Physics-informed neural networks in the energy sector: Progress, trends, and future directions

With rising global energy demands, optimizing energy systems has become increasingly critical. Physics-Informed Neural Networks (PINNs) have emerged as a powerful tool in enhancing the efficiency and reliability of energy technologies. PINNs offer a unique framework that combines the interpretability of physics-based approaches with the adaptability of neural networks (NNs), enabling precise predictions and optimizations across diverse applications. This paper presents a bibliometric analysis and overview of recent advances in applying PINNs across energy domains, including battery management, power grid optimization, and renewable energy integration. The bibliometric analysis highlights the expanding global interest in PINNs, particularly in leading nations such as China and the United States. It provides insights into publication trends, active research themes, and influential contributors in the field. The review delves into specific applications of PINNs, particularly in battery management, where they enable accurate estimates of state-of-charge (SoC) and state-of-health (SoH), which are crucial for enhancing battery performance and longevity. Additionally, the potential of PINNs for optimizing power flow in smart grids and improving predictions of renewable energy output is explored, underscoring their role in boosting operational efficiency and integrating sustainable energy sources. This review identifies key opportunities and challenges for future research in applying PINNs within the energy sector, emphasizing their potential to drive innovation and sustainability. • Bibliometric mapping reveals global trends in energy-focused PINN research. • PINNs enhance accuracy and data efficiency across key energy applications. • Review identifies advances in batteries, power systems, and renewables. • Challenges in scalability and physics gaps highlight the need for future work. • Emerging PINN opportunities include sodium-ion batteries and CCU systems.