Electric Vehicle Battery Charging Research Articles

Non-Integrated and Integrated On-Board Battery Chargers (iOBCs) for Electric Vehicles (EVs): A Critical Review

The rising Greenhouse Gas (GHG) emissions stemming from the extensive use of automobiles across the globe represent a critical environmental challenge, contributing significantly to phenomena such as global warming and the deterioration of air quality. To address these challenges, there is a critical need for research and development in electric vehicles (EVs) and their associated charging infrastructure, including off-board and on-board chargers (OBCs). This paper aims to bridge the gaps in existing review literature by offering a comprehensive review of both integrated and non-integrated OBCs for EVs, based on the authors’ knowledge at the time of writing. The paper begins by outlining trends in the EV market, including voltage levels, power ratings, and relevant standards. It then provides a detailed analysis of two-level and multi-level power converter topologies, covering AC-DC power factor correction (PFC) and isolated DC-DC topologies. Subsequently, it discusses single-stage and two-stage non-integrated OBC solutions. Additionally, various categories of integrated OBCs (iOBCs) are explored, accompanied by relevant examples. The paper also includes comparison tables containing technical specifications and key characteristics for reference and analysis.

Integrated on-board electric vehicle battery charger with multifunctional features

ABSTRACT Because of the continuous demand for reliable, efficient, small-size, light weighted, and low-cost Electric Vehicle (EV) battery charging systems, an integrated on-board charger is proposed with a high-power propulsion motor drive. It utilises stator windings of motor, the 6-leg Voltage Source Converter (VSC), DC-DC converter, and feedback sensors to facilitate vehicular battery charging. With this system, soft switching technique based bidirectional isolated DC-DC converter is proposed that offers the advantage of simple circuitry design due to no usage of extra passive or active components. This paper presents mathematical modelling, analysis, and controller design of this proposed configuration under various operating modes. The complete system is developed in Sim Power system toolbox of MATLAB software and tested in real-time environment. The performance investigations of proposed system are carried out for three operating modes like Grid-to-Vehicle (G2V), Vehicle-to-Grid (V2G), Vehicle-to-Vehicle (V2V) under Constant Current (CC) and Constant Voltage (CV) battery charging modes. Furthermore, the investigations ensure the system’s excellent efficiency, less THD, and nearly unity power factor operation under all operating conditions.

Online adaptive anode potential-controlled fast charging of lithium-ion cells using a validated electrochemical model-based virtual reference electrode

Optimal utilization of the safe operating area of lithium-ion battery cells is fundamental to achieving peak performance. Pushing the currents close to the intrinsic electrochemical cell limits during fast charging of battery electric vehicles reduces charging times noticeably and improves customer satisfaction. With electrochemical model-based state observers embedded in the battery management system, predicting the critical anode potential is feasible. This allows the charging process to be adjusted accordingly for effective prevention of lithium plating. However, validating the model, especially the internal potentials, is particularly challenging, and the real-time capability is often doubted. This work addresses the parameterization and experimental validation of a full-order electrochemical model utilizing cells instrumented with micro-reference electrodes. Special emphasis is put on validating the estimate of the anode surface potential, which enables the model to act as a virtual reference electrode. For the first time, it is shown how this can be efficiently implemented on a next-generation automotive microcontroller, in combination with a charging current controller, for online adaptive anode potential-controlled fast charging. Execution times well below 4 ms prove real-time capability, and reference electrodes validate good agreement between model estimate and measurements in closed-loop charging current control.

Internet of Vehicles (IoV) Based Framework for electricity Demand Forecasting in V2G

