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

Previous Articles    

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

RICH HTML

 2   

PDF (PC)

193

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
1 Lin C, Mu H, Xiong R, et al. Multi-model probabilities based state fusion estimation method of lithium-ion battery for electric vehicles: state-of-energy[J]. Applied Energy, 2016, 194(15): 560-568.
2 Chang J Q, Chi M S, Shen T. Model based state‑of‑energy estimation for LiFePO4 batteries using unscented particle filter[J]. Journal of Power Electronics, 2020(20): 624-633.
3 刘伟龙, 王丽芳, 王立业. 基于电动汽车工况识别预测的锂离子电池SOE估计[J]. 电工技术学报, 2018, 33(1): 17-25.
Liu Wei-long, Wang Li-fang, Wang Li-ye. Estimation of state-of-energy for electric vehicles based on the identification and prediction of driving condition[J]. Transactions of China Electrotechnical Society, 2018, 33(1): 17-25.
4 Liu X T, Wu J, Zhang C B, et al. A method for state of energy estimation of lithium-ion batteries at dynamic currents and temperatures[J]. Journal of Power Sources, 2014(270): 151-157.
5 He H W, Zhang Y Z, Xiong R, et al. A novel gaussian model based battery state estimation approach: state-of-energy[J]. Applied Energy, 2015(151): 41-48.
6 Wang Y J, Zhang C B, Chen Z H. A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries[J]. Applied Energy, 2014(135): 81-87.
7 Zhang W G, Shi W, Ma Z Y. Adaptive unscented kalman filter based state of energy and power capability estimation approach for lithium-ion battery[J]. Journal of Power Sources, 2015(289): 50-62.
8 Dong G Z, Chen Z H, Wei J W, et al. An online model-based method for state of energy estimation of lithium-ion batteries using dual filters[J]. Journal of Power Sources, 2016(301): 277-286.
9 赵琳, 王小旭, 丁继成, 等. 组合导航系统非线性滤波算法综述[J].中国惯性技术学报, 2009, 17(1): 46-52.
Zhao Lin, Wang Xiao-xu, Ding Ji-cheng, et al. Overview of nonlinear filter methods applied in integrated navigation system[J]. Journal of Chinese Inertial Technology, 2009, 17(1): 46-52.
10 Mook D J, Junkins J L. Minimum model error estimation for poorly modeled dynamic systems[J]. Journal of Guidance, Control and Dynamics, 1988, 11(3): 256-261.
11 Crassidis J L, Markley F L. Predictive filtering for nonlinear systems[J]. Journal of Guidance, Control and Dynamics, 1997, 20(3): 566-572.
12 杨一. 神经网络优化方法及其在组合导航中的应用研究[D]. 西安: 西北工业大学自动化学院, 2016.
Yang Yi. Research on the optimization of neural networks method and its application for integrated navigation[D]. Xi'an: College of Automation, Northwestern Polytechnical University, 2016.
13 华寅, 许敏. 基于双非线性预测滤波法的锂离子电池SOH估计[J]. 电源技术, 2018, 42(9): 1321-1324.
Hua Yin, Xu Min. State of health estimation of lithium-ion battery by dual nonlinear predictive filter for electric vehicles[J]. Chinese Journal of Power Sources, 2018, 42(9): 1321-1324.
14 田冬冬, 李立伟, 杨玉新. 基于改进BP-EKF算法的SoC估算[J]. 电源技术, 2020, 44(9): 1274-1278.
Tian Dong-dong, Li Li-wei, Yang Yu-xin. Research on SoC estimation based on improved BP-EKF algorithm[J]. Chinese Journal of Power Sources, 2020, 44(9): 1274-1278.
[1] Guo-yuan QI,Kuo LI,Kun WANG. Attitude constrained control of quadrotor unmanned aerial vehicle based on compensation function observer [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 853-862.
[2] Hong-yan GUO,Wen-ya YU,Jun LIU,Qi-kun DAI. Integrated moving horizon decision⁃making method for lane and speed of intelligent vehicle in complex scenarios [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 693-703.
[3] De-jun WANG,Kai-ran ZHANG,Peng XU,Tian-biao GU,Wen-ya YU. Speed planning and control under complex road conditions based on vehicle executive capability [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 643-652.
[4] Zhuo-jun XU,Yao-xiang WANG,Xing HUANG,Cheng PENG. Ground moving target search and location with multi⁃unmanned aerial vehicles [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 832-840.
[5] De-feng HE,Dan ZHOU,Jie LUO. Efficient cooperative predictive control of predecessor⁃following vehicle platoons with guaranteed string stability [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 726-734.
[6] Long-long CHEN,Tian-yu FENG,Zong-yang LYU,Yu-hu WU. Finite⁃time sliding mode attitude control for coaxial tilt⁃rotor unmanned aerial vehicle [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 883-890.
[7] Guo-yuan QI,Hao CHEN. Observer⁃based control⁃anti⁃disturbance⁃obstacle avoidance of quadrotor unmanned aerial vehicle [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 810-822.
[8] Su-feng WEI,Xin PING,Chun-lin LI,Guo-yong WANG. N⁃doped Co/Co3O4@C core⁃shell nanoparticles as anode materials for lithium⁃ion batteries [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 376-384.
[9] Hong-zhi WANG,Ting-ting WANG,Huang-shui HU,Xiao-fan LU. PID control based on BP neural network optimized by Q⁃learning for speed control of BLDCM [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2280-2286.
[10] Jian-xin FENG,Qiang WANG,Ya-lei WANG,Biao XU. Fuzzy PID control of ultrasonic motor based on improved quantum genetic algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 1990-1996.
[11] Yan MA,Jian-fei HUANG,Hai-yan ZHAO. Method of vehicle formation control based on vehicle to vehicle communication [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(2): 711-718.
[12] DENG Li-fei, SHI Yao-wu, ZHU Lan-xiang, YU Ding-li. Failure detection of closed-loop systems and application to SI engines [J]. 吉林大学学报(工学版), 2017, 47(2): 577-582.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd