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Page 8 of 15 Cui et al. Complex Eng Syst 2023;3:3 I http://dx.doi.org/10.20517/ces.2022.57

where

2 2
¤
¤
 = − − +

 2 2
 8( 2) 8( 1)

1 = [ + − ]


 2 = [ − − ]

The first subsystem named as energy loss is given by



 2
 2
 0 0

 1 = 1 − / + 1 − /



 2
 2
  0 2 0 0
+ 1 − / + 1 − / + (1 − / ) (15)



 :


0
 3 ≤ ≤ 5




0
0
0
0
0
 ≤ , , , , , 0
 ≤

The second subsystem named as road feel is given by



 2

 2
 00 00
 2 = 1 − / + 1 − /



 2
 2
  00 00 2 00
+ 1 − / + 1 − / + 1 − / (16)



 :


00
 3 ≤ ≤ 5




00
00
00
00
 ≤ , , , , 00
 ≤

The third subsystem named as steering sensibility is given by



 2
 2 2

 000 000 000
 3 = 1 − / + 1 − / + 1 − /



 2
 2 2
  000 000 000
+ 1 − / + 1 − / + (1 − / ) (17)



 :


 3 ≤ 000 ≤ 5




000
 ≤ , , , , , 000
000
000
000
000
 ≤

According to the above models, the multi-objective collaborative optimization model of EHPS is showed in
Figure 3.
3.2. Multi-objective optimization algorithm
The NSGA-II algorithm has excellent global search performance and is often used in multi-objective optimiza-
tion. Ontheonehand, theNSGA-IIintroducesanelitestrategyintheprocessofranking, whichavoidstheloss
of non-dominated individuals in the evolution process and speeds up the convergence speed of the algorithm.
On the other hand, the NSGA-II improves the crowded-comparison approach, which ensures the diversity of
the next generation and enhances the global exploratory capability of the algorithm.
The main steps of NSGA-II algorithm can be depicted as follows.
(1) Generate the initial population 0 at random and the size of 0 is ;