Battery Charging Station Research Articles

Design of a Safety Discharge System for Prevent Thermal Runaway in Shared Battery Charging Stations

Design of a Safety Discharge System for Prevent Thermal Runaway in Shared Battery Charging Stations

An Integrated Analysis of Electric Battery Charging Station Selection—Thailand Inspired

An Integrated Analysis of Electric Battery Charging Station Selection—Thailand Inspired

Impacts of Distribution-Level Joint Scheduling of Electric Vehicle Battery Charging and Swapping Stations on Reliability Improvement

Impacts of Distribution-Level Joint Scheduling of Electric Vehicle Battery Charging and Swapping Stations on Reliability Improvement

A lexicographic optimization-based approach for efficient task allocation in industrial transportation multi-robot systems

In industrial transportation applications, multi-robot systems (MRS) are assigned to perform transportation tasks until their batteries are depleted, requiring them to move to battery charging stations. This temporary unavailability of the robots during charging decreases system productivity that measures the number of transportation tasks accomplished with the available robot energy. As a result, maximizing task completion becomes crucial, especially with prioritized tasks and increased workload. This can be achieved through an energy management strategy. In this context, the Lexicographic Optimization-based Multi-Robot Task Allocation (LO-MRTA) approach is proposed to maximize the reward in terms of system productivity with a limited energy consumption. The existing multi-robot energy management approaches consider the energy management during the tasks execution and neglect workload scaling issue with increasing tasks, whereas the LO-MRTA approach accounts for the energy consumption in the large-scale tasks allocation process. The main idea of the proposed approach is to consider the global state of the robots in the MRS system before carrying out the tasks execution, enhancing the task allocation decisions in terms of the energy management. The evaluation scenarios show the promising performance of the LO-MRTA approach in comparison with three existing baselines in terms of the system productivity, overall energy consumption and workload scaling effects.

Energy-efficient Vienna rectifier for electric vehicle battery charging stations

Global DC fast-charging infrastructure is being rapidly developed to accommodate the ever-increasing demand from the electric vehicle market. A major complication in the electric power system's process is the proliferation of ultra-fast charging (UFC) stations, which are known for their enormous power consumption and unpredictable, intermittent performance. By implementing grid-supportive features and ensuring an improved power consumption profile for the grid, installing regional energy storage can solve these challenges. This study presents a control approach for managing the grid-side AC/DC converters of rapid charging stations focused on future-oriented regulation. The paper primarily concentrates on various Vienna rectifier topologies. The technology, characteristics, benefits, and operational aspects of Vienna rectifier topologies are vital to improving the performance, efficiency, and grid integration of electric vehicle charging systems. Fast charging, grid stability, energy economy, and the smooth integration of electric vehicles into the electrical grid are all made possible by Vienna rectifiers. When used in battery energy storage systems (BESS) for electric vehicle charging infrastructure, Vienna rectifiers allow for effective discharge and charging of the batteries. The configurations and assessments of these converters are examined, assessed, and compared based on power output parameters, element count, power factor, THD, and efficiency. The Vienna rectifier has been identified as the best suitable DC fast-charging converter architecture for power levels exceeding 15 kW due to its exceptional efficiency, limited output voltage ripples, high power density, reduced current ripples, and reliable performance. Research on ways to improve electric vehicle charging infrastructure can be spurred by a better grasp of the Vienna rectifier's technological complexities and performance benefits, leading to greener and more efficient transportation options. As a result, the system's power density will eventually rise and elemental stress will decrease.

Operation Model Based on Artificial Neural Network and Economic Feasibility Assessment of an EV Fast Charging Hub

