## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Battery Charging**} & \multicolumn{4}{c}{**Battery Discharging**} & \multicolumn{4}{c}{**Average Error Margin**} \\ \hline Cycles & 1 st & 2 nd & 3 rd & 4 th & 5 th & 1 st & 2 nd & 3 rd & 4 th & 5 th & Charge & Discharge \\ \hline Real Value & 90\% & 90 & 90 & 90 & 90 & 30 & 30 & 30 & 30 & 30 & Charge & Discharge \\ \hline Estimated CC & 95\% & 95 & 95 & 95 & 95 & 35 & 35 & 35 & 35 & 5\% & 5\% \\ \hline Estimated EKF & 92\% & 89 & 89 & 88 & 92 & 31 & 29 & 29 & 29 & 1.6\% & 1\% \\ \hline Estimated UKF & 94\% & 91 & 90 & 90 & 90 & 31.5 & 30 & 30 & 30 & 29.9 & 1\% & 0.32\% \\ \hline CC Error Margin & 5\% & 5 & 5 & 5 & 5 & 5 & 5 & 5 & 5 & 5 & \\ \hline EKF Error Margin & 2\% & 1 & 1 & 2 & 2 & 1 & 1 & 1 & 1 & 1 & \\ \hline UKF Error Margin & 4\% & 1 & 0 & 0 & 0 & 1.5 & 0 & 0 & 0.1 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of real and estimated SOC levels with various estimation techniques. in the error margins of SOC estimation; as the SOC estimation worsens, so does the SOH estimation accuracy drop. This analysis of the SOC level parameter of the BMS, through this comparison of the used estimation methods which is shown in Tables 1 and 2, meets the aim and objective of conducting a comprehensive review of the traditional and modern estimation methods. This concludes that, when comparing the traditional estimation methods with the modern methods, it is evident that the advanced versions of the KF methods are preferred as they are more accurate and adaptive in estimating the SOC and SOH in real-time with frequent variances in the system to work with. While the proposed BMS demonstrates effective real-time tracking, future enhancements could explore lightweight or hybrid filtering techniques to further minimise latency. Such improvements would enhance the system's applicability in scenarios with exceptionally high demands on response time, such as high-congestion environments. It is recommended that, based on the research conducted and the promising results shown in this project report, the modern estimation method is preferable as an advanced BMS for use in an EV if the user requires extremely accurate tracking and estimation of the battery parameters. As the KF-based EKF method is more accurate than the CC method, but not compared to UKF, as the EKF handles the non-linear system by linearising it but tends to lead to more errors appearing in the system when faced with high levels of non-linearities in the system. Whereas the UKF is more robust at handling high non-linearities, hence being more accurate and efficient at handling complex behaviours in the system. The UKF method also shows consistency in estimation and stability compared to the EKF when faced with high non-linearity. It also presents better accuracy when it comes to dealing with high dimensional systems which consist of dealing with large number of data points. Currently, based on the research conducted and the achieved results, the UKF is the most optimum method for SOC and SOH estimation as it is more robust and adaptable than the other proposed methods. However, the modern estimation method can also be complex to implement as it is a mathematical model-based method. The KF-based methods EKF and UKF use complex computations such as Jacobian matrix and unscented transformation sigma points which demand high computation power, and these increase the cost of the manufacturing and operating costs of the BMS. If the user wants a cost-efficient option, then a good alternative can be the traditional estimation approach of the CC method which is commonly used because it is simple to implement and use, requiring the least computational power compared to the modern estimation approaches; this excessively reduces the expenses of the physical implementation of it.