Electric Vehicle Battery Charging Research Articles

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.

Impact of Plug-In Electric Vehicle Battery Charging on a Distribution System Based on Real-Time Digital Simulator

Although the electric vehicles (EVs) have many economic and environmental advantages as compared to the conventional cars, the increased adoption of these vehicles will bring up new concerns for the power distribution grid in terms of introducing new peaks into the system or causing voltage quality problems. This study investigates the impact of the electric vehicles battery charging on the distribution system in terms of voltage unbalance at various locations, transformers overloading, and shifting the peak of the system. In this research, a 12.47 kV real distribution network has been modelled using real time digital simulator, using real data from a power distributor. The study presents four different scenarios of uncoordinated EVs integration for two different charging times (evening and night) and two different charging rates (level I and level II) at different penetration levels ranging from 10% to 100%. Voltage unbalance at different locations is determined and transformer overloading is analysed. The influence of EVs charging on the daily load curve is shown.

Commercial Electric Vehicle Battery Degradation modelling and charging assessing using a real driving cycle

This paper proposes a model to study the degradation of li-ion NMC batteries of commercial electric vehicles. The model takes into account operation variables such as operating C-Rate, Depth of Discharge (DoD), Number of Cycles and Temperature using a 4-D Piecewise Cubic Hermite Interpolating Polynomial (PCHIP). Simulations have been done considering the Worldwide Harmonised Light Vehicles Test Procedure 3 (WLTP3) standard cycle. The model has been implemented in MATLAB. In addition, recommendations on charging procedures are given in order to reduce the degradation of batteries.

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.

High Efficiency Dual-Active-Bridge Converter with Triple-Phase-Shift Control for Battery Charger of Electric Vehicles

An optimal modulation scheme with triple-phase-shift (TPS) control could increase the efficiency in the entire load range for a dual-active-bridge (DAB) converter under wide output voltage range conditions. Therefore, this study proposes a convergent approach to TPS mode selection, coupled with an optimal modulation scheme, ensuring the circuit’s efficiency over the entire range in the realm of a high-power and high-efficiency battery charger for electric vehicles. The convergent approach to TPS mode selection also reduces the numerous cases for small-signal analysis through general average modeling. After verifying the small-signal models under various voltage transfer ratios and load conditions to verify the stability, a converter prototype with a rated power of 15 kW is built and tested. Thus, a peak efficiency of 97.7% can be achieved.

Improving the Efficiency of an Isolated Bidirectional Dual Active Bridge DC–DC Converter Using Variable Frequency

This article presents a control method for an isolated bidirectional dual active bridge DC–DC converter (IBDC) where single phase shift (SPS) and variable frequency (VF) modulations are combined simultaneously with the purpose of improving the efficiency of the converter applied to the bidirectional charging of electric vehicles batteries. A complete power characterization of the dual active bridge (DAB) converter is carried out. The combined control achieves lossless turn-on and turn-off on the primary bridge for a wide range of output powers. A comparative study of power losses between the traditional SPS control and the combined method proposed here was also carried out. A prototype with an output power of 10 kW was built based on Silicon Carbide (SiC) Metal Oxide Semiconductor Field-Effect Transistors (MOSFET), with which a peak efficiency close to 99% was obtained, thus verifying the viability of the method.

Ultra-thin vapour chamber based heat dissipation technology for lithium-ion battery

A powerful thermal management scheme is the key to realizing the extremely fast charging of battery electric vehicles. In this scheme, a water-cooled plate is set at the bottom of the battery modules, which has a remarkable heat dissipation ability but increases the temperature difference between the top and bottom of the battery. This temperature difference problem is more obvious during the high-rate charge/discharge process; in particular, in the area of battery tabs, which are usually located far from the cooling plate, heat accumulates, risking thermal runaway. To address this problem, a thermal management method based on an ultrathin vapour chamber (UTVC) is proposed in this paper to improve temperature uniformity and reduce the highest temperature of batteries. According to the number of applied UTVCs, a unilateral UTVC (U-UTVC) and a bilateral UTVC (B-UTVC) cooling approach were presented, the effectiveness of which was verified by a set of thermal performance experiments. The test data show that the temperature differences in the batteries under the U-UTVC and B-UTVC schemes are 39.77% and 73.54% lower than those under the traditional method at a 25 °C ambient temperature and a 1.39C charge rate, respectively. The temperature difference on the battery surface with the B-UTVC scheme is approximately 3 °C at 25 °C and 40 °C ambient temperature.

