Lithium-ion Battery Chemistries Research Articles

Leveraging Advanced Analytical Chemistry Techniques to Probe Thermo-Electrochemical Aging Effects in Lithium-Ion Battery Electrolytes

As lithium-ion and other battery chemistries gain market traction for grid and industrial-scale applications, pressure is mounting to develop more robust, longer lasting cells that can operate for hundreds of cycles at variable temperatures without sacrificing performance or safety. Electrolyte design is crucial to this effort and requires deep understanding of the correlated physical, chemical, and electrochemical factors that dictate long-term electrolyte performance. In this presentation, I will discuss the value of combined analytical chemistry techniques (solid- and liquid- state NMR, GCMS, and ICPMS) to resolve molecular-level impacts of thermally- and electrochemically- induced aging of lithium-ion electrolytes. In a model electrolyte, High Resolution (HR)-GCMS enables identification of subtle changes in solvent and additive chemistry while NMR reveals the formation of lithium-salt breakdown products. These findings are instructive for battery cycling as demonstrated by full chemical analysis of electrolyte extracted from aged lithium-ion cells, including evolved gas analysis, electrolyte deformulation, and SEI characterization. These cutting-edge analytical chemistry techniques are powerful and underutilized tools for detecting and mitigating failure modes in liquid electrolytes.

Diketopyrrolopyrroles As Performance Enhancing Additives for Lithium-Sulfur Batteries

Although the lithium-ion battery (LIB) has been instrumental in global electrification, more premium applications (such as electrification of aircraft) require capacities and performance beyond the capabilities of traditional LIB chemistries[1]. Lithium-sulfur (Li-S) batteries are a front runner among next generation battery technologies thanks to their very high theoretical capacities (1675 mAh g-1) and impressive gravimetric energy densities (2,700 Wh kg-1) [2,3]. Despite their potential, there are numerous chemical challenges (polysulfide shuttle, dendrite formation etc.) that must be overcome before widespread adoption of the technology [4]. Electrolyte additives have been explored as a means to facilitate the electron transfer reactions that occur within the cell and improve cell capacity and cyclability [5].Diketopyrrolopyrroles (DPPs) are a class of organic π-conjugated compounds that have garnered significant interest in a variety of electrical applications, including solar cells and sensors [6]. The high electrochemical tunability, made possible through the editable organic structure, makes them suitable candidates for redox active electrolyte additives. Here, we explore DPP derivatives as a novel class of electrolyte additive for improving the cell cycling performance of Li-S batteries. A variety of electrochemical and analytical techniques have been utilised to assess the suitability of DPPs as redox active additives, building an understanding of their interactions with active materials in a Li-S cell. Galvanostatic cycling allows us to connect these electrochemical findings to cell cycling performance, where we observe a large enhancement in capacity. We also present an alternative statistical approach to cell data analysis, providing a more concrete method to assessing cycling enhancement than that shown throughout literature. Figure 1. A box plot displaying the discharge capacities at specific cycle numbers for standard (light grey) and DPP (dark grey) cells to provide a statistical comparison.

A study on recovery strategies of graphite from mixed lithium-ion battery chemistries using froth flotation

The growing electric vehicle industry has increased the demand for raw materials used in lithium-ion batteries (LIBs), raising concerns about material availability. Froth flotation has gained attention as a LIB recycling method, allowing the recovery of low value materials while preserving the chemical integrity of electrode materials. Furthermore, as new battery chemistries such as lithium titanate (LTO) are introduced into the market, strategies to treat mixed battery streams are needed. In this work, laboratory-scale flotation separation experiments were conducted on two model black mass samples: i) a mixture containing a single cathode (i.e., NMC811) and two anode species (i.e., LTO and graphite), simulating a mixed feedstock prior to hydrometallurgical treatment; and ii) a graphite-TiO2 mixture to reflect the expected products after leaching. The results indicate that graphite can be recovered with > 98 % grade from NMC811-LTO-graphite mixtures. Additionally, it was found that flotation kinetics are dependent on the electrode particle species present in the suspension. In contrast, the flotation of graphite from TiO2 resulted in a low grade product (<96 %) attributed to the significant entrainment of ultrafine TiO2 particles. These results suggest that flotation of graphite should be preferably carried out before hydrometallurgical treatment of black mass.

