<p>Electric vehicles (EV) and hybrid Electric vehicles have become far more common over the past decade, powered by rechargeable lithium-ion batteries. For safety, performance, and battery life, a battery management system (BMS) is important, and for even greater efficiency, performance, and sustainability, improvements in energy management systems (EMS) are necessary. This paper investigates the advancements of EMS in EV with a particular focus on their critical components, notably the battery. The primary objective is to enhance understanding of the battery’s role in supplying power to electric motors, which is vital for seamless operation. To ensure safe and reliable battery functionality, it emphasizes the necessity of online monitoring and state estimation, achievable through an effective BMS. Novel aspects of this review including a comparison of various optimization algorithms integral to the EMS. This analysis includes Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Model Predictive Control, among others. Each algorithm is scrutinized for its strengths, limitations, and suitability for different operational scenarios, providing insights into their effectiveness in optimizing energy distribution within the EV system. The findings show that advanced EMS techniques can reduce fuel or hydrogen use by 18–25%, improve driving range, extend battery lifespan by up to 20%, and lower overall energy consumption. These findings confirm that EMS performance directly influences energy efficiency, system reliability, and battery durability. Strategic design and the appropriate selection of optimization algorithms can therefore deliver substantial improvements in overall vehicle performance, supporting the development of cleaner, more efficient, and sustainable transportation systems.</p>