摘要
行驶工况下电动汽车锂离子电池剩余使用寿命(RUL)衰退情况复杂,准确的RUL预测可为电池的定期维护和安全稳定运行提供指导,避免安全隐患。为此,该文提出一种适用于行驶工况下电动汽车电池的RUL预测方法。首先,针对行驶工况,提出一种基于轻量型梯度提升机(LightGBM)的RUL预测模型,利用元学习超参数优化方法对其进行超参数调优;其次,搭建行驶工况下电池全生命周期容量测试系统,模拟行驶工况下电池所受振动应力、充放电应力环境和测试电池容量衰退情况;然后,基于动态时间规整对容量衰退的相似性分析结果,使用生成对抗网络(GAN)生成新的容量序列;最后,通过实验数据验证所提模型和生成容量序列的有效性。
The degradation of the remaining useful life(RUL)for EV lithium-ion battery under driving conditions is complicated.The appropriate prediction of RUL can provide guidance for the periodic maintenance and stable operation to avoid the risks.Therefore,a RUL prediction method for driving conditions is proposed in this paper.Firstly,a light gradient boosting machine(LightGBM)based RUL prediction model is constructed,and the coefficients are obtained by the hyper parameter optimization(Hyperopt).Secondly,the experimental bench of battery cycle life capacity is established to simulate the vibration stress and charge-discharge stress,and the RUL degradation of battery under driving conditions is measured.Then,based on the dynamic time warping(DTW),the similarity of RUL degradation between driving conditions and static conditions is analyzed,and a new capacity sequence can be generated by the generative adversarial networks(GAN).Finally,experimental results verify the effectiveness of the proposed model and the generated capacity sequence.
作者
肖迁
焦志鹏
穆云飞
陆文标
贾宏杰
Xiao Qian;Jiao Zhipeng;Mu Yunfei;Lu Wenbiao;Jia Hongjie(Key Laboratory of Smart Grid of Ministry of Education Tianjin University,Tianjin 300072 China;State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology,Tianjin 300130 China)
出处
《电工技术学报》
EI
CSCD
北大核心
2021年第24期5176-5185,共10页
Transactions of China Electrotechnical Society
基金
国家自然科学基金(U2066213,52107121)
中国博士后科学基金(2020M680880)资助项目。
关键词
电动汽车
行驶工况
锂离子电池
剩余使用寿命
轻量型梯度提升机
Electric vehicle
driving conditions
lithium-ion battery
remaining useful life
light gradient boosting machine(LightGBM)
