Shenao Ma

Bio: Shenao Ma is an academic researcher from Jilin University. The author has contributed to research in topics: Brake-by-wire & Detection limit. The author has an hindex of 2, co-authored 2 publications receiving 9 citations.

Vehicle braking force distribution with electronic pneumatic braking and hierarchical structure for commercial vehicle

Abstract: Brake by wire that allows a number of braking functions exists on commercial vehicles under the name electronic pneumatic braking system, which can improve braking comfort and safety of commercial ...

Braking intention recognition algorithm based on electronic braking system in commercial vehicles

Abstract: The aim of this research is to investigate the braking intention identification method adaptive to the electronic braking system (EBS) in commercial vehicles. Based on the neural network, a braking intention identification model is established which takes both emergency braking and general braking into account. Then, considering the complex transportation environment, a multi-condition identification model with respect to four typical braking conditions is developed using the fuzzy logic. The experimental results of the two models demonstrate that the proposed strategy can make good use of driver braking intention. The proposed method provides theoretical guidelines on driver behaviour adaptation on the longitudinal active safety system, which promotes vehicle safety and braking performance.

A Novel Initiative Braking System With Nondegraded Fallback Level for ADAS and Autonomous Driving

Abstract: A novel compact initiative braking system orienting intelligent vehicles and autonomous driving is revealed. The delicate arrangement of on – off switch valves guarantees precise hydraulic pressure modulation. Integrated stroke simulator provides a well-tuned pedal force feedback. The fallback level is intensively designed to be nondegraded. A hierarchical control frame with the underlying hydraulic controller is designed to govern operation procedures. The underlying hydraulic controller is set up based on adaptive gain scheduling proportion differentiation controller and logic threshold control. Hardware-in-loop tests are carried out in full perspectives. The test result of slope-sine combination tracking shows that, compared with the conventional proportion integration differentiation controller, the designed underlying controller achieves higher pressure modulation accuracy with no chattering effect. Controller robustness to accumulator pressure fluctuation is proven by the dual-cylinder tracking test. A batch of step-response tests under different accumulator pressures shows a rapid pressure building capability in emergency situations under all pressure range. The fail-safe test result indicates that the conventional hydraulic brake can be restored in 1.5 s with the operation of the driver, which significantly increases the margin of brake safety for highly autonomous vehicles. The regenerative braking test result suggests the immense potential of the developed system in an application to electrified vehicles.

Road Grade and Vehicle Mass Estimation for Heavy-duty Vehicles Using Feedforward Neural Networks

Abstract: In this paper, a neural network approach is presented for solving the problem of estimating road grade and vehicle mass, for the case of simulated heavy-duty vehicles (HDVs) driving on highways. After training, and using only signals normally available in HDVs, the (feedforward) neural network provides road grade estimates with an average root mean square (RMS) error of around 0.10 to 0.14 degrees, and mass estimates with an average RMS error of around 1%, when applied to two different test data sets (one with synthetic roads and one based on a real road), not used during the training phase. The estimates obtained outperform road grade and mass estimates obtained with other approaches.

A Review on Driving Control Issues for Smart Electric Vehicles

Abstract: Smart electric vehicles (EVs) are attractive because of their clean, zero-emission, low impact on the environment whilst providing a safer and smoother riding experience. To provide the latter, driving control requires appropriate systems and algorithms to optimize smart vehicle performance, maximize vehicle stability and protection, minimize accident probability, heighten driving comfort, and optimize transportation costs. Despite advancements in these areas, the realization of optimal smart EVs still requires considerable effort. This paper reviews driving control systems and algorithms for smart EVs, including the advanced driving assistant system, implementation of sensors, vehicle dynamics, and control algorithms. The major contribution of this review is to identify promising work to assist researchers with the most advanced trends in this area for prospective regulations.

Sliding Mode Control of Vehicle Equipped with Brake-by-Wire System considering Braking Comfort

Abstract: For passengers, the most common feeling during running on the bumpy road is continuous vertical discomfort, and when the vehicle is braking, especially the emergency braking, the instantaneous inertia of the vehicle can also cause a strong discomfort of the passengers, so studying the comfort of the vehicle during the braking process is of great significance for improving the performance of the vehicle. This paper presented a complete control scheme for vehicles equipped with the brake-by-wire (BBW) system aiming at ensuring braking comfort. A novel braking intention classification method was proposed based on vehicle braking comfort, which divided braking intention into mild brake, medium comfort brake, and emergency brake. Correspondingly, in order to improve the control accuracy of the vehicle brake system and to best meet the driver’s brake needs, a braking intention recognizer relying on fuzzy logic was established, which used the road condition and the brake pedal voltage and its change rate as input, output real-time driver's braking intention, and braking intensity. An optimal brake force distribution strategy for the vehicle equipped with the BBW system based on slip rate was proposed to determine the relationship between braking intensity and target slip ratio. Combined with the vehicle dynamics model, improved sliding mode controller, and brake force observer, the joint simulation was conducted in Simulink and CarSim. The cosimulation results show that the proposed braking intention classification method, braking intention recognizer, brake force distribution strategy, and sliding mode control can well ensure the braking comfort of the vehicle equipped with the BBW system under the premise of ensuring brake safety.