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

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
Daniel F. Opila ◽  
Xiaoyong Wang ◽  
Ryan McGee ◽  
R. Brent Gillespie ◽  
Jeffrey A. Cook ◽  
...  
Keyword(s):  
Energy Management ◽  
Fuel Economy ◽  
Real World ◽  
Power Flow ◽  
Design Method ◽  
Management Strategies ◽  
Hybrid Vehicle ◽  
Stochastic Dynamic ◽  
Real World Data ◽  
Industrial Controller

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.

Comparative Study of Energy Management Strategies for Hydraulic Hybrids

2014 ◽  
Vol 137 (4) ◽  
Author(s):  
Timothy O. Deppen ◽  
Andrew G. Alleyne ◽  
Jonathan J. Meyer ◽  
Kim A. Stelson
Keyword(s):  
Energy Management ◽  
Design Method ◽  
Tracking Error ◽  
Management Strategies ◽  
Stochastic Dynamic ◽  
Drive Cycle ◽  
Hybrid Powertrain ◽  
Hydraulic Hybrid ◽  
System A ◽  
Factorial Design Of Experiments

The sensitivity of energy management strategies (EMS) with respect to variations in drive cycle and system parameters is considered. The design of three strategies is presented: rule-based, stochastic dynamic programming (SDP), and model predictive control (MPC). Each strategy is applied to a series hydraulic hybrid powertrain and validated experimentally using a hardware-in-the-loop system. A full factorial design of experiments (DOE) is conducted to evaluate the performance of these controllers under different urban and highway drive cycles as well as with enforced modeling errors. Through this study, it is observed that each EMS design method represents a different level of tradeoff between optimality and robustness based on how much knowledge of the system is assumed. This tradeoff is quantified by analyzing the standard deviation of system specific fuel consumption (SSFC) and root mean square (RMS) tracking error over the different simulation cases. This insight can then be used to motivate the choice of which control strategy to use based on the application. For example, a city bus travels a repeated route and that knowledge can be leveraged in the EMS design to improve performance. Through this study, it is demonstrated that there is not one EMS design method which is best suited for all applications but rather the underlying assumptions of the system and drive cycle must be carefully considered so that the most appropriate design method is chosen.

Development of a driver model to study the effects of real-world driver behaviour on the fuel consumption

2011 ◽  
Vol 225 (11) ◽  
pp. 1518-1530 ◽  
Author(s):  
A McGordon ◽  
J E W Poxon ◽  
C Cheng ◽  
R P Jones ◽  
P A Jennings
Keyword(s):  
Fuel Economy ◽  
Real World ◽  
High Speed ◽  
Driver Model ◽  
Good Representation ◽  
Vehicle Speed ◽  
Data Logging ◽  
Real World Data ◽  
Driver Behaviour ◽  
The Individual

The real-world fuel economy of vehicles is becoming increasingly important to manufacturers and customers. One of the major influences in this is driver behaviour, but it is difficult to study in a controlled and repeatable manner. An assessment of driver models for studying real-world driver behaviour has been carried out. It has been found that none of the currently existing driver models has sufficient fidelity for studying the effects of real-world driver behaviour on the fuel economy of the individual vehicle. A decision-making process has been proposed which allows a driver model with a range of driving tasks to be developed. This paper reports the initial results of a driver model as applied to the conceptually straightforward scenario of high-speed cruising. Data for the driver model have been obtained through real-world data logging. It has been shown that the simulation driver model can provide a good representation of real-world driving behaviour in terms of the vehicle speed, and this is compared with a number of logged driver speed traces. A comparison of the modelled fuel economy for logged and driver model real-world drivers shows good agreement.

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

2012 ◽  
Vol 135 (2) ◽  
Author(s):  
Daniel F. Opila ◽  
Xiaoyong Wang ◽  
Ryan McGee ◽  
J. W. Grizzle
Keyword(s):  
Dynamic Programming ◽  
Real Time ◽  
Energy Management ◽  
Fuel Economy ◽  
Stochastic Dynamic Programming ◽  
Stochastic Dynamic ◽  
Engine Torque ◽  
Simulation Based ◽  
Completion Times ◽  
Engine Start

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 controller's 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.

scholarly journals Energy Management Strategy for Fuel Cell and Battery Hybrid Vehicle Based on Fuzzy Logic

Processes ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 882
Author(s):  
Dongxu Li ◽  
Bing Xu ◽  
Jie Tian ◽  
Zheshu Ma
Keyword(s):  
Fuzzy Logic ◽  
Fuel Cell ◽  
Energy Management ◽  
Fuel Economy ◽  
Cell Hybrid ◽  
Management Strategy ◽  
Hybrid Vehicle ◽  
Hybrid Vehicles ◽  
Energy Management Strategy ◽  
Fuel Cell Hybrid

In order to improve fuel economy and enhance operating efficiency of fuel cell hybrid vehicles (FCHVs), fuzzy logic control (FLC) strategies are available and suggested for adoption. In this paper, the powertrain of a fuel cell hybrid vehicle is designed and the parameters of the motor, battery, and fuel cell are calculated. The FLC strategy and the power following control (PFC) strategy are designed for the studied FCHV. A secondary development for Advanced Vehicle Simulator (ADVISOR) is implemented based on the standard driving cycles, and a Chinese typical city driving cycle is imported. Simulation results demonstrate that the proposed FLC strategy is more valid and reasonable than the traditional PFC strategy. The proposed FLC strategy affects the vehicle characteristics significantly and contributes to better performance in four aspects: fuel economy, efficiency of battery and fuel cell system, battery state of charge (SOC), and battery life. Hence, the FLC strategy is more suitable for the energy management strategy for fuel cell and battery hybrid vehicles.

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