## 6 Conclusions  
In conclusion, the significance of a robust and adaptive BMS for EV battery management application that can measure and estimate battery critical parameters for the optimised management of an enhanced battery for performance, safety, and lifespan is emphasised. The critical parameters of a battery, such as the SOC and SOH levels, are estimated by using various estimation techniques, including the traditional technique, the CC method, which tracks and integrates the current of battery to estimate the SOC level, and modern techniques, the KF-based methods which are data-driven approach algorithms which predict the state of system to estimate the SOC and SOH levels. The novelty of the developed BMS is characterised in this paper as robust, adaptable, optimised for performance and enhanced longevity, sustainable, cost-efficient, and conducts a comprehensive evaluation of the various estimation techniques. The BMS continuously updates the new estimated state of the battery while extensively adapting to improve the accuracy of the BMS. Additionally, it also minimises the degradation of the battery's internal components throughout its operational lifespan to enhance its performance and its lifespan, thereby reducing battery wastage and reducing costs related to battery maintenance and replacement. The results the comprehensive and analytical evaluation of the developed BMS with the Simulink simulation and ThingSpeak IoT platforms demonstrated that, compared to traditional estimation techniques, the modern technique, the UKF method, achieves a higher level of accuracy at estimating the battery SOC and SOH levels. For battery SOC level estimation, the average estimated SOC for a fully charged battery is 90% and for a fully discharged battery is 30%. The error margin for the UKF method-based BMS in tracking the real battery SOC level, compared to the estimated SOC level, is 1% for a battery charging state and 0.32% for a battery discharging state. Overall, the UKF method is the proposed estimation technique for an advanced and robust BMS for application in real-world scenarios for optimum and efficient EV battery management application. Future studies will focus on evaluating the proposed BMS across different EV categories, including passenger vehicles, commercial fleets, and heavy-duty applications. Such an exploration will ensure that the system meets the diverse operational and performance needs of various EV types.  
**Author Contributions:** M.T.M.N.: conceptualisation, methodology, software, validation, formal analysis, investigation, data curation, writing--original draft, visualisation. M.H.F.: writing--review and editing, visualisation, supervision. N.S.: project administration, writing--review and editing, visualisation, supervision. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** No data was used for the research discussed in this article.  
**Acknowledgments:** The author would like to sincerely thank the project supervisor [PERSON] and [PERSON] for their guidance and supervision during the period of the project. The authors would also acknowledge the scholars whose articles and books were used as a reference for the research of the project's solutions. The authors are also grateful to [PERSON] for providing basic knowledge on how to compose the project report. The authors are also thankful for the help and support of colleagues during the project research.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
**Abbreviations**  
A. Amps AC Alternative current BMS Battery management system BBS Battery balancing system C Celsius CC Coulomb counting CO2 Carbon dioxide DC Direct current ECM Equivalent circuit modelling EV Electric vehicle EKF Extended Kalman filter I Current IoT Internet of things K Kelvin KF Kalman filter Li-ion Lithium-ion LoRa Long range MQTT Message queue telemetry transport