Improving the Performance of Lithium Ion Batteries at Low Temperature

The ability for Li-ion batteries to operate at low temperatures is extremely critical for the development of energy storage for electric and hybrid electric vehicle technologies. Currently, Li-ion cells have limited success in operating at temperature below –10 deg C. Electrolyte conductivity at low temperature is not the main cause of the poor performance of Li-ion cells. Rather the formation of a tight interfacial film between the electrolyte and the electrodes has often been an issue that resulted in a progressive capacity fading and limited discharge rate capability. The objective of our Phase I work is to develop novel electrolytes that can form low interfacial resistance solid electrolyte interface (SEI) films on carbon anodes and metal oxide cathodes. From the results of our Phase I work, we found that the interfacial impedance of Fluoro Ethylene Carbonate (FEC) electrolyte at the low temperature of –20degC is astonishingly low, compared to the baseline 1.2M LiPFEMC:EC:PC:DMC (10:20:10:60) electrolyte. We found that electrolyte formulations with fluorinated carbonate co-solvent have excellent film forming properties and better de-solvation characteristics to decrease the interfacial SEI film resistance and facilitate the Li-ion diffusion across the SEI film. The very overwhelming low interfacial impedance for FEC electrolytes will translate into Li-ion cells with much higher power for cold cranking and high Regen/charge at the low temperature. Further, since the SEI film resistance is low, Li interaction kinetics into the electrode will remain very fast and thus Li plating during Regen/charge period be will less likely to happen

Highly Concentrated Electrolytes: Electrochemical and Physicochemical Characteristics of LiPF6 in Propylene Carbonate Solutions

Highly concentrated electrolytes (HCEs) based on LiPF6 in propylene carbonate (PC) have been examined as lithium-ion battery electrolytes. These HCEs have lower ionic conductivities and higher viscosities than ethylene carbonate (EC) electrolytes with 1.2 M LiPF6, but they have higher Li+ ion transference numbers. Electrochemical cycling behaviour of LiNi0.8C0.015Al0.05O2//graphite cells with 3.2 M LiPF6 in PC resembles that of cells with EC-based electrolytes; the HCE cells have higher impedance, which can be lowered by increasing test temperature. By employing Raman and infrared spectroscopy, combined with density functional theory and ab initio molecular dynamics simulations, we reveal that the Li+ solvation structure and speciation are key factors that determine cell performance. Two distinct regimes are observed as a function of salt concentration-in the conventional regime, the solvation number (SN) is mostly constant, while in the HCE regime it decreases linearly. Graphite exfoliation is suppressed only at very high salt concentrations (>2.4 M), where [PC](free)/[Li+] < 1 and [PF6-](free) > [PC](free). Results from the Advanced Electrolyte Model indicate that Li+ desolvation improves at higher LiPF6 concentrations, thereby mitigating PC co-intercalation into the graphite. However, Li+ ion transport is hindered in the HCEs, which increases impedance at both the oxide-positive and graphite-negative electrodes

Differentiable Modeling and Optimization of Battery Electrolyte Mixtures Using Geometric Deep Learning

Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast-charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of DiffMix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces

Battery data integrity and usability: Navigating datasets and equipment limitations for efficient and accurate research into battery aging

