Vehicle Energy Storage Research Articles

A Boost-Inductorless Electrolytic-Capacitorless Single-Stage Bidirectional Isolated AC–DC Converter

With the development of wide-bandgap devices, bidirectional isolated ac–dc converter becomes an attractive solution to realize highly compact, highly efficient power conversion for electric vehicle (EV) chargers and energy storage applications. However, in the existing literature, regardless of two-stage or single-stage isolated ac–dc converters, the circulating current in boost inductors is inversely proportional to the inductance value, resulting in large volume and high loss of the converter. In this article, a boost-inductorless electrolytic-capacitorless single-stage bidirectional ac–dc converter with high-frequency isolation is proposed. With the introduction of a center-tapped transformer, the proposed converter can achieve nearly zero circulating current without any boost inductor, thus overcoming the drawbacks mentioned above. Equivalent circuit analysis is applied to illustrate the operation principle and advantages of the proposed topology. A detailed comparison between the proposed topology and the state-of-the-art is also provided. The experimental results obtained from a 3.6 kW, 140 kHz single-phase EV charger prototype are given as the proof-of-the-concept. It is demonstrated that the proposed converter prototype realizes a peak efficiency of 96.4% with only 38 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</i> H total inductance and 6.33 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</i> F total capacitance.

Real-Time Allocation of Multi-Mobile Resources in Integrated Distribution and Transportation Systems for Resilient Electrical Grid

This article proposes a novel real-time optimization-based critical load restoration strategy for the post-event stages. The key idea is to integrate multi-mobile and stationary resources into the emergency tools of the system operation as grid-support services. The role of the demand response (DR) program and distributed generation (DG) from the resiliency-boosting points of view is investigated. To prevent prolonged and extensive electric outages, mobile emergency generator (MEG), emergency vehicle energy storage (EVES), and electric vehicles (EVs) are aimed to be exploited by forming microgrids. Power system constraints are modelled according to the linearized DistFlow equations. The framework's main aim is to serve critical loads, weighted by their priority, with the coordinated operation of multiple resources. Mixed-integer quadratic programming-based model is solved by a state-of-the-art solver within 30-minute time slots while considering uncertainties in real-time traffic congestion. Different case studies are carried out on the IEEE-33 bus coupled distribution-transportation system and the results are discussed in detail on the load restoration basis.

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.

Multiobjective Optimal Dispatch of Mobile Energy Storage Vehicles in Active Distribution Networks

In active distribution networks (ADNs), mobile energy storage vehicles (MESVs) can not only reduce power losses, shave peak loads, and accommodate renewable energy but also connect to any mobile energy storage station bus for operation, making them more flexible than energy storage stations. In this article, a multiobjective optimal MESV dispatch model is established to minimize the power loss, renewable energy source curtailment, and total operating cost of ADNs. Additionally, a method to directly obtain the compromise optimal solution (COS) of the Pareto optimal solutions (POSs) of a three-objective optimization problem is proposed. Based on the normalized normal constraint (NNC) method for solving the POSs of a three-objective optimization problem, a bilevel optimization model is established to directly obtain the COS. The innerlayer model uses the NNC method to calculate the POS corresponding to a point on the Utopia plane, and the outerlayer model uses the improved Nelder–Mead (INM) method to directly search the POS with the minimum distance to the Utopia point as the COS. Compared with the traditional method, which first solves the POSs of the three-objective optimization problem and then selects the COS, the proposed INM method has higher computational efficiency and obtains a better COS. Finally, case studies on the modified IEEE-33 bus distribution network and an actual 180 bus distribution network demonstrate the effectiveness of the proposed method.

