## 1 Introduction
The LoRa module is a technology that allows communication through a wide area network (WAN), which is used to receive and send data from sensors through a wide area wirelessly. The LoRa sends its data through radio waves at a frequency of 869 MHz. Another wireless data transmission technology available is a Wi-Fi module, which allows for wireless communication using a Wi-Fi network. Another slightly different developed BMS, illustrated in [5], consists of a BMS which has the battery being monitored by a voltage sensor and DHT11 sensor for measuring temperature. The voltage sensor uses a potential divider to measure voltage and has a range of a maximum of 25 V. The DHT11 sensor can measure to a range from \(-40\) to 125 \({}^{\circ}\)C. It has an ATMEGA 328P microcontroller and uses a coulomb counting method for estimating the SOC. The data is directly transferred to an LCD and is also wirelessly transferred to an IoT cloud using MQTT which is a commonly used messaging protocol for communication between a machine and an IoT device. An IoT devices is an appliance or sensor that is capable of exchanging data by sending and receiving information wirelessly through a network, such as the internet; hence, it is interconnected. IoT is commonly used for monitoring data from sensors and remotely controlling the device. The cloud in the network, which supports the IoT, is the place where the transferred data is stored, processed and remotely viewed by the user. The key contributions of this paper can be summarised as follows:  
* Innovative integration of advanced estimation techniques: This paper introduces a novel BMS that integrates advanced KFs and other cutting-edge, data-driven estimation techniques, providing a more accurate and adaptable method for monitoring the state of a battery. This approach not only enhances precision across battery cycles but reduces the error margin significantly, demonstrating a marked improvement over traditional techniques. Real-time BMS implementation with IoT and Simulink integration: The proposed BMS uniquely combines real-time data processing through a Simulink model with physical implementation using an Arduino board and an IoT platform. This integration creates a robust and adaptive BMS capable of real-time battery management, offering a practical and scalable solution that bridges the gap between theoretical models and real-world applications. * Comprehensive benchmarking and analytical comparison: The paper conducts an extensive benchmarking of traditional versus modern estimation techniques within the BMS context, offering a detailed analytical comparison. This evaluation not only highlights the effectiveness of advanced methods but provides a clear academic and practical framework for selecting the most appropriate techniques for different BMS applications. The main motivation behind this research was to further critically analyse and enrich the available knowledge on BMSs, which can act as a catalyst to further accelerate the adoption of EVs in modern society and preserve our planet for future generations. Other reasons were to socially improve society's perception and acceptance of battery powered EVs by improving the safety of the battery during high stress conditions and operations commonly known for creating malfunctions and safety risks in the battery. This mitigates any personal risk or risks to the surrounding environment, like explosions or fire caused by the battery. This further enhances the safeguarding of public health and well-being and helps in promoting the transition to green energy by increasing the awareness and a social ideology shift towards the use of cleaner energy solutions, like battery powered EVs. Vast inspiration was taken from the current pioneers in this field paving the path, companies like Tesla, Mercedes, Toyota, and BYD.