Smart Charging Of Electric Vehicles Research Articles

Integrating Battery Aging in the Optimization for Bidirectional Charging of Electric Vehicles

Smart charging of Electric Vehicles (EVs) reduces operating cost, allows more sustainable battery usage, and promotes the rise of electric mobility. In addition, bidirectional charging and improved connectivity enable efficient power grid support. Today, however, uncoordinated charging, e.g., governed by users’ habits, is still the norm. Thus, the impact of upcoming smart charging applications is mostly unexplored. We aim to estimate the expenses inherent with smart charging, e.g., battery aging costs, and give suggestions for further research. Using typical onboard sensor data we concisely model and validate an EV battery. We then integrate the battery model into a realistic smart charging use case and compare it with measurements of real EV charging. The results show that i) the temperature dependence of battery aging calls for precise thermal models for charging power greater than 7 kW, ii) disregarding battery aging underestimates EVs’ operating cost by approx. 30%, and iii) the profitability of Vehicle-to-Grid (V2G) services based on bidirectional power flow, e.g., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">energy arbitrage</i> , depends on battery aging costs and the electricity price spread.

ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging Research

ACN-Sim is a data-driven, open-source simulation environment designed to accelerate research in the field of smart electric vehicle (EV) charging. It fills the need in this community for a widely available, realistic simulation environment in which researchers can evaluate algorithms and test assumptions. ACN-Sim provides a modular, extensible architecture, which models the complexity of real charging systems, including battery charging behavior and unbalanced three-phase infrastructure. It also integrates with a broader ecosystem of research tools. These include ACN-Data, an open dataset of EV charging sessions, which provides realistic simulation scenarios, and ACN-Live, a framework for field-testing charging algorithms. It also integrates with grid simulators like MATPOWER, PandaPower and OpenDSS, and OpenAI Gym for training reinforcement learning agents.

In-Company Smart Charging: Development of a Simulation Model to Facilitate a Smart EV Charging System

Current electric vehicle (EV) charging systems have limited smart functionality, and most research focuses on load-balancing the national or regional grid. In this article, we focus on supporting the early design of a smart charging system that can effectively and efficiently charge a company’s EV fleet, maximizing the use of self-generated Photo-Voltaic energy. The support takes place in the form of the Vehicle Charging Simulation (VeCS) model. System performance is determined by operational costs, CO2 emissions and employee satisfaction. Two impactful smart charging functions concern adaptive charging speeds and charging point management. Simulation algorithms for these functions are developed. The VeCS model is developed to simulate implementation of a smart charging system incorporating both charging infrastructure and local Photo-Voltaics input, using a company’s travel and energy data, prior to having the EVs in place. The model takes into account travel behaviour, energy input and energy consumption on a daily basis. The model shows the number of charged vehicles, whether incomplete charges occur, and energy flow during the day. The model also facilitates simulation of an entire year to determine overall cost and emission benefits. It also estimates charging costs and CO2 emissions that can be compared to the non-EV situation. With the VeCS model, the impact of various system design and implementation choices can be explored before EVs are used. Two system designs are proposed for the case company; a short-term version with current technology and a future version with various smart functionalities. Overall, the model can contribute to substantiated advice for a company regarding implementation of charging infrastructure.

Dynamic pricing and energy management for profit maximization in multiple smart electric vehicle charging stations: A privacy-preserving deep reinforcement learning approach

Profit maximization of electric vehicle charging station (EVCS) operation yields an increasing investment for the deployment of EVCSs, thereby increasing the penetration of electric vehicles (EVs) and supporting high-quality charging service to EV users. However, existing model-based approaches for profit maximization of EVCSs may exhibit poor performance owing to the underutilization of massive data and inaccurate modeling of EVCS operation in a dynamic environment. Furthermore, the existing approaches can be vulnerable to adversaries that abuse private EVCS operation data for malicious purposes. To resolve these limitations, we propose a privacy-preserving distributed deep reinforcement learning (DRL) framework that maximizes the profits of multiple smart EVCSs integrated with photovoltaic and energy storage systems under a dynamic pricing strategy. In the proposed framework, DRL agents using the soft actor–critic method determine the schedules of the profitable selling price and charging/discharging energy for EVCSs. To preserve the privacy of EVCS operation data, a federated reinforcement learning method is adopted in which only the local and global neural network models of the DRL agents are exchanged between the DRL agents at the EVCSs and the global agent at the central server without sharing EVCS data. Numerical examples demonstrate the effectiveness of the proposed approach in terms of convergence of the training curve for the DRL agent, adaptive profitable selling price, energy charging and discharging, sensitivity of the selling price factor, and varying weather conditions.

