traditional genetic algorithms, including crossover operations, are then used to generate a population that repeats the above steps. By considering the crowding relationship and the crowding sizes of individuals, suitable individuals are selected to form a new parent population of size N. Through crossover and mutation, an offspring population of size N is generated and merged with its parent population. Non-dominated fast sorting is repeated until the number of generations reaches the predetermined value. The algorithm possesses the following advantages:Three model optimization scenarios were developed as follows:Option 1. The optimization of refrigeration systems based on the most severe usage conditions.The mainstream approach for optimizing refrigeration systems is to use a scheme based on the most stringent boundary conditions. This method ensures that the component parameters of the selected system can meet the needs of all other cooling states, thus ensuring system stability under various operating conditions. Additionally, optimizing the system under harsh environmental conditions that often coincide with high energy consumption can effectively reduce system costs. The key operational ideas of this system are as follows in Initially, the general system is optimized for operations at a temperature of −70 °C and a wind speed of 100 m/s in the experimental section of the environmental wind tunnel. The optimization parameters include equipment selection and wind tunnel operation parameters under this condition. To obtain credible results and compare the efficiencies of different methods, the termination conditions of the genetic algorithm are employed to determine if the desired results are achieved. The algorithm terminates when the system population remains unchanged or only changes slightly. Generally, a region is defined, and the optimization process continues until all population members are within that region for a certain number of generations. If the difference between 20 consecutive genetic generations is less than 1‰ of the minimum value, the system is considered stable and reaches termination condition. The system stability condition is determined using specific coefficients.The optimal scheme for the stabilized system is determined as the minimum value, and the system design parameters are sequentially applied to different systems to carry out the system optimization. This process yields a control scheme for the air compression refrigeration system under different conditions. The relevant scheme parameters are sorted out, and evaluation coefficients for high-speed trains’ environmental wind tunnel cooling in different states are calculated.Option 2. Optimization using the cooling evaluation factor as an objective function.In contrast to the first option, which derives wind tunnel evaluation coefficients indirectly, the second option directly utilizes refrigeration evaluation coefficients as functions of the objective coefficients. The approach is presented in Compared with Option 1, Option 2 involves fewer steps but significantly increases the number of variables to be optimized from 9 to 24. Consequently, there is a substantial increase in the number of system optimization algorithms, resulting in a longer computation time. This phenomenon arises because, during system optimization, multiple systems need to be computed simultaneously. If any of these systems fail to meet the requirements, the optimization solution is deemed noncompliant, leading to a large number of potential optimization solutions that cannot be executed and consequently slowing down the convergence of system results. To address this issue, improvement schemes are proposed for different systems to enhance the feasibility of obtaining effective solutions and improve the convergence speed of the optimization system. This approach is particularly effective in precomputed systems.Option 3. Refrigeration systems for two sets of equipment.The previous study focused solely on analyzing a single refrigeration system. However, in practical applications, safety hazards and power underutilization can occur because of issues such as the relative size of the compressor. To address these concerns, it is possible to employ two refrigeration systems operating in parallel, which can effectively resolve these issues. The approach is shown in The system optimization approach for Option 3 remains the same as in Option 2. The only difference is the inclusion of an extra set of refrigeration equipment. The solving process follows the same methodology as Option 2.Because of the addition of a refrigeration system in Option 3, the number of variables to be optimized doubles from 24 to 48. This increase in system variables significantly raises the complexity of system optimization. To address these challenges, we propose a genetic optimization algorithm using fixed-point gene mutation.Gene point mutation techniques involve introducing favorable changes into the target DNA fragment or genome to achieve desired characteristics. Polymerase chain reactions (PCRs) are commonly used to facilitate base addition, deletion, point mutation, and other modifications. These techniques have proven to be valuable