## 4 An Exploration of the Advantages of Genetic Algorithms
### Application of GA-Based Energy Management for HEVs  
In HEV energy management, the application of GAs manifests in three primary areas. Firstly, GAs aid in optimizing energy distribution by reasonably deploying power sources such as the engine, motor, and battery, based on the vehicle state and driving demands, thereby achieving efficient energy supply. Secondly, GAs are instrumental in optimizing battery management through the adjustment of charging and discharging strategies, which helps prevent battery damage and enhances energy utilization efficiency. Thirdly, GAs contribute to optimizing the driving mode by automatically adjusting it according to the driver's preference and the vehicle's state, ensuring a balance between driving performance and energy consumption. To fulfill the aforementioned optimization objectives, the pivotal role of the GA's optimization process cannot be overstated. Typically depicted in Figure 13, this process comprises several integral steps as outlined below:  
(1) Select an initial population within the feasible solution domain to start the algorithm's optimization. (2) Iteratively optimize the initial population through genetic operations, i.e., selection, crossover, and mutation, to gradually generate a new population that continuously approaches the global optimal solution. Figure 12: The non-dominated sorting GAs with elite strategy (NSGA-ll) algorithm optimization step [120]. (3) Referring to the iterative principle of the intelligent optimization algorithm, determine whether the population meets the end criteria, such as reaching the preset number of iterations or the quality of the solution meeting the preset requirements. Only when these conditions are met will the algorithm stop iterating and output the final optimization results. Through this series of steps, the GA plays an indispensable role in HEV energy management. [PERSON] et al. (2022)[PERSON] et al. (2022) introduced an EMS based on a neural network optimized by a genetic algorithm (NNOGA). This strategy aims to minimize hydrogen consumption and enhance fuel cell durability in fuel cell hybrid electric vehicles (FCHEVs). The study employs a genetic algorithm to optimize the neural network's parameters, thereby mitigating the adverse effects of start-stop cycles and rapid load variations on fuel cell durability. In this strategy, the fuel cell's output power serves as the objective function for the training set, with the neural network's weights and thresholds acting as decision variables. The results indicate that NNOGA effectively avoids unnecessary start-stop cycles and rapid power fluctuations, outperforming the unoptimized neural network in this regard. Within the NNOGA framework, the genetic algorithm fine-tunes the neural network's parameters to adhere to the predefined upper and lower limits of the fuel cell's output power. Additionally, a start-stop deceleration module is incorporated into the simulation system to further constrain rapid start-stop cycles of the FC. By integrating the start-stop deceleration module, rapid start-stop cycles of the FC are effectively curtailed, allowing the FC to operate continuously for a duration to charge the auxiliary energy battery. When compared to the  
Figure 13: The flow of the genetic algorithm [89]. ECM strategy, NNOGA reduces the number of start-stop cycles by 33.0% in the NEDC driving cycle, demonstrating the significant impact of the genetic algorithm-optimized neural network on enhancing fuel cell durability. [PERSON] et al. (2021) [183] innovatively applied a GA for multi-objective co-optimization of key engine control parameters, including variable valve timing, ignition advance angle, and exhaust gas recirculation rate. Experimental data revealed that this strategy significantly improved combustion efficiency, reduced fuel consumption by 12.48%, and decreased NOx emissions by 92.64%. Further investigations have demonstrated the efficacy of GAs in control optimization under extreme operating conditions. For instance, [PERSON] et al.