Research on the energy control optimized strategy of electric tractor composite power supply based on improved genetic algorithm

doi:10.12398/j.issn.2096-7217.2023.03.002

Abstract

Abstract:

Aiming to solve the problems of insufficient capacity and high consumption of electric tractors， an optimal control strategy of composite power supply system based on battery plus super capacitor was proposed. By analyzing the topological structure of the electric tractor complex power system and the resistance characteristics of the electric tractor under various operating conditions， a complex power system structure scheme suitable for electric tractor was selected and a test condition suitable for electric tractor was designed. At the same time， the power demand of the electric tractor drive motor under the test condition was determined. According to the charging and discharging characteristics of the main power components of the electric tractor composite power system， the relevant mathematical model was established， and the simulation model of the electric tractor complex power system was built in Simulink. In addition， according to the load demand of the electric tractor under the test condition and the discharge characteristics of the complex power supply system， a fuzzy control strategy of the complex power supply system was formulated. Taking the minimum current at the maximum power discharge of the battery as the optimization goal， the improved genetic algorithm was used to optimize the fuzzy control parameters. The models of the composite power supply system under different conditions such as single power supply and logic threshold control strategy was built for comparative simulation. The results showed that： compared with the single power supply， logic threshold control strategy and fuzzy control strategy， the peak current battery with the improved genetic algorithm can be reduced by 63%， 62.9% and 43.9% respectively， the average current was respectively reduced 24.8%， 3.2% and 8.5%. It is also shown that this strategy is beneficial to reduce the peak current battery and prolong the service life of the battery.

Key words: electric tractor, composite power supply, fuzzy control strategy, genetic algorithm

CLC Number:

S219.4

YU Qiang, LI Xueyan, PAN Xinjia, HE Xionglin, FAN Wentao, WANG Fang, WANG Yu. Research on the energy control optimized strategy of electric tractor composite power supply based on improved genetic algorithm[J]. Journal of Intelligent Agricultural Mechanization, 2023, 4(3): 14-23.

Figures/Tables 12

Figure 1 Topology of dual-source energy storage system

Figure 2 Working condition to be tested

Figure 3 Temperature model

Figure 4 Basic schematic diagram of fuzzy controller

Figure 5 Fuzzy controls membership function

Table 1 Fuzzy control rule table

$K u c$		$P r e q$
$S O C c$	$S O C b$	T	S	M	B	H
NS	NS	T	S	S	M	M
	NM	T	T	S	S	S
	NB	T	T	T	T	T
NM	NS	T	T	S	M	M
	NM	T	T	S	M	B
	NB	T	T	T	S	M
NB	NS	S	M	B	H	H
	NM	T	S	M	B	H
	NB	T	S	M	M	B

Table 1 Fuzzy control rule table

$K u c$		$P r e q$
$S O C c$	$S O C b$	T	S	M	B	H
NS	NS	T	S	S	M	M
	NM	T	T	S	S	S
	NB	T	T	T	T	T
NM	NS	T	T	S	M	M
	NM	T	T	S	M	B
	NB	T	T	T	S	M
NB	NS	S	M	B	H	H
	NM	T	S	M	B	H
	NB	T	S	M	M	B

Figure 6 Flowchart of genetic algorithm optimization

Figure 7 Schematic of the logic threshold control strategy

Figure 8 Variation curve of battery temperature

Figure 9 Variation curve of battery SOC

Figure 10 Variation curve of battery current

Table 2 Battery current comparison table

电流类型	模糊控制策略	单电源	逻辑门限控制策略	改进遗传算法优化	改进效果相对值/%
最大电流/A	52.18	79.01	78.99	29.27	43.9
					63
					62.9
平均电流/A	6.75	8.17	6.38	6.17	8.5
					24.8
					3.2

