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Vehicle Fuel Consumption Optimization using Model Predictive Control based on V2V communication

Jing, Junbo
http://orcid.org/0000-0003-3351-1977
http://rave.ohiolink.edu/etdc/view?acc_num=osu1406201257

Abstract Details

2014, Master of Science, Ohio State University, Electrical and Computer Engineering.
As people are working hard on improving vehicle's fuel economy, a large portion of fuel consumption in everyday driving is wasted by vehicle driver's inexperienced operations and inefficient judgments. This thesis proposes a system that optimizes the vehicle's fuel consumption in automated car-following scenarios. The system is designed able to work in the initial stage of implementing Vehicle-to-Vehicle (V2V) communications. The system is developed based on Model Predictive Control (MPC). With a given prediction of the preceding vehicle's speed, the system controls the vehicle's throttle and brake to follow the preceding vehicle with an optimal velocity profile. The control problem is formed into a quadratic programming optimization problem using real vehicle parameters. Active-set algorithm is adopted for optimization, and the computation speed can satisfy real-time computations. The control results show a significant fuel saving benefit of up to 15%, with car-following safety ensured and ride comfort cared. To provide the prediction horizon for the MPC based system, a preceding vehicle speed prediction algorithm and a leading vehicle speed prediction algorithm are developed in this thesis. The preceding vehicle's speed is predicted by analyzing the transmission of speed disturbances along the convoy using Intelligent Driver Model (IDM). The information needed is obtained through V2V communication, and the algorithm does not require a high V2V penetration rate. The estimated car-following behavioral parameters are clustered online for improved prediction accuracy. The algorithm can provide a prediction horizon of seconds depending on the convoy length. The leading vehicle speed prediction algorithm is developed to extend the prediction horizon. The algorithm predicts the leading vehicle's free road driving and approaching speed when a rather large gap to the next vehicle appears. The leading vehicle's historical speed profile is decoded into a driver operation state sequence and forms a Markov chain. Markov Model is used for speed prediction. The information required by the algorithm is simply speed profiles and car-following distance profiles, which can be easily obtained by cooperating with the already existing Adaptive Cruise Control (ACC) systems.
Umit Ozguner (Advisor)
Giorgio Rizzoni (Committee Member)
121 p.
Automotive Engineering; Electrical Engineering; Energy; Engineering
Vehicle Fuel Consumption Minimization, Model Predictive Control, Quadratic Programming, V2V, Vehicle Speed Prediction, Markov Model

Recommended Citations

Citations

  • Jing, J. (2014). Vehicle Fuel Consumption Optimization using Model Predictive Control based on V2V communication [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1406201257

    APA Style (7th edition)

  • Jing, Junbo. Vehicle Fuel Consumption Optimization using Model Predictive Control based on V2V communication. 2014. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1406201257.

    MLA Style (8th edition)

  • Jing, Junbo. "Vehicle Fuel Consumption Optimization using Model Predictive Control based on V2V communication." Master's thesis, Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1406201257

    Chicago Manual of Style (17th edition)

osu1406201257
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This open access ETD is published by The Ohio State University and OhioLINK.