Open AccessMulti-objective Optimization Design for Battery Pack of Electric Vehicle Based on Neural Network of Radial Basis Function (RBF)
Author(s) -
Yuefag Li
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1684/1/012156
Subject(s) - battery pack , battery (electricity) , artificial neural network , power (physics) , radial basis function , reliability (semiconductor) , automotive engineering , engineering , surrogate model , electric vehicle , computer science , artificial intelligence , machine learning , physics , quantum mechanics
As a carrier of EV batteries, battery pack is a key component that ensures stability, safety, and reliability of energy system for power batteries. In order to complete the requirement of light weight for EV, the battery pack shall have light weight as much as possible while meeting structural strength. This paper uses an Surrogate Models algorithm based on RBF neural network to solve the problem of multi-objective optimization for battery pack. It can be observed from LsDyna simulation results that the battery pack has declined 17.62% in mass and 30.78% in maximum deformation. Therefore, the proposed structure optimization model of battery pack hereof provides an effective design and optimization method for battery pack.