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Proceedings Papers

Artificial Intelligence Based State of Health Estimation With Short-Term Current Profile in Lead-Acid Batteries for Heavy-Duty Vehicles

Sara Luciani, Stefano Feraco, Angelo Bonfitto, Nicola Amati, Andrea Tonoli, Maurizio Quaggiotto
Proc. ASME. IDETC-CIE2022, Volume 1: 24th International Conference on Advanced Vehicle Technologies (AVT), V001T01A015, August 14–17, 2022
Paper No: DETC2022-89512
https://doi.org/10.1115/DETC2022-89512
... Abstract The State of Health (SOH) estimation for automotive batteries is currently assessed with different techniques which may involve long testing procedure or require costly hardware to be implemented. This paper aims at contributing to this domain by exploiting the response of a lead-acid...
View Paper titled, Artificial Intelligence Based <span class="search-highlight">State</span> of <span class="search-highlight">Health</span> Estimation With Short-Term Current Profile in Lead-Acid Batteries for Heavy-Duty Vehicles
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Topics: Artificial intelligence, Artificial neural networks, Construction equipment, Lead-acid batteries
Proceedings Papers

Physics-Informed Machine Learning for Degradation Diagnostics of Lithium-Ion Batteries

Adam Thelen, Yu Hui Lui, Sheng Shen, Simon Laflamme, Shan Hu, Chao Hu
Proc. ASME. IDETC-CIE2021, Volume 3A: 47th Design Automation Conference (DAC), V03AT03A041, August 17–19, 2021
Paper No: DETC2021-71407
https://doi.org/10.1115/DETC2021-71407
... Abstract State of health (SOH) estimation of lithium-ion batteries has typically been focused on estimating present cell capacity relative to initial cell capacity. While many successes have been achieved in this area, it is generally more advantageous to not only estimate cell capacity...
View Paper titled, Physics-Informed Machine Learning for Degradation Diagnostics of Lithium-Ion Batteries
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Topics: Lithium-ion batteries, Machine learning, Physics