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In this paper, a hydrogen consumption rate model of a PEM fuel cell engine is developed based on experimental data. The assumption of the model is that fuel cell stack works in steady-state condition. The relationships between the hydrogen consumption rate and stack current, stack power and Fuel Cell Engine (FCE) power are analyzed. Based on the hydrogen consumption rate model, a hydrogen specific consumption model is developed. Moreover, the models presented in this paper are verified by test data. The results indicate that these models are accurate to reflect the static performance of fuel cell stack and fuel cell engine. The advantages of these models are that theirs structures are very simple, and theirs parameters are easy to be obtained. They are easy to be applied to the study of fuel cell vehicle simulation.

In this paper, on the basis of the extant semi-empirical tire models of non-steady state with pure yaw angle input and pure side slip angle input, two empirical tire models of non-steady state side slip properties are established, one is pure yaw angle input, the other is pure side slip angle input, and both of them have been verified by test data. These two models can be used to approximately express tire force within low frequency. They have their own advantages, and make up for the disadvantages of existing tire models. They provide more choice for the simulation of vehicle dynamics.

The vehicular fuel cell stack is unavoidably impacted by the vibration in the real-world usage due to the road unevenness. However, effects of vibration on stacks have yet to be completely understood. In this work, the mechanical integrity and gas-tightness of the stack were investigated through a strengthen road vibration test with a duration of 200 h. The excitation signals applied in the vibration test were simulated by the acceleration of the stack, which were previously measured in a vehicle vibration test. The load signals of the vehicle vibration test were iterated through a road simulator from vehicle acceleration signals which were originally sampled in the proving ground. Frequency sweep test was conducted before and after the vibration test. During the vibration test, mechanical structure inspection and pressure maintaining test of the stack were conducted at regular intervals.

In this paper, a parasite system power empirical model of a PEM fuel cell engine suitable for the study of fuel cell vehicle simulation is developed by fitting the experimental data with quadratic polynomial. The assumption of the model is that the fuel cell stack is in steady-state condition. The properties of the parasite system power are analyzed. Based on the analysis of the properties of the parasite power, two parameters--- stack power factor RAS and FCE power factor RAF are introduced to evaluate the quality of the stack and the fuel cell engine. Moreover, the parasite system power model is verified by test data. The result indicates that the model is accurate to reflect the static performance of the parasite system. The advantage of the model is that its structure is simple, and its parameters are easy to be obtained. It is easy to apply to the study of fuel cell vehicle simulation.

Durability performance prediction is a critical issue in fuel cell research. During the demonstration operation of fuel cell commercial vehicles in China, this issue has attracted more attention. In this article, the long short-term memory neural network (LSTMNN), which is an improved recurrent neural network (RNN), and the demonstration operation data are used to establish the prediction model to predict the durability performance of the fuel cell stack. Then, a model based on a back-propagation neural network (BPNN) is established to be a control group. The demonstration operation data is divided into training group and validation group. The former is used to train the prediction model, and the latter is used to verify the validity and accuracy of the prediction model. The outputs of the prediction model, as the durability performance evaluation indexes of the fuel cell, are the polarization curve (current-voltage curve) and the voltage decay curve (time-voltage curve).

Fuel 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.