1. Application of MBSE on a
Hybrid Hydraulic/Electric
Mining Shovel Product
Development
Siemens PLM MBSE Trifecta
Seminar
Troy, Michigan, USA
March 17, 2016
Anab Akanda, Joy Global Surface Mining Inc
James Maki, Joy Global Surface Mining Inc
Darren Simoni, Dynamic Simulation Technologies, LLC
2. │ 10 March 2016 │ Our Joy Global Story
AGENDA
Joy Global: Who we are
Introduction: Relevance of Model Based Systems Engineering (MBSE)
Integration of MBSE in Product Development
Application of MBSE in all phases of the 2650CX Hybrid Shovel
– Duty Cycle Loads Development
– Backward Facing Models (Quasi-Static)
Concept selection, component selection/sizing and fuel consumption
– Forward Facing Models (Dynamic)
Performance (Fuel consumption & Cycle Time)
Structural integrity
Algorithms/controls development
Auxiliary hydraulics and driveline TVA
Post-production tuning and future concept assessment
Concluding Remarks
Q/A
2
5. │ 10 March 2016 │ Our Joy Global Story5
The 2650CX Hybrid Shovel
6. │ 10 March 2016 │ Our Joy Global Story
The 2650CX Hybrid Hydraulic/Electric Mining Shovel
6
• 59 metric ton payload, Two 1600hp Diesels Engines, Switch Reluctance
motors drive hoist/crowd/swing. Hydraulic propulsion and implements.
7. │ 10 March 2016 │ Our Joy Global Story
INTRODUCTION
7
• Major engineering challenges in large mining shovel product development:
o Sheer size, complexity, extremely harsh operating environment, and availability;
Makes it very difficult for instrumentation and measurement on existing machines
and/or proto-types. Machine always assembled at customer site and stays there
rest of it’s life. No “proving ground” available, must be coordinated with customer
for any testing/data acquisition. Huge resources required.
o Stochastic nature of loads: operator behavior, mining material, operating
conditions, and bank geometry variation.
o Forced to rely on past experience and limited existing data. Expensive on-site
trial and error is not an option. Therefore, model-based engineering (MBSE) is an
essential tool for successful product development of large mining excavators.
• MBSE Tools: AMESim, MathWorks, C/C++ scripts, MSC Adams and Nastran
8. │ 10 March 2016 │ Our Joy Global Story8
MBSE Integration
9. │ 10 March 2016 │ Our Joy Global Story9
How MBSE was Integrated into Product Development
Program
Target/Functional
Specifications
• Structure
geometry/property
definition
• Vender provided
component parameters
• Loads definition
Concept
Selection
Component
Sizing &
Hard-point
selection
Initial
Performance
Assessment
Backward-Facing Quasi-static Models
• System/sub-system level
hydraulic/electrical controls architecture
selection
• Hydraulic/electrical control system
tuning
• Structural Integrity (Co-simulation)
• Driveline vibration studies
• Power management schemes
Higher Fidelity
Performance
Assessment
Forward-Facing Dynamic Models
PHASE #1
PHASE #2
10. │ 10 March 2016 │ Our Joy Global Story10
Duty Cycle Loads Development
11. │ 10 March 2016 │ Our Joy Global Story11
Duty Cycle Development
Predictive tools need a representative duty cycle for any performance assessment (e.g. Automotive
FUDS/FHDS).
In mining, duty cycle development requires estimation of “Digging Effort”. Very complex process. This
requires a soil resistance or “Bank Reaction” model. Approximation techniques exist:
• White-Box Approach
Discrete-Element-Method (DEM) models coupled with MBSE models via co-simulation.
Computationally very time consuming, more appropriate for digging tool
development/loads and less practical for rapid system level controls/performance iterations
• Black-Box Approach
Completely test based and tied to specific machine geometry and material being
excavated. Very simple and fast but difficult to scale.
• Gray-Box Approach
Math-models based on soil failure. Model coefficients extracted from test observation.
Statistical variation of material properties are easy to include. Very fast solution and
suitable for controls/performance assessment.
