Lithium Iron Phosphate Cells Research Articles

Mechanical methods for materials concentration of lithium iron phosphate (LFP) cells and product potential evaluation for recycling.

The production and sales of lithium-ion batteries (LIB) are rapidly expanding nowadays, causing a significant impact on the consumption of critical raw materials, such as lithium. Thus, developing and improving methods for the separation and recovery of materials from LIBs is necessary to ensure the supply of critical raw materials, as well as to meet the recycling targets set by some countries. This study evaluated and compared two mechanical routes to concentrate materials of LiFePO4 (LPF) cells. In addition, the economic, environmental, and scarcity risk potential of the products obtained through the best mechanical route were evaluated. The first route involved 6 grinding cycles in a knife mill, followed by particle size separation into 3 fractions. The second route involved a single grinding cycle (knife and hammer mill were tested), followed by particle size separation into 6 fractions. The second route showed more promise, with obtaining fractions rich in (1) iron, (2) aluminum and copper, and (3) cathode materials. Additionally, less operating time and energy consumption were necessary. The hammer mill offered a better separation for the iron and the cathodic materials (LiFePO4), while the knife mill proved to be more effective in concentrating the aluminum and copper. The product potential evaluation of the best route revealed that the priority fractions for recycling in economic and environmental assessment in LFP2 are 2 < n < 9.5mm (due Cu and Al) and n < 0.5mm (due Li). Considering the scarcity risk, priority should be assigned to the recycling of the fraction n < 0.5 due to lithium.

Error estimation of Bernardi heat evaluation of Li-ion cells under drive cycle operation

Accurate estimation of heat generation is critical for EV batteries. Conventional means of heat estimation (i.e. Bernardi equation) works only for constant current discharge. However, fluctuating current condition is the normal mode of operation for cells in EV applications. The current study attempts a comprehensive investigation of heat generation and temperature evolution for a cylindrical lithium iron phosphate cell under standard drive cycles (FUDS, US06). An experimentally parameterized electrochemical-heat transfer model is developed. Experimental voltage curve of FUDS drive cycle is used to obtain unknown electrochemical parameters while its fidelity is tested against US06. It is observed that Bernardi method overpredicts heat generation rate by approximately 25% under drive cycle operation. It is noted that Bernardi method is an indirect method of heat estimation based majorly on electrical parameters. It is also observed that the reversible part of Bernardi heat is negligible under drive cycle operation. Moreover, SOC and temperature evolution are accurately estimated by the electrochemical-heat transfer model – errors lie within 7% and 2° C, respectively. Thus, the present study demonstrates the capability of the electrochemical-heat transfer model for estimating heat generation rate of Li-ion cells under drive cycle operation where Bernardi equation is shown to be an inadequate method.

Quantifying the State of the Art of Electric Powertrains in Battery Electric Vehicles: Comprehensive Analysis of the Tesla Model 3 on the Vehicle Level

Data on state-of-the-art battery electric vehicles are crucial to academia; however, these data are not published due to non-disclosure policies in the industry. As a result, simulation models and their analyses are based on assumptions or insider information. To fill this information gap, we present a comprehensive analysis of the electric powertrain of a Tesla Model 3 Standard Range Plus (SR+) from 2020 with lithium iron phosphate (LFP) cells, focusing on the overall range. On the vehicle level, we observe the resulting range in multiple test scenarios, tracing the energy path from source to sink by conducting different test series on the vehicle dynamometer and through alternating current (AC) and direct current (DC) charging measurements. In addition to absolute electric range tests in different operating scenarios and electric and thermal operation strategies on the vehicle level, we analyze the energy density and the power unit’s efficiency on the component level. These tests are performed through procedures on the chassis dynamometer as well as efficiency analysis and electric characterization tests in charge/discharge scenarios. This study includes over 1 GB of attached measurement data on the battery pack and vehicle level from the lab to the real-world environment available as open-source data.

