Advances and Future Trends in Electrified Agricultural Machinery for Sustainable Agriculture

Yue Shen, Feng Yang, Jianbang Wu, Shuai Luo, Zohaib Khan, Lanke Zhang and Hui Liu ()
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Yue Shen: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Feng Yang: Key Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment, Jiangsu University, Zhenjiang 212013, China
Jianbang Wu: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Shuai Luo: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Zohaib Khan: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Lanke Zhang: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Hui Liu: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2025, vol. 15, issue 22, 1-55

Abstract: The global transition toward sustainable and intelligent farming has positioned Electrified Agricultural Machinery (EAM) as a central focus in modern equipment development. By integrating advanced electrical subsystems, high-efficiency powertrains, and intelligent Energy Management Strategies (EMSs), EAM offers considerable potential to enhance operational efficiency, reduce greenhouse-gas emissions, and improve adaptability across diverse agricultural environments. Nevertheless, widespread deployment remains constrained by harsh operating conditions, complex duty cycles, and limitations in maintenance capacity and economic feasibility. This review provides a comprehensive synthesis of enabling technologies and application trends in EAM. Performance requirements of electrical subsystems are examined with emphasis on advances in power supply, electric drive, and control systems. The technical characteristics and application scenarios of battery, series hybrid, parallel hybrid, and power-split powertrains are compared. Common EMS approaches (rule-based, optimization-based, and learning-based) are evaluated in terms of design complexity, energy efficiency, adaptability, and computational demand. Representative applications across tillage, seeding, crop management, and harvesting are discussed, underscoring the transformative role of electrification in agricultural production. This review identifies the series hybrid electronic powertrain system and rule-based EMSs as the most mature technologies for practical application in EAM. However, challenges remain concerning operational reliability in harsh agricultural environments and the integration of intelligent control systems for adaptive, real-time operations. The review also highlights key technical bottlenecks and emerging development trends, offering insights to guide future research and support the wider adoption of EAM.

Keywords: agricultural machinery; energy management strategy; hybrid electric powertrain; deep reinforcement learning; agricultural intelligence (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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