Large-scale Battery Systems Research Articles

Limiting the aging process of spinel-based Li-ion cells by cathode sulfur doping

The degradation of stoichiometric lithium-manganese spinel (LiMn2O4, LMO) is a critical concern in the longevity and efficiency of eco-friendly, cheap lithium-ion batteries. The complex mechanisms of degradation, involving Jahn-Teller distortion of Mn3+, as well as Mn2+ dissolution, are well-known to be accelerated by reactive species like HF. This study explores sulfur substitution into the LMO’s oxygen sublattice as a targeted approach to mitigate these aging factors. The properties and stability in the electrochemical cell of the S-doped LMO were investigated and compared to unmodified LMO using electrothermal atomic absorption spectrometry, X-ray photoelectron spectroscopy, X-ray powder diffraction, scanning and transmission electron microscopy as well as galvanostatic charge/discharge tests and electrochemical impedance spectroscopy. The sulfur doping was found to significantly increase the structural stability of the spinel and enhance the performance of the cells. These improvements were found to be caused by sulfur, which limited passive manganese dissolution and stretched the spinel structure diminishing diffusion constraints and reducing the susceptibility to mechanical strains. Furthermore, the S-doping was found to enhance both the passivation layer stability and PVDF binder durability. Although a full understanding of sulfur’s role in the sipenl-based cell aging process remains intricate, the findings affirm its role in limiting the degradation of the LiMn2O4, especially under severe working conditions. These insights into sulfur doping open new avenues for developing more robust and environmentally friendly LMO-based materials for large-scale battery systems.

Pegylated Viologen Derivatives to Improve Performance of Aqueous Organic Redox Flow Battery

Renewable energy sources like wind and solar power are great alternatives for a clean way to produce electricity but have the inconvenience to fluctuate1. To address this issue and promote the implementation of renewables sources, scientists are developing new ways to store energy while production is at a maximum for a subsequent release when there is demand. Redox flow batteries (RFBs) are the most appropriate solution and are increasingly gaining attention for stationary, large scale electrochemical energy storage2,3. One variation of RFBs are aqueous organic redox flow batteries (AORFBs), where water-soluble organic molecules are use as the redox couple. Viologen type molecules are particularly well-suited as a negative potential species to develop cheaper and better AORFBs with great scalability, reversibility and long-term stability4.In this study, viologen derivatives were synthesized with various N-functional groups to explore the impact of the substituent on their solubility, viscosity and redox potential. We showed that the use of short chains of poly(ethylene glycol) improved the solubility of the viologens derivatives which could lead to batteries with higher capacity. The symmetric viologen (with two identical substituents) provided a great solubility in water and the formal potential was unaffected by the chemical nature of the substituents. We demonstrated that the 1,1’-(bisethylene glycol)-4,4’-bipyridium (PEG2-V-PEG2) showed the most promising properties with a high solubility (2.3 M in 1M KCl) and low viscosity (3.01 mPa*s at 1M). As such, this derivative was selected for further evaluation in an asymmetrical labs-scale RFB prototype cell versus bis(trimethylamoniumpropyl)ferrocene (BTMAP-Fc). The compound was studied at various concentrations to assess the maximum energy density that can be reached and the effect of concentration on cycling life by achieving a comparison to the symmetrical 1,1’-bis(3-sulfonatopropyl)-4,4’-bipyridium (SPr-V-SPr) at 0.5 M. Figure 1: Redox flow battery scheme using Viologen derivative as the negolyte and BTMAP-Fc as the posolyte Reference Shi, X., Qian, Y. and Yang, S. Fluctuation analysis of a complementary wind–solar energy system and integration for large scale hydrogen production. ACS Sustainable Chemistry & Engineering, 8(18), pp.7097-7110. (2020)Sánchez-Díez, E.; Ventosa, E.; Guarnieri, M.; Trovò, A.; Flox, C.; Marcilla, R.; Soavi, F.;Mazur, P.; Aranzabe, E.; Ferret, R., Redox flow batteries: Status and perspective towards sustainable stationary energy storage. Journal of Power Sources, 481, 1-23. (2021)Poullikkas, A., A comparative overview of large-scale battery systems for electricity storage. Renewable and Sustainable energy reviews, 27, pp.778-788. (2013)Gentil, S., Reynard, D. and Girault, H.H. Aqueous organic and redox-mediated redox flow batteries: a review. Current Opinion in Electrochemistry, 21, pp.7-13. (2020) Figure 1

Renewable, Flexible, and Storage Capacities: Friends or Foes?