tools in genetic research across various fields. In medicine, they have been used to correct genetic diseases and treat cancer and other illnesses, and in agriculture, to develop disease-resistant and insect-resistant seeds. Using fixed-point mutation technology has significantly reduced breeding time and improved efficiency.The current genetic algorithms are derived from biologically relevant features and primarily rely on crossover and random mutation to generate offspring. In the refrigeration system optimization process, we employed genetic information crossover and random mutation. However, because of the strong coupling between system variables, crossing two excellent offspring often fails to produce equally excellent offspring. Consequently, obtaining superior offspring through crossover alone becomes difficult. On the other hand, random mutation offers a relatively high chance of generating positive effects in the later stages of refrigeration system optimization. However, during the initial phases, the disorderly nature of evolution leads to an overall low system efficiency. By using fixed-point gene mutation technology, we can effectively address this issue. The specific operational steps are presented in Reduce the dimensional design parameters of all components (excluding the compressor and turbine inversions) and system control variables by 5%;Maintain the refrigeration 1 system component parameters and control variables as they are but reduce the refrigeration 2 system component parameters and control variables by 5%;Keep the parameters of all components unchanged while reducing all state system control variables by 5%;Keep the parameters of all components unchanged. Reduce the system control variables in states 1 and 4 (high-speed states) by 5% while leaving the system control quantities in the remaining states unchanged;Keep the parameters of all components unchanged. Reduce the system control variables in states 2 and 5 (medium-speed states) by 5% while leaving the system control quantities in the remaining states unchanged;Keep the parameters of all components unchanged. Reduce the system control variables in states 3 and 6 (low-speed states) by 5% while leaving the system control quantities in the remaining states unchanged;Keep the parameters of all components unchanged. Reduce the system control variables in states 1, 2, and 3 (low-temperature states) by 5% while leaving the system control quantities in the remaining states unchanged;Keep the parameters of all components unchanged. Reduce the system control variables in states 4, 5, and 6 (room-temperature states) by 5% while leaving the system control quantities in the remaining states unchanged;Keep the parameters of all components unchanged. Maintain the control variables of System I while scaling down the control variables of all states of System II by 5%;Keep the parameters of all components unchanged. Maintain the control variables of System I while reducing the system control variables of states 1 and 4 (high-speed states) of System II by 5%. Leave the system control quantities for the remaining states unchanged;Keep the parameters of all components unchanged. Maintain the control variables of System I while reducing the system control variables of states II and V (medium-speed states) of System II by 5%. Leave the system control quantities for the remaining states unchangedKeep the parameters of all components unchanged. Maintain the control variables of System I while reducing the system control variables of states III and VI (low-speed states) of System II by 5%. Leave the system control quantities for the remaining states unchanged;Keep the parameters of all components unchanged. Maintain the control variables of System I while reducing the system control variables of states I, II, and III (low-temperature states) of System II by 5%. Leave the system control quantities for the remaining states unchanged;Keep the parameters of all components unchanged. Maintain the control variables of System I while reducing the system control variables of states IV, V, and VI (ambient states) of System II by 5%. Leave the system control quantities for the remaining states unchanged.The gene-targeting mutation strategy is as follows:Each row of the matrix represents a feasible direction for the fixed-point mutation. We can draw an analogy with biological fixed-point mutations to conclude that fixed-point mutations have a higher probability of producing improved individuals, thereby accelerating population evolution. Furthermore, since the best individual in the population has a greater chance of being optimized than other individuals, the number of systematically superior individuals in the population rapidly increases, leading to faster convergence. However, using fixed-point mutations also presents challenges, such as reducing population diversity, which results in certain information from the parent generation not being passed on to the offspring, leading to “genetic drift.” To mitigate this, it is necessary to expand the population size and increase the number of parents.We utilize the three different optimization schemes mentioned earlier to optimize the design of the cooling system used in