基于HI-DD-AdaBoost.RT的锂离子动力电池SOH预测

锂离子电池是一个复杂的电化学动态系统,实时准确的健康状态(SOH)估计对电动汽车动力锂电池的维护至关重要,传统建模方法难以实现SOH的在线估算.基于此,从实时评估电池的SOH出发,在增量学习的基础上,选取与电池健康状态相关的指标建立SOH预测模型.考虑到增量学习中的耗时性问题,提出融合滑动窗口技术的HI-DD算法,该算法可以检测概念漂移是否发生,从而指导和确定模型更新位置;设计出HI-DD与AdaBoost.RT结合的模型更新策略,进而提高模型的在线学习性能和预测精度,最后使用CALCE提供的电池老化实验数据对所提出的方法进行验证.结果表明,基于增量学习的HI-DD-AdaBoost.RT预测算法具有较强的在线更新能力和较高的预测精度,能够满足SOH在线预测的实际需求.

Prediction of Li-ion battery SOH based on HI-DD-AdaBoost.RT

Real-time and accurate estimation of the state of health (SOH) is of great importance for the maintenance of Li-ion battery in electric vehicles. Li-ion batteries is a complex electrochemical dynamic system, so traditional modeling methods are difficult to achieve online estimation of SOH. Therefore, starting from real-time assessment of SOH of battery, this paper chooses indicators related to the state of battery health and establishes a SOH prediction model based on incremental learning. A HI-DD algorithm based on sliding window technology is proposed for considering the time-consuming problem of online learning in incremental learning, which can detect whether concept drift occurs, thus guiding and determining the updating position of the model. Furthermore, the model updating strategy based on HI-DD and AdaBoost.RT is designed to improve the online learning performance and prediction accuracy of the model. Finally, the proposed method is verified by using the experimental data of battery aging provided by CALCE. The results show that the HI-DD-AdaBoost.RT prediction algorithm based on incremental learning has strong updating ability of online and high accuracy of prediction, which can meet the actual needs of SOH online prediction.