Electric Vehicle Energy Storage Research Articles

Analysis and simulation of multi-station fusion layered structure based on MMC module

In order to solve the problems of low space utilization rate and insufficient power supply reliability of substations, a multi-station fusion technology with substations as the main body and multiple power stations (5G communication base station, electric vehicle charging station, data center and energy storage station) was proposed. In this paper, the layered structure of the new fusion power station with “multi-station fusion” is proposed to clarify the collaborative working mode of various parts in the new fusion power station through the layered structure, so as to prevent equipment failures caused by interlocking control information. At the same time, the topological structure of the energy transfer layer of the new fusion power station is designed, and the MMC module is used in the energy transfer layer for energy transmission. Then the verification procedure, analysis of the circuit feasibility of the output waveform and control of the signal waveform are realized on the hardware circuit.

Energy Storage Capacity Expansion of Microgrids for a Long-Term

In this paper, we examine the microgrids and the long-term dynamic capacity expansion planning in their architecture. Many resources contribute towards the supply to microgrid such as energy, micro gas turbine, solar and wind storage system. Moreover the electric vehicle charging stations use these microgrids as a source of electricity. The electric vehicles that are used in charging stations are based on vehicle-to-grid wherein it is possible to regulate the charging rate and time and to transmit energy to the microgrid. Hence, these charging stations are found to be present in generating unit or flexible load. In the microgrid, the capacity expansion planning is initiated to expand the capacity of battery, wind turbine, solar and micro turbine energy storage system. We have elaborated a 6–year planning horizon, targeting a long term plan through capacity expansion. On the other hand, we have also conducted a short term plan simultaneously to improve the hourly operation of electric vehicle charging station, energy and micro turbine storage system. An expansion of about 200% on wind system is used such that expansion cost is about 53% and incorporation of further resources will increase it by 58% in terms of cost.

Battery Management System for Electric Vehicle

The healthy system in any country is a sign of healthy lifestyle. The rapid depletion of fossil fuels and harmful effects on the environment are forcing the use of electric vehicles on a large scale. Now days the proper use of energy sources that can available in nature is very important and efficiency point of view electric vehicles are the very good choice. The battery is a basic component of electric vehicles that signals a step forward in the direction of stable mobility. Lithium chemistry is now known as the energy storage in electric vehicles. However, many research points are still open including the optimal selection of cell material and the development of electronic circuits and algorithms for more efficient use of batteries. This paper reviews battery performance and discusses the requirements and standards applicable to the battery management system(BMS). The architecture for implementation battery management and techniques for state of charge and cell balancing are reported.

Analysis of Energy Management Using PI Logic Controller Technique for Supercapacitor Applications

Batteries are the primary energy storage for electric vehicles. Often the power of the battery cannot be adequate to satisfy the demands of heavy loads. Simultaneously with the battery source, secondary capacity, such as a super-capacitor, can be used to fulfil the power demand where ultra-capacitors meet the high-frequency specifications. Ultracapacitor can be used to inhibit the recycling of the high-current transient battery. In the course of the battery recycling impacts battery life, charging device with high current. This paper is focused on a PI control system for electric vehicle management and the selected MATLAB / Simulink simulation framework. The result from the energy management system that is using shows that with PI controller, the voltage and current more stable compared to the design without using PI controller.

Optimal Allocation and Control of EV Energy Storage in Microgrids

Next Generation smart grid (SG) systems blend legacy power system networks and the latest state-of-the-art ICT technologies to ensure the efficiency, robustness as well as reliability of the former (power systems). The duplex flow of both information and energy enhances energy supply and de-mand response, as well as SG-related innovative business-oriented applications and services. Renewable generators (RGs) and Electric Vehicles (EVs) are becoming prominent in any SG setup as they promote environmental friendliness. The presence of both necessitates the optimal allocation of dis-tributed renewable generation (DRG) and energy storage sys-tems (ESS) at both SG and microgrid (MG) levels. In that way, grid stability will be always ensured. The paper describes and discusses an optimized ESS deployment approach for serving EVs (as they are one of the largest consumers of stored energy) and DRGs. Careful consideration of the state of the ESS is also considered by developing and applying a dynamic capacity adjustment algorithm to deal with the none-smooth cost func-tions. The proposed cost function takes into consideration the operation as well as investment cost minimization concurrently. The matrix real-coded genetic algorithm (MRCGA) is used to minimize the cost function of the system while constraining it to meet the customer demand, as well as the security of the system overall. The computational simulation results are presented to verify the effectiveness of the proposed method. The electricity network model is simplified using a virtual subnode concept to alleviate the computational load burden of a node's agent. Simulation results demonstrate the feasibility and stability of this dispatch strategy. Overall, our proposed framework and obtained results set a benchmark for the realization of agent-based coordination algorithms to solve the optimal dispatch problem

ZnO and MXenes as electrode materials for supercapacitor devices.

