Ryan Abraham McGee

An Energy Management Controller to Optimally Trade Off Fuel Economy and Drivability for Hybrid Vehicles

Hybrid vehicle fuel economy performance is highly sensitive to the energy management strategy used to regulate power flow among the various energy sources and sinks. Optimal non-causal solutions are easy to determine if the drive cycle is known a priori. It is very challenging to design causal controllers that yield good fuel economy for a range of possible driver behavior. Additional challenges come in the form of constraints on powertrain activity, such as shifting and starting the engine, which are commonly called “drivability” metrics and can adversely affect fuel economy. In this paper, drivability restrictions are included in a shortest path stochastic dynamic programming (SP-SDP) formulation of the real-time energy management problem for a prototype vehicle, where the drive cycle is modeled as a stationary, finite-state Markov chain. When the SP-SDP controllers are evaluated with a high-fidelity vehicle simulator over standard government drive cycles, and compared to a baseline industrial controller, they are shown to improve fuel economy more than 11% for equivalent levels of drivability. In addition, the explicit tradeoff between fuel economy and drivability is quantified for the SP-SDP controllers.

Real time vehicle speed prediction using a Neural Network Traffic Model

Prediction of the traffic information such as flow, density, speed, and travel time is important for traffic control systems, optimizing vehicle operations, and the individual driver. Prediction of future traffic information is a challenging problem due to many dynamic contributing factors. In this paper, various methodologies for traffic information prediction are investigated. We present a speed prediction algorithm, NNTM-SP (Neural Network Traffic Modeling-Speed Prediction) that trained with the historical traffic data and is capable of predicting the vehicle speed profile with the current traffic information. Experimental results show that the proposed algorithm gave good prediction results on real traffic data and the predicted speed profile shows that NNTM-SP correctly predicts the dynamic traffic changes.

Incorporating drivability metrics into optimal energy management strategies for Hybrid Vehicles

Hybrid Vehicle fuel economy performance is highly sensitive to the energy management strategy used to select among multiple energy sources. Optimal solutions are easy to specify if the drive cycle is known a priori. It is very challenging to compute controllers that yield good fuel economy for a class of drive cycles representative of typical driver behavior. Additional challenges come in the form of constraints on powertrain activity, like shifting and starting the engine, which are commonly called ¿drivability¿ metrics. These constraints can adversely affect fuel economy. The benefits of including drivability restrictions in a Shortest Path Dynamic Programming (SPDP) formulation of the energy management problem are investigated for the first time. It is shown that this method yields up to 10% fuel economy improvement on a representative parallel electric hybrid when compared to a simpler instantaneous optimization formulation. This result is obtained by comparing a SPDP controller designed for drivability to a second SPDP controller, designed for fuel economy only, that uses an additional instantaneous optimization step for the incorporation of drivability. The results also quantify the tradeoff between drivability and fuel economy.

Trip-Oriented Energy Management Control Strategy for Plug-In Hybrid Electric Vehicles

This paper presents a trip-oriented energy management control strategy for plug-in hybrid electric vehicle (PHEV). The proposed strategy provides system optimization and control methods to improve real-world fuel economy (FE) by optimizing the power demand distribution between fuel and battery electricity and the power delivery split between the mechanical and electrical paths in a PowerSplit PHEV architecture. A two degree of freedom system model is established to characterize the actuation dynamics and the power delivery properties of the powertrain. This paper achieves three important contributions to PHEV energy management control research: 1) the optimal control problem is solved considering both the nonlinearity of battery efficiency and the complexity of PowerSplit architecture; 2) a novel trip-oriented energy consumption preplanning method is proposed using a driving pattern-based dynamic programming approach; and 3) a feedback control system is designed to realize the optimal energy consumption process in real applications. The proposed energy management control strategy has been shown to improve FE in Ford Escape PHEVs.

