TY - JOUR
T1 - Thermal Analysis for Lithium-Ion Battery Pack based on Parameter Estimation based on Genetic Algorithm
AU - Wang, Yong
AU - Deng, Yelin
AU - Liu, Weiwei
AU - Hao, Kunkun
AU - Zhang, Hongchao
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2020/4/9
Y1 - 2020/4/9
N2 - Thermal analysis of Lithium-ion battery pack is the important portion of battery management for electric vehicles. The heat produced in charging and discharging will bring about impairment of the safety and service life of batteries. It is thus important to monitor battery temperature for prevention of the battery failure. This paper first sets up a simulation model based on the second-order RC circuit model of the heat generation and dissipation of the battery pack using SIMULINK. The temperature of the battery pack is tested under The New European Driving Cycle conditions. And by comparing with the experimental data of the battery temperature, the heat dissipation coefficient in the simulation model will be optimized by the genetic algorithm using MATLAB. The optimization result shows that the difference between the simulated temperature and the actual temperature is within one degree, so the model based on the optimization result can accurately reflect the actual temperature change.
AB - Thermal analysis of Lithium-ion battery pack is the important portion of battery management for electric vehicles. The heat produced in charging and discharging will bring about impairment of the safety and service life of batteries. It is thus important to monitor battery temperature for prevention of the battery failure. This paper first sets up a simulation model based on the second-order RC circuit model of the heat generation and dissipation of the battery pack using SIMULINK. The temperature of the battery pack is tested under The New European Driving Cycle conditions. And by comparing with the experimental data of the battery temperature, the heat dissipation coefficient in the simulation model will be optimized by the genetic algorithm using MATLAB. The optimization result shows that the difference between the simulated temperature and the actual temperature is within one degree, so the model based on the optimization result can accurately reflect the actual temperature change.
UR - http://www.scopus.com/inward/record.url?scp=85083163260&partnerID=8YFLogxK
U2 - 10.1088/1757-899X/793/1/012015
DO - 10.1088/1757-899X/793/1/012015
M3 - Conference article
AN - SCOPUS:85083163260
SN - 1757-8981
VL - 793
JO - IOP Conference Series: Materials Science and Engineering
JF - IOP Conference Series: Materials Science and Engineering
IS - 1
M1 - 012015
T2 - 2019 International Conference on Energy, Power and Mechanical Engineering, EPME 2019
Y2 - 20 December 2019 through 22 December 2019
ER -