Stationary Lithium-ion Batteries Research Articles

High-power electric vehicle charging: Low-carbon grid integration pathways with stationary lithium-ion battery systems and renewable generation

The energy transition in the mobility sector is well underway. The electrification of road transport is resulting in a shift of the energy demand from the oil and gas sector to the electricity grid. Increasingly aggressive targets for low charging times for Electric Vehicles (EVs) are slated to raise the demand for High-Power Charging (HPC). This is likely to lead to bottlenecks and overloading in vulnerable sections of the electricity grid. Battery Assistance (BA) is a promising grid integration measure for High-Power Charging (HPC) to mitigate these problems. As decarbonization is the primary objective of the energy transition, the determination and comparison of the Global Warming Potential (GWP) footprints for HPC stations with BA is crucial. A comprehensive mathematical framework for the modelling and quantification of GWP footprints for HPC has been developed. The Levelized Emissions of Energy Supply (LEES) methodology has been extended and generalized to handle energy from the grid. A new state variable for the Battery Energy Storage System (BESS) — the State of Carbon Intensity (SOCI) has been introduced to calculate the operation phase GWP footprint of the BESS. The energy consumption GWP footprint for the load is also described by a new quantity — the Load Energy Consumption (LEC) emissions. The effect of incorporation of local Photovoltaic Solar (PV) generation in the energy flows is also investigated. An optimized Energy Management System (EMS) strategy with rolling horizon optimization to minimize emissions has been implemented to regulate energy flows in scenarios with BA and local PV generation. The Levelized Emissions of Energy Supply (LEES) values are obtained for all simulated scenarios and compared against a baseline rule-based EMS strategy. In combination with on-site PV generation, BA could achieve a reduction of 24% in the LEES vis-á-vis the baseline strategy. For reference, two scenarios with Grid Reinforcement (GR) for the grid section with and without local PV generation have also been simulated. With Grid Reinforcement (GR), a reduction of over 2% can be achieved with respect to the baseline EMS strategy for BA. Grid Reinforcement (GR) in conjunction with local PV generation can bring about a further reduction of about 6% with respect to the baseline EMS strategy for BA.

Aging Characteristics of Stationary Lithium-Ion Battery Systems with Serial and Parallel Cell Configurations

The significant market growth of stationary electrical energy storage systems both for private and commercial applications has raised the question of battery lifetime under practical operation conditions. Here, we present a study of two 8 kWh lithium-ion battery (LIB) systems, each equipped with 14 lithium iron phosphate/graphite (LFP) single cells in different cell configurations. One system was based on a standard configuration with cells connected in series, including a cell-balancing system and a 48 V inverter. The other system featured a novel configuration of two stacks with a parallel connection of seven cells each, no cell-balancing system, and a 4 V inverter. The two systems were operated as part of a microgrid both in continuous cycling mode between 30% and 100% state of charge, and in solar-storage mode with day–night cycling. The aging characteristics in terms of capacity loss and internal resistance change in the cells were determined by disassembling the systems for regular checkups and characterizing the individual cells under well-defined laboratory conditions. As a main result, the two systems showed cell-averaged capacity losses of 18.6% and 21.4% for the serial and parallel configurations, respectively, after 2.5 years of operation with 810 (serial operation) and 881 (parallel operation) cumulated equivalent full cycles. This is significantly higher than the aging of a reference single cell cycled under laboratory conditions at 20 °C, which showed a capacity loss of only 10% after 1000 continuous full cycles.

