scholarly journals Multi-objective optimization design for a battery pack of electric vehicle with surrogate models

2016 ◽  
Vol 18 (4) ◽  
pp. 2343-2358 ◽  
Author(s):  
Fengling Gao ◽  
Cheng Lin ◽  
Wenwei Wang ◽  
Xiaokai Chen
Keyword(s):  
Electric Vehicle ◽  
Optimization Design ◽  
Surrogate Models ◽  
Battery Pack ◽  
Multi Objective Optimization ◽  
Multi Objective

Multi objective optimization of structural parameters of air-cooled system for lithium battery pack based on surrogate model

2021 ◽  
pp. 1-24
Author(s):  
Nengsheng Bao ◽  
Wei Li ◽  
Ma Chong ◽  
Fan Yuchen ◽  
Li Tuyan
Keyword(s):  
Electric Vehicle ◽  
Thermal Management ◽  
Management System ◽  
Lithium Battery ◽  
Optimization Method ◽  
Battery Pack ◽  
Maximum Temperature ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Thermal Management System

Abstract The new energy electric vehicle, which takes clean electric energy as the main driving force, has no pollutants and exhaust emissions during its operation. And has a higher energy utilization ratio than the fuel locomotive. Therefore, electric vehicles have been widely developed in recent years. The maximum temperature and temperature consistency of battery pack in electric vehicle have great influence on the life and safety of battery. In this paper, the thermal management system of lithium battery pack was taken as the research object. The temperature distribution and uniformity of battery pack under different heat dissipation conditions were analyzed based on computational fluid dynamics (CFD). The multi-objective optimization method of battery pack thermal management system was carried out by combining sur-rogate model with fast non-dominated sorting genetic algorithm (NSGA-II). The maximum temperature of the battery pack obtained from candidate point 1 is 310.72K, which is 4.99K lower than the initial model temperature, and the temperature standard deviation is 0.76K, with a reduction rate of 51.9%. Experiment results showed that maximum difference between the optimized and experimental value of the maximum temperature is 0.8K, and the error was within 1K. Therefore, the multi-objective optimization method proposed in this paper has high accuracy.

Sign in / Sign up

Export Citation Format

Share Document