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DC fast charging is critical for the widespread adoption of electric vehicles (EVs) due to its impact on convenience, economics, and environmental sustainability. Due to the critical role of DC fast charging in EV adoption, improving its efficiency and performance is paramount. This paper presents a Fractional-Order PI (FOPI) controller for obtaining a well-regulated output voltage of a Dual Active Bridge (DAB) converter, a widely used topology in EV applications. The proposed FOPI is validated to the conventional PI controller in a simulated DAB model that is relevant to DC fast charging applications. The evaluation is performed, and various time-domain parameters and performance indices to evaluate the dynamic response of the model are considered. The results are expected to demonstrate significant improvement in the converter's transient and steady-state response using the proposed FOPI controller compared to the conventional PI controller, contributing to a more efficient and robust DC fast charging system. This improvement can translate to faster charging times, better stability, and potentially reduced stress on the EV battery during DC fast charging. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are inducing revolutionary developments to the transportation and power sectors. Their innumerable benefits are forcing nations to adopt this sustainable mode of transport. Governments are framing and implementing various green energy policies. Nonetheless, there exist several critical challenges and concerns to be resolved in order to reap the complete benefits of E-mobility. The impacts of unplanned EV charging are a major concern. Accurate EV load forecasting followed by an efficient charge scheduling system could, to a large extent, solve this problem. This work focuses on short-term EV demand forecasting using three learning frameworks, which were applied to real-time adaptive charging network (ACN) data, and performance was analyzed. Auto-regressive (AR) forecasting, support vector regression (SVR), and long short-term memory (LSTM) frameworks demonstrated good performance in EV charging demand forecasting. Among these, LSTM showed the best performance with a mean absolute error (MAE) of 4 kW and a root-mean-squared error (RMSE) of 5.9 kW. [ABSTRACT FROM AUTHOR]

The efficiency of a nation's progress is determined by a variety of factors; however, transportation plays a critical role in boosting progress because it facilitates trade and communication between countries. The majority of transportation is powered by fossil fuels such as gasoline or diesel, which will be depleted in less than 50 years. Another option is to operate transportation systems after replacing conventional vehicles with electric vehicles (EV). Powering these vehicles with green electricity contributes zero carbon emissions from production to the final product. Together with the controller, an efficient charger ensures that the entire system is reliable and stable. The current work focuses on charging an off-board EV from greener energy sources (both a fuel cell and PV array forming a micro-grid) based on their availability via an efficient converter controlled by an adaptive multi-objective controller. A novel multi-output-based adaptive neuro fuzzy inference system (ANFIS) controller for charging the off-board EV at a constant current and voltage for both line and load regulations is proposed, in the current work. A comparison study of grid partitioning and subtractive clustering was conducted in order to select an optimized algorithm for generating FIS. Novelty is achieved by ensuring closed-loop stability is the main aim of the work. The entire work was created with the MATLAB/Simulink software. [ABSTRACT FROM AUTHOR]