Guodong Du

Bio: Guodong Du is an academic researcher from Beijing Institute of Technology. The author has contributed to research in topics: Energy management & Reinforcement learning. The author has an hindex of 6, co-authored 12 publications receiving 133 citations.

A Supervisory Control Strategy of Distributed Drive Electric Vehicles for Coordinating Handling, Lateral Stability, and Energy Efficiency

Abstract: A supervisory control strategy, including dynamic control supervisor, handling-stability controller, energy efficiency controller, and coordinated torque allocator, is proposed for distributed drive electric vehicles to coordinate vehicle handling, lateral stability, and energy economy performance. In the dynamic control supervisor, first, the phase plane analysis is implemented to accurately define the vehicle stability boundary so that the lookup table of bounds can be established for online applications. Subsequently, based on the feedback drive conditions and vehicle states, the identified boundary is dynamically quantified by the designed varying weight factor (VWF) in real time. In the handling-stability controller, a unified yaw rate reference of VWF is developed to simultaneously guarantee vehicle maneuverability and lateral stabilization. Then, a novel integral triple-step method is proposed to calculate the proper direct yaw moment for the desired vehicle motion. In the energy efficiency controller, the interaxle torque distribution map is optimized for optimal vehicle energy economy. In the coordinated torque allocator, a torque increment allocation problem is formulated and optimized to realize the desired forces, meanwhile, based on VWF to minimize energy consumption and tire workload usage. The validations of the proposed strategy are conducted under various maneuvers, yielding comprehensive improvements in terms of vehicle handling, lateral stability, and energy performance.

Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook

Abstract: Hybrid Electric Vehicles (HEVs) have been proven to be a promising solution to environmental pollution and fuel savings. The benefit of the solution is generally realized as the amount of fuel consumption saved, which by itself represents a challenge to develop the right energy management strategies (EMSs) for HEVs. Moreover, meeting the design requirements are essential for optimal power distribution at the price of conflicting objectives. To this end, a significant number of EMSs have been proposed in the literature, which require a categorization method to better classify the design and control contributions, with an emphasis on fuel economy, providing power demand, and real-time applicability. The presented review targets two main headlines: (a) offline EMSs wherein global optimization-based EMSs and rule-based EMSs are presented; and (b) online EMSs, under which instantaneous optimization-based EMSs, predictive EMSs, and learning-based EMSs are put forward. Numerous methods are introduced, given the main focus on the presented scheme, and the basic principle of each approach is elaborated and compared along with its advantages and disadvantages in all aspects. In this sequel, a comprehensive literature review is provided. Finally, research gaps requiring more attention are identified and future important trends are discussed from different perspectives. The main contributions of this work are twofold. Firstly, state-of-the-art methods are introduced under a unified framework for the first time, with an extensive overview of existing EMSs for HEVs. Secondly, this paper aims to guide researchers and scholars to better choose the right EMS method to fill in the gaps for the development of future-generation HEVs.

Eco-driving-based cooperative adaptive cruise control of connected vehicles platoon at signalized intersections

Abstract: Vehicle driving patterns greatly impact the sustainability of the transportation system. Based on V2X communication, the ecological cooperative adaptive cruise control (Eco-CACC) is proposed combing the advantages of eco-driving and car-following to minimize the energy consumption of the connected automated vehicles platoon. Herein, the vehicle platoon behavior in the scenario of driving through a signalized intersection exhibits great benefits for sustainability which is even improved along corridors with more traffic lights. In the velocity trajectory planning process, a modified dynamic programming algorithm is formulated with the switching logic gate of two types of optimal control problems to increase the computational speed. By testing in the real-world scenario, the results of the proposed Eco-CACC demonstrate excellent energy performance which improves 8.02% compared to manual driving with the constant acceleration policy. Moreover, energy can be further improved by 2.02% and 1.55% when the car-following strategy is selected with MPC and IDM algorithm.