Problem definition: More than 99% of the new power generation capacity to be installed in the United States from 2023 to 2050 will be powered by wind, solar, and natural gas. Additionally, large-scale battery systems are planned to support power systems. It is paramount for policymakers and electric utilities to deepen the understanding of the operational and investment relations among renewable, flexible (natural gas-powered), and storage capacities. In this paper, we optimize both the joint operations and investment mix of these three types of resources, examining whether they act as investment substitutes or complements. Methodology/results: Using stochastic control theory, we identify and prove the structure of the optimal storage control policy, from which we determine various pairs of charging and discharging operations. We find that whether storage complements or substitutes other resources hinges on the operational pairs involved and whether executing these pairs is constrained by charging or discharging. Through extensive numerical analysis using data from a Florida utility, government agencies, and industry reports, we demonstrate how storage operations drive the investment relations among renewable, flexible, and storage capacities. Managerial implications: Storage and renewables substitute each other in meeting peak demand; storage complements renewables by storing surplus renewable output; renewables complement storage by compressing peak periods, facilitating peak shaving and displacement of flexible capacity. These substitution and complementary effects often coexist, and the dominant effect can alternate as costs change. A thorough understanding of these relations at both operational and investment levels empowers decision makers to optimize energy infrastructure investments and operations, thereby unlocking their full potential. Funding: This research was supported in part by Lilly Endowment, Inc., through its support for the Indiana University Pervasive Technology Institute. This research was also supported by Kelley School of Business, Indiana University, and Haslam College of Business, University of Tennessee. O. Q. Wu thanks Grant Thornton for their generous support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0068 .

A triple-tiered modular equalizer for large-scale battery systems

Among the equalizers based on the dc-dc converter with a single-tiered structure, some problems are widespread: the poor feasibility caused by a tricky control strategy, the rated parameters of switching components relating to the voltage levels of the battery system, and an unsatisfactory balancing speed caused by an uncontrollable balancing current. This paper proposes a triple-tiered, modular equalizer to solve the above-mentioned problems. The balancing current is controllable in the proposed scheme, ensuring a satisfactory balancing speed. The triple-tiered structure used in this work also gives the proposed equalizer a control strategy with the virtue of simplicity. The design of the proposed equalizer makes the rated parameter of switching components entirely independent of the voltage levels of the battery system, promising the modularization of the proposed equalizer. The working principle, efficiency analysis, and balancing strategy of the proposed equalizer are first detailed, and a comparison study between this work and other works follows. Finally, an equalization experiment was carried out, and the experiment results demonstrate the validity of the proposed equalizer.

Battery configuration dependence to power line communication using high-order quadrature amplitude modulation

Power line communication (PLC) within future smart batteries facilitates the communication of high fidelity sensor data between smart cells and external systems, with application areas including intelligent vehicles and smart grids. This interconnected PLC system of smart cells will enhance cell utilisation and safety through cell-to-cell coordination at a system level, leveraging the existing bus bar within the battery and eliminating the need for additional wire harnesses. This paper studies the performance of a PLC system operating at carrier frequencies of 10MHz to 6GHz within four distinct configurations of lithium-ion batteries. This assessment focuses on changes in scattering parameters and data transmission error ratios. Furthermore, quadrature amplitude modulation (QAM) orders of up to 1024 are investigated for their viability within such environments. The results indicate that the addition of cells in parallel increases error ratios with high-order QAM, and that this effect varies substantially with carrier frequency. Using QAM increases data throughput, allowing for data transfers within large-scale battery systems without PLC bus bandwidth saturation. A prospective centre frequency of 3650MHz allows for a wide bandwidth of 300MHz and 1024-QAM with little signal attenuation and data error. At this frequency, the need for signal repeaters and higher signal output power is reduced. These results are used to determine the most suitable arrangement of cells within a smart battery with consideration of the PLC performance. The preliminary performance of a large-scale battery system with PLC in-situ could be derived from the findings in this research based upon the four battery configurations tested.

