Control system (for HEV and HSV)
J.Bokor, P.Gaspar,
P.Bauer, T.Peter, T. Becsi (*)
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(*) BUTE, Budapest
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Model based control
It becomes important to control complicated hybrid systems that consist
of not only a powertrain
but also vehicle systems such as regenerative
braking. Model-based control and calibration enables both control
strategy optimization and control system development efficiency improvement.
Recently, the hybrid
vehicle, which requires the integration of various control
systems, has been introduced to the market. Model based control offers
the potential to derive an accurate controller rapidly and systematically.
It is especially useful for developing a coordinated control system like
those present in a hybrid vehicle.
Model based control is characterized by a plant model that describes
the dynamics of the system to be controlled and allows closed
loop simulation. A high fidelity powertrain model can make
it easy to introduce advanced control technologies including recent developments
in robust, adaptive and nonlinear control. Advanced, high-speed data processing
will provide sophisticated filtering technologies to extract important
information from measured signals. However, fundamental questions remain
for the current control theories. For an example, control designs are
often subject to unrealistic restrictions such as the need to neglect
time delays and manipulation constraints, the linearization of plants
and so on.
For controller design on a given hybrid vehicle, we need to know the
physical layout of the powertrain. Basically it can have series, parallel
or series/parallel structure. We need to know the whole structure containing
internal
combustion engine (ICE), electrical motor / generator (EM),
battery and perhaps solar cells too. We must have, or construct the characteristics
(rpm, torque, efficiency, etc.) and models (MATLAB / SIMULINK) of all
elements.
In case of parallel structure the device which joins the ICE and electrical
motor EM is the most interesting component of the powertrain. It affects
seriously the optimal energy management of the vehicle. It could allow
arbitrary rpm rate between ICE and EM, or only a fixed rate (as a timing
belt). These two cases are very different. In the first case
we can change both the torques and rpms of the motors to achieve optimum
performance split, while in the second case we can change only the torques
of the motors (if cluch is closed).
In literature we can find several control strategies for hybrid electric
vehicles. The global, optimal control strategy could be calculated only
with dynamic programming or other backward algorithms. The main problem
is the lack of knowledge about the whole drive cycle, which could last
hundreds or thousands of seconds. So in real applications only a suboptimal
solution can be found which still requires the use of drive cycle prediction.
For this purpose, several prediction methods can be used for example Neural
Networks [2] or Model Predictive Control.
Another problem is how to create the cost function which reflects the
fuel equivalent of battery power besides the ICE fuel consumption. In
some articles [1] equivalence factors which are control parameters are
used in the cost function. So we have to optimize the drive cycle dependent
equivalence factors too. The searching for cost functions, without additional
factors, can be a second goal of controller design (besides minimal fuel
consumption in global sense). For controller tests and comparison we need
to know the driving schedules (FUDS = Federal Urban Driving Schedule,
FHDS = Federal Highway Driving Schedule etc. [1,2] ) used by other authors.
References
[1] Cristian Musardo, Giorgio Rizzoni, Yann Guezennec és Benedetto Staccia:
A – ECMS: An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management,
European Journal of Control, 2005, pp. 509-524
[2] Ivan Arsie, Marco Graziosi, Cesare Pianese, Gianfranco Rizzo, Marco
Sorrentino: Optimization of Supervisory Control Strategy for Parallel
Hybrid Vehicle with Provisional Load Estimate, AVEC ’04 (Department of
Mechanical Engineering – University of Salerno)
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