A Modular Hybrid SOC-Estimation Framework with a Supervisor for Battery Management Systems Supporting Renewable Energy Integration in Smart Buildings

Mehmet Kurucan, Panagiotis Michailidis, Iakovos Michailidis and Federico Minelli ()
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Mehmet Kurucan: Department of Computer Engineering, Faculty of Computer and Informatics, Adana Alparslan Türkeş Science and Technology University, 01250 Adana, Türkiye
Panagiotis Michailidis: Centre for Research and Technology Hellas (CERTH), Thermi, 57001 Thessaloniki, Greece
Iakovos Michailidis: Centre for Research and Technology Hellas (CERTH), Thermi, 57001 Thessaloniki, Greece
Federico Minelli: Department of Industrial Engineering, University of Naples “Federico II”, 80125 Naples, Italy

Energies, 2025, vol. 18, issue 17, 1-30

Abstract: Accurate state-of-charge (SOC) estimation is crucial in smart-building energy management systems, where rooftop photovoltaics and lithium-ion energy storage systems must be coordinated to align renewable generation with real-time demand. This paper introduces a novel, modular hybrid framework for SOC estimation, which synergistically combines the predictive power of artificial neural networks (ANNs), the logical consistency of finite state automata (FSA), and an adaptive dynamic supervisor layer. Three distinct ANN architectures—feedforward neural network (FFNN), long short-term memory (LSTM), and 1D convolutional neural network (1D-CNN)—are employed to extract comprehensive temporal and spatial features from raw data. The inherent challenge of ANNs producing physically irrational SOC values is handled by processing their raw predictions through an FSA module, which constrains physical validity by applying feasible transitions and domain constraints based on battery operational states. To further enhance the adaptability and robustness of the framework, two advanced supervisor mechanisms are developed for model selection during estimation. A lightweight rule-based supervisor picks a model transparently using recent performance scores and quick signal heuristics, whereas a more advanced double deep Q-network (DQN) reinforcement-learning supervisor continuously learns from reward feedback to adaptively choose the model that minimizes SOC error under changing conditions. This RL agent dynamically selects the most suitable ANN+FSA model, significantly improving performance under varying and unpredictable operational conditions. Comprehensive experimental validation demonstrates that the hybrid approach consistently outperforms raw ANN predictions and conventional extended Kalman filter (EKF)-based methods. Notably, the RL-based supervisor exhibits good adaptability and achieves lower error results in challenging high-variance scenarios.

Keywords: smart buildings; state-of-charge; battery management system; artificial neural networks; finite state automata; reinforcement learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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