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The rapid consumption of fossil fuel and increased environmental damage caused by it have given a strong impetus to the growth and development of fuel-efficient vehicles. Hybrid electric vehicles (HEVs) have evolved from their inchoate state and are proving to be a promising solution to the serious existential problem posed to the planet earth. Not only do HEVs provide better fuel economy and lower emissions satisfying environmental legislations, but also they dampen the effect of rising fuel prices on consumers. HEVs combine the drive powers of an internal combustion engine and an electrical machine. The main components of HEVs are energy storage system, motor, bidirectional converter and maximum power point trackers (MPPT, in case of solar-powered HEVs). The performance of HEVs greatly depends on these components and its architecture. This paper presents an extensive review on essential components used in HEVs such as their architectures with advantages and disadvantages, choice of bidirectional converter to obtain high efficiency, combining ultracapacitor with battery to extend the battery life, traction motors’ role and their suitability for a particular application. Inclusion of photovoltaic cell in HEVs is a fairly new concept and has been discussed in detail. Various MPPT techniques used for solar-driven HEVs are also discussed in this paper with their suitability.

The penetration of electric vehicles (EVs) into society needs extensive charging infrastructure. The existing charging system solely depends on the grid supply, which is essentially fossil fuel-dependent and leads to carbon emissions and environmental pollution. This can be minimized by incorporating renewable energy into the charging grid. This article presents a charging scheme combining photovoltaic (PV) and grid, offering a clean and dependable charging plan to sustain green transport. The proposed work presents the modelling and controlling a 10 kW EV charging/discharging framework integrating PV and grid. This work has multi-fold objectives: i) the development of an intelligent hybrid maximum power point tracking (MPPT) strategy, ii) the design of a fuzzy logic controlled bidirectional charger, iii) the setup of a PV-grid integrated charging system, and iv) the implementation of vehicle-to-grid (V2G) operation. The proposed charging system utilizes PV power and seamlessly switches to grid power whenever required. Since the performance of the PV source is affected by varying temperatures and irradiance, MPPT methods are needed to extract maximum power from the PV source. This paper developed and compared perturb and observe (P&O), Particle swarm optimization (PSO), and hybrid PSO + Adaptive neuro-fuzzy inference system (ANFIS) based algorithm for MPPT. The findings indicate that the PSO + ANFIS-driven method offers the highest tracking efficiency of 99.5%. This algorithm is also tested under dynamic partial shading conditions (PSC) to ensure robustness, and it led to achieving fast convergence and high efficiency despite multiple power peaks. In addition, the designed bidirectional charging system maximizes solar energy collection, minimizes the charging cost, and improves grid stability through demand balancing. The overall system is validated in a hardware-in-loop real-time environment through FPGA-based OPAL-RT.

Presence of an alternative energy source along with the Internal Combustion Engine (ICE) in Hybrid Electric Vehicles (HEVs) appeals for optimal power split between them for minimum fuel consumption and maximum power utilization. Hence HEVs provide better fuel economy compared to ICE based vehicles/conventional vehicle. Energy management strategies are the algorithms that decide the power split between engine and motor in order to improve the fuel economy and optimize the performance of HEVs. This paper describes various energy management strategies available in the literature. A lot of research work has been conducted for energy optimization and the same is extended for Plug-in Hybrid Electric Vehicles (PHEVs). This paper concentrates on the battery powered hybrid vehicles. Numerous methods are introduced in the literature and based on these, several control strategies are proposed. These control strategies are summarized here in a coherent framework. This paper will serve as a ready reference for the researchers working in the area of energy optimization of hybrid vehicles.

The paper provides a quick and robust power control mechanism for electric vehicles with integrated photovoltaic panels. Traditionally, photovoltaic power is solely used to charge the battery which feeds various power loads. However, this process is inefficient due to the incessant charging and discharging losses that occur in the battery. This paper proposes a distribution of power via an intelligent switching mechanism to various accessory loads so as to reduce these losses. Furthermore, a key component of this design is to estimate the maximum power available from the photovoltaic module in arbitrary environmental conditions. To do this, a fast and accurate polynomial regression model is presented. The performance of the model has been compared with several feed-forward neural networks with different hidden layers and nodes. The feed-forward neural network has been trained using the Levenberg–Marquardt back propagation method. The entire simulation has been carried out in MATLAB and Simulink 2018a. To validate the accuracy of this system, it has verified in real time on a hardware-in-the-loop testing platform using MicroLabBox hardware controller. It is shown that the proposed polynomial regression model provides an accurate estimate of maximum power in a much shorter duration compared with the neural networks. The formulated switching mechanism results in greater final SOC as compared to traditional power distribution schemes. This allows for longer cruising range for an electric vehicle ceteris paribus.

This paper proposes an improved deep learning‐based maximum power point tracking (MPPT) in solar photovoltaic cells considering various time series‐based environmental inputs. Generally, artificial neural network‐based MPPT algorithms use basic neural network architectures and inputs which do not represent the ambient conditions in a comprehensive manner. In this article, the ambient conditions of a location are represented through a comprehensive set of environmental features. Furthermore, the inclusion of time‐based features in the input data is considered to model cyclic patterns temporally within the atmospheric conditions leading to robust modeling of the MPPT algorithm. A transformer‐based deep learning architecture is trained as a time series prediction model using multidimensional time series input features. The model is trained on a dataset containing typical meteorological‐year data points of ambient weather conditions from 50 locations. The attention mechanism in the transformer modules allows the model to learn temporal patterns in the data efficiently. The proposed model achieves a 0.47% mean average percentage error of prediction on non‐zero operating voltage points in a test dataset consisting of data collected over a period of 200 consecutive hours; resulting in the average power efficiency of 99.54% and peak power efficiency of 99.98%. The proposed model is validated through real‐time simulations. The proposed model performs power point tracking in a robust, dynamic, and nonlatent manner, over a wide range of atmospheric conditions. In this paper, the authors explore several avenues where the maximum power point tracking (MPPT) algorithm can be improved upon, including the use of more inclusive environmental factors, the inclusion of time‐based factors to model cyclic patterns within the atmospheric conditions, and modeling the MPPT algorithm as a time series problem.

