LFP Battery Research Articles

Thermo-Electrochemical Simulation of Large-Format Li-Ion Cells in 3D Using the Battery Modelling Toolbox (BattMo)

Computer-based modelling and simulation can be a valuable tool when developing electrochemical devices: it offers possibilities for insight into internal properties not available in experiments, it can guide the development and testing of new materials in the device, it can provide time-efficient alternatives for optimizing operating procedures, and more. In this contribution we will give an overview of the Battery Modelling Toolbox (BattMo), which is an open-source framework for continuum modelling of electrochemical-thermal systems. It has been primarily developed for the modelling and simulation of Li-ion battery cells using pseudo X-dimensional (PXD, X=2,3,4) Doyle-Fuller-Newman models, but has also successfully been applied to electrolyzers. BattMo is developed in MATLAB and has a high-performance Julia version (BattMo.jl) in development.The fundamental equations of mass, charge and energy conservation are in BattMo discretized using a low-order implicit finite volume method. These are suitable methods, since they are conservative (meaning that mass, charge, etc., are conserved), and guarantee monotone solutions (meaning that the solutions will be positive if the data is positive). These properties provide a simulation foundation that is robust and stable also on complex 3D grids -- essential features when modelling real-world systems. As with most numerical methods, the most time-consuming part is to solve the resulting linear system of equations. In BattMo we make use of state-of-the-art block-wise multigrid methods to obtain high performance.Working with BattMo may be done using a GUI, which generates a json file which completely specifies a simulation. This json file can be used directly as input to BattMo via the MATLAB/Julia prompt, or used in cloud resources. It is also possible to use a json file as a base configuration and modify the parameters manually or programmatically, for example to investigate a specific system response when changing some parameters of interest. BattMo also provides functionality for automatic gradient-based optimization and parameter estimation that can be used for maximizing or minimizing some goal quantity, and matching to experimental data. A key component for performing efficient gradient-based optimization and parameter estimation in the case of many parameters (for example when having one parameter value in each grid cell) is the use of so-called adjoints. Using adjoints allow for the gradients in the sensitivity analysis to be computed with a constant cost that is independent on the number of parameters, thus avoiding the curse of dimensionality.Much attention is spent on making the BattMo json input compatible with other simulation frameworks such as PyBamm, COMSOL and CideMod. This is to accelerate research and development by taking advantage of the strengths of different packages, as well as providing not only reproducible but also reliable science. BattMo is developed alongside BattINFO, which is a battery interface ontology based on EMMO (Elementary Multiperspective Material Ontology) that is developed in the BIG-MAP EU H2020 project. We also seek to support the BPX standard developed by the PyBamm team.In this talk we will overview the basic features of BattMo and see how the modular approach used in the software simplifies development of new models. BattMo contains many examples, and we will here present thermal simulation of 1D and 3D cells with LFP and LNMO battery chemistries in pouch cell format and investigate the effects of the tabless design in the 4680 cylindrical cell format (see the Figure below).BattMo is available at https://github.com/BattMoTeam and is developed with the support from the EU H2020 innovation programs under the grants Hydra (875527) and BIG-MAP (957189). Figure 1

Removal of lithium, sodium, and phosphate from the Li-ion battery cathode recycling process using column adsorption

Lithium Ion Battery (LIB) is one type of battery whose demand is increasing every year. After the Li-ion battery usage cycle ends, more and more waste is generated. Wastewater from the LFP battery cathode recycling process still contains metals such as lithium, sodium, and phosphate. This experiment was carried out using the adsorption method. The adsorption method is used to remove metal ions in LFP battery waste. The adsorption process took place in a semi-continuous fixed-bed column made of glass cylinders with a height of 50 cm and a diameter of 2.5 cm. The artificial waste solution flowed into a column containing 135 g of activated carbon with variations in flow rates of 10, 15, and 20 mL/min for 2 hours. The concentration of the initial solution and the filtrate was analyzed using an Inductively Coupled Plasma-Optical Emission Spectrometer (ICP-OES). The experimental results showed that the maximum percentage removal of lithium, sodium, and phosphate at a flow rate of 10 mL/min was 95.22%, 96.42%, and 68.02%, respectively. In addition, the results of the adsorption kinetic studies reviewed using the Thomas and Yoon-Nelson models showed that the column adsorption performance for lithium, sodium, and phosphate was most effective at a flow rate of 10 mL/min with an adsorption capacity of 8.75.105 mg/g, 8.85.105 mg/g, and 3.91.105 mg/g, respectively. Meanwhile, the breakthrough times for lithium, sodium, and phosphate were 335 minutes, 441 minutes, and 126 minutes, respectively.

