Norman K. Bucknor

Supervisory Control of Parallel Hybrid Electric Vehicles for Fuel and Emission Reduction

Past research on Hybrid Electric Vehicles (HEVs) focused primarily on improving their fuel economy. Emission reduction is another important performance attribute that needs to be addressed. When emissions are considered for hybrid vehicles with a gasoline engine, horizon-based optimization methodologies should be used because the light- off of the three-way catalytic converter heavily depends on the warming-up of catalyst temperature. In this paper, we propose a systematic design method for a cold-start supervisory control algorithm based on the Dynamic Programming (DP) methodology. First, a system-level parallel HEV model is developed to efficiently predict tailpipe emissions as well as fuel economy.The optimal control problem for minimization of cold-start emissions and fuel consumption is then solved via DP. Since DP solution cannot be directly implemented as a real-time controller, more useful control strategies are extracted from DP solution over the entire state space via the comprehensive extraction method. The DP results indicate that the engine on/off, gear-shift, and power-split strategies must be properly adjusted to achieve fast catalyst warm-up with minimal cold-start engine-out emissions. Based on DP results, we proposed a rule-based control algorithm that is easy to implement and achieves near- optimal fuel economy and emissions performance.

Optimal Energy and Catalyst Temperature Management of Plug-in Hybrid Electric Vehicles for Minimum Fuel Consumption and Tail-Pipe Emissions

Control of plug-in hybrid electric vehicles (PHEVs) poses a different challenge from that of the conventional hybrid electric vehicle (HEV) because the battery energy is designed to deplete throughout the drive cycle. In particular, when the travel distance exceeds the all-electric range (AER) of a PHEV and when tailpipe emissions are considered, optimal operation of the PHEV must consider optimization of the performance over a time horizon. In this paper, we develop a method to synthesize a supervisory powertrain controller (SPC) that achieves near-optimal fuel economy and tailpipe emissions under known travel distances. We first find the globally optimal solution using the dynamic programming (DP) technique, which provides an optimal control policy and state trajectories. Based on the analysis of the optimal state trajectories, a new variable energy-to-distance ratio (EDR), θ, is introduced to quantify the level of battery state-of-charge (SOC) relative to the remaining distance. This variable plays an important role in adjusting both energy and catalyst thermal management strategies for PHEVs. A novel extraction method is developed to extract adjustable engine on/off, gear-shift, and power-split strategies from the DP control policy over the entire state space. Based on the extracted results, an adaptive SPC that optimally adjusts the engine on/off, gear-shift, and power-split strategies under various EDR and catalyst temperature conditions was developed to achieve near-optimal fuel economy and emission performance.

Model Predictive Control of a Two Stage Actuation System using Piezoelectric Actuators for Controllable Industrial and Automotive Brakes and Clutches

High bandwidth actuation systems that are capable of simultaneously producing relatively large forces and displacements are required for use in automobiles and other industrial applications. Conventional hydraulic actuation mechanisms used in automotive brakes and clutches are complex, inefficient and have poor control robustness. These lead to reduced fuel economy, controllability issues and other disadvantages. Recently, a two-stage hybrid actuation mechanism was proposed by combining classical electromechanical actuators like DC motors and advanced smart material devices like piezoelectric actuators. This article discusses the development and implementation of a model predictive control methodology for controlling this two-stage actuation system in tracking various reference inputs. Additionally, this methodology also employs a unit-step delayed disturbance estimate to account for actuator hysteresis, other nonlinearities and unmodeled dynamics in the system. Finally, the article highlights the effectiveness of this control methodology experimentally by tracking various reference inputs.

Optimal Control of Plug-in HEVs for Fuel Economy Under Various Travel Distances

Abstract Control of Plug-in Hybrid Electric Vehicles (PHEVs) poses a different challenge from that of the conventional Hybrid Electric Vehicle (HEV) due to the fact that the battery energy is depleted throughout the drive cycle. In particular, when the travel distance exceeds the All Electric Range (AER) of a PHEV, control of the PHEV is no longer trivial. In this paper, we develop a method for the synthesis of the supervisory powertrain controller (SPC) that achieves near-optimal performance under known travel distances. We first find the globally optimal solution using the dynamic programming (DP) technique, which serves as a benchmark of achievable performance. By analyzing the DP results, a variable Energy-to-Distance Ratio (EDR), θ, is introduced to quantify the level of battery state-of-charge (SOC) with respect to the remaining distance. This variable was found to play an important role in the energy management of PHEVs, and an adaptive SPC that adjusts engine on/off and gear-shift strategies under a wide range of θ conditions was proposed using a comprehensive extraction method. Simulation results confirm that the proposed adaptive SPC achieves near-optimal (