Double-layer state of health equalization based on cooperative coevolution for large-scale lithium battery system

Due to the fast development of electric vehicles, the rational utilization of retired power lithium batteries is an urgent problem needs to be solved. In order to make better use of the batteries, a preferable equalization method is required. However, the existing studies mainly focus on the equalization circuits and the simplification of control models. This paper further proposes a double-layer state of health (SOH) equalization method namely AMDL-equalization, in which a novel cooperatively coevolving algorithm (AMCCPSO-CG) is developed and the model predictive control (MPC) is also introduced. In the developed AMDL-equalization method, the first equalization layer is used to solve the optimal dispatching problem among battery packs, and the degradation rates of the battery packs are also efficiently adjusted in the first layer. In addition, considering the number of packs in a large-scale battery system, the category-based variable grouping mechanism is proposed and a novel co-evolutionary algorithm namely AMCCPSO-CG is developed to improve the optimal power dispatching performance. For the second equalization layer, the MPC-AMCCPSO-CG algorithm is developed to efficiently optimize the depth of discharge (DOD) and adjust the degradation rate for each battery. Experimental results show that the developed AMDL-equalization method can obtain promising performance on reducing the differences among batteries. The developed method can be applied in the optimal operation control of large-scale energy storage systems, in order to satisfy the future trend of high operational safety and efficiency for retired battery systems.

RAC$^+$: Supporting Reconfiguration-Assisted Charging for Large-Scale Battery Systems

RAC$^+$: Supporting Reconfiguration-Assisted Charging for Large-Scale Battery Systems

Faults Diagnosis for Large-Scale Battery Packs via Texture Analysis on Spatial–Temporal Images Converted From Electrical Behaviors

Battery failures have become the most intractable obstacles undermining the market confidence in applications like electric vehicle and power grid energy storage. This paper aims to fashion a generic diagnosis scheme against the faults in large-scale battery systems. First, a voltmeter array-based anomaly perception mechanism against the electrical behaviors of battery packs is developed. Then, system information on spatial arrangement and temporal dynamics is organically fused and drawn as a kind of pseudo 2D images (P2Is). Afterwards, by analyzing the resultant P2Is with the 2D variational mode decomposition (2D-VMD) and gray level co-occurrence matrix (GLCM), some statistical quantities concerning multi-scale texture features, extracted and refined by the principal component analysis (PCA), are found to have strong indicative associations with battery fault type and fault grade. Finally, relying on the multi-class relevance vector machine (M-RVM), feature evidences are synthesized to detect fault occurrence and give judgements on fault specifics of type and severity. Experimental verifications on a li-ion battery pack with 180 cells suggest that the proposed scheme behaves well in fault type isolating, with an accuracy rate of 97.6% and in fault severity grading, with an accuracy rate of 84.67%.

Practical production of heteroatom-bridged and mixed amorphous–crystalline silicon for stable and fast-charging batteries

Mixed amorphous–crystalline silicon microparticles were designed to increase Li-ion diffusivity and electrode integrity using a low-cost synthetic approach. It allows longer cycle life in fast-charging and large-scale battery systems.

Electrical cell-to-cell variations within large-scale battery systems — A novel characterization and modeling approach

Digital twins for large-scale and investment-intensive Li-ion battery systems in marine and stationary applications have drawn increasing interest in recent years. Considering electrical cell-to-cell variations (CtCVs) within the underlying battery model of such a digital twin promises various advantages in the fields of model-based optimization and predictive maintenance. However, the existing approaches for both the characterization and modeling of CtCVs are unsuited for large-scale systems consisting of thousands of individual cells. In this context, this paper introduces a holistic tool chain comprising three main elements: First, a non-destructive method for the in-situ determination of resistance and capacity distributions within a battery system is presented. The method was evaluated on a commercial battery module for stationary applications consisting of 64 Ah pouch cells in 14s2p configuration. In the second step, the obtained distributions were used to parameterize a state-of-the-art multi-cell battery model, which allows the calculation of the voltage distribution within the system. The validation showed that the resulting model is able to calculate the voltage spread (Vcell,max(t)−Vcell,min(t)) with a mean average error of 1.1 mV for a 24 h load profile. In the third step, multivariate statistical analysis was used on the obtained parameters in order to simplify the original model and thereby reducing its computational demands. The simplification approach allows the calculation of envelope voltages curves within which a random cell can be found with a given probability. In comparison to the original model, the simplified model was able to represent the voltage extrema while reducing the computation time by a factor of 27. This renders the simplified model applicable for live digital twin applications for large-scale battery systems.

