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ISSN 2063-5346
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Analysing Real Time Battery Condition Using Machine Learning For Electric Vehicles

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V. Vineeth Raj, T. Shanmuganathan, N. Meenakshi, S. Muruganandam, T N. Sudhahar
» doi: 10.48047/ecb/2023.12.si4.441

Abstract

Batteries are essential electrochemical cells that deliver energy to a variety of electrical devices. These cells must be routinely maintained in order to operate properly. Battery management systems control charge and temperature, lowering possible safety, health, & property issues. To regulate battery performance, these systems use merit measures. Since current approaches over data-driven fault prediction produce good results on the specific processes on which they were trained, they frequently lack the ability to adapt to changes, To address this issue, this research presents a continuous learning neural network strategy that uses a data-driven approach to monitor these parameters. To estimate these values, the machine learning algorithm used in this work finds relevant characteristics from the discharge curves. The efficiency of the suggested technique was assessed using extensive simulations at various voltage as well as temperature levels.

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