# Smoothed Least-Laxity-First Algorithm for EV Charging

arXiv: https://arxiv.org/abs/2102.08610 · HTML: https://mlsanity.com/paper/2102.08610/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2021.

## Built on by (most cited first, 5 of 5)

- [Predictive Control and Regret Analysis of Non-Stationary MDP with Look-ahead Information](https://mlsanity.com/paper/2409.08434/) (2024)
- [Risk Adversarial Learning System for Connected and Autonomous Vehicle Charging](https://mlsanity.com/paper/2108.01466/) (2021)
- [Efficient Representation for Electric Vehicle Charging Station Operations using Reinforcement Learning](https://mlsanity.com/paper/2108.03236/) (2021)
- [CONSENT: A Negotiation Framework for Leveraging User Flexibility in Vehicle-to-Building Charging under Uncertainty](https://mlsanity.com/paper/2601.01581/) (2026)
- [Laxity-Aware Scalable Reinforcement Learning for HVAC Control](https://mlsanity.com/paper/2306.16619/) (2023)

## Abstract (arXiv)

Adaptive charging can charge electric vehicles (EVs) at scale cost effectively, despite the uncertainty in EV arrivals. We formulate adaptive EV charging as a feasibility problem that meets all EVs' energy demands before their deadlines while satisfying constraints in charging rate and total charging power. We propose an online algorithm, smoothed least-laxity-first (sLLF), that decides the current charging rates without the knowledge of future arrivals and demands. We characterize the performance of the sLLF algorithm analytically and numerically. Numerical experiments with real-world data show that it has a significantly higher rate of feasible EV charging than several other existing EV charging algorithms. Resource augmentation framework is employed to assess the feasibility condition of the algorithm. The assessment shows that the sLLF algorithm achieves perfect feasibility with only a 0.07 increase in resources.