Electric motorcycles (EM) are promising solutions for eco-friendly vehicles, but there are some dilemmas caused by the fossil-based energy used for Charging and the limited Charging infrastructure. This article proposes solving these dilemmas by designing a Solar-Powered Mobile Battery Swap Charging Station (MBSCS) for EM infrastructure. MBSCS will integrate solar power plants as a sustainable energy source and using Battery swap system to accommodate EM. Design thinking methodology is used to develop the initial design of MBSCS and technical indicator assessment through focus group discussions with expert panelists. Simulations are conducted using PVSyst software to evaluate various system variants defined according to the selected components. The results of this study provide the MBSCS initial design, technical indicators to assess the MBSCS system, simulation results, and optimal system variant configuration. The findings of this study will mainly contribute to a solution for EM challenges and offer an environmentally friendly Charging infrastructure. This study is expected to serve as an alternative solution for future mobile Charging stations designed to answer the limited Charging infrastructure as well as to demonstrate the potential use of portable solar power plant to overcome dependence on fossil-based energy.

In recent years, the popularity of wireless inductive power transfer (WIPT) system for electric vehicle Battery Charging (EVBC) is always ever-increasing. In the WIPT inductively coupled coil structure is the heart of the system and the mutual inductance (MI) between the coupled coils is the key factor for effective power transfer. This paper presents the analysis of mutual inductance between the spiral square coils based on the crosssectional area ratio of spiral circular and spiral square coupled coils. The analytical computed MI values are compared with FEM (ANSYS Maxwell) simulation and Experimental computed values. Finally, the designed spiral square coils are implemented in a laboratory prototype model and at the receiver side for effective electric vehicle (EV) Battery Charging a closed-loop PID controller is implemented for DC-DC buck converter. The effectiveness of the proposed controller has been tested by providing sudden changes in mutual coupling and change in reference value. The proposed system is suitable for both stationary and dynamic wireless EVBC.

In this paper, a Li-Ion Battery Charger Interface (BCI) circuit with fast and safe Charging for portable electronic devices is proposed. During the Charging of Li-Ion Battery, current spikes due to asynchronous control signals, and temperature are factors that greatly affect Battery performances and life. This circuit has the following features: prevents current spikes and also incorporates a permanent Battery temperature monitoring block. The BCI uses a dual current source and generates a constant current in a large current mode of 1.5 A, further reducing Charging time. The proposed BCI was designed and simulated in Cadence Virtuoso using TSMC 180 nm technology. The simulation results of the control signals show that the proposed architecture was able to eliminate the current drifts and keep the Battery temperature within the normal operating range.

In addressing the critical challenge of developing sustainable energy solutions for electric vehicle (EV) Battery Charging, this study introduces an innovative direct current (DC) microgrid system optimized for areas with high solar irradiance, such as Ain El Ibel, Djelfa. The research confronts two primary difficulties: maximizing solar energy utilization in the microgrid system and ensuring system stability and response accuracy for reliable EV Charging. To tackle these challenges, the study presents two original achievements. Firstly, it develops a neural network-enhanced Maximum Power Point Tracking (MPPT) controller, which is further optimized with Particle Swarm Optimization (PSO) to increase the efficiency of solar energy capture. Secondly, it refines the system's reliability through the advanced calibration of a Fractional Order Proportional-Integral (FOPI) controller using the Grey Wolf Optimization (GWO) technique, marking a notable improvement in microgrid system stability and response accuracy. The integration of a solar panel array, Battery storage, and a supercapacitor, coupled with these advanced optimization techniques, exemplifies a significant leap forward in enhancing efficiency and reliability of EV Battery Charging through renewable energy sources. Comprehensive simulation and evaluation of the system underscore its superiority over conventional methods, demonstrating the effectiveness of combining neural network-based optimization with PSO and GWO. This breakthrough not only advances the field of renewable energy, particularly for solar-powered EV Charging stations, but also aligns with global efforts towards sustainable transportation solutions.

When the Automated Guided Vehicles (AGVs) are transferring the parts from one machine to another in a job shop environment, it is possible that AGVs stop on their guide paths since their batteries are discharged. Consequently, it is essential to establish at least one Battery Charging Storage (BCS) to replace full batteries with empty batteries for the stopped AGVs. Due to non-predictable routes for AGVs in the manufacturing systems, to find the best place to establish the BCS can impact performance of the system. In this paper, an integrated mathematical model of job shop and AGV scheduling with respect to the location of a BCS is proposed. The proposed nonlinear model is transformed into a linear form to be efficiently solved in GAMS software. Finally, several numerical examples are presented to test the validity of the proposed mathematical model. The results show that the optimal cost and location of BCS can be obtained with respect to the number of AGVs, machines, parts, and other problem parameters. In addition, it is concluded that the increasing number of AGVs in a manufacturing system cannot be always a suitable policy for reducing the cost because in such conditions. Further to that, the conflict of AGVs may increase leading to the increase of the makespan. In other words, following the optimal point, increasing AGVs leads to the increase in costs.

In this paper, a sliding mode control of the LLC resonant dc-dc converter low voltage and high current to the Battery charger for electric vehicles is presented. The proposed controller operates at two fixed switching frequencies with sliding mode control implementation. In resonant converter, because of the soft-switching process, switching loss is negligible and converters can be designed at high frequencies to decline their size, weight and price. To analyze this convertor the harmonic approximation method with state space equation is used. Taking advantage of the approximations used in order to avoid uncertainty, sliding mode control method is used. The mentioned convertor with the proposed controlled is exploited within three switching frequency bands that are lower than resonant frequency, resonant frequency and above resonant frequency. Due to the robustness of the sliding mode control, the output voltage regulation occurs with minimum distortion under a wide variation of input voltage, output voltage and output current. As a result, the Battery life increases. In order to verify the results and effectiveness of the proposed converters, the prototype of LLC resonant converters Simulation and experimental results for the input voltage (300-420Vdc), output voltage (36-72Vdc) and current output (0-11A) is presented. The prototype achieves a peak efficiency value of 96%.