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Energies
Volume 18
Issue 21
10.3390/en18215713
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Qiaohong Ming
1,2,*,
Yangyang Wang
1,
Feng Wang
3,
Houran Ying
2,
Hao Zeng
2,
Jie Ren
2 and
Zhiwei Cui
2
1
School of Automotive Studies, Tongji University, Shanghai 201800, China
2
SANY Heavy Industry Co., Ltd., Changsha 410100, China
3
State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
*
Author to whom correspondence should be addressed.
Energies 2025, 18(21), 5713; https://doi.org/10.3390/en18215713 (registering DOI)
Submission received: 25 September 2025 / Revised: 23 October 2025 / Accepted: 29 October 2025 / Published: 30 October 2025
(This article belongs to the Special Issue Drive System and Control Strategy of Electric Vehicle)
Versions Notes

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 electric loader; duty cycle aggregation; operating mode clustering; cross-scenario generalization; Jensen–Shannon divergence

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MDPI and ACS Style

Ming, Q.; Wang, Y.; Wang, F.; Ying, H.; Zeng, H.; Ren, J.; Cui, Z. A Cross-Scenario Generalizable Duty Cycle Aggregation Method for Electric Loaders with Scenario Verification. Energies 2025, 18, 5713. https://doi.org/10.3390/en18215713

AMA Style

Ming Q, Wang Y, Wang F, Ying H, Zeng H, Ren J, Cui Z. A Cross-Scenario Generalizable Duty Cycle Aggregation Method for Electric Loaders with Scenario Verification. Energies. 2025; 18(21):5713. https://doi.org/10.3390/en18215713

Chicago/Turabian Style

Ming, Qiaohong, Yangyang Wang, Feng Wang, Houran Ying, Hao Zeng, Jie Ren, and Zhiwei Cui. 2025. "A Cross-Scenario Generalizable Duty Cycle Aggregation Method for Electric Loaders with Scenario Verification" Energies 18, no. 21: 5713. https://doi.org/10.3390/en18215713

APA Style

Ming, Q., Wang, Y., Wang, F., Ying, H., Zeng, H., Ren, J., & Cui, Z. (2025). A Cross-Scenario Generalizable Duty Cycle Aggregation Method for Electric Loaders with Scenario Verification. Energies, 18(21), 5713. https://doi.org/10.3390/en18215713

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