Greenhouse Gas Emissions of Stationary Battery Installations in Two Renewable Energy Projects

The goal to decrease greenhouse gas (GHG) emissions is spurring interest in renewable energy systems from time-varying sources (e.g., photovoltaics, wind) and these can require batteries to help load balancing. However, the batteries themselves add additional GHG emissions to the electricity system in all its life cycle phases. This article begins by investigating the GHG emissions for the manufacturing of two stationary lithium-ion batteries, comparing production in Europe, US and China. Next, we analyze how the installation and operation of these batteries change the GHG emissions of the electricity supply in two pilot sites. Life cycle assessment is used for GHG emissions calculation. The regional comparison on GHG emissions of battery manufacturing shows that primary aluminum, cathode paste and battery cell production are the principal components of the GHG emissions of battery manufacturing. Regional variations are linked mainly to high grid electricity demand and regional changes in the electricity mixes, resulting in base values of 77 kg CO2-eq/kWh to 153 kg CO2-eq/kWh battery capacity. The assessment of two pilot sites shows that the implementation of batteries can lead to GHG emission savings of up to 77%, if their operation enables an increase in renewable energy sources in the electricity system.

Novel Degradation Model-Based Current Derating Strategy for Lithium-Ion-Batteries

Derating is the operation of an electrical or electronic device at less than its rated maximum capability in order to ensure safety, extend lifetime or avoid system shutdown. Relatively simple derating approaches have been proven effective for lithium-ion batteries. They are typically based on limiting battery charging and discharging currents to prevent operation outside certain operating areas, which are bounded by state-of-charge (SOC), voltage, or temperature levels, taken individually. The manufacturer’s datasheet provides hard limits for these operating areas, defining the so-called safe operating area (SOA). In order to prolong battery lifetime, more restrictive limits than the SOA can be defined, but this leads to reducing battery performance more frequently and intensively. However, it should be noted that these simple derating approaches do not fully capture the complexity of battery degradation mechanisms, since the actual rate of degradation is the result of an interaction of multiple operating conditions. Thus, they may overestimate or underestimate the optimal current limit. Indeed, many advanced degradation models that consider a combination of operating conditions have been proposed in the literature to predict the rate of degradation, in terms of capacity loss and/or internal resistance increase.With this in mind, we propose the integration of an advanced degradation model in the derating strategy and thereby reduce degradation without significant losses in performance. The degradation model calculates the maximum battery current that will ensure reduced degradation rates, both for calendar and cycle related ageing processes. The calendar ageing rate is limited by defining the SOC-dependent maximum temperature that will keep the rate below a certain level, and then limiting the current accordingly, aiming to reduce self-heating effects that lead to temperature rise. The cycle ageing rate is limited by calculating the SOC and temperature-dependent maximum current that will keep the rate below a certain level, and then controlling the current accordingly. The advanced degradation model and the control strategy are parameterized for a lithium iron phosphate/graphite cell.The algorithm is evaluated in simulations for a low-cost battery energy storage system (BESS) with passive thermal management. The BESS is installed outdoors and used as a buffer in a stationary residential photovoltaic application in Berlin, Germany. Two scenarios are used as a benchmark: 1) no derating; and 2) simple SOA-based current derating. In comparison, this novel approach achieves a large increase of 59 % in energy throughput over its lifetime, while system performance is only slightly reduced by 9 % through derating.Hence, we conclude that, in the case of low-cost BESS with passive thermal management, this novel degradation-aware derating strategy enables significant extension in lifetime operation with negligible impact on battery performance.Future work could evaluate the impact of the technology on BESS with active management approaches, such as active thermal management, hybridization, advanced balancing systems or by-passing. Acknowledgements This work was kindly supported by the EPSRC Faraday Institution Multi-Scale Modelling Project (EP/S003053/1, grant number FIRG003). References (1) Schimpe, M., von Kuepach, M. E., Naumann, M., Hesse, H. C., Smith, K., & Jossen, A. (2018). Comprehensive modelling of temperature-dependent degradation mechanisms in lithium iron phosphate batteries. Journal of The Electrochemical Society, 165(2), A181.(2) Schimpe, M., Naumann, M., Truong, N., Hesse, H. C., Santhanagopalan, S., Saxon, A., & Jossen, A. (2018). Energy efficiency evaluation of a stationary lithium-ion battery container storage system via electro-thermal modeling and detailed component analysis. Applied Energy, 210, 211-229.(3) Barreras, J. V., Raj, T., & Howey, D. A. (2018, October). Derating Strategies for Lithium-Ion Batteries in Electric Vehicles. In IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society (pp. 4956-4961). IEEE. Figure 1

