Smart Charging Of Electric Vehicles Research Articles

Artificial Intelligence Optimization for User Prediction and Efficient Energy Distribution in Electric Vehicle Smart Charging Systems

This paper presents an advanced AI-based optimization framework for Electric Vehicle (EV) smart charging systems, focusing on efficient energy distribution to meet dynamic user demand. The study leverages machine learning models such as Random Forest, Support Vector Regression (SVR), Gradient Boosting Regressor, XGBoost, LightGBM, and Long Short-Term Memory (LSTM) to forecast user demand and optimize energy allocation. Among the models, XGBoost demonstrated superior predictive performance, achieving the lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), making it the most effective for real-time user demand prediction in smart charging scenarios. The framework introduces proportional and priority-based allocation strategies to distribute available energy effectively, with a focus on minimizing energy shortfalls and balancing supply with user demand. Results from the XGBoost model reduced prediction error by 15% compared to other models, significantly improving the station’s ability to meet user demand efficiently. The proposed AI framework enhances charging station operations, supports grid stability, and promotes sustainability in the context of increasing EV adoption.

A Hybrid Technique for Bidirectional Smart Charging of EVs Using BLDC Motor and Bidirectional Converter

This paper introduces a novel hybrid approach for bidirectional smart charging of electric vehicles (EVs) powered by brushless DC (BLDC) motors. The proposed technique, named Atomic Orbital Search-Rat Swarm Optimizer (AOS-RSO), leverages the Atomic Orbital Search (AOS) for optimizing energy storage management and the Rat Swarm Optimizer (RSO) for enhancing motor control. The modular hybrid energy storage system (HESS) is designed with five modules, each consisting of a bidirectional DC-DC converter connecting a battery and super capacitor module. A multi-level cascaded converter (MCC) connects the HESS to the BLDC motor inverter of the EV drive, controlling the DC bus voltage. The AOS-RSO approach efficiently manages the state-of-charge (SOC) across the modules, achieving a balanced SOC of 98%. Implemented in MATLAB, the method is evaluated in both open and closed-loop systems and compared with existing techniques. Results show the proposed method achieves 96% efficiency in simulation and 94% in experiments, with a CPU time of 2.84 s. This approach significantly advances smart charging solutions for EVs, offering improved energy management and operational efficiency.

EV Smart-Charging Strategy for Power Management in Distribution Grid with High Penetration of Distributed Generation

Accelerated environmental impacts are a growing concern in the modern world. Electric mobility and the transition to a cleaner energy matrix have become increasingly discussed topics. In this context, this work presents a framework for controlling an electric vehicle (EV)-charging station integrated into a microgrid application as a basis for creating the infrastructure integrated into a smart grid concept. Considering the electrification of the transportation sector future perspectives, a brief review is conducted on the impacts of EV fleet growth in different countries and how smart-charging technologies are identified as solutions for mitigating the negative effects of energy and power consumption associated with EV-charging stations. An analysis of the technical characteristics and the tools that enable the deployment of a fleet-charging operator are examined, specifically focusing on the communication protocol for EVs, such as the OCPP (Open Charge Point Protocol) parameterization/configuration. A new EV-charging station control method is proposed to manage the impacts of distributed solar photovoltaic generation and mitigate the effects of the duck curve. Finally, an integration architecture via IEC 61850 for these elements is proposed, in a practical implementation for variable power control, considering different strategies to deal with distributed generation impact using EV-fleet-charging power demand dynamic management.

Artificial Intelligence-Based Electric Vehicle Smart Charging System in Malaysia

The worldwide transition to electric vehicles (EVs) is gaining momentum, propelled by the imperative to reduce carbon emissions and foster sustainable transportation. In Malaysia, the government is facilitating this transformation through targeted initiatives aimed at promoting the use of electric vehicles (EVs) and developing the required infrastructure. This paper investigates the crucial role of artificial intelligence (AI) in developing intelligent electric vehicle (EV) charging infrastructure, specifically focusing on the context of Malaysia. The paper examines the current electric vehicle (EV) charging infrastructure in Malaysia, highlights advancements led by artificial intelligence (AI), and references both local and international case studies. Fluctuations in the Total Industry Volume (TIV) and Total Industry Production (TIP) reflect changes in market demand and production capabilities, with notable peaks in March 2023 and March 2024. The research reveals that AI technologies, such as machine learning and predictive analytics, can enhance charging efficiency, improve user experience, and support grid stability. A mathematical model for an AI-based smart charging system was developed, and the implemented system achieved 30% energy savings and a 20.38% reduction in costs compared to traditional methods. These findings underscore the system’s energy and cost efficiency. In addition, we outline the potential advantages and challenges associated with incorporating artificial intelligence (AI) into Malaysia’s electric vehicle (EV) charging infrastructure. Furthermore, we offer recommendations for researchers, industry stakeholders, and regulators. Malaysia can enhance the uptake of electric vehicles and make a positive impact on the environment by leveraging artificial intelligence (AI) to enhance its electric vehicle charging system (EVCS).

