To address the challenges of energy and the environment

hybrid vehicles have become a key transitional technology.The core challenge lies in how to intelligently allocate the power between the engine and the electric motor to achieve optimal energy efficiency.The evolution of energy management strategies represents an ongoing endeavor to resolve this complex optimization challenge. A systematic review of its developmental trajectory holds significant guiding importance for driving technological breakthroughs and industrial applications.This study employs a literature review methodology to systematically delineate the developmental trajectory of energy management strategies for hybrid electric vehicles

progressing from rule-based and optimization-based approaches to intelligent predictive control systems. It critically examines the limitations of conventional methods and highlights two major challenges in online optimization: the trade-off between model accuracy and computational efficiency

as well as the coordination of multi-objective optimization. Furthermore

the study synthesizes and evaluates cutting-edge solutions in the field.This study indicates that the field currently faces a core trade-off among model accuracy

computational efficiency

and real-time performance. Existing optimization methods exhibit limitations in addressing system nonlinearities and mode transitions

while vehicle-infrastructure cooperation further amplifies the complexity of multi-objective coordination.Future research should focus on developing high-performance novel mixed-integer solvers and hierarchical intelligent control architectures

signifying a paradigm shift in the field from single-algorithm optimization to system-level collaborative design.