Unlocking the Potential of Battery Storage with the Dynamic Stacking of Multiple Applications

Summary The ability of a battery energy storage system (BESS) to serve multiple applications makes it a promising technology to enable the sustainable energy transition. However, high investment costs are a considerable barrier to BESS deployment, and few profitable application scenarios exist at present. Here, we show that by tapping into multiple revenue streams using the dynamic stacking of applications, profitable operation is viable under current regulatory conditions. We develop a multi-use optimization framework which distinguishes between behind-the-meter and in-front-of-the-meter applications and considers how power capacity is allotted in addition to energy capacity allocation. The algorithm uses a rolling horizon optimization with an integrated degradation model and is fed with real-world data from a stationary lithium-ion battery in Germany. When combining peak shaving with frequency containment reserve, a net present value per Euro invested of 1.00 is achieved, and 1.24 with the addition of arbitrage trading on the intraday continuous market.

The Effect of Pack Configuration on Degradation of Lithium-Ion Battery Packs

Large-scale grid-connected batteries consist of various components, each of which influence the behaviour. Schimpe et al. [1] simulated the contributions of different components to the round-trip efficiency of a 192 kWh battery and found that the power electronics and the load profile have major impacts. The effect on the lifetime of the battery was not considered.Additionally, there are different configurations to connect the cells into modules and racks. Rumph et al. [2] used a multi-physics model to analyse the electrical and thermal behaviour of small modules of series and parallel connected cells. They showed how cell-to-cell variations affect the current distribution during peak currents and how matching cells could reduce the inhomogeneities. However, only short durations could be simulated due to the computational complexity of the models, excluding long-term effects such as degradation or system performance.Finally, Dechent et al. [3] used equivalent circuit models with parametric degradation models to study the degradation of large battery packs under various topologies and cell-to-cell variations. Monte Carlo simulations showed widely varying degradation trends for different systems. However, the coupling of the ageing model with the battery and thermal models was on a longer time scale, and combined with the non-physical nature of the models, this might introduce errors.In this work, SLIDE, a ‘virtual cycler’ physics-based degradation modelling framework for lithium-ion cells [4], is expanded to simulate the degradation of battery systems. Figure 1 shows the degradation trajectories of 10 cells with minor capacity variations grouped in one module during calendar ageing at 25 degrees and 90% state of charge. If all 10 cells are connected in series, small cell-to-cell differences result in a diverging degradation trend, which is not observed for parallel-connected cells. It should be noted that no balancing circuit was included, such that the diverging degradation is due to small differences in the voltages during rest. The smaller capacity cells will rest at higher potentials, accelerating the degradation and exacerbating the different degradation rates, which is prevented in parallel-connected cells.Figure 1 shows how the module configuration can affect the degradation of individual cells and small cell-to-cell variations. Once the model is extended to combine multiple modules, as well as adding thermal coupling, these effects will be studied in more detail.[1] M. Schimpe et al., “Energy efficiency evaluation of a stationary lithium-ion battery container storage system via electro-thermal modeling and detailed component analysis,” Appl. Energy, vol. 210, no. November 2017, pp. 211–229, 2018.[2] K. Rumpf, A. Rheinfeld, M. Schindler, J. Keil, T. Schua, and A. Jossen, “Influence of Cell-to-Cell Variations on the Inhomogeneity of Lithium-Ion Battery Modules,” J. Electrochem. Soc., vol. 165, no. 11, pp. A2587–A2607, Aug. 2018.[3] P. Dechent, I. Schoeneberger, and D. U. Husta, Friedrich Emanuel Sauer, “From Variability and Aging Rate Spread to Quantification of Failure Rates in Lithium-Ion Packs,” in MA2019-04 188, 2019.[4] J. M. Reniers, G. Mulder, and D. A. Howey, “Review and Performance Comparison of Mechanical-Chemical Degradation Models for Lithium-Ion Batteries,” J. Electrochem. Soc., vol. 166, no. 14, pp. A3189–A3200, Sep. 2019. Figure 1

