# Decentralized Smart Charging of Large-Scale EVs using Adaptive Multi-Agent Multi-Armed Bandits

arXiv: https://arxiv.org/abs/2307.10704 · HTML: https://mlsanity.com/paper/2307.10704/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 3 of 3)

- [Multi-Armed Bandit Learning in IoT Networks: Learning helps even in non-stationary settings](https://mlsanity.com/paper/1807.00491/) (2018)
- [Combinatorial Bandits Revisited](https://mlsanity.com/paper/1502.03475/) (2015)
- [Large-scale Charging of Electric Vehicles: A Multi-Armed Bandit Approach](https://mlsanity.com/paper/1605.07578/) (2016)

## Abstract (arXiv)

The drastic growth of electric vehicles and photovoltaics can introduce new challenges, such as electrical current congestion and voltage limit violations due to peak load demands. These issues can be mitigated by controlling the operation of electric vehicles i.e., smart charging. Centralized smart charging solutions have already been proposed in the literature. But such solutions may lack scalability and suffer from inherent drawbacks of centralization, such as a single point of failure, and data privacy concerns. Decentralization can help tackle these challenges. In this paper, a fully decentralized smart charging system is proposed using the philosophy of adaptive multi-agent systems. The proposed system utilizes multi-armed bandit learning to handle uncertainties in the system. The presented system is decentralized, scalable, real-time, model-free, and takes fairness among different players into account. A detailed case study is also presented for performance evaluation.