基于Aseq2seq-PF的实车锂离子动力电池剩余使用寿命预测

doi:10.19562/j.chinasae.qcgc.2023.12.017

汽车工程 ›› 2023, Vol. 45 ›› Issue (12): 2348-2356.doi: 10.19562/j.chinasae.qcgc.2023.12.017

所属专题：新能源汽车技术-动力电池&燃料电池2023年

基于Aseq2seq-PF的实车锂离子动力电池剩余使用寿命预测

兰凤崇,潘威,陈吉清

华南理工大学机械与汽车工程学院，广州 510640
^2.华南理工大学，广东省汽车工程重点实验室，广州 510640

收稿日期:2023-03-16 修回日期:2023-04-13 出版日期:2023-12-25 发布日期:2023-12-21
通讯作者: 陈吉清
基金资助:
国家重点研发计划(2018YFB0104100);广东省科技计划(2015B010137002);全国车辆事故深度调查体系（NAIS）和新能源汽车事故调查协作网资助

Prediction of Remaining Useful Life of Real-World Vehicle Lithium-Ion Power Battery Based on Aseq2seq-PF

Fengchong Lan,Wei Pan,Jiqing Chen

School of Mechanical and Automotive Engineering，South China University of Technology，Guangzhou 510640
^2.South China University of Technology，Guangdong Provincial Automobile Engineering Key Laboratory，Guangzhou 510640

Received:2023-03-16 Revised:2023-04-13 Online:2023-12-25 Published:2023-12-21
Contact: Jiqing Chen

摘要/Abstract

摘要：

锂离子动力电池剩余使用寿命（RUL）预测可评估电池未来状态，实现指导电池维护和降低故障危害。实车工况中电池循环条件不受控制，动态运行条件下的RUL预测仍存在杂乱数据处理困难、预测结果精度较差且无法兼顾老化不确定性等问题，提出注意力机制序列到序列-粒子滤波（Aseq2seq-PF）混合模型，选取公共荷电状态（SOC）充电区间获取归一化容量，采用迭代和直接的融合预测策略，Aseq2seq模型作为迭代部分实现容量序列精确预测，粒子滤波（PF）模型作为直接部分实现容量波动的不确定性预测，外推容量衰退趋势预测RUL。经实车动力电池数据验证，公共SOC充电区间有效获取了清晰容量衰退趋势，混合模型提高了容量衰退长期预测精度，具有良好鲁棒性，对比已有模型平均绝对误差下降56%以上，且输出满足不同应用需求的置信区间，实现老化不确定性描述。

关键词: 锂离子动力电池, 剩余使用寿命（RUL）, 实车数据, 序列到序列, 老化不确定性

Abstract:

Remaining useful life （RUL） prediction of lithium-ion power battery can estimate the future state of batteries， which can guide battery maintenance and reduce the risk of failure. The battery cycle conditions are not controlled in real-world vehicle conditions， and RUL prediction under dynamic operating conditions still suffers from difficulties in processing messy data， poor accuracy of prediction results and inability to take aging uncertainty into account. For this case， the Attention Mechanism Sequence to Sequence-Particle Filter （Aseq2seq-PF） hybrid model is proposed， where the common State of Charge （SOC） charging interval is selected to obtain the normalized battery capacity and a fusion prediction strategy of Iteration and Direct is adopted， with the Aseq2seq model as the Iteration part to achieve accurate prediction of capacity sequences， the PF model as the Direct part to achieve uncertainty prediction of capacity fluctuations， and RUL is predicted by extrapolating the trend of battery capacity degradation. Verified by the real-world vehicle power battery data， the public SOC charging interval effectively obtains a clear trend of capacity degradation. The hybrid model improves the long-term prediction accuracy of the capacity degradation with good robustness， with reduction of average absolute error by more than 56% compared with existing models， and outputs confidence intervals to meet the needs of different application to achieve aging uncertainty description.

Key words: lithium-ion power battery, remaining useful life （RUL）, real-world vehicle data, sequence to sequence, aging uncertainty

兰凤崇,潘威,陈吉清. 基于Aseq2seq-PF的实车锂离子动力电池剩余使用寿命预测[J]. 汽车工程, 2023, 45(12): 2348-2356.

Fengchong Lan,Wei Pan,Jiqing Chen. Prediction of Remaining Useful Life of Real-World Vehicle Lithium-Ion Power Battery Based on Aseq2seq-PF[J]. Automotive Engineering, 2023, 45(12): 2348-2356.

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数据集	预测模型	RMSE	MAE	R²
实车1	PF	0.012 3	0.009 9	-0.818 6
	LSTM	0.008 5	0.007 7	0.131 3
	Seq2seq	0.004 7	0.002 9	0.731 3
	Aseq2seq-PF	0.002 3	0.001 9	0.935 0
实车2	PF	0.003 5	0.003 3	0.767 0
	LSTM	0.008 4	0.007 5	-0.356 2
	Seq2seq	0.006 2	0.004 9	0.260 9
	Aseq2seq-PF	0.001 8	0.001 5	0.934 0

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基于Aseq2seq-PF的实车锂离子动力电池剩余使用寿命预测

Prediction of Remaining Useful Life of Real-World Vehicle Lithium-Ion Power Battery Based on Aseq2seq-PF

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