Estimation of road friction coefficient based on WOA-BP neural network

Abstract

The road friction coefficient is often estimated based on vehicle dynamics models, but the ideal mathematical model is difficult to describe the actual vehicle operation, which in turn leads to deviations between the estimated friction coefficient and the actual situation. In order to take full account of the actual vehicle driving conditions, back propagation (BP) neural network model is often applied to assess the friction coefficient. To further improve the generalization ability of the network model, this paper proposes a method to predict the friction coefficient using the whale optimized algorithm-BP (WOA-BP) neural network model. The whale algorithm improves the weights and thresholds of the BP neural network model to improve generalization ability and convergence speed. First, the vehicle dynamics analysis is utilized to determine the vehicle dynamics parameters as a function of the friction coefficient. Then, the WOA-BP is compared against the BP under various realistic operating scenarios. The results demonstrate that the WOA-BP estimator is accurate and efficient in estimating the friction coefficient, and the proposed method reduces the mean absolute error by 51.9% and the root mean squared error by 49.1% compared to the BP.