Electric Vehicle Energy Storage System Research Articles

Evaluation of cable and busbar system in multiconductor distribution systems in terms of current and magnetic field distributions

The selection of power distribution components is of great importance in electrical facilities. Cable and busbar systems are widely used applications, such as electric vehicle charge stations, microgrids and energy storage systems, for power distribution in the distribution grid. In this study, the current distribution on the parallel conductors and magnetic field distributions around cable and busbar structures is evaluated for studied application where the power is distributed using a cable system between a converter transformer and a converter. All modeling and analyzes are conducted using ANSYS Electronics Suite software, by applying balanced and pure sinusoidal current excitation. Obtained results show that, when the busbar system is installed for power distribution, current distribution between parallel conductors is decreased to the negligible level, and the calculated magnetic field density is about 73.7% lower than the cable system.

A Linear Recursive State of Power Estimation for Fusion Model Component Analysis with Constant Sampling Time

The state of power of lithium-ion batteries, as the main product of choice for electric and hybrid electric vehicle energy storage systems, is one of the precise feedback control parameters for the battery management system. The proposed research establishes a method for the analysis of charging and discharging constitutive factors under the sampling time, realizes the online identification of parameters by building an adaptive forgetting factor recursive least-squares method based on the Thevenin model, and uses the online parameters to achieve an effective characterization of the power state under voltage and current limitations. The results demonstrate that the accuracy error of online parameter identification is less than 0.03 V. Combining the analysis of charging and discharging constitutive factors under-sampling time with the fusion model of voltage and current limitation makes the power state estimation more reliable and accurate. The results demonstrate that the power state estimation error in the discharging state is less than 8%.

Review of electric vehicle energy storage and management system: Standards, issues, and challenges

Renewable energy is in high demand for a balanced ecosystem. There are different types of energy storage systems available for long-term energy storage, lithium-ion battery is one of the most powerful and being a popular choice of storage. This review paper discusses various aspects of lithium-ion batteries based on a review of 420 published research papers at the initial stage through 101 published research articles that have been finally reviewed. This review paper focuses on several topics, including electrical vehicle (EV) systems, energy management systems, challenges and issues, and the conclusions and recommendations for future work. EV systems discuss all components that are included in producing the lithium-ion battery. The energy storage section contains the batteries, super capacitors, fuel cells, hybrid storage, power, temperature, and heat management. Energy management systems consider battery monitoring for current and voltage, battery charge-discharge control, estimation and protection, cell equalization. This paper's challenges and issues discuss some of the critical aspects of lithium-ion batteries, including temperature and safety, life-cycle and memory effects, environmental effects, and recycling processes. The conclusion and recommendation of this paper indicate the future scope of research. This review paper can provide the lithium-ion battery's insight, overall synopsis and contribution, and further research directions to the EV system.

Energy storage sizing in plug-in Electric Vehicles: Driving cycle uncertainty effect analysis and machine learning based sizing framework

Transportation electrification has been considered as one of the promising solutions towards clean environment. On-board Energy Storage System (ESS) has significant influence on the cost and reliability of Electric Vehicles (EVs). Furthermore, the choice of the ESS in terms of type and size, i.e., its capacity heavily depends on the driving patterns which, usually, vary significantly depending on factors like driver characteristics, geographical locations and traffic congestion. Hence, it is crucial to understand the effect of driving patterns/cycles on the sizing of ESS in order to avoid over/under-sizing. In this context, we investigate the effect of driving cycle uncertainties on the optimal sizing of ESS in EVs. We specifically focus on the uncertainty arising from the unpredictability of the future state of speed and acceleration and model the same as a scaling factor on velocity-magnitudes and time-windows of the velocity profile. Subsequently, (i) we derive the quantitative relationship between uncertainty and corresponding change in ESS size based on four thousand test driving cycles; (ii) we develop a framework which provides a guideline towards the choice of appropriate sizing of ESS in EVs subject to uncertainty in driving cycle; and (iii) we propose a machine-learning based framework that can enable a data-driven approach for ESS sizing with lower computational requirement. We choose a city bus application to illustrate our approach and consider three different ESS configurations: (i) Li-ion battery alone, (ii) Supercapacitor alone, and (iii) Battery–Supercapacitor hybrid ESS.

