Energy Management and Drivability Control Problems for Hybrid Electric Vehicles

Aswani

2008 47th IEEE Conference on Decision and Control

et al. 2008

Abstract-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.

Section: A Motivationmentioning

confidence: 99%

“…Most of the past work in hybrid energy management has focused primarily on fuel economy. In this paper we address the "basic" drivability issues of gear selection and engine on/off, rather than more detailed driveline dynamics as in [7].…”

Section: Introductionmentioning

confidence: 99%

Incorporating drivability metrics into optimal energy management strategies for Hybrid Vehicles

Aswani

2008 47th IEEE Conference on Decision and Control

et al. 2008

“…Drivability is a rather vague term that covers many aspects of vehicle performance including acceleration, engine noise, braking, shifting activity, shift quality [12], and other behaviors. All of these contribute to consumer perception of the vehicle, which is crucial in purchasing decisions.…”

Section: A Motivationmentioning

confidence: 99%

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

Wang

2009 American Control Conference

et al. 2009

Abstract-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 today's production hybrids. This paper studies controllers designed using Shortest Path Stochastic Dynamic Programming (SPSDP). The controllers are evaluated on Ford Motor Company's highly accurate proprietary vehicle model over large numbers of realworld drive cycles, and compared to a controller developed by Ford for a prototype vehicle. Results show the SPSDPbased 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.

“…Drivability is a commonly used term that covers many aspects of vehicle performance including acceleration, engine noise, braking, shifting activity, shift quality [7], and other behaviors. All of these contribute to consumer perception of the vehicle, which is crucial in purchasing decisions.…”

Section: Drivability Constraints 31 Motivationmentioning

confidence: 99%

Fundamental Structural Limitations of an Industrial Energy Management Controller Architecture for Hybrid Vehicles

Wang

ASME 2009 Dynamic Systems and Control Conference, Volume 1

et al. 2009

Energy management controllers for hybrid electric vehicles typically contain numerous parameters that must be tuned in order to arrive at a desired compromise among competing attributes, such as fuel economy and driving quality. This paper estimates the Pareto tradeoff curve of fuel economy versus driving quality for a baseline industrial controller, and compares it to the Pareto tradeoff curve of an energy management controller based on Shortest Path Stochastic Dynamic Programming (SPSDP). Previous work had shown important performance advantages of the SPSDP controller in comparison to the baseline industrial controller. Because the baseline industrial controller relies on manual tuning, there was always the possibility that better calibration of the algorithm could significantly improve its performance. To investigate this, a numerical search of possible controller calibrations is conducted to determine the best possible performance of the baseline industrial controller and estimate its Pareto tradeoff curve. Both the SPSDP and baseline controllers are causal (i.e., do not rely on future drive cycle information). The SPSDP controllers achieve better performance (i.e., better fuel economy with equal or better driving quality) over a wide range of driving cycles due to fundamental structural limitations of the baseline controller that can not be overcome by tuning. The message here is that any decisions that restrict controller structure may limit attainable performance, even when many tunable parameters are made available to calibration engineers. The structure of the baseline algorithm and possible sources of its limitations are discussed.