The wheeled loader typically operates in complex and harsh environments, where its working equipment, such as the bucket and boom, divert a significant amount of engine power during operation. Therefore, accurately analyzing the mapping relationship between the power demand of the working equipment and the vehicle speed is critical to optimizing gear ratios for effective power matching. To address this issue, this paper conducts a kinematic analysis of the working equipment of the wheeled loader to obtain the power demand during the loading process. Mathematical and statistical methods are used to extract representative data from V-type operating conditions. This data is used to show how the power demand of working equipment relates to the vehicle speed. Using this as a foundation, a multi-objective optimization model for gear ratios is established, and the effectiveness of the gear ratios optimization is verified using three different V-type operating conditions. Compared to the original gear ratios, the optimized gear ratios reduce fuel consumption by 8.13, 18.56, and 8.25 mL while only decreasing the average traction force by 0.42, 1.16, and 0.39 kN, respectively. Improved the performance of the loader.
WangFQiaoYZhouH, et al. An independent electro-hydraulic variable speed drive to improve energy efficiency of electric wheel loader working system. IEEE Trans Veh Technol2024; 73(12): 18250–18265.
2.
YaoJEdsonCPYuSC, et al. Bucket loading trajectory optimization for the automated wheel loader. IEEE Trans Veh Technol2023; 72(6): 6948–6958.
3.
CaoBWLiuXHChenW, et al. Intelligentization of wheel loader shoveling system based on multi-source data acquisition. Autom Constr2023; 147: 15.
4.
ZhangGXiaoC.Dynamic simulation analysis of the working device of a ZL50 loader. Fluid Dyn Mater Proc2020; 16(4): 699–707.
5.
ShiJRSunDYQinDT, et al. Planning the trajectory of an autonomous wheel loader and tracking its trajectory via adaptive model predictive control. Robot Auton Syst2020; 131: 15.
6.
YuanZWLuYNHongT, et al. Research on the load equivalent model of wheel loader based on pseudo-damage theory. Proc IMechE, Part C: J Mechanical Engineering Science2022; 236(2): 1036–1048.
7.
Masih-TehraniMEbrahimi-NejadS.Hybrid genetic algorithm and linear programming for bulldozer emissions and fuel-consumption management using continuously variable transmission. J Constr Eng Manage2018; 144(7): 9.
8.
JensenKJEbbesenMKHansenMR.Anti-swing control of a hydraulic loader crane with a hanging load. Mechatronics2021; 77: 13.
9.
KanYXieSYinQ, et al. Dynamic power matching for wheel loader based on power reflux hydrodynamic transmission system. Proc IMechE, Part D: J Automobile Engineering2024. DOI: 10.1177/09544070241261107.
10.
YuJShenPFWangZ, et al. Optimization for speed regulation strategy of compound coupled hydro-mechanical transmission. Proc IMechE, Part C: J Mechanical Engineering Science2021; 235(7): 1966–1977.
11.
YouYSunDYQinDT, et al. A new continuously variable transmission system parameters matching and optimization based on wheel loader. Mech Machine Theory2020; 150: 18.
12.
QiHADingLZhengM, et al. Variable wheelbase control of wheeled mobile robots with worm-inspired creeping gait strategy. IEEE Trans Robot2024; 40: 3271–3289.
13.
KanYZSunDYLuoY, et al. Optimal design of the gear ratio of a power reflux hydraulic transmission system based on data mining. Mech Machine Theory2019; 142: 14.
14.
YouYWuJTMengYL, et al. Adaptive shift strategy of a novel power-cycling variable transmission for construction vehicles. Robot Auton Syst2024; 171: 17.
15.
QuHBGuoSZhangY.A novel relative degree-of-freedom criterion for a class of parallel manipulators with kinematic redundancy and its applications. Proc IMechE, Part C: J Mechanical Engineering Science2017; 231(22): 4227–4240.
16.
