Adaptively coordinated optimization of battery aging and energy management in plug-in hybrid electric buses

被引:86
|
作者
Zhang, Shuo [1 ]
Hu, Xiaosong [1 ]
Xie, Shaobo [2 ]
Song, Ziyou [3 ]
Hu, Lin [4 ]
Hou, Cong [5 ]
机构
[1] Chongqing Univ, Dept Automot Engn, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China
[2] Changan Univ, Sch Automot Engn, Xian 710064, Shaanxi, Peoples R China
[3] Univ Michigan, Dept Naval Architecture & Marine Engn, Ann Arbor, MI 48109 USA
[4] Changsha Univ Sci & Technol, Sch Automot & Mech Engn, Changsha 410114, Hunan, Peoples R China
[5] Chongqing Changan Automobile Co Ltd, Powertrain R&D Inst, Chongqing 400023, Peoples R China
基金
中国国家自然科学基金;
关键词
Plug-in hybrid electric vehicle; Energy management; Pontryagin's minimum principle; Battery degradation; Fuel economy; Co-optimization; PONTRYAGINS MINIMUM PRINCIPLE; POWER MANAGEMENT; FUEL-CELL; STRATEGY; MODEL; LIFETIME; VEHICLES; SYSTEM; DESIGN;
D O I
10.1016/j.apenergy.2019.113891
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Plug-in hybrid electric buses with large battery packs exhibit salient advantages in increasing fuel economy and reducing toxic emissions. However, they may be subject to expensive battery replacement caused by battery aging. This paper designs an online, coordinated optimization approach, based on Pontryagin's minimum principle, for a single-shaft parallel plug-in hybrid electric bus, aiming at minimizing the total cost of energy consumption and battery degradation. Specifically, three key contributions are delivered to complement the relevant literature. First, a capacity loss model for lithium ion batteries emulating dynamics of both cycle life and calendar life is exploited in the optimization framework, in order to highlight the importance of considering calendar life and its implication to overall energy management performance in real bus operations. Second, the online adaptive mechanism of the optimization method with respect to varying driving conditions is achieved by tracking two reference trajectories to adjust the state of charge and effective ampere-hour throughput of the battery. Finally, to verify the effectiveness of the proposed scheme, various comparative studies are carried out, accounting for different driving scenarios. Simulation results show that the maximum control errors between the proposed strategy and Pontryagin's minimum principle are only 0.4% in the battery capacity loss and 2.7% in fuel economy under four random driving cycles, which indicates the prominent adaptability and optimization performance of the designed strategy.
引用
收藏
页数:15
相关论文
共 50 条
  • [1] Integrated Component Optimization and Energy Management for Plug-In Hybrid Electric Buses
    Liu, Xiaodong
    Ma, Jian
    Zhao, Xuan
    Zhang, Yixi
    Zhang, Kai
    He, Yilin
    [J]. PROCESSES, 2019, 7 (08)
  • [2] A Study on Coordinated Optimization on Battery Capacity and Energy Management Strategy for a Plug-in Hybrid Electric Bus
    Xie S.
    Xin Z.
    Li H.
    Liu T.
    Wei L.
    [J]. Qiche Gongcheng/Automotive Engineering, 2018, 40 (06): : 625 - 631and645
  • [3] Coordinated management of connected plug-in hybrid electric buses for energy saving, inter-vehicle safety, and battery health
    Xie, Shaobo
    Qi, Shanwei
    Lang, Kun
    Tang, Xiaolin
    Lin, Xianke
    [J]. APPLIED ENERGY, 2020, 268
  • [4] Battery aging conscious intelligent energy management strategy and sensitivity analysis of the critical factors for plug-in hybrid electric buses
    Lopez-Ibarra, Jon Ander
    Goitia-Zabaleta, Nerea
    Isaac Herrera, Victor
    Gazta Naga, Haizea
    Camblong, Haritza
    [J]. ETRANSPORTATION, 2020, 5
  • [5] Optimization Research on Energy Management Strategies and Powertrain Parameters for Plug-In Hybrid Electric Buses
    Wang, Lufeng
    Zhou, Juanying
    Zhao, Jianyou
    [J]. World Electric Vehicle Journal, 2024, 15 (11)
  • [6] Plug-in hybrid electric buses total cost of ownership optimization at fleet level based on battery aging
    Ander Lopez-Ibarra, Jon
    Gaztanaga, Haizea
    Saez-de-Ibarra, Andoni
    Camblong, Haritza
    [J]. APPLIED ENERGY, 2020, 280
  • [7] AGRU and convex optimization based energy management for plug-in hybrid electric bus considering battery aging
    Du, Yi
    Cui, Naxin
    Cui, Wei
    Li, Tao
    Ren, Fei
    Zhang, Chenghui
    [J]. ENERGY, 2023, 277
  • [8] Research on influence of battery aging on energy management economy for plug-in hybrid electric vehicle
    Chen, Zeyu
    Lu, Jiahuan
    Yang, Ying
    Xiong, Rui
    [J]. 2017 CHINESE AUTOMATION CONGRESS (CAC), 2017, : 3117 - 3121
  • [9] Battery anti-aging control for a plug-in hybrid electric vehicle with a hierarchical optimization energy management strategy
    Bai, Yunfei
    He, Hongwen
    Li, Jianwei
    Li, Shuangqi
    Wang, Ya-xiong
    Yang, Qingqing
    [J]. JOURNAL OF CLEANER PRODUCTION, 2019, 237
  • [10] Intelligent Electric Drive Management for Plug-in Hybrid Buses
    Ruiz, Patricia
    Arias, Aaron
    Massobrio, Renzo
    de la Torre, Juan Carlos
    Seredynski, Marcin
    Dorronsoro, Bernabe
    [J]. OPTIMIZATION AND LEARNING, 2020, 1173 : 85 - 97