Demonstrating stability within parallel connection as a basis for building large-scale battery systems

Demonstrating stability within parallel connection as a basis for building large-scale battery systems

The bidding strategies of large-scale battery storage in 100% renewable smart energy systems

Large-scale battery storage solutions have received wide interest as being one of the options to promote renewable energy (RE) penetration. The profitability of battery storages is affected by the bidding strategy adopted by the operator and is highly dependent on the operation of the rest of the energy system. Nevertheless, the coordination between the battery and the energy system has not been investigated in the literature yet. This paper provides a holistic hourly techno-economic analysis of the bidding strategies of large-scale Li-ion batteries in 100% renewable smart energy systems. As a case study, the 2050 Danish energy system is used to demonstrate the relationship between large-scale battery systems and the rest of the energy system. The results show that large-scale battery storage plays a limited role in future energy systems that follow the smart energy system concept. Likewise, the battery solution is only economically feasible in the Danish smart energy system at low battery storage capacities (few hours’ duration) with a low-profit margin rate (approx. 100%) and a short prognostic period (approx. 12 h) for operation planning. The finding of this study provides the general strategies of the battery bidding and operation in 100% RE systems.

A multi‐objective parallel layered equalizer for large‐scale lithium ion battery system

The equalisers available are difficult to give consideration to both high balance efficiency and fast balance speed, and their performances are unstable as the number of the series-connected batteries increases, so they are unsuitable for a large-scale battery system. A multi-objective parallel layered equaliser based on battery working states for the large-scale lithium-ion battery system is presented. The equaliser has two types of balance objects and two corresponding layers, and the first layer and the second layer take the single battery and the battery unit as the balance object, respectively. The multi-objective parallel layered balance means that multiple balance objects are selected as the balance objectives simultaneously at each layer. The balance speed is fast due to the multi-objective parallel balance. The balance efficiency is high due to the short energy path and the complementary pulse width modulation (PWM) control in the first layer, and the double voltage value of the balance object in the second layer. Moreover, the performance of the equaliser is stable, even when the number of series-connected batteries is large. In short, the proposed equaliser features high efficiency, fast speed, and strong scalability. Further, the experimental platform for a battery system with twelve series-connected lithium-ion phosphate batteries is built, and then the balance experiments have been completed. Finally, the effectiveness of the equaliser is verified by the corresponding experimental results.

Synchronous condensers as a viable inertia support mechanism on the future South African grid

Modern electricity networks may face inertia shortages based on increasing inverter-based renewable energy penetration, along with the (often concurrent) decommissioning of aging synchronous thermal generators. In the South African network, the market share of coal generation is projected to decrease significantly based on the country's energy policy, primarily to be replaced by wind and solar PV. In this paper the inertia adequacy on the future South African network is evaluated, and the viability of converting retired synchronous generators to synchronous condensers is assessed based on sufficiency of inertia support provided and economic viability. It is found that by 2030, the South African network will have insufficient inertia for a significant portion of the year. However, significant improvements in Rate of Change of Frequency and frequency nadir are observed when incorporating inertia from converted synchronous condensers during multiple-contingency trip events. Finally, the conversion of synchronous generators to synchronous condensers is found to be the least-cost solution when compared to the installation of new synchronous condensers or of large-scale battery systems. The contributions made in this paper highlight the importance of considering network inertia in establishing a national energy policy, while also informing solutions to future inertia constraints.

Robust Cycling of Ultrathin Li-Metal Enabled By Nitrate-Preplanted Li Powder Electrode

The Li metal anode has been spotlighted for large-scale battery systems such as electric vehicles (EVs) and energy storage systems (ESSs) because of its high theoretical energy density and lowest potential. However, the Li metal anode still has the several challenging issues like Li dendrite growth and many side reactions. Our strategy to this problem is to utilize Li metal powder (LMP), which has superior advantages in reducing local current and utilizing slurry-based coating process for wide-width electrodes. Nevertheless, the spherical shape of LMP with the imperfect protection layer can be lost during the long-term cycling, which leads to dead particles formation and ununiform Li distribution.In this work, we present a LMP electrode having pre-planted nitrates (LN-LMP). The LiNO3 additive in the LMP electrode can chemically replace the native layer with the uniform Li3N-rich passivation layer on the surface during the simple slurry mixing process. Furthermore, residual LiNO3 was continuously released toward electrolyte, thereby repairing the SEI during cycling. As a result, the 20 µm‐thick LN‐LMP electrodes exhibited the robust cycling performances compared to control cells with conventional LMPs and LiNO3‐containing electrolytes.

