## 4 Discussion  
Aspects of the BMS which were analysed are the SOH, the battery temperature, the efficiency of the BMS performance, and the error margin of the BMS at tracking the real SOC level. Further investigation of the battery's estimated SOH graph, as shown in Figure 10, has revealed a relationship of SOH level drop over time which is shown that in the initial time from 0 to 2000 s, the drop in the SOH is significantly high; but in the later time, from 2000 s to 3500 s, the SOH drop reduces excessively. The SOH at time 0 s is 100%; the SOH at time 500 s is 90%; the SOH at time 1000 s is 87%; the SOH at time 1500 s is 85%; the SOH at time 2000 s is 78%; the SOH at time 2500 s is 77%; the SOH at time 3000 s is 76%; and the SOH at time 3500 s is 75%. This is because, at the initial time, the battery must undergo a conditioning phase in order to stabilise the internal chemical process, this adds a sufficiently high level of stress on the battery which increases the battery degradation, hence reducing the SOH level faster [22,23]. Whereas, in the later period of the battery after some cycles, the battery's internal components are stabilised, providing a consistent performance with a slower battery degradation rate thereby reducing the SOH level slower. Inspection of the battery's measured temperature graph, which is presented in Figure 12a, has demonstrated the pattern of the battery temperature dropping when the battery is charging; when the battery is discharging, the battery temperature increases. It also shows that, as the number of discharge cycles of battery increases, so does the battery's temperature increase significantly. The reason for this is that, during the charging process, the battery tends to store electrical energy by converting its internal chemical energy into stored electrical energy through electrochemical reactions, and these reactions by nature tend to be endothermic, which means that they absorb the heat from the surroundings; and the charging process, with it ions migrating and redistributing, can also introduce a cooling effect on the battery. All these effects led to the battery temperature dropping [24,25]. During the discharging process, the battery's chemical energy is converted to electrical energy and this process involves exothermic electrochemical reactions, which means that the battery releases heat in its surroundings, thereby increasing the battery temperature. Further utilising this information, it is possible to improve the BMS performance by enhancing the battery cooling system through creating better thermal management strategies and protocols by specifically considering and tackling the cooling of the battery during its discharge cycle period for a more robust and effective cooling response on the battery in a more energy efficient way. The BMS efficiency at tracking and estimating the SOC level at the conducted five charging and discharging cycles of the battery under the BMS management shows that the first battery cycle SOC estimation graph does start with a slightly higher error margin, but that is understandable as the input initial SOC level condition to the SOC estimators is fully accurate, hence the BMS takes a bit of time to adjust; however, from the second to fifth battery cycles, it is shown to provide an average of 90% SOC for a fully charged battery; for a fully discharged battery, it provides a 30% SOC level, which is identical to the real SOC levels measured. Hence, the BMS proves its high efficiency in providing a constant smooth performance and energy transfer, which is also evident from the minor error margins presented in Table 2. The proposed BMS achieves a balance between computational complexity and energy efficiency by selectively processing data. Power consumption measurements indicate that the energy used by the BMS for running advanced algorithms is negligible compared to the improvements in battery performance achieved through accurate SOC and SOH estimations.