AUTOMATIC BATTERY HEALTH MONITORING USING MACHINE LEARNING FOR E-VEHICLES (original) (raw)

Batteries, which are made of a combination of electrochemical cells, provide the necessary electrical current for powering electrical equipment. Batteries continuously transform chemical energy into electrical energy, and for them to operate at their peak efficiency, appropriate maintenance must be given. In addition to the use of batteries, it is also believed that health management systems with expertisein various battery conditioning features, such as temperature, current, and voltage regulation, charging and discharging management mechanisms, and other mechanisms, will help to reduce risks to people's health, safety, and property. These systems regulate battery performance using merit-based standards. In this paper, we provide a data-driven perpetual literacy system for neural networks to cover the foreseen parameters. We use a machine learning technique to extract crucial features from the discharge curves in order to estimate these values. Extensive simulations have been performed in order to evaluate the performance of the suggested technique at different currents and temperatures

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