Advanced, high-power batteries are key to datacenter power systems. Li-ion batteries are a common choice for datacenter energy storage systems, with significant power density and longevity benefits when appropriately designed to address safety concerns. This talk will discuss the technical challenges of designing Li-ion batteries and management systems for hyperscale datacenters. Specifically, I will address how to design batteries for high-reliability IT systems in a large, distributed network.

I’ll discuss the rationale behind Nyobolt adopting a cell to systems approach to BMS development, which allows the deep technical knowledge within our advanced cell development team to be embedded into the real-time monitoring algorithms running in the BMS. I’ll highlight some of the advanced techniques we use to manage our ultra-fast charging cells and how the approach is reciprocated by feeding detailed application knowledge back into the cell development, which ensures we develop state of the art cells that address the critical needs of many different electrification industries.

Electrochemical impedance spectroscopy (EIS)-based internal temperature monitoring has attracted a significant attention lately, especially in the face of degradation and safety issues that have cost billions in the grid-scale energy storage and electric vehicle industries. Recent trends towards large format cells in both industries have reduced costs and simplified pack design; however, incurring the cost of reduced thermal and electrochemical observability for performance and safety BMS algorithms. In this talk, JHU/APL and Zitara will explore the relationship between internal temperature estimation and BMS algorithm observability, which is critical for the reliability and profitability of large-scale battery-powered applications.

Given the industry push towards extreme fast-charging, there is an increased need to detect lithium plating in real time and adjust fast-charge profiles to maximize charge rate without compromising safety or longevity. This presentation will cover an in-situ lithium plating detection method using pseudo-EIS which can be incorporated into battery management systems. The detection method was demonstrated on an NMC 21700 cell and lithium plating was verified via destructive physical analysis.

The safety of aged lithium-ion batteries beyond a state of health of 80% was investigated for cylindrical type 21700 cells with different chemistries. They have been studied in Heat-Wait-Seek tests for thermal abuse by means of Accelerating Rate Calorimetry (ARC). The presentation will cover both the degradation during aging, as well as the thermal runaway performance after aging.

In today's and future sustainable mobility, electric powertrains play a pivotal role. Among all the components of electric vehicles, the battery holds the highest value. For manufacturers and mobility providers, the competition is determined by the total cost of ownership (TCO). To balance the reduction of TCO and battery costs while ensuring optimal performance, range, efficiency, and most importantly, lifetime, the continuous monitoring of the battery during operation is inevitable.

When a battery rests in an open-circuit condition for an extended period, its voltage is observed to decay. This phenomenon has been called “self-discharge” and arises due to both reversible and irreversible mechanisms. Models that quantify these mechanisms can help a battery-management system predict capacity loss. This talk presents models of self-discharge mechanisms and shows how they accurately predict self-discharge voltage across a range of cell SOC and temperature.

State-of-the-art battery management systems (BMS) rely on accurate battery models and well-designed algorithms to ensure reliable and safe operation. The most electrochemically informative models are physics-based, though these are inherently complex. Reduced complexity, physics-based single-particle models (SPM) are shown to be excellent candidates for next-generation BMS applications.

Thermal runaway, recognized as one of the major obstacles in the safe performance of lithium-ion batteries, has seriously hindered their large deployment. Therefore, it is crucial to develop new approaches that can accurately predict the failure of lithium-ion batteries and prevent catastrophic incidents. For this purpose, this study focuses on analyzing the failure of lithium-ion batteries due to the occurrence of thermal runaway using a data-driven framework called data-driven prognosis.

Dynamic modeling, state estimation, and optimal charging control of Lithium-ion batteries are some of the primary challenges in advanced battery management. While model-based control and estimation algorithms have made significant leaps in the past decade, the integration of battery big data offers further insights into this highly-coupled and intricate electrochemical storage system. Mathematically, combining machine learning with physics is a trending approach for discovering unknown dynamics. This talk will highlight the application of physics-informed neural network and imitation learning.

This talk will provide a high-level exploration of some of the key challenges and opportunities in the model-based control of both lithium-ion and lithium-sulfur batteries, including both solid and liquid electrolyte Li-S batteries. Much of the talk will focus on emerging opportunities for test trajectory optimization and machine learning for both battery types, with a focus on Li-S batteries.