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Research Papers

A Coupled Mechanical–Electrochemical Study of Li-Ion Battery Based on Genetic Programming and Experimental Validation

[+] Author and Article Information
Li Shui, Xiongbin Peng, Jian Zhang

Intelligent Manufacturing Key Laboratory of
Ministry of Education,
Shantou University,
Guangdong 515063, China

Akhil Garg

Intelligent Manufacturing Key Laboratory of
Ministry of Education,
Shantou University,
Guangdong 515063, China
e-mail: akhil@stu.edu.cn

Hoang-do Nguyen, My Loan Phung Le

Applied Physical Chemistry Laboratory,
Department of Physical Chemistry,
Vietnam National University of Ho Chi Minh City
(VNUHCM),
Ho Chi Minh City 700000, Vietnam

1Corresponding author.

Manuscript received February 13, 2018; final manuscript received June 30, 2018; published online August 6, 2018. Assoc. Editor: Partha P. Mukherjee.

J. Electrochem. En. Conv. Stor. 16(1), 011008 (Aug 06, 2018) (7 pages) Paper No: JEECS-18-1017; doi: 10.1115/1.4040824 History: Received February 13, 2018; Revised June 30, 2018

Lithium-ion batteries (LIBs) are the heart of electric vehicle because they are the 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 result 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. This study proposes the evaluation of battery state of health (SOH) based on the mechanical parameter such as stack stress. The objective of this 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 validate the fundamentals, and the robust models are formulated using an evolutionary approach of genetic programming (GP). The findings from this study can pave the way for the design of new battery that incorporates the sensors to estimate its state accurately.

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Figures

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Fig. 1

(a) Battery testing system and (b) schematic presentation of experimental setup

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Fig. 2

Battery container for measurement of stress-capacity

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Fig. 3

The comparison between the full charge stress and zero charge stress during cycling

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Fig. 4

The stress variation with different initial stress

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Fig. 5

The capacity fade after 32 cycles

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Fig. 6

Flowchart showing steps of GP for evaluation and estimation of discharging capacity based on the initial stress, stress variation, and temperature

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Fig. 7

Graph showing convergence of best GP model (iteration 2 from Table 3) with minimum fitness RMSE value of 0.005 on training data at generation value of 130

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Fig. 8

Line fit of actual and predicted capacity data

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Fig. 9

Sensitivity analysis of input data on battery capacity

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Fig. 10

Battery capacity with temperature

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Fig. 11

Battery capacity with initial stress

Tables

Errata

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