Research on Electric Vehicle Parameter Identification Method Based on Battery and Ultracapacitor

Hailan Mou; Chun Wang

doi:10.54097/4fpqwy65

Authors

Hailan Mou
Chun Wang

DOI:

https://doi.org/10.54097/4fpqwy65

Keywords:

Electric vehicle; Battery; Ultracapacitor; Parameter identification; Intelligent optimization algorithm.

Abstract

The accuracy of the model relies heavily on parameter identification. In order to accurately identify the model parameters of batteries and ultracapacitors, three representative intelligent optimization algorithms: Grey Wolf Optimization Algorithm (GWO), Particle Swarm Optimization Algorithm (PSO), and Genetic Algorithm (GA) are selected in this study to identify the parameters of battery and ultracapacitor models, respectively. The results of the study show that the Grey Wolf optimization algorithm demonstrates significant advantages in improving the overall prediction accuracy of the battery and supercapacitor models. Specifically, the Grey Wolf optimization algorithm reduces the root mean square error (RMS), an evaluation metric, by at least 7.4% and 13.8% compared to the particle swarm optimization algorithm and the genetic algorithm, respectively. Therefore, the parameter identification of battery and supercapacitor models using the Grey Wolf optimization algorithm not only has better accuracy and reliability but also provides strong support for the subsequent research of electric vehicle energy storage systems.

Downloads

Download data is not yet available.

References

[1] Xiong R, Sun W, Yu Q, Sun F. Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles. Applied Energy 2020;279:115855. https://doi.org/10.1016/j.apenergy.2020.115855.

[2] Wang C, He H, Zhang Y, Mu H. A comparative study on the applicability of ultracapacitor models for electric vehicles under different temperatures. Applied Energy 2017; 196:268-78. https://doi.org/10.1016/j.apenergy.2017.03.060.

[3] Wang C. Research on modelling and energy management strategies for automotive composite power systems [D]. Beijing Institute of Technology, 2018.DOI: 10.26948/d.cnki.gbjlu.2018.000173.

[4] Xiong R, Li Z, Yang R, Shen W, Ma S, Sun F. Fast self-heating battery with anti-aging awareness for freezing climate applications. Applied Energy 2022; 324:119762. https://doi.org/10.1016/j.apenergy.2022.119762.

[5] Lin C, Mu H, Xiong R, Shen W. A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm. Applied Energy 2016; 166:76-83. https://doi.org/10.1016/j.apenergy.2016.01.010.

[6] Jamadar NM, Jadhav HT. Effectiveness of supercapacitor during braking operation of electric vehicle. Materials Today: Proceedings 2022; 56:314-9. https://doi.org/10.1016/j.matpr.2022.01.168.

[7] Li H, Yang H, Xu C, Yan J, Cen K, Ostrikov K (Ken), et al. Entropy generation analysis in supercapacitor modules based on a three-dimensional coupled thermal model. Energy 2022; 244:123218. https://doi.org/10.1016/j.energy.2022.123218.

[8] Mwambeleko JJ, Kulworawanichpong T. Supercapacitor and accelerating contact lines hybrid tram system. Journal of Energy Storage 2021; 44:103277. https://doi.org/10.1016/j.est.2021.103277.

[9] Zhang S, Zhang Q, Liu D, Dai X, Zhang X. State-of-charge estimation for lithium-ion battery during constant current charging process based on model parameters updated periodically. Energy 2022; 257:124770. https://doi.org/10.1016/j.energy.2022.124770.

[10] Wang C, Fang C, Tang A, Huang B, Zhang Z. A Novel Ultracapacitor State-of-Charge Fusion Estimation Method for Electric Vehicles Considering Temperature Uncertainty. Energies 2022; 15:4309. https://doi.org/10.3390/en15124309.

[11] Ullah I, Liu K, Yamamoto T, Shafiullah M, Jamal A. Grey wolf optimizer-based machine learning algorithm to predict electric vehicle charging duration time. Transportation Letters 2023; 15:889-906. https://doi.org/10.1080/19427867.2022.2111902.

[12] Yan D.C,Ma C,Yang K,et al. Development of Energy Management Strategies for Extended Range Vehicles Based on Operating Conditions and Particle Swarm Optimization[J]. Science, Technology and Engineering,2021,21(26):11396-11404.

[13] Li Y,Yuan H.Y, Yu J.Q,et al. A review of the application of genetic algorithms to optimization problems[J]. Shandong Industrial Technology,2019, (12):242-243+180.DOI: 10.16640/j.cnki.37-1222/t.2019.12.210.