用混合模型精准预测锂电池健康状态，揭示快充对衰减的加速作用
Prognosis Of Lithium-Ion Battery Health with Hybrid EKF-CNN+LSTM Model Using Differential Capacity
- 融合EKF-CNN-LSTM的混合模型，结合差分容量分析
- 在0.2C~1.5C充放电率下误差低于0.001%
- 发现快充会显著加速电池老化，磷酸铁锂更耐久
电池退化是电动汽车和储能系统的主要挑战。现有研究多聚焦于荷电状态（SOC）估算，难以揭示电池内部退化机制。本文基于差分容量分析（DCA），采用三元镍钴铝氧化物（LiNiCoAlO2）与磷酸铁锂（LiFePO4）两种锂离子电池，在0.2C、0.5C、1C、1.5C充电率及0.5C、0.9C、1.3C、1.6C放电率下评估其性能退化。提出融合扩展卡尔曼滤波（EKF）、卷积神经网络（CNN）与长短期记忆网络（LSTM）的混合模型，验证实验数据。模型在均方误差（MSE）与均方根误差（RMSE）上表现优异，充放电速率下的误差均低于0.001%。通过峰值识别法（PIM）分析峰数、位置、高度、面积与宽度，发现正常负载下电池缓慢退化，而快速充放电导致急剧劣化。总体而言，磷酸铁锂电池在不同负载条件下表现出更强的稳定性与一致性。
原文摘要 · Abstract (English)
Battery degradation is a major challenge in electric vehicles (EV) and energy storage systems (ESS). However, most degradation investigations focus mainly on estimating the state of charge (SOC), which fails to accurately interpret the cells' internal degradation mechanisms. Differential capacity analysis (DCA) focuses on the rate of change of cell voltage about the change in cell capacity, under various charge/discharge rates. This paper developed a battery cell degradation testing model that used two types of lithium-ions (Li-ion) battery cells, namely lithium nickel cobalt aluminium oxides (LiNiCoAlO2) and lithium iron phosphate (LiFePO4), to evaluate internal degradation during loading conditions. The proposed battery degradation model contains distinct charge rates (DCR) of 0.2C, 0.5C, 1C, and 1.5C, as well as discharge rates (DDR) of 0.5C, 0.9C, 1.3C, and 1.6C to analyze the internal health and performance of battery cells during slow, moderate, and fast loading conditions. Besides, this research proposed a model that incorporates the Extended Kalman Filter (EKF), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) networks to validate experimental data. The proposed model yields excellent modelling results based on mean squared error (MSE), and root mean squared error (RMSE), with errors of less than 0.001% at DCR and DDR. The peak identification technique (PIM) has been utilized to investigate battery health based on the number of peaks, peak position, peak height, peak area, and peak width. At last, the PIM method has discovered that the cell aged gradually under normal loading rates but deteriorated rapidly under fast loading conditions. Overall, LiFePO4 batteries perform more robustly and consistently than (LiNiCoAlO2) cells under varying loading conditions.
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