RongYZhangXCDouTC, et al. Type synthesis of non-overconstrained and overconstrained two rotation and three translation (2R3T) parallel mechanisms with three branched chains. Mech Sci2023; 14(2): 567–577.
17.
XuJHGaoMYFengXQ, et al. Dexterity distribution design for attitude adjustment of multi-joint robotics based on singularity-free workspace decomposition. Mech Based Des Struct Mech2024; 52(3): 1764–1784.
18.
LiXYuHYFengHB, et al. Design and control for WLR-3P: a hydraulic wheel-legged robot. Cyborg Bionic Syst2023; 4: 17.
19.
PeidróAGilAMarínJM, et al. On the stability of the quadruple solutions of the forward kinematic problem in analytic parallel robots. J Intell Robot Syst2017; 86(3–4): 381–396.
KumarRDwarakanathTABhutaniG, et al. Novel and robust forward kinematic algorithm for real-time control of general six-degree-of-freedom parallel robot for tele-manipulation and tele-navigation. J Mech Robot2024; 16(10): 11.
22.
AhmedAJuHHYangY, et al. An improved unit quaternion for attitude alignment and inverse kinematic solution of the robot arm wrist. Machines2023; 11(7): 28.
23.
LiaoZWJiangGDZhaoF, et al. A novel solution of inverse kinematic for 6R robot manipulator with offset joint based on screw theory. Int J Adv Robot Syst2020; 17(3): 12.
24.
HuangFSangHLiuF, et al. Dimensional optimisation and an inverse kinematic solution method of a safety-enhanced remote centre of motion manipulator. Int J Med Robot Comput Assist Surg2024; 20(1): e2579.
25.
MaBYXieZWJiangZN, et al. Precise semi-analytical inverse kinematic solution for 7-DOF offset manipulator with arm angle optimization. Front Mech Eng2021; 16(3): 435–450.
26.
LeeSHLeeYJKimDH.Inverse kinematic solution extention for a robot to cope with joint angle constraints. Int J Control Autom Syst2023; 21(6): 1899–1909.
27.
TongYCLiuJGLiuYW, et al. Analytical inverse kinematic computation for 7-DOF redundant sliding manipulators. Mech Machine Theory2021; 155: 23.
28.
KanagarajGMasthanSYuVF.Inverse kinematic solution of obstacle avoidance redundant robot manipulator by bat algorithms. Int J Robot Autom2021; 36(1): 18–26.
29.
AhmedAYuMChenFF.Inverse kinematic solution of 6-DOF robot-arm based on dual quaternions and axis invariant methods. Arab J Sci Eng2022; 47(12): 15915–15930.
30.
ShiJRSunDYHuMH, et al. Prediction of brake pedal aperture for automatic wheel loader based on deep learning. Autom Constr2020; 119: 12.
31.
KumarMEkevidTLöweW.Operator model for wheel loader short-cycle loading handling. Autom Constr2024; 167: 19.
32.
WangYLiuXHRenZK, et al. Synchronized path planning and tracking for front and rear axles in articulated wheel loaders. Autom Constr2024; 165: 15.
33.
DengTGanZHXuH, et al. Configuration design and screening of multi-mode double-planetary-gears hybrid powertrains. J Mech Des2022; 144(7): 13.
34.
YangPLiXMengJ, et al. Incident angle optimization with multiple conflicting objectives based on BP-MOPSO in oblique laser interferometry. Opt Commun2024; 563: 130564.
35.
MollajafariMEbrahimi-NejadS.Recent advancements in automotive engineering by using evolutionary algorithms and nature-inspired heuristic optimization, 2025.
36.
ShiJHZhaoBHeJY, et al. The optimization design for the journal-thrust couple bearing surface texture based on particle swarm algorithm. Tribol Int2024; 198: 17.
37.
GuJDZhaoZGChenY, et al. Parameter optimization design of two-planetary-gear power-split hybrid system configuration using the RAD-MOPSO algorithm. Proc IMechE, Part D: J Automobile Engineering2020; 234(14): 3256–3271.
