Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (1): 263-272.doi: 10.13229/j.cnki.jdxbgxb20210556

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SoE estimation of lithium-ion batteries based on improved BPNN-MPF algorithm

Yan MA1,2(),Ze-xuan GUO1   

  1. 1.College of Communication Engineering，Jilin University，Changchun 130012，China
    2.State Key Laboratory of Automotive Simulation and Control，Jilin University，Changchun 130022，China
  • Received:2021-04-28 Online:2023-01-01 Published:2023-07-23

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Abstract:

The State of Energy （SoE） of lithium-ion battery is an important evaluation index for the energy optimization and management of electric vehicles. In order to improve the accuracy and reliability of battery SoE estimation， a SoE estimation method based on the combination of improved Back Propagation Neural Network （BPNN） and Model Predictive Filtering （MPF） was proposed in this paper. Considering the accumulated errors of current or voltage sensor noise， a first-order RC equivalent circuit model was established in this paper. Based on this model， the MPF algorithm was used to estimate the battery SoE. In order to make the estimated results more accurate， the improved BPNN was used to compensate the errors of the estimated results of MPF algorithm. Finally， the accuracy of the proposed method was verified under New European Driving Conditions （NEDC）. The results show that the SoE estimate based on the improved BPNN-MPF algorithm well converges to the real SoE value， compared with traditional MPF algorithm and BPNN-MPF algorithm. The Maximum Absolute Error （MAE） and Root-Mean-Square Error （RMSE） of the estimated values are all within 1%.

Key words: control theory and control engineering, lithium-ion batteries, state of energy, improved bp neural network, model predictive filtering

CLC Number: 

  • U463.63

Fig.1

Schematic diagram of first order RC circuit"

Fig.2

Relation curves of open circuit voltage with SoC and SoE"

Fig.3

Fitting curve of open circuit voltage with SoE"

Fig.4

Instant current and voltage curves of 500 mA discharge standing"

Fig.5

Voltage contrast curve before and after fitting"

Fig.6

Current curve for model validation"

Fig.7

Terminal voltage curves for model validation"

Fig.8

Terminal voltage error curve for model validation"

Fig.9

Flow chart of improved BPNN-MPF algorithm"

Fig.10

Contrast curve of improved BP neural network"

Fig.11

Estimation results of terminal voltage and SoE when initial SoE error is 0"

Table 1

Statistical results of estimation error when initial SoE error is 0"

估计对象最大误差平均误差（MAE）均方根误差（RMSE）
电压/mV1.71.41.4
SoE（MPF）/%0.620.520.52
SoE（BPNN-MPF）/%0.610.510.51
SoE（改进BPNN-MPF）/%0.10.020.02

Fig.12

Estimation results of terminal voltage and SoE when initial SoE error is 0.2"

Table 2

Statistical results of estimation error when initial SoE error is 0.2"

估计对象最大误差平均误差（MAE）均方根误差（RMSE）
电压/mV2.11.51.5
SoE（MPF）/%1.461.051.05
SoE（BPNN-MPF）/%1.451.031.04
SoE（改进BPNN-MPF）/%0.950.530.54
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