A Novel Day Optimal Scheduling Strategy for Integrated Energy System including Electric Vehicle and Multisource Energy Storage

Aiming at problems such as high operating costs, the low utilization efficiency of renewable energy, and the increase in the peak-valley difference in user load caused by disordered charging of electric vehicles (EVs), the operation mode of “setting electricity by heat” or “setting heat by electricity” is adopted in the integrated energy system of intelligent residential areas on the distribution side. In this paper, a method is proposed to use multiple energy storage, demand response technology, and make EV orderly charge and discharge plan to consume renewable resources and realize optimal economic scheduling in the integrated energy system of combined heat and power (CHP). Firstly, the demand response model of electricity load adjusting according to renewable energy output and time-of-use electricity prices has been established. Secondly, considering the comfort of the space thermal load, the orderly charge and discharge plan of electric energy storage (ESS), thermal energy storage (HSS), and EV and aiming at the minimum operating cost of the integrated energy system, the optimal output operation of each equipment was scheduled. Finally, through simulation analysis and comparative experiments, it is verified that the proposed method can promote the local consumption of renewable energy, realize the decoupling of thermoelectric constraints, and reduce the operating cost of the integrated energy system.

A Case of Interdisciplinary Fusion under Dual Carbon Goal: Coordinated Carbon Reduction with Greenhouse Photovoltaics and Electric Vehicles

Building a new type of power system is an important guarantee to support China’s “dual carbon” goal. Due to the inseparable relationship between industrial and agricultural production and electric energy utilization, there must be interdisciplinary integration to achieve the goal of “dual carbon”. The disciplines of horticulture and electric power are taken as examples in this paper to analyze the feasibility of carbon emission reduction through coordinating agricultural photovoltaic (PV) greenhouse and electric vehicle (EV) energy storage. Firstly, the mechanism of carbon emission difference caused by electric energy supplementing during EV charging is analyzed. Secondly, in the context of the contradiction between the reduction of battery life caused by discharging (increasing carbon emission) and the increase in PV output consumption by orderly charging and discharging (reducing carbon emission), an optimization model for the synergistic operation of EV clusters and greenhouse PVs (with the objective of minimizing carbon emissions) is proposed. Finally, the effectiveness of the proposed model is verified through simulation cases. The energy storage characteristics of EVs is capable of realizing the transfer of PV power generation in the time dimension, and the coordinated operation of greenhouse PVs and EVs’ charging and discharging can effectively reduce carbon emission during the EV operation period. In a typical summer scenario of PV output, the carbon emission of EVs in V2G (vehicle to grid) mode was reduced by 69.13% compared to disorderly charging. It is shown that the adequacy of PV generation and the orderly dispatching of the charging and discharging of EVs are the key factors in reducing carbon emission throughout the life cycle of EVs.

Advanced Strategies for Improving Lithium Storage Performance under Cryogenic Conditions

AbstractAs a vital technology in portable electronic equipment, electric vehicle, and stationary energy storage, lithium‐based batteries are currently gaining widespread attention. However, when batteries are used more frequently, there are greater worries regarding their susceptibility to temperature, especially at low temperatures (LTs). In this review, the factors that lead to capacity loss in lithium‐based batteries at LT are analyzed and a summary of the most recent cutting‐edge approaches for improving LT kinetics is provided. In order to achieve satisfactory LT performance, electrode technology, interface chemistry, and electrolyte engineering must be taken into account during the cell design process. The recently revealed unique cryogenic storage mode and design concepts are also thoroughly introduced in this review, which is intended to motivate researchers in related domains. Finally, new insights, intriguing concepts, and workable solutions are proposed to promote the ongoing development of high‐performance cryogenic lithium batteries.

Stationary and mobile storages-based renewable off-grid system planning considering storage degradation cost based on information-gap decision theory optimization

