{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T05:21:58Z","timestamp":1776403318744,"version":"3.51.2"},"reference-count":36,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,13]],"date-time":"2023-08-13T00:00:00Z","timestamp":1691884800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["52102458"],"award-info":[{"award-number":["52102458"]}]},{"name":"National Natural Science Foundation of China","award":["20210301020GX"],"award-info":[{"award-number":["20210301020GX"]}]},{"name":"Jilin Province Science and Technology Development Program","award":["52102458"],"award-info":[{"award-number":["52102458"]}]},{"name":"Jilin Province Science and Technology Development Program","award":["20210301020GX"],"award-info":[{"award-number":["20210301020GX"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2013DP) 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\u2013DP 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\u201384.67%, and the accuracy of the global optimal solution is improved by 0.94\u201316.85% compared to DP. The optimal control of the engine and air conditioning system is also more efficient and reasonable. Furthermore, AGO\u2013DP is applied to explore IETMS energy-saving potential for PHEVs. It is found that the IETMS energy-saving potential range is 3.68\u201323.74% under various driving conditions, which increases the energy-saving potential by 0.55\u20133.26% compared to just doing the energy management.<\/jats:p>","DOI":"10.3390\/s23167149","type":"journal-article","created":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T11:07:10Z","timestamp":1692011230000},"page":"7149","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Research on Global Optimization Algorithm of Integrated Energy and Thermal Management for Plug-In Hybrid Electric Vehicles"],"prefix":"10.3390","volume":"23","author":[{"given":"Junyu","family":"Jiang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China"}]},{"given":"Yuanbin","family":"Yu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China"}]},{"given":"Haitao","family":"Min","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China"}]},{"given":"Weiyi","family":"Sun","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China"}]},{"given":"Qiming","family":"Cao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China"}]},{"given":"Tengfei","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China"}]},{"given":"Deping","family":"Wang","sequence":"additional","affiliation":[{"name":"China FAW Group Co., Ltd., Changchun 130013, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"120393","DOI":"10.1016\/j.apenergy.2022.120393","article-title":"A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: Impacts analysis, collaborative management technologies, and future perspective","volume":"331","author":"Zou","year":"2023","journal-title":"Appl. Energy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1016\/j.jclepro.2019.01.056","article-title":"Energy and environmental assessment of a traction lithium-ion battery pack for plug-in hybrid electric vehicles","volume":"215","author":"Cusenza","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"126498","DOI":"10.1016\/j.energy.2022.126498","article-title":"Two-scale based energy management for connected plug-in hybrid electric vehicles with global optimal energy consumption and state-of-charge trajectory prediction","volume":"267","author":"Jin","year":"2023","journal-title":"Energy"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.pecs.2019.04.002","article-title":"Energy management strategies of connected HEVs and PHEVs: Recent progress and outlook","volume":"73","author":"Zhang","year":"2019","journal-title":"Prog. Energy Combust. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"126772","DOI":"10.1016\/j.energy.2023.126772","article-title":"An energy management strategy for plug-in hybrid electric vehicles based on deep learning and improved model predictive control","volume":"269","author":"Sun","year":"2023","journal-title":"Energy"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Liu, Y., Liang, J., Song, J., and Ye, J. (2022). Research on Energy Management Strategy of Fuel Cell Vehicle Based on Multi-Dimensional Dynamic Programming. Energies, 15.","DOI":"10.3390\/en15145190"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Pu, S., Chu, L., Hu, J., Li, S., and Hou, Z. (2022). An Equivalent Consumption Minimization Strategy for a Parallel Plug-In Hybrid Electric Vehicle Based on an Environmental Perceiver. Sensors, 22.","DOI":"10.3390\/s22249621"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2393","DOI":"10.1109\/TVT.2007.899933","article-title":"Control Strategies for Hybrid Electric Vehicles: Evolution, Classification, Comparison, and Future Trends","volume":"56","author":"Salmasi","year":"2007","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1016\/j.energy.2019.03.155","article-title":"Development of energy management system based on a rule-based power distribution strategy for hybrid power sources","volume":"175","author":"Wang","year":"2019","journal-title":"Energy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2660","DOI":"10.1016\/j.egypro.2017.03.771","article-title":"Energy Management Strategy of a Plug-in Parallel Hybrid Electric Vehicle Using Fuzzy Control","volume":"105","author":"Ming","year":"2017","journal-title":"Energy Procedia"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"125620","DOI":"10.1016\/j.energy.2022.125620","article-title":"Integrated energy management of hybrid power supply based on short-term speed prediction","volume":"262","author":"Wu","year":"2023","journal-title":"Energy"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"120993","DOI":"10.1016\/j.energy.2021.120993","article-title":"Cost-optimal energy management strategy for plug-in hybrid electric vehicles with variable horizon speed prediction and adaptive state-of-charge reference","volume":"232","author":"Guo","year":"2021","journal-title":"Energy"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, S., Hu, M., Gong, C., Zhan, S., and Qin, D. (2018). Energy Management Strategy for Hybrid Electric Vehicle Based on Driving Condition Identification Using KGA-Means. Energies, 11.","DOI":"10.3390\/en11061531"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, S., Chu, L., Hu, J., Pu, S., Li, J., Hou, Z., and Sun, W. (2023). A Novel A-ECMS Energy Management Strategy Based on Dragonfly Algorithm for Plug-in FCEVs. Sensors, 23.","DOI":"10.3390\/s23031192"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"125405","DOI":"10.1016\/j.energy.2022.125405","article-title":"Grey Markov prediction-based hierarchical model predictive control energy management for fuel cell\/battery hybrid unmanned aerial