Dynamic ultrasonic response modeling and accurate state of charge estimation for lithium ion batteries under various load profiles and temperatures

Accurate estimation of the State of Charge (SoC) plays a vital role in ensuring the efficient and safe operation of lithium iron phosphate (LFP) batteries. However, the flat open circuit voltage (OCV) curve of the LFP battery implies a low sensitivity to SoC, which results in large SoC estimation errors in the presence of noisy terminal voltage measurements. To address this challenge, an SoC estimation methodology utilizing an ultrasonic reflection response model is proposed, which is the first methodology regarding highly accurate and robust ultrasonic model-based SoC estimation under dynamic load profiles. Since ultrasound waves enable non-destructive acquisition of battery internal physical property changes directly associated with SoC, the ultrasonic battery near-surface reflection feature is extracted and demonstrated to exhibit a highly linear correlation with and higher sensitivity to SoC. We pioneeringly construct an empirical differential ultrasonic model to describe how the ultrasonic feature depends on the SoC and dynamic current. The advantage of such an ultrasonic model is demonstrated by theoretical and experimental results of an Adaptive Extend Kalman Filter (AEKF) and an Adaptive H-infinity Filter (AHIF) under various dynamic load profiles and temperatures. The Root Mean Square Error (RMSE) for ultrasonic model-based SoC estimation remains at approximately 1% across all tests, reducing by 36.7% compared to the voltage model, which shows its great potential in accurate SoC estimation.

Hierarchical bulk-interface design of MOFs framework for polymer electrolyte towards ultra-stable quasi-solid-state Li metal batteries

The sluggish ion transportation, oxidation instability and severe lithium dendrites impede the development of solid-state lithium metal battery (SSLMBs). Herein, a novel structural design of in-situ polymerized composite electrolyte (CPE) with the utilization of both orientationally arranged and unilaterally sedimented Zeolitic imidazolate framework-67 (ZIF-67) fillers are reported. The in-situ growing of ZIF-67 crystals on 3D polyvinylidene fluoride (PVDF) nanofiber skeleton not only provides high-speed Li+ conduction pathway, but also promotes the antioxidative ability of surrounding polymer matrix by facilitating the ring-open polymerization of 1,3-Dioxolane (DOL) monomer to form long-chain PDOL. Meanwhile, ZIF-67 deposition layer constructed through the natural sedimentation of residual ZIF-67 during PVDF@ZIF-67 framework fabrication regulates surface Li+ flux towards lithium anode and further optimizes SEI composition. As a result, satisfactory ionic conductivity of 1.8 × 10−4 S cm−1 and oxidative potential of 5.9 V (vs. Li+/Li) are acquired for unilateral-sedimented ZIF-67/PDOL CPE (UZE). Encouragingly, symmetric Li battery based on UZE displays a superior stability over 2300 h at 0.1 mA cm−2 and a remarkable cycle durability with 92.5 % of capacity retained after 570 cycles at 2C is also achieved for Li||LFP battery. This study provides a novel guidance in engineering solid electrolyte with excellent ionic transport kinetics and interfacial compatibility for next-generation SSLMBs.

Semi-empirical ageing model for LFP and NMC Li-ion battery chemistries

Semi-empirical ageing model for LFP and NMC Li-ion battery chemistries

Effect of safety valve types on the gas venting behavior and thermal runaway hazard severity of large-format prismatic lithium iron phosphate batteries

The safety valve is an important component to ensure the safe operation of lithium-ion batteries (LIBs). However, the effect of safety valve type on the thermal runaway (TR) and gas venting behavior of LIBs, as well as the TR hazard severity of LIBs, are not known. In this paper, the TR and gas venting behavior of three 100 A h lithium iron phosphate (LFP) batteries with different safety valves are investigated under overheating. Compared to previous studies, the main contribution of this work is in studying and evaluating the effect of gas venting behavior and TR hazard severity of LFP batteries with three safety valve types. Two significant results are obtained: (I) the safety valve type dominates over gas venting pressure of battery during safety venting, the maximum gas venting pressure of LFP batteries with a round safety valve is 3320 Pa, which is one order of magnitude higher than other batteries with oval or cavity safety valve; (II) the LFP battery with oval safety valve has the lowest TR hazard as shown by the TR hazard assessment model based on gray-fuzzy analytic hierarchy process. This study reveals the effect of safety valve type on TR and gas venting, providing a clear direction for the safety valve design.