high-speed train environmental wind tunnels. The goal is to determine cooling schemes for the wind tunnel under different operating conditions. Additionally, we compare the characteristics of the system optimization process using traditional optimization methods with those that use the genetic method employing a fixed-point mutation.According to industry standards and engineering expertise, the experimental requirements for high-speed train testing generally follow a 1:3:1 time ratio of high speed/conventional speed/low speed. The distribution of experimental conditions between high-temperature, medium-temperature, and low-temperature states should be approximately the same. However, since the high-temperature portion of the wind tunnel can be cooled by the tunnel itself, we only need to consider the experimental requirements for the low- and room-temperature sections. The specific experimental requirements for each section and the frequency coefficients for the wind tunnel are shown in The primary cooling demand in the system is influenced by the wind pressure, which is determined by the wind speed requirement in the experimental section. Additionally, there is a fixed cooling demand of 2 MW in the gas section, and the heat dissipation in the system depends on the temperature in the experimental section.First, the three methods are compared on the basis of the cooling evaluation coefficient per unit of time, as shown in The influence of the refrigeration system on the energy consumption of the other systems is illustrated in In this paper, the process was programmed using MATLAB 2018b, based on the improved algorithm mentioned earlier, to optimize and analyze the method of selection and mode of operation of the high-speed train environmental wind tunnel. To verify the effectiveness and feasibility of the second method and compare the running speeds of the two algorithms, each algorithm was independently executed 10 times on a computer system, using an Intel(R) Core(TM) i7-3770K CPU @ 3.50 GHz and 16 GB RAM. Simulink’s model was used to run the wind tunnel refrigeration models. The algorithm parameters were as follows: the population size for optimization was set to 48, the number of populations was set to 200, the stability of the population was determined using a minimum of two elements, and the change in value between optimal solutions for five consecutive generations was ≤0.01. Additionally, the coefficient of variation within the same generation, calculated as {/minimum value}, was ≤1‰.Since the initial population impacts the number of iterations, the same population was used for both methods. For the mutation matrix, the gene-targeting mutation strategy was employed, as shown below in Using the two methods, 10 optimization calculations were performed for the air compression refrigeration cycle. The optimal solutions and the number of optimization generations for each dataset are presented in Taking the sixth calculation of both methods as an example, this section analyzes the advantages and disadvantages of the genetic optimization algorithm, which uses a gene fixation mutation, in detail. A convergence iteration diagram of the objective function is shown in However, as the iterative population reaches the later stages in which the system’s change pattern is less obvious and there are closer coupling relationships between different independent variables, the use of fixed-point gene changes may fail to meet the operational requirements of the system. These individuals are directly eliminated. At this point, the generation of excellent individuals in the population primarily relies on gene mutation to achieve optimization efficiency. As a result, the computational efficiencies of both methods do not differ significantly during the late iterations.In summary, using genetic algorithms with fixed-point mutations can improve the optimization efficiency in the initial stages of the system more rapidly without significantly impacting the optimization results of the system in the long run.This paper focuses on optimizing the refrigeration system of a high-speed train environmental wind tunnel. Before optimizing the refrigeration system, it is necessary to determine the optimization objective. Existing refrigeration system evaluation indexes mostly apply to stable operation systems, lacking evaluation methods for systems with complex working conditions. Therefore, a new system of evaluation for a refrigeration system based on the system’s energy and economic performance is proposed. The selection and operation scheme of the refrigeration system is modeled using a genetic algorithm. The operating characteristics of the system under different optimization methods are compared. Finally, an improved genetic optimization method for refrigeration systems is proposed, which significantly improves the system’s optimization efficiency without compromising operating accuracy.Using the original exergoeconomic analysis method, a weight coefficient for the frequency of different states in the refrigeration system is introduced to evaluate the comprehensive energy consumption of the high-speed refrigeration system under complex working conditions. Additionally, by analyzing