Electric Vehicle Energy Storage System Research Articles

Energy management strategies in distribution system integrating electric vehicle and battery energy storage system: A review

AbstractThe electricity sector is witnessing a rise in renewable energy sources and the widespread adoption of electric vehicles, posing new challenges for distribution system. Additionally, the surge in carbon emissions resulting from industrialization and population growth continues to worsen global warming and climate change. In response, integrating electric vehicles (EVs) and battery energy storage systems (BESS) has emerged as a critical strategy, presenting both challenges and opportunities in effective energy management. BESSs offer potential solutions to mitigate these impacts. Furthermore, this review thoroughly explores issues related to lithium‐ion batteries, particularly in the context of EVs and energy management systems (EMS), identifies challenges, and provides recommendations for future research directions. The article concludes by outlining the current extent of investigation in the field of BESS and EV systems to provide researchers with a clear understanding. The escalation of carbon emissions stemming from industrialization and population expansion has worsened the effects of global warming and climate change. To address this challenge, the integration of Electric EVs and energy storage systems (ESS) has emerged as a pivotal strategy. This study examines optimization techniques, methodologies, and the evolving market landscape in distributed systems, with a focus on EVs and BESS. It also explores issues related to lithium‐ion batteries, particularly in the context of EVs and energy management systems. The article highlights the challenges and opportunities in the field of BESS and EV systems, emphasizing the need for ongoing research. BESSs offer potential solutions to mitigate these impacts. Moreover, it offers an extensive analysis of the existing BESS installations, outlining main areas of interest, pointing out difficulties, clarifying areas of unfinished study, and providing future directions.

Many-objective optimization based mutual feed scheduling for energy system of integrated energy station

The battery storage system of the integrated energy station contains electric vehicles as the distributed energy storage node of the regional power system can help the regional power system to achieve peak shaving and valley filling. This paper proposes a many-objective optimization based mutual feed model of the energy system of the integrated energy station. The model includes multiple modules of electric vehicle (EV), wind turbine (WT), photovoltaic (PV) and battery energy storage system (BESS). BESS was modeled according to the two behaviors of EVs charging and swapping. The model ensures the role of peak shaving and valley filling for regional power, reduces the loss of battery life in the battery storage system of the integrated energy station, and reduces the operating cost of the battery storage system. In the optimized results, the regional power system load peak-to-valley ratio by 28.72 %, and the daily battery charge/discharge life loss is minimized to 0.00036 %. In addition, for known many-objective optimization problems, this paper also proposes a new meta-heuristic many-objective optimization algorithm the non-dominated sorting hunter prey optimization (NSHPO for short) and compared with other four many-objective optimization algorithms by three indexes (Distribution range, Hypervolume index, and Running time). The simulation results show that compared with other algorithms, the HV metrics of NSHPO are lower by about 1.56 %-7.26 %, Spread metrics are better by about 0.39 %-2.73 %, and the proposed NSHPO algorithm is better in terms of convergence and distributivity.

Enhancing passive cell balancing techniques for electric vehicles

This research endeavors to enhance the efficiency and functionality of electric vehicle (EV) energy storage systems by scrutinizing the state of charge (SOC) across three batteries. Utilizing Matlab Simulink, a comprehensive battery management system (BMS) integrating passive cell balancing techniques has been meticulously devised. Additionally, a pioneering protective mechanism has been proposed to prevent undue discharges and overcharges, thus ensuring the longevity and reliability of EV battery systems. The study's findings underline the significance and applicability of the developed model in facilitating prospective enhancements within the realm of EV energy storage systems. By meticulously analyzing SOC dynamics across multiple batteries, the study offers valuable insights into optimizing battery utilization, prolonging battery lifespan, and enhancing overall system performance. Moreover, the integration of passive cell balancing techniques within the BMS framework represents a noteworthy advancement in battery management strategies. This approach not only promotes uniformity in SOC distribution among battery cells but also minimizes energy losses and mitigates the risk of cell degradation, thereby bolstering the operational efficiency and durability of EV energy storage systems. Furthermore, the proposed protective mechanism serves as a proactive measure to safeguard against potential damage caused by extreme operating conditions, such as overdischarge and overcharge events. By implementing real-time monitoring and control mechanisms, the protective system ensures the safe and reliable operation of EV batteries under varying driving conditions, thereby enhancing both safety and performance aspects. In conclusion, this research contributes to the ongoing efforts aimed at advancing the state-of-the-art in EV energy storage systems, holding immense potential for fostering innovation and driving sustainable progress in the field of electric mobility.

