Open AccessThermal Analysis for Lithium-Ion Battery Pack based on Parameter Estimation based on Genetic Algorithm
Author(s) -
Yong Wang,
Yelin Deng,
Weiwei Liu,
Kunkun Hao,
Hongchao Zhang
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/793/1/012015
Subject(s) - battery pack , battery (electricity) , matlab , lithium ion battery , automotive engineering , heat generation , genetic algorithm , thermal management of electronic devices and systems , dissipation , computer science , simulation , control theory (sociology) , nuclear engineering , engineering , mechanical engineering , power (physics) , thermodynamics , physics , control (management) , machine learning , artificial intelligence , operating system
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.