The integration of smart grids with Advanced Metering Infrastructure (AMI) has bridged the realms of the Internet of Vehicles (IoV) and Electric Vehicles (EVs), yet challenges persist in managing EV Battery Range and charging infrastructure effectively. This paper presents an innovative IoV-based framework tailored for EVs, with a specific focus on forecasting electricity consumption in a Vehicle-to-Grid (V2G) scenario. By exploring the hurdles surrounding electric vehicle usage, the research lays the foundation for Electric Vehicles (EVs). The proposed IoV model optimizes IoT sensor utilization through a fog layer and employs a Back Propagation (BP) neural network for battery State of Charge (SoC) estimation, integrating Principal Component Analysis (PCA) for data dimensionality reduction. Leveraging substantial computing capabilities, the cloud layer predicts Electricity Consumption Data (ECD) associated with EVs in V2G scenarios. Performance evaluation metrics like Akaike Information Criteria (AIC), Mean Error (ME), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE) are assessed across state-of-the-art forecasting algorithms. Incorporating EV potential into the optimal model reveals a significant 10% reduction in electricity demand. This research advances IoV-based frameworks, offering insights to enhance EV efficiency within the broader energy infrastructure.

Two‐stage demand‐side management in energy flexible residential buildings

AbstractIn this study, an optimisation model is developed for two‐stage energy management of a residential building to minimise energy cost under monthly power‐based tariffs for peak demand and time‐variable electricity prices. The expected peak demand is determined in the first stage, and then the energy management system minimizes energy costs during the second stage. The second stage's optimisation problem is solved in a rolling time window, facilitating real‐time operation of flexible energy sources in the building. This includes optimal charging and discharging of the battery energy system, electric vehicle battery charging, heating system operation, and determining the optimal start times for washing machines and dishwashers, all close to real‐time. The proposed approach enables users to predict and manage peak demand in daily operation, staying below the predetermined value through a close to real‐time energy management system. The effectiveness of this two‐stage approach in demand‐side management for residential buildings is demonstrated through a realistic case study.

Optimization of vehicle charging and dynamic relocation in free-floating electric carsharing systems with advanced reservations

The electric free-floating carsharing (FFCS) system is an emerging type of shared mobility and is widely adopted in urban areas due to its eco-friendliness, effectiveness, and flexibility. However, as most trips in FFCS are uni-directional, the distributions of vehicles in FFCS are usually spatially uneven. The operators of FFCS must relocate cars during the operation period to maintain the availability of cars to the users and improve the profitability of the system for dealing with imbalanced vehicle distribution caused by uneven demand. To optimize the relocation operations for FFCS operators, this study formulates the problem as a mixed-integer linear programming (MILP) model, which simultaneously addresses dynamic relocation, battery charging of electric vehicles, and scheduling of relocation staff in an FFCS with advanced reservation. A novel, efficient algorithm using a new structure of relocation tuples (RTs) is proposed to solve the problem that facilitates the search for feasible solutions using an RT exchange-based local search algorithm. Acceleration techniques are introduced to strengthen the solution search in large-scale scenarios. Numerical examples are used to evaluate the performance of the solution algorithm and validate the benefit of the proposed methodology. This shows that the methodology could be used by operators for decision-making at different levels.

Modeling, simulation, and experimental studies of a novel battery charger for fuel cell applications based on a new high step‐up DC‐DC converter

SummaryDuring winter, the proton exchange membrane fuel cell (PEMFC) faces operational challenges, potentially leading to ice formation in membranes and gas technology. To achieve favorable operational conditions and output voltage, suitable power electronic devices are indispensable. Consequently, the fuel cell module links to the battery through a DC‐DC converter. Addressing the necessity for an efficient battery charger in fuel cell electric vehicle applications, this article introduces a new high step‐up DC‐DC converter. It employs a boosting structure, incorporating a coupled inductor to elevate voltage gain while substantially reducing voltage and current stresses on components, a fitting attribute for PEMFC electric vehicle battery chargers. Additionally, the proposed converter boasts high efficiency and a minimal component count. Moreover, the study meticulously explores various operating modes of the converter, offering comprehensive analyses and operational relationships. To evaluate dynamic behavior and ensure stability, the study employs small signal modeling and devises a controller based on the maximum power point tracking principle. MATLAB simulations validate the converter's performance, while a laboratory‐built prototype (48 V input, 220 V output, 500 W) substantiates its characteristics, aligning with theoretical projections. Finally, a comparative analysis between simulation, experimentation, and theoretical calculations validates the converter's functionality. Ultimately, this work emphasizes the critical role of a well‐designed high‐efficiency DC‐DC converter in the context of PEMFC electric vehicle battery charging during challenging winter conditions. The proposed converter's innovative design, minimal componentry, efficiency, and validated performance through simulation, experimentation, and theory underscore its potential significance in enhancing fuel cell electric vehicle technology.

