## 4 Discussion
During analysis it is also seen in Figure 8 that, during the battery discharge, the SOC graphs are rougher and have more fluctuations compared to when battery is charging, which smoother. The reason for this is that, during the charging process, the battery's electrochemical reactions are controllable and predictable and even the charging current to the battery is supplied externally, which can also be precisely controlled; hence, the battery having a smooth SOC level increase during charging. Whereas, during the discharging process, the battery is the one providing the discharge current from its internally storedenergy and this discharge process can be affected by factors, like varying load resistance being introduced to the battery, introduced random white noise, changing internal resistance, and battery conditions, which could lead to a rough SOC level graph during battery discharge [23, 25]. The system's robustness to noisy sensor data is demonstrated by the UKF's low error margins, even under simulated inaccuracies. Noise modelling and covariance tuning in the Kalman filters ensured reliable SOC and SOH estimations, maintaining error margins as low as 0.32% during discharge cycles despite the introduced noise. Table 2 shows the comparison of results of the battery's measured real SOC level vs. the estimated SOC levels based on the CC, EKF and UKF methods. The error margin is calculated, which informs by how much the estimated SOC levels diverge from the real SOC measured; this is performed to find the most accurate SOC estimation method: it shows the SOC levels measured over five battery charging and discharging cycles. The average calculated error margin for the CC-based SOC is 5%, for the EKF-based SOC is between 1.6% and 1%, and for the UKF-based SOC is between 1% and 0.32%, which is the lowest. The SOC estimation performance of the proposed BMS was evaluated against industry standards, such as IEC 62660-2 and ISO 12405-4, which specify acceptable error margins for the SOC tracking in electric vehicle applications. These standards typically set thresholds around 1-2% error for advanced systems. The results of this study, with error margins ranging from 0.32% to 1%, demonstrate compliance with and surpass these guidelines, emphasising the robustness and accuracy of the proposed approach. Testing under simulated real-time conditions has shown that the delay introduced by the Kalman filters is negligible for typical EV operations. The system's design prioritises critical actions, such as thermal management and fault detection, over non-critical SOC and SOH updates, ensuring responsiveness in high-demand scenarios. Table 3 presents the comparison of results of the battery's real SOC vs. estimated SOC levels, when different initial SOC levels are given as the initial condition to the SOC estimators. At 40%, the initial SOC level given, the average calculated error margin for the CC-based SOC is 10%; for the EKF, it is between 2.7% and 1%; and for UKF, it is between 1.68% and 0.7%. Whereas, at the 50% initial SOC level, the average calculated error margin for the CC-based SOC is 20%; for the EKF, it is between 4.8% and 0.8%; and for the UKF, it is between 2.4% and 1.8%. This shows that, as inaccuracy of the BMS's SOC estimators is increased purposely to test how the estimator algorithms react to it, the modern methods which are the EKF and UKF methods have proven to be more resilient, adaptive, and robust at minimising the error margin and more accurate at the SOC and SOH estimations. Whereas the traditional method, which is the CC method, has shown to be less accurate and adaptive to this system initial condition changes, which can be seen by the significantly excessive increase