Supercapacitor devices are interesting owing to their broad range of applicability from wearable electronics to energy storage in electric vehicles. One of the key parameters that affect the efficiency of supercapacitor devices is selecting the ideal electrode material for a specific application. Regarding this, recently developed metal oxides, specifically nanostructured ZnO, and MXenes with their defect structures, size effects, as well as optical and electronic properties have been presented as electrode material in supercapacitor devices. The discussion of MXenes along with ZnO, although different in chemistry, also highlights the differences in dimensionality when it comes to defect-driven effects, especially in carrier transport. The volume under the influence of the defect centers is expected to be different in bulk and 2D structures, regardless of composition. Hence, analysis and discussion of both materials provide a fundamental understanding regarding the manner in which 2D structures are impacted by defects compared to bulk. Such an approach would therefore serve the scientific community with the material design tools needed to fabricate the next generation of supercapacitor devices.

Aggregation Optimization Method of Virtual Energy Storage for Electric Vehicles Considering User Elasticity

With the continuous development of electric vehicle charging facilities, the impact of electric vehicles on the power grid is growing. Considering the automatic demand response technology of smart grid, charging pile operators participate in the demand response plan and guide users to charge according to the price signal or incentive mechanism, which can ensure the safety of the power grid and reduce the negative effects of electric vehicles on the grid influence. Based on the theory of demand side response elasticity, this paper analyzes the whole process of electric vehicle users' participation in demand response and its influencing factors, classifies electric vehicle users based on demand response, and then quantitatively analyzes users' demand response from the perspective of electricity price elasticity and consumption psychology. Finally, the demand side response model with user participation is established. The application of the model can effectively improve the enthusiasm of electric vehicle users to participate in demand side response, and reduce the negative impact of electric vehicles on the power grid.

Study on Control Strategy for Composite Power Supply System of EV

At present, countries all over the world are vigorously developing new energy vehicles. Many countries and regions have introduced policies to stop the production and sales of traditional energy vehicles. However, the large-scale industrialization of pure electric vehicles has encountered the constraints of power batteries, and its main problems are reflected in the specific power of the power battery is low. This paper analyzes the current situation of electric vehicle composite power supply, points out the problems and solutions of electric vehicle energy storage system, in order to obtain the expected development and test results.

Virtual inertia provision through data centre and electric vehicle for ancillary services support in microgrid

The proposal focuses on the role of data centres (DCs) and electric vehicle (EV) energy storage systems (ESSs) for frequency regulation and it provides a new opportunity for different resources to participate in the power sector. Frequency fluctuations are introduced with the emergence of inverter-dominated renewable energy sources (RESs) as it does not provide rotational inertia to the grid, such as synchronous generators. In this study, the virtual synchronous generator (VSG) controlled inverter compensates for the lack of inertia. Particularly, this research work analyses the involvement of DC and EV batteries as ESSs in designing an integrated technical virtual power plant (VPP) to support frequency variations using the VSG concept. Microgrid (MG) simulations are performed in MATLAB/ Simulink platform. Case studies are carried out to validate the grid export and grid import performance of the model by using variations in the load and the solar irradiance of the MG. It is verified that bidirectional power flow takes place between the proposed VPP components and the grid that enables the grid with high penetration of RESs. The work provides a conceptual framework for future contributions towards the smarter usage of assets such as ESS and a greener future.

Analysis of Electric Vehicles Motion Modes Influence on Traction Batteries Aging Based on WLTC

The paper considers the growth rates of electric vehicles, taking into account the development of electrochemical energy storage. The main lithium batteries aging mechanisms under conditions of uneven load and reversible nature of energy exchange processes with short modes of recharging batteries during regenerative braking are determined. To assess the aging of the lithium battery, the WLTC driving cycle was taken as a basis, in accordance with which the load on the electric vehicle energy storage unit the was formed. A Nissan Leaf equipped with energy storage units (ESU) of different energy intensity was adopted as a test sample. Current diagrams were obtained for one battery cell, minimum, average and maximum currents, as well as durations of traction, braking and stopping modes in various control modes of electric rolling stock are given. An assumption is made about the growth of the energy intensity of the ESU in the future period and its influence on the load of the storage battery is determined. On the basis of the data processing carried out, the main changes in the processes occurring in the power source, associated with an increase in the energy intensity of the ESU, have been revealed

