Battery Engineering

Archer will share perspectives on cell criteria and qualifications relevant for EVTOL battery design.

A metallized polymer cathode current collector demonstrated consistent isolation of internal short circuits triggered by a slow, shallow (3mm) radial nail penetration in 5Ah, 21700 cells yielding > 265 Wh/kg. A remarkable consistency (16 no thermal runaways (TR) in 16 attempts) while penetrated at 100% SoC occurred with either a commercial ceramic coated polymer separator or a custom made all ceramic separator. In contrast, metal Al foil collector control cells went into immediate TR with the commercial separator under same test conditions. High speed radiography provides unique insights into this phenomena. 

Conventional embedded BMS architectures are fundamentally limited in their ability to accurately estimate the complex states of LFP cells. This talk details a hybrid, cloud-to-BMS framework that overcomes these constraints. Unconstrained by hardware, the cloud executes advanced algorithms to gain insights into battery health and performance. These insights are fed back to the embedded BMS controller, improving SOx accuracy, enhancing operational safety, and maximizing the battery's operational lifespan and value.

State-of-the-art BMS rely on accurate battery models and specialized algorithms to obtain useful estimates of the battery state in order to ensure proper performance and safe operation. Most practical models are simplifications and thus must trade off high accuracy for computational efficiency. This talk examines the implications of using various simplified models in the performance of key BMS tasks.

Most BMS are electronics-only based, focused on monitoring voltage, current, and temperature. To meet the increasing demands on batteries with higher energy and power densities, performance, and endurance, a BMS should manage electronics, mechanical, and thermal aspects synchronously. Engineering design for battery performance and safety—including impedances at the anode, cathode, and electrolyte—and power generation depends upon impedances and internal temperature (T_int). T_int influences impedance, SoH, SoP, and mechanical design should preserve good cells when a rogue cell vents due to high T_int, as well as mechanical design should remove high-energy ejecta expeditiously from rogue cells experiencing thermal runaway to prevent its impact on good cells.

While the electric vehicle (EV) market is rapidly evolving, widespread adoption demands a deeper understanding of battery performance in terms of functionality and reliability. This study proposes a novel data-driven framework, known as data-driven prognosis (DDP), which enables in situ estimation of key constitutive parameters and identifies deviations from expected lithium-ion battery (LIB) degradation patterns. In addition to accurately modeling degradation and capacity, the approach utilizes statistical pattern recognition and machine learning techniques to detect anomalies and predict potential failures in battery systems.

Sandia National Laboratories aims to create a comprehensive safety framework for next-generation batteries, integrating material testing, mechanistic modeling, and safety assessments. This approach will mitigate risks, streamline design, and establish safety criteria crucial for advancing battery technology.

To meet the future demands for fast (>4C) and low temperature charging, Li plating is an issue that must be addressed. This talk will summarize our work on understanding, diagnosing, and overcoming Li plating during charging. A synergistic approach of 3-D electrode architectures and surface coatings is shown to enable 6C fast charging a temperature of -10°C, with a >500% increase in accessible capacity, while eliminating Li plating. Progress towards scale-up and high-throughput manufacturing will also be described.

This presentation covers understanding lithium-ion battery degradation, how to model it, and how close those models are getting to usefully predict lifetime. We will describe our efforts to model lithium plating, SEI layer growth, positive electrode (cathode) decomposition, unequal degradation in silicon carbon composite electrodes, particle cracking, electrolyte consumption and cell dry-out, and how multiple degradation mechanisms are coupled with each other and contribute towards accelerated degradation (the knee point/cliff-edge/etc).

Numerous EV fires during hurricanes highlight the urgent need to understand how saltwater damages batteries under real-world conditions. Post-mortem analysis is difficult due to the complexity of thermal runaway. To address this, a hybrid top-down and bottom-up approach was used to link investigations from cell to vehicle scale. The study identified key factors—immersion mode, battery form factor, electrolysis, and corrosion—as primary contributors influencing the extent, severity, and delayed onset of fires in saltwater-exposed EVs.

Electrification of Class-8 trucks is advancing due to battery technology and cost improvements. Understanding and predicting battery lifespan across vocations is crucial. Laboratory testing of relevant drive cycles provides insights into stress factors, optimizing battery size, cost, and lifespan. This study transforms real-world drive data into simplified power profiles and schedules, emphasizing the need for appropriate pack size and charger needs to support electric heavy-duty trucks.