## 4 An Exploration of the Advantages of Genetic Algorithms
### Application of GA-Based Energy Management for HEVs
(2023) [184] constructed a cold-start emission prediction model for hybrid vehicles across a wide temperature range (\(-20\)\({}^{\circ}\)C to \(40\)\({}^{\circ}\)C) by optimizing the initial weights and threshold parameters of a neural network (BP) using GA. The hybrid GA-BP algorithm was experimentally validated to exhibit a prediction error rate of less than 3.2% for emission indicators such as CO, CO\({}_{2}\), HC, NOx, and PN, providing a reliable theoretical basis for formulating emission control strategies in low-temperature environments. In a study by [PERSON] et al. (2006) [185], a novel GA-based EMS was developed to minimize fuel consumption and emissions while meeting driving performance requirements. The fitness function was defined as the objective function, with fuel consumption and emissions serving as optimization objectives, and driving performance requirements as constraints. Computer simulations validated the methodology's effectiveness, achieving reductions in fuel consumption and emissions while ensuring vehicle performance stability. [PERSON] et al. (2021) [178] further proposed a GA-based EMS for HEVs. They utilized a vehicle model from the ADVISOR database and conducted simulation studies using the MATLAB Simulink environment in conjunction with the GA optimization tool. The simulation results indicated that GA optimization successfully achieved the sub-objectives set in the fitness function, significantly improving HC and NOx emission performance. These studies underscore the significant potential of GA-based EMS in optimizing HEV energy management, effectively coordinating multiple objective functions and decision variables for comprehensive vehicle performance optimization. [PERSON] et al. (2023) [186] proposed a Multi-Island Genetic Algorithm (MIGA)-based optimization strategy aimed at harnessing the energy-saving potential of a Hybrid Energy Storage System (HESS) for the energy management of HEVs. The study explicitly defined two core objective functions: minimizing cumulative battery ampere hours (Ah) to extend battery life and minimizing equivalent fuel consumption (EFC) to enhance fuel economy. Decision variables included the transmission ratio (TR) and several operational parameters in the EMS, which collectively determined the vehicle's power performance and energy consumption. By optimizing these decision variables, the genetic algorithm developed optimal energy management strategies for HEVs in various driving modes. Under WLTP operating conditions, the algorithm performed global optimization to minimize equivalent fuel consumption and battery accumulated ampere hours under HESS, APU, and ECMS cooperative modes. Correlation analysis revealed that the optimized mode of operation improved fuel economy by 4.49% under WLTP conditions while reducing battery accumulated ampere hours by 11.37%. Several researchers have employed GA technology in the optimization of EMS for HEVs and conducted a comparative analysis with other mainstream optimization algorithms. The findings indicate that genetic algorithms demonstrate substantial theoretical advantages in EMS optimization for HEVs, primarily due to their distinctive bionic principles and algorithmic structural characteristics. [PERSON] et al. (2024) [187] utilized the NSGA-II algorithm to achieve synergistic optimization of dynamic performance and economy. This resulted in a 9% reduction in vehicle acceleration time (reaching 100 km/h in 8.29 s), a 7% enhancement in dynamic performance, and a 5% reduction in power loss. They compared the NSGA-II optimization technique with SO, ACA, and other algorithms. The results revealed that in terms of computational performance; the optimization accuracy reached 89.99%. Processing 6000 MB of data took only 49.20 s, and the transmission delay was stable at 20.20 ms, which was significantly superior to comparison algorithms such as PSO and ACA. Its unique binary coding mechanism is inherently suitable for the optimization of discrete control variables.