Forecasting what Matters: Decision-Focused RL for Controlled EV Charging with Unknown Departure Times

2026-06-17

AI Summary

This work addresses the challenge in electric vehicle charging control where uncertain user departure times hinder effective decision-making in reinforcement learning. To overcome this, the authors propose a Decision-Focused Reinforcement Learning framework (DF-RL) that, for the first time, jointly optimizes a departure time prediction model and the charging control policy in an end-to-end manner. Departing from conventional prediction approaches that prioritize accuracy alone, DF-RL explicitly aligns prediction with downstream decision quality. By integrating historical data-driven temporal forecasting with policy gradient training, the method significantly enhances performance: experiments show up to a 14% increase in total reward and a 55% reduction in unmet charging demand compared to a baseline without prediction.

Abstract

The recent growth of EV adoption poses challenges for power systems, including increased peak demand and potential grid instability. Smart control of EV charging -- e.g., based on reinforcement learning (RL) -- can alleviate these issues by learning temporal and contextual patterns from historical data. Yet, in real-world scenarios, key features, such as departure time, often are unavailable. This, in turn, makes it harder for an RL agent to learn and execute an effective charging policy. To mitigate this uncertainty, a trained forecaster can approximate the unknown features from available data. However, since these forecasting models are typically trained for accuracy (rather than their impact on a downstream agent's decision quality), their errors may propagate and hinder the overall performance of a controller that is using the forecasts. To avoid this, we propose a decision-focused RL (DF-RL) framework in which the forecaster is trained end-to-end, i.e., with feedback from the charging policy actions taken by the RL agent. Such joint training of both the forecaster and controller ultimately results in higher-quality actions: our proposed DF-RL method yields superior charging decisions compared to other baselines, achieving up to a 14% improvement in total reward and a 55% reduction of unsupplied energy (i.e., charging that failed to happen because the EV already left), relative to the RL method without departure time forecasting.

Problem

• EV charging
• unknown departure times
• reinforcement learning
• forecasting
• decision-focused learning

Innovation

• Decision-Focused Learning
• Reinforcement Learning
• EV Charging Control
• End-to-End Training
• Uncertain Departure Time