When commercial vehicles drive in a mountainous area, the complex road condition and long slopes cause frequent acceleration and braking, which will use 25% more fuel. And the brake temperature rises rapidly due to continuous braking on the long-distance downslopes, which will make the brake drum fail with the brake temperature exceeding 308°C [
1]. Meanwhile, the kinetic energy is wasted during the driving progress on the slopes when the vehicle rolls up and down. Our laboratory built a model that could calculate the distance from the top of the slope, where the driver could release the accelerator pedal. Thus, on the slope, the vehicle uses less fuel when it rolls up and less brakes when down. What we do in this article is use this model in a real vehicle and measure how well it works. Thus, to improve the safety and economy of commercial vehicles on mountainous areas, the Vehicle Speed Planning and Prompting System based on real-time calculation of resistance is established. The system consists of four parts: Hardware on Vehicle, Microcontroller Unit (MCU), Database on Website, and App on Smartphone. Once the connection between these devices is built, the system works.
-
Firstly, the system obtains the velocity from several sensors. Then, it uses vehicle dynamics to calculate different resistances.
-
Secondly, the MCU of the system obtains parameters about the forward road’s parameters from the Database to calculate where the driver could release the pedal. Then, what the velocity should be every second is calculated when the vehicle rolls up and down the slope.
-
Finally, the App on the smartphone receives the data from the MCU and displays the planned speed on the screen. Then, the drivers could adjust the velocity to obtain better safety and economy. The simulation shows that the error of calculating resistance is below 3%.
The experiment shows that the fuel economy increased by 3% when the average slope is 2.88% and the total braking time is reduced by 83% when the vehicle rolls down the slope. In this article, another method is proposed to recognize the slope angle based on the velocity. The experimental results show that the error of slope recognition is about 18.3%, which needs time to improve in the future.
