SPECIAL ISSUE ARTICLE Models based on mechanical stress, initial stress, voltage, current, and applied stress for Li-ion batteries during different rates of discharge Xujian Cui 1 | Shi Khai Kam 2 | Christina May May Chin 2 | Jihong Chen 3 | Chitti Babu 4 | Xiongbin Peng 1 1 Intelligent Manufacturing Key Laboratory of Ministry of Education, Shantou University, Shantou, China 2 Department of Mechanical, Materials and Manufacturing Engineering, University of Nottingham Malaysia Campus, Semenyih, Malaysia 3 College of Transport and Communications, Shanghai Maritime University, Shanghai, China 4 Department of Electronics & Communication Engineering, Indian Institute of Information Technology Design and Manufacturing Kancheepuram, Chennai, Tamil Nadu, India Correspondence Xiongbin Peng, Intelligent Manufacturing Key Laboratory of Ministry of Education, Shantou University, Shantou, China. Email: xbpeng@stu.edu.cn Abstract The most important criteria for any energy storage system such as the Li-ion batteries are its capacity fading or the state of health (SOH). In real time, the parameters such as voltage, current cannot be used to predict SOH because these are not taken into account the self-discharge. This article proposes exper- imental combined numerical methodology for studying coupled stress- electrochemical performance of Li-ion batteries. The work aims to evaluate and predict the SOH of lithium-ion batteries based on mechanical stress, num- ber of charging cycles, and induced load. Experiments are conducted to mea- sure data corresponding to capacity, initial stress, and applied stress. Artificial neural networks are then applied in formulation of predictive models based on initial stress, stack stress, charging voltage, and discharging voltage. A neural net was successfully trained that managed to achieve correlation coefficient (prediction accuracy) of 0.9909 for capacity and 0.7260 for cycle number. This research was able to identify an ideal network configuration, predicting cycle number, and remaining capacity of a battery after multiple charges, trained from the given data values. KEYWORDS capacity fading, coupled stress-electrochemical performance, electrochemical performance, energy storage systems, state of health 1 | INTRODUCTION The performance of lithium-ion battery had increased since its birth especially its multiplied capacity. Nowadays, lithium-ions battery possesses a lot of market share with the unremitting efforts of several generations of scientists and becomes the most popular energy storage method within electric vehicles. Given the rise of electric vehicles in the market, the battery pack modules, which are made from packaging up to 7000 lithium-ion batteries together within the vehicles, have come under increased attention, particularly how effective they are at energy storage. A key issue that has been plaguing the usage of electric vehicles is the effective lifespan of the rechargeable batteries within the vehicle. After many cycles of charging, it can be observed that battery capacity decreases. This is reflected in a battery's state of health (SOH). The SOH is a rough measurement of the performance of a battery compared to its freshly minted state. It gives Abbreviations: ANN, artificial neural network; Li-on, lithium-ion; SOH, state of health. Received: 6 November 2019 Revised: 15 December 2019 Accepted: 21 December 2019 DOI: 10.1002/est2.126 Energy Storage. 2020;2:e126. wileyonlinelibrary.com/journal/est2 © 2019 John Wiley & Sons, Ltd. 1 of 10 https://doi.org/10.1002/est2.126