Lithium-ion state estimation based on feedback least square support vector machine

doi:10.19799/j.cnki.2095-4239.2019.0268

Abstract

Abstract:

To ensure the safe operation of electric vehicles, the accurate estimation of the state of charge (SOC) of the lithium-ion battery installed in the vehicle is required. SOC is also an important parameter of battery management systems. However, SOC estimation is influenced by the accuracy of the measuring equipment, applied load, and various other factors. Accordingly, to improve the accuracy of SOC estimation, this study proposes a new SOC estimation model for lithium-ion batteries based on least-squares support vector machine (LSSVM) machine learning. The LSSVM model is trained on the measured current, voltage, and temperature of the lithium ion battery, which are input in vector form, and outputs the SOC vector. To improve the accuracy of SOC estimation, the SOC value estimated at the previous time is added as a feedback vector to the input vector before estimating the SOC at the current time. In an experimental evaluation, the proposed method achieved higher SOC estimation accuracy than the LSSVM, SVM, and neural network models. Moreover, the estimation error was controlled within 1%, establishing the validity of the model.

Key words: lithium-ion battery, SOC, least squares support vector machine (LSSVM)

CLC Number:

TM 911

LI Jiabo, LI Zhongyu, JIAO Shengjie, YE Min, XU Xinxin. Lithium-ion state estimation based on feedback least square support vector machine[J]. Energy Storage Science and Technology, 2020, 9(3): 951-957.

Figures/Tables 16

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Table 1

SOC estimation algorithm of LSSVM framework based on feedback loop"

SOC估计算法流程
训练部分 Step1. 在电池的实验工况中，采集相应的数据：电压V、I、T和SOC； Step2. 选取训练集D=(x，y)，其中X是表示当前时刻的电压V(k)、电流I(k)、温度T(k)以及上一时刻的SOC(k)，y是当前时刻SOC(k)；
Step 3. 按照式(12)将所选的数据进行归一化处理； Step 4. 初始化LSSVM模型的两个参数：正则化系数C和核参数σ，分别设置两个参数的范围[0.001,1000]和[0.001,1000]
Step 5. 利用10-折交叉验证法得到模型的最优超参数。
验证部分
Step 6. 选取验证集x，x表示当前时刻的电压V(k)、电流I(k)、温度T(k)以及上一时刻估计的SOC(k-1)，y表示估计的SOC(k) $y = ∑ i = 1 ∞ α i * K x * + b$

Table 1

Table 2

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Table 3

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方法	RMSE	MAE
反馈循环LSSVM	0.48%	1.35%
LSSVM	3.18%	17.57%
SVM	4.62%	36.61%
NN	11.22%	37.05%

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