The electric motor is a device that converts electrical power into mechanical power. The DC motor has some advantages compared to the AC motor on its easier way to control the speed or position and wide adjustable range. However, it still has some issues, such as uncertainty and disturbance; most existing controllers still provide bad system performance or have major drawbacks in solving these issues. The alternative solution for a control method with simple, easy to design, and can be implemented in a multi-input multi-output system is the integral state feedback control technique, such as in Linear Quadratic Regulator (LQR). The weakness of LQR control is that there is no exact method to determine the best value of matrix Q and R used to calculate the state feedback gain; trial or manual tuning method is mostly used. Therefore, this paper proposed an improved LQR control using Particle Swarm Optimization (PSO) method and the Kalman filter estimator. The PSO was used to find optimal Q and R-value, while the Kalman filter reduced the number of sensors used to measure the system state. Both performance and energy consumption were compared using the Integral of Absolute Error (IAE) and total energy consumption. The simulation results showed that the proposed method is superior in performance and energy compared to manually-tuned LQR. However, based on the hardware implementation, although the manually-tuned LQR was found to have the smallest energy consumption, the proposed method has the smallest IAE, which can reduce the IAE by 11.28% with only 1% higher energy compared to manually-tuned LQR.

The advantages of lithium ion batteries, ranging from high energy density, to high service life, make them in great demand. Along with high demand, the use of lithium ion batteries also increases in complexity, for example, the use of electric vehicles and smart grids. The requirement that lithium ion batteries be used in certain conditions, for example as a battery, must have the same voltage as a lithium ion battery if connected in series. If this condition is not met, security and battery life are at stake. Battery Management System (BMS) comes as a solution to this problem. This study aims to design a BMS with three main features: monitoring, balancing and protection. BMS is designed using an Arduino Nano microcontroller. The test results show the performance of BMS to monitor voltage values has a root mean square error (RMSE) of 0.00706 or an accuracy of 99.29%, while the average value of the relative standard deviation (MRSD) is 0.258% or a precision level of 99.74%. The protection feature can function well to overcome problems such as overcharging, overcharging, and excessive temperatures. Balancing works well in setting the battery voltage value at 4.2V.

The braking system in a vehicle has the main role of slowing down the speed or stopping the moving vehicle. Compared to mechanical braking, which utilizes friction, non-contact braking has several advantages, including longer lifetime and less maintenance. One form of non-contact braking systems is the eddy current brake (ECB), an electric braking system that employs eddy currents to operate. This research focuses on the impact of magnetic field sources used in the ECB. In addition, the number of magnetic field sources is also observed. In order to achieve an ECB design that can be easily applied in any types of vehicles, including motorcycles and compact cars, a compact ECB design with an excellent braking torque is required. In this study, a compact design of the ECB is obtained by distributing the required braking torque from the disc brake into multiple electromagnets. Finite element method-based modeling has been performed to study ECB parameters, including the number of coil winding, the number of electromagnets, and the electric current. The results of this study show that the developed compact ECB could produce 93.66% of the torque required for braking.