Optimal Design of a Single-Phase Bidirectional Rectifier

This article outlines the comprehensive design and control approach for a single-phase bidirectional rectifier (SPBR) used in bidirectional charging of electric vehicle batteries. The operational parameters of the inverter are determined through a thorough analysis of all switching sequences to accurately assess power losses, considering the type of switching device chosen in each case, enabling proper component sizing, and understanding converter efficiency. An exclusive electronic control circuit is examined, governing two converter operation modes: boost rectifier with power factor correction (PFC) and sine pulse inverter width modulation (SPWM) with a minimum number of adjustments made automatically. One problem that arises when addressing the design of an SPBR is determining the operating frequency. To address this issue, this study offers to conduct a comparative analysis of losses using various power devices and magnetic circuits to determine the optimal operating frequency for achieving maximum energy efficiency. To validate the design’s feasibility, a prototype with 10 kW output power was constructed, achieving a peak efficiency of approximately 97.5% in both directions, unity power factor (PF), and total harmonic distortion (THD) of less than 7% during full power operation.

New combined control strategy of on-board bidirectional battery chargers for electric vehicles

This paper aims to develop a bidirectional on-board battery charger for electric vehicles (EVs). The studied battery charger is composed of a bidirectional ac-dc converter as the first stage of conversion and a bidirectional dc-dc converter as the second stage. The first one is controlled by a predictive direct power control strategy based on a space vector modulation technique known as P-SVM-DPC, and the second is used to regulate the battery current and regulate the power direction flow by using a direct current control technique. The choice of its topology has taken into consideration the grid-to-vehicles (G2V) and vehicle-to-grid (V2G) power flow directions. During charging or discharging, the DC/DC converter acts likes a buck or boost converter. Using MATLAB/Simulink software, the performance of the battery charger is examined in various operating modes, such as fast charging and quick discharging.

In‐depth analysis of the power management strategies in electric vehicles

AbstractIn electric vehicles, the battery is a key component that calculates vehicle performance. Due to their greater efficiency and the lower cost of power, charging an electric vehicle is more affordable than purchasing gasoline or diesel for travel needs. To increase the battery's lifespan, the accuracy of the battery model for electric vehicles must be enhanced. To operate at their peak efficiency, batteries must be managed properly by a battery management system. This research illustrates the functioning of a rechargeable electric vehicle battery's charging system. There are several types of rechargeable batteries used, including lead acid, lithium‐ion, and nickel‐cadmium batteries. In recent trends, are used in the majority of cars use rechargeable lithium‐ion batteries. In general, accumulated heat, rapid utilization, and total energy throughput have an impact on the battery life of electric vehicles. In this study, 50 papers were analyzed about battery charging in an electric vehicle, which utilized different measures, as well as achievements attained by various methods. This survey reviewed the information from a different journal, along with their advantage, disadvantages, and challenges. The review lays the groundwork for future researchers to gain a deeper understanding of electric vehicles by offering a thorough interpretation of the methods currently in use.

A study of home energy management considering carbon quota

The household energy management system (HEMS) has become an important system for energy conservation and emission reduction. In this study, home energy management considering carbon quota has been established. Firstly, the household photovoltaic output model, load model of various electrical appliances, battery load model, and charging and discharging of electric vehicles (EVs) model are established. Secondly, the carbon emission and carbon quota of household appliances and EVs are considered in these models. Thirdly, the energy optimization model of minimum the household user’s total comprehensive operation cost with the minimum total electricity consumption, carbon trading cost, battery degradation cost, and carbon quota income are proposed, taking into account constraints such as the comfort of users’ energy use time. Subsequently, the improved particle swarm optimization (IPSO) algorithm is used to tackle the problem. Compared to the standard particle swarm optimization (PSO), the IPSO has significantly improved the optimization effect. By comparing the optimization results in different scenarios, the effectiveness of the strategy is verified, and the influence of different carbon trading prices on optimal energy scheduling has been analyzed. The result shows that the comprehensive consideration of carbon trading cost and total electricity cost can reduce the household carbon emissions and the total electricity cost of the household user. By increasing the carbon trading price, the user’s carbon trading income and the EV carbon quota income increase, and the overall operating cost decreases; the guidance and regulation of carbon trading price can make a valuable contribution to HEMS optimization. Compared to the original situation, the household carbon emissions are reduced by 14.58 kg, a decrease of over 21.47%, while the total comprehensive operation cost are reduced by 14.12%. Carbon quota trading can guide household users to use electricity reasonably, reducing household carbon emissions and the total cost of household electricity.

