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Variable inter-vehicle distances influence significantly the wind resistance of platoon vehicles due to the sheltering of airflow. Accurate air drag estimation is extremely important for platoons in scenarios like energy-oriented driving and high-precision tracking. Most aerodynamic researchers have performed qualitative analysis of wind resistance for equally inter-spaced platoons, while the quantitative description of wind resistance variation with coupled inter-vehicle distances is rare. In addition, data measured through offline wind tunnel experiments, Computational Fluid Dynamics (CFD) simulations, or road tests via fuel consumption calibration for a period of time is unsynchronized, which may be unconvincing for real air drag estimation on road. Aiming at the quick and accurate approximation of platoon wind resistance, this paper proposes a novel and universal modeling strategy combining offline CFD simulation, online air drag observation, and real-time parameter identification. The variation characteristics of air drag with distance of a longitudinal platoon consisting of three homogeneous C-class Notchback cars are analyzed by CFD simulation. With appropriate data processing, a well-designed basis function is summarized. Then a novel wind resistance separation method combining Back-Propagation Neural Network (BPNN) and Extended State Observer (ESO) is proposed. Using the observed data stored in experience memory, the hybrid optimization method via particle swarm optimization (PSO) and gradient descent with momentum (GDM) is employed to identify the model parameters toward high accuracy and global optimality. Results of Hardware-In-the-Loop (HIL) experiment show that the proposed modeling strategy realizes effective real-time observation and accuracy description; the developed approximation model can describe the platoon wind resistance with continuous and coupled inter-vehicle distances, with the RMSE less than 11.4%.

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The independently controlled electric wheels of distributed drive vehicles provide faster and more accurate actuators for vehicle slip ratio control. Meanwhile, the estimation of the slip ratio of electric wheels has been of vital importance for the dynamics control of distributed drive electric vehicles. However, the conventional slip ratio estimation method is hard to accurately estimate the slip ratio under steering conditions without multiple observations, increasing the cost and introducing errors. Considering that the output torque and motor rotation rate of electric wheels can be accurately collected, the novel slip ratio estimation method takes advantage of the signals of the electric wheels and requires fewer vehicle sensors. Based on the torsional vibration model of electric wheel, the slip ratio estimation method was proposed and validated by simulations and experiments. With the drum dynamometer, the slip ratio estimation method was applied to a single electric wheel for testing, proving the feasibility and accuracy of the proposed method. The slip ratio estimation was finally applied to a fuel cell heavy truck for road tests, of which the results show that the error index is reduced from 0.0152 to 0.0064 compared to the conventional slip ratio estimation method, confirming the good estimation performance achievable via the proposed method.