A tremendous commitment of resources is needed to acquire, understand and apply battery data in terms of performance and aging behavior. There are many state of performance (SOP) and state of health (SOH) metrics that are useful to guide alignment of batteries to end-use, yet how these metrics are measured or extracted can make the difference between usable, valuable datasets versus data that lacks the necessary integrity to meet baseline confidence levels for SOP/SOH quantification. This work will speak to 1) types of data that support SOP and SOH evaluations on mechanistic terms, 2) measurement conditions needed to assure high data integrity, 3) equipment limitations that can compromise data high fidelity, and 4) the impact of cell polarization on data quality. A common goal in battery research and field use is to work from a data platform that supports economical paths of data capture while minimizing down-time for battery diagnostics. An ideal situation would be to utilize data obtained during normal daily use (“pulses or cycles of convenience”) without stopping the daily duty cycles to perform dedicated SOP/SOH diagnostic routines. However, difficulties arise in trying to make use of daily duty cycle data (denoted as cycle-by-cycle, CBC) that underscores the need for standardization of conditions: temperature and duty cycles can vary over the course of a day and throughout a week, month and year; polarization can develop within an immediate cycle and throughout successive cycles as a hysteresis. If CBC data is envisioned as a data source to determine performance and aging trends, it should be recognized that polarization is a frequent consequence of CBC and thus makes it difficult to separate reversible and irreversible components to metrics such as capacity loss and resistance increase over aging. Since CBC conditions can have a major impact on data usability, we will devote part of this paper to CBC data conditioning and management. Differential analyses will also be discussed as a means to detect changing trends in data quality. Our target cell chemistries will be lithium-ion types NMC/graphite and LMO/LTO

Advanced Technology Development Program for Lithium-Ion Batteries: Gen 2 Performance Evaluation Final Report

The Advanced Technology Development Program has completed performance testing of the second generation of lithium-ion cells (i.e., Gen 2 cells). The 18650-size Gen 2 cells, with a baseline and variant chemistry, were distributed over a matrix consisting of three states-of-charge (SOCs) (60, 80, and 100% SOC), four temperatures (25, 35, 45, and 55°C), and three life tests (calendar-, cycle-, and accelerated-life). The calendar- and accelerated-life cells were clamped at an open-circuit voltage corresponding to the designated SOC and were subjected to a once-per-day pulse profile. The cycle-life cells were continuously pulsed using a profile that was centered around 60% SOC. Life testing was interrupted every four weeks for reference performance tests (RPTs), which were used to quantify changes in cell degradation as a function of aging. The RPTs generally consisted of C1/1 and C1/25 static capacity tests, a low-current hybrid pulse power characterization test, and electrochemical impedance spectroscopy. The rate of cell degradation generally increased with increasing test temperature, and SOC. It was also usually slowest for the calendar-life cells and fastest for the accelerated-life cells. Detailed capacity-, power-, and impedance-based performance results are reported

Localized High-Concentration Electrolytes Get More Localized Through Micelle-Like Structures

Liquid electrolytes in batteries are typically treated as macroscopically homogeneous ionic transport media despite having complex chemical composition and atomistic solvation structures, leaving a knowledge gap of microstructural characteristics. Here, we reveal a unique micelle-like structure in a localized high-concentration electrolyte (LHCE), in which the solvent acts as a surfactant between an insoluble salt in diluent. The miscibility of the solvent with the diluent and simultaneous solubility of the salt results in a micelle-like structure with a smeared interface and an increased salt concentration at the centre of the salt-solvent clusters that extends the salt solubility. These intermingling miscibility effects have temperature dependencies, wherein an exemplified LHCE peaks in localized cluster salt concentration near room temperature and is utilized to form a stable solid-electrolyte interphase (SEI) on Li-metal anode. These findings serve as a guide to predicting a stable ternary phase diagram and connecting the electrolyte microstructure with electrolyte formulation and formation protocols to form stable SEI for enhanced battery cyclability

Improving the Performance of Lithium Ion Batteries at Low Temperature

The ability for Li-ion batteries to operate at low temperatures is extremely critical for the development of energy storage for electric and hybrid electric vehicle technologies. Currently, Li-ion cells have limited success in operating at temperature below –10 deg C. Electrolyte conductivity at low temperature is not the main cause of the poor performance of Li-ion cells. Rather the formation of a tight interfacial film between the electrolyte and the electrodes has often been an issue that resulted in a progressive capacity fading and limited discharge rate capability. The objective of our Phase I work is to develop novel electrolytes that can form low interfacial resistance solid electrolyte interface (SEI) films on carbon anodes and metal oxide cathodes. From the results of our Phase I work, we found that the interfacial impedance of Fluoro Ethylene Carbonate (FEC) electrolyte at the low temperature of –20degC is astonishingly low, compared to the baseline 1.2M LiPFEMC:EC:PC:DMC (10:20:10:60) electrolyte. We found that electrolyte formulations with fluorinated carbonate co-solvent have excellent film forming properties and better de-solvation characteristics to decrease the interfacial SEI film resistance and facilitate the Li-ion diffusion across the SEI film. The very overwhelming low interfacial impedance for FEC electrolytes will translate into Li-ion cells with much higher power for cold cranking and high Regen/charge at the low temperature. Further, since the SEI film resistance is low, Li interaction kinetics into the electrode will remain very fast and thus Li plating during Regen/charge period be will less likely to happen