This paper presents the planning of a hybrid renewable system with wind turbines and bio-waste energy units along with stationary (i.e., batteries) and mobile (i.e., electric vehicles) energy storage. This model minimizes the cost of construction, maintenance and storage degradation. In this model, the energy supply priority is given to renewable sources. Then the mentioned storage devices are used to cover the gap between the load profile and the renewable generation power. The proposed scheme addresses the uncertainties of loads, renewable power and energy consumption of mobile storage devices. Therefore, robust optimization based on information-gap decision theory (IGDT) is utilized to obtain a robust solution against the prediction error of the aforementioned uncertainties. Also, the hybrid optimization algorithm of honey bee mating and artificial bee colony is derived, showing the optimal solution with low dispersion in the final response. The proposed design is applied to the data of the city of Espoo, Finland. The obtained numerical results show the capability of the proposed design in deriving robust economic planning for the proposed hybrid system. So, the used hybrid solution algorithm is able to obtain the best solution in a lower computation time compared to non-hybrid solvers, and it has a low standard deviation around 0.94 % in the final response. The presence of the aforementioned renewable sources leads to attaining an environment-friendly hybrid system. The optimal performance of the storage devices leads to the robustness of the optimal solution against the maximum error of 18.19 % in the prediction of uncertainties. This occurs considering the smart charging management of mobile storage, but the maximum uncertainty level of 11.12 % holds to non-smart management of these storage devices in the proposed scheme. The energy management of mobile storage devices based on smart (non-smart) charging strategy also reduces (increases) the planning cost of the off-grid system by 7.62 % (39.68 %) compared to their absence.

Comparative Studies on Batteries for the Electrochemical Energy Storage in the Delivery Vehicle

The publication presents a proposal of methodology for the evaluation of electric vehicle energy storage, based on examples of three types of batteries. Energy stores are evaluated in different categories such as cost, reliability, total range, energy density, battery life, weight, dependency on ambient temperature, and requirements of battery conditioning system. The performance of the battery systems were analyzed on exemplary 4 × 4 vehicle with 4 independent drives systems composed of inverters and synchronous in-wheel motors. The studies showed that the best results were obtained for energy storage built on LFP prismatic batteries, and the lowest ranking was given to energy storage built on cylindrical NMC batteries. The studies present the method of aggregation of optimization criteria as a valuable methodology for assessing design requirements and the risk of traction batteries in electric vehicles.

A review of technologies for direct lithium extraction from low Li+ concentration aqueous solutions

Under the Paris Agreement, established by the United Nations Framework Convention on Climate Change, many countries have agreed to transition their energy sources and technologies to reduce greenhouse gas emissions to levels concordant with the 1.5°C warming goal. Lithium (Li) is critical to this transition due to its use in nuclear fusion as well as in rechargeable lithium-ion batteries used for energy storage for electric vehicles and renewable energy harvesting systems. As a result, the global demand for Li is expected to reach 5.11 Mt by 2050. At this consumption rate, the Li reserves on land are expected to be depleted by 2080. In addition to spodumene and lepidolite ores, Li is present in seawater, and salt-lake brines as dissolved Li+ ions. Li recovery from aqueous solutions such as these are a potential solution to limited terrestrial reserves. The present work reviews the advantages and challenges of a variety of technologies for Li recovery from aqueous solutions, including precipitants, solvent extractants, Li-ion sieves, Li-ion-imprinted membranes, battery-based electrochemical systems, and electro-membrane-based electrochemical systems. The techno-economic feasibility and key performance parameters of each technology, such as the Li+ capacity, selectivity, separation efficiency, recovery, regeneration, cyclical stability, thermal stability, environmental durability, product quality, extraction time, and energy consumption are highlighted when available. Excluding precipitation and solvent extraction, these technologies demonstrate a high potential for sustainable Li+ extraction from low Li+ concentration aqueous solutions or seawater. However, further research and development will be required to scale these technologies from benchtop experiments to industrial applications. The development of optimized materials and synthesis methods that improve the Li+ selectivity, separation efficiency, chemical stability, lifetime, and Li+ recovery should be prioritized. Additionally, techno-economic and life cycle analyses are needed for a more critical evaluation of these extraction technologies for large-scale Li production. Such assessments will further elucidate the climate impact, energy demand, capital costs, operational costs, productivity, potential return on investment, and other key feasibility factors. It is anticipated that this review will provide a solid foundation for future research commercialization efforts to sustainably meet the growing demand for Li as the world transitions to clean energy.