The energy transition towards a low-emission matrix has motivated efforts to reduce the use of fossil fuels in the transportation sector. The growth of the electric mobility market has been consistent in recent years. In Brazil, there has been an accelerated growth in the sales rate of new electric (and hybrid) vehicles (EVs). Fiscal incentives provided by governments, along with the reduction in vehicle costs, are factors contributing to the exponential growth of the EV fleet—creating a favorable environment for the dissemination of new technologies and enabling the participation of players from sectors such as battery manufacturing and charging stations. Considering the international context, the E-Lounge R&D joint initiative aims to evaluate different strategies to economically enable the electric mobility market, exploring EV charging service sales by energy distribution utility companies in Brazil. This work describes the step-by-step development of an ideal model of a charging hub and discusses its operation based on a real deployment, as well as its associated technical and economic feasibility. Using EV charging data based on the E-Lounge’s operational behavior, an artificial neural network (ANN) is applied to forecast future energy consumption to each EV charging station. This paper also presents an economic analysis of the E-Lounge case study, which can contribute to proposals for electric vehicle charging ecosystems in the context of smart energy systems. Based on the operational results collected, as well as considering equipment usage projections, it is possible to make EV charging enterprises feasible, even when high investments in infrastructure and equipment (charging stations and battery storage systems) are necessary, since the net present value is positive and the payback period is 4 years. This work contributes by presenting real operational data from a charging hub, a projection model aimed at evaluating future operations, and a realistic economic evaluation model based on a case study implemented in São Paulo, Brazil.

Review on Electric Vehicles Battery Swapping Technology

The goal of research and development organizations is to intelligently design the architecture of battery swapping stations (BSSs), with the hope of offering a reliable platform for the successful installation of a large fleet of electric vehicles (EVs) and hybrid electric vehicles (HEVs). The BSS can calibrate its subsystem for the deployment of electric vehicles (EVs) by implementing a similar concept to those found in current gas stations, which involve exchanging or swapping out discharged batteries for partially or fully charged ones after a short while. Because it gives the relevant stakeholders a more comprehensive understanding of the business prospects, the BSS approach has emerged as a promising technological alternative to the conventional EV recharging station approach. This paper addresses the introduction to BSS, covering its methods, infrastructure, advantages over charging stations, and main obstacles. Index Terms: Battery Swapping Station (BSS), Battery Charging Stations (BCS), Electric Vehicles (EVs), Hybrid Electric Vehicles (EHVs).

Power hardware-in-loop emulation of a battery for charging systems and grid applications

Relevance. Batteries are playing an increasingly vital role in power systems due to their utilization in various applications including microgrids, electric vehicles, sustaining geographically isolated communities, and energization of automated devices. Since they are considered as the enabling technology for renewable energy integration, the absence of battery systems from islanded microgrids can result in decreased system reliability and compromised performance due to the intermittency of local sources. Nevertheless, the hazardousness associated with their charging mechanism has led to the urgent continuous development of charging technologies and battery management systems. Aim. To develop a safe testbed to examine the functionality of newly produced battery charging stations and battery managers without employing actual physical batteries to avoid the hazardous manipulation of batteries and increase flexibility during the design and validation stage. This is accomplished by modeling the electrochemical dynamics of the battery system and integrating the device-under-test to a DC converter, which reacts based on these modeled dynamics. Novelty. This work adapts one of the most successful Li-ion battery models available in the literature and utilizes it to interact with power electronic devices that exchange power signals. Unlike other work in this field, the design is based on power hardware-in-loop principles and has minimized power consumption characteristics due to its unique configuration. The constructed computer model can be easily reparametrized to describe the dynamics of various battery capacities. Methods. MATLAB-based simulations of the proposed testbed were conducted for high and low power capacity. A LabView-based program was interfaced with the testbed hardware using a NI-DAQ board to validate the proposed design practically. The testbed hardware components were entirely developed from scratch for experimentation purposes. Results. The proposed testbed successfully imitated the dynamics of the battery, while the practical results concurred the simulated ones.

Neuro-fuzzy-SVPWM switched-inductor-capacitor-based boost inverter for grid-tied fuel cell power generators, design and implementation

Hydrogen energy shows promise as a renewable energy source for various applications like battery and electric vehicle charging stations, as well as grid connections. However, high current ripple from fuel cells (FCs) and inadequate voltages for grid use pose challenges. This study presents a novel solution using neural fuzzy network control in a high-gain DC-DC boost converter to address these issues. The suggested converter charges in parallel and discharges in series, minimizing the current ripple range in the fuel cell network. Additionally, the switch-capacitor cell efficiently increases the output voltage.In this study, a Neuro-fuzzy system with 9 rules is trained meticulously over 50 epochs using hybrid optimization and grid partition methods, achieving a low training error of 0.045 with 522,064 samples. The neural fuzzy network, employing the weighted average method for Defuzzification, produces duty cycle values from 0.02 to 0.5 in response to input signals. Additionally, an innovative Space Vector Pulse Width Modulation (SVPWM) approach within the inverter circuit enhances voltage generation precision and power quality for grid delivery, notably reducing current ripple and ensuring stable power supply. This combined with the neural fuzzy network in the converter efficiently converts hydrogen energy into AC voltage for seamless grid integration, revolutionizing boost converter efficiency and advancing hydrogen energy utilization across various energy sectors.

