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High-precision lateral dynamics model is essential for vehicle lateral state estimation and stability control. Existing physical models suffer from low accuracy due to simplified modeling, while data-driven models cannot guarantee the robustness. To address these problems for vehicle lateral dynamics modeling, a hybrid lateral dynamics model, which combines data-driven and physical models together, is proposed in this study for vehicle control applications. First, the model parameters of the conventional bicycle model including cornering stiffness are treated as time-varying parameters, and a neural network is adopted to describe the nonlinear relationship between the model parameters and the measurable vehicle states. Then, the neural network and bicycle model are integrated, and a neural network-based bicycle model is established to describe vehicle lateral dynamics. To train the neural network for parameter identification without cornering stiffness labels, a training method, which integrates the bicycle model into the loss function, is proposed. With this method, the neural network can be trained based on the entire hybrid model without providing the true values of the cornering stiffness. Simulations are conducted to verify the effectiveness of the proposed hybrid model.

The proton-exchange membrane fuel cell is a promising power source for automobile industry because of its zero pollution. However, its stack structure always faces increased contact resistance caused by assembly errors, leading to substantial energy loss during the working period. To enhance its output performance, the influence of assembly errors on contact resistance is studied for proton-exchange membrane fuel cell. The mechanical simulation model of fuel cell assembly process is established to provide contact resistance distribution with different assembly errors. An improved global sensitivity analysis method is proposed to evaluate the influence coefficient of each assembly error term on contact resistance based on a series of randomized simulation data. The case study of a single-layer fuel cell demonstrates the proposed method achieves higher efficiency than traditional sensitivity analysis methods, and finds out key assembly errors in regard to reducing contact resistance.

Carbon fibre woven composite has been increasingly employed in engineering applications undergoing complex loading conditions. For effective use of composite material in dynamic applications, it is essential to fully understand the mechanical behaviour of composite at different strain rates. In the present study, both in-plane tensile and compressive experiments loaded at 0 degree axial direction and 45 degree off-axial direction of a TC33 carbon fibre woven composite were investigated over the strain rate range from 0.001 to 1000 s-1. High strain rate tests were carried out using Split Hopkinson Pressure and Tensile Bar apparatus respectively. The results indicated that the in-plane mechanical properties and failure patterns were strain rate sensitive under both tensile and compressive loadings. The mechanical properties, failure patterns and strain rate effect also showed highly direction dependent and tension/compression asymmetric characteristic within the considered strain rate range. For higher strain rate sensitivity under compression than that under tension, the asymmetry of mechanical properties was less obvious with the increase of strain rate. Finally, two phenomenal models were proposed to quantitatively fit the relationship between strength property and strain rate.

The fatigue behaviour of two-dimensional weave composites under constant and variable amplitude loading was discussed in this article. The theories of life prediction methods were represented, including cycle counting, S–Nformulation, constant life diagrams (CLD), damage accumulation rule and stress analysis. Special focuses on CLDs were studied through the interpolation method known as the kriging model. Fatigue tests under constant amplitude loading were carried out utilising the servo-hydraulic Shimadzu testing machine. Tension–tension, tension–compression and compression–compression loading patterns were investigated in fatigue testing. The macro-failure mode was obviously different between tensile and compressive loading. Micro-failure surfaces were observed in micro-mechanism with scanning electron microscopy (SEM). The Basquin equation and the linear Palmgren–Miner (P–M) rule were employed to characterize the fatigue properties under random loading conditions. A new constitutive relation was introduced which took into account the tensile-compressive asymmetry of composites. Finally, the benchmarking for the stochastic loading case was executed using proposed methodology and variable amplitude fatigue experiments. Through the contrast between prediction and the experimental data, it was inferred that the proposed prediction method under any loading conditions (stress ratios and ranges) gave an accurate life prediction.