Toward the Development of a Pump Energy Rating System based upon Performance Indexes. Jose Garcia Purdue University

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1 Toward the Development of a Pump Energy Rating System based upon Performance Indexes Jose Garcia Purdue University Jack Johnson IDAS Electrohydraulics Paul Michael Milwaukee School of Engineering Abstract This paper presents a methodology for rating the energy consumption of hydraulic pumps that uses application-based performance indexes derived from machine operation under pre-defined duty cycles. Two gear pumps of different displacements were evaluated in laboratory testing using the ISO standard method. Physical models for pump input power, output power and lost power were derived from the experimental data. Field data was collected from the hydraulic system of a backhoe during trenching and loading operations. This data was used to create histograms of system pressure, temperature and speed. The resulting histograms and pump models were used to create performance indices that quantify power use. Conditions where a reduction in pump efficiency enhanced energy use were documented. These results illustrate the benefit of comparing the input and output power of a component in a specific duty cycle. Introduction ISO 4409:2007 describes the internationally accepted procedure for measuring the efficiency of power generation by hydraulic pumps under steady-state conditions. This method has proven to be well-suited for mapping the power output of pumps under various conditions of pressure, speed and displacement. The current method however, does not provide a measure of energy consumption; which is required to assess the cost of operation. In order to measure energy consumption it is necessary to integrate pump power over time. The US EPA Urban Drive Duty Cycle is used to produce comparison data for vehicle drivetrain fuel consumption and performance. A number of research articles have reported using this standard drive cycle for comparison of various drivetrain systems with respect to a baseline, typically a gasoline engine or diesel engine powered drivetrain. The article by Wallner et. al. summarizes the steady state results of a hydrogen engine powered vehicle on an engine dynamometer. [1] Similar techniques have been used for demonstrating the advantages of hybridization of power trains for on road vehicles using electric hybrid drivetrains. [2-4] Likewise, duty cycle simulations have been used for drive and job performance comparisons of off-road vehicles [5-7] and for drive cycles of combat vehicles. [8] No such method exists for comparing fluid power systems. This paper presents a methodology for mapping ISO 4409 pump performance data to machine duty cycles through the use of time-weighted histograms to produce pump performance indexes.

2 Histogram and Model Development There are at least four different modeling methods or concepts for prediction of pump performance contained in the technical literature: Physical Numerical Analytical Lookup table Physical models are based on a knowledge of physical processes within a machine. Numerical models are based on an exhaustive search for polynomial terms that fit the data without being limited to physical explanations. Analytical models are based on combinations of the physical and numerical techniques. Lookup tables make no attempt at forming macroscopic polynomial functions, but rather interpolate linearly among the closest surrounding cluster of test data points. All four of these methodologies can produce models that are suitable for use in histogrammic analysis. A physical model of gear pumps was employed in this particular instance. The basis for indexing was summarized by Johnson in a hydrostatic transmission application. [9] The first part of the proposed method requires the identification of a suitable duty cycle that characterizes the operating conditions of a specific machine or system performing a particular function. Such conditions of a system are to be obtained by instrumenting the application machine and collecting operational data while the equipment is performing the duty cycle. The resulting data (pressure, speed, temperature, displacement, etc.) is statistically processed to generate a histogram for each parameter. This histogram tabulates the frequency at which the particular physical variable value occurs for the cycle of operation. A histogram of the pressure profile of a backhoe loader during trenching and loading cycle is shown in Figure 1. This histogram describes the relative frequency of occurrence of a pressure, as a function of the measured pressure value. Figure 1: Histogram representation of the pump pressure for a backhoe duty cycle The second step in the process for determining the performance index or energy rating factor is to create a comprehensive mathematical model for the component or system. In the particular case of a pump, the model is based upon performance data acquired via ISO 4409:2007. The resulting data for each of the physical measurements is derived using a polynomial function with best-fit coefficients that when combined with other fitted functions for the other parameters produces a best fit model of the pump. Such model can be used to predict torque and flow provided pressure, speed, displacement and viscosity are known. The third and final step of the process is to calculate the performance index by combining the results from the two steps described above. Eqn. 1 is used to determine the performance index. It contains in the right brackets, the polynomial functions of the pump model used to predict the specific physical value under operating conditions k,l,j,l; and weights its impact on the index based on the probability values obtained from the histogram. This histogram is a

3 Eqn.1: Index p( D ) p( ) p( P ) p( N ) W ( D,, P, N where M l j Mk M M l j Mi W k l j i X k l j i k 1 l1 j1 i1 k the number of bins in the displacement histogram M the number of bins in the viscosity histogram M the number of bins in the pressure histogram M the number of bins in the shaft speed histogram k i D the instantaneous displacement the instantaneous viscosity l P the instantaneous pump pressure j N the instantaneous pump speed i p the probability of a given state during the duty cycle representation of the duty cycle being assessed. A schematic diagram of the process is shown in figure 2. The major advantage of the method is that it evaluates component or system performance while operating in a specific application. Pump and Field Test Descriptions Gear pump efficiencies were evaluated in accordance with ISO4409:2007. Pump displacement and instrumentation details are listed in Table 1. Pump flow rates and input shaft torque were measured under steady state pressure, speed and temperature conditions. Pump inlet pressure, speed and hydraulic fluids differed. Outlet pressures were nominally the same. Table 1: Instrumentation and operating conditions for pump test Pump 1 2 Displacement, cc/rev Flow Webtec Turbine MaxMachine Gear Torque Lebow K HBM T40B Inlet Temp Omega Type K Omega Type J Inlet pressure WIKA C-10 GEMS Outlet pressure WIKA C-10 GP50 Temperature, ⁰C 50 and and 80 Speed, RPM Pressure, Mpa Inlet pressure, psig 1 5 Fluid HM46 SAE 10 Figure 2: Schematic diagram of the histogrammic procedure