Cell teardown and characterization of an automotive prismatic LFP battery

A key challenge in lithium-ion battery research is the need for more transparency regarding the cell design and production processes of battery as well as vehicle manufacturers. This study comprehensively benchmarks a prismatic hardcase LFP cell that was dismounted from a state-of-the-art Tesla Model 3 (Standard Range). The process steps and manufacturing peculiarities were traced by disassembling the cell and analyzing the characteristics on the material level. A butterfly-design with two jelly rolls was found for the cell design in order to minimize the void volume (6.4%). Cross-sections and microscopy analyses from the cell cap revealed the application of multiple laser-welding processes that are needed to provide a high stability and tightness. The coatings of the approximately 22m long electrodes showed a high homogeneity with thickness fluctuations less than 2µm. Scanning electron microscopy images revealed a pure graphite anode and a bimodal particle distribution within the lithium iron phosphate cathode, whereby the edges of the cathode were covered in a 27µm thick aluminum oxide (Al2O3) insulation layer. Electrochemical analyses were performed showing the improved performance of the inherent electrolyte over a common LP572 electrolyte. A gravimetric energy density of 163Wh/kg and a volumetric energy density of 366Wh/l was determined based on a nominal cell capacity of 161.5Ah. Overall, the purpose of this work is to demonstrate which design decisions and manufacturing challenges are addressed in industrial cell production, so that the focus of academic research can align with the state-of-the-art.

Experimental Study on High-Temperature Cycling Aging of Large-Capacity Lithium Iron Phosphate Batteries

Large-capacity lithium iron phosphate (LFP) batteries are widely used in energy storage systems and electric vehicles due to their low cost, long lifespan, and high safety. However, the lifespan of batteries gradually decreases during their usage, especially due to internal heat generation and exposure to high temperatures, which leads to rapid capacity degradation. In-depth research is needed on the degradation characteristics of large-capacity LFP batteries under high temperatures. To study the degradation characteristics of large-capacity LFP batteries at high temperatures, a commercial 135Ah LFP battery is selected for 45°C high-temperature dynamic cycling aging experiments and 25°C reference performance experiments. A detailed analysis of the degradation process is conducted by examining the patterns of changes in charge-discharge voltage curves, capacity, internal resistance, open circuit voltage (OCV), and incremental capacity curve. The study uncovers that the OCV displays diverse degradation patterns at different states of charge (SOC). Furthermore, it identifies the loss of lithium inventory and active material as the fundamental factors contributing to the degradation observed during high-temperature cycling. This study provides references for developing battery life prediction algorithms and designing long-cycle-life battery cells.

A SOC Correction Method Based on Unsynchronized Full Charge and Discharge Control Strategy in Multi-Branch Battery System

LiFePO4 batteries exhibit voltage plateau and voltage hysteresis characteristics during charging and discharging processes; however, the estimation of state-of-charge relies on voltage detection. Thus, the estimation accuracy of SOC is low in a traditional method. In this paper, a full charge and discharge SOC correction method is proposed; i.e., the SOC is corrected to 100% when the battery is fully charged and to 0% when fully discharged, and the actual usable capacity is corrected using the fully discharged capacity after being fully charged. Thereby, the cumulative error of the ampere-hour integration method is dynamically corrected. In engineering applications, however, the battery systems do not always undergo full charge and discharge cycling due to the operating conditions. By making full use of the distributed control characteristics of the multi-branch topology battery system, the present work proposes an optimized system control strategy to realize the unsynchronized full charge and discharge cluster by cluster, which extends the application of the full charge and discharge SOC correction method. The experimental results verify the accuracy of the proposed SOC correction method and the feasibility of the control strategy. A more reliable and efficient battery management scheme is provided for LFP battery system, which has high practical value in engineering.