12. │ 10 March 2016 │ Our Joy Global Story12
Duty Cycle Development cont’d: Gray-box Approach
• Based on simple soil failure model
• Model parameters estimated from test data with known loads via curve-fitting
• Parameter statistics extracted from large data-set
• Statistical model tested against remaining validation data-set
:sStatisticParameter
f
F
F
F
:ModelDigging
Y
X
Dig
,
;
• Parameter statistics assumed to be Gaussian
• Material Property Spatial correlation is assumed to follow Markov Model
• Spatial variation is obtained from prescribed wave-number PSD
• Parameter-library for different type of materials (Over-burden, hard-rock etc)
13. │ 10 March 2016 │ Our Joy Global Story13
Mean and standard deviation of the parameter curve-fit values extracted
Duty Cycle Development cont’d: Gray-box Curve-Fitting
14. │ 10 March 2016 │ Our Joy Global Story14
Duty Cycle Development cont’d: Random Dig-Force
Spatial Variation PSD
Population of Random Tooth Force
Bank Reaction Parameter Variation
Soil Parameter
Statistics (μ,σ)
15. │ 10 March 2016 │ Our Joy Global Story15
Backward-Facing Models
16. │ 10 March 2016 │ Our Joy Global Story16
Backward-Facing Models
• Motion synthesis necessary for constructing dig-cycles initially done in
Adams rigid-body MBD models coupled with dig-force algorithm
• Prescribed drive torque/force and speed curves are the inputs to
AMESim
• Energy accounting performed in AMESim
• All sub-systems such as EM, hydraulics and engines are quasi-static
• Approach only appropriate for initial estimates of fuel consumption and
sizing of EM and hydraulic sub-systems. No controls assessment
possible.
• Also used the same approach for competitive machine performance
estimates
17. │ 10 March 2016 │ Our Joy Global Story17
Forward-Facing Models
18. │ 10 March 2016 │ Our Joy Global Story18
Forward-Facing Models
• Dynamic representation of all major electrical & hydraulic systems/sub-systems
including speed-control loops and main hydraulic circuit. All contained in AMESim.
• Rigid-body 2D representation of machine structure including all major drive-line
transmissions and rope dynamics. Extensive use of 3D flexible MBD models for stress
recovery and loads in Adams.
• Simplified Engine Dynamic model
• Capable of driving the system with Joystick signals or Path-following mode (Speed or
position)
• Three-way Co-simulation: AMESim-Matlab/Simulink-Adams for FEA loads
• Extensive use of AMESim API to couple with external C/C++ codes for soil model,
power management and hydraulic controls
• Predicts electrical and hydraulic controls performance, fuel consumption and cycle
time
19. │ 10 March 2016 │ Our Joy Global Story19
Forward-Facing Models cont’d: Hoist/Crowd/Swing/Engine
20. │ 10 March 2016 │ Our Joy Global Story20
Forward-Facing Models cont’d: Hydraulic System
• Extensive use of Dynamic Execution Blocks and super components. Main hydraulic controls C-code
drives the hydraulic circuit. Pump pressure compensation is also done externally.
• Custom 3-position 8-way electro-proportional spool valves modeled
• Performance of valve curves assessed and developed/modified with the supplier in the loop
21. │ 10 March 2016 │ Our Joy Global Story21
Forward-Facing Models cont’d: Dig Cycles
• Example of possible dig cycles
• Power management & stochastic bank reaction (soil model) active
22. │ 10 March 2016 │ Our Joy Global Story22
Forward-Facing Models cont’d: Performance Prediction
-1500
-1000
-500
0
500
1000
1500
2000
2500
5 10 15 20 25 30 35 40
Power(kW)
Time (sec)
Cycle Power Breakdown
Max Power
Load
Regen
Hotel
Hydraulic
Hoist
Crowd
Swing
Dig
Swing to
Truck
Swing
Decel &
Dump
Dump &
Swing
Return to Tuck
23. │ 10 March 2016 │ Our Joy Global Story23
Forward-Facing Models: Performance Prediction cont’d
0
1
2
3
4
5
6
7
8
9
Frequency
Bin (gal/hour)
Fuel Consumption Histogram
Frequency
0
1
2
3
4
5
6
7
8
Frequency
Bin (seconds)
Cycle Time Histogram
Frequency
• Stochastic bank reaction model & power management active
• Statistical distribution of fuel consumption and cycle time predicted
24. │ 10 March 2016 │ Our Joy Global Story24
Forward-Facing Models: Co-simulation for Loads
ADAMS – MBD( SLAVE)
SIMULINK – CONTROLS (MASTER)
AMESIM – HYDRAULICS (SLAVE)
• Hydraulic sub-system modeled in AMESm is coupled with the Adams flexible body
MBD model to capture the highly nonlinear system response.
