Lithium-Ion Battery Diagnosis: Health and Safety
A special issue of World Electric Vehicle Journal (ISSN 2032-6653).
Deadline for manuscript submissions: 31 October 2024 | Viewed by 9822
Special Issue Editors
Interests: electric and hybrid vehicle design analysis and testing; applications of batteries and ultracapacitors for electric vehicles
Special Issues, Collections and Topics in MDPI journals
Interests: electric vehicles; cyber-BMS; battery diagnosis; machine learning; transportation electrification
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Environmental issues and energy crises have spawned a host of social and economic issues, which has led to attempting to use renewable clean energy. The reliance of the transportation sector on fossil fuels has made it one of the largest emitters of greenhouse gases and toxic pollution. Therefore, the electrification of transportation is seen as a promising way to reduce emissions of carbon and pollution, and to lower the dependence on limited, non-renewable natural resources. The mass marketing of battery-powered electric vehicles (EVs) requires that car buyers have high confidence in the performance, reliability, and safety of the battery in their vehicles. However, although steady progress has been made in developing technologies for battery diagnosis, there are still many challenges to be overcome to accurately predict battery state of health (SOH), cycle life, remaining useful life (RUL), and fault/failure, as well as abuse conditions in field applications. The safety, health, and reliability of lithium-ion batteries are more important now than ever because of their ubiquitous application scenarios. In this case, there is a pressing need to not only investigate physical mechanisms, but also to develop new techniques to model and predict the dynamics of multiphysics and multiscale battery systems. Data-driven approaches offer new opportunities in a more intelligent manner, which would accelerate the technology transfer from academic progress to engineering applications. We hope this Special Issue will be a useful contribution to the field of battery diagnosis in the automotive industry, and will generate maximum practical value.
Prof. Dr. Andrew F. Burke
Dr. Jingyuan Zhao
Dr. Jinrui Nan
Guest Editors
Manuscript Submission Information
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Keywords
- battery diagnosis and prognosis
- battery safety
- fault detection
- thermal runaway
- abuse conditions
- state of health
- cycle life
- remaining useful lifetime
- state of charge
- data-driven
- artificial intelligence
- machine learning
- deep learning
- electric vehicles
- battery management system
- cloud computing and storage
- edge computing
- digital twin
- cyber-physics
- field application
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