Battery Diagnostics and Prognostics

Abstract:  Currently, the transition from Internal Combustion Engine Vehicles (ICEVs) to Battery Electric Vehicles (BEVs) is accelerating and skipping over Hybrids and Plug-in Hybrid Electric Vehicles (xHEVs) due to the simplicity of BEVs and the projections of a clean grid. A favorable total cost of ownership for BEVs relies on lower maintenance and warranty cost due to fewer moving parts. It is actually interesting that in the micro-scale there are many more moving parts in a battery due to the Lithium (de)intercalation in millions of particles in the electrodes of every cell, causing particle expansion and contraction, fracture, and build-up of rust-like films around the particles that consequently increases the cell resistance (lowers power) and decreases lithium inventory (lowers range). Moreover, hundreds, sometimes thousands of cells in a BEV raise the probability of a single cell failure.

The battery management system (BMS) comes to the rescue by protecting the pack, minimizing aging, accounting for cell-to-cell variability, and monitoring battery degradation in real time from field data. Accurate predictions of degradation and lifetime of lithium-ion batteries are essential for reliability, safety, and key to cost-effectiveness and life-cycle emissions. If the battery stays healthy, vehicle to building, vehicle to grid, and other 2nd life applications can provide additional value streams.

I will discuss the challenges of estimating the state of health (SOH) and predicting remaining useful life (RUL) based on electrical (terminal voltage) measurements. I will then introduce you to the less known phenomenon of reversible and irreversible battery swelling that offers a rich life signature similar to “aging wrinkles”. I will conclude by highlighting the ultimate BMS safety task, namely measuring battery swelling from gas evolution for estimating the onset of venting and thermal runaway, and consequently, managing the risk of explosions and fires from failing batteries.

Bio:  Prof. Anna Stefanopoulou, the William Clay Ford Professor of Technology and Professor of Mechanical Engineering at the University of Michigan is leading an interdisciplinary group in powertrain control, especially state estimation, model-based prediction, data-driven fault-detection, and automation of dynamic and multivariable problems in electrochemical and thermomechanical processes for battery management, fuel cells, and engines

***Event will take place in hybrid format. The location for in-person attendance will be room 1311 EECS.   Attendance will also be possible via Zoom. Zoom link and password will be distributed to the Controls Group e-mail list-serv.

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