{"title":"集成地图信息和个性化驾驶特性的串并联混合动力变速器能源管理策略","authors":"","doi":"10.1016/j.etran.2024.100348","DOIUrl":null,"url":null,"abstract":"<div><p>The integration of multi-source intelligent and connected information during a driving trip, along with its online application to globally optimized energy management strategies, has emerged as a crucial technical approach for enhancing the energy-saving effectiveness of hybrid transmissions. However, the action mode of such information and the optimization calculation efficiency of existing dynamic programming (DP) methods limit the online application of the aforementioned strategies with global optimization capabilities. To address these problems, the present study proposes a hierarchical energy management strategy that follows the reference trajectory of the battery state of charge (SoC) and comprehensively considers the multi-source information on the driving trip. First, a global speed prediction model based on personalized driving characteristics is proposed to obtain an accurate driving cycle input for the space-domain DP method. Second, the aforementioned tasks as well as the working-mode decision of the hybrid transmission and the multi-power-source torque distribution calculation tasks are deployed in the dual-core controller. Finally, the hierarchical energy management strategy is verified via vehicle testing. Compared with the DP strategy, the proposed strategy has an energy-saving potential of 4.17% that is yet to be realized. Furthermore, compared with the charge-depleting and charge-sustaining (CD–CS) strategy, the proposed strategy reduces fuel consumption by 0.38 L per 100 km, and its energy-saving effect is significant. This study is the first to apply the DP method to the vehicle controller, thereby facilitating the online application of energy management strategies with global optimization capabilities.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":null,"pages":null},"PeriodicalIF":15.0000,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Energy management strategy of series–parallel hybrid transmission integrating map information and personalized driving characteristics\",\"authors\":\"\",\"doi\":\"10.1016/j.etran.2024.100348\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The integration of multi-source intelligent and connected information during a driving trip, along with its online application to globally optimized energy management strategies, has emerged as a crucial technical approach for enhancing the energy-saving effectiveness of hybrid transmissions. However, the action mode of such information and the optimization calculation efficiency of existing dynamic programming (DP) methods limit the online application of the aforementioned strategies with global optimization capabilities. To address these problems, the present study proposes a hierarchical energy management strategy that follows the reference trajectory of the battery state of charge (SoC) and comprehensively considers the multi-source information on the driving trip. First, a global speed prediction model based on personalized driving characteristics is proposed to obtain an accurate driving cycle input for the space-domain DP method. Second, the aforementioned tasks as well as the working-mode decision of the hybrid transmission and the multi-power-source torque distribution calculation tasks are deployed in the dual-core controller. Finally, the hierarchical energy management strategy is verified via vehicle testing. Compared with the DP strategy, the proposed strategy has an energy-saving potential of 4.17% that is yet to be realized. Furthermore, compared with the charge-depleting and charge-sustaining (CD–CS) strategy, the proposed strategy reduces fuel consumption by 0.38 L per 100 km, and its energy-saving effect is significant. This study is the first to apply the DP method to the vehicle controller, thereby facilitating the online application of energy management strategies with global optimization capabilities.</p></div>\",\"PeriodicalId\":36355,\"journal\":{\"name\":\"Etransportation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":15.0000,\"publicationDate\":\"2024-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Etransportation\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2590116824000389\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Etransportation","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590116824000389","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Energy management strategy of series–parallel hybrid transmission integrating map information and personalized driving characteristics
The integration of multi-source intelligent and connected information during a driving trip, along with its online application to globally optimized energy management strategies, has emerged as a crucial technical approach for enhancing the energy-saving effectiveness of hybrid transmissions. However, the action mode of such information and the optimization calculation efficiency of existing dynamic programming (DP) methods limit the online application of the aforementioned strategies with global optimization capabilities. To address these problems, the present study proposes a hierarchical energy management strategy that follows the reference trajectory of the battery state of charge (SoC) and comprehensively considers the multi-source information on the driving trip. First, a global speed prediction model based on personalized driving characteristics is proposed to obtain an accurate driving cycle input for the space-domain DP method. Second, the aforementioned tasks as well as the working-mode decision of the hybrid transmission and the multi-power-source torque distribution calculation tasks are deployed in the dual-core controller. Finally, the hierarchical energy management strategy is verified via vehicle testing. Compared with the DP strategy, the proposed strategy has an energy-saving potential of 4.17% that is yet to be realized. Furthermore, compared with the charge-depleting and charge-sustaining (CD–CS) strategy, the proposed strategy reduces fuel consumption by 0.38 L per 100 km, and its energy-saving effect is significant. This study is the first to apply the DP method to the vehicle controller, thereby facilitating the online application of energy management strategies with global optimization capabilities.
期刊介绍:
eTransportation is a scholarly journal that aims to advance knowledge in the field of electric transportation. It focuses on all modes of transportation that utilize electricity as their primary source of energy, including electric vehicles, trains, ships, and aircraft. The journal covers all stages of research, development, and testing of new technologies, systems, and devices related to electrical transportation.
The journal welcomes the use of simulation and analysis tools at the system, transport, or device level. Its primary emphasis is on the study of the electrical and electronic aspects of transportation systems. However, it also considers research on mechanical parts or subsystems of vehicles if there is a clear interaction with electrical or electronic equipment.
Please note that this journal excludes other aspects such as sociological, political, regulatory, or environmental factors from its scope.