汽车工程 ›› 2020, Vol. 42 ›› Issue (9): 1189-1196.doi: 10.19562/j.chinasae.qcgc.2020.09.007

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随机放电工况下锂离子电池容量预测方法

孙道明, 俞小莉   

  1. 浙江大学能源工程学院,杭州 310058
  • 出版日期:2020-09-25 发布日期:2020-10-19
  • 通讯作者: 俞小莉,教授,E-mail:yuxl@zju.edu.cn

Capacity Prediction Method of Lithium-ion Battery Under Random Discharge Condition

Sun Daoming, Yu Xiaoli   

  1. College of Energy Engineering, Zhejiang University, Hangzhou 310058
  • Online:2020-09-25 Published:2020-10-19

摘要: 针对锂离子电池容量预测精度不高的问题,提出一种基于人群搜索优化的支持向量机(seeking optimization algorithm-support vector machine, SOA-SVM)的容量预测方法。通过分析锂离子电池随机放电过程,构建反映容量变化的随机放电容量均值和标准差两个指标,并以此作为预测容量的特征参数。采用主成分分析法分析特征参数之间的相关性,并提取主成分。基于部分测试电池第1主成分和容量数据,采用SOA对SVM超参数进行全局优化并训练模型。采用优化后的模型结合其余电池第1主成分数据预测锂离子电池容量。预测结果表明,本文中提出的锂离子电池容量预测方法具有较高的预测精度。

关键词: 锂离子电池, 随机放电工况, 容量预测, SOA, 支持向量机

Abstract: For the problem of low accuracy of lithium-ion battery capacity prediction, a seeking optimization algorithm-support vector machine (SOA-SVM) based capacity prediction method is proposed. By analyzing the random discharge process of lithium-ion battery, two indicators, the mean and standard error of random discharge capacity reflecting the capacity change of lithium-ion battery are constructed which are used as the feature parameters for capacity prediction. The principle component analysis is used to analyze the correlation between the feature parameters and extract the principle components. Based on the first principle component and the capacity data of part of tested batteries, SOA is used to optimize hyper-parameters of SVM and train the model. The optimized model combined with the first principle component date of other batteries is adopted to predict the capacity of lithium-ion batteries. The prediction results show that the proposed capacity prediction method has high prediction accuracy

Key words: lithium-ion battery, random discharge condition, capacity prediction, SOA, support vector machine