Ming L. Kuang

Predictive energy management of a power-split hybrid electric vehicle

In this paper, a Model Predictive Control (MPC) strategy is developed for the first time to solve the optimal energy management problem of power-split hybrid electric vehicles. A power-split hybrid combines the advantages of series and parallel hybrids by utilizing two electric machines and a combustion engine. Because of its many modes of operation, modeling a power-split configuration is complex and devising a near-optimal power management strategy is quite challenging. To systematically improve the fuel economy of a power-split hybrid, we formulate the power management problem as a nonlinear optimization problem. The nonlinear powertrain model and the constraints are linearized at each sample time and a receding horizon linear MPC strategy is employed to determine the power split ratio based on the updated model. Simulation results over multiple driving cycles indicate better fuel economy over conventional strategies can be achieved. In addition the proposed algorithm is causal and has the potential for real-time implementation.

MPC-Based Energy Management of a Power-Split Hybrid Electric Vehicle

A power-split hybrid electric vehicle (HEV) combines the advantages of both series and parallel hybrid vehicle architectures by utilizing a planetary gear set to split and combine the power produced by electric machines and a combustion engine. Because of the different modes of operation, devising a near optimal energy management strategy is quite challenging and essential for these vehicles. To improve the fuel economy of a power-split HEV, we first formulate the energy management problem as a nonlinear and constrained optimal control problem. Then two different cost functions are defined and model predictive control (MPC) strategies are utilized to obtain the power split between the combustion engine and electrical machines and the system operating points at each sample time. Simulation results on a closed-loop high-fidelity model of a power-split HEV over multiple standard drive cycles and with different controllers are presented. The results of a nonlinear MPC strategy show a noticeable improvement in fuel economy with respect to those of an available controller in the commercial Powertrain System Analysis Toolkit (PSAT) software and the other proposed methodology by the authors based on a linear time-varying MPC.

Optimally controlling Hybrid Electric Vehicles using path forecasting

The paper examines path-dependent control of Hybrid Electric Vehicles (HEVs). In this approach we seek to improve HEV fuel economy by optimizing charging and discharging of the vehicle battery depending on the forecasted vehicle route. The route is decomposed into a series connection of route segments with (partially) known properties. The dynamic programming is used as a tool to quantify the benefits offered by route information availability.

Power Smoothing Energy Management and Its Application to a Series Hybrid Powertrain

Energy management strategies in hybrid electric vehicles determine how much energy is produced/stored/used in each powertrain component. We propose an approach for energy management applied to a series hybrid electric vehicle that aims at improving the powertrain efficiency rather than the total fuel consumption. Since in the series configuration the engine is mechanically decoupled from the traction wheels, for a given power request the steady-state engine operating point is chosen to maximize the efficiency. A control algorithm regulates the transitions between different operating points by using the battery to smoothen the engine transients, thereby improving efficiency. Because of the constrained nature of the transient-smoothing problem, we implement the control algorithm by model predictive control. The control strategy feedback law is synthesized and integrated with the powertrain control software in the engine control unit. Simulations of the urban dynamometer driving schedule (UDDS) and US06 cycles using a complete vehicle system model and experimental tests of the UDDS cycle show improved fuel economy with respect to baseline strategies.

Nonlinear Model Predictive Control for power-split Hybrid Electric Vehicles

In this paper, a causal optimal controller based on Nonlinear Model Predictive Control (NMPC) is developed for a power-split Hybrid Electric Vehicle (HEV). The global fuel minimization problem is converted to a finite horizon optimal control problem with an approximated cost-to-go, using the relationship between the Hamilton-Jacobi-Bellman (HJB) equation and the Pontryagins minimum principle. A nonlinear MPC framework is employed to solve the problem online. Different methods for tuning the approximated minimum cost-to-go as a design parameter of the MPC are discussed. Simulation results on a validated high-fidelity closed-loop model of a power-split HEV over multiple driving cycles show that with the proposed strategy, the fuel economies are improved noticeably with respect to those of an available controller in the commercial Powertrain System Analysis Toolkit (PSAT) software and a linear time-varying MPC controller previously developed by the authors.

Engine power smoothing energy management strategy for a series hybrid electric vehicle

Hybrid electric vehicles exploit energy production and energy storage systems to achieve improved fuel economy with respect to conventional powertrains. In order to maximize such improvements, advanced control strategies are needed for deciding the amount of energy to be produced and stored. In this paper we propose an approach for energy management of a series hybrid electric vehicle (SHEV). This approach focuses on maximizing the pointwise powertrain efficiency, rather than the overall fuel consumption. For a given power request the steady state engine operating point is chosen to maximize the efficiency. A control algorithm regulates the transitions between different operating points, by using the battery to smoothen the engine transients. Due to the constrained nature of the transient smoothing problem, we implement the control algorithm by model predictive control. Experimental testing on the UDDS cycle shows improved fuel economy with respect to two baseline strategies.

Intelligent Trip Modeling for the Prediction of an Origin–Destination Traveling Speed Profile

Accurate prediction of the traffic information in real time such as flow, density, speed, and travel time has important applications in many areas, including intelligent traffic control systems, optimizing vehicle operations, and the routing selection for individual drivers on the road. This is also a challenging problem due to dynamic changes of traffic states by many uncertain factors along a traveling route. In this paper, we present an Intelligent Trip Modeling System (ITMS) that was developed using machine learning to predict the traveling speed profile for a selected route based on the traffic information available at the trip starting time. The ITMS contains neural networks to predict short-term traffic speed based on the traveling day of the week, the traffic congestion levels at the sensor locations along the route, and the traveling time and distances to reach individual sensor locations. The ITMS was trained and evaluated by using ten months of traffic data provided by the California Freeway Performance Measurement System along a California Interstate I-405 route that is 26 mi long and contains 52 traffic sensors. The ITMS was also evaluated by the traffic data acquired from a 32-mi-long freeway section in the state of Michigan. Experimental results show that the proposed system, i.e., ITMS, has the capability of providing accurate predictions of dynamic traffic changes and traveling speed at the beginning of a trip and can generalize well to prediction of speed profiles on the freeway routes other than the routes the system was trained on.

Hydraulic brake system modeling and control for active control of vehicle dynamics

Active control of vehicle dynamics has become one of the top competitive features in todays automobiles. Vehicle dynamic control systems include antilock brakes, traction control and yaw control. The realization of these systems relies on the control of hydraulic brakes as well as other vehicle systems. Modeling of the hydraulic brake system is essential to the design of vehicle dynamic control systems. The paper describes the derivation of a hydraulic brake system model using the bond graph technique, and the design of a feedback control system with an adaptive gain schedule PD controller. In addition, simulation and experimental results are presented to illustrate the model validation and the controller performance.

Path-forecasting for HEV optimal energy management (POEM)

This paper studies a control strategy using path-forecasting for Hybrid Electric Vehicle (HEV) optimal energy management (POEM). In the previous work, a receding horizon control (RHC) approach was developed to solve a dynamic programming (DP) formulated optimization problem where preview information of an intended route is utilized to schedule battery state of charge (SoC) usage profile along the route for optimal fuel economy of HEVs. This paper presents our recent work that further developed POEM strategy with refined route segmentation rules and fuel consumption estimation. The work also contains the development of a simulation platform integrating POEM strategy with a production level vehicle control strategy, fuel economy evaluation of POEM under different driving cycles, and the robustness study of the POEM strategy.