Tropical Modeling of Battery Swapping and Charging Station

We propose and investigate a queueing model of a battery swapping and charging station (BSCS) for electric vehicles (EVs). A new approach to the analysis of the queueing model is developed, which combines the representation of the model as a stochastic dynamic system with the use of the methods and results of tropical algebra, which deals with the theory and applications of algebraic systems with idempotent operations. We describe the dynamics of the queueing model by a system of recurrence equations that involve random variables (RVs) to represent the interarrival time of incoming EVs. A performance measure for the model is defined as the mean operation cycle time of the station. Furthermore, the system of equations is represented in terms of the tropical algebra in vector form as an implicit linear state dynamic equation. The performance measure takes on the meaning of the mean growth rate of the state vector (the Lyapunov exponent) of the dynamic system. By applying a solution technique of vector equations in tropical algebra, the implicit equation is transformed into an explicit one with a state transition matrix with random entries. The evaluation of the Lyapunov exponent reduces to finding the limit of the expected value of norms of tropical matrix products. This limit is then obtained using results from the tropical spectral theory of deterministic and random matrices. With this approach, we derive a new exact formula for the mean cycle time of the BSCS, which is given in terms of the expected value of the RVs involved. We present the results of the Monte Carlo simulation of the BSCS’s operation, which show a good agreement with the exact solution. The application of the obtained solution to evaluate the performance of one BSCS and to find the optimal distribution of battery packs between stations in a network of BSCSs is discussed. The solution may be of interest in the case when the details of the underlying probability distributions are difficult to determine and, thus, serves to complement and supplement other modeling techniques with the need to fix a distribution.

Day-ahead bidding and dispatch strategy of novel battery charging and swapping station under multiple uncertainties

The novel battery charging and swapping station (NBCSS) has great operational flexibility due to its integration of wind power, photovoltaic power, gas turbine and energy storage. This paper presents a day-ahead bidding and dispatch strategy of NBCSS under multiple uncertainties, which consists of battery demand, market price, renewable energy and load. Firstly, the optimized backup method is used to address the uncertainty of battery demand. Then, a two-stage stochastic robust optimization model is further applied to address the uncertainties of market price, renewable energy and load. The model’s goal is to minimize the total operational cost in the worst-case scenario, which is limited by the corresponding uncertainty set. Especially, to control the conservatism of the model, the total adjustable budget and temporal adjustable budget of uncertainties are considered together. Next, the two-stage model is solved efficiently based on the strong duality theory and column-and-constraint generation (C&CG) method. Finally, case studies are given to verify the effectiveness and performance of the proposed model. The results demonstrate that the NBCSS can be efficiently deployed, enabling market arbitrage and reducing the overall cost. Additionally, the conservatism of the proposed model can be adjusted according to the budgets of uncertainties.

Implementation of battery storage system in a solar PV-based EV charging station

Commercial production of solar-powered battery charging stations for electric vehicles has recently started largely as a result of the growing popularity of electric vehicle (EV) technology as well as the falling cost of photovoltaic (PV) systems. The rise in popularity of EVs may be attributed to both the progression of technology and rising awareness of environmental issues. In this work, a 400V DC bus voltage-based EV charging station is designed which is powered by both a PV system and a utility grid. Also, battery energy storage units are used to overcome the unpredictable behavior of the environment. An Energy management algorithm (EMA) has been presented to balance the power flow among PV, EVs, BESS, grid, and residential load. A sharing coefficient-based power sharing is done between the grid and battery to reduce the stress from the battery units. Then, a multi-step constant current charging method is used to charge the EVs in the charging station to maintain the proper state of charge (SOC) of EV batteries. During this study, different operating conditions are considered according to available PV power and load to validate the power management scheme in the EV charging station. The model is run using MATLAB simulation and opal-RT simulation. The obtained results have been demonstrated with an explanation and system performance verification.

