# Meta-Heuristic Optimization and Comparison for Battery Pack Thermal Systems Using Simulink
## 4 Results and Discussion
### Optimized Results Analysis
lifespan and potential safety concerns. Therefore, while short-term energy efficiency might improve at higher temperatures, there could be long-term drawbacks to this approach. Figure 11 delivers a comparison of pump power usage over time, further elaborating on the observations from Figure 10. The pump instant power graph in Figure 11 indicates that, in the basic scenario, the pump is excessively utilized between 0 to 500 s. Despite employing a pump capable of reaching its maximum specified performance, the control was inefficient, resulting in an undue reduction in battery cell temperature. In contrast, the PSO and GA outcomes reveal that, by maintaining a steady pump use at 1000 rpm, effective cooling capacity control is achievable. This optimization results in comparable, if not better, battery usage, solidifying the benefits of the continuous engagement of the battery cooling mechanism. This is further corroborated by Figure 11b. These data aggregate and accumulate the torque and revolution values of the pump over time for each scenario. Observing the cumulative pump usage, it is evident that efficient cooling was achieved even with the pump's sustained operation. Consequently, the objective value was decreased by using the PSO and GA algorithms, ensuring a more effective maintenance of battery temperature alongside efficient pump utilization. What makes PSO more advantageous than GA in terms of battery cell temperature, as observed in Figures 9-11, is its ability to reduce pump usage further, lower the temperature slightly, and simultaneously save more on battery SOC. This confirms that the target pursued by the PSO algorithm chose to sacrifice lowering the temperature for the overall objective function. However, in the case of the GA algorithm, its focus on optimizing temperature cooling had a negative impact on the overall objective function. Therefore, it can be confirmed that although PSO may consume some computational time, the optimization algorithm operated in a way that took into account all the overall parameters. Additionally, the concentration of glycol significantly influences the thermal system. As illustrated in Figure 2, water possesses superior thermal conductivity compared to glycol. Thermal conductivity gauges a material's heat transfer capability. Hence, a fluid with high thermal conductivity is more adept at heat transmission. A mixture leaning towards water rather than glycol inherently boasts better heat conduction. Water stands out for its remarkable specific heat capacity, one of the highest known for any substance. This implies it can store substantial heat without a notable rise in temperature. A mixture with a high water content retains this trait, allowing for more heat transfer. Conversely, a high glycol concentration can make the mixture more viscous, hindering fluid motion and  
Figure 11: Pump usage over time for different meta-heuristics. (**a**) Instant pump power use. (**b**) Accumulated pump power. diminishing convective heat transfer. The lower the glycol concentration, the more it favors turbulent flow, which increases convective heat transfer. Notably, water is generally denser than glycol, impacting the mixture's heat transfer traits. While glycol's inclusion in cooling systems primarily offers antifreeze properties and combats corrosion, it can compromise the fluid's thermal efficiency. Hence, for prime heat transfer, minimizing the glycol fraction is recommended, a fact underscored by optimization outcomes, provided that freezing protection remains uncompromised. Furthermore, while glycol's detrimental effect on thermal transfer is acknowledged, the fluid composition in a thermal system can extend beyond glycol, potentially involving multiple fluids. The optimal ratio in such blends can be pinpointed through heuristic optimization strategies.