Research Tools for Smart Electric Vehicle Charging: An introduction to the adaptive charging network research portal

Millions of Electric Vehicles (EVs) will enter service in the next decade, generating gigawatthours of additional energy demand. Charging these EVs cleanly, affordably, and without excessive stress on the grid will require advances in charging system design, hardware, monitoring, and control. Collectively, we refer to these advances as smart charging.

Solar Charging Station for Electric Vehicles

This paper describes design of solar powered charging station for charging of electric vehicle that solves the key downside of fuel and pollution. While EV charging has traditionally been grid-based, use of solar powered chargers has emerged as an interesting opportunity. As worlds resources are diminishing, government agencies and non-government organization are pushing greener solution through the use of renewable energy sources. In order to reduce the pollution through the motor vehicles, there is a large scope of increase in electric vehicles all over the world. To run the electric vehicle the fuel required is the electricity which can be storable through the use of solar energy. Electric vehicles that run on the Electric vehicle smart charging station which is the promising alternative and environmentally sustainable solution to meet up the energy crisis. This paper investigates the possibility of charging the battery of electric vehicles at a various working place like offices, colleges, hospitals, universities etc in India using solar energy. In this paper, the charging station successfully developed as desired features for electric vehicle from renewable energy resources with solar panel, solar charge controller, batteries storage and DC-DC converter. A laboratory-scale experimental prototype was also developed, and the performance of the proposed charging station was investigated.

Probabilistic load flow analysis of electric vehicle smart charging in unbalanced LV distribution systems with residential photovoltaic generation

Several studies have presented electric vehicle smart charging schemes to increase the temporal matching between photovoltaic generation and electric vehicle charging, including a smart charging scheme with an objective to minimize the net-load variance. This method has proved, through simulations, that the self consumption could be increased, but the benefit of the approach has not been tested on a low voltage distribution system. To increase the quality of grid impact analyses of the smart charging scheme, probabilistic methods that include input and spatial allocation uncertainties are more appropriate. In this study, a probabilistic load flow analysis is performed by modelling the variability of electric vehicle mobility, household load, photovoltaic system generation, and the adoption of photovoltaic system and electric vehicle in society. The results show that the smart charging scheme improves the low voltage distribution system performance and increases the correlations between network nodes. It is also shown that concentrated allocation has more severe impacts, in particular at lower penetration levels. This paper can form the basis for the development of probabilistic impact analysis of smart charging to allow society to integrate more electric vehicles and photovoltaic systems for a more sustainable future.

Qualifying quantum approaches for hard industrial optimization problems. A case study in the field of smart-charging of electric vehicles

In order to qualify quantum algorithms for industrial NP-Hard problems, comparing them to available polynomial approximate classical algorithms and not only to exact exponential ones is necessary. This is a great challenge as, in many cases, bounds on the reachable approximation ratios exist according to some highly-trusted conjectures of Complexity Theory. An interesting setup for such qualification is thus to focus on particular instances of these problems known to be “less difficult” than the worst-case ones and for which the above bounds can be outperformed: quantum algorithms should perform at least as well as the conventional approximate ones on these instances, up to very large sizes. We present a case study of such a protocol for two industrial problems drawn from the strongly developing field of smart-charging of electric vehicles. Tailored implementations of the Quantum Approximate Optimization Algorithm (QAOA) have been developed for both problems, and tested numerically with classical resources either by emulation of Pasqal’s Rydberg atom based quantum device or using Atos Quantum Learning Machine. In both cases, quantum algorithms exhibit the same approximation ratios as conventional approximation algorithms or improve them. These are very encouraging results, although still for instances of limited size as allowed by studies on classical computing resources. The next step will be to confirm them on larger instances, on actual devices, and for more complex versions of the problems addressed.

