The evolution of driving technology has recently progressed from active safety features and ADAS systems to fully sensor-guided autonomous driving. Bringing such a vehicle to market requires not only simulation and testing but formal verification to account for all possible traffic scenarios. A new verification approach, which combines the use of two well-known model checkers: model checker for multi-agent systems (MCMAS) and probabilistic model checker (PRISM), is presented for this purpose. The overall structure of our autonomous vehicle (AV) system consists of: (1) A perception system of sensors that feeds data into (2) a rational agent (RA) based on a belief–desire–intention (BDI) architecture, which uses a model of the environment and is connected to the RA for verification of decision-making, and (3) a feedback control systems for following a self-planned path. MCMAS is used to check the consistency and stability of the BDI agent logic during design-time. PRISM is used to provide the RA with the probability of success while it decides to take action during run-time operation. This allows the RA to select movements of the highest probability of success from several generated alternatives. This framework has been tested on a new AV software platform built using the robot operating system (ROS) and virtual reality (VR) Gazebo Simulator. It also includes a parking lot scenario to test the feasibility of this approach in a realistic environment. A practical implementation of the AV system was also carried out on the experimental testbed.

A teleoperated robotic system offers a rapid deployment for performing tasks in dangerous environments. An effective teleoperation requires a proper feedback system such as a vision system using cameras. Despite its effectiveness of using camera, the limited field of view (FoV) become one of the main burdens of the operation. To deal with this problem, some researchers used wider-view camera or multiple cameras. In contrast, this research proposed to achieve a better teleoperation by eliminating FoV limit by using 360 camera. This research also aimed to improve the immersive experience by utilizing virtual reality (VR) headset. The proposed framework then developed to accommodate all the requirements and then evaluated by some numerous trials by some participants. An AprilTag marker was placed on the robot and tracked by a ceiling mounted camera to record the trajectory data. The obtained trajectories then measured its performance by using a standard performance metrics. The experiments demonstrated that the proposed method achieved lower collision area, closer to desired ideal path and smoother than using a standard camera as the feedback system. However, as the 360-video streaming induced higher latency, the proposed approach was slower. The measurements justified that the proposed approach achieved a better teleoperation especially for mission critical tasks.

Battery management systems are essential in electric vehicles and renewable energy applications, especially in terms of ensuring optimal battery health and performance and regarding the state of charge (SOC) in batteries consisting of many cells. The lifetime and efficiency of the battery depend on the accuracy of the SOC parameter estimation. Moreover, systems that apply active balancing technology are able to move cells with high SOC data to cells with low SOC. Many methods have been developed, but their long execution time makes them less optimal when applied. High-speed SOC estimation is required in active balancing technology, in addition to high accuracy. Therefore, this study proposes the estimation of SOC parameters using a statistical and metaheuristic approach from voltage and current input data in each battery cell. The experimental results showed that the metaheuristic-based method (ANFIS) had better RSME and R² values compared with the polynomial and linear regression or even the machine learning-based method (recurrent neural network) for training data.

One of the technologies of this power source is a brine battery. In this study, testing the effect of electrolyte salinity levels on the performance of brine batteries with two different types of electrodes was carried out, namely using magnesium anodes and aluminum anodes. In its cathode part used inert metal or carbon. Variations are carried out on the electrolyte solution used in the form of brine containing Na and Cl ions as electron conductors as well as reductors. Because the reaction of the brine battery system is relatively complex, a study was conducted in this study to see the effect of electrolyte salinity levels (27, 37, and 47 ppt) on the dc voltage produced. The measured parameters include the value of the dc voltage and the resulting current as well as the change in the salinity level after operating for a certain period of time. Tests were performed on batteries to drain the 0.47Ω resistor load operating for 24 hours non-stop where the parameter measurement setup was performed automatically using a PLC. The values of such parameters are monitored and recorded during 24-hour operation. Based on the results of measurements, the average energy capacity that can be used in Mg electrode cells is 17.6% of the total energy generated from the reaction of battery cells, then the average usable battery capacity on the Al electrode cell is 2.6% of the total energy produced.

VAWT (Vertical Axis Wind Turbine) is a turbine that has an upright mechanical structure with its blades rotating toward a perpendicular z-axis. Based on the experimental results, it is found that there is a relationship between the rotational speed of turbine that rotates generator with the output voltage and power. Thus, it is necessary to control the wind turbine speed so it can rotate according to the set point to be achieved. The contribution of this research is the development of a Fuzzy logic-based control system to control the speed of VAWT turbine where the speed of turbine is used as feedback. To design a Fuzzy rule base, the characteristics of the wind turbine’s response to wind speed are investigated first. Then Fuzzy logic-based controller is created and implemented. To test the effectiveness of the Fuzzy controller made, the implementation is carried out on a VAWT turbine while the simulation is applied to PMSG model using wind turbine through Simulink/Matlab. Based on simulation and experiment results, the performance of the control system using Integral Absolute Error (IAE) for each turbine speed set point value (35, 45, 85, and 100 RPM), it is found that for a small set point value, the IAE value will be larger than higher setpoints. The percentage of the average IAE value for the simulation is 10.25% higher than the experiment. It further shows that the control turbine speed at low speeds is relatively more difficult than at higher speeds.

Al-Ikhlas Yatim Dhuafa Islamic Boarding School is ± 12.6 km from Malang City. If you look at it geographically, the location of this community service program is located at coordinates 7°58'27.0"S 112°48'14.8"E. To support activities in this tourist area, electrical energy is really needed, especially for lighting facilities and charging electronic equipment. This Community Service Program will be implemented between April and November 2022 with activities in the form of building a pico hydro power plant in the Al-Ikhlas Yatim Dhuafa Islamic Boarding School area, Singosari District, Malang Regency. In carrying out this community service program, there is one activity partner, namely the Al-Ikhlas Yatim Dhuafa Islamic Boarding School with a total of 6 personnel. The results of this community service program are the installation and operation of a pico hydro power plant with a capacity of 1000 watts. This pico hydro power plant can meet electricity needs in the Islamic boarding school area with a total power of around 450 watts. In order for the pico hydro power plant to operate according to its capacity, it is necessary that the operation and maintenance of the system equipment be carried out correctly and on a schedule. For this reason, the transfer of knowledge and skills to work partners, namely the management of the Al-Ikhlas Yatim Dhuafa Islamic Boarding School, has been carried out.