Dongpu Cao

Integrated Optimization of Battery Sizing, Charging, and Power Management in Plug-In Hybrid Electric Vehicles

This brief presents an integrated optimization framework for battery sizing, charging, and on-road power management in plug-in hybrid electric vehicles. This framework utilizes convex programming to assess interactions between the three optimal design/control tasks. The objective is to minimize carbon dioxide (CO2) emissions, from the on-board internal combustion engine and grid generation plants providing electrical recharge power. The impacts of varying daily grid CO2 trajectories on both the optimal battery size and charging/power management algorithms are analyzed. We find that the level of grid CO2 emissions can significantly impact the nature of emission-optimal on-road power management. We also observe that the on-road power management strategy is the most important design task for minimizing emissions, through a variety of comparative studies.

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.

Energy Management in Plug-in Hybrid Electric Vehicles: Recent Progress and a Connected Vehicles Perspective

Plug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet.

System Modeling and Pressure Control of a Clutch Actuator for Heavy-Duty Automatic Transmission Systems

Precision control of clutch pressure is critical in heavy-duty automatic transmission applications in which the fast response of the clutch actuator is required. A conventional clutch actuator system with a pressure-reducing valve (PRV) is not applicable in this kind of application due to the fact that a large transient flow and high output power for power-on shift are necessary. In this paper, a pilot-operated PRV is developed for heavy-duty automatic transmission systems. The developed PRV can make the clutch actuator system have a fast response and a high flow capacity simultaneously. The PRV utilizes a three-stage structure with a high-speed proportional solenoid valve (PSV) as the pilot stage to do the tradeoff between the valve response and the flow capacity. First, a linearized input-output dynamic analytical model for the clutch pressure control system is developed based on fluid dynamics. Then, the parameters are identified, and the model is validated by using experimental data. For the validated input-output model, both open- and closed-loop (feedback) pressure control strategies are designed and implemented in a test setup. It infers from the experimental results that the feedback control can lead to excellent control precision. The developed clutch actuator system is applicable for heavy-duty automatic transmissions.

Real-Time Trajectory Planning for Autonomous Urban Driving: Framework, Algorithms, and Verifications

This paper focuses on the real-time trajectory planning problem for autonomous vehicles driving in realistic urban environments. To solve the complex navigation problem, we adopt a hierarchical motion planning framework. First, a rough reference path is extracted from the digital map using commands from the high-level behavioral planner. The conjugate gradient nonlinear optimization algorithm and the cubic B-spline curve are employed to smoothen and interpolate the reference path sequentially. To follow the refined reference path as well as handle both static and moving objects, the trajectory planning task is decoupled into lateral and longitudinal planning problems within the curvilinear coordinate framework. A rich set of kinematically feasible path candidates are generated to deal with the dynamic traffic both deliberatively and reactively. In the meanwhile, the velocity profile generation is performed to improve driving safety and comfort. After that, the generated trajectories are carefully evaluated by an objective function, which combines behavioral decisions by reasoning about the traffic situations. The optimal collision-free, smooth, and dynamically feasible trajectory is selected and transformed into commands executed by the low-level lateral and longitudinal controllers. Field experiments have been carried out with our test autonomous vehicle on the realistic inner-city roads. The experimental results demonstrated capabilities and effectiveness of the proposed trajectory planning framework and algorithms to safely handle a variety of typical driving scenarios, such as static and moving objects avoidance, lane keeping, and vehicle following, while respecting the traffic rules.

High-Precision Hydraulic Pressure Control Based on Linear Pressure-Drop Modulation in Valve Critical Equilibrium State

High precision and fast response are of great significance for hydraulic pressure control in automotive braking systems. In this paper, a novel sliding mode control based high-precision hydraulic pressure feedback modulation is proposed. Dynamical models of the hydraulic brake system including valve dynamics are established. An open loop load pressure control based on the linear relationship between the pressure-drop and coil current in valve critical open equilibrium state is proposed, and also experimentally validated on a hardware-in-the-loop test rig. The control characteristics under different input pressures and varied coil currents are investigated. Moreover, the sensitivity of the proposed modulation on valves key structure parameters and environmental temperatures are explored with some unexpected drawbacks. In order to achieve better robustness and precision, a sliding mode control based closed loop scheme is developed for the linear pressure-drop modulation. Comparative tests between this method and the existing methods are carried out. The results validate the effectiveness and superior performance of the proposed closed loop modulation method.

