Chenchen He

Bio: Chenchen He is an academic researcher from Beijing Institute of Technology. The author has contributed to research in topics: Electric vehicle & Reduction (mathematics). The author has an hindex of 2, co-authored 2 publications receiving 86 citations.

A Novel Dynamic Cooperative Traffic Control Algorithm for the Reduction of Traffic Delay

Abstract: One of the most serious and worsening problems in much of the world is traffic congestion, which represents an undoubted menace to the quality of urban life, including wasting of time, and environmental pollution. Therefore, this study proposes a new dynamic cooperative traffic control algorithm based on Vehicle-to-Infrastructure connections to solve this problem. The proposed model contains three parts. First of all, when each vehicle enters the communication zone, it sends its estimated range of arrival time to the control center in the intersection; then the optimal passing sequence is calculated in the control center by dynamically grouping the compatible trajectories with the respect of the related safety constraints; finally, each vehicle optimizes its appropriate speed profile based on the given optimal time and speed to enter the intersection, which is sent by the control center. Several simulation cases are executed for different traffic volumes, whose results prove that vehicles under the proposed algorithm can pass the intersection with less travel time compared with traditional traffic control methods and other algorithms. Therefore, the proposed method can reduce the time delay and improve traffic efficiency.

Noise and vibration suppression in hybrid electric vehicles: State of the art and challenges

Abstract: The need for more efficient and renewable means of transport makes the development of hybrid electric vehicles (HEVs) an important topic for both automobile manufacturers and academic researchers. Noise, vibration, and harshness (NVH) of a vehicle are important factors for vehicle users and essential for successful commercialization of this vehicle type. This paper is a compact state-of-the-art review of both NVH characteristics and relevant suppression methods for HEVs. The NVH-related problems are categorized into three categories: engine, powertrain, motor-related. A detailed overview of each category/problem-type is provided, the NVH-suppression methods for different types of HEVs are introduced, and their respective advantages discussed. In addition, emerging developments and ideas for future improvements are summarized. As a result, the paper should be particularly helpful for researchers, who are interested in the development of high-performance HEVs that can provide both substantially improved ride-comfort and reduced energy-consumption.

Lane Keeping Control of Autonomous Vehicles With Prescribed Performance Considering the Rollover Prevention and Input Saturation

Abstract: This paper investigates the lane keeping control of autonomous ground vehicles (AGVs) considering the rollover prevention and input saturation An enhanced state observer-based sliding mode control (SMC) strategy is proposed to achieve the control purpose and maintain the lane keeping errors as well as the roll angle within the prescribed performance boundaries Three contributions are made in this paper First, a prescribed performance function (PPF) is proposed in the controller design, aiming to implement the error transformation so as to constrain the controlled variables within the prescribed performance boundaries Second, a modified sliding surface is developed incorporating two nonlinear functions, whose specialities and benefits are taken advantage of: one is a barrier function to restrict the load transfer ratio (LTR) in a safe boundary to guarantee the roll stability; another is a monotonely decreasing function to adaptively change the damping ratio of the closed-loop system to improve the transient performance, including reducing the transient overshoots and steady-state errors Third, a modified multivariable adaptive SMC controller is proposed to achieve the integrated lane-keeping and roll control in the presence of the input saturation and bound-unknown disturbances The stability of the closed-loop system is rigorously proved via the Lyapunov function Finally, the effectiveness of the proposed control strategy is verified with a high-fidelity and full-car model via the CarSim platform

Online Energy Management for Multimode Plug-In Hybrid Electric Vehicles

Abstract: An online energy management controller is presented in this paper for a plug-in hybrid electric vehicle (PHEV), which is based on driving conditions recognition and genetic algorithm (GA). The proposed controller can be used in the real-time application. First, the studied multimode PHEV is modeled and four traction operation modes are introduced in detail. Second, the principal component analysis (PCA) algorithm is utilized to classify the real historical driving conditions data. Four types of driving conditions are constructed to describe the representative scenarios. Then, GA is applied to search the optimal values for seven control actions offline. These parameters for different driving conditions are preserved and can be activated online. Finally, the driving condition is identified online and the corresponding control actions are loaded and adopted. Simulation results indicate that the proposed approach is close to the globally optimal method, dynamic programming, and is superior to the charge-depleting/charge-sustaining technique. Also, hardware-in-the-loop experiment is built to validate the real-time characteristic of the proposed strategy.