Design of shifting strategy for capacity adjustment device of power reflux hydraulic transmission systems based on working condition identification

Abstract

The performance of a wheel loader (WL) can be improved with the application of a power reflux hydraulic transmission system (PRHTS) with a capacity adjustment device (CA-device). However, due to CA-device’s unique working principle, the traditional shifting strategy is not suitable for CA-device. Apply traditional shifting strategy to CA-device will result in frequent or incorrect shifting. In order to solve this problem, this paper proposes applying the working condition identification to the CA-device shifting strategy. Combining the working principle of PRHTS with CA-device and the analysis of WL V-condition, the requirement for the working condition identification algorithm of the CA-device shifting strategy is obtained, and a working condition identification algorithm for WL V-condition is designed. Simulation results show that applying the working condition identification algorithm to the CA-device shifting strategy can avoid frequent shifts and incorrect shifts. A hardware-in-the-loop platform is built to verify the real-time performance of the shifting strategy. Results show that the proposed shifting strategy exhibits a satisfactory level of real-time performance.

Keywords

Wheel loader power reflux hydraulic transmission system shifting strategy working condition identification support vector machine

Get full access to this article

View all access options for this article.

References

Nezhadali

Eriksson

and IEEE. Optimal control of wheel loader operation in the short loading cycle using two braking alternatives. In: 2013 9th IEEE vehicle power and propulsion conference, Beijing, 15–18 October 2013, pp.447–452. New York: IEEE.

Frank

Kleinert

Filla

. Optimal control of wheel loader actuators in gravel applications. Autom Constr 2018; 91: 1–14.

Zeng

Yang

Peng

, et al. Research on energy saving control strategy of parallel hybrid loader. Autom Constr 2014; 38: 100–108.

You

Sun

Qin

. Research on vehicle starting control based on reflux power condition. Mech Mach Theory 2019; 134: 289–307.

Kim

, et al. Integrated wheel loader simulation model for improving performance and energy flow. Autom Constr 2015; 58: 129–143.

Nezhadali

Frank

Eriksson

. Wheel loader operation-optimal control compared to real drive experience. Contr Eng Pract 2016; 48: 1–9.

Wang

Sun

. Optimal matching between a diesel engine and a PRHTS transmission. J Braz Soc Mech Sci Eng 2017; 39: 3375–3387.

Cai

, et al. Variable-capacity matching of continuously variable-capacity double-impeller torque converter applied to a loader. Adv Mech Eng 2017; 9: 1687814017690694.

Kan

Sun

Luo

, et al. Optimal design of power matching for wheel loader based on power reflux hydraulic transmission system. Mech Mach Theory 2019; 137: 67–82.

10.

Wang

Sun

Qin

. A new continuously variable transmission system applied to transmission system of the roadheader’s cutting unit. Proc IMechE, Part C: J Mechanical Engineering Science 2017; 231: 3590–3600.

11.

Krogerus

Hyvonen

Huhtala

. Recognition of operating states of a wheel loader for diagnostics purposes. SAE Int J Commer Veh 2013; 6: 412–418.

12.

Wang

Liu

, et al. Active control of hydropneumatic suspension parameters of wheel loaders based on road condition identification. Int J Adv Robot Syst 2018; 15.

13.

Huang

Tan

. An intelligent multifeature statistical approach for the discrimination of driving conditions of a hybrid electric vehicle. IEEE Trans Intell Transp Syst 2011; 12: 453–465.

14.

Zhang

Dong

, et al. Embedded feature-selection support vector machine for driving pattern recognition. J Franklin Inst 2015; 352: 669–685.

15.

Zhao

Hofman

, et al. Intelligent synthesis of driving cycle for advanced design and control of powertrains. In: 2018 IEEE intelligent vehicles symposium (IV), Changshu, 26–30 June 2018, pp.1608–1613. New York: IEEE.

16.

Zhao

Hofman

. Driving-cycle-aware energy management of hybrid electric vehicles using a three-dimensional Markov chain model. Automot Innov 2019; 2: 146–156.

17.

Chang

Zhao

Lyu

. Loader power-split transmission system based on a planetary gear set. Adv Mech Eng 2018; 10: 1–8.

18.

Cavina

Olivi

Corti

, et al. Development and implementation of hardware in the loop simulation for dual clutch transmission control units. SAE Int J Passeng Cars-Electron Electr Syst 2013; 6: 458–466.

19.

Ngo

Hofman

Steinbuch

, et al. Gear shift map design methodology for automotive transmissions. Proc IMechE, Part D: J Automobile Engineering 2014; 228: 50–72.

20.

Sun

, et al. Automatic gear-shifting strategy for fuel saving by tractors based on real-time identification of draught force characteristics. Biosyst Eng 2020; 193: 46–61.

21.

Yun

, et al. An investigation of energy efficiency of a wheel loader with automated manual transmission. J Mech Sci Technol 2016; 30: 2933–2940.

22.

Zhang

Liu

, et al. Prediction method of the fuel consumption of wheel loaders in the V-type loading cycle. Math Probl Eng 2015; 2015: 1–12.

23.

Zhang

. Research on the prediction method of power performance and fuel economy of loader based on V-type loading cycle. Master Thesis, Jinlin University, China, 2016.

24.

Feng

Sun

Wang

, et al. Sound feature space effects on the performance of annoyance evaluation model based on support vector machine. Appl Acoust 2019; 154: 99–113.

25.

Wang

Zhu

. Automatic detection of individual oil palm trees from UAV images using HOG features and an SVM classifier. Int J Remote Sens 2019; 40: 7356–7370.

26.

Zeng

Espino

Roy

, et al. Using natural language processing and machine learning to identify breast cancer local recurrence. BMC Bioinformatics 2018; 19; 65–74.

27.

Pan

Fan

Chen

, et al. Development of HL-2M data integration and acquisition system for magnetic diagnostics. Plasma Sci Technol 2019; 21: 105602.

28.

Tyagi

Yadav

Patel

, et al. Development of real-time controller-based data acquisition system for Indian test facility of ITER DNB. IEEE Trans Plasma Sci 2019; 47: 2775–2782.