the coupling relationship between the refrigeration system and other systems in the train’s wind tunnel, the impact cost of the refrigeration system in the wind tunnel system is introduced. This cost reflects the coupling relationship between the refrigeration system and the other systems during operation and is incorporated into the new method of evaluating refrigeration systems via energy prices. Ultimately, a system for evaluating a refrigeration system based on the system’s energy and economic performance is developed.A congestive genetic algorithm with shared adaptation values is employed to optimize the system under different schemes. It is observed that although Option 3 has inferior construction costs and cooling efficiencies at low temperatures and high speeds compared with the other two schemes, it outperforms the other two schemes overall because of its excellent performance under ambient operating conditions. The energy cost of Option 3 per unit of time is approximately 2204.81 USD/h, which is about 43% lower than that of Option 3, resulting in a cost saving of around 1724.78 USD/h.An improved genetic optimization algorithm using fixed-point gene mutation is proposed. By comparing the two algorithms, it is found that the new method enhances the optimization efficiency of the genetic algorithm in the early stages of the system. This leads to a reduction in the number of optimization iterations from 984.4 to 812.9 generations and a decrease in the average computation time from 37.69 to 31.12 h, resulting in a 17% increase in computational efficiency.The key conclusions are as follows:


Power distribution and battery thermal management are important technologies for improving the energy efficiency of plug-in hybrid electric vehicles (PHEVs). In response to the global optimization of integrated energy thermal management strategy (IETMS) for PHEVs, a dynamic programming algorithm based on adaptive grid optimization (AGO–DP) is proposed in this paper to improve optimization performance by reducing the optimization range of SOC and battery temperature, and adaptively adjusting the grid distribution of state variables according to the actual feasible region. The simulation results indicate that through AGO–DP optimization, the reduction ratio of the state feasible region is more than 30% under different driving conditions. Meanwhile, the algorithm can obtain better global optimal driving costs more rapidly and accurately than traditional dynamic programming algorithms (DP). The computation time is reduced by 33.29–84.67%, and the accuracy of the global optimal solution is improved by 0.94–16.85% compared to DP. The optimal control of the engine and air conditioning system is also more efficient and reasonable. Furthermore, AGO–DP is applied to explore IETMS energy-saving potential for PHEVs. It is found that the IETMS energy-saving potential range is 3.68–23.74% under various driving conditions, which increases the energy-saving potential by 0.55–3.26% compared to just doing the energy management. With the development of new energy technologies in the automotive industry, the plug-in hybrid electric vehicle (PHEV) has become one of the important approaches to reducing carbon emissions and the usage of fossil fuels and achieving the electrification of transport and sustainable development ,2. The PThe energy management strategies (EMS) of PHEV are used to reasonably and effectively distribute the output power between the engine and battery to meet the vehicle demand power ,5, whichMeanwhile, PHEVs are equipped with large-capacity batteries, so efficient battery thermal management can extend battery lifetime and ensure battery safety. Optimizing thermal management strategies (TMS) is also an important method to reduce vehicle energy consumption . Wei et DP algorithm is the most common solving algorithm to obtain the global optimal power distribution solution for PHEVs. Battery state-of-charge (SOC) and engine output power are usually taken as the PHEV system state variable and control variables, respectively. Xu et al. also treated the vehicle work mode as a system state variable and added a penalty function to reduce the number of vehicle work mode switches . Zhou etTo address the above issues in existing research, a novel global optimization algorithm is proposed in this work to solve the IETMS global programming problem for PHEV. Firstly, the PHEV state equation with SOC and battery temperature as variables is established. Secondly, an improved dynamic programming algorithm based on adaptive grid optimization (AGO–DP) is proposed, which has a shorter computation time and higher optimization accuracy than the traditional DP algorithm. Finally, the IETMS energy-saving potential of PHEV under different driving conditions is analyzed.The remainder of this paper is organized as follows. The control-oriented model of PHEV is developed in The powertrain architecture of the plug-in hybrid electric sport-utility vehicle (SUV) studied in this paper is shown in The vehicle demand power of PHEV is used to provide energy for the drive motor, AC system, and auxiliary system. Therefore, the power balance equation of PHEV can be expressed as:Due to the complexity of the