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Journals / Politeknik Dergisi / 2017 / Cilt: 20 - Sayı: 1

Model Based Predictive Engine Torque Control for Improved Driveability

Murat ÖTKÜR
Orhan ATABAY
İsmail Murat EREKE
Pages
71–82
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Simulation results indicate that MPC plus P Controller is capable of obtaining desired acceleration and deceleration profiles achieving improved driveability.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:An engine brake torque based Model Predictive Control (MPC) algorithm with an additional anti-shuffle control element is developed to manipulate the pedal map oriented brake torque demand signal in an automotive powertrain application.
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Key Findings & Data:In order to capture the longitudinal vehicle dynamics of a front wheel drive vehicle, a simplified 4 mass powertrain model is generated.
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Conclusion & Impact:Simulation results indicate that MPC plus P Controller is capable of obtaining desired acceleration and deceleration profiles achieving improved driveability.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Drivability · Driveline Modelling · Model Predictive Control

Abstract

An engine brake torque based Model Predictive Control (MPC) algorithm with an additional anti-shuffle control element is developed to manipulate the pedal map oriented brake torque demand signal in an automotive powertrain application. In order to capture the longitudinal vehicle dynamics of a front wheel drive vehicle, a simplified 4 mass powertrain model is generated. Model validation is performed with vehicle tests using a typical tip-in and back-out acceleration pedal signal input manoeuvre. Comparison of simulation results and vehicle tests reveals that simplified model is capable of capturing vehicle acceleration profile with the error states for the specified input signals. MPC scheme based on 2 mass vehicle model is developed in “MATLAB / Simulink” environment to obtain a smooth and responsive acceleration profile without error states like excessive jerks and shuffles. An additional engine to wheel speed difference based proportional controller employed in order to further reduce powertrain oscillations without compromising from system response speed. Simulation results indicate that MPC plus P Controller is capable of obtaining desired acceleration and deceleration profiles achieving improved driveability.

Özet

An engine brake torque based Model Predictive Control (MPC) algorithm with an additional anti-shuffle control element is developed to manipulate the pedal map oriented brake torque demand signal in an automotive powertrain application. In order to capture the longitudinal vehicle dynamics of a front wheel drive vehicle, a simplified 4 mass powertrain model is generated. Model validation is performed with vehicle tests using a typical tip-in and back-out acceleration pedal signal input manoeuvre. Comparison of simulation results and vehicle tests reveals that simplified model is capable of capturing vehicle acceleration profile with the error states for the specified input signals. MPC scheme based on 2 mass vehicle model is developed in “MATLAB / Simulink” environment to obtain a smooth and responsive acceleration profile without error states like excessive jerks and shuffles. An additional engine to wheel speed difference based proportional controller employed in order to further reduce powertrain oscillations without compromising from system response speed. Simulation results indicate that MPC plus P Controller is capable of obtaining desired acceleration and deceleration profiles achieving improved driveability.

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Cite This Article

ÖTKÜR, M., ATABAY, O., EREKE, İ. M. (2017). Model Based Predictive Engine Torque Control for Improved Driveability. Politeknik Dergisi, Cilt: 20 - Sayı: 1, 71–82. https://ajindex.com/articles/307167

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