The transportation sector, a significant contributor to carbon dioxide emissions as of 2020, confronts a pressing challenge in mitigating pollution. Electric Vehicles (EVs) present a promising solution, offering a cleaner alternative; however, their limited travel range poses a constraint. Hybrid Electric Vehicles (HEVs) and Hybrid Energy Storage System Electric Vehicles (HESS EVs) emerge as economically feasible compromises. Nonetheless, the effective management of energy and the optimization of power source size remain crucial challenges for both HEVs and HESS EVs. Among various Energy Management Strategies (EMS), the Fuzzy Logic Controller (FLC) stands out for its performance, simplicity, and real-time applicability. This article comprehensively explores the diverse applications of FLC as an EMS in both HEVs and HESS EVs, providing a comparative analysis with other EMS methods and delving into the advantages and challenges associated with each approach. A detailed examination of various FLC types employed as EMS has been conducted, drawing insights from a multitude of references. Each class of FLC EMS is scrutinized, presenting a broad overview of proposed methodologies within each category. By providing this comprehensive information, the article equips readers with foundational knowledge and insights for the continued development of FLC EMS in hybrid electric and hybrid energy storage system electric vehicles.

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.

Industries use numerous drives and actuators, including DC motors. Due to the wide-ranged and adjustable speed, DC motor is widely used in many industries. However, the DC motor is prone to external disturbance and parameter changes, causing its speed to be unstable. Thus, a DC motor requires an appropriate controller design to obtain a fast and stable speed with a small steady-state error. In this study, a controller was designed based on the PID control method, with the controller gains tuned by trial-and-error and MATLAB Tuner with an identification system. The proposed controller design was implemented using PLC OMRON CP1E NA20DRA in the hardware implementation. Each tuning method was repeated five times so that the system performances could be compared and improved. Based on hardware implementation results, the trial-error method gave acceptable results but had steady-state errors. On the other hand, the use of MATLAB Tuner provided fast system responses with no steady-state error but still had oscillations with high overshoot during the transition. Therefore, the PID controller gains acquired from MATLAB Tuner must be tuned finely to get better system responses.

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.

Temperature forecasting is a crucial aspect of meteorology and climate change studies, but challenges arise due to the complexity of time series data involving seasonal patterns and long-term trends. Traditional methods often fall short in handling this variability, necessitating more advanced solutions to enhance prediction accuracy. LSTM and GRU models have emerged as promising alternatives for modeling temperature data. This study is a literature review comparing the effectiveness of LSTM and GRU based on previous research in temperature forecasting. The goal of this review is to evaluate the performance of both models using various evaluation metrics such as MSE, RMSE, and MAE to identify gaps in previous research and suggest improvements for future studies. The method involves a comprehensive analysis of previous studies using LSTM and GRU for temperature forecasting. Assessment is based on RMSE values and other metrics to compare the accuracy and consistency of both models across different conditions and temperature datasets. The analysis results show that LSTM has an RMSE range of 0.37 to 2.28. While LSTM demonstrates good performance in handling long-term dependencies, GRU provides more stable and accurate performance with an RMSE range of 0.03 to 2.00. This review underscores the importance of selecting the appropriate model based on data characteristics to improve the reliability of temperature forecasting.

In the digitalization and automation era, the internet has become an inseparable part of human life It provides a place for devices that are connected and can be controlled wirelessly through a network infrastructure, which is called the internet of things (IoT). In this research, the dual mode system of smart home based on IoT is proposed. In this system, the smart home can be controlled both manually and automatically. The key component for the proposed system is the relay mode which can be controlled to select the mode. The hardware implementation was done to test the proposed system with good result. Blynk app is used to control in automatic mode with virtual switch. When the manual mode is selected, the automatic mode is turned off and vice versa.