transient model of the AC system, which involves numerous state variables, it is difficult to directly apply it to global optimization. Therefore, a steady-state model is used to describe the cooling characteristics of the AC system. Specifically, if the electric power of the AC system is constant, its actual cooling power also remains constant, satisfying the following relationship:The engine working point of the PHEV series studied in this paper can be adjusted arbitrarily. Hence, the minimum fuel consumption e engine , as showThe internal resistance equivalent circuit model is used to describe the electrical characteristics of the battery, as shown in Moreover, according to the Ampere-hour integration method, the battery SOC can be expressed as Equation (6). Where The battery thermal model describes the heat generation, heat exchange, and temperature change processes of the battery . AccordiThe battery heat exchange process mainly includes the heat exchange power between the battery and the external environment, as well as the heat exchange power between the battery and the AC system. After determining the heat generation power and heat exchange power of the battery, its temperature can be described as:The empirical model based on the Arrhenius equation proposed by Song et al.  is used To study the global optimization problem of IETMS, it is necessary to determine the control and state variables of the PHEV system. Considering the need to ensure the thermal comfort of the cabin during the driving process in a high-temperature environment, the cabin temperature is generally maintained in a constant suitable temperature range, which decreases significantly only in the initial driving phase. Therefore, it is assumed that the cabin temperature and cabin cooling power C system . Further−4. It means that if the engine start state The goal of PHEV energy management is to improve vehicle fuel economy. During the actual driving process of the vehicle, the battery SOC decreases and consumes electricity, leading to battery life degradation. Therefore, the total driving costs of fuel consumption research ,16. The e 0.55 g  to avoid 176/kWh .Due to the system characteristics of vehicle components, the engine output power, battery output power, and battery cooling power are only taken within a certain range. For the state variables, their value range also needs to be limited according to the actual situation. Otherwise, the cabin thermal comfort is preferred to the battery cooling, so the maximum battery cooling power is the difference between the maximum cooling power of the AC system Calculate cost-to-go function Calculate cost-to-go function According to the optimal cost-to-go function Repeat the above steps until the initial state, and complete the backward optimization process of the DP algorithm. Then, through the state equation of the PHEV system, the optimal state and optimal control sequences of each time stage are solved in a forward direction to determine the global optimal solution.When solving global optimization problems using the DP algorithm, it is needed to decompose multi-stage decisions into a series of single-stage optimization problems. The initial and terminal values of state variables are determined in advance. Subsequently, to formulate the optimal decision sequence, each single-stage optimization problem is sequentially solved in a backward direction to minimize the cost-to-go function. Finally, the optimal state of each stage is calculated forward from the initial state according to the optimal decision sequence. The solving principle of the DP algorithm is shown in The constraints in Equation (15) specify the range of the control variables and state variables, but there is a coupling relationship between these. Due to the finite value range of the control variables, the change of the state variable SOC and battery temperature in adjacent time stages is also limited according to Equations (6) and (8), and there are no sudden changes in state variables. Therefore, if the initial and terminal values of the state variables are determined in advance, the actual feasible region of the state at each time may be less than the constraint range in Equation (15). AGO–DP is proposed in this paper to determine the actual value range of the state variables at different time stages and remove the infeasible region in the state constraints by combining the system state equation. At the same time, according to the practical, feasible region of state variables at different time stages, the interval of state grid points is adaptively adjusted to improve the accuracy of global optimization by DP without increasing the additional computation time.The variation range of SOC at each time stage is related to the maximum and minimum output power of the battery at that time. The output power limit of the battery at time After determining the actual limit of the battery output power, the recursive calculation in a forward direction can be performed from the SOC initial state. Then the upper and lower limits of SOC at the next time stage correspond to the minimum output power and maximum output power of the battery, which is expressed as:The recursive calculation in a