Transfer learning from synthetic data for open-circuit voltage curve reconstruction and state of health estimation of lithium-ion batteries from partial charging segments

Data-driven models for battery state estimation require extensive experimental training data, which may not be available or suitable for specific tasks like open-circuit voltage (OCV) reconstruction and subsequent state of health (SOH) estimation. This study addresses this issue by developing a transfer-learning-based OCV reconstruction model using a temporal convolutional long short-term memory (TCN-LSTM) network trained on synthetic data from an automotive nickel cobalt aluminium oxide (NCA) cell generated through a mechanistic model approach. The data consists of voltage curves at constant temperature, C-rates between C/30 to 1C, and a SOH-range from 70% to 100%. The model is refined via Bayesian optimization and then applied to four use cases with reduced experimental nickel manganese cobalt oxide (NMC) cell training data for higher use cases. The TL models’ performances are compared with models trained solely on experimental data, focusing on different C-rates and voltage windows. The results demonstrate that the OCV reconstruction mean absolute error (MAE) within the average battery electric vehicle (BEV) home charging window (30% to 85% state of charge (SOC)) is less than 22mV for the first three use cases across all C-rates. The SOH estimated from the reconstructed OCV exhibits an mean absolute percentage error (MAPE) below 2.2% for these cases. The study further investigates the impact of the source domain on TL by incorporating two additional synthetic datasets, a lithium iron phosphate (LFP) cell and an entirely artificial, non-existing, cell, showing that solely the shifting and scaling of gradient changes in the charging curve suffice to transfer knowledge, even between different cell chemistries. A key limitation with respect to extrapolation capability is identified and evidenced in our fourth use case, where the absence of such comprehensive data hindered the TL process.

Life cycle testing and reliability analysis of prismatic lithium-iron-phosphate cells

ABSTRACT A cell’s ability to store energy, and produce power is limited by its capacity fading with age. This paper presents the findings on the performance characteristics of prismatic Lithium-iron phosphate (LiFePO4) cells under different ambient temperature conditions, discharge rates, and depth of discharge. The accelerated life cycle testing results depicted a linear degradation pattern of up to 300 cycles. Linear extrapolation reveals that at 25°C temperature, an increase in the discharge rate from 0.5 C to 0.8 C reduces the cycle life significantly by 52.9%. On the other hand, at a constant discharge rate, an increase in temperature reduced predicted cycle life in the range of 23.2–41.36%. Lithium-ion cells’ reliability modeling and analysis was carried out using an exponential distribution showing the increasing failure rate with age, with the temperature significantly reducing the expected life of the cells.

Investigating electrochemical impedance spectroscopy for state of health estimation of LiFePO4 batteries in idle state

Online methods for determining the State of Health (SOH) and predicting the Remaining Useful Lifetime of lithium iron phosphate (LiFePO4) cells are becoming increasingly relevant. Due to the requirements for safety and availability of all-electric subsea applications, measurement methods for an accurate non-destructive battery cell diagnosis are especially important. Currently used methods are not applicable for power to safety applications, because they change the battery State of Charge (SOC) during measurement. This study aims to determine the extent to which the application of EIS provides a battery state estimation in idle state. A Design of Experiments (DoE) has been developed and realized. The EIS measurement results are analyzed in the Nyquist plot to identify curve characteristic points that refer to the SOH. The DVA is used to provide a capacity reference for the EIS evaluation. The local maximum point of the EIS is identified as a possible analysis point as it has an almost linear correlation with the DVA. It is discussed whether the gained results on cell level can be applied in further research to develop a diagnosis method that can be used on pack level in field applications.

