In the rapidly evolving landscape of smart buildings and renewable energy integration, accurate battery management is emerging as a critical challenge. A recent study published in the journal *Energies*—translated from Turkish as “Energies”—offers a promising solution, blending cutting-edge artificial intelligence with traditional control systems to optimize energy storage. Led by Mehmet Kurucan of the Department of Computer Engineering at Adana Alparslan Türkeş Science and Technology University in Türkiye, the research introduces a modular hybrid framework designed to enhance state-of-charge (SOC) estimation in lithium-ion batteries, a cornerstone of modern energy management systems.
At the heart of this innovation lies a sophisticated fusion of artificial neural networks (ANNs) and finite state automata (FSA). The framework employs three distinct ANN architectures—feedforward neural networks (FFNN), long short-term memory (LSTM), and 1D convolutional neural networks (1D-CNN)—to extract temporal and spatial features from raw battery data. However, ANNs alone can sometimes produce physically unrealistic SOC values, a limitation that Kurucan’s team addressed by integrating an FSA module. This module ensures that the predictions remain within feasible operational bounds, applying domain constraints based on battery states.
But the true breakthrough comes in the form of an adaptive dynamic supervisor layer, which dynamically selects the best-performing ANN+FSA model in real time. Two supervisor mechanisms were developed: a lightweight rule-based supervisor that makes transparent decisions based on recent performance scores and quick signal heuristics, and a more advanced double deep Q-network (DQN) reinforcement-learning supervisor. The latter continuously learns from reward feedback, adapting to changing conditions to minimize SOC estimation errors.
“This RL agent dynamically selects the most suitable model, significantly improving performance under varying and unpredictable operational conditions,” Kurucan explained. The results are striking: the hybrid approach consistently outperforms raw ANN predictions and conventional extended Kalman filter (EKF)-based methods, particularly in high-variance scenarios.
For the energy sector, the implications are profound. As smart buildings increasingly rely on rooftop photovoltaics and lithium-ion energy storage systems, accurate SOC estimation becomes essential for aligning renewable generation with real-time demand. Kurucan’s framework offers a robust solution, enhancing the reliability and efficiency of battery management systems. The research suggests that future developments in this field could leverage similar hybrid approaches, combining the strengths of machine learning with traditional control systems to create more adaptive and resilient energy solutions.
As the energy sector continues to evolve, innovations like this one will play a pivotal role in shaping a more sustainable and efficient future. With the publication of this study in *Energies*, the stage is set for further exploration and implementation of these advanced techniques in real-world applications.