Battery State-of-Charge Estimation Based on Regular/Recurrent Gaussian Process Regression
Battery State-of-Charge Estimation Based on Regular/Recurrent Gaussian Process Regression
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DOI：
10.1109/tie.2017.2764869
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发表时间：
2018-05-01
影响因子：
7.7
通讯作者：
Wada, Toshihiro
中科院分区：
文献类型：
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作者：
Sahinoglu, Gozde O.;Pajovic, Milutin;Wada, Toshihiro
提出了一种基于机器学习的锂离子电池荷电状态估计方法，该方法采用高斯过程回归（GPR）框架。测量的电池参数，例如电压、电流和温度，被用作常规GPR的输入，而在先前样本处的SoC估计被反馈并被并入到用于递归GPR的输入向量中。所提出的方法包括两个部分。在第一部分中，执行训练，其中确定所选核函数的最佳超参数以对数据属性建模。在第二部分中，根据训练好的模型进行在线SoC估计。GPR框架的实际优点之一是量化估计的不确定性，因此能够对电池SoC估计进行可靠性评估。所提出的方法的性能进行评估，通过使用模拟数据集和两个实验数据集，一个恒定的和其他动态的充电和放电电流。仿真和实验结果表明，所提出的方法相比，国家的最先进的技术，包括支持向量机，相关向量机和神经网络的优越性。
This paper presents novel machine-learningbased methods for estimating the state of charge (SoC) of lithium-ion batteries, which use the Gaussian process regression (GPR) framework. The measured battery parameters, such as voltage, current, and temperature, are used as inputs for regular GPR, whereas the SoC estimate at the previous sample is fed back and incorporated into the input vector for recurrent GPR. The proposed methods consist of two parts. In the first part, training is performed wherein the optimal hyperparameters of a chosen kernel function are determined to model data properties. In the second part, online SoC estimation is carried out according to the trained model. One of the practical advantages of a GPR framework is to quantify estimation uncertainty and, hence, to enable reliability assessment of the battery SoC estimate. The performance of the proposed methods is evaluated by using a simulated dataset and two experimental datasets, one with constant and the other with dynamic charge and discharge currents. The simulations and experimental results show the superiority of the proposed methods in comparison to state-of-the-art techniques including a support vector machine, a relevance vector machine, and a neural network.