International Journal of Electronics Signals and Systems


Electric Vehicles (EVs) are becoming more and more financially viable as the operating costs of EVs fall dramatically in comparison to Internal Combustion Engine Vehicles (ICEVs). To boost consumer trust in EVs even further, accurate State of Health - SOH measurement is essential. SOH in a battery is determined by a number of parameters, including current, voltage, age, and temperature. Estimating the SOH of a Lithium -ion battery chemistry is of a difficult task. Because lithium-ion batteries are extremely nonlinear, time-variant, and complicated electrochemical systems, this is the case. Two machine learning techniques are used in this article to estimate SOH from Lithium-ion battery cell experimental test data. Experiments are carried out using data from NASA's Prognostic Center of Excellence.





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