Electrification and Smartification for Modern Tractors: A Review of Algorithms and Techniques

Chaoxian Zhang, Jun Li, Chuxi Li, Peihan Lin, Linlin Shi and Boyi Xiao ()
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Chaoxian Zhang: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Jun Li: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Chuxi Li: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Peihan Lin: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Linlin Shi: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Boyi Xiao: College of Engineering, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2025, vol. 15, issue 18, 1-30

Abstract: Agricultural tractors account for a substantial portion of greenhouse gas emissions in the farming sector, necessitating the development of sustainable machinery solutions. This study systematically reviews the latest advancements in electrification and smartification technologies for modern tractors, with a particular focus on algorithmic control strategies and their applications. Architecturally, the study provides a comparative analysis of four key configurations, pure electric, series hybrid, parallel hybrid, and series-parallel hybrid, detailing their respective advantages and challenges in energy efficiency and operational performance. From an algorithmic perspective, three primary methodologies—rule-based control strategies, optimization algorithms, and reinforcement learning—are examined for their applicability in energy management and control systems. The research further explores the integration of intelligent systems in unmanned farming scenarios, addressing critical challenges such as adaptive path planning in unstructured environments and multi-machine collaborative operations. A case study on battery-electric tractors demonstrates practical advancements in battery technology and energy management systems. Lifecycle cost analysis confirms the long-term economic viability of electrification, while outlining a forward-looking technological roadmap for sustainable and intelligent agricultural machinery.

Keywords: electric tractors; electrification; optimization algorithms; energy management; unmanned farms (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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