Optimization of Photovoltaic Capacity and Electric Vehicle Battery Charging at Electric Vehicle Charging Stations Using Particle Swarm Optimization

The transportation sector is one of the largest producers of carbon gas emissions. This encouraging electrification to reduce the impact of environmental damage. However, the impact of large-scale electrification causes an increase in peak load demand on the distribution network. This results in an energy crisis, economic losses, and reduces the reliability of the power system if not handled seriously. Important solutions involve optimizing electric vehicle charging stations (EVCS), accelerating the use of renewable energy for transportation and controlling electric vehicle charging. The hybrid microgrid system at EVCS can be an effective solution. This system is able to manage its own electricity needs through an energy management system with a combination of energy sources. This research discusses optimizing photovoltaic capacity and charging electric vehicle batteries at EVCS using particle swarm optimization (PSO). The aim of this research is to increase efficiency in investment and increase profits. The model is determined as an EVCS that is interconnected with photovoltaic (PV). The parameters determined include: charging load data, investment costs, power import tariff provisions and charging rates. PSO is used to size photovoltaic (PV) and battery energy storage systems (BESS). The results show that PSO can be used for optimization. This is proven by obtaining the position of the combination of PV and BESS with the best profit with a combination of PV of 494 kW and BESS of 581 kW, with an NPV of $1,652,604.89, an IRR of 46.60%, and a payback period of 2.15 years. The combination with the best PV contribution, namely PV of 3,146 kW and BESS of 1,811 kW, produces energy of 75,504 kWh per month, power import of 35,946 kWh and battery usage level of 58%. These two combinations of optimization results meet the investment acceptance requirements with an internal rate of return (IRR) of at least 9%.

Development of Wireless Charging System Using Square-Circular Coupled Coils with Different Misalignments

Now a days, inductive power transfer (IPT) has gained a lot of attention from researchers as it has ease of use and realiability for electric vehicle (EV) battery charging systems. This paper examines the increasing attention from researchers towards inductive power transfer (IPT) as a means of charging electric vehicle (EV) batteries. This interest originates from the user-friendly characteristics and notable reliability associated with IPT. The evaluation of mutual inductance (MI) holds importance within the domain of Inductive Power Transfer (IPT) systems, as it serves a critical function in enabling effective power transfer. Therefore, it is essential to perform a comprehensive analysis of the mutual inductance between the two coils that are connected through inductive coupling. This study provides an examination of mutual inductance (MI) and efficiency within the context of interoperability conditions of interconnected coils. The transmitter coil is represented as a square structure, denoted as TxS, whereas the receiving coil is represented as a circular structure, denoted as RxC. Furthermore, the application of ferrite cores and steel chassis inclosures, in combination with coils, is utilised for the objective of electric vehicle (EV) battery charging. The magnetic induction (MI) analysis is performed by the utilisation of finite element method (FEM) simulation. The finite element method (FEM) simulation outcomes of the interconnected coils with misalignments, encompassing both non-core and steel chassis configurations, are juxtaposed with the corresponding empirical observations.

