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首页> 外文期刊>International Journal of Electrochemical Science >A Novel Joint Support Vector Machine - Cubature Kalman Filtering Method for Adaptive State of Charge Prediction of Lithium-Ion Batteries
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A Novel Joint Support Vector Machine - Cubature Kalman Filtering Method for Adaptive State of Charge Prediction of Lithium-Ion Batteries

机译：一种新型关节支持向量机 - 锂离子电池充电预测自适应状态的Cubature Kalman滤波方法

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摘要

Accurate estimation of SOC of lithium-ion batteries has always been an important work in the battery management system. However, it is often very difficult to accurately estimate the SOC of lithium-ion batteries. Therefore, a novel joint support vector machine - cubature Kalman filtering (SVM-CKF) method is proposed in this paper. SVM is used to train the output data of the CKF algorithm to obtain the model. Meanwhile, the output data of the model is used to compensate the original SOC, to obtain a more accurate estimate of SOC. After the SVM-CKF algorithm is introduced, the amount of data needed for prediction is reduced. By using Beijing Bus Dynamic Stress Test (BBDST) and the Dynamic Stress Test (DST) condition to verify the training model, the results show that the SVM-CKF algorithm can significantly improve the estimation accuracy of Lithium-ion battery SOC, and the maximum error of SOC prediction for BBDST condition is 0.800%, which is reduced by 0.500% compared with CKF algorithm. The maximum error of SOC prediction under DST condition is about 0.450%, which is 1.350% less than that of the CKF algorithm. The overall algorithm has a great improvement in generalization ability, which lays a foundation for subsequent research on SOC prediction.
机译：精确估计锂离子电池的SOC始终是电池管理系统中的重要工作。然而，通常非常难以准确地估计锂离子电池的SOC。因此，本文提出了一种新颖的关节支持向量机 - Cubature Kalman滤波（SVM-CKF）方法。 SVM用于培训CKF算法的输出数据以获得模型。同时，模型的输出数据用于补偿原始SOC，以获得对SoC的更准确估计。在介绍SVM-CKF算法之后，预测所需的数据量减少。通过使用北京总线动态应力测试（BBDST）和动态应力测试（DST）条件来验证培训模型，结果表明，SVM-CKF算法可以显着提高锂离子电池SOC的估计精度，最大与CKF算法相比，BBDST条件的SOC预测误差为0.800％，减少0.500％。 DST条件下SOC预测的最大误差约为0.450％，比CKF算法小于1.350％。整体算法对泛化能力有了很大的提高，这为后续研究SoC预测奠定了基础。

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