Using Optimized Long Short-Term Memory For Time-Series Forecasting of Electric Vehicles Battery Charging

The last decade has seen a significant rise in the adoption and development of Electric Vehicles (EVs), driven by environmental concerns, technological advancements, and governmental support. Batteries, central to EVs, have witnessed groundbreaking innovations in terms of energy density, charging speeds, and longevity. Expanding charging infrastructure and the automotive industry's investment in EV research have made them more mainstream. Effective Battery Management (BM), which includes monitoring essential parameters and thermal management, is critical for the longevity and reliability of EVs. Accurate charge prediction, in particular, aids in trip planning, reduces range anxiety and facilitates cost-effective charging coordinated with dynamic electricity pricing. Traditional models like linear regression and Autor-Rgressive Integrated Moving Average (ARIMA) have been standard for EV battery charge prediction. However, these often struggle with the dynamic nature of EV charging data. Even models like the vanilla Long Short-Term Memory (LSTM), which are adept at recognizing long-term patterns, require meticulous hyperparameter tuning. This work introduces the DWT-DE-LSTM model, which utilizes the Discrete Wavelet Transform (DWT) to dissect battery charging data at different resolutions and a Differential Evolution (DE) strategy for model optimization. Tests using the Panasonic 18650PF Li-ion Battery Dataset revealed the superior efficacy of the DWT-DE-LSTM model, emphasizing its suitability for real-world battery charge prediction.

Energy management of grid connected PV with efficient inverter based wireless electric vehicle battery charger: A hybrid CSA-QNN technique

A Hybrid CSA-QNN approach is proposed in this manuscript for grid-connected PV with an efficient inverter-based wireless electric vehicle (EV) battery charger. The proposed hybrid method combines the performance of both the circle search algorithm (CSA) and quantum neural networks (QNN), commonly named the CSA-QNN technique. The Circle Search Algorithm helps find the best charging spot by creating a virtual circle, while the Quantum Neural Network optimizes the overall power flow and charging efficiency. Together, these technologies contribute to making wireless charging for EVs more efficient and convenient. The major goal of the manuscript is the design of a wireless EV battery charger with PV integration. Wireless EV charging systems (WEVCS) may be a feasible alternative technology for charging EVs without a plug-in problem. The CSA-QNN method is performed in the MATLAB platform and it is compared to different existing approaches. The CSA-QNN method shows better results than the existing approaches like the Salp Swarm Algorithm (SSA), Wild horse optimizer (WHO), and Particle Swarm optimization (PSO).

Digital Sliding Mode Control of a 3-Phase AC-DC rectifier for Ultra-Fast Charging of EV Battery

This paper describes a digital sliding mode control (SMC) technique applied to a three-phase four-wire rectifier operating at a fixed frequency for ultra-fast charging of electric vehicle (EV) battery. The control algorithm employs three decoupled sliding mode controllers to achieve loss-free resistor (LFR) behavior in each phase for power factor correction (PFC). The design of the sliding mode controller is twofold. The first one is to guarantee convergence of the sliding variable to zero. The equivalent control and the discrete-time dynamic model of the rectifier are obtained by imposing sliding-mode regime in discrete-time. The second one is to stabilize the inner-loop under the obtained control law. Theoretically, the resulting inner-loop is stable with a deadbeat behavior in the inner current loop. The results are validated by numerical simulations using on a 350 kW AC-DC rectifier for EV battery ultra-fast charging applications. The numerical simulation results performed on the switched model implemented in PSIM© software are in close agreement with the theoretical analysis.

Design of Single-Stage Light Electric Vehicles Battery Charger based on Isolated Bridgeless Modified SEPIC Converter with Reduced Switch Stress

Design of Single-Stage Light Electric Vehicles Battery Charger based on Isolated Bridgeless Modified SEPIC Converter with Reduced Switch Stress

Optimizing DC Microgrid Systems for Efficient Electric Vehicle Battery Charging in Ain El Ibel, Algeria

Optimizing DC Microgrid Systems for Efficient Electric Vehicle Battery Charging in Ain El Ibel, Algeria

Bi-Directional Power Flow Rapid Charging System Using a Carrier Wave Modulated Self-Commutated Voltage Source Converter for Electric Vehicle

This paper examines a high-performance battery charger for electric vehicles (EVs). Active power line filtering capabilities in the battery charger can be used to develop a fast-charging infrastructure that is advantageous for EV users as well as power distributors. This article discusses battery charger modeling and design difficulties in depth using a carrier wave modulated self-commutated voltage source converter architecture. A full study of the controller's features is also provided using model-based controller synthesis. The battery charger's active filtering performance is emphasized. In active filter applications, the model-based control system's intrinsic phase deviation and sensitivity to system factors decrease the active filter's performance, which is disclosed in this study. Based on numerous integrators in diverse reference frames, we provide an improved control structure for active filters to offset the model-based controller's degradation. The suggested controller demonstrates minimal sensitivity to system factors and fully compensates for the intrinsic phase deviation, as shown both theoretically and empirically. The suggested active filter controller has outstanding steady-state conditioning performance.

Recent Advancement of Electrical Vehicle Battery Charger

Recent Advancement of Electrical Vehicle Battery Charger

Modeling of a Hybrid Controller for Electric Vehicle Battery Charging Using Photovoltaic Panels

Modeling of a Hybrid Controller for Electric Vehicle Battery Charging Using Photovoltaic Panels