## 3 Results  
Figure 7 shows the graphs of the measured battery parameters using the dedicated sensors, such as voltage and current. Its shows that, when the battery is charging, it ranges from 3.7 V to a maximum voltage of 4.3 V; when the battery is discharging, it ranges from 4.3 V to 2.9 V. When the battery is charging the maximum current being supplied is 15 Amps. Figure 6: Practical BMS Model Implementation. Figure 7: Measured Voltage and Current Graphs. Figure 8 demonstrates the battery's calculated SOC graphs, which consists of the measured real SOC and the estimated SOC graphs of the battery. The real SOC is the yellow-coloured graph; the CC method-based estimated SOC is the blue-coloured graph; the EKF method-based estimated SOC is the red-coloured graph; and the UKF method-based estimated SOC is the green-coloured graph. The real SOC graph starts plotting the battery charging graph from 0.30 which, in the percentage format, is 30 percent, and it goes up to 90%, at which point the battery is fully charged. Whereas the battery discharging graph goes from 90% to 30%, at which point the battery is fully discharged. The battery repeats this charging and discharging cycle for five cycles. It also shows that the estimated SOC graphs are trying to track and follow the real SOC graph. It is evidently presented from the results that the SOC estimation method which achieved the worst results in accurately tracking the real SOC graph is the CC method; the method which was most accurate at tracking the real SOC is the UKF method, which can be seen by the green-coloured UKF-based SOC graph overlapping the yellow-coloured real SOC graph. The EKF-based SOC graph is also giving very accurate results, but it can also be seen that it has some unwanted spikes that appear at some places in the red-coloured SOC graph, due to errors or disturbances in the system. Figure 9 presents the zoomed-in graph of Figure 8 that focuses on only the first and second cycles of the battery for a closer look at the battery SOC graph and to obtain a better understanding of the battery dynamics. Figure 10 presents the battery's estimated state of health (SOH) graphs, which consists of the estimated SOH based on the EKF method that is the yellow-coloured graph, and the estimated SOH based on the UKF method which is the blue-coloured graph. The plotting of the SOH graph starts from 1 which, in the percentage format, is 100%; hence, the battery being fully healthy, which means it is basically a brand new battery. Then, as the time increases while the battery performs multiple charging and discharging cycles, the battery's SOH level starts to decrease causing battery degradation from the excessive usage such that the SOH level drops between the range of 70% and 80%. It also shows that the UKF-based SOH estimation graph is smoother and more accurate as it narrows the SOH level range to as close to the real SOH of the battery. While the EKF-based SOH estimation graph is more widespread and covers a large range and fluctuates a lot, thus being less accurate than the UKF-based SOH graph and being just a rough estimate of the battery's SOH level. Figure 11 demonstrates the battery's estimated internal resistance (R0) graphs, which consists of the estimated R0 based on the EKF method, which is the yellow-coloured graph, and the estimated R0 based on the UKF method, which is the blue-coloured graph. The plotting of the R0 graph starts from 0.008 ohms resistance, which represents the initial resistance of the battery. Then, as the time increases while the battery executes multiple cycles, the battery's R0 level starts to increase from battery usage along with an increase in the battery temperature to the R0 level being between the range of 0.010 and 0.015 ohms resistance. It also shows that the UKF-based SOH estimation graph is smoother and more accurate, while the EKF-based R0 estimation graph is fluctuating more and is less accurate then UKF-based R0 graph. Figure 10: Estimated SOH Graphs.