An Integrated Self-Modularized Battery Equalizer and Supercapacitor Charger for Hybrid Electric Vehicle Energy Storage System

An Integrated Self-Modularized Battery Equalizer and Supercapacitor Charger for Hybrid Electric Vehicle Energy Storage System

Development of Electro-Thermal Model for Air Cooling Study of Electric Vehicle Lithium-Ion Battery Module Operating in Indian Conditions and Drive Cycle

Lithium-ion cells have higher energy density at a specific power, making them a primary choice for battery packs in energy storage of modern technology, powering from smartphones to Electric vehicle (EV) and grid-scale energy storage systems. However, their performance, safety and lifespan are temperature sensitive and developing a fail-safe mechanism is considered as a pressing priority. In the present study, a 2D electro-thermal model is developed in MATLAB to characterize the thermal behaviour of cylindrical cells (individual and inline) during discharge. The thermal model solves the transient heat conduction equation with internal heat generation. The model is validated with the available experimental data for a single cell at a constant discharge rate and found to be in good agreement within 0.5°C. The analysis is extended to a battery module with 6 cell-inline configuration and validated. This validated model is used to study the impact of multiple cells (1-10) in inline configuration. For Indian drive cycle analysis, the current profile is estimated based on the drive cycle and its impacts on cell temperature rise in battery module is studied when subjected to variation in monthly average ambient temperature. Battery module maximum temperature rises to 60 °C and Δ T (Tmax - Tmin) across cells is 8.8 °C during summer conditions under natural convection due to multiple drive cycle loading. The battery module temperature can be kept under safe operating limits (Tmax≤ 45 °C, Δ T ≤ 4 °C) with forced convection of 2 m/s and 25 °C. The study aims to provide insights into the thermal behaviour of Lithium-ion cells that can be used to design safe and cost-effective air-cooled thermal management system for battery module.

A Dynamic Peer-to-Peer Electricity Market Model for a Community Microgrid With Price-Based Demand Response

Peer-to-Peer (P2P) energy sharing enables prosumers within a community microgrid to directly trade their local energy resources such as solar photovoltaic (PV) panels, small-scale wind turbines, electric vehicle battery storage among each other based on an agreed cost-sharing mechanism. This paper addresses the energy cost minimization problem associated with P2P energy sharing among smart homes which are connected in a residential community. The contribution of this paper is threefold. First, an effective Home Energy Management System (HEMS) is proposed for the smart homes equipped with local generation such as rooftop solar panels, storage and appliances to achieve the demand response (DR) objective. Second, this paper proposes a P2P pricing mechanism based on the dynamic supply-demand ratio and export-import retail prices ratio. This P2P model motivates individual customers to participate in energy trading and ensures that not a single household would be worse off. Finally, the performance of the proposed pricing mechanism, is compared with three popular P2P sharing models in the literature namely the Supply and Demand Ratio (SDR), Mid-Market Rate (MMR) and bill sharing (BS) considering different types of peers equipped with solar panels, electric vehicle, and domestic energy storage system. The proposed P2P framework has been applied to a community consisting of 100 households and the simulation results demonstrate fairness and substantial energy cost saving/revenue among peers. The P2P model has also been assessed under the physical constrains of the distribution network.

Opportunities, Challenges and Strategies for Developing Electric Vehicle Energy Storage Systems under the Carbon Neutrality Goal

Developing electric vehicle (EV) energy storage technology is a strategic position from which the automotive industry can achieve low-carbon growth, thereby promoting the green transformation of the energy industry in China. This paper will reveal the opportunities, challenges, and strategies in relation to developing EV energy storage. First, this paper clarifies the strategic value and potential of developing EV energy storage under the carbon neutrality goal. Second, this paper demonstrates strategic opportunities and challenges during the development. Third, this paper proposes methods for creating a good market environment and business models. Finally, this paper suggests that relevant policies and regulations should be formulated and charts the course of technology development. The results show that EV energy storage technology has potential in terms of technology, the scale of development, and the user economy. The proposal of the carbon neutrality goal, the increasing market share of EVs, lower-cost and higher-efficiency batteries, etc., have all further accelerated the development of EV energy storage. The EV energy storage field should focus on developing battery technology, make advancements toward delivering longer cycle lives and improving the safety and availability of battery materials, and ramp up the R&D efforts with respect to developing vehicle-to-grid (V2G) management technologies. Simultaneously, it is necessary to create a business ecosystem centered on V2G operating platforms, constituting a process to which various players can contribute and achieve mutually beneficial results. It is also essential to formulate top-level strategic plans across industries and organizations, develop an electricity-trading mechanism as soon as possible, and promote the implementation of technical standards related to EV energy storage.

