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Description
Abstract:
Lithium-ion batteries (LIBs) have been widely adopted in electric vehicles (EVs) because of their high energy and power density, high efficiency, and long cycle life. However, the safety, performance, and lifespan of LIBs are greatly affected by the temperature. High temperatures not only accelerate the battery aging rate but also bring some safety hazards to LIBs such as thermal runaway. Low temperatures, can slow down the reactions inside LIBs, which undermines the energy and power capability of LIBs. Moreover, charging LIBs at low temperatures will trigger lithium plating, causing significant and irreversible capacity loss. Accurate monitoring of battery temperature serves as the basis of effective battery management and thermal management. Therefore, this project develops accurate and robust algorithms for battery state-of-temperature based on artificial intelligence and big data, which would allow for battery performance and lifetime maximization.
Funding: Villum Fonden
Lithium-ion batteries (LIBs) have been widely adopted in electric vehicles (EVs) because of their high energy and power density, high efficiency, and long cycle life. However, the safety, performance, and lifespan of LIBs are greatly affected by the temperature. High temperatures not only accelerate the battery aging rate but also bring some safety hazards to LIBs such as thermal runaway. Low temperatures, can slow down the reactions inside LIBs, which undermines the energy and power capability of LIBs. Moreover, charging LIBs at low temperatures will trigger lithium plating, causing significant and irreversible capacity loss. Accurate monitoring of battery temperature serves as the basis of effective battery management and thermal management. Therefore, this project develops accurate and robust algorithms for battery state-of-temperature based on artificial intelligence and big data, which would allow for battery performance and lifetime maximization.
Funding: Villum Fonden
| Status | Active |
|---|---|
| Effective start/end date | 01/01/2022 → 31/12/2024 |
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Projects
- 1 Active
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CROSBAT: SMART BATTERY
Teodorescu, R., Stroe, D., Kulkarni, A., Che, Y., Zheng, Y., Sui, X., Vilsen, S. B., Bharadwaj, P., Weinreich, N. A., Christensen, M. D. & Steffensen, B.
01/09/2021 → 31/08/2027
Project: Research
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Combing physics-based thermal model and machine learning for battery temperature estimation: The impact of model accuracy
Zheng, Y., Che, Y., Sui, X. & Teodorescu, R., 2024, 2024 IEEE 10th International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia. IEEE Signal Processing Society, p. 4946-4951 6 p. (2024 IEEE 10th International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia).Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review
Open AccessFile3 Downloads (Pure) -
Online Sensorless Temperature Estimation of Lithium-Ion Batteries Through Electro-Thermal Coupling
Zheng, Y., Che, Y., Hu, X., Sui, X. & Teodorescu, R., 2024, In: IEEE/ASME Transactions on Mechatronics. p. 1-12 12 p.Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile2 Citations (Scopus)37 Downloads (Pure) -
Real-time Sensorless Temperature Estimation of Lithium-ion Batteries Based on Online Operando Impedance Acquisition
Zheng, Y., Che, Y., Guo, J., Weinreich, N. A., Kulkarni, A., Nadeem, A., Sui, X. & Teodorescu, R., 2024, (Accepted/In press) In: IEEE Transactions on Power Electronics . p. 1-14 14 p.Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile10 Downloads (Pure)