Power Utility Tests for Multi-MW High Energy Batteries

This paper reviews the procedures, layouts and metrics described in the new test manual issued by the Electric Power Research Institute (EPRI), in order to determine the performance and functionality of a utility scale battery energy storage system (BESS). In this approach, the large battery unit is connected to the dc-link of a bidirectional power conversion system (PCS), which may be interfaced with either the utility grid or a load bank for the purpose of estimating the BESS operation and performance characteristics, some of which includes; available charge/discharge energy at rated power, continuous charge/discharge duration, battery ramp rate, and ac round trip efficiency (RTE). The bidirectional converter is operated with different charge and discharge cycles relevant to each specification and the battery state of charge along with electrical measurements at the ac and dc side are monitored and recorded. Also, an electrical equivalent circuit for a utility scale battery unit was developed based on the 1MW/2MWh operational BESS at the LG &E and KU E.W. Brown facility. This model was formulated using an improved method for estimating the battery cell parameters

Modelling and experimental evaluation of parallel connected lithium ion cells for an electric vehicle battery system

Highlights: • Experimental evaluation of energy imbalance within parallel connected cells. • A validated new method of combining equivalent circuit models in parallel. • Interdependence of capacity, voltage and impedance for calculating cell currents. • A 30% difference in impedance can result in a 60% difference in peak cell current. • A difference of over 6% in charge throughput was observed during cycling. Abstract: Variations in cell properties are unavoidable and can be caused by manufacturing tolerances and usage conditions. As a result of this, cells connected in series may have different voltages and states of charge that limit the energy and power capability of the complete battery pack. Methods of removing this energy imbalance have been extensively reported within literature. However, there has been little discussion around the effect that such variation has when cells are connected electrically in parallel. This work aims to explore the impact of connecting cells, with varied properties, in parallel and the issues regarding energy imbalance and battery management that may arise. This has been achieved through analysing experimental data and a validated model. The main results from this study highlight that significant differences in current flow can occur between cells within a parallel stack that will affect how the cells age and the temperature distribution within the battery assembly

Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications

The second-generation hybrid and Electric Vehicles are currently leading the paradigm shift in the automobile industry, replacing conventional diesel and gasoline-powered vehicles. The Battery Management System is crucial in these electric vehicles and also essential for renewable energy storage systems. This review paper focuses on batteries and addresses concerns, difficulties, and solutions associated with them. It explores key technologies of Battery Management System, including battery modeling, state estimation, and battery charging. A thorough analysis of numerous battery models, including electric, thermal, and electro-thermal models, is provided in the article. Additionally, it surveys battery state estimations for a charge and health. Furthermore, the different battery charging approaches and optimization methods are discussed. The Battery Management System performs a wide range of tasks, including as monitoring voltage and current, estimating charge and discharge, equalizing and protecting the battery, managing temperature conditions, and managing battery data. It also looks at various cell balancing circuit types, current and voltage stressors, control reliability, power loss, efficiency, as well as their advantages and disadvantages. The paper also discusses research gaps in battery management systems.publishedVersio

Battery Management System for Future Electric Vehicles

The future of electric vehicles relies nearly entirely on the design, monitoring, and control of the vehicle battery and its associated systems. Along with an initial optimal design of the cell/pack-level structure, the runtime performance of the battery needs to be continuously monitored and optimized for a safe and reliable operation and prolonged life. Improved charging techniques need to be developed to protect and preserve the battery. The scope of this Special Issue is to address all the above issues by promoting innovative design concepts, modeling and state estimation techniques, charging/discharging management, and hybridization with other storage components

Kernel Based Model Parametrization and Adaptation with Applications to Battery Management Systems.

With the wide spread use of energy storage systems, battery state of health (SOH) monitoring has become one of the most crucial challenges in power and energy research, as SOH significantly affects the performance and life cycle of batteries as well as the systems they are interacting with. Identifying the SOH and adapting of the battery energy/power management system accordingly are thus two important challenges for applications such as electric vehicles, smart buildings and hybrid power systems. This dissertation focuses on the identification of lithium ion battery capacity fading, and proposes an on-board implementable model parametrization and adaptation framework for SOH monitoring. Both parametric and non-parametric approaches that are based on kernel functions are explored for the modeling of battery charging data and aging signature extraction. A unified parametric open circuit voltage model is first developed to improve the accuracy of battery state estimation. Several analytical and numerical methods are then investigated for the non-parametric modeling of battery data, among which the support vector regression (SVR) algorithm is shown to be the most robust and consistent approach with respect to data sizes and ranges. For data collected on LiFePO4 cells, it is shown that the model developed with the SVR approach is able to predict the battery capacity fading with less than 2% error. Moreover, motivated by the initial success of applying kernel based modeling methods for battery SOH monitoring, this dissertation further exploits the parametric SVR representation for real-time battery characterization supported by test data. Through the study of the invariant properties of the support vectors, a kernel based model parametrization and adaptation framework is developed. The high dimensional optimization problem in the learning algorithm could be reformulated as a parameter estimation problem, that can be solved by standard estimation algorithms such as the least-squares method, using a SVR special parametrization. The resulting framework uses the advantages of both parametric and non-parametric methods to model nonlinear dynamics, and greatly reduces the required effort in model development and on-board computation. The robustness and effectiveness of the developed methods are validated using both single cell and multi-cell module data.PhDNaval Architecture and Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116688/1/chsweng_1.pd

Li+ solvation in pure, binary and ternary mixtures of organic carbonate electrolytes

