A framework integrates map data, Bidirectional LSTM driver behavior modeling, and quasi-steady physics-based energy consumption to generate personalized BEV velocity, power, and SOC profiles that capture individual patterns across urban, freeway, and hilly routes.
Velocity prediction based on map data for optimal control of electrified vehicles using recurrent neural networks (LSTM),
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Personalized electric vehicle energy consumption estimation framework that integrates driver behavior with map data
A framework integrates map data, Bidirectional LSTM driver behavior modeling, and quasi-steady physics-based energy consumption to generate personalized BEV velocity, power, and SOC profiles that capture individual patterns across urban, freeway, and hilly routes.