Burst noise in rechargeable LiFePO4 batteries and its changes after long-term calendar aging

Noise analysis can provide insight into both internal electrochemical processes and the health of batteries. Here we show noise measurements taken in 2017, during discharging, both in the frequency and in the time domains for lithium iron phosphate (LiFePO4) cells manufactured by Hailei. The low freuency noise was substantially higher when the cell voltage and the discharge current exceeded certain thresholds. Moreover, the mean square voltage spectral density was remarkably constant until the cell voltage decreased below the noise threshold voltage and these results were almost unaffected by up to 500 charge-discharge cycles. The same cells, characterized again in 2023, exhibited increased noise, lower noise threshold voltages and much more variable mean square voltage spectral densities. For comparison, measurements on different LiFePO4 cells (Enix) and lithium-ions batteries (RS PRO, Samsung and Ansmann) confirmed that, unlike for other lithium-ion batteries, for new LiFePO4 cells the mean square voltage spectral density was almost constant until the cell voltage decreased below the noise threshold voltage. This is the first report on the peculiar characteristics of the noise of LiFePO4 cells and its dramatic changes after long-term calendar aging, thus paving the way to the age estimation of LiFePO4 cells by noise analysis.

Increasing the lifetime profitability of battery energy storage systems through aging aware operation

Lithium-ion cells are subject to degradation due to a multitude of cell-internal aging effects, which can significantly influence the economics of battery energy storage systems (BESS). Since the rate of degradation depends on external stress factors such as the state-of-charge, charge/discharge-rate, and depth of cycle, it can be directly influenced through the operation strategy. In this contribution, we propose a model predictive control (MPC) framework for designing aging aware operation strategies. By simulating the entire BESS lifetime on a digital twin, different aging aware optimization models can be benchmarked and the optimal value for aging cost can be determined. In a case study, the application of generating profit through arbitrage trading on the EPEX SPOT intraday electricity market is investigated. For that, a linearized model for the calendar and cyclic capacity loss of a lithium iron phosphate cell is presented. The results show that using the MPC framework to determine the optimal aging cost can significantly increase the lifetime profitability of a BESS, compared to the prevalent approach of selecting aging cost based on the cost of the battery system. Furthermore, the lifetime profit from energy arbitrage can be increased by an additional 24.9% when using the linearized calendar degradation model and by 29.3% when using both the linearized calendar and cyclic degradation model, compared to an energy throughput based aging cost model. By examining price data from 2019 to 2022, the case study demonstrates that the recent increases in prices and price fluctuations on wholesale electricity markets have led to a substantial increase of the achievable lifetime profit.

Robust Estimation of State of Charge in Lithium Iron Phosphate Cells Enabled by Online Parameter Estimation and Deep Neural Networks

This paper addresses the state of charge estimation problem in lithium iron phosphate (LFP) battery cells. LFP cells are particularly challenging because their fat open circuit voltage (OCV) curve means OCV-based battery models are weakly observable. This means standard methods for SOC estimation don't easily converge to the true SOC. Additionally, in practice, estimates must be accurate in the face of biased noise on current input, as well as mean-zero noise on measurements. As such, we aim to create an estimator that is accurate when facing these types of noise. We accomplish this with a three-layer estimation technique that uses an adaptive Kalman filter, a Neural Network, and a Kalman Filter to estimate the state of charge. This method achieves an SOC estimation with an RMSE of 2.248%, even in the presence of a 0.2A current measurement bias and 5mA and 5mV random measurement noise. Notably, the proposed approach outperforms state-of-the-art methods like the extended Kalman filter.

Environmental Impacts of Specific Recyclates in European Battery Regulatory-Compliant Lithium-Ion Cell Manufacturing

Since environmental benefits and supply chain resilience are commonly assumed for circular economy strategies, this study tests this hypothesis in the context of lithium-ion battery recycling and cell manufacturing. Therefore, the use of recyclates from different cathode active materials and from different recycling routes, namely hydrometallurgy and direct recycling, in a subsequent cell production is modelled with the recyclate quotas prescribed by the amended European Battery Regulation and analysed using life cycle assessment methodology. This study concludes that both, negative and positive environmental impacts can be achieved by the usage of recyclates, depended on the cell technology and the recycling process chosen. Newly constructed lithium iron phosphate (LFP) cells using a share of 11.3% of recyclates, which are obtained from LFP cells by a hydrometallurgical process, achieve a deterioration in the ecology by 7.5% for the global warming potential (GWP) compared to LFP cells without any recyclate share at all. For the same recyclate quota scenario, hydrometallurgical recyclates from lithium nickel manganese cobalt oxide cells (NMC), on the other hand, achieve savings in GWP of up to 1.2%. Recyclates from direct recycling achieve savings in GWP for LPF and NMC of a maximum of 6.3% and 12.3%, by using a recyclate share of 20%. It can be seen that circular economy can raise large savings potentials ecologically, but can also have a contrary effect if not properly applied.

