Deep Learning and Optimization Approaches in An Efficient Hybrid Ladybug Beetle and Physics Informed Neural Network for Electric Vehicle Energy Management: A Review

Abstract

The rapid advancement of electric vehicles has intensified the need for intelligent energy management systems capable of optimizing energy utilization, battery performance, and real-time power distribution. Traditional methods often lack adaptability and efficiency in handling nonlinear, multi-objective challenges associated with modern EV powertrains, necessitating advanced hybrid intelligent frameworks. This paper presents a comprehensive review of hybrid approaches integrating the Ladybug Beetle Optimization (LBO) algorithm with Physics-Informed Neural Networks (PINNs) for EV energy management. The LBO algorithm provides efficient global optimization through strong exploration–exploitation capabilities, while PINNs incorporate physical laws governing battery dynamics, thermal behavior, and power flow into the learning process. This combination ensures accurate, interpretable, and physically consistent predictions while optimizing energy distribution and system performance. Applications include battery electric, hybrid electric, and fuel cell vehicles across standard driving cycles. Comparative studies demonstrate that the LBO-PINN framework outperforms traditional optimization and deep learning methods in energy efficiency, battery health preservation, and computational performance. Despite these advantages, challenges such as scalability, computational complexity, and real-time deployment remain. This review highlights the potential of combining metaheuristic optimization and physics-informed learning to develop intelligent, robust, and efficient energy management systems for next-generation electric vehicles.