• Simulink added as a method to manage the communication hurdles and provide simple
controls.
25. │ 10 March 2016 │ Our Joy Global Story25
Forward-Facing Models: Co-simulation for Loads cont’d
Hoisting Corner Tooth Impact
26. │ 10 March 2016 │ Our Joy Global Story26
Forward-Facing Models: Speed Controls Example
Hoist Drive Motor Response
PI Integral Sum PI Proportional Term
• Bucket Hoist Joystick Step Input
• Proportional and Integral gains of
speed loop estimated from the MBSE
model and implemented in the Machine
controls software
Control Gain Model Based
Recommendation
Hardware Implementation
Proportional: Kp 250.0 232.0
Integral: Ki 400.0 333.3
27. │ 10 March 2016 │ Our Joy Global Story27
Forward-Facing Models: Speed Controls Example cont’d
Drum Inertia
Rope
Equivalent
Bucket Mass
Gear Ratio
Motor
rK rC
bm
ddJ ,
mmJ ,
Ndm
bx
mTTorque
• Based on AMESim model, simplified RT capable state-space models developed for HIL
• Works remarkably well for controls application!
00
00
00
00
1000
0100
0010
0001
10
00
01
00
1000
0010
22
2
2
2
2
2
2
DCB
m
C
m
K
Nm
RC
Nm
RK
N
N
J
J
RC
N
N
J
J
RK
N
N
J
J
RC
N
N
J
J
RK
A
b
r
b
r
b
r
b
r
d
m
r
d
m
r
d
m
r
d
m
r
0 5 10 15 20 25
-1500
-1000
-500
0
500
1000
1500
Time (s)
RPM
Validation of 2DOF Model
Test
2DOF Model
28. │ 10 March 2016 │ Our Joy Global Story28
Forward-Facing Models: Power-train TVA
Drive-shaft FEA
6th order @
1800rpm: 180Hz
112.8Hz Resonance
29. │ 10 March 2016 │ Our Joy Global Story29
Forward-Facing Models: Hoist Transmission TVA
SRM Torque Ripple Risk Assessment
Planetary Transmission Response
Machine MBD
30. │ 10 March 2016 │ Our Joy Global Story30
Forward-Facing Models: Auxiliary Hydraulic Circuits
Boarding Ladder Circuit
• Effect of oil viscosity change, hydraulic
routing circuit logic verification
31. │ 10 March 2016 │ Our Joy Global Story31
Forward-Facing Models: Auxiliary Hydraulic Circuits cont’d
Other ...• Lubrication flow, effect of oil viscosity
change, hydraulic routing circuit logic
verification
Crowd Transmission
Lubrication Routing
32. │ 10 March 2016 │ Our Joy Global Story32
Forward-Facing Models: Other Applications
• Main control valve curves tuned based on AMESim system response: Striking a balance
between efficiency and active/passive over-running load control. Implemented in the
machine hardware.
• Power management control code developed, verified in the MBSE model and validated
on the hardware. Software implemented in the production machine.
• Payload estimation algorithm developed/tested on the MBSE model and validated with
test data. This code is now being implemented on the machine.
• MBSE models currently used to trouble-shoot post-production hydraulic and electrical
field issues.
• Models also been used for assessing alternative energy storage devices to improve
efficiency.
33. │ 10 March 2016 │ Our Joy Global Story33
Concluding Remarks
34. │ 10 March 2016 │ Our Joy Global Story34
Upfront and strategic engagement of MBSE can be very effective in new
product development. The 2650CX machine is a good example.
Model fidelity must be adjusted based on what questions are to be answered.
Adding thousands of states with the hope of capturing absolute truth often leads
to “Analyses Paralysis” raising more questions than answers.
When applicable, easily modifiable, low-order models should be preferred. They
are far more effective in fast-paced product development cycles.
