A Modified Strategy Using the KNN-Markov Chain for SOH Estimation of Lithium Batteries
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摘要: 锂离子电池的健康状态(State of health, SOH)是决定电池使用寿命的关键因素.由于锂电池生产工艺、工作环境和使用习惯等的差异性导致其衰退特性具有较大差异, 因此锂电池SOH难以精确估算.本文采用数据驱动的方式通过对采集的电压数据进行特征提取, 使用贝叶斯正则化神经网络对锂电池SOH进行预测, 同时引入KNN-马尔科夫修正策略对预测结果进行修正.实验结果证明, 贝叶斯正则化算法对锂电池SOH的预测准确度较高, KNN-马尔科夫修正策略提高了预测的精确度和鲁棒性, 组合预测模型对锂电池SOH的平均预测误差小于$1\,\%$, 与采用数据分组处理方法(Group method of data handling, GMDH)、概率神经网络(Probabilistic neural network, PNN)、循环神经网络(Recurrent neural network, RNN)的预测精度进行对比, 该模型的预测精度分别提高了$33.3\,\%$、$48.7\,\%$和$53.1\,\%$.Abstract: The state of health (SOH) of lithium batteries is a critical factor in determining the battery's end-of-service-life. The differences of the Lithium-ion battery's production process, work environment, and use habit etc. lead to the massive differences of the battery's fade characteristics, which, in turn, inaccurate estimation of their battery's SOH. In this paper, the data-driven method was employed for experimental feature extraction. Besides, this paper presents an SOH estimation method based on the Bayesian-regularization neural network and the KNN-Markov chain used for amending the prediction results. Experimental results show that the Bayesian-regularization neural network applied to the SOH estimation could obtain superior accuracy performance, and by combining the KNN-Markov chain, the prediction accuracy (the average prediction error of SOH less than 1 %) could be improved. On the whole, the combined model shows good robustness. Compared with the group method of data handling (GMDH), probabilistic neural network (PNN) and recurrent neural network (RNN), the prediction accuracy of the model was improved by 33.3 %, 48.7 % and 53.1 % respectively.
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Key words:
- Lithium battery SOH /
- feature extraction /
- multilayer feedforward neural network /
- Bayesian regularization /
- Markov chain
1) 本文责任编委 曹向辉 -
图 5 MC-BRNN、GMDH、PNN、RNN预测结果对比
Fig. 5 Comparative results of MC-BRNN, GMDH, PNN and RNN
表 1 BRNN预测值相对误差及状态划分
Table 1 BRNN prediction error and state division
序号 实测值 预测值 相对误差(%) 归一化相对误差(%) 状态 1 0.6445 0.6431 -0.2172 0.4569 2 2 0.7480 0.7253 -3.0347 0.1425 1 3 0.9665 0.9646 -0.1965 0.4496 2 4 0.9502 0.9508 0.0631 0.4865 3 5 0.5802 0.5695 -1.8442 0.3194 1 6 0.7556 0.7344 -2.8057 0.1647 1 7 0.9294 0.9301 0.0753 0.4879 3 8 0.9222 0.9440 2.3639 0.7994 3 9 0.6808 0.6927 1.7479 0.6533 1 10 0.8615 0.8503 -1.3001 0.3123 1 11 0.7556 0.7619 0.8338 0.5706 3 12 0.6445 0.6431 -0.2172 0.4569 2 
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表 2 BRNN预测误差及马尔科夫修正误差
Table 2 BRNN prediction and Markov correction error
序号 实测值 预测值 修正前误差(%) 修正后误差(%) 1 1.0000 0.9809 1.9090 1.4446 2 0.9942 0.9753 1.8847 1.4203 3 0.9941 0.9741 2.0029 1.5385 4 0.9878 0.9720 1.5855 1.1211 5 0.9665 0.9646 0.1857 -0.2787 6 0.9607 0.9623 -0.1558 -0.6202 7 0.9554 0.9333 2.2035 1.7391 8 0.9387 0.9590 -2.0253 -0.3699 9 0.9329 0.9486 -1.5736 0.0818 10 0.9222 0.9440 -2.1778 -0.5224
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表 3 KNN-马尔科夫修正结果
Table 3 KNN-Markov correction results
编号 $5\#$电池 $6\#$电池 $7\#$电池 有无修正 无 有 无 有 无 有 MAE (%) 0.47 0.35 0.52 0.43 0.44 0.37 MSE (%$^2$) 0.49 0.37 0.52 0.40 0.48 0.37
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表 4 各算法准确度对比
Table 4 Comparison of accuracy of each algorithm
编号 $5\#$电池 $6\#$电池 $7\#$电池 算法 MC-BRNN GMDH PNN RNN MC-BRNN GMDH PNN RNN MC-BRNN GMDH PNN RNN MAE ($\%$) 0.35 0.52 0.64 0.67 0.43 0.61 0.81 0.93 0.37 0.59 0.79 0.83 MSE ($\%^2$) 0.37 0.67 0.94 1.21 0.40 0.88 1.30 2.35 0.37 0.58 1.57 1.95
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表 5 各算法的时间复杂度对比(s)
Table 5 Comparison of time complexity of each algorithm (s)
算法 MC-BRNN GMDH PNN RNN 5号电池 1.5101 1.1184 0.2994 4.5942 6号电池 1.4361 1.1948 0.2442 3.7446 7号电池 1.5559 1.1103 0.2748 4.8092 平均值 1.5007 1.1399 0.2726 4.3826
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