## 1 Introduction  
Personal transportation has been a crucial means of travelling for humans for decades. However, with current petrol vehicles not being eco-friendly, as they pollute the air, the demand for greener transport, like electric vehicles (EVs), has increased. However, world institutions have not been able to drastically adopt EVs because the Li-ion batteries used in EVs are not fully stable and efficient, as the battery overheats and degrades over time from usage. The global demand for Li-ion batteries is increasing exceedingly, and it is expected to increase from the required 700 GWh in 2022 to around 4.7 TWh by 2030 [1]. Hence, research on technologies such as battery management systems (BMSs), which will play major role in meeting this market demand, has become a hot topic; this report evaluates these technologies. The main subject of this report is to design and build a BMS for EVs, a system that monitors battery condition and critical parameters, such as voltage (V), current (I), temperature (T), state of charge (SOC), and state of health (SOH), and alerts the end-user when the battery conditions are abnormal through a user-interface display [2, 3]. Current challenges in the development of a BMS for electric vehicles include ensuring accurate estimation of battery states (SOC and SOH) under dynamic operational and environmental conditions, such as varying temperatures and load profiles. Additionally, balancing the computational demands of advanced algorithms with real-time constraints and maintaining cost-effectiveness while integrating features like the IoT and thermal management systems remain significant hurdles. This research addresses these challenges by developing a robust BMS that integrates UKF-based estimation for high precision, IoT-enabled remote monitoring for predictive maintenance and adaptive thermal management to enhance safety and efficiency. The findings aim to provide a scalable and practical solution for optimising EV battery performance and lifespan. The main reason that BMS technology is relevant for the EV industry is it can improve the battery safety by monitoring and managing it so that it is not being overcharged or discharged. This also optimises battery usage and minimises energy loss, increasing the battery's reliability for giving a constant smooth high performance. This further leads to the battery having a longer life span, as its health degrades far slower, thereby reducing the cost of battery service and maintenance. This paper aims to critically analyse and evaluate various existing BMSs to develop and simulate the most optimum BMS for EVs which is robust and adaptive at accurately estimating the battery's critical parameters in real-time and managing the battery for optimum performance by efficiently conserving battery energy and health throughout its operational life cycle. To achieve this, a comprehensive review of the literature was conducted on the scientific theories and methods that can be used to monitor and estimate battery parameters in BMSs, such as the traditional and modern estimation methods. A BMS simulation and physical model were created with an IoT-based user interface that consisted of an interface between the BMS simulation program and the Arduino board. The relevant previously developed BMS is presented in [4], which consists of a BMS that has a load connected to a battery, being measured by an INA219 sensor for monitoring current and a MAX6675 sensor for measuring temperature. The INA219 sensor has range to measure a maximum of 26 V and 3.2 A. The MAX6675 sensor is a K-type thermocouple which can measure in a range from 0 to 1024 C. It has an ESP32 microcontroller which processes the sensors data and uses a coulomb counting method to calculate the SOC. The long range (LoRa) module is used for the wireless transmission of data to another LoRa, which then passes it to another ESP32, which then transfers the data to an OLED screen and on to an IoT cloud for data display.