In the process of preventing rollover, the expected path of the driver to achieve better anti-rollover effect is often ignored, which may lead to the deviation of vehicle from the original path. Aiming at this problem, this paper considers both anti-rollover and path tracking performance, and proposes an integrated controller based on active steering and active braking. On the one hand, it can reduce the lateral acceleration and rollover risk by restraining the front wheel angle as tracking the driver’s expected path. On the other hand, through reasonably distributing the braking force of the four tires, it can offset the additional yaw moment caused by uneven distribution and reduce the impact on vehicle trajectory as the risk of rollover occurs. In addition, an improved index of rollover is put forward to give early warning to the future moment and to prevent rollover accident effectively. Simulation and hardware-in-the-loop test results show that the proposed integrated controller can ensure that the vehicle tracks the expected path well and achieves rollover prevention effectively.

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The precise estimation of the battery’s state of charge is one of the most significant and difficult techniques for battery management systems. In order to improve the accuracy of estimation of the state of charge, the forgetting-factor recursive least-squares method is used to achieve online identification of the model parameters based on the first-order RC battery model, and a back-propagation neural-network-assisted adaptive Kalman filter algorithm is proposed. A back-propagation neural network is established by using the MATLAB neural network toolbox and is trained offline on the basis of the battery test data; then the trained back-propagation neural network is used to realize the online optimized results of an adaptive Kalman filter algorithm for estimation of the state of charge. The proposed methodology for estimation of the state of charge is demonstrated using experimental lithium-ion battery module data in dynamic stress tests. The results indicate that, in comparison with the common adaptive Kalman filter algorithm, the back-propagation–adaptive Kalman filter algorithm significantly improved precise estimation of the state of charge.