Real-Time Implementation and Hardware Testing of a Hybrid Vehicle Energy Management Controller Based on Stochastic Dynamic Programming

An energy management controller based on shortest path stochastic dynamic programming (SP-SDP) is implemented and tested in a prototype vehicle. The controller simultaneously optimizes fuel economy and powertrain activity, namely gear shifts and engine on–off events. Previous work reported on the controllers design and its extensive simulation-based evaluation. This paper focuses on implementation of the controller algorithm in hardware. Practical issues concerning real-time computability, driver perception, and command timing are highlighted and addressed. The SP-SDP controllers are shown to run in real-time, gracefully handle variations in engine start and gear-shift-completion times, and operate in a manner that is transparent to the driver. A hardware problem with the test vehicle restricted its maximum engine torque, which prevented a reliable fuel economy assessment of the SP-SDP controller. The data that were collected indicated that SP-SDP controllers could be straightforwardly designed to operate at different points of the fuel economy tradeoff curve and that their fuel economy may equal or exceed that of a baseline industrial controller designed for the vehicle.

Driving pattern identification for EV range estimation

This paper presents a driving pattern recognition method based on trip segment clustering. Driving patterns categorize various driving behaviors that contain certain energy demand property in common. It can be applied to various applications including intelligent transportation, emission estimation, passive/active safety controls and energy management controls. In this paper, pattern features are first identified from high impact factors from static and quasi-static environmental and traffic information. A feature based trip/route partitioning algorithm is then developed based on data clustering methods. The driving patterns are finally recognized by synthesizing all partitioned feature zones along the trip/route where each partitioned road section is distinguished by an attribute of feature combination that will result in a distinctive drive energy demand property. The driving pattern recognition is a critical technology especially in solving problems like range estimation and energy consumption preplanning for the plug-in capable electrified vehicles.

Performance comparison of hybrid vehicle energy management controllers on real-world drive cycle data

Hybrid Vehicle fuel economy and drivability performance are very sensitive to the “Energy Management” controller that regulates power flow among the various energy sources and sinks. Many methods have been proposed for designing such controllers. Most analytical studies evaluate closed-loop performance on government test cycles. Moreover, there are few results that compare stochastic optimal control algorithms to the controllers employed in todays production hybrids. This paper studies controllers designed using Shortest Path Stochastic Dynamic Programming (SPSDP). The controllers are evaluated on Ford Motor Companys highly accurate proprietary vehicle model over large numbers of real-world drive cycles, and compared to a controller developed by Ford for a prototype vehicle. Results show the SPSDP-based controllers yield 2–3% better performance than the Ford controller on real-world driving data, with even more improvement on a government test cycle. In addition, the SPSDP-based controllers can directly quantify tradeoffs between fuel economy and drivability.

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.

Trip-oriented Energy Management Control strategy for plug-in hybrid electric vehicles

This paper presents a trip-oriented Plug-in Hybrid Electric Vehicle (PHEV) energy management control (EMC) strategy that aims to improve the real world PHEV energy usage efficiency, economy and flexibility. The designed control architecture and methodologies enable the energy management control to utilize different levels of available trip foreknowledge, from as limited as distance between charges to as much as driving patterns, routing and real time traffic information, to optimize the onboard energy (fuel and electricity) usage. The proposed EMC first programs a battery SOC profile along a specific trip that governs an optimized energy consumption process with respect to a customers energy usage budget and foreseeable trip information. Next, a feedback controller manages the fuel consumption to electricity depletion ratio to achieve the preplanned energy consumption process by following the SOC profile and controlling the PHEV powertrain to its most efficient admissible operating states.

Model based control system design and verification for a hybrid electric vehicle

A hybrid electric vehicle requires a complex control system to effectively manage vehicle level attributes while maximizing fuel efficiency. The control system interactions necessitate a hierarchical control structure in which one controller, the vehicle system controller, directs the functions of the lower level controllers. This paper outlines a model-based method that allows a controls team to design and validate a vehicle system controller for use in a hybrid electric vehicle.