KiKi0016/State-of-Health-Estimation-of-Electric-Vehicle-Batteries-Using-DeTransformer
Deep learning of lithium-ion battery SOH using the DeTransformer model learns the aging characteristics of the battery and then makes predictions about the battery SOH in order to monitor the health of batteries in electric vehicles.
Monitors the health of electric vehicle batteries by analyzing their aging characteristics. It takes data on how lithium-ion batteries perform over time, especially during fast charging, and provides predictions about their remaining lifespan and current health status. This is for battery engineers, fleet managers, or EV manufacturers who need to track the condition of batteries.
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Use this if you need to accurately predict the State of Health (SOH) and Remaining Useful Life (RUL) of lithium-ion batteries in electric vehicles based on their aging data.
Not ideal if you are monitoring battery types other than lithium-ion or need real-time, on-vehicle diagnostics without historical aging data.
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Feb 06, 2024
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