Search Results (44)

Driven by the rapid advancement of Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) technologies, improving power quality and system stability during charging and discharging has become a research focus. To address this, this paper proposes a Model Predictive Control (MPC) strategy for Active Neutral-Point Voltage Balancing (ANPVB) in a single-phase three-level converter used in on-board V2G chargers. Traditional converters rely on passive balancing using redundant vectors, which cannot ensure neutral-point (NP) voltage stability under sudden load changes or frequent power fluctuations. To solve this issue, an auxiliary leg is introduced into the converter topology to actively regulate the NP voltage. The proposed method avoids complex algorithm design and weighting factor tuning, simplifying control implementation while improving voltage balancing and dynamic response. The results show that the proposed Model Predictive Current Control-based ANPVB (MPCC-ANPVB) and Model Predictive Direct Power Control-based ANPVB (MPDPC-ANPVB) strategies maintain the NP voltage within ±0.7 V, achieve accurate power tracking within 50 ms, and reduce the total harmonic distortion of current (THDi) to below 1.89%. The proposed strategies are tested in both V2G and G2V modes, confirming improved power quality, better voltage balance, and enhanced dynamic response. Full article

A battery charger for solid-state lithium battery packs was developed and implemented. The power stage used a phase-shifted full-bridge converter integrated with a current-doubler rectifier and synchronous rectification. Dual voltage and current control loops were employed to enable constant-voltage and constant-current charging modes. To improve the lifespan of the output filter capacitor, the current-doubler rectifier was adopted to effectively reduce output current ripple. During the initial start-up phase, as the charger transitions from constant-voltage to constant-current output mode, the use of proportional–integral control in the voltage and current loop error amplifiers may cause current overshoot during the step-rising phase, primarily due to the integral action. Therefore, this study incorporated fuzzy control, proportional control, and proportional–integral control strategies into the current-loop error amplifier. This approach effectively reduced the current overshoot during the step-rising phase, preventing the charger from mistakenly triggering the overcurrent protection mode. The analysis and design considerations of the proposed circuit topology and control loop are presented. Experimental results agree with theoretical predictions, thereby confirming the validity of the proposed approach. Full article

The rapid growth of electric vehicle (EV) adoption necessitates advanced energy management strategies to ensure sustainable, reliable, and efficient operation of charging infrastructure. This study proposes a hybrid AI-based framework for optimizing residential EV charging systems through the integration of Reinforcement Learning (RL), Linear Programming (LP), and real-time grid-aware scheduling. The system architecture includes smart wall-mounted chargers, a 120 kWp rooftop solar photovoltaic (PV) array, and a 60 kWh lithium-ion battery energy storage system (BESS), simulated under realistic load conditions for 800 residential units and 50 charging points rated at 7.4 kW each. Simulation results, validated through SCADA-based performance monitoring using MATLAB/Simulink and OpenDSS, reveal substantial technical improvements: a 31.5% reduction in peak transformer load, voltage deviation minimized from ±5.8% to ±2.3%, and solar utilization increased from 48% to 66%. The AI framework dynamically predicts user demand using a non-homogeneous Poisson process and optimizes charging schedules based on a cost-voltage-user satisfaction reward function. The study underscores the critical role of intelligent optimization in improving grid reliability, minimizing operational costs, and enhancing renewable energy self-consumption. The proposed system demonstrates scalability, resilience, and cost-effectiveness, offering a practical solution for next-generation urban EV charging networks. Full article

The increasing adoption of intermittent renewable energy sources and electric vehicles in households necessitates effective energy management systems (EMS) in the residential sector. This study quantifies the economic benefits of using a state-of-the-art EMS for optimally controlling a grid-connected smart home, which includes PV panels, a battery, and an EV charging station with either monodirectional or bidirectional charging modes. The EMS uses a two-layer approach: the first layer handles strategic decisions with day-ahead forecasts and solving a mixed-integer linear program (MILP) model; the second layer manages the real-time control decisions based on a heuristic strategy. Tested on 396 real-world case studies (based on measured data) with varying user types and energy systems (different PV plant sizes, with or without BESS, and different EV charging modes), different EV models, and weekly commutes, the results demonstrate the EMS’s cost-effectiveness compared to current non-predictive heuristic strategies. Annual cost savings exceed 20% in all cases and reach up to 900 €/year for configurations with large (6 kW) PV plants. Additionally, while installing a battery is not economically advantageous, bidirectional EV chargers yield 10–15% additional savings compared to monodirectional chargers, increasing with more weekly remote working days. Full article