A Bayesian optimized machine learning approach for accurate state of charge estimation of lithium ion batteries used for electric vehicle application

In recent times, battery technology used in electric vehicles has drawn numerous researchers' attention. Monitoring the battery condition, particularly the State of charge, is required to ensure the battery's safe and reliable performance. Despite the fact that many SOC estimation methods have been proposed, further research is necessary to identify an approach that adapts versatile lithium-ion battery chemistries. In the recent study it has been demonstrated that Machine Learning approaches have better prediction accuracy compared to conventional methods. To maximize the performance of Machine learning models, it is imperative to select the optimal hyperparameters and employ appropriate input parameters. At present, researchers employ, established heuristics methods to select hyperparameters, which may involve manual tuning or exhaustive search techniques such as random search and grid search. These techniques make the models less accurate and inefficient. In this paper, a systematic, automated process for selecting hyperparameters with a Bayesian optimization algorithm is proposed. In addition, along with the battery parameters (voltage, current and temperature), vehicle velocity, road condition, motor characteristics and environmental conditions are used as the input parameters for accurate SOC prediction. The highly correlated input features are selected through the MRMR algorithm. The performance of six ML algorithms, namely SVM, ANN, GPR, Ensembler, linear regression and Decision Tree, is tested and validated with and without hyperparameter tuning for different data sets. The experimental results demonstrate that the hyperparameter-tuned model outperforms the standard model in estimating the State of charge (SOC). In addition, the model's estimation error is <1 % across a range of vehicle velocity, road condition, motor torque, battery voltage, current, and temperature.

Polymeric Binder Design for Sustainable Lithium-Ion Battery Chemistry.

The design of binders plays a pivotal role in achieving enduring high power in lithium-ion batteries (LIBs) and extending their overall lifespan. This review underscores the indispensable characteristics that a binder must possess when utilized in LIBs, considering factors such as electrochemical, thermal, and dispersion stability, compatibility with electrolytes, solubility in solvents, mechanical properties, and conductivity. In the case of anode materials, binders with robust mechanical properties and elasticity are imperative to uphold electrode integrity, particularly in materials subjected to substantial volume changes. For cathode materials, the selection of a binder hinges on the crystal structure of the cathode material. Other vital considerations in binder design encompass cost effectiveness, adhesion, processability, and environmental friendliness. Incorporating low-cost, eco-friendly, and biodegradable polymers can significantly contribute to sustainable battery development. This review serves as an invaluable resource for comprehending the prerequisites of binder design in high-performance LIBs and offers insights into binder selection for diverse electrode materials. The findings and principles articulated in this review can be extrapolated to other advanced battery systems, charting a course for developing next-generation batteries characterized by enhanced performance and sustainability.

A Comprehensive Study of the Parameters Affecting Magnesium Plating/Stripping Kinetics in Rechargeable Mg Batteries

Beyond the lithium ion battery (LIB) technology, alkali and alkaline earth metal (Li, Na, Mg, Ca etc.) anodes boost the prospect of next generation battery chemistry through their highly promising theoretical specific capacities.1,2 Mg metal anode based battery is thus one of the strong contenders for sustainable and alternative rechargeable battery systems, with its natural abundance and eco-friendly resources.3 Despite the first proof-of-concept of a Mg battery established back in 2000,4 its complete rechargeability still remains elusive due to the lack of suitable conventional electrolytes and low mobility of Mg2+ ions in typical intercalation cathodes. Although potentially safer than Li metal (less prone to dendritic growth), reversible Mg electrodeposition in non-aqueous liquid electrolytes is a challenge due to poor salt solubility and formation of ionically blocking surface passivation films.5,6 The study of the interphase between a Mg metal anode and electrolyte is therefore of critical worth as it can dictate stability and reversibility for a particular electrode/electrolyte combination.7 In this work, we explore the role of various parameters, including electrolyte composition, concentration, solvent and substrate nature (various metals and carbon based substrates) and their impact on Mg plating/stripping kinetics and coulombic efficiency. The deposit morphology was investigated via ex situ scanning electron microscopy (SEM) and the reductive stability of the electrolytes was studied by cyclic voltammetry (CV), X-ray Photoelectron Spectroscopy (XPS) and electrochemical quartz crystal microbalance (EQCM). Overall, a comprehensive discussion about the relationship between cation solvation structure, composition and stability of passivation layer (if any) and the degree of reversibility of Mg electrodeposition will be presented. Keywords: Beyond LIB chemistry, metal anode, magnesium, EQCM, plating/stripping.

