Personalized electric vehicle energy consumption estimation framework that integrates driver behavior with map data

TL;DR

This paper introduces a personalized BEV energy consumption estimation framework that combines driver behavior, map data, and physics-based models to accurately predict SOC depletion.

Contribution

It integrates driver-specific velocity prediction with map features and physics models, providing a novel personalized energy consumption estimation approach.

Findings

Accurately predicts power and SOC trajectories across various routes.

Captures key driver behavioral patterns such as deceleration and speed tracking.

Demonstrates effectiveness of combining learned behavior with map and physics data.

Abstract

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. Evaluation across urban, freeway, and hilly routes demonstrates that the proposed approach captures key driver behavioral patterns such as deceleration at intersections, speed-limit tracking, and road grade-dependent responses, while producing accurate power and SOC trajectories. The results highlight the effectiveness of combining learned driver behavior with map-based context and physics-based energy consumption modeling to produce accurate, personalized BEV SOC depletion profiles.