The invention discloses a battery residual charge estimation method based on Gaussian process regression and Kalman filtering. The method specifically comprises the following steps: step 1, extracting an initial feature set according to an existing battery data set; 2, predicting the health degree of the battery by taking the initial feature set as the input of Gaussian process regression; 3, substituting the predicted health degree of the battery obtained in the step 2 into a Kalman filtering method to estimate the state of charge of the battery; according to the method, the SOC estimation of the battery of the electric vehicle is not influenced by sampling noise and other interference, the SOH of the battery is predicted, and the method has the advantages of being high in battery residual charge estimation precision, accurate in battery state prediction, easy to implement, high in robustness for noise measurement and the like, and can be conveniently applied to application scenes where the battery residual charge needs to be accurately estimated.
本发明公开了基于高斯过程回归和卡尔曼滤波的电池剩余电荷估算方法,具体包括以下步骤:步骤1,根据已有电池数据集提取初始特征集;步骤2,将初始特征集作为高斯过程回归的输入预测电池健康度;步骤3,将步骤2获得的预测电池健康度代入卡尔曼滤波法对电池荷电状态进行估算;本发明方法对于电动汽车电池SOC估计不受采样噪声及其他干扰影响,且对电池SOH进行预测,具有电池剩余电荷估计精度高,电池状态预测准确,实现简单,对于测量噪声具有强鲁棒性等优点,便于应用于电池剩余电荷需要精确估算的应用场景中。
Battery residual charge estimation method based on Gaussian process regression and Kalman filtering
基于高斯过程回归和卡尔曼滤波的电池剩余电荷估算方法
2024-12-27
Patent
Electronic Resource
Chinese
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