# Smart Online Charging Algorithm for Electric Vehicles via Customized Actor-Critic Learning

arXiv: https://arxiv.org/abs/2106.00854 · HTML: https://mlsanity.com/paper/2106.00854/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2021.

## Built on by (most cited first, 9 of 9)

- [A Deep Reinforcement Learning-Based Charging Scheduling Approach with Augmented Lagrangian for Electric Vehicle](https://mlsanity.com/paper/2209.09772/) (2022)
- [MARL for Decentralized Electric Vehicle Charging Coordination with V2V Energy Exchange](https://mlsanity.com/paper/2308.14111/) (2023)
- [Non-Intrusive Load Monitoring for Feeder-Level EV Charging Detection: Sliding Window-based Approaches to Offline and Online Detection](https://mlsanity.com/paper/2312.01887/) (2023)
- [Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks](https://mlsanity.com/paper/2308.08792/) (2023)
- [Safety-Aware Reinforcement Learning for Electric Vehicle Charging Station Management in Distribution Network](https://mlsanity.com/paper/2403.13236/) (2024)
- [Progress and summary of reinforcement learning on energy management of MPS-EV](https://mlsanity.com/paper/2211.04001/) (2022)
- [Deep Reinforcement Learning for Community Battery Scheduling under Uncertainties of Load, PV Generation, and Energy Prices](https://mlsanity.com/paper/2312.03008/) (2023)
- [Centralized vs. Decentralized Multi-Agent Reinforcement Learning for Enhanced Control of Electric Vehicle Charging Networks](https://mlsanity.com/paper/2404.12520/) (2024)
- [Agent-Based Decentralized Energy Management of EV Charging Station with Solar Photovoltaics via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2505.18750/) (2025)

## Abstract (arXiv)

With the advances in the Internet of Things technology, electric vehicles (EVs) have become easier to schedule in daily life, which is reshaping the electric load curve. It is important to design efficient charging algorithms to mitigate the negative impact of EV charging on the power grid. This paper investigates an EV charging scheduling problem to reduce the charging cost while shaving the peak charging load, under unknown future information about EVs, such as arrival time, departure time, and charging demand. First, we formulate an EV charging problem to minimize the electricity bill of the EV fleet and study the EV charging problem in an online setting without knowing future information. We develop an actor-critic learning-based smart charging algorithm (SCA) to schedule the EV charging against the uncertainties in EV charging behaviors. The SCA learns an optimal EV charging strategy with continuous charging actions instead of discrete approximation of charging. We further develop a more computationally efficient customized actor-critic learning charging algorithm (CALC) by reducing the state dimension and thus improving the computational efficiency. Finally, simulation results show that our proposed SCA can reduce EVs' expected cost by 24.03%, 21.49%, 13.80%, compared with the Eagerly Charging Algorithm, Online Charging Algorithm, RL-based Adaptive Energy Management Algorithm, respectively. CALC is more computationally efficient, and its performance is close to that of SCA with only a gap of 5.56% in the cost.