The effect of electric vehicle energy storage on the transition to renewable energy

The most viable path to alleviate the Global Climate Change is the substitution of fossil fuel power plants for electricity generation with renewable energy units. This substitution requires the development of very large energy storage capacity, with the inherent thermodynamic irreversibility of the storage-recovery process. Currently, the world experiences a significant growth in the numbers of electric vehicles with large batteries. A fleet of electric vehicles is equivalent to an efficient storage capacity system to supplement the energy storage system of the electricity grid. Calculations based on the hourly demand-supply data of ERCOT, a very large electricity grid, show that a fleet of electric vehicles cannot provide all the needed capacity and the remaining capacity must be met by hydrogen. Even though the storage capacity of the batteries is close to 1–2% of the needed storage capacity of the grid, the superior round-trip storage efficiency of batteries reduces the energy dissipation associated with the storage and recovery processes by up to 38% and the total hydrogen storage capacity by up to 50%. The study also shows that anticipated improvements in the round-trip efficiencies of batteries are almost three times more effective than improvements in hydrogen storage systems.

Fault isolating and grading for li-ion battery packs based on pseudo images and convolutional neural network

Battery-related faults have become the most intractable problem hindering the further prosperity of fields like electric vehicle and grid energy storage. This paper is devoted to constructing a novel diagnostic framework for the faults in series battery packs, resorting to signal imaging and convolutional neural network (CNN) techniques. First, the voltage synchronicity between adjacent cells in a pack is quantified using the recursive correlation coefficient which can percept system anomalies sensitively. Then, reliant on the Gramian Angular Field (GAF) and Markov Transition Field (MTF) transformations, the correlation coefficient series is converted into pseudo images, the textures of which are full of informative details regarding system state. Finally, CNN models are employed to analyze the images for fault symptoms, thereby detecting fault occurrence, inferring fault type and evaluating fault grade. To obtain realistic dataset, different types and severities of faults are physically triggered on a li-ion battery pack. Experimental verification results indicate that the proposed framework can give accurate and reliable judgements on fault specifics, with the accuracy rates of fault type isolating and severity grading as 99.63% and 63.6% on GAF images, and as 99.75% and 58.7% on MTF images, respectively.

Reactive Power Compensation Using Electric Vehicle and Data Center by Integrating Virtual Power Plant

This article proposes a virtual power plant (VPP) theory for reactive power support consisting of electric vehicle (EV) and data center (DC) UPS battery energy storage in the power system. As enhanced grid regulations have been developed to enable large-scale distributed generation, progressive controllers are required for reactive power control considering nonlinear and industrial type load. Data center batteries are deep cycle batteries; they discharge over a much longer time and EV batteries also have great potential to absorb/release electricity from/back to the grid when needed. This paper presents the bidirectional VPP operation with a novel virtual synchronous generator (VSG)-driven reactive power controller for reactive power support. Four case studies are performed in MATLAB/Simulink platform to evaluate the performance characteristics, which include grid import/export operations, time-varying nonlinear load conditions, nonlinear industrial load conditions, and transient disturbance conditions. The obtained results validate the reactive power support in all discussed case studies.

Review of Key Technologies of mobile energy storage vehicle participating in distribution network dispatching under the high proportion of renewable energy access

With modern society’s increasing reliance on electric energy, rapid growth in demand for electricity, and the increasingly high requirements for power supply quality, sudden power outages are bound to cause damage to people’s regular order of life and the normal functioning of society. Currently, the commonly used emergency power protection equipment is mainly based on diesel generator sets, while there is also flywheel energy storage equipment in the application of emergency power protection. In today’s society, we strongly advocate green, energy-saving, and emission reduction background, and the demand for new mobile power supply systems becomes very urgent. Mobile energy storage vehicles can not only charge and discharge, but they can also facilitate more proactive distribution network planning and dispatching by moving around. The basic model and typical application scenarios of a mobile power supply system with battery energy storage as the platform are introduced, and the input process and key technologies of mobile energy storage devices under different operation modes are elaborated to provide strong support for further input and reasonable dispatch of mobile energy storage vehicles.