Lithium-ion battery design optimization based on a dimensionless reduced-order electrochemical model

Model-based optimal cell design is an efficient approach to maximize the energy density of lithium-ion batteries. This maximization problem is solved in this paper for a lithium iron phosphate (LFP) cell. We consider half-cells as opposed to full-cells typically considered, which are intermediate steps during battery manufacturing for electrode characterization and they are gaining popularity by themselves as lithium-metal batteries. First, a dimensionless reduced-order electrochemical model is used instead of high-order models. Second, sensitivity equations are analyzed to determine the ranking of the design parameters according to their effect on the energy density, which is often lacking in other contributions. Three parameters, namely electrode thickness, LFP particle radius and electrode cross sectional area, are shown to have the most influential effects. Third, a novel adaptive particle swarm optimization with a specific stopping criterion is used for LIB design optimization. The proposed optimization framework is tested in simulation on a LFP half-cell battery. The results show that the design optimization yields 250 Wh kg−1 for an LFP electrode of 310μm thickness, 10 nm particle radius and 2⋅10−4 m2 cross-sectional area, which is an increase of energy density of 61 Wh kg−1 with respect to an initial design proposed in the literature.

Experimental investigation of a micelle encapsulator F-500 on suppressing lithium ion phosphate batteries fire and rapid cooling

Thermal runaway (TR) in lithium-ion batteries (LIBs) and LIB fires have attracted a considerable amount of attention. In this study, the micelle encapsulator F-500 was experimentally investigated to understand its extinguishing effect and cooling capacity for lithium iron phosphate (LFP) cells in modules. The gases produced from the batteries were collected and analysed. The extinguishing effectiveness and cooling capacity of a 3% F-500 solution and pure water mist (WM) were compared and discussed. Furthermore, the concentration of H2 was evaluated during the tests. The experimental results showed that H2 is the major gas released during thermal runaway. The combustion of LFP batteries requires external ignition and fire-intensified TR propagation in the LFP battery module. The cooling capacity of the 3% F-500 solution was appropriately three times that of WM according to temperature reduction calculations. The peak concentration for H2 was 14 ppm when the micelle encapsulator was employed, while the peak concentration was 217 ppm when WM was applied. The control mechanisms were qualitatively discussed by comparing the connection between thermal runaway and fire progress. The cooling effect was identified as the most significant factor not only for rapidly extinguishing LIB flames but also for preventing TR propagation. These results are expected to provide guidelines for fighting LIB fires and for cooling LIB systems.

Neural Ordinary Differential Equations for Grey-Box Modelling of Lithium-Ion Batteries on the Basis of an Equivalent Circuit Model

Lithium-ion batteries exhibit a dynamic voltage behaviour depending nonlinearly on current and state of charge. The modelling of lithium-ion batteries is therefore complicated and model parametrisation is often time demanding. Grey-box models combine physical and data-driven modelling to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling. Differential equations given by physical laws and NODEs can be combined in a single modelling framework. Here we demonstrate the use of NODEs for grey-box modelling of lithium-ion batteries. A simple equivalent circuit model serves as a basis and represents the physical part of the model. The voltage drop over the resistor–capacitor circuit, including its dependency on current and state of charge, is implemented as a NODE. After training, the grey-box model shows good agreement with experimental full-cycle data and pulse tests on a lithium iron phosphate cell. We test the model against two dynamic load profiles: one consisting of half cycles and one dynamic load profile representing a home-storage system. The dynamic response of the battery is well captured by the model.

