Vehicle Simulation

The goal of this research is to improve the design of a fuel cell battery hybrid vehicle through the use of an advanced vehicle powertrain simulator. The first part of this project involves the development of the simulation package from an existing Matlab / Simulink based model. This model is modified and improved to more accurately simulate the realworld vehicle and also to allow for various parametric studies to be performed. The resulting simulation package is called Light, Fast, and Modifiable or LFM, and has been validated and used for several studies.

The first involves vehicle design parameters. This experiment studies the effect of various vehicle parameters such as tire rolling resistance, vehicle mass, drag coefficient on vehicle performance. The second study involves hybrid control strategy parameters. The hybrid control strategy controls the flow of power between each of the power sources and is vital to proper vehicle performance. This experiment studies the effect of various scaling factors in the hybrid control strategy. The third parametric study involves the input drive cycle. A drive cycle is a speed vs. time data set that effectively "drives" the simulation. This experiment studies how different statistical quantities that describe a drive cycle (such as average speed, average acceleration, etc.) affect vehicle performance.

A new hybrid control strategy, called Drive Cycle Recognition (DCR), is developed and investigated. This strategy involves classification of the current vehicle drive cycle (speed vs. time history) based on certain representative drive cycles. The identification is performed using key statistical information from the cycle such as average speed, stop ratio, and average acceleration. These values are weighted according to relative importance. The weight factors were obtained through the sensitivity study. It is shown that DCR can be used to significantly improve upon the current strategy by addressing faults in the most sensitive parameters.

The LFM simulator was used to perform a Degree of Hybridization (DOH) analysis, which involves experimenting with different balances between the power sources available in a hybrid vehicle. It is shown that an optimum balance exists for each driving style, but a significant compromise is needed to cover all driving styles the vehicle is likely to encounter.

Specific energy consumption (energy to travel a given distance) for different mixes of battery and hybrid power supply.

Most recently, LFM has been used to develop and test a "prediction-based" power management strategy. The main feature of this strategy is that, if the total amount of energy required to complete a particular drive cycle can be reliably predicted, then the energy stored in the onboard electrical storage system can be depleted in an optimal manner that permits the fuel cell to operate in its most efficient regime.

Single stack fuel cell system efficiency at different power outputs.

The proposed strategy is shown to provide significant improvement over the conventional charge-maintaining strategy in average fuel cell system efficiency while reducing hydrogen consumption. It has been demonstrated with the LFM simulation that the prediction-based power management strategy can maintain a stable power request to the fuel cell thereby improving fuel cell durability, and that the battery is depleted to the desired state-of-charge at the end of the drive cycle.A sensitivity analysis has also been conducted to study the effects of inaccurate predictions of the remaining portion of the drive cycle on hydrogen consumption and the final battery state-of-charge. Finally, the advantages of the proposed control strategy over the conventional strategy have been validated through implementation in the University of Delaware's fuel cell hybrid bus with operational data acquired from onboard sensors.

Comparison of predictive control and the "baseline" hybrid control strategy on the University's Phase 1 fuel cell bus. An 11.7% savings in fuel consumption was realized with predictive control.
Brown D., Alexander M., Brunner D., Advani S.G., and Prasad A.K., "Drive-train simulator for a fuel cell hybrid vehicle," Journal of Power Sources, Vol. 183, pp. 275-281, August 15, 2008. doi:10.1016/j.jpowsour.2008.04.089
Bubna P., Brunner D., Advani S.G., and Prasad A.K., "Prediction-based optimal power management in a fuel cell/battery plug-in hybrid vehicle," Journal of Power Sources, Vol. 195, pp. 6699-6708, October 1, 2010. doi:10.1016/j.jpowsour.2010.04.008