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Battery aging- and temperature-aware predictive energy management for hybrid electric vehicles

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成果类型:
期刊论文
作者:
Du, Ronghua;Hu, Xiaosong*;Xie, Shaobo*;Hu, Lin;Zhang, Zhiyong;...
通讯作者:
Hu, Xiaosong;Xie, Shaobo
作者机构:
[Hu, Lin; Du, Ronghua; Zhang, Zhiyong] Changsha Univ Sci & Technol, Sch Automot & Mech Engn, Changsha 410114, Peoples R China.
[Hu, Xiaosong] Chongqing Univ, Dept Automot Engn, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China.
[Xie, Shaobo] Changan Univ, Sch Automot Engn, Southern 2nd Rd, Xian 710064, Peoples R China.
[Lin, Xianke] Ontario Tech Univ, Dept Automot Mech & Mfg Engn, 2000 Simcoe St N, Oshawa, ON L1G 0C5, Canada.
通讯机构:
[Hu, Xiaosong; Xie, Shaobo] C
Chongqing Univ, Dept Automot Engn, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China.
Changan Univ, Sch Automot Engn, Southern 2nd Rd, Xian 710064, Peoples R China.
语种:
英文
关键词:
Automotive batteries;Dynamic programming;Dynamics;Energy management;Energy management systems;Energy utilization;Global optimization;Lithium-ion batteries;Markov chains;Model predictive control;Predictive control systems;Stochastic models;Stochastic systems;Thermal aging;Urban transportation;Battery temperature;Electrical thermal aging;Energy management strategies;Energy management strategies (EMS);Global optimization method;Optimal control frameworks;Parallel hybrid electric vehicles;Total energy consumption;Hybrid vehicles
期刊:
Journal of Power Sources
ISSN:
0378-7753
年:
2020
卷:
473
期:
Oct.15
页码:
228568
基金类别:
This work was supported in part by the NSF of China (Grant No. 51875054 , 61973047 ), the Hunan Science Foundation for Distinguished Young Scholars of China (Grant No. 2019JJ20017 ), and Shaanxi Province Natural Science Foundation (Grant No. 2019JQ-439 ). This work was supported in part by the NSF of China (Grant No. 51875054, 61973047), the Hunan Science Foundation for Distinguished Young Scholars of China (Grant No. 2019JJ20017), and Shaanxi Province Natural Science Foundation (Grant No. 2019JQ-439).
机构署名:
本校为第一机构
院系归属:
汽车与机械工程学院
摘要:
Lithium-ion battery degradation is one of the most important issues in hybrid electric vehicles. In order to minimize degradation and its equivalent battery life cost, the degradation of Li-ion batteries needs to be considered in energy management strategies. The existing methods mainly consider the battery aging in energy management strategies while ignoring its thermal dynamics. This paper proposes a battery aging- and temperature-aware predictive energy management strategy for parallel hybrid electric vehicles. This method is developed based...

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