A Jaya-driven quasi-predictive optimization strategy with adaptive window scheduling for EV thermal management

Jiayi Li, Yan Ma, Jinwu Gao and Yunfeng Hu

Energy, 2025, vol. 334, issue C

Abstract: Grappling with the trade-off between long-range planning, driven by the battery’s sluggish thermal response, and low computational cost remains a core challenge for model-based optimization methods in electric vehicle (EV) thermal management. This study proposes a lightweight quasi-predictive optimization strategy driven by the non-model-based Jaya algorithm, designed to emulate predictive control while ensuring real-time responsiveness. A dual-mode coordinated cooling model, integrating air and liquid circuits, is developed to capture multi-source thermal interactions between the battery and cabin. To adapt to real-time thermal dynamics, an entropy-guided adaptive window scheduling mechanism with dynamic objective weighting is proposed, enabling balanced trade-offs among window length, control accuracy, and computational efficiency. Simulation results indicate that the proposed strategy reduces energy consumption by 11.48% while maintaining a millisecond-level computation time (0.4 ms per step) over conventional fixed-window approaches. These findings confirm that the proposed strategy offers a computationally efficient, adaptive, and real-time capable solution for scalable thermal management in connected EV applications, supporting future deployment in embedded control systems.

Keywords: Quasi-predictive optimization; Entropy-guided scheduling; Adaptive window control; Thermal management system (TMS); Electric vehicle (EV) (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225032359
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:334:y:2025:i:c:s0360544225032359

DOI: 10.1016/j.energy.2025.137593

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().