backward direction is performed from the SOC terminal state. The upper and lower limits of SOC at the previous time correspond to the maximum output power and minimum output power of the battery, respectively, which is expressed as:According to the above method, the forward recursive boundary According to the SOC feasible region range at each time stage, the SOC grid interval is adaptively adjusted to ensure that all state grid points are within the feasible region. In this paper, the state grid points are evenly distributed, and the number of SOC grid points in the global optimization problem is set to To reduce the range of SOC and optimize the grid distribution, it is necessary to recursively calculate the SOC variation ranges along the forward and backward directions. Furthermore, combine the upper and lower limits of SOC constraints to take the intersection of these ranges as the actual feasible region of SOC at each time stage. The specific steps are as follows:If the maximum output power If the plus–minus sign of the maximum battery output power and the minimum battery output power are opposite, it means that the battery does not need to be operated at this moment, and the demand power can be met only by the engine. Hence, the maximum heat generation can still be calculated according to Equation (29), while the minimum heat generation is 0.The battery heat exchange boundary at each moment can be calculated by:The recursive calculation in a forward direction is performed from the battery temperature’s initial state. The upper and lower boundaries of battery temperature at each time can be calculated by:The recursive calculation in a backward direction is performed from the battery temperature terminal state. The boundaries of battery temperature can be expressed as:According to the forward recursive boundary Similarly, the state grid points of battery temperature are evenly distributed. The number of grid points of battery temperature is set to When optimizing the feasible region of battery temperature during the vehicle driving process, the key lies in determining the real-time output current range, thereby calculating the maximum and minimum heat generation of the battery at each time stage. According to Equation (8), it can be seen that when the absolute value of the battery current is the highest, and there is no battery cooling power, the battery has the maximum temperature rise. Conversely, when the absolute value of the battery current is the smallest and the cooling power is the highest, the battery temperature decreases to the maximum allowable extent. The feasible region optimization steps of battery temperature are as follows:In order to verify the superiority of AGO–DP in solving the IETMS global optimization problem of PHEV, three standard driving cycles are selected as test cycles: urban dynamometer driving schedule (UDDS), highway fuel economy test driving cycle (HWFET) and world light-duty vehicle test cycle (WLTC), and the speed curves are shown in 2, and the setpoint temperature of the cabin is 25 °C. According to the cabin thermal load expression proposed by Zhao [The simulation time step is set to 1 s to discretize the driving condition. The ambient temperature and the light intensity are set to 35 °C and 800 W/m by Zhao , the cabThe difference between the AGO–DP algorithm and the traditional DP algorithm is mainly reflected in the feasible region of the state variables. Taking the simulation result of the 5 × HWFET driving condition as an example, the practical, feasible region of SOC and battery temperature during the entire driving process optimized by AGO–DP is shown in The three test cycles in It can be seen from To verify the impact of state-feasible region reduction on global optimization performance, three sets of driving conditions are selected for simulation analysis under different grid point numbers of the state SOC and battery temperature variables. The traditional DP and AGO–DP are used for IETMS global optimization, respectively. The global optimal solution and computation time vary with the state grid point numbers, as shown in The global optimization performance of AGO–DP is significantly better than that of the traditional DP algorithm under various driving conditions. The optimal driving cost of AGO–DP decreases slightly with the increase of the grid point number, and its convergence rate is faster. For the driving conditions of WLTC and HWFET, even if the state variable grid is only 30, an ideal optimal cost can also be obtained. The optimal cost is basically stable when the grid point number reaches 50. However, DP has poor optimization performance when the grid point number is less than 100, and its convergence rate of the optimal cost is also slow. Meanwhile, the optimal driving cost of DP in a stable stage is slightly higher than AGO–DP. This is because the state grid points of DP are uniformly distributed within the limits of state variables, resulting in a large number of state grid points of DP being outside the actual feasible region, especially in the initial and final stages of the driving conditions. Not only does it increase the unnecessary computational burden, but it also has varying degrees of impact