HPPC Test Methodology Using LFP Battery Cell Identification Tests as an Example

The aim of this research was to create an accurate simulation model of a lithium-ion battery cell, which will be used in the design process of the traction battery of a fully electric load-hull-dump vehicle. Discharge characteristics tests were used to estimate the actual cell capacity, and hybrid pulse power characterization (HPPC) tests were used to identify the Thevenin equivalent circuit parameters. A detailed description is provided of the methods used to develop the HPPC test results. Particular emphasis was placed on the applied filtration and optimization techniques as well as the assessment of the quality and the applicability of the acquired measurement data. As a result, a simulation model of the battery cell was created. The article gives the full set of parameter values needed to build a fully functional simulation model. Finally, a charge-depleting cycle test was performed to verify the created simulation model.

Multi sensor fusion methods for state of charge estimation of smart lithium-ion batteries

Multi-dimensional sensing is the key characteristic of next generation smart batteries. But the existing researches on multi-sensor fusion methods haven't focused on algorithm mechanism, the global optimal solution has not been built, the superiority has not been proved theoretically. For the first time, this research built the global optimal structure of multi-sensor fusion state estimation algorithm. Specifically, the state of charge (SoC) estimation problem of lithium iron phosphate (LFP) batteries is studied, cooperating with voltage signal, expansion force (EF) signal is introduced. Firstly, a normalization algorithm is introduced to overcome the drift of EF under different cycles and different pre-tightening force. Secondly, the non-monotonic relationship of LFP battery's EF-SoC curve is addressed with a forced monotone transformation method in the transition areas. Then the global optimal multi-sensor fusion method is built, theoretical reductions are carried out to prove the higher precision of multi-sensor fusion methods compared to single-signal methods. Experiments are conducted to verify the effectiveness of the methods, even under the most serious situations, the fusion methods exhibit powerful correction ability, and the root mean square error can be controlled within 3 %. Moreover, the proposed algorithms shows strong tolerance to error sources.

Boron nitride nanofibers enhanced composite PEO-based solid-state polymer electrolytes for lithium metal batteries

Polyethylene oxide (PEO)-based solid-state polymer electrolytes (SPEs) are limited by their poor cyclic stability and inferior ionic conductivity for applicating in high-safety, long-cycling and high-energy-density lithium metal batteries. Herein, porous boron nitride nanofibers (BNNFs) are filled into PEO-based SPE, which significantly suppresses Li dendrites growth and enhances the electrochemical performance of Li metal battery. BNNFs with high porosity have more active sites to connect with PEO, which can effectively reduce the crystallinity of the PEO matrix and enhance its ionic conductivity. Moreover, owing to the hardness and good stability of BNNFs, BNNFs/PEO/LiTFSI electrolyte exhibits a wider electrochemical window, better mechanical property and higher thermal stability compared with PEO/LiTFSI electrolyte. Consequently, the Li symmetric cell composed of 1% BNNFs/PEO/LiTFSI performs good cyclic stability (>1800 h), and the Li||1% BNNFs/PEO/LiTFSI||LFP full battery shows obviously improved performances in charge-discharge polarization voltage, discharge specific capacity, rate performance and cyclic stability than the Li||PEO/LiTFSI||LFP battery.

The influence of current in off-grid PV systems on lead–acid battery lifetime and hybridization with LFP battery as solution

This paper describes test results of two sets of VRLA GEL batteries that included various phenomena which might accelerate their degradation process in off-grid PV systems like: incomplete charges (bad recharges), low start current and remaining in partially discharge state. The test shown in the paper included two cycling experiments lasting 45 days and 98 days during which the batteries where constantly charged and discharged in different cycles. The results show strong acceleration of degradation process for incomplete charges (bad recharges) which are specifically difficult to avoid in off-grid systems. In order to avoid the premature destruction of lead–acid battery in off-grid PV systems the hybrid battery system consisting of lithium-iron-phosphate and lead–acid batteries connected with DC/DC converter is proposed.