Classical molecular dynamics (MD) simulations and quantum chemical density functional theory (DFT) calculations have been employed in the present study to investigate the solvation of lithium cations in pure organic carbonate solvents (ethylene carbonate (EC), propylene carbonate (PC) and dimethyl carbonate (DMC)) and their binary (EC-DMC, 1:1 molar composition) and ternary (EC-DMC-PC, 1:1:3 molar composition) mixtures. The results obtained by both methods indicate that the formation of complexes with four solvent molecules around Li+, exhibiting a strong local tetrahedral order, is the most favorable. However, the molecular dynamics simulations have revealed the existence of significant structural heterogeneities, extending up to a length scale which is more than five times the size of the first coordination shell radius. Due to these significant structural fluctuations in the bulk liquid phases, the use of larger size clusters in DFT calculations has been suggested. Contrary to the findings of the DFT calculations on small isolated clusters, the MD simulations have predicted a preference of Li+ to interact with DMC molecules within its first solvation shell and not with the highly polar EC and PC ones, in the binary and ternary mixtures. This behavior has been attributed to the local tetrahedral packing of the solvent molecules in the first solvation shell of Li+, which causes a cancellation of the individual molecular dipole vectors, and this effect seems to be more important in the cases where molecules of the same type are present. Due to these cancellation effects, the total dipole in the first solvation shell of Li+ increases when the local mole fraction of DMC is high

Energy efficiency improvement of Li-ion battery packs via balancing techniques

Due to worldwide energy consumption increase, different energy strategies are growing in order to reduce fossil fuel consumption, increase renewable energy impact and increase energy efficiency. Renewable energy impact in the electric grid is increased by combination with energy storage systems. Energy storage systems storage energy during low consumption periods and insert energy during high power demand time. The efficiency and the stability of the electric grid are improved. The thesis work is focused on the energy improvement of Li-ion based energy storage systems. To improve the energy of series connected Li-ion energy storage system balancing systems are required. The thesis deals with the analysis of unbalancing processes in series connected Li-ion cells and the balancing system design to improve the Li-ion battery pack energetic behavior. The search of a low complexity active balancing system to compete against the passive balancing system is one of the most important research lines.Mundu mailako energia kontsumoa igotzen ari denez, araudi energetiko berriak sortzen ari dira erregai fosilen kontsumoa murritzeko, energia berriztagarriak ezartzeko eta efizientzia energetikoa handitzeko. Energia berriztagarrien ezartzea eta beraien erabilpena sare elektrikoan, asko hobetzen da metatze sistemen laguntzarekin. Metatze sistemek energia batzen dute kontsumo txikiko uneetan energia txertatuz sare elektrikora kontsumo handiko aldiuneetan, sare elektrikoaren efizientzia eta egonkortasuna hobetuz. Tesi lana litio ioizko metatze sistemen energia efizientzia hobetzean datza. Litio ioizko metatze sistemak litio zelden serie konekzioak dira. Seriean konektatuko sistema hauen efizientzia hobetzeko beharrezkoa da sistema orekatzaileak erabiltzea zelden artean sortutako desberdintasunak konpentsatzeko. Tesi hau zelden arteko desoreken analisian eta desoreka hauek konpentsatzeko beharrezkoak diren oreka sistemen diseinuan zentratzen da. Oreka sistema aktibo konpetitiboen diseinua, oreka sistema pasiboekin lehiatzeko da tesiaren lan inguru nagusienetakoa

Offline and Online Blended Machine Learning for Lithium-Ion Battery Health State Estimation

This article proposes an adaptive state of health (SOH) estimation method for lithium-ion batteries using machine learning. Practical problems with feature extraction, cell inconsistency, and online implementability are specifically solved using a proposed individualized estimation scheme blending offline model migration with online ensemble learning. First, based on the data of pseudo-open-circuit voltage measured over the battery lifespan, a systematic comparison of different incremental capacity features is conducted to identify a suitable SOH indicator. Next, a pool of candidate models, composed of slope-bias correction (SBC) and radial basis function neural networks (RBFNNs), are trained offline. For online operation, the prediction errors due to cell inconsistency in the target new cell are then mitigated by a proposed modified random forest regression (mRFR) based ensemble learning process with high adaptability. The results show that compared to prevailing methods, the proposed SBC-RBFNN-mRFR-based scheme can achieve considerably improved SOH estimation accuracy (15%) with only a small amount of early-age data and online measurements are needed for practical operation. Furthermore, the applicability of the proposed SBC-RBFNN-mRFR algorithms to real-world operation is validated using measured data from electric vehicles, and it is shown that a 38% improvement in estimation accuracy can be achieved

2021 roadmap on lithium sulfur batteries

Batteries that extend performance beyond the intrinsic limits of Li-ion batteries are among the most important developments required to continue the revolution promised by electrochemical devices. Of these next-generation batteries, lithium sulfur (Li–S) chemistry is among the most commercially mature, with cells offering a substantial increase in gravimetric energy density, reduced costs and improved safety prospects. However, there remain outstanding issues to advance the commercial prospects of the technology and benefit from the economies of scale felt by Li-ion cells, including improving both the rate performance and longevity of cells. To address these challenges, the Faraday Institution, the UK's independent institute for electrochemical energy storage science and technology, launched the Lithium Sulfur Technology Accelerator (LiSTAR) programme in October 2019. This Roadmap, authored by researchers and partners of the LiSTAR programme, is intended to highlight the outstanding issues that must be addressed and provide an insight into the pathways towards solving them adopted by the LiSTAR consortium. In compiling this Roadmap we hope to aid the development of the wider Li–S research community, providing a guide for academia, industry, government and funding agencies in this important and rapidly developing research space