Bi-Level Coordinated Power System Restoration Model Considering the Support of Multiple Flexible Resources

When power systems encounter outages and large-scale blackouts, system restoration is critical and should be carried out with dedicated schemes. In this past, most studies divided the power system restoration into three stages (i.e., black-start zone partitioning, network reconfiguration, and load restoration) and deal with them separately. After that, few studies considering the three stages together were emerging while the support of multiple flexible resources, i.e., renewable energy source (RES), electric vehicle system (EVS) and energy storage system (ESS), were not considered comprehensively. Therefore, a bi-level coordinated power system restoration (BiCPSR) model is proposed in this work considering the support of multiple flexible resources. In the upper level, two network topology indices that describe the “reachability” and “shortest reachable distance” of buses in power systems, and the restoration characteristics of generators and loads are utilized for optimizing the start-up sequence of generators and network reconfiguration. In the lower level, the uncertainties of RES and EVS are considered by various scenarios and the support of multiple flexible resources is utilized cooperatively for accelerating the restoration process and maximizing the restorable load. Case studies on the revised IEEE 39-bus, WECC 179-bus and the actual Zhejiang power systems are performed to illustrate the basic features of the proposed model and its availability in bulk power systems. The comparisons between the proposed model and other models are also performed to illustrate the strengths of the proposed model.

Design and Control Method of a Battery/Ultra-Capacitor Energy Storage System for EVs

This paper presents a battery/ultra-capacitor (UC) energy storage system for the operation of permanent magnet synchronous motor drives in electric vehicles (EVs). In this system, when the EV is used for accelerated operation, the battery provides a stable voltage to the inverter through the DC-DC converter. At the same time, the battery and UC jointly release energy to the inverter to share the larger current demand of the EV motor. In constant speed operation, the battery supplies energy to the inverter through a shunt diode to avoid additional power losses while the DC-DC converter is operating. During the regenerative braking mode, the current produced by the EV motor is charged to the UC to reduce pulse currents flowing into the battery, which may shorten battery life. To validate the effectiveness of the circuit design, several operation scenarios including buck and boot voltage conversion for the operation of proposed battery/UC ESS were examined in the experimental studies. Test results consolidates the feasibility and practicality of the circuit method for the EV applications.

Thermal Investigation of Cylindrical Lithium-ion Batteries for Different Loading using Experimental and Numerical Techniques

With growing concerns over climate change due to automotive emissions and fossil fuel depletion, electric vehicles (EVs) have gained more interest as a mode of transportation. Mostly, lithium-ion batteries are being used as an energy storage system in EVs. Temperature plays a major role in lithium-ion battery performance, degradation, and safety. Thermal investigation of cylindrical lithium-ion batteries of different chemistry and shape factors (18650 NMC and 21700 NCA) is conducted for different charging/discharging rates (0.5 C, 1 C, 1.5 C) and surrounding temperatures (26 °C and 45 °C) using numerical and experimental techniques. The numerical technique involves the determination of the surface temperature of the battery by solving the energy balance equation using a suitable numerical method. The numerical technique proposed in this study is validated using the experimental technique and can predict the temperature with at least 90% accuracy during the entire charging and discharging cycle from a lower to higher loading rate and different surrounding temperatures. The impact of different battery internal and external parameters on battery temperature is carried out using validated numerical technique. Thermal investigation conducted in this research can help in deciding the operating strategies of the battery management system and further in designing the proper thermal management system.

Towards Sustainable Fuel Cells and Batteries with an AI Perspective

With growing environmental and ecological concerns, innovative energy storage systems are urgently required to develop smart grids and electric vehicles (EVs). Since their invention in the 1970s, rechargeable lithium-ion batteries (LIBs) have risen as a revolutionary innovation due to their superior benefits of high operating potential and energy density. Similarly, fuel cells, especially Proton Exchange Membrane Fuel Cells (PEMFC) and Solid-Oxide Fuel Cells (SOFC), have been developed as an energy storage system for EVs due to their compactness and high-temperature stability, respectively. Various attempts have been made to explore novel materials to enhance existing energy storage technologies. Materials design and development are significantly based on trial-and-error techniques and require substantial human effort and time. Additionally, researchers work on individual materials for specific applications. As a viewpoint, we present the available sustainable routes for electrochemical energy storage, highlighting the use of (i) green materials and processes, (ii) renewables, (iii) the circular economy approach, (iv) regulatory policies, and (v) the data driven approach to find the best materials from several databases with minimal human involvement and time. Finally, we provide an example of a high throughput and machine learning assisted approach for optimizing the properties of several sustainable carbon materials and applying them to energy storage devices. This study can prompt researchers to think, advance, and develop opportunities for future sustainable materials selection, optimization, and application in various electrochemical energy devices utilizing ML.