Analysis of the aging effects on the thermal runaway characteristics of Lithium-Ion cells through stepwise reactions

The current political vision to drastically reduce carbon emissions pushes the electrified powertrain into an increasingly important role in the transport sector. However, concerns related to the battery's effectiveness as an energy source need to be overcome to make this technology widespread. One such concern is safety, catching attention as development races towards greater battery energy density. In this way, cathodic chemistry is important since exothermic reactions are unleashed from the components that originally formed the active material. Furthermore, the aging process reduces the battery capacity, reducing the amount of active material and thickening the solid electrolyte interface, which increases the joule effect. For these reasons, thermal runaway under aging conditions must be investigated to assess potential safety issues. Using an accelerating rate colorimeter, the heat-induced thermal runaway tests were performed with two-cathode chemistry (NMC and LFP) under pristine and aged battery conditions. For aging the batteries, the ARC was coupled with a bidirectional source. Two ambient temperatures, 20 °C and 50 °C, were used for the aging tests, being the batteries cycled up to 250 cycles with a determined protocol for charge and discharge. A numerical model was fed with experimental tests, targeting optimizing the battery output parameters and obtaining geometric aspects that are difficult to measure. Unlike the single step model, a stepwise reactions model was created to assess the heat release from different battery components for pristine and aged conditions. The higher endothermic behavior from cathode decomposition and less oxygen released during this reaction make the LFP battery safer than the NMC. For aged batteries, the SEI growth consumes lithium and electrolyte, decreasing the quantity of both components in the anode. Thus, the anode and electrolyte reaction after SEI decomposition is lower, improving battery safety.

Neural network for the estimation of LFP battery SOH cycled at different power levels

This work presents a method to quantify and estimate the degradation level of lithium-ion battery cell cycle aged at different power levels. Experimental results previously reported by the authors are analysed further, now with artificial intelligence workflows to establish a method to estimate the cells degradation rate, in term of State of Health. The neural network estimation capability is dependent on the type of input signal used for training, and the relative proportion of training vs. testing data. The development of a feedforward neural network which elaborates the information of voltage and current differences during sudden power changes significantly increased the predictive capability of the method, reaching State-of-Health estimation best-case errors lower than 1 %, in line with more complex Artificial Intelligence approaches found in literature. In addition, the results obtained with the feedforward neural network are then compared with a regression learning – based estimation function, trained and tested over the same dataset. As last test, both these two methods, trained with dataset coming from cycle aging experimental tests, are used to estimate the State of Health of a lithium cell aged due to calendar phenomena only.

Plant Leaf-Inspired Separators with Hierarchical Structure and Exquisite Fluidic Channels for Dendrite-Free Lithium Metal Batteries.

Lithium (Li) metal batteries are among the most promising devices for high energy storage applications but suffer from severe and irregular Li dendrite growth. Here, it is demonstrated that the issue can be well tackled by precisely designing the leaf-like membrane with hierarchical structure and exquisite fluidic channels. As a proof of concept, plant leaf-inspired membrane (PLIM) separators are prepared using natural attapulgite nanorods. The PLIM separators feature super-electrolyte-philicity, high thermal stability and high ion-selectivity. Thus, the separators can guide uniform and directed Li growth on the Li anode. The Li//PLIM//Li cell with limited Li anode shows high Coulombic efficiency and cycling stability over 1500h with small overpotential and interface impedance. The Li//PLIM//S battery exhibits high initial capacity (1352 mAh g-1 ), cycling stability (0.019% capacity decay per cycle at 1 C over 500 cycles), rate performance (673 mAh g-1 at 4 C), and high operating temperature (65°C). The separators can also effectively improve reversibility and cycling stability of the Li/Li cell and Li//LFP battery with carbonate-based electrolyte. As such, this work provides fresh insights into the design of bioinspired separators for dendrite-free metal batteries.

