Battery Intelligence

Lethargic oxygen evolution reaction (OER) hinders proton exchange membrane water electrolyzer adoption for green hydrogen production. While iridium- and ruthenium-based catalysts are prevalent, this study proposes a high-throughput screening approach, predicting 61 potential acid OER candidates from 6912 pyrochlore compounds, including promising p-block metal dopants. These findings highlight pyrochlore compounds as versatile materials for various applications.

This talk discusses the integration of machine learning and robotic experimentation for rapid advancement in battery materials, offering insights into the cutting-edge developments driving innovation in energy storage.

From F35 to F1: an overview of Saft Power Capability. Saft has established a market-leading position in providing batteries with very-high power capability. Recent efforts have led to an improvement in various dimensions of Saft’s very-high power–capable products, making them even more well-suited for the applications in which they are used. A summary of the work undertaken and the performance advantages that have resulted is presented.

Optimization of lifetime performance, early identification of anomalies during production, and description of the health of the fleet are just some of the imperative insights that a battery analytics solution should provide. Enterprise Battery Intelligence leverages best-in-class battery data analytics to provide OEMs with a digital thread across their battery lifecycle. We will discuss why this digital thread is business-critical and will provide use cases from the automotive industry.

Reliable and accurate aging estimation and prediction remains challenging due to the nonlinear nature of lithium-ion batteries that stems from internal electrochemical reactions and intrinsic parameter variability across cells. In this talk, we will introduce our current work in battery aging monitoring and prediction based on large-scale field data with machine learning.

The manufacturing process of lithium-ion batteries is a complex procedure that encompasses multiple steps and various parameters. It is crucial to develop tools that can facilitate the preparation of future gigafactory workers for this intricate task. In this presentation, I will introduce a series of innovative virtual reality digital twins that have been developed in my research group. These digital twins give the promise to revolutionize the effective teaching of battery concepts and the efficient training of battery scientists, engineers, and operators.

Battery energy storage systems (BESS) involve a decision hierarchy that spans cell design, performance benchmarking, aging trends, and application matching. Ultimate disposition of used BESS considers second-use versus materials-recycle. INL tools support this hierarchy though a physics and AI platform that predicts battery aging based on arbitrary duty cycle inputs and materials attributes. Battery aging can be tracked through first-use and into second-use options, looking directly at path-dependent stress factors along the timeline. Predictions over extended-life applications (e.g., long-duration energy storage) are made more feasible with our methods. Case studies will be provided for Li-ion systems.

How can the time-to-market of new batteries and electrical vehicles be shortened? Virtual testing can cut development and test time in different use cases like cell aging prediction using neural networks or battery degradation modelling using machine learning. This presentation explains AVL's approach on how to optimize battery development and testing with a comprehensive methodology supported by a dedicated software solution open for any test equipment hardware.

This talk will discuss the long-term testing and methodology development efforts for adaptive health-aware derating of charging rates led by a collaborative team of researchers at the University of Connecticut, the University of South Carolina, and the University of Delaware.

As the battery sector scales to meet rising global demand, companies spanning the battery ecosystem are increasingly understanding the centrality of data analytics to achieving business goals across the product lifecycle. From development to production to battery passports, the imperative to understand battery quality, performance, and health at every stage is clear. Rather than assemble a patchwork of siloed systems to meet these needs, companies that take an integrated, full-lifecycle approach to achieving electrochemical insights at scale will learn faster than the competition, serve customer needs better, and ultimately win in the marketplace.