Battery energy management plays a crucial role in fuel economy improvement of
charge-sustaining parallel hybrid electric vehicles. Currently available control strategies
consider battery state of charge (SOC) and driver’s request through the pedal input in
decision-making. This method does not achieve an optimal performance for saving fuel
or maintaining appropriate SOC level, especially during the operation in extreme
driving conditions or hilly terrain. The objective of this thesis is to develop a control
algorithm using forthcoming traffic condition and road elevation, which could be fed
from navigation systems. This would enable the controller to predict potential of
regenerative charging to capture cost-free energy and intentionally depleting battery
energy to assist an engine at high power demand.
The starting point for this research is the modelling of a small sport-utility vehicle by
the analysis of the vehicles currently available in the market. The result of the analysis
is used in order to establish a generic mild hybrid powertrain model, which is
subsequently examined to compare the performance of controllers. A baseline is
established with a conventional powertrain equipped with a spark ignition direct
injection engine and a continuously variable transmission. Hybridisation of this vehicle
with an integrated starter alternator and a traditional rule-based control strategy is
presented. Parameter optimisation in four standard driving cycles is explained, followed
by a detailed energy flow analysis.
An additional potential improvement is presented by dynamic programming (DP),
which shows a benefit of a predictive control. Based on these results, a predictive
control algorithm using fuzzy logic is introduced. The main tools of the controller
design are the DP, adaptive-network-based fuzzy inference system with subtractive
clustering and design of experiment. Using a quasi-static backward simulation model,
the performance of the controller is compared with the result from the instantaneous
control and the DP. The focus is fuel saving and SOC control at the end of journeys,
especially in aggressive driving conditions and a hilly road. The controller shows a
good potential to improve fuel economy and tight SOC control in long journey and hilly
terrain. Fuel economy improvement and SOC correction are close to the optimal solution by the DP, especially in long trips on steep road where there is a large gap
between the baseline controller and the DP. However, there is little benefit in short trips
and flat road. It is caused by the low improvement margin of the mild hybrid powertrain
and the limited future journey information.
To provide a further step to implementation, a software-in-the-loop simulation model is
developed. A fully dynamic model of the powertrain and the control algorithm are
implemented in AMESim-Simulink co-simulation environment. This shows small
deterioration of the control performance by driver’s pedal action, powertrain dynamics
and limited computational precision on the controller performance.
| Identifer | oai:union.ndltd.org:CRANFIELD1/oai:dspace.lib.cranfield.ac.uk:1826/2589 |
| Date | January 2008 |
| Creators | Cho, B |
| Contributors | Vaughan, N. D. |
| Publisher | Cranfield University |
| Source Sets | CRANFIELD1 |
| Language | English |
| Detected Language | English |
| Type | Thesis or dissertation, Doctoral, PhD |
| Rights | © Cranfield University, 2008. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright holder. |
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