Energy Management of Sowing Unit for Extended-Range Electric Tractor Based on Improved CD-CS Fuzzy Rules

Author:

Wu Zhengkai12,Wang Jiazhong12,Xing Yazhou12,Li Shanshan12,Yi Jinggang12,Zhao Chunming3

Affiliation:

1. College of Mechanical and Electrical Engineering, Agricultural University of Hebei, Baoding 071001, China

2. Hebei Intelligent Agricultural Equipment Technology Innovation Center, Baoding 071001, China

3. Tianjin Yidingfeng Power Technology Co., Ltd., Tianjin 300380, China

Abstract

In order to ensure the continuity and endurance mileage requirements during sowing operations, it is necessary to establish accurate modeling for the working condition of the electric tractor sowing unit by adopting a reasonable energy management strategy and realizing accurate energy prediction. The existing electric tractor sowing unit battery energy management strategy is not optimal since it is mostly based on extensive rules. In this paper, according to the requirements of the sowing conditions, a precise model of electric energy consumption in the sowing cycle was established and an energy management strategy of sowing unit of extended-range electric tractor with power CD-CS was proposed. Fuzzy control rules of the dynamic SOC correction factor were established in the battery maintenance stage, and the NSGA-II algorithm was used to optimize the fuzzy control rules to optimize the battery charging and discharging efficiency. A hardware-in-the-loop simulation test platform was built, and the proposed CD-CS strategy was compared with the fuzzy improvement strategy. The simulation results show that the proposed fuzzy improvement strategy extended the battery life of the power consumption stage by 2131.9 s, which is a significant improvement. The field practical results showed that the SOC decreased by 7.21% and the simulation by 4.94% in terms of power consumption in a cycle. The power consumption variance was within a reasonable range, which further verifies the feasibility of the strategy.

Funder

Key Technology Research and Intelligent Equipment Research

Integrated Application of Key Technologies and Intelligent Equipment of Automatic Wheat Precision Planter

Publisher

MDPI AG

Subject

Plant Science,Agronomy and Crop Science,Food Science

Reference30 articles.

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2. Zhan, W. (2022). Unmanned Dense Planting of Wheat Planting Design and Test of the Unit, Hebei Agricultural University.

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5. Chen, H., Xie, H., Sun, L., and Shang, T. (2023). Research on Tractor Optimal Obstacle Avoidance Path Planning for Improving Navigation Accuracy and Avoiding Land Waste. Agriculture, 13.

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