'Energy Efficiency in Mechatronics: Enabler or just another
cost function?'
Vehicle system design optimization:
integrating powertrain and control design
Dr. Ir. Theo Hofman, Control Systems Technology, TU/e
Organised by Leuven.Inc and VeroTech. Powered by Flanders Make
Trend in power train electrification
Range (km)
500 ~ 800
Leaf
C3
IMA
Micro
3-10%
Mild
15-25%
Prius
Full / Plug-in
20-30%
EV Tesla S
100%
Volt
MiEV
Pure electric driving
1
2
4
16
Size (kW)
100 ~ 200
24
85
Storage capacity (kWh)
> 30
7-12
3-5
14 42
144
288 330
400
500
600
System voltage level (V)
[Source: [1] T.Hofman, “Hybrid drivetrain technologies”, (2014)]
2
Trend in technology development
2015
[Source: ERTRAC (European Road Transport Research Advisory Council)]
2050: 50% plug-in vehicle (H)EV
3
Just more than ‘electrified’ conventional cars…
• Research projects:
Solar Team Eindhoven: Stella
4 seats; mass: 380 kg; 2x wheel hub motors (20 kW); 6
m2 PV; Battery: 16 kWh/66 kg; Top speed: 120 km/h
URE: 05-09 (‘10-’15)
1 seat; mass: 200 kg; 4x wheel hub motors
(100 kW); Bat.: 20 kWh/55 kg; Top speed:
130 km/h; 0-100 km/h: 2.5 s.
TU/ecomotive: Isa
EM-01 - 03
1L:500 km
VW Lupo EL
4 seats; mass: 1060 kg; 1x motor (24 kW/50 kW peak);
Bat: 27 kWh/273 kg; Top spd: 130 km/h; 0-100 km/h: 12 s.
2 seats; mass: 200 kg; 1x motor (x kW);
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Battery: x kWh/12 kg; Top speed: 60 km/h;
System Design Engineering
Route-navigation systems (GPS, Maps)
Powertrain design
-
GIS
GPS
Integrated
EMS
more accurate state estimations…
[Source: [2] V. van Reeven, et al , “Extending Energy Management in Hybrid Electric Vehicles with Explicit
Control of Gear Shifting and Start-Stop”, (2012)]
-
Human-Machine Interaction (Psychology)
Energy efficiency improvement:
• Conventional: +5 / +10%
• Hybrid:
+20 / +40%
[Source: [3] R. Thijssen, et al, “Towards persuasive technology for enhancing the fuel economy of commercial vehicles”, (2014)]
5
System Design Engineering
Developing integrated design methods that produce system-optimal designs for
complex dynamic engineering systems.
HCU/ECU
: Operation strategy
Environment
Gear box
: Drive cycle
: Gear box ratio
Combustion engine
: Compresion ratio
: Displacement
Battery
Electric machine
: Type
: Max. power
: Battery capacity
6
System Design Engineering
• Applied to automotive systems
technology
– Control- & design-oriented system models:
scaleable systems, drive train components &
power nets;
– Dynamic optimization: efficient optimization
methods for adaptive integrated energy
management systems;
– Topology optimization: optimal layout of
components from system level (engine,
battery, etc.) to component level (planetary
gears, clutches, etc.);
– Co-design frameworks: bi-level/simultaneous
(optimal) versus sequential or iterative design
(suboptimal) procedures.
Global optimality is not guaranteed: nonlinear nonconvex problem
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Sequential Design
• Sub-optimal design proces for activelycontrolled dynamic systems
Plant Design Optimization
Control Design Optimization
iterative (initialize, first plant, then control, update plant, etc.)
• Static, passive, active plant design
• Single-/multi-objective design with approximated/original objective function
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Iterative Design
• Block Coordinate Decend (BCD) method:
set k = 1 & initialize designs
and design tolerances, ε
• Step 1: x = [xck, xpk]
min (design objective | control design)
s.t. constraints
• Step 2: xpk+1
min (control objective | plant design)
s.t. constraints
• Step 3: xck+1
If tolerances are met: terminate
• Step 4: |xk+1 – xk| < ε
k = k+1, go to step 2
[Source: [4] J.T. Allison and D.R. Herber, “Multidisciplinary Design Optimization of Dynamic Engineering Systems,” (2014)]
• Step 5
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Mathematical example
• Cost function:
• a = [1,5,-4]T and b = [1,2]T;
• Optimal solution: x* = [25,12]T with ε = 1⋅10-5
in 57 BCD iterations;
• Increasing the magnitude of a3 (coupling
strenth) increase computational expense!
