Experimental Combined Numerical Approach for Evaluation of Battery Capacity Based on the Initial Applied Stress, the Real-Time Stress, Charging Open Circuit Voltage, and Discharging Open Circuit Voltage

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@Article{Yun:2018:MPE,

• author = "Liu Yun and Biranchi Panda and Liang Gao and Akhil Garg and Xu Meijuan and Dezhi Chen and Chin-Tsan Wang",
• title = "Experimental Combined Numerical Approach for Evaluation of Battery Capacity Based on the Initial Applied Stress, the Real-Time Stress, Charging Open Circuit Voltage, and Discharging Open Circuit Voltage",
• journal = "Mathematical Problems in Engineering",
• year = "2018",
• volume = "2018",
• number = "1",
• pages = "Article ID 8165164",
• keywords = "genetic algorithms, genetic programming, Li",
• ISSN = "1024-123X",
• bibsource = "OAI-PMH server at oai.repec.org",
• identifier = "RePEc:hin:jnlmpe:8165164",
• oai = "oai:RePEc:hin:jnlmpe:8165164",
• URL = "https://downloads.hindawi.com/journals/mpe/2018/8165164.pdf",
• DOI = "doi:10.1155/2018/8165164",
• size = "16 pages",
• abstract = "With the intensification of energy crisis, considerable attention has been paid to the application and research of lithium-ion batteries. A significant progress has also been made in the research of lithium-ion battery capacity evaluation using electrochemical and electrical parameters. In this study, the effect of mechanical characteristic parameter (i.e., stack stress) on battery capacity is investigated using the experimental combined numerical approach. The objective of the proposed approach is to evaluate the capacity based on the initial applied stress, the real-time stress, charging open circuit voltage, and discharging open circuit voltage. Experiments were designed and the data is fed into evolutionary approach of genetic programming. Based on analysis, the accuracy of the proposed GP model is fairly high while the maximum percentage of error is about 5percent. In addition, a negative correlation exists between the initial stress and battery capacity while the capacity increases with real-time stress.",

}

Genetic Programming entries for Liu Yun Biranchi Narayan Panda Liang Gao Akhil Garg Xu Meijuan Dezhi Chen Chin-Tsan Wang

Citations