| Autor: |
Dinh Hoa Nguyen |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 12, Pp 155904-155914 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3482319 |
| Popis: |
This research studies the control of electro-thermal dynamics of cylindrical Li-ion battery packs in electric vehicles (EVs). These dynamics are coupled by the charging-discharging current which generates the Joule heating that directly affects to the operation of battery cells. Hence, to guarantee the battery cells’ temperatures in a desired range for their best operation, a model predictive iterative learning control (MPILC) design is proposed, which composes of an iterative learning controller (ILC) and a model predictive controller (MPC). The former controller with iteration-varying learning gains helps better track slightly variant daily state-of-charge (SoC) patterns of the battery pack. A constant upper bound is derived for the tracking error norm, based on which the iteration-varying learning gains can be designed to make the tracking error converge to zero. The latter controller employs the result of the former as a predicted disturbance to design the cooling-heating temperature input for the battery pack by minimizing its consumed energy while driving the battery cells’ temperatures to a desired range. Simulations are then carried out to illustrate the effectiveness of the proposed MPILC design in tracking daily-variant SoC profiles while guaranteeing battery cells’ temperatures in expected intervals. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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