RESEARCH

Battery Safety

Battery safety will become increasingly crucial as the battery market grows along with the demand for transportation electrification and the increasing energy density in battery systems. Reliable fault detection (leaks, venting gas, temp, pres, and voltage) and effective mitigation methods (discharge & cooling) are required to alert, advise on needed actions based on predicting evolution, and reduce the risk. Our group is developing methods to address these issues and guide emergency response to better prevent and eliminate the hazards.

Publications

  • Ting Cai, et al Detection of Li-ion battery failure and venting with Carbon Dioxide sensors. eTransportation, 2021. [ bib | DOI ]

  • Ting Cai, et al. Modeling li-ion battery temperature and expansion force during the early stages of thermal runaway triggered by internal shorts. JECS (2019) [ bib | DOI ]

  • T. Cai, V. Tran, et al, "Modeling Li-ion Battery First Venting Events Before Thermal Runaway", Modeling, Estimation, and Control Conference (MECC), 2021 [pre-print]

Battery reversible expansion corresponds to electrode material phase transitions. Unlike the differential voltage, the double differential expansion peaks are observable up to 1C rate, at low depth of discharge, and are less path-dependent, which makes the differential expansion an excellent method for capacity and health estimation. To understand why that is the case, and at the same time develop a model-based estimator a reduced ordered electrochemical/mechanical model is developed. The electrochemical model is primarily based on the well-known Doyle Fuller Newman (DFN) model and includes a particle expansion model.

Publications

  • Mohtat, P., et al (2022), “Comparison of expansion and voltage differential indicators for battery capacity fade,” JPS [DOI]

  • Mohtat, P., et al (2021). “Reversible and Irreversible Expansion of Lithium-Ion Batteries Under a Wide Range of Stress Factors”. JECS. [DOI]

  • Mohtat, P., et al. (2021). "An Algorithmic Safety VEST For Li-ion Batteries During Fast Charging." Modeling, Estimation and Controls Conference [preprint].

  • Mohtat, P., at al. (2020). “Differential Expansion and Voltage Model for Li-ion Batteries at Practical Charging Rates”. JECS, 167(11), 110561. [preprint | DOI]

Battery Degradation Modeling and Lifetime Prediction

To enable reusability of batteries (2nd Life Use) there is a need for enhanced modeling for prediction and accelerated simulation of how batteries degrade.

We model physics-based degradation mechanisms (SEI growth, lithium plating, and particle swelling and cracking) to understand their interactions and predict the knee point where accelerated aging starts. Classical and model-predictive control techniques can then guide degradation-conscious EV fast-charging, 2nd life BMS, adaptive power denials, ESS derating and allow adaptive accelerated simulations of a battery's entire life.

Finally, we also work with data-driven methods to predict battery lifetime based on features in the charge/discharge curves.

Publications

  • Pannala, et al. (2021) "Methodology for Accelerated Inter-Cycle Simulations of Li-ion Battery Degradation with Intra-Cycle Resolved Degradation Mechanisms". Submitted to American Control Conference 2022.

  • Sulzer, V., et al. (2020). “Promise and Challenges of a Data-Driven Approach for Battery Lifetime Prognostics”. Submitted to American Control Conference 2021. [preprint]

Manufacturing Diagnostics

The ability to quickly assess battery lifetime is critically important for enabling battery manufacturers to make smart design choices that balance initial performance with longevity.

Towards this goal, we are analyzing data generated during the battery manufacturing process to identify diagnostic signals that can be used to rapidly screen new materials and manufacturing processes alike.

Partnering with the University of Michigan Battery Lab, we are building prototype pouch cells with varying process parameters and deploying low-cost signals into the manufacturing process. We are also performing cycle life characterization to study the connection between early-life signals and end-of-life outcomes.

Publications

A. Weng, et al, "Predicting the impact of formation protocols on battery lifetime immediately after manufacturing" Joule 2021 5, 1-22, November 17, 2021, doi: 10.1016/j.joule.2021.09.015.


Electrification Workforce Development Needs

Figure from paper submitted to 2022 IFAC AAC titled "US Automotive Sector Employment and Early Evidence of the Employment Impact of the Transition to Battery Electric Vehicles" by Rebecca Pickens and Anna Stefanopoulou (pre-print here)


Notes from the figure caption: US manufacturing and repair jobs that are directly ICE-related are represented in gray shading, based on 2019 average employment from QCEW in the ICE-related categories represented in Table 1 (BLS, 2022a); Alaska and Hawaii are not shown and each have fewer than 200 jobs that are directly ICE-related and no announced or current final vehicle or EV battery manufacturing; Final automotive assembly plants (as of 2021 and announced through 2025) are shown on the map by circles, with 2021 battery manufacturing locations and 2021 battery manufacturing announced through 2025 identified by stars (Automotive News, 2021; Ford, 2021c, 2021b, 2021a; Kane, 2021, 2022; Loveday, 2022; Tesla, 2022); model types produced were identified by EPA classification (US EPA, 2021) combined with Automotive News (2021) assembly plants data.