# Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning

arXiv: https://arxiv.org/abs/1912.03408 · HTML: https://mlsanity.com/paper/1912.03408/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2019.

## Built on by (most cited first, 3 of 3)

- [Improving Operational Efficiency In EV Ridepooling Fleets By Predictive Exploitation of Idle Times](https://mlsanity.com/paper/2208.14852/) (2022)
- [Hybrid Information-driven Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2102.01004/) (2021)
- [Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning](https://mlsanity.com/paper/2109.05024/) (2021)

## Abstract (arXiv)

New forms of on-demand transportation such as ride-hailing and connected autonomous vehicles are proliferating, yet are a challenging use case for electric vehicles (EV). This paper explores the feasibility of using deep reinforcement learning (DRL) to optimize a driving and charging policy for a ride-hailing EV agent, with the goal of reducing costs and emissions while increasing transportation service provided. We introduce a data-driven simulation of a ride-hailing EV agent that provides transportation service and charges energy at congested charging infrastructure. We then formulate a test case for the sequential driving and charging decision making problem of the agent and apply DRL to optimize the agent's decision making policy. We evaluate the performance against hand-written policies and show that our agent learns to act competitively without any prior knowledge.