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In order to ensure the normal operation of OBD (On Board Diagnostics) system, manufacturers are required to conduct self-examination and supervision of OBD system, which is the performance evaluation of production vehicles. Multi-objective GA (Genetic Algorithm) simulates the process of biological evolution, dealing with a population, and can generate a large number of non-inferior solutions in one optimization process, so it can search the approximate Pareto optimal solution set of multi-objective optimization problems. In this paper, a fault diagnosis model based on multi-objective GA is proposed to diagnose the faults of automobile engines. The main idea is to use advanced multi-objective GA NSGA-II to adjust the parameters of the production car performance. The simulation results show that the fault samples can be completely and accurately identified by the established model, and the identification rate reaches 91.27%. Therefore, the fault diagnosis model based on multi-objective GA proposed in this paper can be used for fault diagnosis of automobile engine.

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