Problem: Remanufacturers want to increase consumer interest in refurbished products, which motivates the need to understand which product features are important to buyers of refurbished products such as mobile phones. Research Questions: This study addresses two questions. First, which product features are most important for buyers of refurbished iPhones? Second, how do those preferences differ from the preferences of buyers of new iPhones? Methods: Online reviews of iPhones are obtained and converted into a document–term matrix. Using this text model, three subsets of features are identified using statistical analysis of frequency of mention: most frequent, average, and least frequent. A logistic regression (LR) model is then used to identify which features are most predictive of whether a review is for a new or refurbished phone. Results: Buyers of refurbished phones mention battery health, screen/display, shell condition, and brand significantly more often than other features. Directly contrasting reviews of refurbished versus new phones shows that shell condition, brand, speaker, and charger are found to be the most predictive product features indicated in reviews for refurbished phones. Of those, the shell condition is significantly more predictive than the others. Implications: The results identify product features that remanufacturers of iPhones can emphasize to increase customer demand. Full article

The article presents the development of a Rules-Based Energy Management System for a nanogrid that serves an electric vehicle charging station. This nanogrid is composed of photovoltaic generation, a wind turbine, a battery energy storage system, and a fast electric vehicle charger. The objective is to prioritize the use of renewable energy sources, reducing costs and promoting energy efficiency. The methodology includes forecasting models based on an Artificial Neural Network for photovoltaic generation, a parametric estimation for wind generation, and a Monte Carlo simulation to predict the energy consumption of electric vehicles. The developed algorithm makes decisions every 15 min, considering variables such as energy tariff, battery state of charge, renewable generation forecast, and energy consumption forecast. The results showed that the system adequately balances energy generation, consumption, and storage, even under forecasting uncertainties. The use of the Monte Carlo simulation was crucial for evaluating the financial impacts of forecast errors, enabling robust decision-making. This energy management system proved to be effective and sustainable for nanogrids dedicated to electric vehicle charging, with the potential to reduce operational costs and increase energy reliability and the use of renewable energy sources. Full article

Electric-drive-reconstructed onboard chargers (EDROCs), also known as electric-drive-reconstructed power management systems, are a promising alternative to conventional onboard chargers due to their characteristics of low cost and high power density. The model predictive control offers a fast dynamic response, simple implementation, and the ability to control multiple targets simultaneously. In this paper, a two-stage multi-vector model predictive current control (MPCC) of a six-phase EDROC for solar-powered electric vehicles (EVs) is proposed. Firstly, the topology for the EDROC incorporating a six-phase symmetrical permanent magnet synchronous machine (PMSM) is introduced, and the operation principles of the DC charge mode, the drive mode, and, especially, the in-motion charge mode are analyzed in detail. After that, a two-stage multi-vector MPCC method is proposed by using the multi-vector MPC technique and designing a two-stage MPC structure to eliminate the regulation of the weighting factor of the MPC. Finally, the effectiveness of the proposed method is verified on a self-designed 2 kW EDROC platform. Full article

This study focuses on the transformation of Jaguar XJ40 vehicles to electric power, with the main equipment being a permanent-magnet synchronous motor (PMSM), lithium iron phosphate (LFP) batteries, an on-board charger (OBC) system, and a battery management system (BMS). The process involves integrating the PMSM with the vehicle’s existing transmission system. This research compares the driving range of battery electric vehicles (BEVs) using different testing methods under the same conditions: simulation, dynamometer (dino), and actual on-road testing. Based on Raminthra’s public roads (RITA drive cycle), one drive cycle covers 7.64 km in 11.25 min. The simulation test by MATLAB/SIMULINK R2016a predicts a driving distance of up to 282.14 km. The dino test, using a chassis dynamometer to simulate driving conditions while the vehicle remains stationary, indicates a driving distance of 264.68 km. In contrast, actual on-road tests show a driving distance of 259.09 km, accounting for real-world driving conditions, including variations in speed, road types, weather, and traffic. The motor achieves 95% efficiency at 2400 rpm and 420 Nm torque. The simulated distance differs from the actual road distance by approximately 8.17%, suggesting reasonable accuracy of the model. Full article