Integrated energy management of hybrid power supply based on short-term speed prediction

The energy management strategy and output limitation of energy storage system affect the actual regenerative braking recovery. In order to optimize the performance and energy efficiency of vehicle energy storage system in the process of braking energy recovery, an integrated energy management strategy based on short-term speed prediction is proposed in this paper. Firstly, support vector machine classification method is used for off-line training and on-line recognition of driving patterns, and then input-output hidden Markov model and Gaussian mixture model are fused to establish speed prediction models under urban, suburban, and high-speed conditions for real-time prediction of speed sequences. Then, the power is distributed by the dynamic programming algorithm according to the short-term speed prediction results, and the distribution coefficient is corrected through the integrated strategy of braking force distribution and power distribution. Finally, the simulation results under continuously changing driving conditions show that the pattern recognition with changeable span is conducive to improving its accuracy during the transition period, and the proposed integrated energy management strategy is helpful to optimizing the performance of the hybrid energy storage system. The renewable energy utilization efficiency index indicates the efficiency of the integrated energy management strategy.

Cover Feature: Sustainable Polyurethane‐Derived Heteroatom‐Doped Electrode Materials for Advanced Supercapacitors (ChemElectroChem 19/2022)

The Cover Feature illustrates the conversion of the rigid polyurethane foam (RPUF) waste to N, O co-doped porous carbon with three-dimensional honeycomb-like porous structure rich in micro and mesopores. The high capacitance exhibited by this porous carbon, when applied as a supercapacitor electrode, comes from the combined presence of electrical double layer capacitance and pseudocapacitance, which is promising to be used in electric vehicle and renewable energy storage device. More information can be found in the Research Article by J. Tang et al.

Two-Stage Stochastic Optimization for the Pre-Position and Reconfiguration of Microgrid Defense Resources against Natural Disasters.

With the aggravation and evolution of global warming, natural disasters such as hurricanes occur more frequently, posing a great challenge to large-scale power systems. Therefore, the pre-position and reconfiguration of the microgrid defense resources by means of Mobile Energy Storage Vehicles (MEVs) and tie lines in damaged scenarios have attracted more and more attention. This paper proposes a novel two-stage optimization model with the consideration of MEVs and tie lines to minimize the shed loads and the outage duration of loads according to their proportional priorities. In the first stage, tie lines addition and MEVs pre-position are decided prior to a natural disaster; in the second stage, the switches of tie lines and original lines are operated and MEVs are allocated from staging locations to allocation nodes according to the specific damaged scenarios after the natural disaster strikes. The proposed load restoration method exploits the benefits of MEVs and ties lines by microgrid formation to pick up more critical loads. The progressive hedging algorithm is employed to solve the proposed scenario-based two-stage stochastic optimization problem. Finally, the effectiveness and superiority of the proposed model and applied algorithm are validated on an IEEE 33-bus test case.

Lithiation-Driven Crystallization of Ni-Based Cathode Materials during Calcination

