基于混合模型及LSTM的锂电池SOH与剩余寿命预测

Estimation of SOH and remaining life of lithium batteries based on a combination model and long short-term memory

预测电池健康状态(state of health,SOH)的传统方法,一般以历史数据为依据,既难以预测电池实时状态,也无法估计锂电池剩余使用寿命。针对实时预测电池SOH的问题,文章依据采集的大量实车电池数据,结合机器学习与安时积分法对其进行建模预测,处理特征并训练数据。基于模型测试结果,文章提出融合LightGBM与CatBoost算法的实时SOH混合预测模型。通过两辆实车为载体进行混合模型的验证,所测算的实时SOH预测绝对平均误差为0.009。针对电池剩余使用寿命的问题,研究的目标为获取SOH衰减曲线。因此建立长短记忆(LSTM)神经网络模型预测电池SOH的未来衰减曲线,以固定时间间隔内的SOH差值为特征,减小差值波动,保证数据近似具有相同分布规律。通过对某原始设备制造商提供的实时监视数据集的验证,得出未来衰减曲线预测的绝对平均误差为0.021。总体结果表明:文章研究的锂电池实时SOH预测模型与剩余寿命预测模型,预测精度较高,电池使用方可以更好掌握锂电池的实时状态,为相关决策提供依据。

The traditional method of predicting the state of health(SOH)of a battery is generally based on historical data.Predicting the real-time state of a lithium battery or estimating its remaining service life is difficult.Aiming at the real-time prediction of battery SOH,we introduce a large amount of real-vehicle battery data(combined with machine learning and ampere-hour integration method to model and predict SOH)to process features and train data.On the basis of the model test results,this article proposes a real-time SOH hybrid prediction model combining the LightGBM and CatBoost algorithms.By verifying the hybrid model with two real vehicles as the carrier,the measured absolute average error of the real-time SOH prediction is 0.009.Our research intends to obtain the SOH attenuation curve to predict the remaining battery life.Therefore,we establish a long short-term memory(LSTM)neural network model to predict the future decay curve of battery SOH,characterized by the difference in SOH within a fixed time interval.This reduces the fluctuation of the difference and ensures that the data have similar distribution laws.By verifying the real-time monitoring data set provided by an original equipment manufacturer,the absolute average error of the future attenuation curve prediction is 0.021.The overall results show that the real-time SOH prediction model and the remaining life prediction model of the lithium battery studied in the article have high prediction accuracy.The battery user can better grasp the real-time status of the lithiumbattery and provide a basis for relevant decisionmaking.