# Dynamic Scheduling for Charging Electric Vehicles: A Priority Rule

arXiv: https://arxiv.org/abs/1602.00372 · HTML: https://mlsanity.com/paper/1602.00372/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2016.

## Built on by (most cited first, 6 of 6)

- [Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control](https://mlsanity.com/paper/2210.01452/) (2022)
- [An Efficient Distributed Multi-Agent Reinforcement Learning for EV Charging Network Control](https://mlsanity.com/paper/2308.12921/) (2023)
- [Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewards](https://mlsanity.com/paper/2502.18526/) (2025)
- [Centralized vs. Decentralized Multi-Agent Reinforcement Learning for Enhanced Control of Electric Vehicle Charging Networks](https://mlsanity.com/paper/2404.12520/) (2024)
- [Online Decision-Making Under Uncertainty for Vehicle-to-Building Systems](https://mlsanity.com/paper/2601.03476/) (2026)
- [Emergent Charging Coordination in Electric Delivery Fleets](https://mlsanity.com/paper/2609.07689/) (2026)

## Abstract (arXiv)

We consider the scheduling of multiple tasks with pre-determined deadlines under random processing cost. This problem is motivated by the potential of large scale adoption of plug-in (hybrid) electric vehicles (PHEVs) in the near future. The charging requests of PHEVs usually have deadline constraints, and the electricity cost associated with PHEV charging is usually random due to the uncertainty in both system load and renewable generation. We seek to properly schedule the battery charging of multiple PHEVs so as to minimize the overall cost, which is derived from the total charging cost and the penalty for not completing charging before requested deadlines. Through a dynamic programming formulation, we establish the Less Laxity and Longer remaining Processing time (LLLP) principle that improves any charging policy on a sample-path basis, when the non-completion penalty is a convex function of the additional time needed to fulfill the uncompleted request. Specifically, the LLLP principle states that priority should be given to vehicles that have less laxity and longer remaining processing times. Numerical results demonstrate that heuristic policies that violate the LLLP principle, for example, the earliest deadline first (EDF) policy, can result in significant performance loss.