Training Electric Vehicle Charging Controllers with Imitation Learning
Not on the map. Why?
Abstract (arXiv)
The problem of coordinating the charging of electric vehicles gains more importance as the number of such vehicles grows. In this paper, we develop a method for the training of controllers for the coordination of EV charging. In contrast to most existing works on this topic, we require the controllers to preserve the privacy of the users, therefore we do not allow any communication from the controller to any third party. In order to train the controllers, we use the idea of imitation learning -- we first find an optimum solution for a relaxed version of the problem using quadratic optimization and then train the controllers to imitate this solution. We also investigate the effects of regularization of the optimum solution on the performance of the controllers. The method is evaluated on realistic data and shows improved performance and training speed compared to similar controllers trained using evolutionary algorithms.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 11 references that are in the map. First posted on arXiv 21 Jul 2021.
Builds on
3 of 11 references.
References · 3
- 2016Generative Adversarial Imitation Learningcited by 1,576
- 2017OSQP: An Operator Splitting Solver for Quadratic Programscited by 156
- 2018Controlling the Charging of Electric Vehicles with Neural Networkscited by 1
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2022Optimized cost function for demand response coordination of multiple EV charging stations using reinforcement learning
- 2024Deep-MPC: A DAGGER-Driven Imitation Learning Strategy for Optimal Constrained Battery Charging
- 2018Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning
- 2022Learning energy-efficient driving behaviors by imitating experts
- 2021Reinforcement Learning for Battery Energy Storage Dispatch augmented with Model-based Optimizer
- 2025Economic Battery Storage Dispatch with Deep Reinforcement Learning from Rule-Based Demonstrations
- 2026Infeasible optimization problems and the hierarchical augmented Lagrangian method in imitation learning
- 2026LLM-Enhanced Multi-Agent Reinforcement Learning for Unified Electric Vehicles-Charging Station-Grid Optimization in Public Charging Systems