The sensitivity of energy management strategies (EMS) with respect to variations in drive cycle and system parameters is considered. The design of three strategies is presented: rule-based, stochastic dynamic programming (SDP), and model predictive control (MPC). Each strategy is applied to a series hydraulic hybrid powertrain and validated experimentally using a hardware-in-the-loop system. A full factorial design of experiments (DOE) is conducted to evaluate the performance of these controllers under different urban and highway drive cycles as well as with enforced modeling errors. Through this study, it is observed that each EMS design method represents a different level of tradeoff between optimality and robustness based on how much knowledge of the system is assumed. This tradeoff is quantified by analyzing the standard deviation of system specific fuel consumption (SSFC) and root mean square (RMS) tracking error over the different simulation cases. This insight can then be used to motivate the choice of which control strategy to use based on the application. For example, a city bus travels a repeated route and that knowledge can be leveraged in the EMS design to improve performance. Through this study, it is demonstrated that there is not one EMS design method which is best suited for all applications but rather the underlying assumptions of the system and drive cycle must be carefully considered so that the most appropriate design method is chosen.

References

References
1.
Davis
,
C. S.
,
Diegel
,
W. S.
, and
Boundy
,
G. R.
,
2012
,
Transportation Energy Data Book
,
31st ed.
,
Oak Ridge National Laboratory
,
Oak Ridge, TN
.
2.
Sciarretta
,
A.
, and
Guzzella
,
L.
,
2007
, “
Control of Hybrid Electric Vehicles
,”
IEEE Control Syst. Mag.
,
27
(
2
), pp.
60
70
.10.1109/MCS.2007.338280
3.
Backe
,
W.
,
1993
, “
Present and Future of Fluid Power
,”
Proc. Inst. Mech. Eng., Part I
,
207
(
4
), pp.
193
212
.10.1243/PIME_PROC_1993_207_080_02
4.
Yan
,
Y.
,
Liu
,
G.
, and
Chen
,
J.
,
2010
, “
Integrated Modeling and Optimization of a Parallel Hydraulic Hybrid Bus
,”
Int. J. Automot. Technol.
,
11
(
1
), pp.
97
104
.10.1007/s12239-010-0013-5
5.
Filipi
,
Z.
, and
Kim
,
Y. J.
,
2010
, “
Hydraulic Hybrid Propulsion for Heavy Vehicles: Combining the Simulation and Engine-in-the-Loop Techniques to Maximize the Fuel Economy and Emission Benefits
,”
Oil Gas Sci. Technol.
,
65
(
1
), pp.
155
178
.10.2516/ogst/2009024
6.
Wu
,
B.
,
Lin
,
C. C.
,
Filipi
,
Z.
,
Peng
,
H.
, and
Assanis
,
D.
,
2004
, “
Optimal Power Management for a Hydraulic Hybrid Delivery Truck
,”
Veh. Syst. Dyn.
,
42
(
1–2
), pp.
23
40
.10.1080/00423110412331291562
7.
Johri
,
R.
, and
Filipi
,
Z.
,
2010
, “
Low-Cost Pathway to Ultra Efficient City Car: Series Hydraulic Hybrid System With Optimized Supervisory Control
,”
SAE Int. J. Engines
,
2
(
2
), pp.
505
520
.
8.
Stelson
,
K. A.
, and
Meyer
,
J. J.
,
2008
, “
Optimization of a Passenger Hydraulic Hybrid Vehicle to Improve Fuel Economy
,”
The 7th JFPS International Symposium on Fluid Power, Toyama, Japan, Sept. 15–18
.
9.
Deppen
,
T. O.
,
Alleyne
,
A. G.
,
Stelson
,
K. A.
, and
Meyer
,
J. J.
,
2011
, “
Optimal Energy Use in a Light Weight Hydraulic Hybrid Passenger Vehicle
,”
ASME J. Dyn. Syst., Meas., Control
,
134
(
4
), p.
041009
.10.1115/1.4006082
10.
Çağatay Bayindir
,
K.
,
Gözüküçük
,
M. A.
, and
Teke
,
A.
,
2011
, “
A Comprehensive Overview of Hybrid Electric Vehicle: Powertrain Configurations, Powertrain Control Techniques and Electronic Control Units
,”
Energy Convers. Manage.
,
52
(
2
), pp.
1305
1313
.10.1016/j.enconman.2010.09.028
11.
Kum
,
D.
,
Peng
,
H.
, and
Bucknor
,
N. K.
,
2011
, “
Supervisory Control of Parallel Hybrid Electric Vehicles for Fuel and Emission Reduction
,”
ASME J. Dyn. Syst., Meas., Control
,
133
(
6
), p.
061010
.10.1115/1.4002708
12.
Meyer
,
J. J.
,
Stelson
,
K. A.
,
Alleyne
,
A. G.
, and
Deppen
,
T. O.
,
2010
, “
Power Management Strategy for a Parallel Hydraulic Hybrid Passenger Vehicle Using Stochastic Dynamic Programming
,”
Proceedings of 7th International Fluid Power Conference
, Aachen, Germany, March 22–24.
13.
Serrao
,
L.
,
Onori
,
S.
, and
Rizzoni
,
G.
,
2011
, “
A Comparative Analysis of Energy Management Strategies for Hybrid Electric Vehicles
,”
ASME J. Dyn. Syst., Meas., Control
,
133
(
3
), p.
031012
.10.1115/1.4003267
14.
Zhang
,
R.
,
Alleyne
,
A. G.
, and
Prasetiawan
,
E.
,
2002
, “
Modeling and H2/H∞ MIMO Control of an Earthmoving Vehicle Powertrain
,”
ASME J. Dyn. Syst., Meas., Control
,
124
(
4
), pp.
625
636
.10.1115/1.1515326
15.
Prasetiawan
,
E. A.
,
Zhang
,
R.
,
Alleyne
,
A. G.
, and
Tsao
,
T. C.
,
1999
, “
Modeling and Control Design of a Powertrain Simulation Testbed for Earthmoving Vehicles
,”
International Mechanical Engineering Congress & Exposition: The Fluid Power and Systems Technology Division
.
16.
Carter
,
D.
, and
Alleyne
,
A.
,
2003
, “
Load Modeling and Emulation for an Earthmoving Vehicle Powertrain
,”
Proc. Am. Control Conf.
, Vol. 6, pp.
4963
4968
.10.1109/ACC.2003.1242510
17.
Zhang
,
R.
, and
Alleyne
,
A.
,
2005
, “
Dynamic Emulation Using an Indirect Control Input
,”
ASME J. Dyn. Syst., Meas., Control
,
127
(
1
), pp.
114
124
.10.1115/1.1876496
18.
Bellman
,
R. E.
,
1957
,
Dynamic Programming
,
Princeton University Press
,
Princeton, NJ
.
19.
Bertsekas
,
D. P.
,
2005
,
Dynamic Programming and Optimal Control
,
Athena Scientific
,
Belmont, MA
.
20.
Gosavi
,
A.
,
2003
,
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
,
Kluwer Academic Publishers
,
Norwell, MA
.
21.
ADVISOR 2004
,
2004
, AVL, www.avl.com
You do not currently have access to this content.