A Low Impact V2H Battery Charging Station Using an AC Green Plug Switched Filter Scheme

A Low Impact V2H Battery Charging Station Using an AC Green Plug Switched Filter Scheme

Design solutions for construction of autoelectric refueling stations in the conditions of rocky soils

In today’s world, the electric car is no longer a fantasy, but an ordinary vehicle that also requires a battery charging station. This is impossible without infrastructure development, especially in mountainous and rocky regions. The appearance of electric charging stations on public roads shows the development of the region, which affects the transportation system, namely its expansion and demand. Despite their simplicity, buildings on rocky soils are difficult to design and require special engineering solutions. The structures themselves should act, as one of the ideas of the electric car, as a picture of ecology, that is, they should be made of eco material and not harm the environment with harmful vapors and waste. If we take apart the design of electric refueling station, the charging stations can act not only as a place to replenish the charge to the battery, but also points of interest, such as: observation decks, places for recreation, etc., as well. Overall, the information raised in this article may help the future of the construction industry as possible designs will soon become ubiquitous.

Prospective life cycle inventory datasets for conventional and hybrid-electric aircraft technologies

Hybrid-electric aircraft represent a promising solution for the urgent need to decarbonize short-haul flights and bolster aviation sustainability. Nevertheless, the realization of hybrid-electric aircraft demands rigorous environmental impact analysis, given the substantial investments, time, and research required for technology development. This study offers a comprehensive life cycle inventory spanning the years 2030, 2040, and 2050 for both conventional and hybrid-electric aircraft configurations. Our inventory datasets are meticulously constructed through a systematic approach, ensuring data harmonization by drawing upon scientific literature, industry expertise, and primary data sources. This extensive dataset encompasses all pertinent systems necessary to model the environmental footprint of flights covering distances ranging from 200 to 600 nautical miles, utilizing a 50-passenger aircraft with the ATR42 as a reference model. Additionally, we furnish supplemental data for end-of-life considerations and uncertainty analysis. The systems under examination include the airframe, powertrain, power electronics and drives, batteries, fuel cells, hydrogen onboard storage, airport infrastructure, and battery charging stations. Notably, the carbon footprint of conventional aircraft aligns with data from the ecoinvent v3.8 database; however, our provided datasets are more than tenfold more detailed and incorporate a forward-looking perspective. These meticulously curated life cycle inventories can be amalgamated to simulate the potential environmental ramifications of conventional aircraft powered by kerosene or alternative aviation fuels, hybrid-electric aircraft utilizing battery technology, and hybrid-electric aircraft employing hydrogen as a fuel in conjunction with batteries. In this context, our findings play a pivotal role in nurturing the development of technology roadmaps that prioritize environmental sustainability within the realm of regional aviation.

Distributed distributionally robust optimization of distribution network incorporating novel battery charging and swapping station

This paper addresses the joint optimization problem of the distribution network (DN) and novel battery charging and swapping (NBCSS) under multi-entities and uncertainties. A distributed distributionally robust optimization (DRO) framework is proposed to facilitate the coordination of DN and NBCSS. Firstly, the DRO models for the DN and NBCSS are developed respectively for the DN and NBCSS, taking into account the uncertainties associated with wind and photovoltaic power. Both models minimize the worst-case expected total cost over a family of distributions characterized by an ambiguity set. Then, the constraints of exchanging power are used to couple the DN and NBCSS. Specifically, in the model of NBCSS, an integrated modeling method of batteries based on the state of charge (SOC) interval is constructed, and the charging and discharging priorities are embedded. Further, the primitive DRO model is transformed into a tractable mixed-integer linear program (MILP) via the affine decision rule and duality theory. Moreover, to protect the information privacy of different entities, the analytical target cascading (ATC) method is developed to decouple the centralized DRO model into two independent subproblems, which can be solved separately. Finally, case studies are carried out to demonstrate the effectiveness of the proposed method.

