基于机器学习的锂离子电池荷电状态多步预测

先进电池管理技术依赖于对未来一段时间荷电状态变化的预测，难点在于误差积累和时间依赖性降低引起的预测精度下降。提出采用机器学习结合多步预测策略来提升荷电状态多步预测精度，利用实际锂电池数据研究了不同多步预测策略的效果。结果表明，实际锂电池荷电状态预测在充电过程中具有显著线性特性，放电过程表现出非线性特性。预测步长为 15个时，LR模型、KNN模型、RF模型的 MAPE均低于 6%，R2均大于 0.90。线性回归结合 MIMO策略具有最大的实际应用潜力。

Multi-Step Ahead Forecasting of Lithium-Ion Battery State of Charge Based on Machine Learning

Advanced battery management technology relies on the near-future prediction of state of charge (SOC). However, the accumulation of errors and the diminished time-dependency lead to a decline in prediction accuracy. In this paper, the machine learning algorithms combined with multi-step prediction strategies are proposed to improve the accuracy of SOC over multiple steps ahead. The effects of different multi-step prediction strategies are studied based on actual lithium battery data. The results show that the actual lithium battery SOC prediction exhibits a significant linear characteristic during the charging phase,and conversely, a nonlinear characteristic in the discharging process. Furthermore, with the prediction step size of 15, the MAPEs of the LR, KNN, and RF models are below 6%, and the R2 values are greater than 0.90. It is found that the LR combined with MIMO shows the greatest potential for practical applications.