CARAMEL: results on a secure architecture for connected and autonomous vehicles detecting GPS spoofing attacks

The main goal of the H2020-CARAMEL project is to address the cybersecurity gaps introduced by the new technological domains adopted by modern vehicles applying, among others, advanced Artificial Intelligence and Machine Learning techniques. As a result, CARAMEL enhances the protection against threats related to automated driving, smart charging of Electric Vehicles, and communication among vehicles or between vehicles and the roadside infrastructure. This work focuses on the latter and presents the CARAMEL architecture aiming at assessing the integrity of the information transmitted by vehicles, as well as at improving the security and privacy of communication for connected and autonomous driving. The proposed architecture includes: (1) multi-radio access technology capabilities, with simultaneous 802.11p and LTE-Uu support, enabled by the connectivity infrastructure; (2) a MEC platform, where, among others, algorithms for detecting attacks are implemented; (3) an intelligent On-Board Unit with anti-hacking features inside the vehicle; (4) a Public Key Infrastructure that validates in real-time the integrity of vehicle’s data transmissions. As an indicative application, the interaction between the entities of the CARAMEL architecture is showcased in case of a GPS spoofing attack scenario. Adopted attack detection techniques exploit robust in-vehicle and cooperative approaches that do not rely on encrypted GPS signals, but only on measurements available in the CARAMEL architecture.

Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging

We describe the architecture and algorithms of the Adaptive Charging Network (ACN), which was first deployed on the Caltech campus in early 2016 and is currently operating at over 100 other sites in the United States. The architecture enables real-time monitoring and control and supports electric vehicle (EV) charging at scale. The ACN adopts a flexible Adaptive Scheduling Algorithm based on convex optimization and model predictive control and allows for significant over-subscription of electrical infrastructure. We describe some of the practical challenges in real-world charging systems, including unbalanced three-phase infrastructure, non-ideal battery charging behavior, and quantized control signals. We demonstrate how the Adaptive Scheduling Algorithm handles these challenges, and compare its performance against baseline algorithms from the deadline scheduling literature using real workloads recorded from the Caltech ACN and accurate system models. We find that in these realistic settings, our scheduling algorithm can improve operator profit by 3.4 times over uncontrolled charging and consistently outperforms baseline algorithms when delivering energy in highly congested systems.

Plug‐in electric vehicles smart charging mechanisms for cost minimization and ancillary service provision

The increasing number of Plug-in Electric Vehicles (PEV) connected to the grid will create challenges to be addressed and opportunities that can be ceased. One challenge is the load shape dynamics, which might result in increasing the loading level on the distribution lines. On the other hand, there is an opportunity to use the PEV stored energy to provide ancillary services to the grid. Therefore, this paper proposes a new PEV charging mechanisms that not only aid in ancillary service provision, but also help in reducing the charging cost and avoiding the extra investment in peak generation units while maximizing the integrated PEVs. The proposed approach utilizes three stages to ensure the satisfaction of PEV owners with different preferences and to respect the grid limitations. The main objective is to develop different charging mechanisms for charging PEV's in the parking lots to ensure the maximum satisfaction of the PEV owners by fulfilling their charging requirements with the minimum possible cost. The developed algorithm relies on a moving time window to address the real-time changes and considers the participation of PEVs in the frequency regulation and peak shaving. The effectiveness of the proposed algorithm is verified through simulation using real data.

Smart electric vehicle charging strategies for sectoral coupling in a city energy system

The decarbonization of city energy systems plays an important role to meet climate targets. We examine the consequences of integrating electric cars and buses into the city energy system (60% of private cars and 100% of public buses), using three different charging strategies in a modelling tool that considers local generation and storage of electricity and heat, electricity import to the city, and investments to achieve net-zero emissions from local electricity and heating in 2050. We find that up to 85% of the demand for the charging of electric cars is flexible and that smart charging strategies can facilitate 62% solar PV in the charging electricity mix, compared to 24% when cars are charged directly when parked. Electric buses are less flexible, but the timing of charging enables up to 32% to be supplied by solar PV. The benefit from smart charging to the city energy system can be exploited when charging is aligned with the local value of electricity in the city. Smart charging for cars reduces the need for investments in stationary batteries and peak units in the city electricity and heating sectors. Thus, our results point to the importance of sectoral coupling to exploit flexibility options in the city electricity, district heating and transport sectors.

