# Meta-Heuristic Optimization and Comparison for Battery Pack Thermal Systems Using Simulink
## 4 Results and Discussion
### Optimized Results Analysis  
This analysis starts with understanding how meta-heuristic optimization data influence optimal design and its tangible physical implications. A primary aspect to evaluate is the efficacy of the PSO or GA algorithms in optimizing battery consumption, temperature, and pump usage, given that the distance covered by the electric car remains constant. We begin by examining a graph, as depicted in Figure 8, which compares battery consumption to a baseline scenario over a span of 2500 s for a distance of 17.7 km, based on the FTP-75 standard. The results from both graphs indicate comparable EV performance, with negligible differences, with an error rate of less than 1.5%. Thus, while the data can produce consistent outcomes, the optimization results can vary, indicating the potential gains in battery efficiency, reduced pump usage, and improved heat transfer. State of charge (SOC) refers to the used battery gauge equivalent to a fuel gauge in a traditional gasoline-powered vehicle in the context of batteries, especially rechargeable batteries like those found in electric vehicles or renewable energy storage systems. It represents the current battery capacity as a percentage of its maximum capacity. If it is 50% or 0.5, the battery is half-charged; if it is 0%, the battery is completely discharged. Figure 9 displays the SOC results over time, segmented into basic, PSO, and GA outcomes. As illustrated, the battery's consumption seems to significantly improve in the optimized results compared to the basic results. While the basic case ends with an SOC of 0.76, the PSO outcome yields a SOC of 0.77, a 1% difference. This 1% variance might appear minimal, but in practical terms, it equates to an additional driving distance of between 4 and 6 km. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Initial Condition** & **PSO** & **GA** \\ \hline
1. Initial battery temperature & 30 \({}^{\circ}\)C & 40 \({}^{\circ}\)C & 40 \({}^{\circ}\)C \\
2. Radiator.solution.glycol & 0.5 (50\%) & 0.1 (10\%) & 0.1 (10\%) \\
3. Pump.omega & 4000 rpm & 1000 rpm & 1000 rpm \\
4. Pump.response.delay & 20 & 20.5 & 19 \\
5. Pump.displacement & 20 m & 12.7 m & 19.8 m \\ Fitness & 1.22 \(\times\) 10\({}^{-4}\) kW/K & 0.356 \(\times\) 10\({}^{-3}\) kW/K & 0.406 \(\times\) 10\({}^{-3}\) kW/K \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimized results by PSO and GA at 20 particles and 10 iterations. Figure 8: Battery voltage and driving distance of EV over time for different optimized results. (a) Battery usage. (b) Accumulated distance. Hence, this underlines that the battery's overall reliability and driving performance are enhanced through optimal design using meta-heuristics. Figure 10 depicts the cooling achieved due to the influence of battery cell temperature in each scenario. As illustrated, in scenarios where the battery temperature begins at a relatively higher level, such as in the PSO and GA cases, the temperature consistently decreases over time. This trend occurs because, while the battery's temperature rises due to resistance during its use in driving, it cools down more rapidly due to the activation of the liquid pump. Conversely, in the basic scenario, the battery temperature only experiences a brief decline during driving due to the pump's operation. Additionally, when the temperature of a battery increases, its internal resistance generally decreases. This reduction in resistance means that, for a given load, the battery can deliver the required power with less internal energy loss. As a result, less energy is wasted in the form of heat within the battery, leading to a potentially slower rate of SOC consumption. Additionally, at higher temperatures, the electrolyte's ionic conductivity increases, further aiding in efficient charge and discharge processes. However, it is crucial to note that consistently operating a battery at elevated temperatures can lead to a reduced  
Figure 10: Battery cell temperature over time. Figure 9: Battery SOC tendency over time.