SOC estimation of Li-ion battery based on gaussian mixture regression

doi:10.19799/j.cnki.2095-4239.2019.0231

Abstract

Abstract:

The state of charge (SOC) of a power battery must be accurately estimated as it determines the endurance mileage and is the basic premise for the energy management of electric vehicles. However, SOC estimation of battery systems is degraded by nonlinearity, instability, and other factors. Accordingly, the characteristic state data of a lithium battery contain nonlinearities, fluctuations, and external interference. This study proposes an SOC prediction method based on Gaussian mixture regression (GMR), which resolves the problems of abnormal values embedded in the state data and noise in the traditional Gaussian procession model (GPM). The hyper-parameters of the Gaussian mixture model are sequentially optimized by k-means clustering and an EM algorithm. The GMR predicts the SOC output. In an experimental validation and a comparative analysis of GMR and GPM, the GMR algorithm achieved superior prediction accuracy and effectiveness in SOC estimation.

Key words: power battery, state of charge, Gaussian process regression, Gaussian mixture regression

CLC Number:

TM 911

WEI Meng, LI Jiabo, YE Min, GAO Kangping, XU Xinxin. SOC estimation of Li-ion battery based on gaussian mixture regression[J]. Energy Storage Science and Technology, 2020, 9(3): 958-963.

Figures/Tables 10

Table 1

Battery SOC estimation based on GMR"

算法：基于GMR的电池SOC估计
Step1.输入训练集数据
Step2.通过K-means进行聚类
Step3.建立电流，电压和SOC的联合密度GMM模型 $P (x) = ∑ k = 1 k α k N (μ k, Σ k)$
Step4.利用EM算法得到模型的最优参数 $Q = α 1, μ 1, Σ 1, α 2, μ 2, Σ 2, . . ., α n, μ n, Σ n$
Step5.输入测试数据，并计算相应高斯分量测试集的后验概率 $μ j x = μ j y + Σ j y x ? Σ j x x - 1 (x - μ j x)$ $Σ j x = Σ j y y - ?? Σ j y x ? Σ j x x - 1 Σ j x y$
Step6.根据GMR，输出预测结果 $μ x = ∑ j = 1 k α j x μ j x$ $ν x = ∑ j = 1 k α j x μ j x 2 + Σ j 2 - ?? ∑ j = 1 k α j x μ j x 2$

Table 1

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方法	RMSE/%	MAPE/%
GPR（UDDS）	3.34	3.79
GMR（UDDS）	2.37	2.23
GPR（DST）	0.91	2.24
GMR（DST）	0.64	2.22

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