mlsanityData to 14 Oct 2026

A Hybrid Physics-Based and Reinforcement Learning Framework for Electric Vehicle Charging Time Prediction

arXiv 2512.06287 ↗First posted 6 Dec 2025eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

In this paper, we develop a hybrid prediction framework for accurate electric vehicle (EV) charging time estimation, a capability that is critical for trip planning, user satisfaction, and efficient operation of charging infrastructure. We combine a physics-based analytical model with a reinforcement learning (RL) approach. The analytical component captures the nonlinear constant-current/constant-voltage (CC--CV) charging dynamics and explicitly models state-of-health (SoH)--dependent capacity and power fade, providing a reliable baseline when historical data are limited. Building on this foundation, we introduce an RL component that progressively refines charging-time predictions as operational data accumulate, enabling improved long-term adaptation. Both models incorporate SoH degradation to maintain predictive accuracy over the battery lifetime. We evaluate the framework using 5,000 simulated charging sessions calibrated to manufacturer specifications and publicly available EV charging datasets. Our results show that the analytical model achieves R2=98.5% and MAPE=2.1%, while the RL model further improves performance to R2=99.2% and MAPE=1.6%, corresponding to a 23% accuracy gain and 35% improved robustness to battery aging.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 19 references that are in the map. First posted on arXiv 6 Dec 2025.

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5 of 19 references.

References · 5

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2021Machine learning pipeline for battery state of health estimationcited by 29
  3. 2023Rollout-Based Charging Strategy for Electric Trucks with Hours-of-Service Regulations (Extended Version)cited by 1
  4. 2023Distributed Charging Coordination of Electric Trucks with Limited Charging Resourcescited by 1
  5. 2024Distributed Charging Coordination for Electric Trucks under Limited Facilities and Travel Uncertaintiescited by 1

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  2. 2025Physics-Informed Reinforcement Learning for Large-Scale EV Smart Charging Considering Distribution Network Voltage Constraints
  3. 2024Advancements in Electric Vehicle Charging Optimization: A Survey of Reinforcement Learning Approaches
  4. 2024EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and Benchmarking
  5. 2022Optimized cost function for demand response coordination of multiple EV charging stations using reinforcement learning
  6. 2025Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewards
  7. 2026Forecasting what Matters: Decision-Focused RL for Controlled EV Charging with Unknown Departure Times
  8. 2024Adaptive Safe Reinforcement Learning-Enabled Optimization of Battery Fast-Charging Protocols