Providing a Control System for Charging Electric Vehicles Using ANFIS

Frequency control, especially when incorporating distributed generation units such as wind and solar power plants, is crucial for maintaining grid stability. To address this issue, a study proposes a method for controlling the connection status of electric vehicles (EVs) to prevent frequency fluctuations. The method utilizes an adaptive neural-fuzzy inference system (ANFIS) and a whale optimization algorithm to regulate the charging or discharging of EV batteries based on frequency fluctuations. The objective is to minimize and adjust the frequency fluctuations to zero. The proposed method is evaluated using a real microgrid composed of a wind power plant, a solar power plant, a diesel generator, a large household load, an industrial load, and 711 electric vehicles. The ANFIS system serves as the primary controller, taking inputs such as electric vehicle and battery status and generating outputs that determine the charging or discharging of the electric vehicles. Several investigations are conducted to assess the effectiveness of this model, and the results obtained are compared with the normal state where electric vehicles only consume power. By implementing this method, it is expected that the connection status of electric vehicles can be optimized to help stabilize the grid and minimize frequency fluctuations caused by the integration of distributed renewable energy sources. This study highlights the importance of automatic frequency control in smart grids and offers a potential solution using ANFIS and the whale optimization algorithm.

Primary side control strategies for battery charging regulation in wireless power transfer systems for EV applications

AbstractResonant inductive‐based wireless power transfer (WPT) for battery charging has potential applications in electric vehicles (EVs). The EV battery charging process requires the regulation of both charging voltage and current. Duty ratio or frequency control is generally preferred to manage the power flow between the transmitter and receiver coils in the WPT system. In the case of WPT charging, misalignment between the coils and parameter variations are unavoidable issues that result in changes to the output power. Therefore, it is essential to control the power flow to maintain constant current (CC) and constant voltage (CV) modes during battery charging. To address these challenges, various primary‐side control techniques, such as asymmetric clamped mode (ACM), asymmetric duty cycle (ADC), and phase‐shift (PS) fixed frequency control strategies, have been proposed for WPT systems. This paper conducts a comparative analysis of these control methods, considering their output voltage ranges and their ability to maintain zero‐voltage switching (ZVS) for the entire control range. Furthermore, the paper presents a generalized design for reduced‐order small signal modelling, utilizing an extended describing function. The designed controller, based on small signal modelling, will undergo real‐time testing to evaluate its dynamic performance in the series‐series resonant converter.

Pro-environment consumer behaviour and electric vehicle adoptions: a comparative regional meta-analysis

ABSTRACT Numerous studies have explored the relationship between the environment and the adoption of electric vehicles (EVs); however, a consensus has not been reached among empirical findings. This study aims to conduct a comprehensive meta-regression analysis by synthesizing and scrutinizing data from existing literature to identify potential sources of heterogeneity among effect sizes in individual studies. To achieve this goal, we collected 429 estimates from 65 previously published studies spanning the period from 1998 to 2021. We investigate the connection between pro-environmental consumer behaviour and EV adoption at the aggregate level and compare this relationship across three distinct regions: North America, Western Europe, and emerging markets. Our research employs advanced empirical techniques, including Bayesian model averaging, frequentist model averaging, and weighted average least squares. The outcome of our analysis reveals a positive, albeit relatively weak synthesized effect of environmental factors on EV adoption. Notably, our meta-analysis findings indicate that the impact of environmental concerns on EV adoption varies significantly depending on the specific type of EV being considered. Consumers prefer versatile hybrid and plug-in hybrid electric vehicles due to fuel flexibility, especially in areas where battery electric vehicle charging infrastructure is scarce.

Optimization model of battery electric vehicle charging facility layout towards embedded system and data mining algorithm

Abstract Battery electric vehicles have been developing rapidly in recent years due to their advantages of energy saving and emission reduction. However, the development of battery electric vehicles is still in its infancy due to the relatively immature charging technology and the lack of infrastructure development. In many researches, there have been many solutions to adopt remote monitoring systems in battery electric vehicles to monitor and collect them in real time for statistical analysis and fault diagnosis. However, the current monitoring systems are carried out statistically and it is difficult to extract much valuable information from the data. In this paper, after analyzing the construction mode of battery electric vehicle charging facilities, the battery electric vehicle charging facility system platform using embedded system was analyzed. Several functions were simulated to meet the problems encountered by users in using it, studying the battery electric vehicle charging facility layout optimization model based on data mining algorithm. Focusing on the coverage problem in the siting problem, the State-of-Charge (SOC) of the battery charge state was analyzed. Taking residential areas as an example and 4 weeks as a cycle, it was found that during the first to fourth weeks, the negative power at 1:00, 20:00 and 24:00 was higher. At the same type of time point, different charging thresholds had a significant impact on the charging load demand of users. The larger the charging threshold of battery electric vehicles appeared, the earlier the peak moment and the higher the peak load. The battery state of the battery electric vehicle in the work area is 60 % at 1:00, and it reaches a relatively full state of 90 % at 8:00. In different regions, the allocation of charging thresholds for battery electric vehicles was different, and the Markov theory was derived from the analysis of the coverage problem to enable fast and effective siting of charging piles while satisfying charging convenience and high utilization of charging facilities to complement the research on the optimization model of battery electric vehicle charging facility layout.

