期刊
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
卷 44, 期 4, 页码 2801-2818出版社
WILEY
DOI: 10.1002/er.5096
关键词
battery aging model; battery management system; diffusion coefficient; energy conversion; genetic programming
资金
- HUST Academic Frontier Youth Team [2017QYTD04]
- HUST Graduate Innovation and Entrepreneurship Fund [2019YGSCXCY037]
- State Key Lab of Digital Manufacturing Equipment & Technology (Huazhong University of Science and Technology) [DMETKF2018019]
In this paper, a method composed of state of health (SOH) testing experiments and artificial intelligence simulation is proposed to carry out the study on the change of battery characteristic during its operation and generate mathematical models for the prediction of aging behaviour of battery. An experiment comprising of multidisciplinary parameters-based SOH detection is conducted to study the battery aging characteristics from several aspects (ie, electrochemistry, electric, thermal behaviour and mechanics). In total, 200 sets of data (corresponding 200 charging/discharging cycles) are collected from the experiment. The data obtained from the first 150 cycles are employed in generation of the models. The result of sensitivity analysis based on the obtained genetic programming models shows that it is better to apply voltage value at the end of charging step, charging time and cycle number to predict the operational performance of the battery. The average predicted accuracy of model (without stress) is 94.52%, whereas the average predicted accuracy of model (with stress effect) is 99.42%. The proposed models could be useful for defining the optimised charging strategy, fault diagnosis and spent batteries disposal strategies.
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