Applications of two-dimensional material-functional separator in lithium battery

In recent years, people’s continuous attention to energy crisis and environmental problems has promoted the development of electric energy storage technology. The emergence of lithium-ion battery (LIB) has profoundly impacted on society by providing energy storage for portable electronic devices, medical devices and electric vehicles. However, the electrode materials can only contribute a low capacity due to the intercalation reaction chemistry, specific capacity of Li-ion batteries cannot be further improved. Therefore, lithium secondary battery systems with higher energy and power density need to be developed for the next generation of electric vehicles.The lithium metal has an extremely high theoretical specific capacity (3860 mAh/g) and low redox potential (–3.04 V vs. standard hydrogen electrode), giving the lithium battery a high energy density when used as a anode material. And when sulfur used as a cathode material, its theoretical specific capacity reaches 1672 mAh/g, thus the theoretical energy density of the lithium sulfur (Li-S) battery system is as high as 2600 Wh/kg. However, the polysulfides in Li-S battery seriously affect the safety and electrochemical performance of the cell. When lithium metal used as anode materials, the uncontrolled dendrite growth also brings serious safety risks to users. Therefore, all the basic components of lithium batteries, including the anode, cathode, separator and electrolyte, need to be optimized. Most of the current research has focused on the development of high-capacity anode and cathode materials. In fact, functional separators have an important impact on the safety and electrochemical performance of lithium secondary batteries. Compared with other materials that modified commercial polyolefin membranes, two-dimensional materials offer the advantages of ultra-thin laminate structure, high mechanical strength, high specific area, and adjustable surface chemical properties. Therefore, in Li-S batteries, two-dimensional material-functionalized separators can inhibit the shuttle effect of polysulfides through chemical adsorption and confined pore channels. In Li-metal batteries, the inhibition of dendritic growth by two-dimensional functional membranes is mainly due to its high mechanical strength, thermal conductivity and the ability to regulate ion transport flux through defect sites. And in Li-ion batteries, the two-dimensional functional separator mainly improves the wettability and poor thermal stability of routine polyolefin membrane, thus it greatly enhances the electrochemical performance and safety performance of lithium cells. This review first introduces the unique advantages of two-dimensional materials in the membrane, including parallel self-assembly feature, excellent mechanical strength and flexibility, ultrahigh specific surface area, surfaces are easily modified and element doped and ultrathin laminar structure. Then the review briefly describes the method of constructing two-dimensional materials separators and its characterization. Subsequently, this paper focuses on the application of different types of two-dimensional materials functional membranes in solving polysulfide shuttle effect in Li-S batteries, dendrite growth of anode in Li-metal batteries, as well as poor wettability and thermal stability of commercial separator in Li-ion batteries. Finally, the remaining opportunities and challenges of two-dimensional materials in Li battery separator materials are briefly discussed.

Optimal scheduling of smart microgrids considering electric vehicle battery swapping stations

Smart microgrids belong to a set of networks that operate independently. These networks have technologies such as electric vehicle battery swapping stations that aim to economic welfare with own resources of smart microgrids. These resources should support other services, for example, the supply of energy at peak hours. This study addresses the formulation of a decision matrix based on operating conditions of electric vehicles and examines economically viable alternatives for a battery swapping station. The decision matrix is implemented to manage the swapping, charging, and discharging of electric vehicles. Furthermore, this study integrates a smart microgrid model to assess the operational strategies of the aggregator, which can act like a prosumer by managing both electric vehicle battery swapping stations and energy storage systems. The smart microgrid model proposed includes elements used for demand response and generators with renewable energies. This model investigates the effect of the wholesale, local and electric-vehicle markets. Additionally, the model includes uncertainty issues related to the planning for the infrastructure of the electric vehicle battery swapping station, variability of electricity prices, weather conditions, and load forecasting. This article also analyzes how both the user and the providers maximize their economic benefits with the hybrid optimization algorithm called variable neighborhood search - differential evolutionary particle swarm optimization. The strategy to organize the infrastructure of these charging stations reaches a reduction of 72% in the overall cost. This reduction percentage is obtained calculating the random solution with respect to the suboptimal solution.

