Development of an Algorithm for Identifying Fuel Blending Ratios for Methanol/Gasoline Flex-Fuel Engines

2025-01-7107

01/31/2025

Features
Event
SAE 2024 Vehicle Powertrain Diversification Technology Forum
Authors Abstract
Content
Flex-fuel vehicles play a crucial role in energy conservation and emission reduction; however, they often rely on expensive fuel identification sensors at the nozzle to accurately control the blending ratio. To reduce costs and enhance engine flexibility, this paper presents a flexible fuel proportion identification algorithm that utilizes exhaust oxygen content measured by the oxygen sensor and engine air intake data. Additionally, the algorithm incorporates air intake feedback control and λ feedback control, which adjusts both the throttle opening and fuel mass of the flex-fuel engine, ensuring optimal operating conditions at all times. A methanol-gasoline flex-fuel engine model was developed using GT-Power, and the algorithm model was implemented in Simulink software. Then, a co-simulation model of GT-Power and Simulink is established. In the GT-Power engine model, three parameters—engine speed, load, and methanol blending ratio—are set for the sweep points. The algorithm model in Simulink calculates the methanol blending ratio based on the data output from the GT-Power sweep points. Finally, the calculated blending ratio is compared with the actual blending ratio set in GT-Power to verify the accuracy of the algorithm described in this paper. Results indicate that the error in the methanol blending ratio calculated by the algorithm is less than 2%. The algorithm presented in this paper utilizes real-time simulation technology based on fully algebraic equations, resulting in high efficiency, accuracy, and sensitivity.
Meta TagsDetails
DOI
https://doi.org/10.4271/2025-01-7107
Pages
11
Citation
Qian, P., Nan, T., Luo, W., Du, Y. et al., "Development of an Algorithm for Identifying Fuel Blending Ratios for Methanol/Gasoline Flex-Fuel Engines," SAE Technical Paper 2025-01-7107, 2025, https://doi.org/10.4271/2025-01-7107.
Additional Details
Publisher
Published
Jan 31
Product Code
2025-01-7107
Content Type
Technical Paper
Language
English