Optimum Engine Power Point Determination Method to Maximize Fuel Economy in Hybrid Vehicles

2021-01-0419

04/06/2021

Features
Event
SAE WCX Digital Summit
Authors Abstract
Content
One of the advantages of hybrid vehicles is the ability to operate the engine more optimally at a low brake specific fuel consumption (BSFC) as compared to conventional vehicles. This ability of hybrid vehicles is a major factor contributing to the fuel economy improvement over conventional vehicles. Unlike conventional gasoline powertrains, hybrid powertrains allow engine to be switched off and use battery power to propel vehicles. In order to maintain battery state of charge neutral operation between the start and end of a drive cycle, the net electrical energy consumption from the battery requires to be zero. An optimization algorithm can be developed and calibrated in different ways to achieve net zero battery energy over the cycle. For instance, the engine can be operated at powers higher than the power of the drive cycle to charge the battery. This accumulated energy can be used for all-electric propulsion by turning off the engine. In this paper, the authors have developed an algorithm to determine the optimum power at which engine should operate for best fuel economy for hybrid configurations. The algorithm evaluates the energy consumption for different levels of battery charging by the engine to achieve net battery energy neutral operation and determines the most optimum strategy for fuel economy. Furthermore, it can be used to determine optimum engine torque/speed regions considering the overall system throughput efficiencies of hybrid powertrain architectures comprising engine, transmission, motors and battery. This empowers calibration teams to calibrate battery subjective costs for optimum engine operation.
Meta TagsDetails
DOI
https://doi.org/10.4271/2021-01-0419
Pages
9
Citation
Rane, O., Mortuza, S., and Bhide, S., "Optimum Engine Power Point Determination Method to Maximize Fuel Economy in Hybrid Vehicles," SAE Technical Paper 2021-01-0419, 2021, https://doi.org/10.4271/2021-01-0419.
Additional Details
Publisher
Published
Apr 6, 2021
Product Code
2021-01-0419
Content Type
Technical Paper
Language
English