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

11 Matlab implementation 113
left in the Simulink model shown in Figure 11.2. The upper one estimates the model
errors for the cabin humidity and temperature as well as for the windscreen temperature.
The lower one contains a simulation of the carbon dioxide concentration in the cabin air
as no sensor is available.
The yellow block in the middle is the core of the controller, the optimiser. It in-
cludes a general implementation of the IMEA-algorithm presented in Chapter 6. The
cabin system is coded into a function as well as each objective. The used system pa-
rameters are recapitulated in a table in Appendix C.1. The set of objectives presented
in Chapter 8 is reduced by the ones not applicable. The humidity minimum cannot be
changed. No air flow is directed towards the passengers as well. On the other hand, a
battery air flow constraint described in Section 10.5 is added. An overview of the objec-
tives and the used (standard) priorities, weights and limits are found in Appendix C.2.
The design variables related and general controller settings can be found there, too. Some
objective parameters (e.g. the power consumption weight) and design variables bound-
aries are adapted online to the environment conditions and user or system settings, as
described in the Sections 8.4 and 8.2.
Finally, the set-point values for the inlet air are translated into control output values
in the rightmost, red block. Here, no closed-loop control is involved since the fuel heater
is not suited for this use (see Section 10.3). With look-up tables the physical values are
translated into a percentage of fan PWM, heater set-point and flap opening angle outputs.
A cold air prevention mechanism, as explained in Section 7.5, is implemented. The desired
output air mass flow is fed back to the simulator since no measured information about this
value is available. The status of the heater (on/off) is fed back to the power estimation
in the controller’s system simulation, which then can tell whether the glow plug needs to
be activated.
Warnings are generated by the blocks if an estimation limitation was reached or a
panic (exceeded objective limit) occurred in the controller. An error signal is indicated if
the control is not able to allocate the required memory. These signals are joined into one
number and output as the controller error signal.