Control Strategy Development for Parallel Plug-In Hybrid Electric Vehicle Using Fuzzy Control Logic

2016-01-2222

10/17/2016

Event
SAE 2016 International Powertrains, Fuels & Lubricants Meeting
Authors Abstract
Content
The Hybrid Electric Vehicle Team of Virginia Tech (HEVT) is currently developing a control strategy for a parallel plug-in hybrid electric vehicle (PHEV). The hybrid powertrain is being implemented in a 2016 Chevrolet Camaro for the EcoCAR 3 competition. Fuzzy rule sets determine the torque split between the motor and the engine using the accelerator pedal position, vehicle speed and state of charge (SOC) as the input variables. The torque producing components are a 280 kW V8 L83 engine with active fuel management (AFM) and a post-transmission (P3) 100 kW custom motor. The vehicle operates in charge depleting (CD) and charge sustaining (CS) modes. In CD mode, the model drives as an electric vehicle (EV) and depletes the battery pack till a lower state of charge threshold is reached. Then CS operation begins, and driver demand is supplied by the engine operating in V8 or AFM modes with supplemental or loading torque from the P3 motor. The 0 - 60 mph acceleration time with the Fuzzy control strategy is 4.8 seconds, which is close to the 4.9 seconds result yielded by a deterministic rule-based control strategy. The total energy consumption result for the Fuzzy control strategy is 555 Wh/km, which slightly beats the 560 Wh/km yielded by the deterministic rule-based strategy. Although the Fuzzy control strategy does not vastly improve the energy consumption or performance results, it proves to be a functional starting strategy that meets HEVT goals for EcoCAR 3.
Meta TagsDetails
DOI
https://doi.org/10.4271/2016-01-2222
Pages
14
Citation
Marquez, E., and Nelson, D., "Control Strategy Development for Parallel Plug-In Hybrid Electric Vehicle Using Fuzzy Control Logic," SAE Technical Paper 2016-01-2222, 2016, https://doi.org/10.4271/2016-01-2222.
Additional Details
Publisher
Published
Oct 17, 2016
Product Code
2016-01-2222
Content Type
Technical Paper
Language
English