[Source: [4] J.T. Allison and D.R. Herber, “Multidisciplinary Design Optimization of Dynamic Engineering Systems,” (2014)]
10
Mathematical example
• Min. iterations at a3 = {-3.75, 0, 4};
• Since, φ(.) is ‘quadratic’, one step with Newton’s method
would be sufficient independent of a3.
[Source: [4] J.T. Allison and D.R. Herber, “Multidisciplinary Design Optimization of Dynamic Engineering Systems,” (2014)]
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Remarks
• Sequential design can be improved with BCD
design using a few iterations:
– Often used in design practice in and ad hoc
manner, iteration continue until time or budget
constraints are reached;
– Inexact BCD approach produces far from systemoptimal for strongly coupled co-design problems.
Coupling strength (mathematical) between power train & control design plays an
important role in selecting optimal design framework that is a trade off between,
accuracy, speed & flexibility.
[Source: [4] J.T. Allison and D.R. Herber, “Multidisciplinary Design Optimization of Dynamic Engineering Systems,” (2014)]
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Co-design: {T,T,C} ⊗ {sy,su,co}
Optimizing:
• Technology
• Topology
• Control
At multi-levels:
• system
• subsystem
• component
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System-level design
Topology
generation
Topology
optimization
Technology
Control
Design Space Increase
• Nested approach
[Source: [5] E. Silvas, et al, “Review of Optimization Strategies for System-Level Design in Hybrid Electric Vehicles,” (submitted), (2015)]
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Nested algorithms: control design
• Direct and indirect method combined:
Dynamic
Programming (DP)
Pontryagin’s Minimum
Principle (PMP)
Discrete dynamics
Continuous dynamics
• Convex methods using iterative scheme. Co-states
each time step alternated until constraints are satified
System design method: combination of global (evolutionary) solvers
with nested (bi-level) methods for optimal control.
[Source: [6] D.V. Ngo, et al, “Optimal control of the gear shift command for hybrid electric vehicles,” (2012)]
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System design cases: HD Truck
• Powertrain design: engine, electric machine, battery
Topology generation (n/a)
Topology optimization: 1x topology
Technology: kW, kWh
Control: power split, gear shifting
Not applicable
Fixed (parallel hybrid)
SQP, DIRECT, PSO, GA
Dynamic Programming
[Source: [7] E. Silvas, et al, “Comparison of Bi-level Optimization Frameworks for Sizing and Control of a Hybrid Electric Vehicle”, (2014)]
• Auxiliary design: electric machine, transmission
Topology generation
Topology optimization: 6x topologies
Heuristics
Exhaust search
Technology: gear values, kW
SQP
Control: speed ratio, clutches
Dynamic Programming
[Source: [8] E. Silvas, et al, “Design of Power Steering Systems for Heavy-Duty Long-Haul Vehicles”, (2014)]
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Automatic Topology Generation
• Goal: topology optimization for HEV trucks
– How can we find all possible feasible
topologies for a fixed number of mechanical
components?
– How can we define a method to automatically
do the same study with extra proporties or
extra elements?
Topology
optimization
Technology
Control
[Source: [9] E. Silvas, et al, “Functional and cost-based automatic generator for hybrid vehicle topologies,” (submitted), (2015)]
Design Space Increase
Topology
generation
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Automatic Topology Generation
• Example: graph representation
Planetary Gear Set
Clutch
Final Drive + Wheels
Internal Combustion Engine
Transmission / Gearbox
Battery+ Electric Machine
Brake
3 Node
Connector
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Automatic Topology Generation
ICE
Planetary Gear Set
Gearbox
Clutch
Final Drive
+Wheels
Final Drive + Wheels
Internal Combustion Engine
Transmission / Gearbox
Battery+ Electric Machine
Brake
3 Node
Connector
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Automatic Topology Generation
EM
ICE
Planetary Gear Set
Gearbox
Clutch
Internal Combustion Engine
Final Drive
+Wheels
Final Drive + Wheels
Transmission / Gearbox
Battery+ Electric Machine
Brake
3 Node
Connector
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Automatic Topology Generation
• When is a topology feasible?
– Can ensure energy is deliverd to the wheels;
– Represents a hybrid electric configuration;
– Avoids the redundant usage of components; and,
– Can ensure hybrid modes (functionalities)
[Source: [9] E. Silvas, et al, “Functional and cost-based automatic generator for hybrid vehicle topologies,” (submitted), (2015)]
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Automatic Topology Generation
• Goal: Series, parallel, seriesparallel
• Possible topolgies 5.7⋅1045 (!)
