## 4 Simulation Results
The water temperature at the entrance changes gently, possibly because the cooling water will be stored in the water tank, before entering the electric reactor. Due to the large capacity of the water tank, the heat will be spread evenly throughout the coolant in the water tank. The main reason for the obvious fluctuation of water temperature at the outlet is that there is less liquid in the cooling water passage, and the absorption of a small amount of heat will cause the temperature to change. Temperature changes of the MEA layer under the control of the diploid genetic algorithm, haploid genetic algorithm, and fuzzy PID are shown in Figure 15a. In the time period without load demand, the temperature controlled by the three control strategies is kept at 343.5 K, 343.6 K and 344.0 K. In the range of 0-800 s, when the fuel cell has the load demand, the temperature difference between the two genetic algorithms is not large. The temperature controlled by diploid genetic algorithm is the lowest in the range of 800-1180 s. When the load peak is reached, the haploid genetic algorithm shows no obvious difference from the fuzzy PID control. In the whole driving cycle, compared with  
Figure 13: Diagram of temperature variation inside a fuel cell. the two genetic algorithms, haploid genetic algorithm has a poor temperature control ability at peak load. The mean temperature of the MEA layer was 343.75 K, 343.88 K and 344.32 K, respectively. Compared with the haploid genetic algorithm and the fuzzy PID control, the diploid genetic algorithm optimized the temperature control deviation by 18% and 48%, respectively. Temperature changes in the bipolar plate with three control strategies are shown in Figure 15b. The temperature of the bipolar plate under fuzzy PID control is relatively high, ranging from 343.9 to 344.4 K. The temperature of the bipolar plate under the control of the diploid genetic algorithm is low, ranging from 343.4 K to 343.7 K. The temperature fluctuation of the bipolar plate under the control of the haploid genetic algorithm is the largest, which is between 343.4 and 344.1 K. By comparison with Figure 14a, the fluctuation  
Figure 14: Diagram of temperature variation at inlet and outlet of reactor. Figure 15: (**a**) MEA temperature change chart of three strategies; (**b**) Temperature variation diagram of bipolar plate with three strategies. of the bipolar plate temperature has no significant influence on the temperature of the MEA layer. The main reason is that compared with the MEA layer, the volume of the bipolar plate is smaller, and the specific heat capacity is lower, therefore the heat exchange with the MEA layer is limited. At the same time, the thermal conductivity of the MEA layer is low, and the influence of the bipolar plate on the MEA layer is weakened. The water flow changes of the three strategies are shown in Figure 16. The water flow controlled by the diploid genetic algorithm is larger, ranging from 0.006 to 0.011 kg/s. The fuzzy PID control water flow is small, between 0.004-0.007 kg/s. The water flow rate controlled by the haploid genetic algorithm is not much different from that controlled by the diploid genetic algorithm, around 0-800 s. Between 800 and 1200 s, the fuel cell load demand increases sharply, but the water flow controlled by the haploid genetic algorithm is unable to keep up with the rate of change, resulting in large fluctuations. Compared to the fuzzy PID, the overall water flow of the haploid genetic algorithm increased by 37.8%, and the temperature decreased by 0.44 K. The overall water flow increased by 38.5% and the temperature decreased by 0.55 K. In the three strategies, the water flow controlled by the double body genetic algorithm is maintained at a high flow rate, but when the reactor temperature drops to a certain value, through thermal management, the increase of the coolant flow rate has a nonlinear relationship with the decrease of the reactor temperature. The greater the water flow, the less obvious the cooling effect.