18 2. CO-DESIGN OPTIMIZATION FOR CYBER-PHYSICAL VEHICLE SYSTEM
Table 2.4: Weight selection for different styles
Driving Style Weights
ω
1
ω
2
ω
3
ω
4
Aggressive
10 10 0
Moderate
10 0 10
Conservative
0 0 1 1
1
1
and energy efficiency is considered, with a much greater weight on the side of dynamic perfor-
mance:
˚
i
g
; k
SMC
; ˇ
D arg min
E
reg
!
1
t
acc
C !
2
t
brk
C !
3
j !
4
E
reg
: (2.28)
(2) Moderate-driving-style based optimization: In this case, the multi-objective optimiza-
tion problem is set as a trade-off between dynamic performance and ride comfort:
˚
i
g
; K
P
; K
I
; ˇ
D arg min
j
!
1
t
acc
C !
2
t
brk
C !
3
j !
4
E
reg
: (2.29)
(3) Conservative-driving-style based optimization: As mentioned before, under the conser-
vative driving style, the drivers’ behavior is usually mild with intentions of saving energy and
ensuring comfort. us, in this mode, the trade-off elements are switched to ride comfort and
energy efficiency:
˚
i
g
; K
P
; K
I
; ˇ
D arg min
E
reg
!
1
t
acc
C !
2
t
brk
C !
3
j !
4
E
reg
: (2.30)
For weighting selection, a much greater value would be put on the side of each featured perfor-
mance under different driving styles, and the weight on non-considered performance is set as
zero. e difference of the weights between featured and sub-featured performances are set to
be an order of magnitude. e detailed set-up for the weightings under different driving styles
is summarized in Table 2.4. e overall optimization flow and procedure are shown in Fig. 2.7.
2.5 OPTIMIZATION RESULTS AND ANALYSIS
Based on the proposed co-design method, the performance exploration and system optimiza-
tion are carried out in MATLAB/Simulink. e simulations are implemented iteratively with
developed models under defined driving events at each operating point (i.e., each selected value
of plant and control parameters) for the three driving styles, generating multiple performances.
e detailed results with each driving style are reported as follows.
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