An efficient gel polymer electrolyte for dendrite-free and long cycle life lithium metal batteries

Lithium metal batteries (LMBs) are plagued with non-uniform and dendritic electrodeposition of Li when used with liquid electrolytes, resulting in poor cycle life and Coulombic efficiency, and safety hazards. Herein, we report a novel gel polymer electrolyte (GPE) for LMBs enabling uniform and nondendritic Li electrodeposition, long cycle life, and high Columbic efficiency. The GPE is made by immobilization of liquid electrolytes within a crosslinked polymer matrix. At ambient temperature, the GPE shows a Li ion conductivity of 1.5 mS cm−1. Topology and morphology of the electrochemically deposited Li in the GPE were studied by operando optical microscopy and scanning electron microscopy (SEM). Cryogenic transmission electron microscopy (cryo-TEM) and X-ray photon spectroscopy (XPS) characterizations indicate that the solid electrolyte interphase (SEI) layer at the Li/GPE interface is a thin and LiF-rich while the SEI layer is thick and LiF-poor at the Li/liquid electrolyte interface. Density functional theory (DFT) calculations show that LiF crystals facilitate and regulate Li ions transport. Lithium|lithium iron phosphate (Li|LFP) cells with our GPE deliver an initial specific capacity of ∼130 mAh g−1 and ∼70% capacity retention after 1000 cycles at 2C charge/discharge rate. This study offers a promising approach for engineering a stable and conductive Li/polymer electrolyte interface for dendrite-free LMBs.

Experimental Investigation of the Collective Impact of Electric Current and Ambient Temperature on the Thermal and Electrical Parameters of Lithium Iron Phosphate Cells

The performance of a lithium‐ion battery depends on the collective impact of discharge current and ambient temperature. Herein, three aspects of such impact are investigated. The first is the relationship between the thermal parameters. At 0 ºC ambient temperature and 1 C discharge rate, the heat generation rate initially increases to more than 50 kW m−3 and then dips by almost 20% due to a consequent rise in battery temperature. The second aspect is the relationship between the thermal and the electrical parameters. The steep rise in battery temperature at low ambient temperatures lifts up the voltage and improves the electrical performance more than expected. The third aspect is the collective impact of ambient temperature and electric current on the battery performance. At 0 °C ambient temperature, an increase in current from 1 to 1.5 C increases the temperature rise rate by more than two. This increases the battery capacity by at least 6% compared with that at a 1 C rate. The investigation results are expected to improve the understanding of the collective impact of current and ambient temperature on battery performance and provide a guideline for developing an optimized thermal management system.

Linear parameter-varying modeling and identification of lithium-ion battery for control-oriented applications

Model-based approaches are popularly used for real-time management of lithium-ion batteries. However, building accurate models to characterize the inherent nonlinear dynamic properties of electrochemical processes is still a challenge for control-oriented applications. Instead of building a model independently from physical laws or experimental data, a linear parameter-varying (LPV) modeling method for lithium-ion batteries is proposed in this research. In the LPV modeling structure, the nonlinear relationships are considered to be dependent on the so-called scheduling variables. In combination with the subspace identification algorithm, a global method is proposed to identify the LPV battery model based on a single data set with constantly changing scheduling variables. The validity of this modeling approach is verified by an experimental study of lithium iron phosphate (LiFePO4) cells. Experimental results reveal that state-of-charge (SOC) and SOC change rate are key scheduling variables that determine the model nonlinearities, and the identified model provides good results in terms of prediction accuracy and robustness. Meanwhile, comparison results show that the global LPV modeling approach can describe the nonlinear battery dynamics adequately with first-order model, which makes it a promising method for control-oriented applications.

Experimental Evaluation of Aging Indicators for Lithium–Iron–Phosphate Cells

Degradation mechanism of batteries has to be carefully studied when considering their utilization in electrical power systems. This paper presents the results of an extensive experimental campaign, through which three different lithium–iron–phosphate (LFP) cells were subjected to different electrical cycling stresses. The purpose of the campaign was to evaluate the cells’ aging, as well as to try to find parameters on the cell behavior before its end of life, able to act as state-of-life (SOL) (or aging) indicators. The considered stress consists of the cyclic repetition of fixed-duration discharge steps, followed by full charge phases. The three cells under study were subjected to the very same stress pattern but with three different discharge and charge power levels: low, medium, and high. The results showed that the end-of-discharge voltage and the cell internal resistance can be used as good SOL indicators. However, both are significant functions of the cell conditions, such as the state of charge (SOC) and the cell temperature.

