A Comprehensive Review of An Efficient Hybrid Ladybug Beetle and Physics Informed Neural Network for Electric Vehicle Energy Management

Abstract

The global transition toward sustainable transportation has accelerated the adoption of electric vehicles, increasing the need for intelligent energy management systems capable of handling complex, nonlinear, and dynamic energy flows. Modern EV powertrains integrating batteries, supercapacitors, and fuel cells require advanced optimization strategies to ensure efficiency, battery longevity, and real-time adaptability under varying operating conditions. This paper presents a comprehensive review of hybrid energy management frameworks combining the Ladybug Beetle Optimization (LBO) algorithm with Physics-Informed Neural Networks (PINNs). The LBO algorithm offers efficient global optimization through adaptive exploration–exploitation strategies, while PINNs incorporate physical laws and system dynamics into the learning process, ensuring accurate and physically consistent predictions of battery state, thermal behavior, and motor performance. The integration of these approaches enables reliable, data-driven, and physics-aware decision-making for EV energy management. Applications include battery electric vehicles, plug-in hybrid vehicles, and fuel cell vehicles operating under diverse   driving cycles. Comparative studies demonstrate that the LBO-PINN framework outperforms conventional methods in energy efficiency, thermal stability, and computational performance. However, challenges such as computational complexity, scalability, and real-time deployment remain. This review highlights the potential of combining metaheuristic optimization with physics-informed learning to develop intelligent, interpretable, and robust energy management systems for next-generation electric vehicles.