# Emergent Charging Coordination in Electric Delivery Fleets

arXiv: https://arxiv.org/abs/2609.07689 · HTML: https://mlsanity.com/paper/2609.07689/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 65 references that are in the map.
- First posted on arXiv 7 Sep 2026.

## Builds on (most cited first, 17 of 17)

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- [Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning](https://mlsanity.com/paper/1712.06567/) (2017)
- [OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks](https://mlsanity.com/paper/1611.01890/) (2016)
- [Settling the Variance of Multi-Agent Policy Gradients](https://mlsanity.com/paper/2108.08612/) (2021)
- [Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms](https://mlsanity.com/paper/2401.11963/) (2024)
- [EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and Benchmarking](https://mlsanity.com/paper/2404.01849/) (2024)
- [Learning to Operate an Electric Vehicle Charging Station Considering Vehicle-grid Integration](https://mlsanity.com/paper/2111.01294/) (2021)
- [Dynamic Scheduling for Charging Electric Vehicles: A Priority Rule](https://mlsanity.com/paper/1602.00372/) (2016)
- [Toward Efficient Transportation Electrification of Heavy-Duty Trucks: Joint Scheduling of Truck Routing and Charging](https://mlsanity.com/paper/2302.00240/) (2023)
- [Electric Vehicle Fleet and Charging Infrastructure Planning](https://mlsanity.com/paper/2306.10178/) (2023)
- [Dynamic Routing for the Electric Vehicle Shortest Path Problem with Charging Station Occupancy Information](https://mlsanity.com/paper/2305.11773/) (2023)
- [TensorNEAT: A GPU-accelerated Library for NeuroEvolution of Augmenting Topologies](https://mlsanity.com/paper/2504.08339/) (2025)

## Abstract (arXiv)

In electric delivery fleets, mid-shift charging is non-trivial: each vehicle must decide when, where and how much to charge to finish on time with battery above a safety floor. The choices are coupled: queues build where too many vehicles pick the same station. Prior work resolves this coupling with central dispatching, precomputed schedules or reservations, machinery that charging infrastructure rarely supports. Instead, we use a family of learning agents under purely local control: every vehicle runs the same policy, deciding alone from its time budgets and broadcast station occupancies, leading to emergent coordination without central control or messaging. We validate this paradigm in simulation on real OpenStreetMap networks of twenty cities, each with a frozen scenario calibrated by an omniscient Oracle (99.5% of shifts completed on time), whereas a naive greedy rule (nearest station on low battery) completes just 73%. Agents trained with neuroevolution (NEAT) and policy gradients (PPO) on four cities and deployed zero-shot across all twenty, sixteen never seen in training, complete 96.8% and 98.6% of shifts, with the policy-gradient controllers proving more robust when demand or vehicle characteristics drift beyond the trained regime. In contrast, tuned threshold heuristics that read vehicle urgency alone fall short in contended cities (~80%). Through training, these learning agents rediscover partial charging and short opportunistic sessions, and route around busy stations, cutting per-session queue waits from about 45 minutes to under 2. In summary, this coordination paradigm balances local urgency against public occupancy, reaching near-Oracle performance at minimal implementation cost.