Measuring the reversible heat of lithium-ion cells via current pulses for modeling of temperature dynamics

This article introduces a novel calorimetric measurement method, namely the ‘Double Pulse Method’, to measure reversible heat in lithium-ion battery cells. In Li-ion cells, reversible heat has a material-dependent characteristic as it is closely related to both entropy change and the temperature dependence of the open circuit voltage. The proposed method measures reversible heat as a highly resolved function of the cell’s state of charge. The determination of reversible heat is based on the evaluation of the temperature difference generated by two current pulses of opposite polarity. Unlike established potentiometric methods, the Double Pulse Method is simple to set up, fast, and cost-effective. The accuracy of the Double Pulse Method is demonstrated for an automotive lithium iron phosphate (LFP) cell, and the entropy changes are compared with measurements of the same cell using the established potentiometric method. Simulation results indicate that the cell temperature is more accurate when determined by the reversible heat measurement obtained by the Double Pulse Method than from the potentiometric method.

Electrical and Structural Characterization of Large‐Format Lithium Iron Phosphate Cells Used in Home‐Storage Systems

This article presents a comparative experimental study of the electrical, structural, and chemical properties of large‐format, 180 Ah prismatic lithium iron phosphate (LFP)/graphite lithium‐ion battery cells from two different manufacturers. These cells are particularly used in the field of stationary energy storage such as home‐storage systems. The investigations include 1) cell‐to‐cell performance assessment, for which a total of 28 cells are tested from each manufacturer; 2) electrical charge/discharge characteristics at different currents and ambient temperatures; 3) internal cell geometries, components, and weight analysis after cell opening; 4) microstructural analysis of the electrodes via light microscopy and scanning electron microscopy; 5) chemical analysis of the electrode materials using energy‐dispersive X‐ray spectroscopy; and 6) mathematical analysis of the electrode balances. The combined results give a detailed and comparative insight into the cell characteristics, providing the essential information needed for system integration. The study also provides complete and self‐consistent parameter sets for the use in cell models needed for performance prediction or state diagnosis.

Performance benchmark of state-of-the-art high-power lithium-ion cells and implications for their usability in low-voltage applications

For vehicle electrical systems, high-power optimized lithium-ion batteries offer superior cycle stability, compactness and weight compared to conventional lead–acid batteries. To identify lithium-ion cell candidates during early concept and development phases, both performance characteristics and a comparison of commercialized lithium-ion cells covering different cell chemistries are needed. Since the market share of high-power lithium ion cells is limited, scientific studies and extensive characterizations are rare. This study closes the gap by benchmarking state-of-the-art high-power cells considering the requirements of 12V/48V applications. The sensible begin-of-life parameters OCV, internal resistance, and capacity were investigated by stepwise OCV measurement, pulse power characterization and capacity measurement regarding the dimensions: SOC (0% to 100%), temperature (−25°C to +55°C) and current rate (up to 30C). All cells exhibit temperature dependent OCV curves, with ambient temperatures above zero hardly affecting the OCV hysteresis. A SOC dependency of the internal resistance of the tested lithium titanate oxide cell reduces the power capability, available cell capacity and energy efficiency. This cell, in contrast to the graphite-based cells, enables a neglection of a Butler–Volmer dependency and offers high charge acceptance at negative temperatures. The internal resistance of the lithium iron phosphate cell is less affected by SOC which allows for constant power output. Above 25°C and up to 15C, energy efficiencies of the graphite-based cells exceed 95%. We conclude that the lithium iron phosphate cell is best suited for 12V applications due to its voltage band and discharge characteristics. None of the cells stand out for use in 48V applications. Our findings from benchmarking among different cell chemistries are beneficial to other research areas such as battery simulation, battery management systems, or cell/system design.