基于DTV-IGPR模型的锂离子电池SOH估计方法

doi:10.19562/j.chinasae.qcgc.2021.11.017

Abstract

Abstract:

The state of health （SOH） of lithium-ion batteries is a key factor to ensure the safe and reliable operation of electric vehicles. The existing SOH estimation methods usually ignore the temperature information that can characterize battery aging in the process of capacity degradation. In view of this， this paper proposes a method to obtain the differential temperature voltammetry （DTV） curve based on the battery surface temperature and a filtering method combining moving average （MA） and Kalman filtering （KF） to extract the health feature. In addition， the combined kernel function is used to improve the traditional Gaussian process regression （GPR） algorithm to fit the two trends of overall decline and local fluctuations of battery capacity， so as to establish a DTV-IGPR battery-aging model for SOH estimation. Single cell and multi cell verification are carried out using the Oxford and NASA datasets， which are collected at two different ambient temperatures. The results show that the proposed method has high SOH estimation accuracy and strong robustness.

Key words: lithium-ion battery, state of health, health feature, temperature, Gaussian process regression

Ping Wang,Xiangyuan Peng,Ze Cheng. SOH Estimation Method for Lithium-ion Batteries Based on DTV-IGPR Model[J].Automotive Engineering, 2021, 43(11): 1710-1719.

Figures/Tables 12

References 24

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电池	RMSE			MAE
电池	SE+PER	SE	PER	SE+PER	SE	PER
Cell1	0.010 9	0.025 6	0.024 5	0.011 2	0.027 4	0.023 5
Cell2	0.010 8	0.078 1	0.072 9	0.010 3	0.089 4	0.079 9
Cell3	0.015 8	0.048 0	0.040 2	0.016 9	0.055 3	0.046 7
Cell4	0.013 0	0.026 2	0.031 8	0.012 3	0.023 5	0.029 2
Cell5	0.033 8	0.084 2	0.076 7	0.033 6	0.057 2	0.042 7
Cell6	0.013 7	0.041 1	0.046 2	0.014 7	0.030 9	0.036 7
Cell7	0.006 7	0.038 7	0.045 8	0.006 9	0.038 5	0.046 7
Cell8	0.008 9	0.023 2	0.026 7	0.009 0	0.023 1	0.026 8
B0005	0.010 2	0.175 1	0.166 5	0.014 9	0.218 0	0.218 7
B0006	0.012 1	0.082 8	0.091 0	0.016 6	0.091 8	0.085 7
B0018	0.016 9	0.079 8	0.128 9	0.019 5	0.091 0	0.172 7

电池	RMSE			MAE
电池	SE+PER	SE	PER	SE+PER	SE	PER
Cell1	0.010 4	0.017 4	0.017 2	0.008 5	0.013 4	0.012 7
Cell2	0.010 8	0.022 1	0.027 8	0.010 1	0.016 3	0.020 5
Cell3	0.014 7	0.043 8	0.044 4	0.011 8	0.041 5	0.036 6
Cell4	0.009 8	0.021 1	0.017 5	0.008 7	0.017 0	0.015 1
Cell5	0.011 5	0.038 2	0.049 6	0.008 8	0.020 4	0.031 5
Cell6	0.008 7	0.011 0	0.025 6	0.006 8	0.007 6	0.014 2
Cell7	0.009 8	0.021 7	0.021 0	0.008 9	0.016 6	0.017 3
Cell8	0.011 1	0.014 8	0.030 8	0.009 7	0.013 1	0.026 7
B0005	0.014 3	0.045 2	0.091 0	0.012 9	0.037 5	0.085 7
B0006	0.016 8	0.084 0	0.117 2	0.016 7	0.068 1	0.102 2
B0018	0.011 3	0.102 7	0.085 6	0.019 8	0.090 4	0.081 3

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SOH Estimation Method for Lithium-ion Batteries Based on DTV-IGPR Model

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