The Need

Battery design is becoming increasingly complex with the increased applications for electric vehicles and power storage for renewable energy such as solar and wind. Designers continue to pursue more efficient power designs with resilient characteristics to ensure long battery life over continued cycling. The design considerations for batteries include electrochemical reactions, heat transfer, structural stress, and strain on the components due to power cycling. Improvements in modeling capability can aid in the design by maximizing the battery's power capacity while minimizing non-reactive parts that don’t contribute to power storage. As the battery market continues to grow, small advantages in modeling capability can result in large increases in market share.

Technology publications available for licesning and collaboration:

1. Brodsky Ringler, Polina, Matthew Wise, Prashanth Ramesh, Jung Hyun Kim, Marcello Canova, Chulheung Bae, Jie Deng, and Heechan Park. "Modeling of Lithium Plating and Stripping Dynamics during Fast Charging." Batteries 9, No. 7, 2023.

2. A. Misley, A. Sergent, M. D’Arpino, P. Ramesh, M. Canova,” Design Space Exploration of Lithium-ion Battery Packs for Hybrid-Electric Regional Aircraft Applications”. AIAA Journal of Propulsion and Power, 1–14, 2022

3. C. Dangwal, M. Canova, “Parameter Identification for Electrochemical Models of Lithium ion Batteries using Sensitivity Analysis”, ASME Letters of Dynamics Systems and Control 1(4), 041014 (2021).

4. Z. Salyer, M. D’Arpino, M. Canova. Extended Physics-Based Reduced-Order Capacity Fade Model for Lithium-Ion Battery Cells. ASME Letters in Dynamic Systems and Control, 1(4), 041002 (2021).

5. G. Fan, X. Li, M. Canova, “A Reduced-Order Electrochemical Model of Li-ion Batteries for Control and Estimation Applications”, IEEE Transactions on Vehicular Technology, 67(1), 76-91, 2017.

6. X. Li, G. Fan, K. Pan, G. Wei, C. Zhu, G. Rizzoni, M. Canova. “A Physics-Based Fractional Order Model and State of Energy Estimation for Lithium-Ion Batteries. Part I: Model Development and Observability Analysis”, Journal of Power Sources 367, 187-201, 2017.

7. X. Li, G. Fan, K. Pan, G. Wei, C. Zhu, G. Rizzoni, M. Canova. “A Physics-Based Fractional Order Model and State of Energy Estimation for Lithium-Ion Batteries. Part II: Parameter Identification and State of Energy Estimation for LiFePO4 Battery”, Journal of Power Sources 367, 202-213, 2017.

8. G. Fan, K. Pan, G. Storti, M. Canova, J. Marcicki, X. Yang, “A Reduced-Order Multi-Scale, Multi-Dimensional Model for Performance Prediction of Large-Format Li-Ion Cells”, Journal of Electrochemical Society, Vol. 164, Is. 2, A252-A264, 2017.

9. X. Li, G. Fan, G. Rizzoni, M. Canova, C. Zhu, G. Wei, “A Simplified Multi-Particle Model for Lithium Ion Batteries via a Predictor-Corrector Strategy and Quasi-Linearization”, Energy, Volume 116, Part 1, 2016.

10. G. Fan, K. Pan, M. Canova, J. Marcicki, X. Yang, “Modeling of Li-Ion Cells for Fast Simulation of High C-Rate and Low Temperature Operations”, Journal of Electrochemical Society, 163.5: A666-A676, 2016.

11. J. Marcicki, M. Canova, A.T. Conlisk, G. Rizzoni, “Design and Parametrization Analysis of a Reduced-Order Electrochemical Model of Graphite/LiFePO4 Cells for SOC/SOH Estimation”, Journal of Power Sources, Vol. 237, Pages 310–324, 2013.

12. M. Muratori, M. Canova, Y. Guezennec: “A Spatially-Reduced Dynamic Model for the Thermal Characterization of Li-Ion Battery Cells”, Int. J. Vehicle Design, Vol. 58, No. 2/3/4, pp.134–158, 2012.