Combined PV–EV hosting capacity assessment for a residential LV distribution grid with smart EV charging and PV curtailment

Photovoltaic (PV) systems and electric vehicles (EVs) integrated in local distribution systems are considered to be two of the keys to a sustainable future built environment. However, large-scale integration of PV generation and EV charging loads poses technical challenges for the distribution grid. Each grid has a specific hosting capacity limiting the allowable PV and EV share. This paper presents a combined PV–EV grid integration and hosting capacity assessment for a residential LV distribution grid with four different energy management system (EMS) scenarios: (1) without EMS, (2) with EV smart charging only, (3) with PV curtailment only, and (4) with both EV smart charging and PV curtailment. The combined PV–EV hosting capacity is presented using a novel graphical approach so that both PV and EV hosting capacity can be analyzed within the same framework. Results show that the EV smart charging can improve the hosting capacity for EVs significantly and for PV slightly. While the PV curtailment can improve the hosting capacity for PV significantly, it cannot improve the hosting capacity for EVs at all. From the graphical analysis, it can be concluded that there is a slight positive correlation between PV and EV hosting capacity in the case of residential areas. • Combined PV–EV grid integration study in a residential LV grid is presented. • A novel graphical analysis approach for combined PV–EV HC assessment is presented. • Both HC for PV and EV are analyzed in the same framework. • Smart EV charging and PV curtailment are included to assess possible HC enhancement. • Correlation between HC for PV and EV in the case of residential areas is obtained.

Smart charging of electric vehicles to minimize the cost of charging and the rate of transformer aging in a residential distribution network

Smart charging of electric vehicles to minimize the cost of charging and the rate of transformer aging in a residential distribution network

Hierarchical Volt-VAR Optimization Framework Considering Voltage Control of Smart Electric Vehicle Charging Stations Under Uncertainty

Smart electric vehicle charging stations (EVCSs) equipped with distributed energy resources (DERs), such as photovoltaic (PV) systems and energy storage systems (ESSs), are promising entities for maintaining voltage quality in power distribution networks through voltage regulation using the smart inverters of DERs. This study proposes a hierarchical Volt-VAR optimization (VVO) framework that reflects the voltage regulation capability of smart EVCSs, which consists of global and local voltage control stages. At the global stage, smart inverters of EVCSs cooperate with conventional voltage regulators, such as an on-load tap changer (OLTC) and capacitor banks (CBs)), and smart inverters of PV systems to minimize the total active power losses and voltage deviations along with the determination of optimal parameters for local droop control functions of the smart inverters. At the local stage, smart inverters of EVCSs and PV systems quickly mitigate local voltage violations using dynamically varying local droop control functions with their optimal parameters calculated from the global stage. Under uncertainties in PV generation outputs and driving patterns of electric vehicle users, the deterministic optimization-based VVO problem at the global stage is reformulated into the chance-constrained optimization-based VVO problem. A simulation study was performed in an IEEE 33-bus distribution system with an OLTC, CBs, PV systems, and smart EVCSs. The results demonstrate the effectiveness of the proposed framework in terms of total active power loss/voltage deviation, optimized local droop control function, and probability level of chance constraints.

A Decision-Making Framework for the Smart Charging of Electric Vehicles Considering the Priorities of the Driver

During the last decade, the technologies related to electric vehicles (EVs) have captured both scientific and industrial interest. Specifically, the subject of the smart charging of EVs has gained significant attention, as it facilitates the managed charging of EVs to reduce disturbances to the power grid. Despite the presence of an extended literature on the topic, the implementation of a framework that allows flexibility in the definition of the decision-making objectives, along with user-defined criteria is still a challenge. Towards addressing this challenge, a framework for the smart charging of EVs is presented in this paper. The framework consists of a heuristic algorithm that facilitates the charge scheduling within a charging station (CS), and the analytic hierarchy process (AHP) to support the driver of the EV selecting the most appropriate charging station based on their needs of transportation and personal preferences. The communications are facilitated by the Open Platform Communications–Unified Architecture (OPC–UA) standard. For the selection of the scheduling algorithm, the genetic algorithm and particle swarm optimisation have been evaluated, where the latter had better performance. The performance of the charge scheduling is evaluated, in various charging tasks, compared to the exhaustive search for small problems.