Cost-Effective Milled Silicon and Exfoliated Graphene Anode for High-Performance Li-Ion Batteries

Lithium-ion batteries play a significant role in modern electronics and electric vehicles. However, current lithium-ion battery chemistries are unable to satisfy the increasingly heightened expectations regarding energy demand and reliability. To boost the overall energy density while ensuring the safety of Li batteries, researchers have focused on developing the battery materials [1]. Silicon has an ultrahigh theoretical capacity and has been regarded as the best choice for next-generation lithium-ion battery anodes, but it also suffers from dramatic expansion during cycling [2]. Hence mitigating volume expansion is one of the main challenges towards developing a successful Si based anodes. Herein, a facile air-controlled electrospray method to directly deposit silicon/graphene (Si/Gr) onto current collectors was described. This method is inspired by an electrospinning process and possesses all the advantages that electrospinning has such as low cost, easy scaling up, and simultaneous solvent evaporation during the spraying process. Cost-effective silicon/graphene (Si/Gr) composite electrodes have been investigated for the multiple benefits of Gr in providing mechanical integrity to buffer the silicon volume expansion while maintaining electronic contact with Si to provide a Li-ion transport pathway and to form a uniform solid electrolyte interface (SEI) layer. Our results revealed a superior battery performance including a capacity of 1000 mAh/g at 2C rates and minor capacity loss during long cycling. The details of synthesis, characterization, and lithium-ion battery results of the hybrid anodes will be presented.

A generalized equivalent circuit model for lithium-iron phosphate batteries

Most of the equivalent circuit battery models available in the literature have been developed specifically for one cell and require extensive measurements to calibrate cell electrical parameters in different operating conditions. In this work, a generalized equivalent circuit model for lithium-iron phosphate batteries is proposed, which only relies on the nominal capacity, available in the cell datasheet. Using data from cells previously characterized, a generalized zeroth-order model is developed. This novel approach allows to avoid time-consuming and expensive experiments and reduces the test matrix. In spite of not relying on detailed data on the dependence of the electrical parameters with respect to state of charge, c-rate and temperature, the model provides an excellent description of the electrical behavior for both low-energy and high-energy cells, the error being always kept below 2 %. The internal resistance of the cell is expressed as a function of a new characteristic coefficient, which is typical of this lithium-ion battery chemistry. This coefficient is fitted to an exponential function of the temperature, which is physically meaningful, as the internal resistance has an Arrhenius-like behavior with respect to temperature. This model, due to its simplicity and flexibility, is particularly useful for control-oriented applications, and for off-line analyses.

Evaluating the feasibility of the spinel-based Li4Ti5O12 and LiNi0.5Mn1.5O4 materials towards a battery supercapacitor hybrid device

Hybrid devices consisting of energy-dense lithium-ion battery chemistry and power-dense supercapacitors in a single unit cell are vital to improve the energy density of supercapacitors. Herein, we demonstrate a lithium-ion capacitor containing a composite of LiNi0.5Mn1.5O4 and a high surface area carbon composite as the cathode and carbon-coated Li4Ti5O12 as an anode. The composite cathode combines redox and adsorption mechanisms to store ions, producing more capacity. In contrast, the nanostructure anode can store Li-ions through a faster diffusion-type intercalation process. As a result, the lithium-ion capacitor delivers energy densities of 48 and 30 Wh kg−1 at power densities of 250 and 7900 W kg−1, respectively. Furthermore, upon cycling at 1 A g−1, the full cell retains ∼70 % specific capacitance till 4000 cycles. The use of spinel materials in both electrodes boosts the electrochemical performances of this lithium-ion capacitor because of their inherent properties like stable phase and 3D network-type structure for Li-ion diffusion. Additionally, battery-type cathodes like LiNi0.5Mn1.5O4 (4.7 V vs. Li/Li+) with a wider potential window improve the energy density, whereas better safety and cycling stability arise from the zero-strain Li4Ti5O12 anode. The studies on impedance, self-discharge, and leakage current analyses prove the practical feasibility of the lithium-ion capacitor.