4 Duty cycled data was collected using a US Army HMEE-III Backhoe Loader (BHL). [10] The BHL is a pneumatic tired, diesel engine driven front end loader / backhoe used for excavation of small emplacements, material handling and general construction tasks. A tandem gear pump provides hydraulic power for the steering, pilot controls, front end loader and backhoe functions. Hydraulically actuated cylinders lift, tilt and dump the front end loader bucket to perform earthmoving functions. Backhoe swing, boom and bucket cylinders are hydraulically powered for excavating operations. This machine operates at a nominal 3300 psi and 2300 rpm. An oil cooler maintains the hydraulic fluid temperature at 100 F to 120 F above ambient temperature. A fully instrumented backhoe loader was evaluated by the U.S. Army TARDEC in a field trial at Naval Base Ventura County in California, USA. Duty cycles were limited to trenching and truck loading with the backhoe. Trenching cycles consisted of cutting a 2 meter deep trench at bucket width with spoil deposited 1 meter to the side. Truck loading cycles consisted of cutting a 6' deep trench at bucket width with spoil loaded 90 degrees into a dump truck. Just over an hour of data was collected. Results and Discussion Pressure, speed and fluid temperature histograms were created from BHL data. Physical models were created based on ISO 4409:2007 performance data produced for the two gear pumps. The pump models defined pump input power, output power and lost power in terms of pump speed, outlet pressure and oil viscosity as shown in Eqn. 2 and 3. Coefficients were derived through regression of the experimental data. The R-sq values exceeded 99.97%. Pump losses were quantified based upon the difference between input and output power. Three scenarios are compared in the example shown in Table 2. Eqn. 2: Eqn. 3: where Q = flow output T = torque input N speed P pressure kinematic viscosity Q C N P T C P N 2 Table 2: Results produced by histogrammic method Scenario Displacement, cc/rev Oil viscosity, mm 2 Shaft input power, kw Pump output power, kw Overall Efficiency, % Lost power, kw In scenarios 1 and 2, two pumps of different displacement were compared using the histogrammic method under identical duty cycle conditions (pump speed and system pressure). The larger of the two pumps has higher operational power losses as one might expect. In scenario 3, the identical pressure histogram was applied to the larger pump but the speed was scaled-down to yield a power output nominally equal to that of the smaller pump. As can be seen by these results, reducing the speed decreased the shaft input power, lost power and overall efficiency. Input power determines the raw energy cost while losses limit the conversion of energy to work. In this example, the pump was less efficient in scenario 3 than in scenario 2, yet scenario 3 is preferable in terms of energy use. These results illustrate the benefit of

5 comparing the input and output power of a component in a specific duty cycle. Conclusion High energy costs, competing power transmission technologies, and environmental awareness have increased the need for efficient fluid power components and systems. Application-based performance indexing of ISO 4409 data to predefined machine duty cycles through a combination of mathematical models and histograms provides a reproducible and cost effective means of comparing energy consumption. References 1. Wallner, Thomas, Henning Lohse- Busch, and Neeraj Shidore. "Operating strategy for a hydrogen engine for improved drive-cycle efficiency and emissions behavior," International Journal of Hydrogen Energy 34, no. 10 (2009): Lohse-Busch, Henning, Jeremy Diez, and Jerry Gibbs. "The Measured Impact of Vehicle Mass on Road Load Forces and Energy Consumption for a BEV, HEV, and ICE Vehicle." SAE International Journal of Alternative Powertrains 2, no. 1 (2013): Vehicle and Systems Simulation and Testing 2012 Annual Progress Report, DOE/EE-0834, Section III.C. Extended Level 2 Benchmarking of Advanced Technology LD Vehicles Peugeot 3008 Hybrid4, p Campbell, Robert. "Battery Characterization and Optimization for use in Plug-in Hybrid Electric Vehicles: Hardwarein-the-loop duty cycle testing." (2011). 5. Hui, Sun, Yang Lifu, Jing Junqing, and Luo Yanling. "Control strategy of hydraulic/electric synergy system in heavy hybrid vehicles." Energy Conversion and Management 52, no. 1 (2011): Achten, Peter AJ. "A serial hydraulic hybrid drive train for off-road vehicles." In Proceedings of the National Conference on Fluid Power, vol. 51, (2008): Zimmerman, Joshua, Rohit Hippalgaonkar, and Monika Ivantysynova. "Optimal control for the series-parallel displacement controlled hydraulic hybrid excavator." In ASME 2011 dynamic systems and control conference and Bath/ASME symposium on fluid power and motion control, pp American Society of Mechanical Engineers (2011): Brudnak, Mark, Mike Pozolo, Victor Paul, Syed Mohammad, Wilford Smith, Marc Compere, Jarrett Goodell, Dale Holtz, Todd Mortsfield, and Andrey Shvartsman. Soldier/hardware-in-the-loop simulationbased combat vehicle duty cycle measurement: duty cycle experiment 2. No. TARDEC Tank Automotive Research Development and Engineering Center, Warren, MI, Johnson, Jack L, Linear Mathematical Models are used to Conduct Pre-Development Feasibility Studies Regarding the Doubly-Compensated Hydrostatic Transmission, Proceedings of the 52nd National Conference on Fluid Power National Fluid Power Association, NCFP I Las Vegas, NV (2011): Bramer, J. Puzzuoli, A. Michael, P. and Wanke, T. Hydraulic Fluid Efficiency Effects in External Gear Pumps, Proceedings of the 53rd National Conference on Fluid Power National Fluid Power Association, Paper NCFP I , Las Vegas, NV (2014)