As the global shift toward sustainable transportation gains momentum, the integration of electric vehicles (EVs) becomes imperative, necessitating a robust and environmentally friendly charging infrastructure. Leveraging the abundant solar potential in the region, this study examines the technical, economic, and environmental feasibility of deploying photovoltaic electric vehicle charging stations (PV-EVCSs) in Hail City, Saudi Arabia, as a case study. This study examines factors such as the energy demand, grid integration, and user accessibility, aiming to address the challenges and opportunities presented by the urban fabric. The proposed solar charging station network seeks to catalyze a paradigm shift toward a cleaner and more sustainable transportation ecosystem, embodying a forward-thinking approach to meeting the evolving needs of urban mobility in the 21st century. The analysis encompasses many scenarios, encompassing a range of car battery sizes, charger powers, and car slots per station. Zone 4 is identified as the most crucial area, where seven charging stations are needed to fulfill the expected demand in the absence of any private charging alternatives. The economic evaluation of the 1047.35 kWp PV system reveals an estimated conventional payback time of 11.69 years, accompanied by a return on assets of 10.17%. The system generates accumulated cash flows amounting to SR 7,169,294.62 over 30 years, while the estimated operational and maintenance expenses are predicted to be SR 50,000 per year. The overall investment cost for the solar PV and EV charging stations is SR 4,487,982. This cost is offset by the yearly electricity savings from solar and grid sources, which can reach up to SR 396,465.26 by year 30. This work presents a detailed plan for the future of sustainable transport. It combines technical, environmental, and economic aspects to promote a cleaner and more sustainable urban mobility system. Full article

This paper investigates the competition between two charge point operators at the same site for future battery electric long-haul trucks. The charge point operators are located along one of the busiest highways in Sweden. The investigation is carried out using an agent-based model where trucks select charge point operators based on charging prices and the length of any queues, while charge point operators adjust their prices and number of chargers to improve their profitability. The study aims to predict conditions for trucks and charge point operators in a future public fast-charging market. Our findings indicate the potential for a well-functioning future public fast-charging market with small queuing problems, high utilisation, and reasonable prices for public fast charging. Assuming a price for electricity of EUR 0.08/kWh and a minimum profit margin of EUR 0.001/kWh for charge point operators, the findings indicate that the price level outside rush hours will be low, approximately EUR 0.1/kWh. The prices during rush hours will likely be much higher, but it is harder to predict the value due to uncertainties of how charge point operators will act in the future market. Still, from the model result, the price during rush hours is suggested to be just above EUR 0.5/kWh, with an average charging price of around EUR 0.15/kWh. It also seems likely that it is profitable for charge point operators to build enough chargers so that charging queues are short. Full article

This paper presents a new electric drive-reconfigured on-board charger and initial electromagnetic torque suppression method. This proposed reconfigured on-board charger does not need many components added to the original electric drive system: only a connector is needed, which is easy to add. Specifically, the inverter for propulsion is reconfigured as a buck chopper and a conduction path to match the reconfigured windings. Two of the machine phase windings serve as inductors, while the third phase winding is reutilized as a common-mode inductor. In addition, the initial charging torque is generated at the outset of the charging process, which may cause an instant shock or even rotational movement. In order to prevent vehicle movement, the reason for the charging torque and suppression method were analyzed. Further, predictive control of the model based on mutual inductance analysis was adopted, where the charging torque was directly used as a control object in the cost function. Finally, experimental performances were applied to verify the proposed reconfigured on-board charger under constant current and constant voltage charging. Full article

This paper investigates optimizing the power exchange between electric vehicles (EVs) and the grid, with a specific focus on the DC-DC converters utilized in vehicle-to-grid (V2G) systems. It specifically explores using model predictive control (MPC) in synchronous boost converters to enhance efficiency and performance. Through experiments and simulations, this paper shows that replacing diodes with SIC MOSFETs in boost converters significantly improves efficiency, particularly in synchronous mode, by minimizing the deadtime of SIC MOSFETs during switching. Additionally, this study evaluates MPC’s effectiveness in controlling boost converters, highlighting its advantages over traditional control methods. Real-world validations further validate the robustness and applicability of MPC in V2G systems. This study utilizes TMS320F28379D, one of Texas Instruments’ leading digital signal processors, enabling the implementation of MPC with a high PWM frequency of up to 200 MHz. This processor features dual 32-bit CPUs and a 16-bit ADC, allowing for high-resolution readings from sensors. Leveraging digital signal processing technologies and advanced electronic circuits, this study advances the development of high-performance boost converters, achieving power outputs of up to 48 watts and output voltages of 24 volts. Electronic circuits (PCB boards) have been devised, implemented, and evaluated to showcase their significance in advancing efficient V2G integration. Full article

