工程科学学报.第44卷，第X期：1-10.2022年X月 Chinese Journal of Engineering,Vol.44,No.X:1-10,X 2022 https://doi.org/10.13374/j.issn2095-9389.2021.03.01.005;http://cje.ustb.edu.cn 基于Pareto的电池容量衰退权衡优化控制策略 林歆悠四，叶常青，苏炼 福州大学机械工程及自动化学院，福州350002 ☒通信作者，E-mail:linxyfzu@126.com 摘要由于插电式混合动力汽车电池可以通过电网获取比较廉价的电量，传统的控制策略只考虑充分利用电池电量，但忽 略了过度使用电池，会加快动力电池容量的衰退.因此，如何权衡充分利用电池电量与抑制电池容量衰退是新的研究重点. 基于电池的半经验衰退模型，引入电池利用程度因子，建立权衡电池容量衰退的能量管理策略.通过Pato非劣目标域选取 合适的权重因子，将多目标优化问题转化为单目标问题，采用动态规划算法获得权重系数全局最优解，通过权衡不同权重下 的油耗和电池容量衰退程度选择最优权重系数.在燃油消耗相当的情况下，当权重系数为09时，可有效抑制电池寿命的衰 减速度.最后，通过在线等效油耗最小策略仿真与在同一权重下的动态规划解进行比较来验证其有效性 关键词电池老化：能量管理策略：燃油消耗：权重系数；动态规划 分类号U461 Pareto-based optimal control strategy for battery capacity decline LIN Xin-you,YE Chang-qing,SU Lian School of Mechanical Engineering and Automation,Fuzhou University,Fuzhou 350002.China Corresponding author,E-mail:linxyfzu@126.com ABSTRACT As environmental problems become increasingly severe,achieving qualitative breakthroughs in the energy consumption and emissions of traditional internal combustion engine vehicles is difficult.In contrast,new energy vehicles are environmentally friendly and have low fuel consumption,which is important for the future development of vehicles.A plug-in hybrid electric vehicle (PHEV)is widely regarded as the most promising alternative solution for improving energy efficiency and reducing emissions.The optimization of the energy management strategy (EMS)mainly focuses on reducing fuel consumption and improving the economy. However,the durability of the power battery also needs attention,as the lack of life remains a major obstacle to the large-scale commercialization of PHEVs.Because PHEV batteries can obtain relatively cheap power through the grid,the traditional control strategy only considers the full use of the battery power but ignores its excessive use,which will accelerate the decline of the power battery capacity.Therefore,determining how to make full use of the battery power and control the decline of the battery capacity is a new research focus.Based on the semiempirical decay model of the battery,the energy management strategy of balancing the degradation of the battery capacity was established by introducing the battery utilization degree factor.The multiobjective optimization problem was transformed into a single-objective problem by selecting the appropriate weight factor through the Pareto noninferior target domain.A dynamic programming algorithm was used to obtain the global optimal solution of the weight coefficient.The optimal weight coefficient was selected by weighing the fuel consumption and battery capacity decline degree under different weights.In the case of equivalent fuel consumption,the decay rate of battery life can be effectively inhibited when the weight coefficient is 0.9.Finally,the validity of the proposed solution is verified by comparing the online equivalent consumption minimization strategy(ECMS)simulation 收稿日期：2021-03-01 基金项目：福建省自然科学基金资助项目(202001449)：国家自然科学基金资助项目(51505086)：安徽工程大学检测技术与节能装置安徽 省重点实验室开放研究基金资助项目(JCKJ2021A04)基于 Pareto 的电池容量衰退权衡优化控制策略 林歆悠苣，叶常青，苏    炼 福州大学机械工程及自动化学院，福州 350002 苣通信作者， E-mail: linxyfzu@126.com 摘    要    由于插电式混合动力汽车电池可以通过电网获取比较廉价的电量，传统的控制策略只考虑充分利用电池电量，但忽 略了过度使用电池，会加快动力电池容量的衰退. 因此，如何权衡充分利用电池电量与抑制电池容量衰退是新的研究重点. 基于电池的半经验衰退模型，引入电池利用程度因子，建立权衡电池容量衰退的能量管理策略. 通过 Pareto 非劣目标域选取 合适的权重因子，将多目标优化问题转化为单目标问题，采用动态规划算法获得权重系数全局最优解，通过权衡不同权重下 的油耗和电池容量衰退程度选择最优权重系数. 在燃油消耗相当的情况下，当权重系数为 0.9 时，可有效抑制电池寿命的衰 减速度. 最后，通过在线等效油耗最小策略仿真与在同一权重下的动态规划解进行比较来验证其有效性. 关键词    电池老化；能量管理策略；燃油消耗；权重系数；动态规划 分类号    U461 Pareto-based optimal control strategy for battery capacity decline LIN Xin-you苣 ，YE Chang-qing，SU Lian School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350002, China 苣 Corresponding author, E-mail: linxyfzu@126.com ABSTRACT    As environmental problems become increasingly severe, achieving qualitative breakthroughs in the energy consumption and  emissions  of  traditional  internal  combustion  engine  vehicles  is  difficult.  In  contrast,  new  energy  vehicles  are  environmentally friendly and have low fuel consumption, which is important for the future development of vehicles. A plug-in hybrid electric vehicle (PHEV)  is  widely  regarded  as  the  most  promising  alternative  solution  for  improving  energy  efficiency  and  reducing  emissions.  The optimization  of  the  energy  management  strategy  (EMS)  mainly  focuses  on  reducing  fuel  consumption  and  improving  the  economy. However,  the  durability  of  the  power  battery  also  needs  attention,  as  the  lack  of  life  remains  a  major  obstacle  to  the  large-scale commercialization  of  PHEVs.  Because  PHEV  batteries  can  obtain  relatively  cheap  power  through  the  grid,  the  traditional  control strategy only considers the full use of the battery power but ignores its excessive use, which will accelerate the decline of the power battery capacity. Therefore, determining how to make full use of the battery power and control the decline of the battery capacity is a new  research  focus.  Based  on  the  semiempirical  decay  model  of  the  battery,  the  energy  management  strategy  of  balancing  the degradation of the battery capacity was established by introducing the battery utilization degree factor. The multiobjective optimization problem was transformed into a single-objective problem by selecting the appropriate weight factor through the Pareto noninferior target domain. A dynamic programming algorithm was used to obtain the global optimal solution of the weight coefficient. The optimal weight coefficient was selected by weighing the fuel consumption and battery capacity decline degree under different weights. In the case of equivalent fuel consumption, the decay rate of battery life can be effectively inhibited when the weight coefficient is 0.9. Finally, the validity of the proposed solution is verified by comparing the online equivalent consumption minimization strategy (ECMS) simulation 收稿日期: 2021−03−01 基金项目: 福建省自然科学基金资助项目（2020J01449）；国家自然科学基金资助项目（51505086）；安徽工程大学检测技术与节能装置安徽 省重点实验室开放研究基金资助项目（JCKJ2021A04） 工程科学学报，第 44 卷，第 X 期：1−10，2022 年 X 月 Chinese Journal of Engineering, Vol. 44, No. X: 1−10, X 2022 https://doi.org/10.13374/j.issn2095-9389.2021.03.01.005; http://cje.ustb.edu.cn