Research on Health Assessment Method of Lithium-ion Battery Based on Data-model Hybrid Drive

doi:10.11985/2022.04.004

Abstract

Abstract:

In this work, a battery’s state of health(SOH) estimation method is developed with capacity and energy as characterization parameters. Two methods are used to estimate the SOH. First, the metabolic grey algorithm(MGA) is used to predict the battery capacity and energy by insetting the original battery capacity and energy sequence directly. Second, the original model parameters are imput, the parameters of simplified electrochemical model(SEM) are predicted by using grey prediction algorithm, the predicted parameter values are brought back to the model, the battery terminal voltage curve is fit, and the battery capacity and energy are obtained by integration method. Aiming at the decay rate and estimation accuracy of the two characterization parameters, and a comprehensive battery health state estimation method based on data-model hybrid drive is developed to realize the accurate prediction of battery SOH.

Key words: Lithium-ion battery, state of health, metabolic grey prediction algorithm, simplified electrochemical-aging model

CLC Number:

TM561

FANG Deyu, CHU Xiao, LIU Tao, LI Junfu. Research on Health Assessment Method of Lithium-ion Battery Based on Data-model Hybrid Drive[J]. Journal of Electrical Engineering, 2022, 17(4): 20-31.

Figures/Tables 25

相应过程描述	模型方程
固相离子扩散	${{y}_{\mathrm{surf}}}(t)={{y}_{\mathrm{avg}}}(t)+\Delta y(t)$ ${{x}_{\mathrm{surf}}}(t)={{x}_{\mathrm{avg}}}(t)-\Delta x(t)$ $\Delta y\left( t \right)=\Delta {{y}_{1}}\left( t \right)+\frac{2}{7}\frac{{{\tau }_{\mathrm{p}}}}{{{Q}_{\mathrm{p}}}}\cdot I\left( t \right)$ $\Delta x\left( t \right)=\Delta {{x}_{1}}\left( t \right)+\frac{2}{7}\frac{{{\tau }_{\mathrm{n}}}}{{{Q}_{\mathrm{n}}}}\cdot I\left( t \right)$
正负集流体处液相锂离子浓度差	$\begin{matrix} \Delta c({{t}_{\mathrm{k}+1}})=\Delta c({{t}_{\mathrm{k}}})+ \\ \frac{1}{{{\tau }_{\mathrm{e}}}}\cdot \left[ {{P}_{\mathrm{con}}}\cdot I({{t}_{\mathrm{k}}})-\Delta c({{t}_{\mathrm{k}}}) \right]\cdot ({{t}_{\mathrm{k}+1}}-{{t}_{\mathrm{k}}}) \\ \end{matrix}$
浓差极化过电势	${{\eta }_{\mathrm{con}}}=\frac{2RT}{F}\left( 1-{{t}_{+}} \right)\cdot \left( \ln \frac{{{c}_{0}}+\Delta {{c}_{0}}(t)}{{{c}_{0}}-\Delta {{c}_{0}}(t)} \right)$
反应极化过电势	${{\eta }_{\mathrm{act}}}=\frac{2RT}{F}\cdot \left[ \ln \left( \sqrt{m_{\mathrm{n}}^{2}+1}+{{m}_{\mathrm{n}}} \right)+\ln \left( \sqrt{m_{\mathrm{p}}^{2}+1}+{{m}_{\mathrm{p}}} \right) \right]$ ${{m}_{\mathrm{n}}}\left( t \right)=\frac{1}{6\cdot {{Q}_{\mathrm{n}}}{{\left( 1-{{x}_{\mathrm{surf}}} \right)}^{0.5}}{{\left( {{x}_{\mathrm{surf}}} \right)}^{0.5}}c_{0}^{0.5}}\cdot {{P}_{\mathrm{act}}}\cdot I\left( t \right)$ ${{m}_{\mathrm{p}}}\left( t \right)=\frac{1}{6\cdot {{Q}_{\mathrm{p}}}{{\left( 1-{{y}_{\mathrm{surf}}} \right)}^{0.5}}{{\left( {{y}_{\mathrm{surf}}} \right)}^{0.5}}c_{0}^{0.5}}\cdot {{P}_{\mathrm{act}}}\cdot I\left( t \right)$
欧姆极化过电势	${{\eta }_{\mathrm{ohm}}}={{R}_{\mathrm{ohm}}}\cdot I\left( t \right)$
端电压	$\begin{matrix} & {{U}_{\mathrm{OCV}}}\left( t \right)={{U}_{\mathrm{p}}}\left( {{y}_{\text{surf}}} \right)-{{U}_{\mathrm{n}}}\left( {{x}_{\text{surf}}} \right)- \\ & \ \ \ {{\eta }_{\text{con}}}\left( t \right)-{{\eta }_{\text{act}}}\left( t \right)-{{\eta }_{\text{ohm}}}\left( t \right) \\ \end{matrix}$

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参数	定义
y₀, x₀	正/负电极初始嵌锂浓度分数
Q_p, Q_n /(A·s)	正/负极活性材料容量
y_ofs	正负极配比偏移
τ_p, τ_n/s	正/负极固相扩散时间常数
τ_e/s	液相扩散时间常数
P_con /(mol·m^-3·A^-1)	液相扩散比例系数
P_act /(m^-1.5·mol^0.5·s)	反应极化系数
R_ohm/Ω	内阻
U_p,n/V	正负极电极电势
c_max,p,c_max,n/(mol/m³)	正/负极最大含锂浓度

参数	定义
y_surf/y_avg, x_surf/x_avg	正负电极表面/平均嵌锂浓度分数
Q_all/(A·s)	放电容量
D_y, D_x	正/负电极嵌锂浓度分数变化范围
η_con/V	浓差极化电势
η_act/V	反应极化电势
η_ohm/V	欧姆极化电势
U₀/U_OCV/V	端电压/开路电压
U_set/V	设定的放电截止电压

电池编号	放电倍率	恒压充电电压/V	放电深度(%)	温度/℃
3-1，3-2	1C	4.2	100	25
3-3，3-4	1C	4.2	50	25
3-5，3-6	2C	4.2	100	25

工况		不同循环下的仿真误差
1C	N 误差	20 0.011 4	80 0.017 9	140 0.034 7	240 0.055 5
2C	N 误差	20 0.012 7	80 0.024 6	160 0.036 7	260 0.051 1

电池编号	MGA平均误差(%)	SEM-MGA平均误差(%)
3-1	2.250	2.392
3-2	2.651	3.151
3-3	1.917	2.428
3-4	2.079	2.794
3-5	0.796	2.029
3-6	1.224	2.056