Driven by the increasing need of energy storage for electric vehicles and electronic devices, the technological advancements of lithium-ion batteries (LIBs) are urgently in demand to increase energy density while reducing cost. LiNixMnyCo1-x-yO2 (NMC) is among the most promising low-cost, high-energy-density cathodes for next-generation LIBs.[i] ,[ii] NMC cathode materials are generally synthesized through high-temperature calcination of NMC and lithium hydroxides. Control of the calcination conditions has been found crucial to obtaining high-performance NMC cathodes, wherein structural ordering and morphology of the final products are finely tuned to maximize capacity while minimizing surface areas exposed to the electrolyte.[iii] ,[iv] However, making NMC cathode materials of desired structural properties has been challenging due to the complexity of the calcination process, involving phase transformation accompanied with non-equilibrium crystallization of intermediates prior to forming the layered oxides.[v] ,[vi] In this work, a combination of in situ X-ray spectroscopy and microscopy techniques is applied to track the calcination process during synthesis of the practical system LiNi0.8Mn0.1Co0.1O2 (NMC811) and the archetypal LiNiO2 (LNO). The reaction pathways and structural evolution of the involved phases were identified, revealing the strong compositional dependence of phase progression, structural ordering, and crystal growth during the calcination process. A strong correlation was found between the degree of lithiation in the rocksalt phase and the progression of the layered phase (see figure 1). In NMC811, the lithiation is facilitated by Co/Mn leading to the highly lithiated rocksalt and resulting in rapid layering at low temperatures. The nucleation of the layered phase is predominately driven by lithiation, while the growth process is kinetically limited by the sluggish Ni diffusion in LNO and further hindered in NMC811 due to the presence of Co/Mn. The findings from this study, with insights into crystallization thermodynamics and kinetics, may provide guidance to the design and synthesis of high-performance Ni-based NMC cathode materials. [i] N. Nitta, F. Wu, J. T. Lee, and G. Yushin, Materials today, 2015, 18(5), 252-264. [ii] M. S. Whittingham, Chemical reviews, 2014, 114(23), 11414-11443. [iii] J. C. Garcia, J. Bareño, J. Yan, G. Chen, A. Hauser, J. R. Croy, H. Iddir, The Journal of Physical Chemistry C, 2017, 121(15), 8290-8299. [iv] Y. Duan, L. Yang, M. J. Zhang, Z. Chen, J. Bai, K. Amine, F. Pan, F. Wang, Journal of Materials Chemistry A, 2019, 7(2), 513-519. [v] M. Bianchini, F. Fauth, P. Hartmann, T. Brezesinski, J. Janek, Journal of materials Chemistry A, 2020, 8(4), 1808-1820. [vi] F. Wang, J. Bai, Batteries & Supercaps, 2021. (https://doi.org/10.1002/batt.202100174) Figure 1

Impact of Electric Vehicle Load Demand and Energy Storage Device in Integrated Renewable Energy Sources

ABSTRACT The intermittent nature of renewable-based generation may cause the dip or rise generation and load imbalances. Besides, the higher penetration of Electric Vehicles can affect the voltage profile and imbalances. The main contribution of the proposed work is to determine (i) the realistic load model of electric vehicle (EV) charging station (ii) the size of battery energy storage (BES) considering the EV load demand with the most realistic ZIP load. (iii) the cost-benefit analysis and the probabilistic modeling of EV load demand are considered for BES sizing. (iv) minimization of the daily energy loss (v), maximizing the total benefit of the system, (vi) the nature of the electric vehicle load demand has been modeled for size determination of energy storage devices. The fuel cost of the thermal generator has been minimized, and the operating and maintenance cost of the Solar Photovoltaic (PV), and Wind Turbine (WT) have been considered in the objective function. The daily energy loss has been reduced to 30.085%. The voltage deviation has been reduced to 1.165%. The analysis has been carried out for the IEEE-33 bus test system. The iterative Monte Carlo Simulation and Nonlinear Programming have been used to solve the multi-objective problem.

Improved Operation Strategy for the High Voltage Input Stage of a Multi-Port Smart Transformer

Smart transformers are considered a crucial part of future smart grids as they will operate as the energy router and be able to control the power flows from and to the microgrids since they are placed in the border. A multi-port transformer can integrate different energy resources, loads and energy storage systems, optimizing the power flows between these elements. Combining both concepts, a multi-port smart transformer is obtained that is able to integrate efficiently distributed and renewable energy resources, electric vehicle chargers, prosumers and energy storage in both AC and DC microgrids. Nevertheless, the operation of these transformers, composed of several modules connected in series to the high-voltage grid is not easy, mainly due to the different power consumed or generated by each module. In this paper this issue is analyzed and different operation strategies for coordinating the series-connected modules at the input side are studied by simulation. The paper will expose how it is possible to extend the proper operation of the system if a reactive power controller is implemented.