Abstract
The distributed driven agricultural vehicle platform studied in this paper is a basic carrying platform that can be used for a variety of agricultural activities (pesticide spraying, weeding, and other plant protection operations). It does not need to rely on traditional fuel engine to drive but is driven by electric power, which is more environmentally friendly and conducive to the development of ecological agriculture in the future. In this paper, according to the driving and structure characteristics of distributed driving electric agricultural vehicles, in order to ensure the vehicle trajectory tracking ability, under conditions of agricultural operation, the driving attitude and drive coordination control of electric agricultural vehicles are taken as the key research objectives. In this way, the driving path of the vehicle platform can be accurately controlled during the operation of pesticide spraying and herbicide, so as to reduce the excessive use or misuse of pesticides and herbicides and greatly reduce the field pollution. Considering the specific driving environment as well as complex and changeable motion patterns of agricultural vehicles, when a single motion model is used to track and estimate the driving state, there will be low filtering accuracy or even loss of the target during vehicle maneuvering. In this paper, interactive multiple model (IMM) algorithm is combined with extended Kalman filter to effectively track changes of the target’s motion mode, thereby avoiding low filtering accuracy or serious state estimation inaccuracy. Finally, through the distributed electric drive agricultural operation experimental platform developed by the research group, the working conditions close to the actual agricultural production activities was set up according to the needs of actual agricultural production activities in this paper, and applicability and accuracy of the algorithm state estimation are verified by experiments.
Similar content being viewed by others
Data availability
All related data are within the manuscript.
References
Auat Cheein FA, Carelli R (2013) Agricultural robotics: unmanned robotic service units in agricultural tasks. IEEE Ind Electron M 7:48–58
Blackmore S, Stout B, Wang MH et al (2005) Robotic agriculture-the future of agricultural mechanization. In: European Conference on Precision Agriculture, 5th edn. Wageningen Academic Publishers, Netherlands, pp 621–628
Capasso C, Veneri O (2014) Experimental analysis on the performance of lithium based batteries for road full electric and hybrid vehicles. Appl Energy 136:921–930
Chau KT, Chan CC, Liu C (2008) Overview of permanent-magnet brushless drives for electric and hybrid electric vehicles. IEEE T Ind Electron 55:2246–2257
Chen JT, Zhu ZQ, Iwasaki S, Deodhar RP (2011) A novel hybrid-excited switched-flux Brushless AC machine for EV/HEV applications. IEEE T Veh Technol 60:1365–1373
Cherouat H, Braci M, Diop S (2005) Vehicle velocity, side slip angles and yaw rate estimation. Proceedings of the IEEE International Symposium on Industrial Electronics. Dubrovnik, Croa-tia:349–354
Denis D, Thuilot B, Lenain R (2016) Online adaptive observer for rollover avoidance of reconfigurable agricultural vehicles. Comput Electron Agric 126:32–43
Doumiati M (2011) Real-time estimation of vehicle lateral tire–road forces and sideslip angle. IEEE/ASME T Mech 16:601–615
Edwards C (2005) Sliding mode observers for vehicle mode detection. Veh Syst Dyn 11:1309–1322
Fernandes HR, Garcia AP (2018) Design and control of an active suspension system for unmanned agricultural vehicles for field operations. Biosyst Eng 174:107–114
Foito D, Guerreiro M (2008) Anti-slip wheel controller drive for EV using speed and torque observers. Proceedings of the 2008 International Conference on Electrical Machines 978–985
Furuichi H, Huang J, Fukuda T, Matsuno T (2014) Switching dynamic modeling and driving stability analysis of three-wheeled narrow tilting vehicle. IEEE/ASME T Mech 19:107–114
Kise M, Zhang Q (2006) Sensor-in-the-loop tractor stability control: look-ahead attitude prediction and field tests. Comput Electron Agric 52:107–118
Kuang M, Qi ZM (2017) A human-centered design of general-purpose unmanned electric vehicle chassis for agriculture task payload. J Comput Inf Sci Eng 17:031004
Laura RR (1999) Nonlinear state and tire force estimation for advanced vehicle control. IEEE T Contr Syst T:111–125
Lee H (2006) Reliability indexed sensor fusion and its application to vehicle velocity estimation. T ASME:236–244
Moreda GP, Muñoz-García MA, Barreiro P (2016) High voltage electrification of tractor and agricultural machinery – A review. Energ Convers Manage 115:117–131
Mutoh N, Hayano Y (2007) Electric braking control methods for electric vehicles with independently driven front and rear wheels. IEEE T Ind Electron:1166–1168
O’brien R, Kiriakidis K (2006) A comparison of H∞ with Kalman filtering in vehicle state and parameter identification. American Control Conference, Minneapolis:3954–3959
Pierzchała M, Giguère P, Astrup R (2018) Mapping forests using an unmanned ground vehicle with 3D LIDAR and graph-SLAM. Comput Electron Agric 145:217–225
Reid JF, Zhang Q, Noguchi N, Dickson M (2000) Agricultural automatic guidance research in North America. Comput Electron Agric 25:155–167
Shi G, Li XS, Jiang ZF (2018) An improved yaw estimation algorithm for land vehicles using MARG sensors. Sensors 18:3251
Tian G, Ren Y, Feng Y, Zhou M, Zhang H, Tan J (2019) Modeling and planning for dual-objective selective disassembly using AND/OR graph and discrete artificial bee colony. IEEE T Ind Inform 15:2456–2468
Unger I, Isermann R (2006) Fault tolerant sensors for vehicle dynamics control. Proceedings of the American Control Conference. Minneapolis, Minnesota, USA, June 3948–3953
Wenzel T, Burnham K, Blundell M (2006) Dual extended Kalman filter for vehicle state and parameter estimation. Veh Syst Dyn 44:153–171
Yim S, Choi J, Yi K (2012) Coordinated control of hybrid 4WD vehicles for enhanced maneuverability and lateral stability. IEEE T Veh Technol 61:1946–1950
Zhang N, Wang M, Wang N (2002) Precision agriculture-a worldwide overview. Comput Electron Agric 36:113–132
Zhu QY, Chen W, Hu H, Wu X, Xiao C, Song X (2019) Multi-sensor based attitude prediction for agricultural vehicles. Comput Electron Agric 156:24–32
Funding
This work was supported by the national key research and development plan (No. 2016YFD0701003).
Author information
Authors and Affiliations
Contributions
Xuesheng Zhou built the electric agricultural vehicle platform, performed the experiments, and wrote the paper. Jun Zhou designed the experiment and analyzed the data.
Corresponding author
Ethics declarations
Ethical approval
There are no studies about human participants or animals in the article performed by any of the authors.
Consent to participate
All authors were participated in the work.
Consent for publication
All authors agree to publish.
Conflict of interest
The authors declare no competing interests.
Additional information
Responsible Editor: Philippe Garrigues
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
ESM 1
(DOC 1344 kb)
Rights and permissions
About this article
Cite this article
Zhou, X., Zhou, J. Optimization of autonomous driving state control of low energy consumption pure electric agricultural vehicles based on environmental friendliness. Environ Sci Pollut Res 28, 48767–48784 (2021). https://doi.org/10.1007/s11356-021-14125-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-021-14125-9