Cyclopentylmethyl Ether, a Non‐Fluorinated, Weakly Solvating and Wide Temperature Solvent for High‐Performance Lithium Metal Battery

AbstractWhile recent work demonstrates the advantages of weakly solvating solvents in enhancing the cyclability of LMBs, both new designs and design strategies for high performance weakly solvating solvent, especially physicochemical properties, are still lacking. Here, we propose a molecular design to tune the solvating power and physicochemical properties of non‐fluorinated ether solvent. The resulting cyclopentylmethyl ether (CPME) have a weak solvating power and wide liquid‐phase temperature range. By optimizing the salt concentration, the CE is further promoted to 99.4 %. Besides, the improved electrochemical performance of Li−S battery in CPME‐based electrolytes is obtained at −20 °C. The Li||LFP (17.6 mg cm−2) battery with developed electrolyte maintains >90 % of the original capacity over 400 cycles. Our design concept for solvent molecule provides a promising pathway to non‐fluorinated electrolytes with weakly solvating power and wide temperature window for high‐energy‐density LMBs.

Cyclopentylmethyl Ether, a Non-Fluorinated, Weakly Solvating and Wide Temperature Solvent for High-Performance Lithium Metal Battery.

While recent work demonstrates the advantages of weakly solvating solvents in enhancing the cyclability of LMBs, both new designs and design strategies for high performance weakly solvating solvent, especially physicochemical properties, are still lacking. Here, we propose a molecular design to tune the solvating power and physicochemical properties of non-fluorinated ether solvent. The resulting cyclopentylmethyl ether (CPME) have a weak solvating power and wide liquid-phase temperature range. By optimizing the salt concentration, the CE is further promoted to 99.4 %. Besides, the improved electrochemical performance of Li-S battery in CPME-based electrolytes is obtained at -20 °C. The Li||LFP (17.6 mg cm-2 ) battery with developed electrolyte maintains >90 % of the original capacity over 400 cycles. Our design concept for solvent molecule provides a promising pathway to non-fluorinated electrolytes with weakly solvating power and wide temperature window for high-energy-density LMBs.

Engineering of hydrogel electrolyte for aqueous Zn||LiFePO4 battery on subzero-temperature adaptability, long cycles and mechanical safety

Aqueous Zn||LiFePO4 (Zn||LFP) hybrid batteries are gaining increasing interest because of their stable charging/discharging, non-flammability, low cost, and environmental friendliness. However, aqueous hybrid batteries still face the challenges of sluggish charge transfer and ion diffusion processes due to electrolyte freezing at subzero temperatures. Herein, we designed an antifreeze hydrogel electrolyte for low-temperature Zn||LFP battery. This hydrogel electrolyte is comprised of double-network κ-carrageenan/PAAm containing moderately concentrated electrolyte (1 M ZnCl2 and 7 M LiCl), possesses high elasticity with a fracture strain of 834% and high ionic conductivity of 14.8 mS cm−1 at −40 °C. It also enables a stable Zn plating/stripping efficiency with reduced interface resistance and alleviates undesired H2 evolution. The corresponding flexible Zn||LFP battery with hydrogel electrolyte displays outstanding cycle stability with capacity retentions of 95.2% and 80.5% after 2000 cycles at −20 °C and −40 °C, respectively. In addition, the battery exhibits low self-discharge rate and retains good electrochemical stability under different destructive mechanical testing including bending, folding, and puncturing. Owing to the ion-orientation effect, cooperative solvation effect, and interface wettability, the hydrogel electrolyte endows Zn||LFP battery with prolonged cyclability, subzero-temperature operation, and mechanical safety as compared to the liquid electrolyte counterpart.

Assessing the Limits of Equivalent Circuit Models and Kalman Filters for Estimating the State of Charge: Case of Agricultural Robots

The battery State of Charge (SoC) is critical information to overcome agricultural robots’ limitations related to battery and energy management. Although several SoC estimation methods have been proposed in the literature, the performance of these methods has not been validated for different battery chemistries in agricultural mobile robot applications. Compared to previous work, this paper evaluates the limits of the SoC estimation using the RC model and the Thevenin model for a Lithium Iron Phosphate (LFP) battery and a Sealed Lead Acid (SLA) battery. This evaluation used a custom agricultural robot in a controlled indoor environment. Consequently, this work assessed the limitations of two ECM-based SoC estimation methods using battery packs, low-cost sensors and discharge cycles typically used in agricultural robot applications. Finally, the results indicate that the RC model is not suitable for SoC estimation for LFP battery; however, it achieved a mean absolute error (MAE) of 2.2% for the SLA battery. On the other hand, the Thevenin model performed properly for both chemistries, achieving MAE lower than 1%.