Multiple-Model Switching Control of Vehicle Longitudinal Dynamics for Platoon-Level Automation

Platooning of autonomous vehicles has the potential to significantly benefit road traffic. This paper presents a new robust acceleration tracking control of vehicle longitudinal dynamics toward platoon-level automation. Based on a multiple-model switching structure, this design divides the large uncertainties of vehicle dynamics into small uncertainties and, accordingly, develops multiple robust controllers for the multiple-model set. The switching control system automatically selects the most appropriate candidate controller into the loop, according to the errors between current vehicle dynamics and multiple models. This technique offers more consistent and approximately linear node dynamics for upper level platoon control, even under relatively large vehicle uncertainties. Simulation comparison with a sliding model controller and a fixed H-infinity controller is conducted for a passenger car to demonstrate the enhanced robustness of the switching control method. The experimental test for the same car is performed for further validation.

Condition Monitoring in Advanced Battery Management Systems: Moving Horizon Estimation Using a Reduced Electrochemical Model

Efficient battery condition monitoring is of particular importance in large-scale, high-performance, and safety-critical mechatronic systems, e.g., electrified vehicles and smart grid. This paper pursues a detailed assessment of optimization-driven moving horizon estimation (MHE) framework by means of a reduced electrochemical model. For state-of-charge estimation, the standard MHE and two variants in the framework are examined by a comprehensive consideration of accuracy, computational intensity, effect of horizon size, and fault tolerance. A comparison with common extended Kalman filtering and unscented Kalman filtering is also carried out. Then, the feasibility and performance are demonstrated for accessing internal battery states unavailable in equivalent circuit models, such as solid-phase surface concentration and electrolyte concentration. Ultimately, a multiscale MHE-type scheme is created for State-of-Health estimation. This study is the first known systematic investigation of MHE-type estimators applied to battery management.

System Modeling, Coupling Analysis, and Experimental Validation of a Proportional Pressure Valve With Pulsewidth Modulation Control

This paper presents system modeling and coupling analysis of a proportional solenoid valve (PSV) with pulsewidth modulation (PWM) control mode. The coupling analysis is adopted to reveal the internal relations among the electromagnetic, mechanical, fluid, and electrical subsystems based on the integrate proportional pressure valve mode for the PSV system. Then, a simplified and practical PWM strategy is proposed to design the PSV. The dynamic performance of each subsystem is analyzed with respect to a duty cycle set. Consequently, simulation using a commercial software is presented. It infers from the results that the valve has a fast response to the control signal and can reach a small flow area at the instantaneous moment during opening and closing operations. Finally, the analytical results of the designed PSV are validated by using experimental tests. It follows from the testing results that the established model is precise and the corresponding coupling analysis results are valuable for the performance design and optimization of PSV.

Simultaneous Observation of Hybrid States for Cyber-Physical Systems: A Case Study of Electric Vehicle Powertrain

As a typical cyber-physical system (CPS), electrified vehicle becomes a hot research topic due to its high efficiency and low emissions. In order to develop advanced electric powertrains, accurate estimations of the unmeasurable hybrid states, including discrete backlash nonlinearity and continuous half-shaft torque, are of great importance. In this paper, a novel estimation algorithm for simultaneously identifying the backlash position and half-shaft torque of an electric powertrain is proposed using a hybrid system approach. System models, including the electric powertrain and vehicle dynamics models, are established considering the drivetrain backlash and flexibility, and also calibrated and validated using vehicle road testing data. Based on the developed system models, the powertrain behavior is represented using hybrid automata according to the piecewise affine property of the backlash dynamics. A hybrid-state observer, which is comprised of a discrete-state observer and a continuous-state observer, is designed for the simultaneous estimation of the backlash position and half-shaft torque. In order to guarantee the stability and reachability, the convergence property of the proposed observer is investigated. The proposed observer are validated under highly dynamical transitions of vehicle states. The validation results demonstrates the feasibility and effectiveness of the proposed hybrid-state observer.