Power system benefits of simultaneous domestic transport and heating demand flexibility in Great Britain’s energy transition

The increasing share of variable renewables in power generation leads to a shortage of affordable and carbon neutral options for grid balancing. This research assesses the potential of demand flexibility in Great Britain to fill this gap using a novel linear optimisation model PyPSA-FES, designed to simulate optimistic and pessimistic transition pathways in National Grid ESO Future Energy Scenarios. PyPSA-FES models the future power system in Great Britain at high spatiotemporal resolution and integrates demand flexibility from both smart charging electric vehicles and thermal storage-coupled heat pumps. The model then optimises the trade-off between reinforcing the grid to align charging and heating profiles with renewable generation versus expanding dispatchable generation capacity. The results show that from 2030, under optimistic transition assumptions, domestic demand flexibility can enable an additional 20–30 TWh of renewable generation annually and reduce dispatchable generation and distribution network capacity by approximately 20 GW each, resulting in a total cost reduction of around £5bn yearly. However, our experiments suggest that half of the total system cost reduction is already achieved by only 25% of electric vehicles alone. Further, the findings indicate that once smart electric vehicle charging reaches this 25% penetration rate in households, minimal benefits are observed for implementing smart 12-hour thermal storages for heating flexibility at the national level. Additionally, smart heating benefits decrease by 90% across all metrics when only pre-heating (without thermal storages) is considered. Spatially, demand flexibility is often considered to alleviate the need for north–south transmission grid expansion. While neither confirmed nor opposed here, the results show a more nuanced dynamic where generation capacities are moved closer to demand centres, enhancing connectivity within UK sub-regions through around 1000 GWkm of additional transmission capacity.

Integration of smart charging of large-scale electric vehicles into generation and storage expansion planning: a case study in south china

This paper studies how to integrate the smart charging of large-scale electric vehicles (EVs) into the generation and storage expansion planning (GSEP), while analyzing the impact of smart charging on the GSEP of a real power system in south China. For this purpose, a random simulation-based method is first developed to provide the tractable formulations of the adjustable charging load and reserve provision from EVs. This method avoids the unrealistic assumption that EVs drive and charge every day, which often exists in prior relevant approaches. Based on the random simulation, this paper proposes a novel GSEP optimization model which incorporates the weekly adjustable charging load of EVs. In the proposed model, the total charging load of EVs can be co-optimized with the investment and operational decisions of various generation and storage units. This GSEP model is applied to a provincial power system in south China. The numerical results show that the implementation of smart charging can significantly alter the decisions of GSEP. As the participation rate of smart charging improves from 0% to 90%, there is an additional 1,800 MW installation in wind and solar power, while the need to build new batteries is noticeably reduced; also, depending on the level of EV uptake, the annualized total system cost decreases by 5.11%–7.57%, and the curtailment of wind and solar power is reduced by 10.34%–19.64%. Besides, numerical tests reveal that the traditional assumption that EVs drive and charge every day can mislead the evaluation of adjustable charging load and overestimate the daily charging power peak by averagely 24.72%.

Charge Manipulation Attacks Against Smart Electric Vehicle Charging Stations and Deep Learning-Based Detection Mechanisms

Charge Manipulation Attacks Against Smart Electric Vehicle Charging Stations and Deep Learning-Based Detection Mechanisms

Harmonics mitigation in distribution networks comprising smart online electric vehicles chargers based on equal sharing algorithm

