Shengbo Eben Li

Model Predictive Multi-Objective Vehicular Adaptive Cruise Control

This paper presents a novel vehicular adaptive cruise control (ACC) system that can comprehensively address issues of tracking capability, fuel economy and driver desired response. A hierarchical control architecture is utilized in which a lower controller compensates for nonlinear vehicle dynamics and enables tracking of desired acceleration. The upper controller is synthesized under the framework of model predictive control (MPC) theory. A quadratic cost function is developed that considers the contradictions between minimal tracking error, low fuel consumption and accordance with driver dynamic car-following characteristics while driver longitudinal ride comfort, driver permissible tracking range and rear-end safety are formulated as linear constraints. Employing a constraint softening method to avoid computing infeasibility, an optimal control law is numerically calculated using a quadratic programming algorithm. Detailed simulations with a heavy duty truck show that the developed ACC system provides significant benefits in terms of fuel economy and tracking capability while at the same time also satisfying driver desired car following characteristics.

Stability and Scalability of Homogeneous Vehicular Platoon: Study on the Influence of Information Flow Topologies

In addition to decentralized controllers, the information flow among vehicles can significantly affect the dynamics of a platoon. This paper studies the influence of information flow topology on the internal stability and scalability of homogeneous vehicular platoons moving in a rigid formation. A linearized vehicle longitudinal dynamic model is derived using the exact feedback linearization technique, which accommodates the inertial delay of powertrain dynamics. Directed graphs are adopted to describe different types of allowable information flow interconnecting vehicles, including both radar-based sensors and vehicle-to-vehicle (V2V) communications. Under linear feedback controllers, a unified internal stability theorem is proved by using the algebraic graph theory and Routh-Hurwitz stability criterion. The theorem explicitly establishes the stabilizing thresholds of linear controller gains for platoons, under a large class of different information flow topologies. Using matrix eigenvalue analysis, the scalability is investigated for platoons under two typical information flow topologies, i.e., 1) the stability margin of platoon decays to zero as 0(1/N2) for bidirectional topology; and 2) the stability margin is always bounded and independent of the platoon size for bidirectional-leader topology. Numerical simulations are used to illustrate the results.

Minimum Fuel Control Strategy in Automated Car-Following Scenarios

Fuel consumption of traditional ground vehicles is significantly affected by how the vehicles are driven. This paper focuses on the servo-loop control design of a Pulse-and-Gliding (PnG) strategy to minimize fuel consumption in automated car following. A switching-based framework is proposed for real-time implementation. The corresponding controller was synthesized for ideal conditions and subsequently enhanced to compensate for practical factors such as powertrain dynamics, speed variations, and plant uncertainties. Simulations in both uniform and naturalistic traffic flows demonstrate that, compared with a linear quadratic (LQ)-based benchmark controller, the PnG controller improves fuel economy up to 20%. The significant fuel saving is achieved while maintaining precise range bounds so that the negative impact on safety/traffic flow is contained. The developed algorithm can potentially be embedded in adaptive cruise control systems to achieve fuel-saving function.

Economy-oriented vehicle adaptive cruise control with coordinating multiple objectives function

A recent design issue of adaptive cruise control systems is how to reduce fuel consumption when following a preceding vehicle. High fuel economy is achievable through reducing acceleration level, however, it is also significantly restrained by two other functional demands, track capability and driver desired response. In the framework of multi-objective coordination, this paper develops and experimentally validates an economy-oriented headway control algorithm for a passenger car with internal combustion engine. The control algorithm is synthesised in a hierarchical structure. The upper controller, undertaking a major coordinating task, is designed based on the model predictive control theory. Fuel economy, tracking capability, and the driver desired response are formulated as its cost function and constraints in a finite prediction horizon. As further analysis indicated, such a design inevitably results in infeasible control inputs in some extreme cases, e.g. urgent situations involving rapid acceleration/deceleration. A constraint softening method is adopted to enlarge the feasible region in the cost of somewhat sacrificing the optimality of the original cost function. Finally, a prototyping controller is developed based on xPC toolbox and equipped in a passenger car. The followed field tests show that, compared to a linear quadratic controller, such an algorithm improves both fuel economy and tracking capability while also being more responsive to driver car-following behaviours.

Advanced Machine Learning Approach for Lithium-Ion Battery State Estimation in Electric Vehicles

To fulfill reliable battery management in electric vehicles (EVs), an advanced State-of-Charge (SOC) estimator is developed via machine learning methodology. A novel genetic algorithm-based fuzzy C-means (FCM) clustering technique is first used to partition the training data sampled in the driving cycle-based test of a lithium-ion battery. The clustering result is applied to learn the topology and antecedent parameters of the model. Recursive least-squares algorithm is then employed to extract its consequent parameters. To ensure good accuracy and resilience, the backpropagation learning algorithm is finally adopted to simultaneously optimize both the antecedent and consequent parts. Experimental results verify that the proposed estimator exhibits sufficient accuracy and outperforms those built by conventional fuzzy modeling methods.

