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Due to the difference of response time and braking type between the motor and the pneumatic braking system, it is still difficult to coordinate the motor braking and the pneumatic braking to ensure the vehicle stability and maximal energy regeneration. To address this challenge, a bilevel electromechanical compound braking coordinated control strategy for electric vehicles is proposed considering general and emergency braking state. First, in general braking state, considering the delay characteristics of the pneumatic braking system, a Lagrange quadratic interpolation prediction algorithm is designed to start the pneumatic braking system in advance. Second, in emergency braking state, a model predictive control method is proposed to optimize the braking torque distribution while controlling the wheel slip ratio in a stable range. In order to obtain the optimal control effect, a modified adaptive cuckoo search algorithm is put forward, in which three adaptive impact factors are added. Finally, the proposed control strategy is verified under three road conditions and compared with the conventional control strategy. The results demonstrate significant improvements under gravel road condition, including a 7% increase in energy recovery efficiency, a 92.1% enhancement in the following effect of pneumatic braking torque, and a 43.5% reduction in wheel fluctuation. [ABSTRACT FROM AUTHOR]

For the sake of better achieving the productivity of leather damage detection in industrial production, this paper proposes a Robust Breakage Detection Network (RBD-Net) model for leather breakage detection. The model is an optimized model of You Only Look Once (YOLO) v5 to identify and detect the degrees of damage in leather and the type of damage in leather without any damage from the image. Firstly, the backbone network is replaced by Cross Stage Partial Densely Connected Networks (CSP-DenseNet), which can better achieve the reuse of features and prevent the loss of excessive gradient flow information; secondly, Bi-directional Feature Pyramid Network (BiFPN) is added in the feature refinement stage, which can better balance the feature information at different scales; finally, the addition of the Decision Network allows for capturing not only local shapes, but also global shapes spanning a large area of the image to better identify leather breakage in the image. By conducting experiments and making comparisons, it concludes that the method performs better than existing detection models in both breakage detection, and the accuracy of cutting, etched surface, brand stigma, hole and bleaching of five types of leather is 83.9%, 80.4%, 82.4%, 95.5%, and 89.4%, respectively, which satisfies the requirement for the balance of accuracy and robustness in industrial production, and also provides some ideas for other breakage detection research. [ABSTRACT FROM AUTHOR]