on the global optimization accuracy of each driving condition.The two algorithms have the greatest difference in global optimization performance for UDDS. Due to the low driving demand power of UDDS, the descent of SOC feasible region boundary is the slowest, as shown in The solid and dotted lines in It can also be seen from The IETMS global optimization performance of AGO–DP is significantly better than that of DP, and the average difference between the two algorithms is 6.99%. UDDS has the largest reduction ratio of feasible regions, resulting in the largest reduction of 16.85% in driving costs. In other types of driving conditions, the advantage of AGO–DP has weakened, but it is still better than the traditional DP algorithm.To illustrate the importance of developing IETMS for PHEVs, the energy-saving potential of IETMS and EMS global optimization is compared in this section. IETMS-DP strategy conforms to the development of the IETMS global optimization mentioned above. EMS-DP strategy only does EMS global optimization and ignores battery thermal management. Meanwhile, rule-based EMS (EMS-RB) and rule-based IETMS (IETMS-RB) are introduced for comparative analysis. Rule-based strategy uses charge-depleting and charge-sustaining (CD/CS) control  and propThe driving cost increases linearly with the number of driving cycles. This is because the energy the battery provides is the same in different driving conditions. As the number of cycles increases, the proportion of engine energy supply gradually increases, leading to a gradual increase in fuel consumption and driving cost. Although battery thermal management improves battery efficiency and slows down battery aging, the energy consumption cost of battery thermal management is higher than the cost of reducing battery aging. Therefore, the cost of IETMS is higher than that of EMS under the same driving condition. However, battery thermal management is crucial for the long-term health and safety of batteries, so although it increases the driving cost, it is indispensable. The distribution of driving costs varies under different driving conditions. The high-speed driving demand power of HWFET is relatively high, and the engine can operate in a high-efficient region in various strategies. The energy consumption for the battery thermal management is relatively small compared to the driving energy consumption, so the driving cost difference of various strategies is the smallest, and the energy-saving potential is also the smallest. Under this driving condition, the average energy-saving potential of EMS is only 2.28%, while the energy-saving potential of IETMS can reach over 4%. This is because, considering the thermal management, the battery can be cooled down to the appropriate temperature range, thereby reducing the battery capacity degradation caused by the high current and high-temperature operation, reducing driving costs, and improving energy-saving potential. The urban driving condition of UDDS is mainly low speed, with low driving demand power. The battery is the main power source, so the driving cost is the lowest. Moreover, the thermal management energy consumption under this condition accounts for a relatively large proportion, so the average energy-saving potential of IETMS has increased to 8.39%, which is 3.17% higher than EMS. The comprehensive driving conditions of WLTC include different speed ranges, making it difficult to achieve a good driving economy because of significant fluctuations in engine output power based on rule-based strategies. Global optimization can balance the output power of the engine and power battery based on global driving conditions to achieve optimal driving costs. This driving condition has the most obvious energy-saving potential, reaching a maximum of over 20%. Meanwhile, IETMS only improves by 0.55–1.57% compared to EMS.An improved DP algorithm is proposed in this paper to solve IETMS global optimization problem for PHEVs by reducing the optimization range of SOC and battery temperature and adaptively adjusting the grid distribution of state variables according to the actual feasible region. The feasible region of state variables under different driving conditions is effectively optimized by AGO–DP. The maximum reduction ratio of the feasible region reaches 74.1% of HWFET. Although the effect of feasible region reduction is weakened as the increase of driving cycle number, the reduction ratio is still nearly 30%, which reduces the computational burden for IETMS global optimization. Meanwhile, AGO–DP can obtain better global optimal driving costs more rapidly and accurately than DP. The global optimization computation time is reduced by 33.29–84.67%, and the accuracy of the global optimal solution is improved by 0.94–16.85%. The engine and AC system in AGO–DP both operate in a more efficient region. Furthermore, the IETMS energy-saving potential range for PHEVs is 3.68–23.74% under different driving conditions, which increases the energy-saving potential by 0.55–3.26% compared to EMS. In future research, AGO–DP proposed in this work will be used as a reference benchmark to guide the development of an online control strategy and further improve vehicle energy efficiency.