Electric Vehicle Battery Replacement Costs Under Realistic Fast Charging Behaviors

Fast charging networks are being constructed in the United States based on the assumption that electric vehicles can sustainably fast charge. However, fast charging has been shown to shorten calendar and cycle life across multiple lithium-ion battery chemistries. Public information on degradation is limited for realistically fast charged commercial cells and extensions from academic work on commercial-adjacent chemistries is difficult. Here, we model realistic consumer charging behavior by cycling commercial NCA 21700 and LFP 26650 cells under 20 different fast charging behaviors, mixing fast charging (2C) and Level 2 charging (C/6|C/5) under different voltage bounds and under well-ventilated thermal conditions. The degradation observed in these tests were used in an economic model to estimate the number and present value cost of battery pack replacements over an electric vehicle’s lifetime, in which results map to a worst case, upper bound scenario. Overall, the tested LFP cells showed extremely low rates of degradation compared to the tested NCA cells. Cycling the NCA cells under the most aggressive charging scenario (2.5-4.2V, 100% of charging at 2C) showed more than 100 pack replacements over 8 years and a present value cost of more than $1M. Cycling the LFP cells under equivalent conditions (2-3.65V, 100% of charging at 2C) showed only 0-1 pack replacements.

Co-Intercalation-Free Fluorinated Ether Electrolytes for Lithium-Ion Batteries

Lithium-ion batteries are widely used to power portable electronics because of their high energy densities and have shown great promise in enabling the electrification of transport. However, the commercially used carbonate-based electrolytes are limited by a narrow operating temperature window and suffer against next generation lithium-ion battery chemistries such as silicon-containing anodes. The lack of non-carbonate electrolyte alternatives such as ether-based electrolytes is due to undesired solvent co-intercalation that occurs with graphitic anodes. Recently, fluorinated ether solvents have become promising electrolyte solvent candidates for lithium metal batteries but their applications in other battery chemistries have not been studied. In this work, we synthesize a group of novel fluorinated ether solvents and study them as electrolyte solvents for lithium-ion batteries. Using X-ray diffraction (XRD) and solid-state nuclear magnetic resonance (ssNMR), we show that fluorinated ether electrolytes support reversible lithium-ion intercalation into graphite without solvent co-intercalation at conventional salt concentrations. To the best of our knowledge, they are the first class of ether solvents that intrinsically suppress solvent co-intercalation without the need for high or locally high salt concentration. In full cells using graphite anode, fluorinated ether electrolytes enable much higher energy densities compared to conventional glyme ethers, and better thermal stability over carbonate electrolytes (operation up to 60°C). As single-solvent-single-salt electrolytes, they remarkably outperform carbonate electrolytes with fluoroethylene carbonate (FEC) and vinylene carbonate (VC) additives when cycled with graphite-silicon composite anodes. Using X-ray photoelectron spectroscopy (XPS), NMR and density functional theory (DFT) calculations, we show that fluorinated ethers produce a solvent-derived solid electrolyte interphase, which is likely the key to suppressing solvent co-intercalation. Rational molecular design of fluorinated ether solvents will produce novel electrolytes that enable next generation lithium-ion batteries with higher energy density and wider working temperature window.

Computational investigation for reduction in auxiliary energy consumption with different cell spacing in battery pack

The need for battery storage systems is expected to grow quickly in the coming decade for a variety of applications, including electric vehicles, stationary storage and off-grid applications. Internal heat production in the lithium-ion battery chemistry is substantial in high-power applications, which is adverse to battery efficiency, long-term performance, and battery safety. Maintaining the battery temperature in the range of 15–45 °C is required for improved performance and safety of lithium-ion battery modules. The cooling methods consist of active and passive cooling, in which active cooling needs of additional power such as liquid cooling, forced air cooling, whereas in passive cooling, there is no need of additional power and employs phase change material cooling, and heat pipe, which require no additional power in the design of Battery thermal management systems. We focused on a designing of a forced-air cooling system for a battery module in this research. A Numerical model of a battery module has been developed in ANSYS Fluent software consist of ECM battery model and validated using the data of the previous study. The novelty of this work is that the subgrouping of the cells in the pack creates turbulence in the flow of heat extracting air. Ability of inlet air to extract heat at an end rows is increased and due to this, the end rows in the pack cool effectively. The modified design shows the improved cooling performance by 21.2 %. The reduced temperature rise in battery pack is achieved without using extra auxiliary power and extra volume. Modified design gives reduction in power consumption by 12.7 %.

Calendar ageing modelling using machine learning: an experimental investigation on lithium ion battery chemistries.

