基于GA-ELM的锂离子电池RUL间接预测方法

Indirect Prediction Method of RUL for Lithium-ion Battery Based on GA-ELM

针对锂离子电池在线剩余寿命预测时容量难以直接测量及预测精度不高等问题,提出一种间接预测方法。首先,分析电池寿命状态特征参数,选取等压降放电时间作为锂电池间接健康因子;其次,引入遗传算法优化极限学习机模型参数,建立锂电池剩余使用寿命间接预测模型;最后,基于NASA锂电池实验数据和自主实验数据验证该预测方法的正确性和有效性。实验结果表明,相较高斯过程回归方法和极限学习机方法,该方法准确有效、测试速度快,并且预测结果输出稳定,精度较高。

Aiming at the problem that it is difficult to directly measure the capacity of the lithium-ion battery when the remaining useful life(RUL)of the battery is predicted and the prediction result is inaccurate,an indirect prediction method was proposed.First,after fully analyzing the parameters of battery life sates,the equivalent voltage drop discharge time was chosen as the indirect health index of the battery.Secondly,genetic algorithm was introduced to optimize the extreme learning machine parameters to establish an indirect RUL prediction model for lithium-ion battery.Finally,the correctness of the GA-ELM method was verified based on NASA data and independent experimental data.The results showed,compared with the ELM method and Gaussian process regression method,this new method is accurate and effective,with high predicting speed,and its output is stable.