2022-Sustainable Industrial Processing Summit
SIPS2022 Volume 14 Yazami Intl. Symp Secondary Battery Manufacturing & Recycling and Electrochemistry
| Editors: | F. Kongoli, K. Aifantis, C. Capiglia, A. Fox, V. Kumar, A. Tressaud, Z. Bakenov, A. Qurashi. |
| Publisher: | Flogen Star OUTREACH |
| Publication Year: | 2022 |
| Pages: | 158 pages |
| ISBN: | 978-1-989820-60-5(CD) |
| ISSN: | 2291-1227 (Metals and Materials Processing in a Clean Environment Series) |
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TOWARD SMART BATTERIES FOR THE ENERGY TRANSITION
Rachid Yazami1; 0;1KVI PTE LTD, Singapore, Singapore;
Type of Paper: Plenary
Id Paper: 463
Topic: 14
Abstract:
Lithium-ion batteries (LIB) will play a major role in the future energy transition owing to
outstanding performances in energy, power, lifespan, costs, and environment friendliness [1].
Electric vehicles are among the most LIB using systems since most of the automobile
manufacturers will stop producing internal combustion engine vehicles (ICV) by 2030-35 to
move to hybrid and full electric vehicles (EV). Accordingly, LIB should offer the same
conveniences to the end-user of EV as for current ICV, which includes ultra-fast charging
(full charge below 15 min), long driving range between charges (>500 km), long life (>10
years), affordable prize (<10% premium vs. ICV) and high safety (reduced thermal
runaway’s events).
Current LIB charging protocols based on constant current (CC) fall short to fully charge an
LIB EV pack in less than 60 min due to overheating. To overcome this limitation, we have
developed a voltage-controlled charging protocol coined as “Non-Linear Voltammetry”
(NLV). By tuning the NLV parameters to the battery characteristics (chemistry, state of
heath, design, engineering…) ultra-fast charging has been successfully achieved at both the
cell and pack levels enabling from 0 to 100% state of charge to be complete below 20
minutes in most cases and below 10 min in specially designed LIB. Artificial intelligence
methods [2] are used to adjust the NLV parameters as the LIB ages to ensure safety and life
span owing to temperature control. Other applications of NLV such as enhanced energy
density will be presented and discussed.
References:
[1] Ghassan Zubi, Rodolfo Dufo-L 贸pez, Monica Carvalho, Guzay Pasaoglu,The lithium-ion battery: State of the art and future perspectives,
Renewable and Sustainable Energy Reviews, 89(2018)292-308
[2] Samanta, A.; Chowdhuri, S.; Williamson, S.S. Machine Learning-Based Data-Driven
Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review. Electronics 2021, 10,
1309. https://doi.org/10.3390/electronics10111309