Hydrometallurgical Extraction of Li and Co from LiCoO2 Particles–Experimental and Modeling

The use of lithium-ion batteries as energy storage in portable electronics and electric vehicles is increasing rapidly, which involves the consequent increase of battery waste. Hence, the development of reusing and recycling techniques is important to minimize the environmental impact of these residues and favor the circular economy goal. This paper presents experimental and modeling results for the hydrometallurgical treatment for recycling LiCoO2 cathodes from lithium-ion batteries. Previous experimental results for hydrometallurgical extraction showed that acidic leaching of LiCoO2 particles produced a non-stoichiometric extraction of lithium and cobalt. Furthermore, the maximum lithium extraction obtained experimentally seemed to be limited, reaching values of approximately 65–70%. In this paper, a physicochemical model is presented aiming to increase the understanding of the leaching process and the aforementioned limitations. The model describes the heterogeneous solid–liquid extraction mechanism and kinetics of LiCoO2 particles under a weakly reducing environment. The model presented here sets the basis for a more general theoretical framework that would describe the process under different acidic and reducing conditions. The model is validated with two sets of experiments at different conditions of acid concentration (0.1 and 2.5 M HCl) and solid to liquid ratio (5 and 50 g L−1). The COMSOL Multiphysics program was used to adjust the parameters in the kinetic model with the experimental results.

Joint optimization of charging station and energy storage economic capacity based on the effect of alternative energy storage of electric vehicle

This paper studies the capacity of electric vehicle charging station (EVCS) and energy storage, and the optimization problem and model of electric vehicle (EV) charging scheduling plan. Based on the alternative energy storage effect of EVs, it is committed to improve the renewable energy consumption capacity in micro-grid, reduce the EVCS and energy storage capacity, and improve the comprehensive benefits of micro-grid investors. In this paper, considering the factors that affect the efficiency and system security, the determination model of EVCS and energy storage capacity is established, and the traditional simulated annealing algorithm (SA) is improved to create a disturbance mechanism for the optimization of charging scheduling plan, and the joint optimization is realized. The empirical analysis results show that the EVCS and energy storage capacity are reduced by about 50%, and the benefit of micro-grid investors is increased by about 7%. Therefore, this study can provide solutions for the planning of micro-grid with distributed energy, conventional users, energy storage and adjustable load, maximize renewable energy consumption and improve the satisfaction of all participants. Keywords: Electric vehicle charging station; Alternative energy storage effect; Net annual value; Improved simulated annealing algorithm; Joint capacity optimization.

Real-Time Building Smart Charging System Based on PV Forecast and Li-Ion Battery Degradation

This paper proposes a two-stage smart charging algorithm for future buildings equipped with an electric vehicle, battery energy storage, solar panels, and a heat pump. The first stage is a non-linear programming model that optimizes the charging of electric vehicles and battery energy storage based on a prediction of photovoltaïc (PV) power, building demand, electricity, and frequency regulation prices. Additionally, a Li-ion degradation model is used to assess the operational costs of the electric vehicle (EV) and battery. The second stage is a real-time control scheme that controls charging within the optimization time steps. Finally, both stages are incorporated in a moving horizon control framework, which is used to minimize and compensate for forecasting errors. It will be shown that the real-time control scheme has a significant influence on the obtained cost reduction. Furthermore, it will be shown that the degradation of an electric vehicle and battery energy storage system are non-negligible parts of the total cost of energy. However, despite relatively high operational costs, V2G can still be cost-effective when controlled optimally. The proposed solution decreases the total cost of energy with 98.6% compared to an uncontrolled case. Additionally, the financial benefits of vehicle-to-grid and operating as primary frequency regulation reserve are assessed.

Comparative analysis of the supercapacitor influence on lithium battery cycle life in electric vehicle energy storage

