Solar‐based Distributed Generation (DG) powered Electric Vehicles (EVs) charging stations are widely adopted nowadays in the power system networks. In this process, the distribution grid faces various challenges caused by intermittent solar irradiance, peak EVs load, while controlling the state of charge (SoC) of batteries during dis(charging) phenomena. In this paper, an intelligent energy management scheme (IEMS)‐based coordinated control for photovoltaic (PV)‐based EVs charging stations is proposed. The proposed IEMS optimizes the PV generation and grid power utilization for EV charging stations (EVCS) by analysing real‐time meteorological and load demand data. The coordinated control of EMS provides power flow between PV generation, distribution grid, and EVs battery storage in a manner which results in the reduction of peak power demand by a factor of two. Further, the adaptive neuro‐based fuzzy control approach includes forecasting solar‐based electricity generation and EVs loads demand predictions to optimize IEMS according to the Indian power scenario. The proposed IEMS optimally utilizes the buffer batteries system for reducing the peak electricity demand with low system losses and reducing the impact of EVs charging load on distribution grid. The results are analysed using the digital simulation model and validated with real‐time hardware‐in‐loop experimental setup.

In recent years, the integration of bidirectional converters in the grid for V2G (vehicle-to–grid) applications of Electric Vehicles (EVs) has gained significant attention due to its potential to enhance grid stability, energy efficiency, and economic benefits. This analytical review highlights the different topologies of bidirectional converters and discusses various control techniques for efficient power flow between the vehicle and grid to enhance the performance of V2G and G2V functionalities. Further, the topologies of bidirectional converters highlighting key parameters such as the count of switches and their voltage stress, current ripple, efficiency, etc., are explored. The distinct advantages and limitations of each classification are also discussed. Many multilevel topologies that generate significant gain with low stress on switches and lesser harmonics have also been reviewed. The review then examines various control strategies, including power flow control and voltage regulation. These techniques are classified according to their control methodologies, which include traditional proportional-integral (PI) control, predictive control, sophisticated model-based control, and optimization. The synthesis of current advances in bidirectional converters and control techniques sheds light on developing trends and technologies, identifying important problems and future research objectives in the field. The economic and environmental implications of the G2V and V2G case studies also reinforce the review. Thus, this evaluation aims to promote further breakthroughs in EV integration with the grid, eventually leading to a more sustainable and resilient energy ecology.

The presence of bypass diodes in photovoltaic (PV) arrays can mitigate the negative effects of partial shading conditions (PSCs), which can cause multiple peak characteristics at the output. However, conventional maximum power point tracking (MPPT) methods can develop errors and detect the local maximum power point (LMPP) instead of the global maximum power point (GMPP) under certain circumstances. To address this issue, several artificial intelligence (AI)‐based methods have been proposed, but they result in complicated and unreliable methodologies. This study introduces the driving training‐based optimization (DTBO) method, which aims to address the partial shading (PS) problem quickly and reliably in maximum power point (MPP) detection for PV systems. DTBO improves tracking speed and reduces fluctuations in the power output during the tracking period. The proposed method is extensively verified using the Typhoon hardware‐in‐the‐loop (HIL) 402 emulator and compared to conventional methods such as particle swarm optimization (PSO), and JAYA, as well as the recently proposed adaptive JAYA (AJAYA) method for MPPT in a PV system under similar conditions.