A Hierarchical Evolutionary Search Framework with Manifold Learning for Powertrain Optimization of Flying Vehicles

Chenghao Lyu, Nuo Lei, Chaoyi Chen () and Hao Zhang ()
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Chenghao Lyu: School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Nuo Lei: Tsintelink Technology Co., Ltd., Beijing 100084, China
Chaoyi Chen: School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Hao Zhang: Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA

Energies, 2025, vol. 18, issue 13, 1-20

Abstract: Hybrid electric vertical take-off and landing (HEVTOL) flying vehicles serve as effective platforms for efficient transportation, forming a cornerstone of the emerging low-altitude economy. However, the current lack of co-optimization methods for powertrain component sizing and energy controller design often leads to suboptimal HEVTOL performance. To address this, this paper proposes a hierarchical manifold-enhanced Bayesian evolutionary optimization (HM-BEO) approach for HEVTOL systems. This framework employs lightweight manifold dimensionality reduction to compress the decision space, enabling Bayesian optimization (BO) on low-dimensional manifolds for a global coarse search. Subsequently, the approximate Pareto solutions generated by BO are utilized as initial populations for a non-dominated sorting genetic algorithm III (NSGA-III), which performs fine-grained refinement in the original high-dimensional design space. The co-optimization aims to minimize fuel consumption, battery state-of-health (SOH) degradation, and manufacturing costs while satisfying dynamic and energy management constraints. Evaluated using representative HEVTOL duty cycles, the HM-BEO demonstrates significant improvements in optimization efficiency and solution quality compared to conventional methods. Specifically, it achieves a 5.3% improvement in fuel economy, a 7.4% mitigation in battery SOH degradation, and a 1.7% reduction in system manufacturing cost compared to standard NSGA-III-based optimization.

Keywords: hybrid electric flying vehicles; multi-objective optimization; Bayesian evolutionary optimization; powertrain sizing; energy controller design (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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