Factors Affecting Capacity Design of Lithium-Ion Stationary Batteries

Lead-acid batteries are currently the most popular for direct current (DC) power in power plants. They are also the most widely used electric energy storage device but too much space is needed to increase energy storage. Lithium-ion batteries have a higher energy density, allowing them to store more energy than other types of batteries. The purpose of this paper is to elaborate on the factors affecting the capacity design of lithium-ion stationary batteries. Factors that need to be considered in calculating the capacity of stationary lithium-ion batteries are investigated and reviewed, and based on the results, a method of calculating capacity of stationary lithium-ion batteries for industrial use is proposed. In addition, the capacity and area required for replacing the lead-acid batteries for nuclear power plants with lithium-ion batteries are reviewed as part of this case study.

Energy efficiency evaluation of a stationary lithium-ion battery container storage system via electro-thermal modeling and detailed component analysis

Energy efficiency is a key performance indicator for battery storage systems. A detailed electro-thermal model of a stationary lithium-ion battery system is developed and an evaluation of its energy efficiency is conducted. The model offers a holistic approach to calculating conversion losses and auxiliary power consumption. Sub-models for battery rack, power electronics, thermal management as well as the control and monitoring components are developed and coupled to a comprehensive model. The simulation is parametrized based on a prototype 192 kWh system using lithium iron phosphate batteries connected to the low voltage grid. The key loss mechanisms are identified, thoroughly analyzed and modeled. Generic profiles featuring various system operation modes are evaluated to show the characteristics of stationary battery systems. Typically the losses in the power electronics outweigh the losses in the battery at low power operating points. The auxiliary power consumption dominates for low system utilization rates. For estimation of real-world performance, the grid applications Primary Control Reserve, Secondary Control Reserve and the storage of surplus photovoltaic power are evaluated. Conversion round-trip efficiency is in the range of 70–80%. Overall system efficiency, which also considers system power consumption, is 8–13 percentage points lower for Primary Control Reserve and the photovoltaic-battery application. However, for Secondary Control Reserve, the total round-trip efficiency is found to be extremely low at 23% due to the low energy throughput of this application type.

A long-life lithium-ion battery with a highly porous TiNb2O7 anode for large-scale electrical energy storage

A TiNb2O7 material with a nanoporous structure was prepared by a facile approach and can be used as an anode with excellent rate and cycling performance for long-life stationary lithium-ion batteries.

Optimal management of stationary lithium-ion battery system in electricity distribution grids

The present article proposes an optimal battery system management model in distribution grids for stationary applications. The main purpose of the management model is to maximise the utilisation of distributed renewable energy resources in distribution grids, preventing situations of reverse power flow in the distribution transformer. Secondly, battery management ensures efficient battery utilisation: charging at off-peak prices and discharging at peak prices when possible. This gives the battery system a shorter payback time. Management of the system requires predictions of residual distribution grid demand (i.e. demand minus renewable energy generation) and electricity price curves (e.g. for 24 h in advance).Results of a hypothetical study in Great Britain in 2020 show that the battery can contribute significantly to storing renewable energy surplus in distribution grids while being highly utilised. In a distribution grid with 25 households and an installed 8.9 kW wind turbine, a battery system with rated power of 8.9 kW and battery capacity of 100 kWh can store 7 MWh of 8 MWh wind energy surplus annually. Annual battery utilisation reaches 235 cycles in per unit values, where one unit is a full charge-depleting cycle depth of a new battery (80% of 100 kWh).

Olivine Type Cathodes for Stationary Lithium-Ion Batteries

not Available.

Polyanion Type Cathodes for Stationary Lithium Ion Batteries

not Available.

Management System for Large-Capacity Stationary Lithium-Ion Batteries

not Available.

439 通信用据置リチウムイオン電池の要素技術検討(蓄熱・電力貯蔵技術)

439 通信用据置リチウムイオン電池の要素技術検討(蓄熱・電力貯蔵技術)