基于电化学模型的锂离子电池健康状态估算
摘要: 针对电动汽车锂离子电池健康状态在线估算问题，提出了一种基于伪二维模型参数的估算方法. 该方法通过拆解同类估算目标电池，以扫描电镜测量电池结构参数，利用遗传算法辨识其他未知电化学模型参数，建立一种新的基于化学计量比的电池正极容量计算法则，估算电池健康状态. 同时考虑老化对电池正极化学计量比的影响，进一步提高健康状态估算精度. 采用电池老化数据集验证该方法的有效性，结果表明所提出的估算方法能在短时动态工况下实现电池健康状态的准确在线估算.
Abstract: To estimate online health state of Li-Ion batteries in electric vehicles accurately, a method was proposed based on the parameters of a pseudo-two-dimensional model. Firstly, disassembling congeneric objective batteries and measuring their structural parameters using scanning electron microscopy, the method was arranged to get some unknown parameters based on genetic algorithm for an electrochemical model. Then, a new stoichiometry ratio-based battery positive capacity calculation was established to estimate the health state of battery. Considering the influence of aging on the stoichiometry ratio in the positive electrode, the estimation accuracy of health state was further improved. Finally, a battery aging dataset was used to verify the validity of the method. The results show that the proposed estimation method can achieve an accurate online estimation of battery health state in short dynamic loading.
图 1 老化实验流程
Figure 1. The flowchart of aging experiment
图 2 电极材料及局部放大
Figure 2. Electrode material and partial zoom
图 3 电池结构厚度测量
Figure 3. Thickness measurement
图 4 P2D模型评估
Figure 4. Evaluation of P2D model
图 5 不同优化方法的辨识精度
Figure 5. Identification accuracy of different optimization methods
图 6 老化过程中的正极化学计量比的变化
Figure 6. The stoichiometric proportion of aging process
表 1 SOH估算误差
Table 1 Errors of SOH estimation
容量测试序号电池真实容量/Ah修正前误差/%修正后误差/%SAM1SAM2SAM1SAM2SAM1SAM2 12.6282.7100.000.000.000.0052.5972.6721.161.461.111.46102.5822.6560.141.59−0.170.61152.4642.600 5.052.892.520.39202.2442.5063.642.841.66−0.17251.9292.4064.402.880.720.54301.9812.941.91MAE2.402.091.030.73RMSE3.152.331.350.97表 2 SOH估算误差对比
Table 2 Comparison of errors of SOH estimation
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1.03, 0.82 0.97 ,
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