Abstract-This project is about the design of PID controllers and the improvement of outputs in multivariable processes. The optimisation of PID controller for the Shell oil process is presented in this paper, using Genetic Algorithms (GAs). GAs are used to automatically tune PID controllers according to given specifications. They use an objective function, which is specially formulated and measures the performance of controller in terms of time-domain bounds on the responses of closed-loop process. A specific objective function is suggested that allows the designer for a single-input, single-output (SISO) process to explicitly specify the process performance specifications associated with the given problem in terms of time-domain bounds, then experimentally evaluate the closed-loop responses. This is investigated using a simple two-term parametric PID controller tuning problem. The results are then analysed and compared with those obtained using a number of popular conventional controller tuning methods. The intention is to demonstrate that the proposed objective function is inherently capable of accurately quantifying complex performance specifications in the time domain. This is something that cannot normally be employed in conventional controller design or tuning methods. Finally, the recommended objective function will be used to examine the control problems of Multi-Input-Multi-Output (MIMO) processes, and the results will be presented in order to determine the efficiency of the suggested control system.

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Because of their inherent safety, fast charging capacity, and extended cycle life, lithium-ion batteries are preferred over other types of batteries in electric vehicle applications. It's critical to be able to determine state factors like state of charge and state of health in order to generate an accurate battery model. The state of charge estimation algorithms for generic Lithium-ion batteries was enhanced using LA92 drive cycle experiment data. To begin, a mathematical model for an analogous circuit battery was created with the goal of accurately imitating the behavior of a lithium-ion battery. The Thevenin model is created by 2 RC branches and identifies the model parameters with the Extended Kalman Filter. The Hybrid Pulse Power Characterization (HPPC) test data obtained at 40°C, 25°C, 10°C, 0°C, and -10°C are used to calculate the SOC 3-dimensional curve as a function of SOC and T. A comparison of the two methods is shown, indicating that the EKF method of battery SOC evaluation is more accurate than the coulomb counting method. The error observed from the EKF results is less than 1% and it shows EKF is reliable for estimating the battery's states. Keywords; Electric Vehicles (EV); Lithium-ion batteries (Li-Ion); State of Charge (SOC); Extended Kalman Filter (EKF)I.

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