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.

Smoothing control of wind power fluctuations with battery energy storage system of electric vehicles

With the rapid development of wind industry, its integration causes inevitable and rapid power fluctuations in the power grid. To promote the integration of wind power and investigate the energy storage capability of electric vehicles (EVs), a smoothing control strategy is proposed to smooth the wind power fluctuations considering the battery energy storage system of EVs (EESS). Based on a proposed maximum operation area of an individual EV considering its traveling behaviors and traveling comfort level, the response parameters are defined to describe the response characteristics of the EV aggregator (EVA). The EESS can operate as an energy storage system composed of several EVAs with different response parameters. A smoothing control strategy is then developed to smooth the wind power fluctuations with the EESS. Considering the power fluctuation rate and the response capability of the EESS, the required power change of each EVA in the EESS is determined in the smoothing control. Then the required power output change is allocated to individual EVs in the EVA with the developed SOC-adaptive method and the power output limits constrained by the maximum operation area of each EV. Comparative results are provided to validate the effectiveness of the proposed smoothing control strategy. The EESS is able to smooth the wind power fluctuations, and the power output limits and the traveling comfort levels of individual EVs are ensured with the SOC-adaptive method and the constraints from the maximum operation areas.

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.

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.

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.

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.

Optimization of a Cascaded H-Bridge Inverter for Electric Vehicle Applications Including Cost Consideration

This paper presents a method to find the optimal configuration for an electric vehicle energy storage system using a cascaded H-bridge (CHB) inverter. CHB multilevel inverters enable a better utilization of the battery pack, because cells/modules with manufacturing tolerances in terms of capacity can be selectively discharged instead of being passively balanced by discharging them over resistors. The balancing algorithms have been investigated in many studies for the CHB topology. However, it has not yet been investigated to which extend a conventional pack can be modularized in a CHB configuration. Therefore, this paper explores different configurations by simulating different switch models, switch configurations, and number of levels for a CHB inverter along with a reference load model to find the optimal design of the system. The configuration is also considered from an economically point of view, as the most efficient solution might not be cost-effective to be installed in a common production vehicle. It is found that four modules per phase give the best compromise between efficiency and costs. Paralleling smaller switches should be preferred over the usage of fewer, larger switches. Moreover, selecting specific existing components results in higher savings compared to theoretical optimal components.

Detailed Modeling and Novel Scheduling of Plug-in Electric Vehicle Energy Storage Systems for Energy Management of Multi-microgrids Considering the Probability of Fault Occurrence

Detailed Modeling and Novel Scheduling of Plug-in Electric Vehicle Energy Storage Systems for Energy Management of Multi-microgrids Considering the Probability of Fault Occurrence

Adhesive and Heat Resistant Separator for Long-Term Stable Lithium Ion Batteries

Next-generation lithium ion batteries (LIBs), the promising energy sources for an electric vehicle and grid energy storage systems, should satisfy the continuous needs for high reliability and safety for long-term high-energy-density operation. A novel separator has a functional multi layer coating on both side of PE separator. It shows superior heat resistance, high electrolyte uptake, long stable cyclability when equipped in the cell, and persistent adhesion with the electrode. Multi coating layers are composed of a 2-μm-thick Al2O3 ceramic layer that enhances mechanical strength and thermal stability of the conventional polyethylene separator, and a 1.5-μm-thick adhesive layer produced from acrylic polymer beads that combine consistently between the electrode and the separator forming electrode/separator assembly during cell operation. These multi-layer coated separators enable high capacity retention and reduce impedance build up with cycling, when used in coin or pouch cells. Fabrication of multi-coating layers by aqueous coating without organic solvents not only offers environmental benefits but is also effective in cost reduction. This approach paves the way for the separator, which has long been considered an inactive major component of LIBs, to contribute towards the energy density as an active component for obtaining longer stable cyclability.

