Aspects of Parameter Sensitivity for Series Hydraulic Hybrid Light-Weight Duty Vehicle Design

2016 ◽  
Author(s):  
Katharina Baer ◽  
Liselott Ericson ◽  
Petter Krus
Keyword(s):  
Design Parameters ◽  
Drive Cycle ◽  
Hydraulic Hybrid ◽  
Component Sizing ◽  
Alternative Approach ◽  
Simulation Based ◽  
Simulation Based Optimization ◽  
Hydraulic Hybrid Vehicle ◽  
Definition Of ◽  
And Control

Hybridization of a vehicle’s drivetrain can in principle help to improve its energy efficiency by allowing for recuperation of kinetic energy and modulating the engine’s load. How well this can be realized depends on appropriate sizing and control of the additional components. The system is typically designed sequentially, with the hardware setup preceding the development and tuning of advanced controller architectures. Taking an alternative approach, component sizing and controller tuning can be addressed simultaneously through simulation-based optimization. The results of such optimizations, especially with standard algorithms with continuous design variable ranges, can however be difficult to realize, considering for example limitations in available components. Furthermore, drive-cycle based optimizations are prone to cycle-beating. This paper examines the results of such simulation-based optimization for a series hydraulic hybrid vehicle in terms of sensitivity to variations in design parameters, system parameters and drive cycle variations. Additional relevant aspects concerning the definition of the optimization problem are pointed out.

Simulationsbasierte Optimierung von Fabrikabläufen*/Simulation-based optimization of factory processes

2018 ◽  
Vol 108 (04) ◽  
pp. 221-227
Author(s):  
T. Donhauser ◽  
L. Baier ◽  
T. Ebersbach ◽  
J. Franke ◽  
P. Schuderer
Keyword(s):  
Calcium Silicate ◽  
Optimization Approach ◽  
Process Technology ◽  
High Quality ◽  
Decomposition Approach ◽  
Planning And Control ◽  
Simulation Based ◽  
Simulation Based Optimization ◽  
And Control

Die Kalksandsteinherstellung weist aufgrund prozesstechnisch und zeitlich divergierender Teilprozesse einen hohen Planungs- sowie Steuerungsaufwand auf. Durch Einsatz eines simulationsgestützten Optimierungsverfahrens kann diese Komplexität bewältigt werden. Um bei hoher Lösungsqualität eine Laufzeit zu erreichen, die einen operativen Einsatz des Verfahrens gestattet, wird auf Basis einer vorangegangenen Studie ein Dekompositionsansatz implementiert und dessen Eignung durch Testläufe validiert.   Calcium silicate masonry production requires a great deal of planning and control due to the fact that subprocesses vary in terms of process technology and time. To overcome this complexity, a simulation-based optimization approach is applied. As a short runtime that allows the method to be used operationally and yet still offers a high quality of solution is crucial, a decomposition approach is implemented on the basis of a previous study and its suitability is validated by means of test runs.

Design Optimisation Strategies for a Hydraulic Hybrid Wheel Loader

2018 ◽  
Author(s):  
Karl Uebel ◽  
Henrique Raduenz ◽  
Petter Krus ◽  
Victor Juliano de Negri
Keyword(s):  
Energy Management ◽  
Design Parameters ◽  
Linear Component ◽  
Concept Design ◽  
Energy Management Strategy ◽  
Design Optimisation ◽  
Hydraulic Hybrid ◽  
Deterministic Dynamic ◽  
Control Optimisation ◽  
And Control

This paper deals with design optimisation of hydraulic hybrid drivelines during early concept design phases. To set the design parameters of a hybrid driveline such as gear ratios, pump/motor displacements and size of energy storage, the energy management of the hybrid machine needs to be considered as well. This is problematic since a nested design and control optimisation normally requires substantial computer power and is time-consuming. Few previous studies have treated combined design and control optimisation of hydraulic hybrid vehicles using detailed, non-linear component driveline models. Furthermore, previously proposed design optimisation methods for on-road vehicles are not suitable for heavy off-road machines operating in short repetitive cycles with high transient power output. The paper demonstrates and compares different optimisation approaches for design and control optimisation combining deterministic dynamic programming and non-gradient based numerical optimisation. The results show that a simple rule-based energy management strategy can be sufficient to find the optimal hardware design even though non-optimal control laws are used.

System Parameter Study for a Light-Weight Series Hydraulic Hybrid Vehicle

2014 ◽  
Author(s):  
Katharina Baer ◽  
Liselott Ericson ◽  
Petter Krus
Keyword(s):  
Fuel Efficiency ◽  
Hybrid Vehicle ◽  
Urban Traffic ◽  
Parameter Study ◽  
Hydraulic Hybrid ◽  
Component Sizing ◽  
Vehicle Propulsion ◽  
System Pressure ◽  
Light Duty Vehicle ◽  
Hydraulic Hybrid Vehicle

Amongst the hybrid vehicle propulsion solutions aiming to improve fuel efficiency, hybrid electric solutions currently receive most attention, especially on the market. However, hydraulic hybrids are an interesting alternative, especially for heavier vehicles due to higher power density which is beneficial if higher masses are moved. As a step towards a comprehensive design framework to compare several possible hydraulic hybrid architectures for a specified application and usage profile, the model of a series hydraulic hybrid vehicle was previously introduced and initially studied concerning component sizing for an exemplary light-duty vehicle in urban traffic. The vehicle is modeled in the Hopsan simulation tool. A comparably straight-forward engine management is used for the vehicle control; both pump and engine controls are based on the hydraulic accumulator’s state-of-charge. The model is developed further with respect to the accumulator component model. Based on that, the influence of several system and component parameters, such as maximum system pressure and engine characteristics, as well as controller parameters on the vehicle’s performance is analyzed. The goal is to allow for more understanding of the system’s characteristics to facilitate future optimization of the system.

scholarly journals Minimizing the Discrepancy between Simulated and Historical Failures in Turbine Engines: A Simulation-Based Optimization Method

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Ahmed Kibria ◽  
Krystel K. Castillo-Villar ◽  
Harry Millwater
Keyword(s):  
Optimization Method ◽  
Statistical Distributions ◽  
Confidence Bounds ◽  
Reliability Modeling ◽  
Turbine Engine ◽  
Correct Definition ◽  
Simulation Based ◽  
Simulation Based Optimization ◽  
Distribution Parameters ◽  
Definition Of

The reliability modeling of a module in a turbine engine requires knowledge of its failure rate, which can be estimated by identifying statistical distributions describing the percentage of failure per component within the turbine module. The correct definition of the failure statistical behavior per component is highly dependent on the engineer skills and may present significant discrepancies with respect to the historical data. There is no formal methodology to approach this problem and a large number of labor hours are spent trying to reduce the discrepancy by manually adjusting the distribution’s parameters. This paper addresses this problem and provides a simulation-based optimization method for the minimization of the discrepancy between the simulated and the historical percentage of failures for turbine engine components. The proposed methodology optimizes the parameter values of the component’s failure statistical distributions within the component’s likelihood confidence bounds. A complete testing of the proposed method is performed on a turbine engine case study. The method can be considered as a decision-making tool for maintenance, repair, and overhaul companies and will potentially reduce the cost of labor associated to finding the appropriate value of the distribution parameters for each component/failure mode in the model and increase the accuracy in the prediction of the mean time to failures (MTTF).

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