Abstract
Owing to the benefits of a longer life, lithium-ion power batteries with high specific power and energy have been widely used in the transportation sector. However, the safety issues brought about by the incorrect calculation and forecasts of battery health have drawn a lot of interest from academics. The fundamental explanations of the “State of Health” (SoH) and the process by which they degrade were summarized from both local and foreign literature in this research. Three perspectives were taken into consideration when discussing the estimation and prediction approaches for lithium-ion power batteries: model based approaches using fusion technology, and data-driven methodologies. The benefits and drawbacks of the standard estimation of SoH and prediction methods used today are outlined in this paper. It is vitally necessary to develop an efficient management of energy storage system that can assess the lithium-ion battery’s overall health and charging condition. Lithium-ion batteries’ state of charge (SoC), which defines how much charge is still in them, is a crucial EV metric. Additionally, SoC offers details regarding the charging/discharging procedure, preventing either an overcharge or an over-discharge of the battery. The different SoC estimate approaches are in-depth examined in this research. SoC estimate methods are thoroughly explored, along with their methodology, mathematical model, advantages, disadvantages, and error rate. The research closes by making several significant recommendations for the improvement of SoC estimates in electric vehicles and applications.
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Gandhi, R., Jha, A., Bhavsar, K. (2023). Intelligent and Conventional Methods for SoC and SoH Estimation. In: Choudrie, J., Mahalle, P.N., Perumal, T., Joshi, A. (eds) IOT with Smart Systems. ICTIS 2023. Lecture Notes in Networks and Systems, vol 720. Springer, Singapore. https://doi.org/10.1007/978-981-99-3761-5_36
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