scholarly journals A Study on a New Independent Metering Valve for Hydraulic Boom Excavator

2022 ◽  
Vol 12 (2) ◽  
pp. 605
Author(s):  
Thanh-Ha Nguyen ◽  
Tri-Cuong Do ◽  
Kyoung-Kwan Ahn
Keyword(s):  
Construction Industry ◽  
Fuel Economy ◽  
Control Strategy ◽  
Fossil Fuel ◽  
Test Bench ◽  
Position Signal ◽  
Hydraulic Excavators ◽  
Pollution Emissions ◽  
Spool Valve ◽  
Real Test

Nowadays, hydraulic excavators are an indispensable part of the construction industry; however, conventional hydraulic excavators consume a great deal of fossil fuel and release a large amount of pollution emissions into the environment. This causes many unwanted costs, therefore, effective solutions are required to solve the above-mentioned problems. In this paper, a new independent metering system is proposed to improve energy-saving and reduce costs of a conventional system. In detail, a directional valve is used to control movement and three electro-hydraulic poppet valves are integrated to adjust the flow rate at the inlet and outlet ports of the boom cylinder. In addition, a control strategy based on the coordination between the speed of the pump and the opening area of the spool valve is designed to improve the performance of the system. Specifically, the valves are controlled based on the strategy that the meter-in valve is opened fully to reduce throttling losses and that the meter-out valve is controlled to reduce leakage. The speed of the pump is adjusted according to the feedback position signal. To demonstrate the effectiveness of the new configuration, a real test bench of the boom system was built under laboratory conditions. From the experimental results, the new independent metering valve system not only works with a high tracking precision, but it also reduces energy consumption. Compared with a conventional independent metering system, the fuel economy of the proposed structure can achieve a reduction of approximately 6.5%.

Optimal adaptation equivalent factor of energy management strategy for plug-in CVT HEV

2018 ◽  
Vol 233 (4) ◽  
pp. 877-889 ◽  
Author(s):  
Xinyou Lin ◽  
Qigao Feng ◽  
Liping Mo ◽  
Hailin Li
Keyword(s):  
Energy Management ◽  
Fuel Economy ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Adaptation Strategy ◽  
Management Control ◽  
Energy Management Strategy ◽  
Driving Mode ◽  
Driving Cycles ◽  
Equivalent Factor

This study presents an adaptive energy management control strategy developed by optimally adjusting the equivalent factor (EF) in real-time based on driving pattern recognition (DPR), to guarantee the plug-in hybrid electric vehicle (PHEV) can adapt to various driving cycles and different expected trip distances and to further improve the fuel economy performance. First, the optimization model for the EF with the battery state of charge (SOC) and trip distance were developed based on the equivalent consumption minimization strategy (ECMS). Furthermore, a methodology of extracting the globally optimal EF model from genetic algorithm (GA) solution is proposed for the design of the EF adaptation strategy. The EF as the function of trip distances and SOC in various driving cycles is expressed in the form of map that can be applied directly in the corresponding driving cycle. Finally, the algorithm of DPR based on learning vector quantization (LVQ) is established to identify the driving mode and update the optimal EF. Simulation and hardware-in-loop experiments are conducted on synthesis driving cycles to validate the proposed strategy. The results indicate that the optimal adaption EF control strategy will be able to adapt to different expected trip distances and improve the fuel economy performance by up to 13.8% compared to the ECMS with constant EF.

scholarly journals Design and Optimization of the Power Management Strategy of an Electric Drive Tracked Vehicle

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Qunzhang Tu ◽  
Xiaochen Zhang ◽  
Ming Pan ◽  
Chengming Jiang ◽  
Jinhong Xue
Keyword(s):  
Power Management ◽  
Electric Drive ◽  
Fuel Economy ◽  
Control Strategy ◽  
Fuzzy Controller ◽  
Management Control ◽  
Drive System ◽  
Tracked Vehicles ◽  
Hybrid Particle Swarm Optimization ◽  
Electric Drive System

This article studies the power management control strategy of electric drive system and, in particular, improves the fuel economy for electric drive tracked vehicles. Combined with theoretical analysis and experimental data, real-time control oriented models of electric drive system are established. Taking into account the workloads of engine and the SOC (state of charge) of battery, a fuzzy logic based power management control strategy is proposed. In order to achieve a further improvement in fuel economic, a DEHPSO algorithm (differential evolution based hybrid particle swarm optimization) is adopted to optimize the membership functions of fuzzy controller. Finally, to verify the validity of control strategy, a HILS (hardware-in-the-loop simulation) platform is built based on dSPACE and related experiments are carried out. The results indicate that the proposed strategy obtained good effects on power management, which achieves high working efficiency and power output capacity. Optimized by DEHPSO algorithm, fuel consumption of the system is decreased by 4.88% and the fuel economy is obviously improved, which will offer an effective way to improve integrated performance of electric drive tracked vehicles.

Depleting Mode Control Strategies for Plug-in Hybrid Electric Vehicles

2011 ◽  
Vol 130-134 ◽  
pp. 2211-2215
Author(s):  
Bing Zhan Zhang ◽  
Han Zhao ◽  
An Dong Yin
Keyword(s):  
Electric Vehicles ◽  
Fuel Economy ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
Control Strategies ◽  
Great Influence ◽  
Torque Control ◽  
Drive Cycle ◽  
Vehicle Simulation ◽  
Hybrid Electric

Control strategy is the most important issue in the Plug-in Hybrid electric vehicles (PHEV) design, which has two modes: charge depleting mode (CD) and charge sustaining mode (CS). The different control strategies in depleting mode will have a great influence on PHEV dynamic performance and fuel economy. The engine optimal torque control strategy was proposed in the paper. The vehicle simulation model in Powertrain Systems Analysis Toolkit (PSAT) was adopted to evaluate the proposed control strategy. The aggressive highway drive cycle Artemis_hwy and a random drive cycle generated by Markov Process were used. The simulation results indicate the proposed control strategy has great improvement in fuel economy.

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