Background: The phenomenon of calendar ageing continues to have an impact on battery systems worldwide by causing them to have undesirable operation life and performance. Predicting the degradation in the capacity can identify whether this phenomenon is occurring for a cell and pave the way for placing mechanisms that can circumvent this behaviour. Methods: In this study, the machine learning algorithms, Extreme Gradient Boosting (XGBoost) and artificial neural network (ANN) have been used to predict the calendar ageing data belonging to six types of cell chemistries namely, Lithium Cobalt Oxide, Lithium Iron Phosphate, Lithium Manganese Oxide, Lithium Titanium Oxide, Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide. Results: Prediction results with overall Mean Absolute Percentage Error of 0.0126 have been obtained for XGBoost algorithm. Among these results, Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide type cell chemistries stand out with their mean absolute percentage errors of 0.0035 and 0.0057 respectively. Also, algorithm fitting performance is relatively better for these chemistries at 100% state of charge and 60°C temperature compared to ANN results. ANN algorithm predicts with mean absolute error of approximately 0.0472 overall and 0.0238 and 0.03825 for Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide. The fitting performance of ANN for Nickle Manganese Cobalt Oxide at 100% state of charge and 60°C temperature is especially poor compared to XGBoost. Conclusions: For an electric vehicle battery calendar ageing prediction application, XGBoost can establish itself as the primary choice more easily compared to ANN. The reason is XGBoost's error rates and fitting performance are more usable for such application especially for Nickel Cobalt Aluminum Oxide and Nickel Manganese Cobalt Oxide chemistries, which are amongst the most demanded cell chemistries for electric vehicle battery packs.

Calendar ageing modelling using machine learning: an experimental investigation on lithium ion battery chemistries

Calendar ageing modelling using machine learning: an experimental investigation on lithium ion battery chemistries

Multiscale Polymeric Materials for Advanced Lithium Battery Applications.

Riding on the rapid growth in electric vehicles and the stationary energy storage market, high-energy-density lithium-ion batteries and next-generation rechargeable batteries (i.e., advanced batteries) have been long-accepted as essential building blocks for future technology reaching the specific energy density of 400Wh kg-1 at the cell-level. Such progress, mainly driven by the emerging electrode materials or electrolytes, necessitates the development of polymeric materials with advanced functionalities in the battery to address new challenges. Therefore, it is urgently required to understand the basic chemistry and essential research directions in polymeric materials and establish a library for the polymeric materials that enables the development of advanced batteries. Herein, based on indispensable polymeric materials in advanced high-energy-density lithium-ion, lithium-sulfur, lithium-metal, and dual-ion battery chemistry, the key research directions of polymeric materials for achieving high-energy-density and safety are summarized and design strategies for further improving performance are examined. Furthermore, the challenges of polymeric materials for advanced battery technologies are discussed.

Calendar ageing modelling using machine learning: an experimental investigation on lithium ion battery chemistries

Background: The phenomenon of calendar ageing continues to have an impact on battery systems worldwide by causing them to have undesirable operation life and performance. Predicting the degradation in the capacity can identify whether this phenomenon is occurring for a cell and pave the way for placing mechanisms that can circumvent this behaviour. Methods: In this study, the machine learning algorithms, Extreme Gradient Boosting (XGBoost) and artificial neural network (ANN) have been used to predict the calendar ageing data belonging to six types of cell chemistries namely, Lithium Cobalt Oxide, Lithium Iron Phosphate, Lithium Manganese Oxide, Lithium Titanium Oxide, Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide. Results: Prediction results with overall Mean Absolute Percentage Error of 0.0126 have been obtained for XGBoost algorithm. Among these results, Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide type cell chemistries stand out with their mean absolute percentage errors of 0.0035 and 0.0057 respectively. Also, algorithm fitting performance is relatively better for these chemistries at 100% state of charge and 60°C temperature compared to ANN results. ANN algorithm predicts with mean absolute error of approximately 0.0472 overall and 0.0238 and 0.03825 for Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide. The fitting performance of ANN for Nickle Manganese Cobalt Oxide at 100% state of charge and 60°C temperature is especially poor compared to XGBoost. Conclusions: For an electric vehicle battery calendar ageing prediction application, XGBoost can establish itself as the primary choice more easily compared to ANN. The reason is XGBoost’s error rates and fitting performance are more usable for such application especially for Nickel Cobalt Aluminum Oxide and Nickel Manganese Cobalt Oxide chemistries, which are amongst the most demanded cell chemistries for electric vehicle battery packs.

