Persistent Vehicle-to-Building Integration via Neuro-Symbolic Control

Rishav Sen, Fangqi Liu, Jose Paolo Talusan, Ava Pettet, Yoshinori Suzue, Ayan Mukhopadhyay, Abhishek Dubey

Proceedings of the HSCC/ICCPS 2026: 29th ACM International Conference on Hybrid Systems: Computation and Control and 17th ACM/IEEE International Conference on Cyber-Physical Systems 2026 HSCC/ICCPS '26

Acceptance rate: 28%; Regular Paper; Track: Systems and Applications

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Why This Matters

Vehicle-to-building systems present a complex control challenge combining real-time physical constraints with long-horizon stochastic effects of user behavior, where traditional decomposition approaches fail to capture crucial dependencies. The innovation lies in explicitly leveraging user persistence—the observation that EV users exhibit recurring patterns—as a key input alongside technical constraints, enabling more intelligent demand charge management. This bridges control theory and behavioral modeling, providing a principled way to incorporate user behavioral patterns into cyber-physical system optimization.

What We Did

P-V2B introduces a neuro-symbolic framework for vehicle-to-building charging that incorporates user persistence information alongside technical optimization. The work addresses the persistent user problem where electric vehicles exhibit recurring arrival patterns over time at buildings, enabling buildings to anticipate charging demand and schedule charging strategically. The approach combines a neuro-symbolic control framework integrating Monte Carlo Model Predictive Control with a learned value function to handle both short-horizon feasibility and long-horizon demand-charge prediction, accounting for user behavior patterns while managing real-time constraints.

Key Results

Evaluation on real EV fleet data from a major California manufacturer demonstrates substantial improvements in demand charge reduction and total operating costs compared to both heuristic baselines and prior work that ignore user persistence. The neuro-symbolic framework achieves significant cost savings while ensuring feasibility and full compliance with user charging requirements, validating the effectiveness of persistence-aware control strategies.

Full Abstract

Vehicle-to-Building (V2B) integration is a cyber–physical system (CPS) where Electric Vehicles (EVs) enhance building resilience by serving as mobile storage for peak shaving, reducing monthly peak-power demand charges, supporting grid stability, and lowering electricity costs. We introduce the Persistent Vehicle-to-Building (P-V2B) problem, a long-horizon formulation that incorporates user-level persistence, where each EV corresponds to a consistent user identity across days. This structure captures recurring arrival patterns and travel-related external energy use, common in employee-based facilities with regular commuting behavior. Persistence enables multi-day strategies that are unattainable in single-day formulations, such as over-charging on low-demand days to support discharging during future high-demand periods. Real-time decision making in this CPS setting presents three key challenges: (i) uncertainty in long-term EV behavior and building load forecasts, which causes traditional control and heuristic methods to degrade under stochastic conditions; (ii) inter-day coupling of decisions and rewards, where early actions affect downstream feasible charging and discharging opportunities, complicating long-horizon optimization; and (iii) high-dimensional continuous action spaces, which exacerbate the curse of dimensionality in reinforcement learning (RL) and search-based approaches. To address these challenges, we propose a neuro-symbolic framework that integrates a constraint-based Monte Carlo Model Predictive Control (MC-MPC) layer with a learned Value Function (VF). The MC–MPC enforces physical feasibility and manages environmental uncertainty, while the VF provides long-term strategic foresight. Evaluations using real building and EV fleet data from an EV manufacturer in California demonstrate that the hybrid framework substantially outperforms state-of-the-art baselines, significantly reducing demand charge and total energy costs, while ensuring feasibility and full compliance with user charging requirements.

Cite This Paper

@inproceedings{iccps2026_pv2b,
  author = {Sen, Rishav and Liu, Fangqi and Talusan, Jose Paolo and Pettet, Ava and Suzue, Yoshinori and Mukhopadhyay, Ayan and Dubey, Abhishek},
  title = {Persistent Vehicle-to-Building Integration via Neuro-Symbolic Control},
  year = {2026},
  booktitle = {Proceedings of the HSCC/ICCPS 2026: 29th ACM International Conference on Hybrid Systems: Computation and Control and 17th ACM/IEEE International Conference on Cyber-Physical Systems},
  location = {Saint Malo, France},
  abstract = {Vehicle-to-Building (V2B) integration is a cyber–physical system (CPS) where Electric Vehicles (EVs) enhance building resilience by serving as mobile storage for peak shaving, reducing monthly peak-power demand charges, supporting grid stability, and lowering electricity costs. We introduce the Persistent Vehicle-to-Building (P-V2B) problem, a long-horizon formulation that incorporates user-level persistence, where each EV corresponds to a consistent user identity across days. This structure captures recurring arrival patterns and travel-related external energy use, common in employee-based facilities with regular commuting behavior. Persistence enables multi-day strategies that are unattainable in single-day formulations, such as over-charging on low-demand days to support discharging during future high-demand periods. Real-time decision making in this CPS setting presents three key challenges: (i) uncertainty in long-term EV behavior and building load forecasts, which causes traditional control and heuristic methods to degrade under stochastic conditions; (ii) inter-day coupling of decisions and rewards, where early actions affect downstream feasible charging and discharging opportunities, complicating long-horizon optimization; and (iii) high-dimensional continuous action spaces, which exacerbate the curse of dimensionality in reinforcement learning (RL) and search-based approaches. To address these challenges, we propose a neuro-symbolic framework that integrates a constraint-based Monte Carlo Model Predictive Control (MC-MPC) layer with a learned Value Function (VF). The MC–MPC enforces physical feasibility and manages environmental uncertainty, while the VF provides long-term strategic foresight. Evaluations using real building and EV fleet data from an EV manufacturer in California demonstrate that the hybrid framework substantially outperforms state-of-the-art baselines, significantly reducing demand charge and total energy costs, while ensuring feasibility and full compliance with user charging requirements.},
  keywords = {vehicle-to-building, EV charging, demand charge management, user persistence, neuro-symbolic control, Monte Carlo tree search, model predictive control},
  note = {Acceptance rate: 28\%; Regular Paper; Track: Systems and Applications},
  series = {HSCC/ICCPS '26}
}

Persistent Vehicle-to-Building Integration via Neuro-Symbolic Control

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