| TOWARD SMART BATTERIES FOR THE ENERGY TRANSITION Rachid Yazami1; 0; 1KVI PTE LTD, Singapore, Singapore; PAPER: 463/Battery/Plenary (Oral) SCHEDULED: 11:30/Mon. 28 Nov. 2022/Similan 2 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 |