Design and practical implementation of non‐fragile controller for non‐isolated on‐board battery charger for electric vehicle

SummaryThis paper proposes a non‐fragile control approach for a non‐isolated two‐switch buck‐boost converter used as an on‐board charger for electric vehicles. The control scheme is based on a cascaded two‐loop configuration with non‐fragile Proportional Integral controllers in the inner current and the outer voltage loop. The control approach is based on designing a control law that can stabilize the closed‐loop system subject to uncertainties in the controller parameters while satisfying the H∞ norm bound constraint on disturbance attenuation. The proposed control technique helps to deal with inevitable uncertainties during the implementation of any control scheme. The proposed control technique is compared with the conventional approach and validated through experimental results. The experimental results are obtained from a 1‐kW laboratory prototype of a TSBB converter with an output voltage range of 150–400 V. The results demonstrate that the proposed approach effectively achieves good robustness against controller parameter variations and disturbances under various operating conditions.

Intelligent Control of DC Microgrid Involving Multiple Renewables for Fast Charging Control of Electric Vehicles

Electric vehicles (EVs) are now considered to be a cutting-edge form of transportation. However, the electrical grid infrastructure is not sufficiently developed to meet the rising charging demand for electric vehicles. Therefore, it is impossible to rely entirely on grid-generated electricity. This article discusses a control approach for extremely rapid charging of EV batteries driven by a hybrid DC microgrid, consisting of isolated, Photovoltaic (PV), Wind Turbine, Fuel Cell and Energy Storage Systems. An adaptive sliding mode controller is designed to provide the appropriate power for charging and discharging EVs and energy storage units (ESUs) under a variety of power generation and demand scenarios. The proposed adaptive sliding mode controller operates in a decentralized manner to maintain power distribution amongst the microgrid DC-link EVs and the ESUs. In order to validate the effectiveness of the sliding mode controller in battery charging applications, a comprehensive comparison between fuzzy logic and adaptive sliding mode control is conducted. The proposed controller operates exceptionally well in the presence of unpredictable operating point variations such as rapid changes in EV charging load, fluctuations in solar irradiation, unexpected wind conditions, and so on. Finally, the proposed controller is verified by both simulation and experimental studies.

Managing BEV Charge to Obtain a Positive Impact on a National Power System

This paper’s research question is to evaluate the potential impact of large numbers of battery electric vehicles (BEVs) on the future electric grid, and whether the flexibility of BEV charging can induce enough system benefits to remunerate BEV users for the change in their recharging pattern. The considered scenario refers to the Italian situation and what might occur through the year 2040, where a share of BEV stock of 40% can be foreseen, as well as significant increases in PV and wind generation. Although this study is focused on Italy, its results are applicable, with minor differences, to several EU countries. This paper first shows that the future impact of increasing penetration of BEVs appears to be compatible with the expected growth of generation from renewable energy sources (RES) and the corresponding reduction in fossil fuel-based generation. It also gives an estimate of the CO2 emission reduction resulting from these changes, considering an unmanaged BEV charge profile and two different managed profiles that shift the car’s charging period to hours of the day when they have no negative impact on the grid and maximize the utilization of RES. Finally, it shows an evaluation of the economic benefits of displacing private car charging ranging from 4 to 10 cEUR/kWh, which could be used as tariff incentives to stimulate this displacing in recharging time.