Optimal scheduling of DG and EV parking lots simultaneously with demand response based on self‐adjusted PSO and K‐means clustering

AbstractRecently, the proliferation of distributed generation (DG) has been intensively increased in distribution systems worldwide. In distributed systems, DGs and utility‐owned electric vehicle (EV) to grid aggregators have to be efficiently scaled for cost‐effective network operation. Accordingly, with the penetration of power systems, demand response (DR) is considered an advanced step towards a smart grid. To cope with these advancements, this study aims to develop an innovative solution for the day‐ahead sizing approach of energy storage systems of EVs parking lots and DGs in smart distribution systems complying with DR and minimizing the pertinent costs. The unique feature of the proposed approach is to allow interactive customers to participate effectively in power systems. To accurately solve this optimization model, two probabilistic self‐adjusted modified particle swarm optimization (SAPSO) algorithms are developed and compared for minimizing the total operational costs addressing all constraints of the distribution system, DG units, and energy storage systems of EV parking lots. The K‐means clustering and the Naive Bayes approach are utilized to determine the EVs that are ready to participate efficiently in the DR program. The obtained results on the IEEE‐24 reliability test system are compared to the genetic algorithm and the conventional PSO to verify the effectiveness of the developed algorithms. The results show that the first SAPSO algorithm outperforms the algorithms in terms of minimizing the total running costs. The finding demonstrates that the proposed near‐optimal day‐ahead scheduling approach of DG units and EV energy storage systems in a simultaneous manner can effectively minimize the total operational costs subjected to generation constraints complying with DR.

Investigation of Thermal Behavior of Cylindrical Lithium-Ion Batteries for Electric Vehicle

With growing concerns over climate change due to automotive emissions, fossil fuel depletion, and the increasing price of crude oil, electric vehicles have gained more interest as a mode of transportation. Various electric vehicles have been developed in recent years, including pure electric vehicles (EVs), hybrid electric vehicles (HEVs), and plug-in hybrid electric vehicles (PHEVs). One of the keen focus area in the ongoing development of EVs is their energy storage systems. Mostly lithium-ion batteries are being used as an energy storage system in EVs. Various complicated reaction occurs during charging/discharging of batteries and the thermal behavior of the batteries are coupled to these reactions. Electrochemical reactions affect the heat generation rate, and higher temperatures further increase the speed of the electrochemical reactions. Adverse operating temperatures can impact battery performance, degradation, and safety. Hence it is become very important to investigate the thermal behavior of the lithium-ion batteries. Thermal investigation of cylindrical lithium-ion batteries of different chemistry and shape factor is conducted for different charging/discharging rate and atmospheric conditions using numerical technique. The numerical technique includes the determination of surface temperature of battery by solving the energy balance equation using suitable numerical method. The accuracy of numerical techniques is validated using the experimental techniques. Thermal investigation conducted in this research can help in deciding the operating strategies of battery management system and further in designing the proper thermal management system.

Multi-objective risk-based optimal power system operation with renewable energy resources and battery energy storage system: A novel Hybrid Modified Grey Wolf Optimization–Sine Cosine Algorithm approach

This research work proposes a Hybrid Modified Grey Wolf Optimization–Sine Cosine Algorithm for the multi-objective optimal scheduling of hybrid power system taking into consideration the risk factor arising due to the intermittent/uncertain nature of the renewable power generation sources. The hybrid power system is modelled considering the thermal generation units, wind energy system, solar photo voltaic system, electric vehicle and battery energy storage system. The multi-objective optimization problem is proposed based on the simultaneous minimization of the total operating cost and system risk. The conditional value at risk is introduced as the risk index to analyse the system risk due to uncertainties in power deliveries by the renewable energy resources, electric vehicle and battery energy storage system during the scheduling process. The integral contribution of this research work focuses on the establishment an optimal generation schedule based on the combined optimization of the total operating cost and system risk. The simultaneous minimization of the operating cost and the risk index is performed with the multi-objective Hybrid Modified Grey Wolf Optimization–Sine Cosine Algorithm and has been used to develop a Pareto-optimal front. The implementation of the fuzzy min–max technique is opted to fetch the best compromised solution. The standard test systems of IEEE-30 bus and Indian-75 bus system are used to validate the potency of the proposed approach. Comparative analysis has been established to highlight the results obtained with the proposed approach is appreciable than other optimization techniques.