References 21

1	王早霞. 实现“双碳”目标必须依靠科技创新［N］. 山西日报， 2022-08-25（006）.
2	农业农村部农业机械化管理司. 农业机械化统计年报［M］. 2021.
3	杨洲，陈朝海，段洁利，等. 果园施肥用便携式电动挖穴机性能试验［J］. 农业工程学报， 2013， 29（12）： 25-31.
	YANG Zhou， CHEN Chaohai， DUAN Jieli， et al. Performance test of hand-held electric hole-digger for fertilization in orchard ［J］. Transactions of the Chinese Society of Agricultural Engineering， 2013， 29（12）： 25-31.
4	白学峰，常江雪，滕兆丽，等. 我国智能农业拖拉机关键技术研究进展［J］. 智能化农业装备学报（中英文）， 2022， 3（2）： 10-21.
	BAI Xuefeng， CHANG Jiangxue， TENG Zhaoli， et al. Research progress on key technologies of intelligent agricultural tractors in China ［J］. Journal of Intelligent Agricultural Mechanization， 2022， 3（2）： 10-21.
5	沈文龙，周俊，姬长英，等. 中国电动拖拉机研究进展［J］. 中国农机化学报， 2017， 38（10）： 102-107.
	SHEN Wenlong， ZHOU Jun， JI Changying， et al. Research review about electric tractor in China ［J］. Journal of Chinese Agricultural Mechanization， 2017， 38（10）： 102-107.
6	高辉松，朱思洪. 电动拖拉机传动系设计理论与方法研究［J］. 南京农业大学学报， 2009， 32（1）： 140-145.
	GAO Huisong， ZHU Sihong. Study on design theory and method for driving line of electric tractor ［J］. Journal of Nanjing Agricultural University， 2009， 32（1）： 140-145.
7	夏长高，孙闫，周雯雯. 双电源电动拖拉机能量管理仿真研究［J］. 农机化研究， 2019， 41（1）： 234-240.
	XIA Changgao， SUN Yan， ZHOU Wenwen. Study on energy management of double power electric tractor ［J］. Journal of Agricultural Mechanization Research， 2019， 41（1）： 234-240.
8	刘孟楠，周志立，徐立友，等. 基于随机载荷功率谱的电动拖拉机复合能量系统研究［J］. 农业机械学报， 2018， 49（2）： 358-366.
	LIU Mengnan， ZHOU Zhili， XU Liyou， et al. Electric tractor energy system and management strategy research based on load power spectral density ［J］. Transactions of the Chinese Society for Agricultural Machinery， 2018， 49（2）： 358-366.
9	李银平，刘立，靳添絮，等. 基于动态规划的电动拖拉机动力电源能量控制策略研究［J］. 农业机械学报， 2020， 51（4）： 403-410.
	LI Yinping， LIU Li， JIN Tianxu， et al. Energy control strategy of electric tractor power supply based on dynamic programming ［J］. Transactions of the Chinese Society for Agricultural Machinery， 2020， 51（4）： 403-410.
10	TRITSCHLER P J， BACHA S， RULLIÈRE E， et al. Energy management strategies for an embedded fuel cell system on agricultural vehicles ［C］// The XIX International Conference on Electrical Machines. IEEE， 2010.
11	BARTHEL J， GRGES D， BELL M， et al. Energy management for hybrid electric tractors combining load point shifting， regeneration and boost ［C］// 2014 IEEE Vehicle Power and Propulsion Conference （VPPC）. IEEE， 2015.
12	王琪，孙玉坤. 一种混合动力汽车复合电源能量管理系统控制策略与优化设计方法研究［J］. 中国电机工程学报， 2014， 34（S1）： 195-203.
	WANG Qi， SUN Yukun. Research on the control strategy and optimization of energy management system of hybrid energy storage in a hybrid electric vehicle ［J］. Proceedings of the CSEE， 2014， 34（S1）： 195-203.
13	UEKA Y， YAMASHITA J， SATO K， et al. Study on the development of the electric： Specification and traveling and performance of a prototype electric tractor ［J］. Engineering in Agriculture， Environment and Food， 2013， 6（4）： 160-164.
14	谢斌，张超，陈硕，等. 双轮驱动电动拖拉机传动性能研究［J］. 农业机械学报， 2015， 46（6）： 8-13.
	XIE Bin， ZHANG Chao， CHEN Shuo， et al. Transmission performance of two-wheel drive electric tractor ［J］. Transactions of the Chinese Society for Agricultural Machinery， 2015， 46（6）： 8-13.
15	商高高，张建舟，张家俊. 履带式电动拖拉机驱动系统控制策略的研究［J］. 重庆理工大学学报（自然科学）， 2017， 31（11）： 32-38.
	SHANG Gaogao， ZHANG Jianzhou， ZHANG Jiajun. Research on control strategy of tracked electric tractor drive system ［J］. Journal of Chongqing University of Technology（Natural Science）， 2017， 31（11）： 32-38.
16	商高高，张家俊. 电动拖拉机驱动控制策略开发［J］. 中国农机化学报， 2016， 37（6）： 149-153.
	SHANG Gaogao， ZHANG Jiajun. Development of electric tractor powertrain control strategy ［J］. Journal of Chinese Agricultural Mechanization， 2016， 37（6）： 149-153.
17	葛安东. 基于遗传算法和模糊控制的某型纯电动汽车整车控制器的研究［D］. 合肥：合肥工业大学， 2015.
	GE Andong. Research on a pure electric vehicle controller based on genetic algorithm and fuzzy control ［D］. Hefei： Hefei University of Technology， 2015.
18	孙闫. 基于超级电容辅能的纯电动拖拉机能量管理的研究［D］. 镇江：江苏大学， 2018.
	SUN Yan. Study on energy management of pure electric tractor based on super capacitor auxiliary ［D］. Zhenjiang： Jiangsu University， 2018.
19	王志豪. 纯电动汽车复合电源能量控制策略研究［D］. 长春：长春工业大学， 2022.
	WANG Zhihao. Research on energy control strategy of pure electric vehicle composite power supply ［D］. Changchun： Changchun University of Technology， 2022.
20	曾梦远. 纯电动汽车用复合电源的建模与仿真研究［D］. 锦州：辽宁工业大学， 2016.
	ZENG Mengyuan. Modeling and simulation research on hybrid energy storage system for pure electric vehicles ［D］. Jinzhou： Liaoning University of Technology， 2016.
21	KERMANI S， DELPRAT S， GUERRA T M， et al. Predictive energy management for hybrid vehicle ［J］. Control Engineering Practice， 2012， 20（4）： 408-420.