## 2 Literature Review and Methodology
### Simulation and Practical BMS Model Implementation  
Figure 5a demonstrates the simulation model of the BMS which is built using Simulink software version R2024a. The developed BMS consists of a battery model which is based on the ECM, the battery is set up to produce ten charging and discharging cycles, one full cycle per 1 h. The battery provides an output voltage range from 3.6 V to 4.2 V, a current of 15 Amps, and produces a temperature ranging from 295 to 336 K. The initial conditions of the battery have the SOC level at 30% and the R0 at 0.008 ohms, which can be seen in Table 1. Dedicated sensors, like voltage, current and temperature sensors, are used to monitor and measure the battery parameters. Random white noise is added together with the measured sensor values to make the measured battery parameters as realistic as possible to a real environment. Random noise will be introduced in the sensor measurements. Temperature  
Figure 4: SOC and SOH Estimation Block Diagram. fluctuations are simulated by introducing controlled ramps and random noise variations to emulate realistic environmental conditions. These variations cover a range of operating temperatures (295-336 K), reflecting typical scenarios, such as ambient cooling and heating effects, during charging and discharging cycles. Three SOC estimator algorithms are used, each algorithm assigned to a different estimation method, such as the first SOC estimator using the CC method, the second SOC estimator using the EKF method, and the third SOC estimator using the UKF method. The measurement of the battery parameters, such as voltage, current, and temperature, are the essential input parameters for the SOC estimator algorithms to function properly, and the estimated battery parameters output are the current SOC level and the internal resistance (R0) of battery. Whereas the initial SOC level given to the SOC estimators is 35%, and this is given to make it realistic, in a real environment the BMS will not know the exact initial SOC level of the battery, but it makes a guess; hence, there being slight initial inaccuracy. Two SOH estimator algorithms were also used, the first SOH estimator using the EKF method and the second SOC using the UKF method. The SOH estimator algorithm uses the outputs of the SOC estimators and battery temperature as their input parameters to calculate the SOH level of the battery. Digital display scopes are used to display the battery's parameters, like the real and estimated SOC levels, the estimated SOH levels, estimated R0, and the measured battery temperature. The BMS simulation model is designed to run in synchronisation to real-time by using the device's digital clock; hence the battery completes 5 charging and discharging cycles in 10 h [20,21]. Figure 5: BMS Model (**a**) and User-interface (**b**) for BMS Simulation Simulink Model Implementation. Battery thermal management is essential for maintaining safe and efficient battery operations, and the developed BMS does this by regulating the battery temperature at regular intervals using a smart cooling system that has been added to the BMS. The cooling system is based on a passive cooling solution which consists of using a parallel channel cooling plate connected to the battery's thermal source, a reservoir thermal liquid and a pump used for pumping the thermal liquid throughout the pipes or channels in the cooling plate, and a switch for turning on and off the pump. The battery cooling plate has a pair of distributor channels for inlet and outlet flow of the thermal liquid.