Optimized operation of large scale battery systems

Abstract In the decentralized renewable driven electric energy system, economically viable battery systems become increasingly important for providing grid-related services. End of 2016, STEAG has successfully started the commercial operation of six 15 MW large scale battery systems which have been incorporated in STEAG’s primary control pool. During the commissioning phase, extensive effort has been spent in optimizing the operational efficiency of these systems with promising results. However, the operation experience has shown that there is still significant potential for improving the system behavior as well as reducing the aging of the battery cells. By analyzing historical data of the charging power associated with the state of charge management, opportunities for significantly reducing the operational costs have been identified. By means of more involved model-based control strategies, which adequately consider the specific characteristics of the battery system, and by using mathematical optimization and artificial intelligence, adapting the state of charge management strategy to new applications, these additional cost savings can be obtained. Apart from giving insights into the operational experience with large scale battery systems, the contribution of this paper lies in proposing strategies for reducing the operational costs of the battery system using classical approaches as well as mathematical optimization and neural networks. These approaches will be illustrated by simulation results.

Sensor Placement With Optimal Precision for Temperature Estimation of Battery Systems

The temperature distribution in the battery significantly impacts the short-term and long-term performance of battery systems. Therefore, efficient, safe, and reliable battery system operation requires an accurate estimation of the temperature field. The current industry standard for sensors to battery cell ratio is quite frugal. Thus, the problem of sensor placement for accurate temperature estimation becomes non-trivial, especially for large-scale systems. In this letter, we explore a greedy approach for sensor placement suitable for large-scale battery systems. An observer to estimate the thermal field is designed in an <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> framework while simultaneously minimizing the sensor precisions, thus lowering the overall thermal management system's economic cost.

Stability within Parallel Connection: A Basis for Building Large-Scale Battery Systems

Stability within Parallel Connection: A Basis for Building Large-Scale Battery Systems

Ultra-Thin Nitrate-Preplanted Li Powder Composite for Lithium Metal Secondary Battery

Due to the high theoretical energy density and lowest negative potential, Li metal is being revalued as a candidate for large-scale battery systems, such as electric vehicles (EVs) and energy storage systems (ESSs). Nevertheless, the Li metal suffer from several issues that the formation of dendrite and continuous side reactions during repeated cycling. In our previous contribution, we tackled this problem by using Li powder (LMP), because the simple manufacturing using slurry coating, flexible cell design, reduced exchanged current density. However, the LMP-based electrode also has severe morphological drawback, which induced the imperfect Li2CO3 passivation layer. During the long-term cycling, the spherical shape can be completely lost due to extensive Li loss at individual LMPs, leading to a failure of the conformal Li distribution.Herein, we report a pre-planted nitrate LMP composite electrode (LN-LMP). The LiNO3 additives play important roles not only allowed chemically induced homogeneous nitration on surfaces but also released toward electrolytes as additives. Therefore, due to the significant features of the LN‐LMP composite design, LMBs with 20 µm‐thick LN‐LMP composite anodes demonstrated excellent cycling performances compared to cells with conventional LMPs and LiNO3‐containing electrolytes. Furthermore, we further verified that the LN‐LMP electrode with compatible electrolytes consistently exhibits better performances under practical cell design conditions.

Practical state estimation using Kalman filter methods for large-scale battery systems

The system states of battery energy storage systems (BESSs) such as state of charge (SOC) and state of health (SOH) are essential for the functions of the system, such as frequency support services and energy trading. However, the complexity of a large-scale battery system makes the estimations more difficult than at the cell-level. This is further compounded by real-world limitations on system monitoring data granularity, accuracy and quality. In this paper it is shown how cell-level state estimation techniques can be utilised on large-scale BESSs using experimental data from a 2MW, 1MWh BESS. The results show how a Dual Sigma point Kalman Filter (DSPKF) SOC estimation provides more accurate results compared to the commercial BESS battery management system SOC. It is shown how the DSPKF parameters can be tuned by a genetic algorithm to simplify selection and generalise the approach for different BESSs. Furthermore, it shows how this method of SOC estimation can be combined with a total least-squares (TLS) method for capacity estimation to less than 1% error. Online system state estimation is demonstrated using both designed tests and real-world operational profiles where the BESS has provided contracted frequency response services to the national electricity grid in the UK.