Evolving LLM-Derived Control Policies for Residential EV Charging and Vehicle-to-Grid Energy Optimization

• URL: http://arxiv.org/abs/2602.07275v1
• Date: Fri, 06 Feb 2026 23:59:33 GMT
• Title: Evolving LLM-Derived Control Policies for Residential EV Charging and Vehicle-to-Grid Energy Optimization
• Authors: Vishesh Purnananda, Benjamin John Wruck, Mingyu Guo,
• Abstract summary: This research presents a novel application of Evolutionary Computation to the domain of residential electric vehicle (EV) energy management.<n>While reinforcement learning (RL) achieves high performance in vehicle-to-grid (V2G) optimization, it typically produces opaque "black-box" neural networks that are difficult for consumers and regulators to audit.<n>We propose a search framework that leverages Large Language Models (LLMs) as intelligent mutation operators within an iterative prompt-fidelity repair loop.
• Score: 7.073682493135313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This research presents a novel application of Evolutionary Computation to the domain of residential electric vehicle (EV) energy management. While reinforcement learning (RL) achieves high performance in vehicle-to-grid (V2G) optimization, it typically produces opaque "black-box" neural networks that are difficult for consumers and regulators to audit. Addressing this interpretability gap, we propose a program search framework that leverages Large Language Models (LLMs) as intelligent mutation operators within an iterative prompt-evaluation-repair loop. Utilizing the high-fidelity EV2Gym simulation environment as a fitness function, the system undergoes successive refinement cycles to synthesize executable Python policies that balance profit maximization, user comfort, and physical safety constraints. We benchmark four prompting strategies: Imitation, Reasoning, Hybrid and Runtime, evaluating their ability to discover adaptive control logic. Results demonstrate that the Hybrid strategy produces concise, human-readable heuristics that achieve 118% of the baseline profit, effectively discovering complex behaviors like anticipatory arbitrage and hysteresis without explicit programming. This work establishes LLM-driven Evolutionary Computation as a practical approach for generating EV charging control policies that are transparent, inspectable, and suitable for real residential deployment.

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