In order to deal with many influence factors of electric vehicles in driving under complex conditions, this paper establishes the system state equation based on the longitudinal dynamics equation of vehicle. Combined with the improved Sage–Husa adaptive Kalman filter algorithm, the road slope estimation model is established. After the driving speed and rough slope observation are input into the slope estimation model, the accurate road slope estimation at the current time can be obtained. The road slope estimation method is compared with the original Sage–Husa adaptive Kalman filter road slope estimation method through three groups of road tests in different slope ranges, and the accuracy and stability advantages of the proposed algorithm in road conditions with large slopes are verified.

A novel architecture of tightly-coupled SINS/ GPS integrated navigation system based on FPGA for target missile is proposed in this paper.The whole system is built on a single single FPGA chip containing a Nios II soft-core processor. In addition, the embedded real-time operating system μC/OS-IIis transplanted to the Nios II processor for managing each module in the system. The system can still provide the high-precision navigation data to integrated control computer of target missile when the number of available satellites is less than 4 by means of processing the pseudorange and pseudorange rate seprately. Therefore, the system has the strong application significance in terms of reducing the route shortcut of target missile.

Accurate real-time information on road slopes and the capacity to forecast future moment gradient values are critical for the vehicle control, stability, and driving comfort. Thus, this study proposes a stacking model method for road slope estimation of electric vehicles. Gated Circulation unit (GRU), Convolutional Neural Network (CNN), and CNN-GRU are used as the base classifiers, and Multilayer Perceptron (MLP) is used as a meta-classifier. The vehicle dynamics equations are examined to select the appropriate parameters to feed into the base classifier for training. The meta-classifier is trained using the estimated results from the basic classifier. The current slope values are estimated by slicing the training set by data sampling time and windowing the training data set to predict the future slope values in 2s, 3s, and 4s. Road experiments are conducted, and error indicators are selected for evaluation. The stacking model is compared with each base classifier, Adaptive Kalman filter, Recursive Least Squares with Forgetting Factor and Back Propagation Neural Network for estimating the current moment slope, and it is verified that the stacking model can better estimate the current slope value and outperform the conventional algorithm. Comparing the stacking model with the predicted results of each base classifier for future time slope prediction shows that the stacking model is more accurate at predicting the slope values in the short future time.

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