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June 3, 2026Electronics0 citationsOpen Access

A Mamba State-Space Sequence Model for AI-Driven Dynamic Aggregation and Predictive Control of Electric Vehicle Clusters in Vehicle-to-Grid Energy Management

JTJinyi TangChangsha University of Science and Technology XZXuan ZhouChangsha University of Science and Technology QYQin YanChangsha University of Science and Technology

Key Points

This research aims to enhance real-time energy management for large electric vehicle (EV) clusters through a predictive, constraint-aware framework.
Proposed the Physics-Constrained Mamba-3 MIMO Aggregator model for efficient energy management.
Evaluated on large EV clusters using real-world charging data from ACN-Data and ElaadNL.
Compared performance metrics with other state-of-the-art models under a 10,000-vehicle stress test.
Improved speed with inference times of 0.34 seconds for 10,000 EVs, significantly faster than alternatives.
Reduced normalized envelope Hausdorff distance from 9.7% to 3.4% compared to previous models.
Achieved zero feasibility violations across all evaluated sessions.

Abstract

Real-time energy management for large electric vehicle (EV) clusters requires both fast aggregate flexibility estimation and executable per-vehicle dispatch. Classical LP/MILP/MPC formulations provide strong feasibility and optimality guarantees when the model is fully specified, but their online solve time increases rapidly with cluster size; learning-based methods are fast but often rely on soft constraint penalties or external feasibility repair. We propose the Physics-Constrained Mamba-3 MIMO Aggregator (PC-M3), an amortized, constraint-aware sequence model that integrates a MIMO Mamba backbone, a history-dependent differentiable projection, a sparse routing layer, and an aggregation–disaggregation consistency loop, scaling AI-EMS from a single battery to ten-thousand-vehicle clusters in one forward pass. PC-M3 assigns every EV to one channel of a multi-input multi-output (MIMO) state-space recurrence and embeds the per-vehicle state-of-charge, power and energy constraints as a differentiable in-loop projection, jointly producing the cluster-level flexibility envelope and the per-vehicle charging trajectory. A sparse Routing-Mamba mixture-of-experts layer adaptively allocates capacity to behaviourally distinct sub-populations without supervised labels, and a consistency-trained aggregation–disaggregation loop binds the predicted envelope to the executed dispatch, forming a digital-twin-style predictive EMS pipeline that couples cluster dispatch with per-vehicle SoC evolution. On a single NVIDIA A100, PC-M3 sustains 0.34 s inference for 10,000 EVs over a 24-h horizon, about 18× faster than an Informer baseline and 2.4× faster than PowerMamba. Evaluated on the open ACN-Data and ElaadNL workplace and public charging corpora and on a 10,000-vehicle NREL dsgrid-TEMPO 2030 stress test, PC-M3 reduces the normalised envelope Hausdorff distance from 9.7% (PowerMamba) to 3.4%, cuts closed-loop cluster tracking RMSE from 1.45 MW (model predictive control) to 0.82 MW, and maintains zero observed feasibility violations with respect to the specified or imputed per-vehicle polytopes on every evaluated session. The framework provides a scalable, predictive, constraint-aware AI-EMS for V2G/G2V virtual-power-plant operation of large EV fleets.

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