• Using functional constraints:
• Graph consistency;
• Power train hybridization &
modes;
• Components/subsystem correct
functionality.
• Using cost constraints:
– Avoid redundant usability of
components (e.g., 3 clutches, or
nodes in series)
16 components
Constraint satisfaction
problem
(integer numbers + finite #
components)
Constrain Logic Programming
over Finite Domains program
in SWI© (Prolog)
[Source: [9] E. Silvas, et al, “Functional and cost-based automatic generator for hybrid vehicle topologies,” (submitted), (2015)]
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Automatic Topology Generation
• Functional and Cost-based principles
25 functional + 22 cost constraints
[Source: [9] E. Silvas, et al, “Functional and cost-based automatic generator for hybrid vehicle topologies,” (submitted), (2015)]
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Automatic Topology Generation
Complexity increase
Mercedes Atego
BleuTec Hybrid
Chevrolet Volt/Amperia
[Source: [9] E. Silvas, et al, “Functional and cost-based automatic generator for hybrid vehicle topologies,” (submitted), (2015)]
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Automatic Topology Generation
• Clustering 4779
topologies:
• Complexity:
construction &
control:
[Source: [9] E. Silvas, et al, “Functional and cost-based automatic generator for hybrid vehicle topologies,” (submitted), (2015)]
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Final remarks
• Complex dynamic
engineering systems require
a system design approach
• Nested approaches for
optimal design discussed;
• Novel method for dynamic
system topology design
discussed;
• Future: development of
dynamic models for
automated generated
Contact: dr. ir. T. Hofman/ME/CST group
E-mail: [email protected]
topologies.
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Tel.: 040-2472827
References
1.
2.
3.
4.
5.
6.
7.
8.
9.
T. Hofman, “Hybrid drive train technologies for vehicles,” in Alternative fuels and Advanced Vehicle Technologies
for Improved Environmental Performance. Towards Zero Carbon Transportation; Editors: Prof. R. Folkson, Elsevier,
Woodhead Publishing Series in Energy, Number 57, Part III, Chapter 18, pp. 567 – 631, DOI:
10.1533/9780857097422.3.567, (2014).
V. van Reeven, T. Hofman, M. Steinbuch, R. Huisman, “Extending Energy Management in Hybrid Electric Vehicles
with Explicit Control of Gear Shifting and Start-Stop”, in Proc. of the American Control Conference; Editors:
ACC2012, Fair Mont Queen Elizabeth,Montréal, Canada, CD rom with proceedings, (2012). ISBN: 978-1-46732102-0.
R. Thijssen, T. Hofman, J. Ham, “Towards persuasive technology for enhancing the fuel economy of commercial
vehicles,” Transportation Research Part F: Traffic Psychology and Behavior, 22, pp. 249 – 260, (2014). DOI:
10.1016/j.trf.2013.12.015.
J.T. Allison and D.R. Herber, “Multidisciplinary Design Optimization of Dynamic Engineering Systems,” AIAA Journal,
52(4):691-710, (2014). DOI: 10.2514/1.J052182
D.V. Ngo, T. Hofman, M. Steinbuch, A. Serrarens, “Optimal control of the gear shift command for hybrid electric
vehicles,” IEEE Transactions on Vehicular Technology, 61(8):3531–3543, (2012). DOI: 10.1109/TVT.2012.2207922.
E. Silvas, T. Hofman, N. Murgovski, P. Etman, M. Steinbuch, “Review of Optimization Strategies for System-Level
Design in Hybrid Electric Vehicles,” (submitted), (2015)
E. Silvas, E. Bergshoeff, T. Hofman, M. Steinbuch, “Comparison of Bi-level Optimization Frameworks for Sizing and
Control of a Hybrid Electric Vehicle”, in Proc. of the 9th IEEE Vehicle Power and Propulsion Conference; Editors:
IEEE VPPC, Coimbra, Portugal, USB with proceedings, (2014)
E. Silvas, E. Backx, Henk Voets, T. Hofman, M. Steinbuch, “Design of Power Steering Systems for Heavy-Duty LongHaul Vehicles”, in Proc. of the 19th IFAC World Congress; Editors: IFAC, Cape Town, South Africa, (2014)
E. Silvas, T. Hofman, A. Serebrenik, M. Steinbuch, “Functional and cost-based automatic generator for hybrid
vehicle topologies,” (submitted), (2015)
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Extra
• Results on non-gradient based optimization
algorithms
[Source: [5] E. Silvas, et al, “Review of Optimization Strategies for System-Level Design in Hybrid Electric Vehicles,” (submitted), (2015)]
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