Design of an Automatic Control Algorithm for Energy-Efficient ...

4 Control strategy 39
limits are directly given for climatisation. For cars only an indirect regulation through
fuel consumption and CO2 emissions exists. Finally, there are less restrictions on weight
or vibration behaviour while those are more on durability. This allows the use of advanced
control algorithms and led to more research in this field in recent years.
In addition, most of the comfort experiments are done for indoor environments
to quantify and improve the ease of persons in their homes or offices. Based on theri
results, controller ideas have been tested. In the examples presented by Freire, Oliveira
and Mendes, the PMV (predicted mean vote) is calculated with the available data and
used as a control variable of a closed-loop control [27]. The control algorithms used were a
PID, a fuzzy control and a model predictive control (MPC). Carbon dioxide concentration
sensors have been taken as base for the control, too [13].
Multi-objective solutions do not take account of only one objective as air-quality
or thermal comfort, but they add other goals as low power consumption. One approach
by Freire, Oliveira and Mendes was using common MPC [27] but also the use of a genetic
algorithm [28] was tested by Nassif, Kajl and Sabourin. Both try to minimise the energy
demand while maximising the comfort in a building. They are used to optimise the
set-points of a second loop, controlling the HVAC-system.
While coming close to a suitable controller scheme for the vehicle application, these
strategies cannot simply be used as they are in a car. Lower computational power limit
the complexity. Moreover, the thermal system “car” is unsteady compared to a building.
4.3 The control concept
The system which has to be controlled is nonlinear and non-stationary (in contrast to a
building, for example). It can easily be separated into two components, as it was described
in Chapter 3 and as implemented in commercial products (cf. Section 4.1). This gives
the structure of a cascade control as seen in Figure 4.1. The HVAC-unit determines the
air flow and its temperature, and is the “actuator” for the cabin system. It has to be
controlled to reach desired input conditions to the cabin in the best possible way. In the
outer loop these values for the input air have to be determined.
The objectives for this are competing. Accordingly, the best possible compromise