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Self-adapting J-type air-based battery thermal management system via model predictive control.
- Source :
-
Applied Energy . Apr2020, Vol. 263, pN.PAG-N.PAG. 1p. - Publication Year :
- 2020
-
Abstract
- • A dynamic J-type battery thermal control system is established. • The mode switching control succeeds to maintain the battery thermal performance. • The energy efficiency is improved by 15.8% by employing model predictive control. Battery thermal control plays an indispensable role in terms of the safety and performance for electric vehicles. For air-based cooling technologies, one of the most pressing challenges is to balance the temperature uniformity and constrain the maximum temperature simultaneously under varying driving conditions. This paper proposes a self-adaptive intelligent neural network-based model predictive control strategy for a J -type air-based battery thermal management system. The J -type structure is first optimized through surrogate-based optimization to improve the temperature uniformity before control. Based on the optimized J -type configuration, an operation mode switching module is developed to mitigate the temperature unbalance. The thermal control approach is tested using an integrated driving cycle, and its evaluations are threefold: (i) the neural network-based control without mode switching fails to meet the thermal requirements; (ii) the control with mode switching succeeds in constraining the maximum temperature and maintaining the temperature uniformity within 1.33 K; (iii) the added model predictive control approach slightly enhances the thermal performance but improves the energy efficiency significantly by 15.8%. The results show that the J -type structure with its appropriate control strategy is a promising solution for light-duty electric vehicles using an air-cooling technology. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03062619
- Volume :
- 263
- Database :
- Academic Search Index
- Journal :
- Applied Energy
- Publication Type :
- Academic Journal
- Accession number :
- 142319289
- Full Text :
- https://doi.org/10.1016/j.apenergy.2020.114640