CAREER: An Ecologically Inspired Approach to Battery Lifetime Analysis and Testing

Michigan Technological University, Houghton MI

Investigators

Abstract

As transportation and grid applications increase their dependency on batteries, challenges related to battery operation and aging dependency on the individual context circumstances remain. This is a particularly relevant problem as batteries perform multiple tasks in each application (e.g. driving, recharging, grid services, etc.) which can contribute to its aging differently. Furthermore, batteries not only perform multiple tasks in a single application, but migrate to a second application as a second life battery. This CAREER proposal aims to understand battery aging dynamics as context-dependent and to provide a unified theory and modeling that can link context events and lives with cell and module aging events. This will benefit all battery applications and the emerging battery repurposing sector by providing tangible methods to improve battery testing, estimation and management. As educational components, this project will propose new hands-on distributed laboratory capabilities for undergraduate and graduate students to explore battery technologies in the context of grid and vehicle applications. Outreach includes hosting female Hispanic students through the Michigan College and University Partnership, and also participating in the Society for Hispanic Professional Engineering mentoring and conferences. Batteries are subjected to highly uncertain scenarios depending on their context, present cell to module and pack variations due to its space and function distribution and different monitoring capabilities at different scales. This CAREER proposal will consider that these conditions are comparable to ecological systems, such as fishery, forestry, etc. and that battery lifetime and aging should tackle the multi-scale and multi-life problem under ecological approaches and methods. For this, testing methods will include low-cost large-scale distributed testing that will experimentally probe individuals and populations of batteries across context variations and different lives. Data from these tests will be used to develop probabilistic reasoning networks to link causality for battery aging and will provide the ability to establish monitoring and data needs across lives. To formulate the battery aging and life modeling, the proposal will focus on studying intraspecific trait variations (variations inside a species) that arise from battery aging. For this purpose, populations of batteries will be identified through the establishment of a patch hierarchy to identify the structure and functional distribution of intraspecific trait per patch at the individual, sub-population and population level. The intraspecific traits will be modeled for each patch using individual-based, mixed models and integral projection models that are used in ecological systems to model population variations. These approaches will provide a probabilistic model across the population. However, as battery populations are monitored at different scales (pack and cells sparsely depending on the technology), the models will consider incomplete data availability and develop scaling ladders. These ladders will scale the intraspecific trait models from individuals to populations and vice versa to adapt to different data availability and mixed approaches. These models will be implemented in battery management systems to learn the traits models from scavenged data. Trait filters will also be formulated and deployed to identify and model internal and external factors that will determine the trait variations for each life. The models and ecology-based theory obtained will be experimentally validated through the large-scale population testing and real electric vehicle and grid-scale battery deployments.