Impact of sector coupling on the market value of renewable energies – A model-based scenario analysis

Decarbonizing the energy supply by substituting fossil fuels with renewable energy sources (RES) is a key task for the coming decades in order to achieve the EU's ambitious climate protection targets. Information about the possible development and marketability of RES in the electricity sector is essential for assessing future funding needs. However, rising shares of fluctuating RES generation in the energy system reduce the average market prices and increase price volatility. Balancing price variations requires a considerable degree of flexibility. Additional flexibility in the electricity market through closer interconnection between the electricity sector and other demand sectors makes it possible to keep the market values of RES closer to the general market price level, irrespective of their shares. Such sector coupling can thus contribute to a cost-efficient transition to a low-carbon energy system. This paper examines the impact of efficient sector coupling on the market values of RES in a European energy system with ambitious decarbonization. We analyze different scenarios by applying the Enertile model, which uses an integrated cost optimization approach with flexibility options due to sector coupling and provides a detailed future development of RES. In our analysis, we examine three flexibility options: smart charging of electric vehicles, decentralized heat pumps in buildings, and multivalent district heating grids. We show that the flexible use of electricity in district heating has a significant impact on market values, while the impact of both flexible electric vehicle charging and flexible heating with heat pumps is rather small. Short-term flexibility due to load shifting of the charging or heating process shows only a limited effect on market values. Fuel switching in district heating, however, offers the possibility to change the absolute demand for electricity in direct response to RES feed-in and drastically reduces the curtailment of RES.

Research Group Introduction: IKEDA Fuka Laboratory, Dept.of Electrical Engineering, National Institute of Technology, Ube College

Our research focuses on the compensation performance of fundamental reactive, unbalanced active, and harmonics currents with constant dc-capacitor voltage control of the smart charger for electric vehicles in single-phase three-wire distribution feeders.

Smart charging of mobile electric vehicles

The paper proposes to create a digital economy for the development of electric charging networks of mass-use road transport in a business environment, in fact, these are mechanisms of public-private partnership. To go along the path of purchasing electric charging stations of Korean, Japanese, and German manufacturers and placing them for their own money on communications of Federal or territorial highways or in organizations and enterprises of various forms of ownership is a poorly managed and not rational process. It is proposed to move away from the schemes of location of specific electric charging stations and switch to the schemes of non-powered sockets. The technical solution refers to the technical means of electric charging (recharging) of mobile electric vehicles. The goal is to reduce the cost of creating a network electric charging infrastructure for electric vehicles. The technical result is the ability to ensure the charging mode of the electric vehicle directly from the existing distributed power supply system in space, by using the phase state modes of the electric connector cable that is powered or not powered by electric current. Based on the materials of the paper, patents of Russia and Kazakhstan were obtained.

Bidirectional smart charging of electric vehicles considering user preferences, peer to peer energy trade, and provision of grid ancillary services

Electric vehicles (EVs) are witnessing increased utilization throughout the world as an alternative to fossil–fueled vehicles. The extensive deployment of EVs can bring challenges to the grid if not properly integrated. Such challenges, however, could be exploited as opportunities if the huge unused capacity of the battery storage in millions of EVs are utilized for ancillary services to the grid and peer–to–peer (PtP) energy trade. Given that there is at least one human user per vehicle, human input must be considered to improve the scheduling process. To that end, this paper presents a new algorithm for bidirectional smart charging of EVs considering user preferences, PtP energy trade, and provision of ancillary services to the grid. The preferences of an EV user as input to the model are embedded into the scheduling process enabling the model to be adaptive to various conditions. Optimization slack variables are utilized for optimal management of EV battery SOC and energy allocation for multiple services. New indices are developed and introduced for quantification of the EV participation in ancillary services and PtP transactions. Real–world data has been collected and utilized for model specification and simulation to make the assumptions more realistic. The efficacy and feasibility of the proposed model are validated using numerical studies. The results indicate that incorporating users’ preferences into the scheduling process would improve the aggregated revenue generated by the EV scheduling model which in turn could offset the charging costs by up to 100%. Further, an increase of about 90% in peer-to-peer energy transactions among EVs and 11% in ancillary services provision to the grid are achieved through the developed user-centric smart charging model.