A Robust Model Predictive Control Framework for Ecological Adaptive Cruise Control Strategy of Electric Vehicles

The recent advancement in vehicular networking technology provides novel solutions for designing intelligent and sustainable vehicle motion controllers. This work addresses a car-following task, where the feedback linearisation method is combined with a robust model predictive control (RMPC) scheme to safely, optimally and efficiently control a connected electric vehicle. In particular, the nonlinear dynamics are linearised through a feedback linearisation method to maintain an efficient computational speed and to guarantee global optimality. At the same time, the inevitable model mismatch is dealt with by the RMPC design. The control objective of the RMPC is to optimise the electric energy efficiency of the ego vehicle with consideration of a bounded model mismatch disturbance subject to satisfaction of physical and safety constraints. Numerical results first verify the validity and robustness through a comparison between the proposed RMPC and a nominal MPC. Further investigation into the performance of the proposed method reveals a higher energy efficiency and passenger comfort level as compared to a recently proposed benchmark method using the space-domain modelling approach

LMI-based robust model predictive control for a quarter car with series active variable geometry suspension

This paper proposes a robust model predictive control-based solution for the recently introduced series active variable geometry suspension (SAVGS) to improve the ride comfort and road holding of a quarter car. In order to close the gap between the nonlinear multi-body SAVGS model and its linear equivalent, a new uncertain system characterization is proposed that captures unmodeled dynamics, parameter variation, and external disturbances. Based on the newly proposed linear uncertain model for the quarter car SAVGS system, a constrained optimal control problem (OCP) is presented in the form of a linear matrix inequality (LMI) optimization. More specifically, utilizing semidefinite relaxation techniques a state-feedback robust model predictive control (RMPC) scheme is presented and integrated with the nonlinear multi-body SAVGS model, where state-feedback gain and control perturbation are computed online to optimise performance, while physical and design constraints are preserved. Numerical simulation results with different ISO-defined road events demonstrate the robustness and significant performance improvement in terms of ride comfort and road holding of the proposed approach, as compared to the conventional passive suspension, as well as, to actively controlled SAVGS by a previously developed conventional H-infinity control scheme.Comment: 13 pages, 11 figures, 2 tables, IEEE Transactions on Control Systems Technolog

A Computationally Efficient Robust Model Predictive Control Framework for Ecological Adaptive Cruise Control Strategy of Electric Vehicles

The recent advancement in vehicular networking technology provides novel solutions for designing intelligent and sustainable vehicle motion controllers. This work addresses a car-following task, where the feedback linearisation method is combined with a robust model predictive control (RMPC) scheme to safely, optimally and efficiently control a connected electric vehicle. In particular, the nonlinear dynamics are linearised through a feedback linearisation method to maintain an efficient computational speed and to guarantee global optimality. At the same time, the inevitable model mismatch is dealt with by the RMPC design. The control objective of the RMPC is to optimise the electric energy efficiency of the ego vehicle with consideration of a bounded model mismatch disturbance subject to satisfaction of physical and safety constraints. Numerical results first verify the validity and robustness through a comparison between the proposed RMPC and a nominal MPC. Further investigation into the performance of the proposed method reveals a higher energy efficiency and passenger comfort level as compared to a recently proposed benchmark method using the space-domain modelling approach.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

A Convex Optimal Control Framework for Autonomous Vehicle Intersection Crossing

Cooperative vehicle management emerges as a promising solution to improve road traffic safety and efficiency. This paper addresses the speed planning problem for connected and autonomous vehicles (CAVs) at an unsignalized intersection with consideration of turning maneuvers. The problem is approached by a hierarchical centralized coordination scheme that successively optimizes the crossing order and velocity trajectories of a group of vehicles so as to minimize their total energy consumption and travel time required to pass the intersection. For an accurate estimate of the energy consumption of each CAV, the vehicle modeling framework in this paper captures 1) friction losses that affect longitudinal vehicle dynamics, and 2) the powertrain of each CAV in line with a battery-electric architecture. It is shown that the underlying optimization problem subject to safety constraints for powertrain operation, cornering and collision avoidance, after convexification and relaxation in some aspects can be formulated as two second-order cone programs, which ensures a rapid solution search and a unique global optimum. Simulation case studies are provided showing the tightness of the convex relaxation bounds, the overall effectiveness of the proposed approach, and its advantages over a benchmark solution invoking the widely used first-in-first-out policy. The investigation of Pareto optimal solutions for the two objectives (travel time and energy consumption) highlights the importance of optimizing their trade-off, as small compromises in travel time could produce significant energy savings.Comment: 16 pages, 11 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Dynamic Analysis of Double Wishbone Front Suspension Systems for Sport Motorcycles

In this paper, two alternative front suspension systems and their influence on motorcycle dynamics are investigated. Based on an existing motorcycle mathematical model, the front end is modified to accommodate both Girder and Hossack suspension systems. Both of them have in common a double wishbone design that varies the front end geometry on certain manoeuvrings and, consequently, the machine’s behaviour. The kinematics of the two systems and their impact on the motorcycle performance is analysed and compared to the well known telescopic fork suspension system. Stability study for both systems is carried out by means of root-loci methods, in which the main oscillation modes, weave and wobble, are studied and compared to the baseline model

Advances in Active Suspension Systems for Road Vehicles

Active suspension systems (ASSs) have been proposed and developed for a few decades, and have now once again become a thriving topic in both academia and industry, due to the high demand for driving comfort and safety and the compatibility of ASSs with vehicle electrification and autonomy. Existing review papers on ASSs mainly cover dynamics modeling and robust control; however, the gap between academic research outcomes and industrial application requirements has not yet been bridged, hindering most ASS research knowledge from being transferred to vehicle companies. This paper comprehensively reviews advances in ASSs for road vehicles, with a focus on hardware structures and control strategies. In particular, state-of-the-art ASSs that have been recently adopted in production cars are discussed in detail, including the representative solutions of Mercedes active body control (ABC) and Audi predictive active suspension; novel concepts that could become alternative candidates are also introduced, including series active variable geometry suspension, and the active wheel-alignment system. ASSs with compact structure, small mass increment, low power consumption, high-frequency response, acceptable economic costs, and high reliability are more likely to be adopted by car manufacturers. In terms of control strategies, the development of future ASSs aims not only to stabilize the chassis attitude and attenuate the chassis vibration, but also to enable ASSs to cooperate with other modules (e.g., steering and braking) and sensors (e.g., cameras) within a car, and even with high-level decision-making (e.g., reference driving speed) in the overall transportation system—strategies that will be compatible with the rapidly developing electric and autonomous vehicles

Fuel efficiency optimization methodologies for series hybrid electric vehicles

This paper provides an overview of various optimization formulations that can lead to improved fuel economy for a series hybrid electric vehicle (HEV). The relevance and improvement to the current state-of-the-art are discussed. The formulated optimal control problems (OCP) consist of two individual optimization challenges: vehicle speed optimization and powertrain power-split optimization. These OCPs can be merged leading to a practical and global problem, where all the aspects are optimized simultaneously for a prescribed route and traveling time. Alternatively, the global problem can be approximated by solving individual OCPs, one for each aspect, in steps and combined a posteriori. The optimal solutions in each case are investigated and compared by simulation examples to expose the trade-off between optimality of fuel economy achieved by global optimization and reduction in computational complexity and hence practicality of the two-step solution approximation.</p