Computational Investigations of Surface Adsorption for Improving Catalysts

In this work we develop computational tools for initializing and performing molecular simulations of organic molecules in the presence of catalysts. Specifically, our work enables the adsorption of organic molecules including ethane and ethylene on the M1 catalyst to be investigated as a function of temperature. This work provides capabilities for understanding which surface modifiers may energetically stabilize the catalyst while not hindering catalyst stability

Highly Quantitative Electrochemical Characterization of Non-Aqueous Electrolytes & Solid Electrolyte Interphases

The methods to measure solid electrolyte interphase (SEI) electrochemical properties and SEI formation capability of non-aqueous electrolyte solutions are not adequately addressed in the literature. And yet, there is a strong demand in new electrolyte generations that promote stabilized SEIs and have an influence to resolve safety, calendar life and other limitations of Li-ion batteries. To fill this gap, in situ electrochemical approach with new descriptive criteria for highly quantitative characterization of SEI and electrolytes is proposed. These criteria are: SEI formation capacity, SEI corrosion rate, SEI maintenance rate, and SEI kinetic stability. These criteria are associated with battery parameters like irreversible capacity, self-discharge, shelf-life, power, etc. Therefore, they are especially useful for electrolyte development and standard fast screening, allowing a skillful approach to narrow down the search for the best electrolyte. The characterization protocol also allows retrieving information on interfacial resistance for SEI layers and the electrochemical window of electrolytes, the other important metrics of characterization. The method validation was done on electrolyte blends containing phosphazenes, developed at Idaho National Laboratory, as 1.2M LiPF6 [80 % EC-MEC (2:8) (v/v) + 20% Phosphazene variety] (v/v), which were targeted for safer electrolyte variations

ADHD and Common Mental Disorders: Effect on Academic Success in SA First-Years

Objective. Attention-deficit/hyperactivity disorder (ADHD) symptoms are highly prevalent among university students. These symptoms, particularly the inattentive cluster, predispose students to poorer academic performance and worse academic adjustment. Moreover, ADHD symptoms are often comorbid with other common mental disorders; this comorbidity also leads to poor outcomes. South African students often have fewer resources to successfully transition to university. Hence, our longitudinal study used data from a sample of South African first-year undergraduate students to investigate the (a) association between ADHD symptoms and academic performance / adjustment, (b) separate influence of the inattentive and hyperactivity-impulsivity clusters on academic performance / adjustment, and (c) influence of the combination of ADHD and psychiatric comorbidities on academic performance / adjustment. Method. We collected data three times through the first semester of 2023. Predictors within our regression models included sociodemographic variables, psychological variables (self-reported symptoms of ADHD, depression, anxiety, risky alcohol use symptoms), and high school academic performance. Outcomes were first-semester GPA and self-reported academic adjustment. Results. Analyses showed that, unlike academic performance (N = 506), academic adjustment (N = 180) was significantly (p < .05) predicted by ADHD symptoms and the combination of ADHD, depression, and anxiety symptoms. Inattentive ADHD symptoms predicted both academic performance and academic adjustment. Conclusions. Our findings suggest that ADHD (both with and without other common mental disorders) influences academic adjustment, and that inattentive symptoms of ADHD affect both academic performance and academic adjustment. These findings are significant in informing future interventions targeting the academic outcomes of first-year university students.</p