Optimal catalyst temperature management of Plug-in Hybrid Electric Vehicles

For driving cycles that require use of the engine (i.e. the trip distance exceeds the All Electric Range (AER) of a Plug-in Hybrid Electric Vehicle (PHEV) or a driving cycle demands power exceeding the battery peak power), the catalyst temperature management for reduced tailpipe emissions is a challenging control problem due to the frequent and extended engine shut-down and catalyst cool-down. In this paper, we develop a method to synthesize a supervisory powertrain controller (SPC) that achieves near-optimal fuel economy and tailpipe emissions under known travel distances. We first find the globally optimal solution using dynamic programming (DP), which provides an optimal control policy and state trajectories. Based on the analysis of the optimal state trajectories, a variable Energy-to-Distance Ratio (EDR) is introduced to quantify the level of battery state-of-charge (SOC) relative to the remaining distance. A novel two-dimensional extraction method is developed to extract engine on/off, gear-shift, and power-split control strategies as functions of both EDR and the catalyst temperature from the DP control policy. Based on the extracted results, an adaptive SPC that optimally adjusts the engine on/off, gear-shift, and power-split strategies under various EDR and catalyst temperature conditions was developed to achieve near-optimal fuel economy and emission performance.

Modeling and control of hybrid electric vehicles for fuel and emission reduction

Control of Hybrid Electric Vehicles (HEVs) is an active research area. Much of the past research focused on one aspect of hybrid vehicle performance–fuel economy. While fuel economy is important for HEVs, reduction of emissions is another major performance of interest, due to ever-tightening emission regulations. Minimization of fuel consumption may have a trickle-down effect but does not guarantee reduced emissions. In fact, over-zealous pursuit of fuel consumption reduction may compromise emission. This paper investigates the emissions formation mechanism, develops an emission model that predicts tail-pipe emissions, and formulates a supervisory control problem of emissions reduction. The Dynamic Programming (DP) technique is employed to solve the optimal control problem of parallel HEVs for both emission reduction and fuel economy. The DP solution of the optimal control problem shows that tail-pipe emissions could be significantly reduced with negligible loss of fuel economy.Copyright

Two-stage actuation system using DC motors and piezoelectric actuators for controllable industrial and automotive brakes and clutches

High bandwidth actuation systems that are capable of simultaneously producing relatively large forces and displacements are required for use in automobiles and other industrial applications. Conventional hydraulic actuation mechanisms used in automotive brakes and clutches are complex, inefficient and have poor control robustness. These lead to reduced fuel economy, controllability issues and other disadvantages. This paper involves the design, development, testing and control of a two-stage hybrid actuation mechanism by combining classical actuators like DC motors and advanced smart material actuators like piezoelectric actuators. The paper also discusses the development of a robust control methodology using the Internal Model Control (IMC) principle and emphasizes the robustness property of this control methodology by comparing and studying simulation and experimental results.

Automatic Determination of Transmission Powerflow Mechanizability Using Graph Theory

A methodology has been developed to automatically generate planetary geartrain topologies based on criteria such as the number of desired speed ratios and the number of torque transmitting mechanisms [1]. This paper describes an algorithm and computer code for automatically determining the mechanizability of the candidate geartrains. The mathematical description of each topology is interpreted as a graph, a collection of vertices and connecting edges, which can then be tested for planarity using graph-processing algorithms. A planar graph implies mechanizability. Using another algorithm based on graph theory, the computer code also detects and eliminates duplicate designs that may be hard to detect via visual inspection. The computer program significantly reduces the time needed to manually process design data sets by eliminating those design candidates that are infeasible.Copyright

Dynamic Modeling of the Traveling Chain Transmission

The Traveling Chain Transmission (TCT) is a novel two-speed chain drive developed at the General Motors R&D Center [1]. In order to predict potential performance and develop design requirements, a multi-body dynamic model of the Traveling Chain Transmission (TCT) was developed and validated using dynamometer test data from the first TCT prototype hardware. A vehicle drivetrain model incorporating the TCT as part of a multi-speed transmission was also developed in order to study the potential performance of the TCT in a vehicle environment. The simulated transmission is a nominal 4-speed automatic transmission converted to a 5-speed transmission by replacing the fixed speed-ratio drop chain by the dual speed-ratio TCT. An upshift from 4th to 5th gear is simulated and the resulting torque and speed transients are predicted.Copyright

Dynamic Modeling of the Organic Rankine Cycle for the Waste Heat Recovery of Internal Combustion Engines

Using state-of-the-art engine technologies, current gasoline internal combustion engines of passenger vehicles convert only 25∼35% of fuel energy into useful power, and 65∼75% of fuel energy are wasted as heat through engine cooling and exhaust gas systems. One of the promising technologies that can dramatically improve fuel economy is the waste heat recovery system using Organic Rankine Cycle (ORC). The working fluid of the ORC, however, undergoes both liquid and gas phases throughout the cycle, and it is a challenge to develop heat exchanger models that can be used in a simple and efficient dynamic ORC model. In this study, a simplified ordinary differential equation (ODE) ORC model is developed for system-level design and control studies. First, the first principles model of heat exchange dynamics is described by two partial differential equations (PDE) and one ODE, and then the moving-boundary approach is used to lump the distributed parameters of the heat exchanger by integrating three governing equations over each length of three phases (gas, two-phase, and liquid). The simulation results demonstrate that the proposed dynamic ORC model provides key transient dynamics of the ORC with much less computational load.Copyright