Lithium-ion cell equivalent circuit model identification by galvanostatic intermittent titration technique

When modelling lithium-ion batteries thorough identification of parameters across the entire operation domain is necessary to capture non-linear variations of properties caused by temperature or state of charge. This work presents a parameter identification method using galvanostatic intermittent titration technique (GITT) to create high resolution look-up tables and response surfaces for equivalent circuit models (ECM). Significant improvements are proposed over other parameter estimation method, such as HPPC. These improvements are open-circuit voltage change compensation, and ohmic resistance correction, which yield to better overall accuracy of the model. An iterative parameter identification algorithm (IPIA) is introduced to increase the robustness of the non-linear least square curve fitting for higher orders ECM. The method is applied to identify the parameters of 1RC and a 2RC ECM. The use of GITT in conjunction with IPIA has allowed greater fidelity of model response surfaces than previously published pulse identification methods. Experiments were conducted on Nickel-Manganese-Cobalt cathode lithium-ion cells. The methodology presented in this paper is intended to be applicable to any lithium-ion battery format or chemistry with minor adjustments.

Enhancing Li+ Transport in NMC811||Graphite Lithium-Ion Batteries at Low Temperatures by Using Low-Polarity-Solvent Electrolytes.

LiNix Coy Mnz O2 (x+y+z=1)||graphite lithium-ion battery (LIB) chemistry promises practical applications. However, its low-temperature (≤ -20 °C) performance is poor because the increased resistance encountered by Li+ transport in and across the bulk electrolytes and the electrolyte/electrode interphases induces capacity loss and battery failures. Though tremendous efforts have been made, there is still no effective way to reduce the charge transfer resistance (Rct ) which dominates low-temperature LIBs performance. Herein, we propose a strategy of using low-polarity-solvent electrolytes which have weak interactions between the solvents and the Li+ to reduce Rct , achieving facile Li+ transport at sub-zero temperatures. The exemplary electrolyte enables LiNi0.8 Mn0.1 Co0.1 O2 ||graphite cells to deliver a capacity of ≈113 mAh g-1 (98 % full-cell capacity) at 25 °C and to remain 82 % of their room-temperature capacity at -20 °C without lithium plating at 1/3C. They also retain 84 % of their capacity at -30 °C and 78 % of their capacity at -40 °C and show stable cycling at 50 °C.

(Invited) Zeolite-Templated Carbon As a Model Material for Electrochemical Energy Storage in Nanometre-Spaced Carbon Channels

Beyond lithium-ion battery (LIB) chemistries demand new electrode materials exhibiting unique features to accommodate the larger and/or multivalent active ions. Zeolite-templated carbon (ZTC) is an ordered microporous carbon scaffold that is molecularly thin, electrically conductive, and covalently connected in three dimensions. Its narrow pore size distribution is centered at 1.2 nm, permitting the introduction and ultrafast conduction of large molecular guests. In this work, we demonstrate the serviceability of ZTC as a stable cathode material across wide voltage ranges for both bulky, polyatomic anions (as in dual-ion batteries, DIBs, and/or hybrid capacitors, HCs) and multivalent cations (e.g., magnesium-ion batteries, MIBs) in high energy density and high power density electrochemical cells. Charge storage proceeds by a purely capacitive mechanism at the surface of ZTC, mated with the faradaic mechanism of metal plating and stripping at the anode. A suite of techniques including solid-state NMR spectroscopy sheds insight into the role of the solvent, anion, and cation in electrochemical insertion/deinsertion of the active ions within the microporous framework of ZTC. Figure 1

Implications of the Electric Vehicle Manufacturers’ Decision to Mass Adopt Lithium-Iron Phosphate Batteries

Lithium-ion batteries are the ubiquitous energy storage device of choice in portable electronics and more recently, in electric vehicles. However, there are numerous lithium-ion battery chemistries and in particular, several cathode materials that have been commercialized over the last two decades, each with their own unique features and characteristics. In 2021, Tesla Inc. announced that it would change the cell chemistry used in its mass-market electric vehicles (EVs) from Lithium-Nickel-Cobalt-Aluminum-Oxide (NCA) to cells with Lithium-Iron-Phosphate (LFP) cathodes. Several other automakers have followed this trend by announcing their own plans to move their EV production to LFP. One of the reasons stated for this transition was to address issues with the nickel and cobalt supply chains. In this paper, we examine the trend of adopting LFP for mass-market electric vehicles, explore alternative reasons behind this transition, and analyze the effects this change will have on consumers.