Stability Margin Improvement of Vehicular Platoon Considering Undirected Topology and Asymmetric Control

The platooning of autonomous vehicles has the potential to significantly improve traffic capacity, enhance highway safety, and reduce fuel consumption. This paper studies the scalability limitations of large-scale vehicular platoons moving in rigid formation, and proposes two basic ways to improve stability margins, i.e., enlarging information topology and employing asymmetric control. A vehicular platoon is considered as a combination of four components: 1) node dynamics; 2) decentralized controller; 3) information flow topology; and 4) formation geometry. Tools, such as the algebraic graph theory and matrix factorization technique, are employed to model and analyze scalability limitations. The major findings include: 1) under linear identical decentralized controllers, the stability thresholds of control gains are explicitly established for platoons under undirected topologies. It is proved that the stability margins decay to zero as the platoon size increases unless there is a large number of following vehicles pinned to the leader and 2) the stability margins of vehicular platoons under bidirectional topologies using asymmetric controllers are always bounded away from zero and independent of the platoon size. Simulations with a platoon of passenger cars are used to demonstrate the findings.

Distributed Model Predictive Control for Heterogeneous Vehicle Platoons Under Unidirectional Topologies

This paper presents a distributed model predictive control (DMPC) algorithm for heterogeneous vehicle platoons with unidirectional topologies and a priori unknown desired set point. The vehicles (or nodes) in a platoon are dynamically decoupled but constrained by spatial geometry. Each node is assigned a local open-loop optimal control problem only relying on the information of neighboring nodes, in which the cost function is designed by penalizing on the errors between the predicted and assumed trajectories. Together with this penalization, an equality-based terminal constraint is proposed to ensure stability, which enforces the terminal states of each node in the predictive horizon equal to the average of its neighboring states. By using the sum of local cost functions as a Lyapunov candidate, it is proved that asymptotic stability of such a DMPC can be achieved through an explicit sufficient condition on the weights of the cost functions. Simulations with passenger cars demonstrate the effectiveness of the proposed DMPC.

An overview of vehicular platoon control under the four-component framework

The platooning of autonomous ground vehicles has potential to largely benefit the road traffic, including enhancing highway safety, improving traffic utility and reducing fuel consumption. The main goal of platoon control is to ensure all the vehicles in the same group to move at consensual speed while maintaining desired spaces between adjacent vehicles. This paper presents an overview of vehicular platoon control techniques from networked control perspective, which naturally decomposes a platoon into four interrelated components, i.e., 1) node dynamics (ND), 2) information flow topology (IFT), 3) distributed controller (DC) and, 4) geometry formation (GF). Under the four-component framework, existing literature are categorized and analyzed according to their technical features. Three main performance metrics, i.e. string stability, stability margin and coherence behavior, are also discussed.

Efficient Exhaustive Search of Power-Split Hybrid Powertrains With Multiple Planetary Gears and Clutches

Planetary gear (PG) power-split hybrid powertrains have been used in producing hybrid and plug-in hybrid vehicles from the Toyota, General Motor, and Ford for years. Some of the most recent designs use clutches to enable multiple operating modes to improve launching performance and/or fuel economy. Adding clutches and multiple operating modes, however, also increases production cost and design complexity. To enable an exhaustive but fast search for optimal designs among a large number of hardware configurations, clutch locations, and mode selections, an automated modeling and screening process is developed in this paper. Combining this process with the power-weighted efficiency analysis for rapid sizing method (PEARS), an optimal and computationally efficient energy management strategy, the extremely large design space of configuration, component sizing, and control becomes feasible to search through. This methodology to identify optimal designs has yet to be reported in the literature. A case study to evaluate the proposed methodology uses the configuration adopted in the Toyota Hybrid Synergy (THS-II) system used in the Prius model year 2010 and the Hybrid Camry. Two designs are investigated to compare with the simulated Prius design: one uses all possible operating modes; and the other uses a suboptimal design that limits the number of clutches to three. [DOI: 10.1115/1.4031533]

Longitudinal collision mitigation via coordinated braking of multiple vehicles using model predictive control

The vehicular collision can lead to serious casualties and traffic congestions, especially multiple-vehicle collision. Most recent studies mainly focused on collision warning and avoidance strategies for two consecutive vehicles, but only a few on multiple-vehicle situations. This study proposes a coordinated brake control CBC strategy for multiple vehicles to minimize the risk of rear-end collision using model predictive control MPC framework. The objective is to minimize total impact energy by determining the desired braking force, where the impact energy is defined as the relative kinetic energy for a consecutive pair of vehicles. Under the MPC framework, this problem is further converted to a quadratic programming at each time step for numerical computations. To compare the performance, three other control strategies, i.e. direct brake control DBC, driver reaction based brake control DRBC and linear quadratic regulator LQR control are also considered in this paper. The simulation results, in both a typical scenario and a huge number of scenarios under stochastic situations, show that CBC strategy has the best performance among these four strategies. The proposed CBC strategy has the potential to avoid the collision among a group of vehicles, and to mitigate the impact in cases where the collision is unavoidable.