This study focuses on analyzing the distribution network's performance due to the scheduled integration of electric vehicles (EVs). In this context, an online equal sharing scheduled algorithm is utilized to monitor and manage the charging process of EVs inside the charging stations (CSs). By this way, the planned charging demand may aid in minimizing the power stresses at the point of common coupling (PCC). Afterwards, the penetration of CSs into practical distribution networks is investigated and resolved from power quality perspectives. Meanwhile, voltage and current harmonic distortions of 3.3 % and 8.3 %, respectively resulted at the PCC due to installing these harmonic injection sources. It is worth mentioning that diverse topologies of passive filters are interrogated in this research for harmonics mitigation purposes. Single tuned passive filters demonstrate their superiority over other architectures in harmonics suppression with the simplest and most cost-effective solution. Additionally, the overloading stresses over the installed package substation of about 7 % are also alleviated permitting the CS to charge with full capacity. Finally, this work presents a forward step in the fields of EVs smart charging and harmonics mitigation in practical distribution gids.

Price Cannibalization Effect on Long-Term Electricity Prices and Profitability of Renewables in the Baltic States

This paper aims to evaluate price cannibalization effects in forecasts of long-term electricity prices and substantiate their relevance on the profitability of renewables in the Baltic States from 2024 to 2033. Statistical data analysis, literature review, scenario method, and PLEXOS modeling were applied. Five scenarios were analyzed for developing renewable energy sources (RES) and load in Lithuania. In contrast, scenarios for Estonia and Latvia were based on assumptions derived from the countries’ national RES strategies. The results showed that the increase in RES capacities will halve electricity market prices from around 130 EUR/MWh in 2024 to 58 EUR/MWh in Latvia, 60 EUR/MWh in Estonia, and 60–77 EUR/MWh in Lithuania in 2033. In time-waving, the absolute and relative price cannibalization effects of renewables were found. In 2033, the loss of revenue from solar photovoltaic (PV) generators was estimated to be 5.5–17.0 EUR/MWh in Lithuania, 7.1 EUR/MWh in Latvia, and 5.6 EUR in Estonia. The case of onshore wind demonstrated revenue losses of 10.5–22.0 EUR/MWh in Lithuania, 12.0 EUR/MWh in Latvia, and 10.0 EUR/MWh in Estonia. After 2029, revenues received by RES electricity generators could not guarantee project profitability; therefore, market flexibility options will be required. The key innovative strategy to mitigate the price cannibalization effect is the demand-side response when leveraging demand flexibility. Typically, this is achieved by sending price signals to the consumers who, if they have any, shift their demand to lower price periods. This is easily applied within HVAC systems, smart electric vehicle charging, and smart home appliance usage. Such behavior would allow the price cannibalization effect to be decreased.

A multi-stage techno-economic model for harnessing flexibility from IoT-enabled appliances and smart charging systems: Developing a competitive local flexibility market using Stackelberg game theory

The increasing integration of renewable energy sources with power systems has led to greater uncertainties, heightening the need for more flexible services. Demand-side resources can provide significant flexibility cost-effectively, making it crucial to design new business models to harness their flexible capacities. Hence, this paper introduces a three-stage techno-economic framework designed to enhance the exploitation of flexible capacities from demand-side resources such as Internet-of-Things (IoT)-Enabled Appliances (IEAs), Battery-Integrated Rooftop Solar Panels (BIRSPs), and Electric Vehicle (EV) smart charging systems in the local flexibility market. The proposed framework operates in three stages. First, smart homes with IEAs, BIRSPs, and smart charging systems assess their flexibility and report it to their Microgrid (MG) operators. Next, MG operators optimize strategies to maximize market returns and report capacities to the Distribution System Operator (DSO). Finally, the DSO manages flexibility transactions based on MG inputs. A Stackelberg game theory model using a gradient-based method updates the coupling variables between market parties, ensuring convergence in the second and third stages within a decentralized space with limited information sharing. Implemented on a modified IEEE 69-node distribution system, the model fully utilizes the potential of smart homes, including IEAs, BIRSPs, and smart charging systems in the local flexibility market. It reduces the Upstream Network (UN) share in the local flexibility market by 56.86% and decreases DSO costs for energy balancing by 22.36%.