The current study investigates public intentions to use an innovative, off-grid renewably powered EV charging technology called FEVER (Future Electric Vehicle Energy networks supporting Renewables). We report the findings of a questionnaire-based survey (QBS) conducted at a zoo in the south of England, exploring the prospect of demonstrating FEVER. The QBS was designed around a context-specific technology acceptance model (TAM) and administered both face-to-face (n = 63) and online (n = 158) from April to May 2023. The results indicate that most participants were willing to pay to use FEVER, particularly where revenue would benefit the zoo. The participants agreed they intended to use the chargers, and that they would be useful and easy to use. The participants agreed that there would be normative pressure to use the chargers, but that their use would be enjoyable. Of greatest concern was that the chargers would be blocked by others. The participants were ambivalent about concerns over charging duration and charge sufficiency. Structural equation modelling confirmed that the context-specific TAM explained 58% of people’s use intentions. The core relationships of the TAM were confirmed, with ‘perceived usefulness’ additionally predicted by subjective norms and ‘perceived ease of use’ additionally predicted by anticipated enjoyment. Of the other variables, only concern that the chargers would be blocked was retained as a marginal predictor of ‘perceived ease of use’. The implications of these findings for the co-design and demonstration of FEVER are discussed. Full article

Reducing greenhouse emissions can be done via the electrification of the transport industry. However, there are challenges related to the electrification such as the lifetime of vehicle batteries as well as limitations on the charging possibilities. To cope with some of these challenges, a charge scheduling method for fleets of electric vehicles is presented. Such a method assigns the charging moments (i.e., schedules) of fleets that have more vehicles than chargers. While doing the assignation, the method also maximizes the total Remaining Useful Life (RUL) of all the vehicle batteries. The method consists of two optimization algorithms. The first optimization algorithm determines charging profiles (i.e., charging current vs time) for individual vehicles. The second algorithm finds the charging schedule (i.e., the order in which vehicles are connected to a charger) that maximizes the RUL in the batteries of the entire fleet. To reduce the computational effort of predicting the battery RUL, the method uses a Machine Learning (ML) model. Such a model predicts the RUL of an individual battery while taking into account common stress factors and fabrication-related differences per battery. Simulation results show that charging a single vehicle as late as possible maximizes the RUL of that single vehicle, due to the lower battery degradation. Simulations also show that the ML model accurately predicts the RUL, while taking into account fabrication-related variability in the battery. Additionally, it was shown that this method schedules the charging moments of a fleet, leading to an increased total RUL of all the batteries in the vehicle fleet. Full article

Residential chargers are going to become the standard in the near future. Their operational cycles are closely tied to users’ daily routines, and the power consumption fluctuates between zero and peak levels. These types of installations are particularly challenging for the grid, especially concerning the balance of electricity production and consumption. Using battery storage in conjunction with renewable sources (e.g., photovoltaic power plants) represents a flexible solution for grid stabilization, but it is also associated with additional costs. Nowadays, grid authorities penalize a destabilization of the grid resulting from an increased imbalance between electricity generation and consumption and reward contributions to the system balance. Hence, there is a motivation for larger prosumers to make use of this mechanism to reduce their operational costs by better aligning their energy needs with the grid. This study explores the possibility of utilizing battery storage when it is not needed to fulfil its primary function of supporting charging electric vehicles, to generate some additional profit from providing a counter-imbalance. To test this idea, we develop an optimization model that maximizes the economic profit, considering system imbalance penalties/rewards, photovoltaic production, electric vehicle charging demand, and battery storage utilization. By means of computer simulation, we assess the overall operational costs while varying key installation parameters such as battery capacity and power, the installed power of photovoltaic panels and the prediction model’s accuracy. We identify conditions when counter-imbalance has proven to be a viable way to reduce installation costs. These conditions include temporal distribution of charging demand, electricity prices and photovoltaic production. For the morning time window, with a suitable setting of the installation parameters, the cost reduction reaches up to 14% compared to the situation without counter-imbalance. Full article