Abstract Modern tendencies together with growing consciousness about the negative environmental influence of the transportation technologies have yielded electric vehicles as one of the approaches for phasing out fuel-based vehicles. Different directives aimed at increasing demand for electric vehicles have been put in place, however, due to different reasons initial predictions resulted in an insufficient number of sold units and suboptimal solutions like hybrid vehicles. Consequently, the reduction in fuel consumption and pollution generation is insignificant on a global scale. The main deficiency of the electric vehicle is its battery-based storage unit, which due to the current state of development makes the electric vehicle less admissible for consumers. Relatively short cycle life, high sensitivity to ambient conditions, environmental hazards, and relatively limited output power are only some of the deficiencies of the current battery technology. However, a novel type of electric storage technology has recently emerged as a promising long term complement to the battery technology. Long cycle life, huge power density, and no environmental hazards make supercapacitor technology a viable and assuring addition to the battery storage. Furthermore, supercapacitors may offer the possibility of making the electric vehicle more competitive on the market as well as a more sustainable solution. Although the supercapacitor technology is still considered to be immature and more research is needed, this paper examines the possibilities and effects of using supercapacitors as part of the electric vehicle energy storage. The main goal is the analysis of the positive effects that the supercapacitor storage can have on the battery cycle life as well as on the electric vehicle performance and economy. Conclusions drawn from the herein presented research are based on the theoretical analysis, numerical models, and experimental verification, therefore it can be confirmed that using supercapacitors as a part of electric vehicle energy storage substantial improvements in performance, durability, and economy could be achieved.

Economic Analysis of Electric Vehicle Operators in Participating Auxiliary Services Market

In recent years, with the rapid development of electric vehicles, the security of power system has been affected by the wide access, and the economic value potential of its participation in auxiliary services has been paid more and more attention. In this paper, through the analysis of the problems of electric vehicle participating in peak shaving auxiliary service, the economic value of electric vehicle energy storage participating in peak shaving auxiliary service is reflected, and the cost and potential benefits of electric vehicle energy storage system are analyzed, which is helpful to study the economy of electric vehicle energy storage further.

Two-stage stochastic home energy management strategy considering electric vehicle and battery energy storage system: An ANN-based scenario generation methodology

This study implements two-stage stochastic programming in a smart home application to reduce the electricity procurement cost of an ordinary household. In this concern, vehicle to home (V2H) capability of the available electric vehicle (EV) is used in coordination with battery energy storage system (BESS) under control of a home energy management system. The stochastic decision variables are the charge-discharge power of these components. The uncertainties derived from the power production of the roof-mounted solar photovoltaic panels, household’s load demand, real-time electricity price are assimilated into the problem. Besides, to create the stochastic process, an artificial neural network (ANN) is trained using historical time series. Furthermore, as one of the main contributions, a proper analytical battery degradation cost model is integrated into the problem. Hence, different schemes such as with and without degradation cost, with and without BESS and uncoordinated charging are investigated under various charging rates. Also, the sensitivity of the problem for different charging rates of the EV and BESS is analyzed. Furthermore, the influence of probable future battery storage cost reductions on the home energy management system is investigated. Eventually, the efficiency of the stochastic programming method is analyzed by the value of stochastic solution (VSS) metric.

Optimal Power Allocation in Battery/Supercapacitor Electric Vehicles Using Convex Optimization

This paper presents a framework for optimizing the power allocation between a battery and supercapacitor in an electric vehicle energy storage system. A convex optimal control formulation is proposed that minimizes total energy consumption whilst enforcing hard constraints on power output and total energy stored in the battery and supercapacitor. An alternating direction method of multipliers (ADMM) algorithm is proposed, for which computational and memory requirements scale linearly with the length of the prediction horizon (and can be reduced using parallel processing). The optimal controller is compared with a low-pass filter against an all-battery baseline in numerical simulations, where it is shown to provide significant improvement in battery degradation (inferred through reductions of 71.4% in peak battery power, 21.0% in root-mean-squared battery power, and 13.7% in battery throughput), and a reduction of 5.7% in energy consumption. It is also shown that the ADMM algorithm can solve the optimization problem in a fraction of a second for prediction horizons of more than 15 minutes, and is therefore a promising candidate for online receding-horizon control.

Artificial Neural Network–Based Multisensor Monitoring System for Collision Damage Assessment of Lithium‐Ion Battery Cells

The sales of electric vehicles (EVs) have seen a significant upward trend in recent years. The occurrence of severe collision accidents has raised awareness concerning safety issues of lithium‐ion batteries (LIBs) which are the core components of EV energy storage systems. When collision accidents of vehicles occur, in most cases, the battery cells will immediately burn and even explode, whereas in some cases, despite not directly showing visual damages, the battery cells may lead to delayed catastrophic failures. Herein, the assessment of the battery cells collision damage based on sensor signal data is focused on by identifying potentially unsafe cells. A campaign of collision experimental tests and a series of electrical performance tests are conducted on single‐cell specimens. A cell damage characterization procedure is proposed and implemented on the battery cells after the collision tests. The impact force and z‐axis acceleration signal features and respective damage classes are input to an artificial neural network (ANN) pattern classifier to train a model to assess the battery cell collision damage.