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research-article

A coupled Mechanical-Electrochemical Study of Li-ion Battery based on Genetic Programming and Experimental validation

[+] Author and Article Information
Li Shui

Intelligent Manufacturing Key Laboratory of Ministry of Education, Shantou University, Guangdong, China
16sli3@stu.edu.cn

Xionygbin Peng

Intelligent Manufacturing Key Laboratory of Ministry of Education, Shantou University, Guangdong, China
xbpeng@stu.edu.cn

Jian Zhang

Intelligent Manufacturing Key Laboratory of Ministry of Education, Shantou University, Guangdong, China
jianzhang@stu.edu.cn

Akhil Garg

Intelligent Manufacturing Key Laboratory of Ministry of Education, Shantou University, Guangdong, China
akhil@stu.edu.cn

Hoang-Nguyen Do

Applied Physical Chemistry Laboratory, Department of Physical Chemistry, Viet Nam National University of Ho Chi Minh city (VNUHCM), Ho Chi Minh city
dhnguyen2110@gmail.com

My Loan Phung Le

Applied Physical Chemistry Laboratory, Department of Physical Chemistry, Viet Nam National University of Ho Chi Minh city (VNUHCM), Ho Chi Minh city
lmlphung@hcmus.edu.vn

1Corresponding author.

ASME doi:10.1115/1.4040824 History: Received February 13, 2018; Revised June 30, 2018

Abstract

Lithium-ion batteries are the heart of electric vehicle because it is main source of its power transmission. The current scientific challenges include the accurate and robust evaluation of battery state such as the discharging capacity, so that the occurrence of unforeseen dire events can be reduced. State-of-the-art technologies focused extensively on evaluating the battery states based on the models, whose measurements rely on determination of parameters such as the voltage, current and temperature. Experts have well argued that these models have poor accuracy, computationally expensive and best suited for laboratory conditions. This forms the strong basis of conducting research on identifying and investigating the parameters that can quantify the battery state accurately. The unwanted, irreversible chemical and physical changes in the battery results in loss of active metals (lithium ions). This shall consequently result in decrease of capacity of the battery. Therefore, measuring the stack stress along with temperature of the battery can be related to its discharging capacity. The present study proposes the evaluation of battery SOH based on the mechanical parameter such as stack stress. The objective of the study will be to establish the fundamentals and the relationship between the battery state, the stack stress and the temperature. The experiments were designed to validates the fundamentals and the robust models are formulated using an evolutionary approach of genetic programming. The findings from the study can pave the way for the design of new battery that incorporates the sensors to estimate its state accurately.

Copyright (c) 2018 by ASME
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