Detecting Distributed Denial-of-Service (DDoS) attacks that generate false authentications on Electric Vehicle (EV) charging infrastructure

In recent years, smart grid-based Electric Vehicle (EV) charging systems have increasingly faced vulnerabilities to Distributed Denial of Service (DDoS) attacks, especially through malicious authentication failures. These attacks typically involve monopolizing the Grid Server (GS), thereby hindering the authentication process for legitimate EVs. Despite the severity of this issue, no research (to the best of our knowledge) has focused on detecting DDoS attacks exploiting weaknesses in EV authentication. This study introduces a DDoS attack detection model specifically designed for EV authentication. The approach involves developing a machine learning model involving unique feature selection and combination. The proposed approach has been evaluated using a new DDOS attack dataset. The model is engineered to optimize feature combination, aiming for high sampling resolution, minimal information loss, and robust performance under 16 distinct attack scenarios. The feature combination used in this study shows improved accuracy over traditional DDoS detection methods based on access time variation while minimizing information loss.

Tariff option preferences for smart and bidirectional charging: Evidence from battery electric vehicle users in Germany

Smart charging of battery electric vehicles (BEVs) can contribute to flexibility in power grids and help integrate renewable electricity. Tapping into this potential requires high user acceptance for smart charging and corresponding tariffs. In this paper, we analyze the preferences of current BEV users, representing the potential near-term adopters of smart charging, for different smart charging tariff design elements by conducting a discrete choice experiment with 689 participants in Germany. In doing so, we (1) provide an overview of current BEV users' preferences, (2) identify and characterize BEV user groups with substantial differences in their preferences, and (3) identify barriers for smart charging implementation from the perspective of current BEV users. More specifically, we find that potential cost savings along with the pricing scheme and charging mode are the most important tariff elements, whereof a pre-defined price corridor with an emergency price for grid bottlenecks and charging a safety buffer before applying smart charging are most preferred. We identify three user groups, with a large share of innovative adopters. Moreover, driving range or reluctance regarding data sharing can represent barriers for smart charging adoption. Based on our findings, we derive implications for decision-makers in policy and industry.

Enhancing smart charging in electric vehicles by addressing paused and delayed charging problems

Smart charging of electric vehicles can alleviate grid congestion and reduce charging costs. However, various electric vehicle models currently lack the technical capabilities to effectively implement smart charging since they cannot handle charging pauses or delays. These models enter sleep mode when charging is interrupted, preventing resumption afterwards. To avoid this, they should be continuously charged with their minimum charging power, even when a charging pause would be desirable, for instance with high electricity prices. This research examines this problem to inform various stakeholders, including policymakers and manufacturers, and stimulates the adoption of proactive measures that address this problem. Here, we demonstrate through technical charging tests that around one-third of tested car models suffer from this issue. Through model simulations we indicate that eliminating paused and delayed charging problems would double the smart charging potential for all applications. Lastly, we propose concrete legal and practical solutions to eliminate these problems.

Smart EV Charging Station Monitoring and User Feedback System

The efficiency and reliability of electric vehicle (EV) charging stations are pivotal for user satisfaction and broader EV adoption. This research introduces a system that monitors essential parameters of EV charging stations, such as temperature, voltage, and current, using advanced sensors and the ESP32 microcontroller. The collected data is transmitted through the Bylnk platform, for real-time monitoring and analysis. To enhance the user experience, a mobile application has been developed, allowing users to review these critical details and receive notifications. The app provides a detailed overview of each charging station's performance, helping users identify the best stations based on historical data and user reviews. By offering insights into the operational status and efficiency of charging stations, the system aids users in making informed decisions about where to charge their vehicles. This integration of real-time monitoring with user-friendly mobile access improves user convenience and optimizes the EV charging infrastructure management.

Role of smart charging of electric vehicles and vehicle-to-grid in integrated renewables-based energy systems on country level

The energy transition based on variable renewable energy sources raises concerns on satisfying the electricity demand at all hours of a year, which is amplified by the ongoing energy system electrification. Balancing variable electricity supply and inelastic demand requires additional sources of flexibility in the system: flexible electricity generation, energy storage, grids, flexible demand, and overall sector coupling. Electrification of transport increases the electricity demand; however, it offers the opportunity to use additional low-cost flexibility from electric vehicles batteries. The LUT Energy System Transition Model was modified to model the smart charging and vehicle-to-grid functionality of electric vehicles in integrated energy systems. The results show that, in countries with a large fleet of electric vehicles, smart charging and vehicle-to-grid allow for a substantial reduction of energy storage requirements, reducing the electricity and heat storage capacity by 35% and 25%, respectively and leading to 4% lower system cost. Flexible operation of electrolysers required for e-fuels synthesis have a similar effect and, in combination with smart charging and vehicle-to-grid, can lead to 8% lower system cost. The impact of vehicle-to-grid naturally depends on assigned operational cost; however, it contributes to system cost reduction even at high costs of 50 or 100 €/MWh.

