A Cross-Scenario Generalizable Duty Cycle Aggregation Method for Electric Loaders with Scenario Verification

Qiaohong Ming (), Yangyang Wang, Feng Wang, Houran Ying, Hao Zeng, Jie Ren and Zhiwei Cui
Additional contact information
Qiaohong Ming: School of Automotive Studies, Tongji University, Shanghai 201800, China
Yangyang Wang: School of Automotive Studies, Tongji University, Shanghai 201800, China
Feng Wang: State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
Houran Ying: SANY Heavy Industry Co., Ltd., Changsha 410100, China
Hao Zeng: SANY Heavy Industry Co., Ltd., Changsha 410100, China
Jie Ren: SANY Heavy Industry Co., Ltd., Changsha 410100, China
Zhiwei Cui: SANY Heavy Industry Co., Ltd., Changsha 410100, China

Energies, 2025, vol. 18, issue 21, 1-22

Abstract: With the rapid advancement of construction machinery electrification, optimizing the energy efficiency of electric loaders requires representative duty cycles that accurately capture real-world operating characteristics. However, most existing studies rely on simplified test-track cycles, which fail to reflect the complexity of actual operations. To address this gap, this paper takes a commercial concrete mixing plant as a case study and proposes a cross-scenario generalization method for the duty cycle aggregation of electric loaders. The method integrates multi-source signal acquisition, task-segment partitioning, feature extraction, and dimensionality reduction via Principal Component Analysis (PCA), enabling the clustering of typical operating modes and reconstruction of representative duty cycles through segment concatenation. The aggregated duty cycles are validated using Jensen–Shannon divergence, showing similarity levels above 93% compared with field measurements from mixing plants in Yiwu and Kunshan. These results demonstrate the method’s strong temporal adaptability and cross-scenario transferability. The proposed approach provides a solid foundation for energy consumption assessment, powertrain matching, and control strategy optimization of electric loaders while also supporting the development of duty cycle databases and future industry standardization.

Keywords: electric loader; duty cycle aggregation; operating mode clustering; cross-scenario generalization; Jensen–Shannon divergence (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
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/21/5713/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/21/5713/ (text/html)

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:gam:jeners:v:18:y:2025:i:21:p:5713-:d:1783382

Access Statistics for this article

Energies is currently edited by Ms. Cassie Shen

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().