Optimal Control and Energy-Saving Analysis of Common Pressure Rail Architectures: HHEA and STEAM

Author(s):  
Jacob Siefert ◽  
Perry Y. Li

Abstract In recent years several novel hydraulic architectures have been proposed with the intention of significantly increasing system efficiency. Two of these architectures, Steigerung der Energieefflzienz in der Arbeitshydraulik mobiler Arbeitsmaschinen (STEAM), and the Hybrid Hydraulic-Electric Architecture (HHEA), use a system of multiple common pressure rails (CPRs) to serve the multiple degrees-of-freedom of the machine. The key difference is that STEAM throttles hydraulic power from these rails while HHEA combines electric and hydraulic power to meet actuator demands. As a throttle-less architecture, HHEA is expected to save more energy than STEAM at the expense of added complexity. Therefore, it is useful to quantify this additional energy saving. Both systems have discrete operational choices corresponding to how the CPRs are utilized for each actuator. It is necessary to determine optimal operation for each of these architectures for analysis and fair comparison. Techniques for optimal operation of the HHEA have been developed previously from the Langrange multiplier method. Applying the same optimal control method to STEAM encountered some technical challenge leading to the optimal control algorithm not being able to satisfy certain constraints. The issue is analyzed and solved by adding noise to the optimization. Using this proposed algorithm, case studies are performed to compare the energy-saving potentials of STEAM and HHEA for two sizes of excavators and a wheel-loader performing representative duty cycles. The baseline is a standard load-sensing architecture. Results show that STEAM and HHEA can reduce energy consumption between 35–65% and 50–80% respectively.

Author(s):  
Nicola Dal Bianco ◽  
Roberto Lot ◽  
Marco Gadola

In this work, optimal control theory is applied to minimum lap time simulation of a GP2 car, using a multibody car model with enhanced load transfer dynamics. The mathematical multibody model is formulated with use of the symbolic algebra software MBSymba and it comprises 14 degrees of freedom, including full chassis motion, suspension travels and wheel spins. The kinematics of the suspension is exhaustively analysed and the impact of tyre longitudinal and lateral forces in determining vehicle trim is demonstrated. An indirect optimal control method is then used to solve the minimum lap time problem. Simulation outcomes are compared with experimental data acquired during a qualifying lap at Montmeló circuit (Barcelona) in the 2012 GP2 season. Results demonstrate the reliability of the model, suggesting it can be used to optimise car settings (such as gearing and aerodynamic setup) before executing track tests.


2011 ◽  
Vol 135-136 ◽  
pp. 10-14
Author(s):  
Fu Lai Yao ◽  
He Xu Sun

This paper presents a class of optimization functions, and gives the optimal value. The optimal conclusion is applied to energy optimization for general devices. When the total load is fixed and the devices are used with same model, the optimal control method is given: adjusting each device to the same load, the minimum energy is required.


Author(s):  
Xing Xu ◽  
Minglei Li ◽  
Feng Wang ◽  
Ju Xie ◽  
Xiaohan Wu ◽  
...  

A human-like trajectory could give a safe and comfortable feeling for the occupants in an autonomous vehicle especially in corners. The research of this paper focuses on planning a human-like trajectory along a section road on a test track using optimal control method that could reflect natural driving behaviour considering the sense of natural and comfortable for the passengers, which could improve the acceptability of driverless vehicles in the future. A mass point vehicle dynamic model is modelled in the curvilinear coordinate system, then an optimal trajectory is generated by using an optimal control method. The optimal control problem is formulated and then solved by using the Matlab tool GPOPS-II. Trials are carried out on a test track, and the tested data are collected and processed, then the trajectory data in different corners are obtained. Different TLCs calculations are derived and applied to different track sections. After that, the human driver’s trajectories and the optimal line are compared to see the correlation using TLC methods. The results show that the optimal trajectory shows a similar trend with human’s trajectories to some extent when driving through a corner although it is not so perfectly aligned with the tested trajectories, which could conform with people’s driving intuition and improve the occupants’ comfort when driving in a corner. This could improve the acceptability of AVs in the automotive market in the future. The driver tends to move to the outside of the lane gradually after passing the apex when driving in corners on the road with hard-lines on both sides.


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