Inventory management of battery swapping and charging stations considering uncertainty

The battery swapping mode of electric vehicles (EVs) is expected to play an essential role in transportation and power systems. Plenty of batteries are managed by the battery swapping and charging stations (BSCSs) as the core assets. Proper inventory scale and charging/discharging control of batteries are essential to the planning and operation of BSCSs. This paper addresses two critical aspects of BSCS batteries: the optimal amount of inventory batteries and the tradeoff between the initial number of depleted and fully-charged batteries at the beginning of each scheduling horizon. Aiming at these, the time coupling between initial inventory, charging/discharging strategy, and swapping time sequence is analyzed. Afterward, it establishes the initial inventory based charging/discharging (IIC) model. The uncertain swapping demand is fully considered by utilizing a data-driven distributionally robust optimization (DRO) approach with an improved K-means clustering algorithm. A duality-free column-and-constraint generation (C&CG) algorithm solves the problem iteratively. The simulation results demonstrate the validity of the IIC model and show that the obtained optimization results achieve the balance of economy and robustness. Moreover, the impact of battery technology development on inventory batteries is analyzed, and suggestions are given.

Dispatchable capacity optimization strategy for battery swapping and charging station aggregators to participate in grid operations

Taking the aggregator as a unit, battery swapping and charging stations (BSCSs) for electric vehicles (EVs) can be aggregated and dispatched by grid operators, to realize the demand-side resource regulation. Considering the characteristics of an aggregator’s multilateral services, in this study, BSCSs need to ensure the quality of swapping service for EV users and participate in the demand-side regulation response. Firstly, we analyze the operation mechanism of a BSCS in the aggregation mode and propose a state transition model for EV batteries. On this basis, the EV demand uncertainty is incorporated by a distributed robust optimization (DRO) approach for multi-timescale inventories, and an optimization model to maximize the BSCSs’ income is established, which obtains the optimal load planning and dispatchable capacity scheduling for a BSCS aggregator. Extensive simulations and numerical results show that the BSCS aggregator with demand-side regulation capacity can increase its income by 59.05% and 36.78% on working and non-working days, respectively. Also, the aggregator does not worsen the original power load while meeting the EV swapping demand and can decrease the daily load fluctuations by 0.65% and 12.89%, reduce the peak–valley difference by 5.81% and 7.80%, and increase the load rate by 3.67% and 4.08% in working and non-working day situations through providing the dynamic dispatchable capacity for the grid.

Multi-objective optimization of distribution network considering battery charging and swapping station

Battery charging and swapping station (BCSS) can provide flexibility for the distribution network due to accumulating a large number of batteries. This paper proposes a multi-objective optimization method of distribution network considering BCSS, aiming to minimize the transmission losses, voltage deviation and curtailment of wind and photovoltaic power. Besides, The present study puts forth a novel integrated modeling methodology for batteries, which is founded upon the State of Charge (SOC) interval. This approach includes the total energy constraint, as well as the charging and discharging priorities of the batteries. Finally, numerical tests are carried out to validate the correctness and rationality of the presented method. The outcomes of the study show that 2% reduction in wasted energy is achieved with BCSS.

Design and analysis of solar hybrid battery swapping station

Abstract Electric vehicles are in vogue nowadays and it has been gaining some ground in India after Tesla entered into the Indian automobile market. Electric vehicles do not require conventional fuel sources such as fuel, oil, etc., so they do not pollute the environment. BLDC motors are used as the motor of electric vehicles, as they provide a higher speed as well as higher efficiency as compared to other motors. Since the vehicles are powered electrically, it is imperative for us to charge the vehicles just like cell phones. Just like fuel cars have fuel stations for filling up fuel for the operation of vehicles, there must also be battery charging or swapping stations for charging the battery of electric vehicles. This paper would determine how we must charge the electric vehicle using solar along with the traditional grid charging architecture. These two must work in tandem to provide the necessary electrical power to assuring the sufficiently charged battery at the swapping station for uninterrupted use of electric vehicles even in emergency cases. Here, the solar PV along with the traditional grid (Renewable and Conventional both respectively) is used to charge the battery at the swapping station, so it is named a hybrid battery swapping station (HBSS). Also, the concept of charging the battery packs in batches with scheduling is used which reduces the installation cost and the area requirement for the swapping stations compared to other existing battery swapping stations (BSS) or battery charging stations (BCS).