🤖 AI Summary
Existing electric vehicle energy consumption prediction methods struggle to simultaneously model individual driving behavior and road-environment context, limiting state-of-charge (SOC) estimation accuracy. This work proposes a high-fidelity SOC prediction framework that integrates map semantics, personalized driving patterns, and a physics-based energy model. By parsing route geometry and road attributes, the method generates reference speeds through rule-based logic, then employs a bidirectional LSTM to forecast individual speed profiles, which drive a PID-controlled vehicle dynamics simulator. A quasi-steady inverse energy model subsequently computes traction power, regenerative braking, and SOC evolution. For the first time, this approach tightly couples learned driving styles with map context and physical modeling, accurately capturing behaviors such as intersection deceleration, speed-limit compliance, and grade response across urban, highway, and hilly scenarios, thereby significantly improving the accuracy of both power demand and SOC trajectory predictions.
📝 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.