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An independent-wheel-drive electric vehicle has the advantage of better implementation of precise motion and stability control. However, when the vehicle is moving on a road that has complex slopes and various adhesion coefficients and is subjected to the structural limitations of the independent-wheel-drive systems, the driving performance will deteriorate. In order to make full use of the drive torque of every motor to improve the vehicle’s climbing and accelerating abilities, on the basis of the designs of a dual-motor coaxial-coupling independent-wheel-drive system and a sliding-mode controller, a coaxial-coupling traction control system was developed. Simulations on coaxial-coupling traction control for a four-wheel-independent-drive electric vehicle were completed. With the innovative coaxial-coupling equipment, the drive torque can be satisfactorily transferred between the wheels at the two sides of one drive shaft. When one of the driving wheels begins to slip, the torque transmission will increase rapidly, the probability that wheel slipping occurs will be reduced and the vehicle’s driving force can be enhanced. Also, the chatter of the traction control system can be quietened effectively, and the dynamicity and trafficability can be improved. In addition, with the additional yaw moment generated by the torque coupling, the system also has the auxiliary effect of improving the high-velocity lateral stability of the vehicle on a road which has a low adhesion coefficient.

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Achieving accurate speed prediction provides the most critical support parameter for high-level energy management of plug-in hybrid electric vehicles. Nowadays, people often drive a vehicle on fixed routes in their daily travels and accurate speed predictions of these routes are possible with random prediction and machine learning, but the prediction accuracy still needs to be improved. The prediction accuracy of traditional prediction algorithms is difficult to further improve after reaching a certain accuracy; problems, such as over fitting, occur in the process of improving prediction accuracy. The combined prediction model proposed in this paper can abandon the transitional dependence on a single prediction. By combining the two prediction algorithms, the fusion of prediction performance is achieved, the limit of the single prediction performance is crossed, and the goal of improving vehicle speed prediction performance is achieved. In this paper, an extraction method suitable for fixed route vehicle speed is designed. The application of Markov and back propagation (BP) neural network in predictions is introduced. Three new combined prediction methods, all named Markov and BP Neural Network (MBNN) combined prediction algorithm, are proposed, which make full use of the advantages of Markov and BP neural network algorithms. Finally, the comparison among the prediction methods has been carried out. The results show that the three MBNN models have improved by about 19%, 28%, and 29% compared with the Markov prediction model, which has better performance in the single prediction models. Overall, the MBNN combined prediction models can improve the prediction accuracy by 25.3% on average, which provides important support for the possible optimization of plug-in hybrid electric vehicle energy consumption.