Optimal battery cycling strategies in workplaces with electric vehicle chargers, energy storage systems and renewable energy generation

Efficient unit commitment strategy in a modern workplace facilitated with electric vehicle (EV) chargers and energy storage systems requires implementation of optimal battery cycling for both local storage system and electric vehicle batteries. In order to achieve this goal, it is necessary to address the battery health in the energy management strategies of commercial buildings. A fair battery cycling approach that could consider the interests of both parties in a workplace (including the system operator and the EV owners) requires access to detailed information on battery performance and degradation-associated costs. In this study, a detailed investigation is carried out on the optimal battery cycling in a workplace that is facilitated with an EV charging station, energy storage system and renewable energy generation. This is carried out by employment of a tailored unit commitment model that can address the battery health for EVs, individually. This study illustrates how a business owner and the employees that own electric vehicles can benefit from bidirectional battery cycling in an equitable way without compromising their financial interests in the energy market.

Composite Non-Linear Control of Hybrid Energy-Storage System in Electric Vehicle

The underlying circuit control is a key problem of the hybrid energy-storage system (HESS) in electric vehicles (EV). In this paper, a composite non-linear control strategy (CNC) is proposed for the accurate tracking current/voltage of the fully-active HESS by combining the exact feedback linearization method and the sliding mode variable structure control technology. Firstly, by analyzing the circuit characteristics of HESS, the affine non-linear model of fully-active HESS is derived. Then, a rule-based energy management strategy (EMS) is designed to generate the reference current value. Finally, the HESS is linearized by the exact feedback linearization method, and the proposed CNC strategy is developed combined with sliding mode variable structure control technology to ensure fast response, high performance, and robustness. At the same time, the stability proof based on the Lyapunov method is given. Moreover, the performance of the CNC strategy is thoroughly investigated and compared with simulation studies with the traditional PI control and a modified sliding mode control, and its effectiveness under different driving conditions is fully verified.

Robust Errorless-Control-Targeted Technique Based on MPC for Microgrid with Uncertain Electric Vehicle Energy Storage Systems

Regarding the microgrid with large-scale electric vehicle (EV) energy storage systems working at the vehicle-to-grid (V2G) mode, uncertain factors (e.g., the number of EVs feeding the microgrid shifts frequently) make the system unfixed, leading to the fact that it is difficult to precisely determine the real-time droop coefficients of the system, thereby degrading the performance of the traditional inverter control strategies that rely on the droop coefficients. To solve the problem, this paper proposes an errorless-control-targeted double control loop (DCL) technique based on robust MPC to control the microgrid with EV energy storage systems without using droop coefficients. Firstly, the structure of the DCL method is developed, with each component in the structure detailed. Compared to the traditional control strategies, the novel one regards the frequency, voltage, and currents as the control objectives instead of active/inactive power. It deserves to be mentioned that the frequency and voltage are regulated by proportional-integral controllers, while the currents are regulated by the finite control set model predictive control (FCS-MPC) method. Secondly, the impacts of system parameter uncertainties on the prediction accuracy of the FCS-MPC controller are analyzed clearly, illustrating that it is necessary to develop effective techniques to enhance the robustness of the controller. Thirdly, sliding mode observers (SMO) based on a novel hyperbolic function are constructed to detect the real-time disturbances, which can be used to generate voltage compensations by using automatic disturbance regulators. Then, the voltage compensations are adopted to establish a modified predicting plant model (PPM) used for the FCS-MPC controller. By using the proposed SMO-based disturbance detection and compensation techniques, the MPC controller gains a strong robustness against parameter uncertainties. Finally, a simulation is conducted on a microgrid system to verify the effectiveness of the proposed techniques, and the obtained results are compared with the traditional virtual synchronous machine (VSG) strategy relying on droop coefficients.

Parameter Matching Method of a Battery-Supercapacitor Hybrid Energy Storage System for Electric Vehicles

To satisfy the high-rate power demand fluctuations in the complicated driving cycle, electric vehicle (EV) energy storage systems should have both high power density and high energy density. In order to obtain better energy and power performances, a combination of battery and supercapacitor are utilized in this work to form a semi-active hybrid energy storage system (HESS). A parameter matching method of battery-supercapacitor HESS for electric vehicles (EVs) is proposed. This method can meet the performance indicators of EVs in terms of power and energy for parameter matching. The result shows that optimized parameter matching is obtained by reducing the weight and cost.

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%.