XiaoYong Wang

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 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.

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.

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

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 demonstrated 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. The SPSDP and baseline controllers are causal; they 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 cannot be overcome by tuning. The message here is that any decisions that specify or 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.Copyright

Real-World Robustness for Hybrid Vehicle Optimal Energy Management Strategies Incorporating Drivability Metrics

Hybrid vehicle fuel economy and drive quality are coupled through the “Energy Management” controller that regulates power flow among the various energy sources and sinks. This paper studies energy management controllers designed using Shortest Path Stochastic Dynamic Programming (SP-SDP), a stochastic optimal control design method which can respect constraints on drivetrain activity while minimizing fuel consumption for an assumed distribution of driver power demand. The performance of SP-SDP controllers is evaluated through simulation on large numbers of real-world drive cycles and compared to a baseline industrial controller provided by a major auto manufacturer. On real-world driving data, the SP-SDP-based controllers yield 10% better fuel economy than the baseline industrial controller, for the same engine and gear activity. The SP-SDP controllers are further evaluated for robustness to the drive cycle statistics used in their design. Simplified drivability metrics introduced in previous work are validated on large real-world data sets.

Fault-type identification and fault estimation of the active steering system of an electric vehicle in normal driving conditions

Fault-type identification and fault estimation for the active steering system of a vehicle are considered in this paper. The vehicle studied is an electric ground vehicle, which operates mainly under normal driving conditions. First, a two-degree-of-freedom dynamic model of the lateral motion and the yaw motion of the vehicle is established and verified by experimental data. Then, an adaptive observer is proposed to estimate the steering motor fault by using measurements of the yaw rate of the vehicle. Residuals are defined for specific fault-type identification purposes. Experimental tests on the electric ground vehicle are carried out to assess the fault identification and estimation performance as well as to reveal the limitations of and possible improvement in the proposed method.