Personalized electric vehicle energy consumption estimation framework that integrates driver behavior with map data
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. The predicted individual-specific velocity profiles are coupled with a quasi-steady backward energy consumption model to compute tractive power, regenerative braking, and State-of-Charge (SOC) evolution. Evaluat...
| Authors | Sreechakra Vasudeva Raju Rachavelpula, Sangwhan Cha |
|---|---|
| Primary category | eess.SY |
| Published | 22 April 2026 |
| Last updated at source | 22 April 2026 |
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