Evaluation of batteries residual energy for battery pack recycling: Proposition of stack stress-coupled-AI approach

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@Article{GARG:2019:JES,

• author = "Akhil Garg and Li Wei and Ankit Goyal and Xujian Cui and Liang Gao",
• title = "Evaluation of batteries residual energy for battery pack recycling: Proposition of stack stress-coupled-{AI} approach",
• journal = "Journal of Energy Storage",
• volume = "26",
• pages = "101001",
• year = "2019",
• ISSN = "2352-152X",
• DOI = "doi:10.1016/j.est.2019.101001",
• URL = "http://www.sciencedirect.com/science/article/pii/S2352152X1930790X",
• keywords = "genetic algorithms, genetic programming, Energy storage, Battery pack recycling, Residual energy",
• abstract = "It is predicted that by 2025, approximately 1 million metric tons of spent battery waste will be accumulated. How to reasonably and effectively evaluate the residual energy of the lithium-ion batteries embedded in hundreds in packs used in Electric Vehicles (EVs) grows attention in the field of battery pack recycling. The main challenges of evaluation of the residual energy come from the uncertainty of thermo-mechanical-electrochemical behavior of battery. This motivates the notion of facilitating research on establishing a model which can detect and predict the state of battery based on parameters enable to be measured, such as voltage and stack stress. Thus, the present work proposes a stack stress-coupled-artificial intelligence approach for analyzing the residual energy (remaining) in the batteries. Experiments are designed and performed to verify the fundamentals. A robust model is formulated based on artificial intelligence approach of genetic programming. The findings in the study can provide an optimized recycling strategy for spent batteries by accurately predicting the state of battery based on stack stress",

}

Genetic Programming entries for Akhil Garg Li Wei Ankit Goyal Xujian Cui Liang Gao

Citations