To reduce apace extraction of natural resources, to plummet the toxic emissions, and to increase the fuel economy for road transportation, hybrid vehicles are found to be promising. Hybrid vehicles use batteries and engine to propel the vehicle which minimizes dependence on liquid fuels. Battery is an important component of hybrid vehicles and is mainly characterized by its state of charge level. Here a modified state of charge estimation algorithm is applied, which includes not only coulomb counting but also open circuit voltage, weighting factor, and correction factor to track the run time state of charge efficiently. Further, presence of battery and engine together needs a prevailing power split scheme for their efficient utilization. In this paper, a fuel efficient energy management strategy for power-split hybrid electric vehicle using modified state of charge estimation method is developed. Here, the optimal values of various governing parameters are firstly computed with genetic algorithm and then fed to Pontryagin’s minimum principle to decide the threshold power at which engine is turned on. This process makes the proposed method robust and provides better chance to improve the fuel efficiency. Engine efficient operating region is identified to operate vehicle in efficient regions and reduce fuel consumption.

To overcome the air pollution and ill effects of IC engine-based transportation (ICEVs), demand of electric vehicles (EVs) has risen which reduce * gasoline consumption, environment degradation and energy wastage, but barriers—short driving range, higher battery cost and longer charging time—slow down its wide adoptions and commercialization. Although to overcome such issues, EV variants —HEVs and PHEVs—were also brought into the market but not that successful either. The use of ICE in HEVs and PHEVs increases fossil fuel dependency. Thus, the research focus shifted towards fuel cell-powered electric vehicles (FCEVs) which offer negligible emission and higher efficiency than EV variants. Though a moderate research work has been done on FCEVs, still its wide expansion is limited, facing severe challenges commonly related to fuel cost, selection of energy units, power electronic interfacing, component sizing and energy management. This paper presents an extensive exploration on EV variants, their issues, an in-depth comparison of latest topologies for FCEVs and optimum arrangement of HESS, designed by energy unit’s integration, i.e. FC, battery and UCs, to encounter the dynamic power demand and develop a performant model for transportation. In last, progress and possible future research areas are discussed. In short, this paper reveals all contemporary information of FCHEV technology to the scientists and scholars who are working in this particular arena.

The increasing oil price, energy demand, and environmental concern are leading to a global switch towards Plug-In Hybrid Electric Vehicles (PHEVs). In a PHEV, Li-ion battery is considered as the primary propelling source. Therefore, an accurate battery model is required to predict the I-V characteristic and dynamic behavior of a battery. This paper presents a highly effective thermoelectric model of Li-ion battery developed in Simulink. An algorithm is proposed for estimation of state of charge (SOC) and open circuit voltage (OCV) adaptively to notify the exact SOC level for better utilization of battery power and optimal vehicle performance. Thermal behavior of Li-ion battery is investigated for wide temperature range and its effect on resistance, capacity, and OCV is recorded. The minimum SOC level to which battery can get depleted is calculated using gradient method. The proposed simulation results are analyzed with those of earlier models and found to be better.

Electric vehicles (EVs) have proved capable of solving many of the environment’s problems such as reducing harmful pollutants’ emission along with having greater motor efficiency than gasoline vehicles. This study presents a bibliometric analysis of 10,426 publications from the year 1989 to 2020, obtained from Web of Science™ (WoS) core collection (CC). An initial citation analysis was done using Histcite to identity the leading nations, institutes, authors, and journals performing research related to EVs. Following this, a co-citation analysis was performed using VOSviewer, which generates clusters that are further analyzed to identify the key domains in EV research. A research overview in EVs over the last three decades is presented that can serve various stakeholders in this field of study. The results of this study will highlight the critical research areas in the field of EVs. Additionally, it will also provide various insights that may help the policymakers, practitioners and associations to accelerate EV adoption by the end-users.

Power loss and malfunctioning of equipment due to current and voltage harmonics produced by nonlinear loads are major concerns for both power suppliers and consumers. This paper employs DSTATCOM to mitigate these harmonic distortions in distribution systems. DSTATCOM is a current controlled voltage source PWM converter with passive components. It supplies compensating current that eliminates current harmonics and provides reactive power compensation thereby providing a smooth source current. The performance of DSTATCOM is controlled using proportional integral and fuzzy logic–based control (FLC) schemes to improve power quality at dynamic loading. As the penetration of photovoltaic (PV) systems in energy production is significant, the power quality issues for PV-integrated systems have also been investigated. The maximum power point for a PV array is tracked using perturb and observe and FLC methods. The complete system is simulated in MATLAB and extensively investigated for a large range of balanced and unbalanced nonlinear load conditions. The simulation results are validated in real time through the hardware in loop (HIL) test bench using Opal-RT controller. The proposed PV-integrated DSTATCOM with FLC yields better performance in terms of DC link voltage settling time, total harmonics distortion, and smooth source current as compared to other schemes.