Special Section on New Trends in Residential Energy Management

The eleven papers in this special section focus on new trends in residential and home energy management. As an important branch of power demand side management, residential energy management plays an important role in reducing the emission and enhancing the energy efficiency in the energy delivery side. Recent technical advances bring significant transformations to energy end-users. First, increasing penetrations of residential renewable energy source, electric vehicle, and residential energy storage system have been transforming residential energy consumers to be “Energy Prosumers (Producer and Consumer. Second, the two-way communication infrastructure enables residential energy entities interact and exchange information flows with the external environment. Third, recent advances in ubiquitous sensing and metering technologies, such as Internet of Things, nonintrusive load monitoring, and advanced metering infrastructure, enable the deep understanding on behaviors of energy end-users and related environments. These technical advances consequently drive residential energy entities to become complex cyber-physical-social systems, which require newsolutions for coordinating, managing, and optimizing residential energy resources with the active participations of end users.

HYBRID ENERGY MANAGEMENT SYSTEM OF EV USING GRID TO VEHICLE SYSTEM

To implement the Electric Vehicle-Energy Storage System (EV-ESS) to control of battery charging and discharging and control between the power generation and load. The proposed system implement a interlinking converter, which act as gateway between AC and DC sub grids in micro grid system. The converter has an able to control active and reactive power controller, which implement within the converter interlink controller. The design of reactive power controller operate depends on the RMS voltage. The PV systems are used to grid tried hybrid AC/DC micro grid. For wind energy conversion using Doubly Fed Induction Generator (DFIG) used for grid tied hybrid AC/DC micro grid. The Simulation and analysis are using MATLAB SIMULINK software. The result proves the effectiveness of the proposed controller and shows the EV-ESS Charging/Discharging control and reactive power control performance efficient.

Model predictive control for building loads connected with a residential distribution grid

Aggregated control of electrical loads in a large cluster of buildings has been a challenge due to the complexity of the system involving generators, grid constraints, load serving entities complex load models, and people behavior. This paper introduces a novel load aggregation method in an electricity distribution system with Model Predictive Controlled (MPC) loads. This method closes the control loop from power generation to people behavior, resulting in a more stable and efficient integrated buildings-to-grid system. A behavior-driven price-based MPC is introduced for a residential building energy management system, which controls the air conditioner (AC), electric vehicle (EV), water heater, and battery energy storage system. A nodal pricing method is introduced representing power generation and distribution costs, which is mathematically proven to stabilize the system with MPC controlled loads. The method is tested in a 342-node residential building distribution network with 15,000 buildings which is inverse sampled from hundreds of actual smart meter data. The results show a 21% reduction in generation cost, 17% reduction in peak load, and reduced nodal voltage drop from the coordinated control system.

Lithium–Sulfur Battery State-of-Charge Observability Analysis and Estimation

Lithium–Sulfur (Li–S) battery technology is considered for an application in an electric-vehicle energy storage system in this study. A new type of Li-S cell is tested by applying load current and measuring cell's terminal voltage in order to parameterize an equivalent circuit network model. Having the cell's model, the possibility of state-of-charge (SOC) estimation is assessed by performing an observability analysis. The results demonstrate that the Li–S cell model is not fully observable because of the particular shape of cell's open-circuit voltage curve. This feature distinguishes Li-S batteries from many other types of battery, e.g., Li-ion and NiMH. As a consequence, a Li-S cell's SOC cannot be estimated using existing methods in the literature and special considerations are needed. To solve this problem, a new framework is proposed consisting of online battery parameter identification in conjunction with an estimator that is trained to use the identified parameters to predict SOC. The identification part is based on the well-known prediction-error minimization algorithm; and the SOC estimator part is an adaptive neuro-fuzzy inference system in combination with coulomb counting. Using the proposed method, a Li-S cell's SOC is estimated with a mean error of 4% and maximum error of 7% in a realistic driving scenario.