AI RESEARCH
Personalized electric vehicle energy consumption estimation framework that integrates driver behavior with map data
arXiv CS.LG
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ArXi:2604.20764v1 Announce Type: cross This paper presents a personalized Battery Electric Vehicle (BEV) energy consumption estimation framework that integrates map-based contextual features with driver-specific velocity prediction and physics-based energy consumption modeling. The system combines route selection, detailed road feature processing, a rule-based reference velocity generator, a PID controller-based vehicle dynamics simulator, and a Bidirectional LSTM model trained to reproduce individual driving behavior.