| Optimizing Lightweight and Rollover Safety of Bus Superstructure with Multi-Objective Evolutionary Algorithm
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| Han Chi Hong 1, Jing Yan Hong 1, Luigi D’Apolito 1, Qian Fan Xin 2 |
1School of Mechanical and Automotive Engineering , Xiamen University of Technology
2School of Mechanical Engineering , Tianjin University |
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| ABSTRACT |
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This paper aims to study an optimization method for the lightweight design of bus superstructure. According to the requirements of ECE R66, the bus rollover finite element model has been developed, and the bus rollover process has been simulated and validated by experimental tests. The maximum error between test results and simulation results was 6.8%, which indicated that the simulation of bus rollover had good accuracy. A multi-objective optimization method for rollover safety has been proposed by combining a back propagation (BP) neural network model with a non-dominated sorting genetic algorithm (NSGA-II). The neural network model considered a different joint scheme of closed loop, wall thickness of side frame longitudinal beam, section of side frame longitudinal beam, wall thickness of side frame beam and section of side frame beam as inputs. It took the minimum distance of the survival space from the side column and the mass of the superstructure as outputs. The results show that the coupling optimization of the BP neural network and NSGA-II can reduce the total mass of the bus by 7.7%, which verifies the feasibility of applying the intelligent algorithm to the lightweight design of the bus.
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| Key Words:
NSGA-II · Neural network · Bus superstructure · Lightweight · ECE R66 |
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Corresponding Author.
Han Chi Hong , Email. 
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