OptimHome: A Shrinking Horizon Control Architecture for Bidirectional Smart Charging in Home Energy Management Systems

This study aims to develop an adaptable home energy management system capable of integrating the bidirectional smart charging of electric vehicles. The final goal is to achieve a user-defined objectives such as cost minimization or maximizing renewable self-consumption. Industrialwise, the present work yields valuable outcomes in identifying operational frameworks and boundary conditions. Optimal scheduling benefits both users and the electric network, thus enhancing grid utilization and increasing renewable energy integration. By coordinating power interactions with dynamic time-of-use tariffs, the energy management system minimizes user costs and aids the grid by cutting peak hour energy consumption. Charging and discharging operations in electric vehicles comply with energy level constraints outlined by bidirectional charging protocols. The proposed approach ensures the scheduling of cycles that minimize detrimental effects on battery health when evaluating an economically ageing mechanism. Compared to uncontrolled charging, optimal scheduling resulted in a significant reduction in the total operational cost of the dwelling. Trade-off conditions between renewable integration and potential savings are identified and numerically evaluated by means of multiobjective optimization. In contrast to scheduling-based models, the proposed architecture possesses the ability to iteratively adapt decision variables in response to system changes, thus responding effectively to external stochastic uncertainty.

Smart and Sustainable Wireless Electric Vehicle Charging Strategy with Renewable Energy and Internet of Things Integration

This study addresses the challenges associated with electric vehicle (EV) charging in office environments. These challenges include (1) reliance on manual cable connections, (2) constrained charging options, (3) safety concerns with cable management, and (4) the lack of dynamic charging capabilities. This research focuses on an innovative wireless power transfer (WPT) system specifically designed for use in office parking areas. This system incorporates renewable energy resources (RERs) and uses the transformative power of the Internet of Things (IoT). It employs a mix of solar energy systems and battery storage solutions to facilitate a sustainable and efficient energy supply to EVs. The integration of IoT technology allows for the automatic initiation of charging as soon as an EV is parked. Additionally, the implementation of the Blynk application offers users real-time access to information regarding the operational status of the photovoltaic system and the battery levels of their EVs. The system is further enhanced with IoT and RFID technologies to provide dynamic updates on the availability of charging slots and to implement strict security protocols for user authentication and protection. The research also includes a case study focusing on the application of this charging system in office settings. The case study achieves a 95.9% IRR, lower NPC of USD 1.52 million, and 56.7% power contribution by RERs, and it reduces annual carbon emissions to 173,956 kg CO2.

Enhanced EV charging algorithm considering data-driven workplace chargers categorization with multiple vehicle types

The increasing penetration of Electric Vehicles (EVs) presents significant challenges in integrating EV chargers. To address this, precise smart EV charging strategies are imperative to prevent a surge in peak power demand and ensure seamless charger integration. In this article, a smart EV charging pool algorithm employing optimal control is proposed. The main objective is to minimize the charge point operator’s cost while maximizing its EV chargers’ flexibility. The algorithm adeptly manages the charger pilot signal standard and accommodates the non-ideal behavior of EV batteries across various vehicle types. It ensures the fulfillment of vehicle owners’ preferences regarding the departure state of charge. Additionally, we develop a data-driven characterization of EV workplace chargers, considering power levels and estimated battery capacities. A novel methodology for computing the EV battery’s arrival state of charge is also introduced. The efficacy of the EV charging algorithm is evaluated through multiple simulation campaigns, ranging from individual charger responses to comprehensive charging pool analyses. Simulation results are compared with those of a typical minimum-time strategy, revealing cost reductions and significant power savings based on the flexibility of EV chargers. This novel algorithm emerges as a valuable tool for accurately managing the power demanded by an EV charging station, offering flexible services to the electrical grid.

Decentralized Smart Charging of Electric Vehicles in Residential Settings: Algorithms and Predicted Grid Impact

Decentralized Smart Charging of Electric Vehicles in Residential Settings: Algorithms and Predicted Grid Impact

Three-Stage Deep Reinforcement Learning for Privacy-and Safety-Aware Smart Electric Vehicle Charging Station Scheduling and Volt/VAR Control

Three-Stage Deep Reinforcement Learning for Privacy-and Safety-Aware Smart Electric Vehicle Charging Station Scheduling and Volt/VAR Control