vehicles","volume":"262","author":"Yao","year":"2023","journal-title":"Energy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"126971","DOI":"10.1016\/j.energy.2023.126971","article-title":"Reinforcement learning-based real-time intelligent energy management for hybrid electric vehicles in a model predictive control framework","volume":"270","author":"Yang","year":"2023","journal-title":"Energy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"12667","DOI":"10.1109\/TVT.2022.3196113","article-title":"A Novel Model Predictive Control Based Co-Optimization Strategy for Velocity Planning and Energy Management of Intelligent PHEVs","volume":"71","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"114970","DOI":"10.1016\/j.enconman.2021.114970","article-title":"Comparative study on fuel saving potential of series-parallel hybrid transmission and series hybrid transmission","volume":"252","author":"Xu","year":"2022","journal-title":"Energy Convers. Manag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"119098","DOI":"10.1016\/j.apenergy.2022.119098","article-title":"Decoding the optimal charge depletion behavior in energy domain for predictive energy management of series plug-in hybrid electric vehicle","volume":"316","author":"Zhou","year":"2022","journal-title":"Appl. Energy"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"114408","DOI":"10.1016\/j.enconman.2021.114408","article-title":"Energy management strategy on a parallel mild hybrid electric vehicle based on breadth first search algorithm","volume":"243","author":"Hao","year":"2021","journal-title":"Energy Convers. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"227024","DOI":"10.1016\/j.jpowsour.2019.227024","article-title":"Load-adaptive real-time energy management strategy for battery\/ultracapacitor hybrid energy storage system using dynamic programming optimization","volume":"438","author":"Liu","year":"2019","journal-title":"J. Power Source"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6310","DOI":"10.3934\/mbe.2020333","article-title":"A-EMCS for PHEV based on real-time driving cycle prediction and personalized travel characteristics","volume":"17","author":"Yu","year":"2020","journal-title":"Math. Biosci. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4312","DOI":"10.1109\/TVT.2021.3073126","article-title":"Model Predictive Control with Stochastically Approximated Cost-to-Go for Battery Cooling System of Electric Vehicles","volume":"70","author":"Park","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wei, C., Hofman, T., Caarls, E.I., and Iperen, R.V. (2019, January 14\u201317). Evolution and Classification of Energy and Thermal Management Systems in Electrified Powertrains. Proceedings of the 2019 IEEE Vehicle Power and Propulsion Conference (VPPC), Hanoi, Vietnam.","DOI":"10.1109\/VPPC46532.2019.8952261"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.1109\/TCST.2019.2923792","article-title":"Cabin and Battery Thermal Management of Connected and Automated HEVs for Improved Energy Efficiency Using Hierarchical Model Predictive Control","volume":"28","author":"Amini","year":"2020","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"100196","DOI":"10.1016\/j.etran.2022.100196","article-title":"Battery lifetime extension through optimal design and control of traction and heating systems in hybrid drivetrains","volume":"14","author":"Widmer","year":"2022","journal-title":"eTransportation"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.jpowsour.2018.10.047","article-title":"Dynamic programming for New Energy Vehicles based on their work modes part I: Electric Vehicles and Hybrid Electric Vehicles","volume":"406","author":"Zhou","year":"2018","journal-title":"J. Power Source"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"116920","DOI":"10.1016\/j.apenergy.2021.116920","article-title":"Optimal mesh discretization of the dynamic programming for hybrid electric vehicles","volume":"292","author":"Maino","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"123825","DOI":"10.1016\/j.energy.2022.123825","article-title":"Determination of vehicle working modes for global optimization energy management and evaluation of the economic performance for a certain control strategy","volume":"251","author":"Xu","year":"2022","journal-title":"Energy"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, H., Wang, C., Zhao, X., and Guo, C. (2018). An Adaptive-Equivalent Consumption Minimum Strategy for an Extended-Range Electric Bus Based on Target Driving Cycle Generation. Energies, 11.","DOI":"10.3390\/en11071805"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"104844","DOI":"10.1016\/j.est.2022.104844","article-title":"Modeling and total cost optimization of battery thermal management system in a hybrid electric vehicle","volume":"52","author":"Asef","year":"2022","journal-title":"J. Energy Storage"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1016\/j.apenergy.2015.08.120","article-title":"The optimization of a hybrid energy storage system at subzero temperatures: Energy management strategy design and battery heating requirement analysis","volume":"159","author":"Song","year":"2015","journal-title":"Appl. Energy"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"126606","DOI":"10.1016\/j.energy.2022.126606","article-title":"A two-stage eco-cooling control strategy for electric vehicle thermal management system considering multi-source information fusion","volume":"267","author":"Zhao","year":"2023","journal-title":"Energy"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"111194","DOI":"10.1016\/j.rser.2021.111194","article-title":"A review of pivotal energy management strategies for extended range electric vehicles","volume":"149","author":"Xiao","year":"2021","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"232513","DOI":"10.1016\/j.jpowsour.2022.232513","article-title":"Differences in the deterioration behaviors of fast-charged lithium-ion batteries at high and low temperatures","volume":"556","author":"Du","year":"2023","journal-title":"J. Power Source"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"229939","DOI":"10.1016\/j.jpowsour.2021.229939","article-title":"A real-time optimization energy management of range extended electric vehicles for battery lifetime and energy consumption","volume":"498","author":"Li","year":"2021","journal-title":"J. Power Source"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7149\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:32:45Z","timestamp":1760128365000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7149"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,13]]},"references-count":36,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["s23167149"],"URL":"https:\/\/doi.org\/10.3390\/s23167149","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,13]]}}}