Performance Prediction of Automotive Fuel Cell Stack with Genetic Algorithm-BP Neural Network Tongji University

Format:

Conference/Event

Author/Creator:

Hou, Hou, author.

Contributor:

Huang, Yanwei

Conference Name:

WCX World Congress Experience (2018-04-10 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2018

Summary:

AbstractFuel cell vehicle commercialization and mass production are challenged by the durability of fuel cells. In order to research the durability of fuel cell stack, it is necessary to carry out the related durability test. The performance prediction of fuel cell stack can be based on a short time durability test result to accurately predict the performance of the fuel cell stack, so it can ensure the timeliness of the test results and reduce the cost of test. In this paper, genetic algorithm-BP neural network (GA-BPNN) is proposed to modeling automotive fuel cell stack to predict the performance of it. Based on the strong global searching ability of genetic algorithm, the initial weights and threshold selection of neural networks are optimized to solve the shortcoming that the random selection of the initial weights and thresholds of BP neural network which can easily lead to the local optimal value. In this paper, the model is trained and tested by the actual road running data of a fuel cell vehicle, and the actual road running data of the fuel cell vehicle is divided into the training group, the validation group and the performance prediction group. Thetraining group data is used to train the GA-BPNN model, the validating group data is used to verify the accuracy of the model, and the performance prediction group is used to validate the accuracy of the model for the performance prediction of automotive fuel cell stack. The results show that the GA-BPNN model compared with the traditional BP neural network model, the prediction accuracy of automotive fuel cell stack performance is greatly improved

Notes:

Vendor supplied data

Publisher Number:

2018-01-1313

Access Restriction:

Restricted for use by site license