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Junbo Dissertation Thesis Final.pdf (5.1 MB)

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Vehicle Predictive Fuel-Optimal Control for Real-World Systems

Jing, Junbo
http://orcid.org/0000-0003-3351-1977
http://rave.ohiolink.edu/etdc/view?acc_num=osu1534506777487814

Abstract Details

2018, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
In response of the world's increasing concern on carbon emissions, vehicle real-world fuel economy potential is being further developed by autonomy and connectivity, so as to achieve superior judgment and to eliminate fuel waste by imperfect human operations. A starting point is by redesigning the existing Adaptive Cruise Control (ACC) system with route preview and optimal control calculation, which is regarded as predictive optimal control in this work. The work targets to provide algorithm solutions for designing and implementing predictive optimal control in a real-world vehicle system, covering the aspects of control, estimation, and prediction. For control development, two algorithms are designed for the scenarios of optimal car-following speed control and optimal cruise control on a hilly route. The two designs share a common concept that by previewing the upcoming condition change, the vehicle control can be scheduled with a constrained modulation range in trade of improved operation cost. For the problem of optimal car-following speed control, which contains a mixed-integer programming problem caused by gearshifts, optimization complexity is broken down by a hybrid solver of Quadratic Programming (QP) and Pontryain's Minimum Principle (PMP). The solver partitions the problem into simplified sub-problems with quick quasi-optimal solutions, so that the search space is efficiently reduced to achieve constrained optimal control solutions in real time. Control results show major fuel saving benefits with clean gear shifts. For the problem of cruising on a hilly route, where the vehicle drives on a high gear and the engine operates near the torque capacity curve, control solving encounters the challenges by non-convex & non-affine constraints, along with state-dependent system switching. To achieve flexibility in optimal control solving, a PMP analytical solution set is developed to self-expand forward in time, detecting the system's constraints and switches while solving for the optimal control. To achieve the deterministic optimal control solutions with robustness and high efficiency, a shooting solver is developed to combine with the PMP solution set and to work in a decoupled search method. The solver package is demonstrated to beat the equivalent QP solver in optimality, computation time, and memory usage. To maintain optimal control result validity and accuracy for the real-world vehicle system, issues for online estimating model parameters with robustness and efficiency are addressed. The control-oriented model is analyzed and adapted in balance of fitting fidelity, measurement feasibility, and computation complexity. Estimation robustness enforcement measures for resisting external noise and disturbances are developed, using the example of road grade map error. Furthermore, statistical ways of validating model estimation results without ground truth knowledge are presented. The work also provides two different vehicle speed prediction methods to generate the prediction horizon for control calculation. One method is developed using model-based approach, being a forward-looking behavioral response predictor for a partial V2V condition. The other method is developed using data driven approach, being a backward-looking probabilistic predictor by only historical speed data. The methods demonstrate their effectiveness by examples from simulation and test data.
Umit Ozguner (Advisor)
Giorgio Rizzoni (Committee Member)
Vadim Utkin (Committee Member)
200 p.
Electrical Engineering
Model Predictive Control, Optimal Control, Vehicle Fuel Economy, Model Estimation, Vehicle Speed Prediction

Recommended Citations

Citations

  • Jing, J. (2018). Vehicle Predictive Fuel-Optimal Control for Real-World Systems [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1534506777487814

    APA Style (7th edition)

  • Jing, Junbo. Vehicle Predictive Fuel-Optimal Control for Real-World Systems. 2018. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1534506777487814.

    MLA Style (8th edition)

  • Jing, Junbo. "Vehicle Predictive Fuel-Optimal Control for Real-World Systems." Doctoral dissertation, Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1534506777487814

    Chicago Manual of Style (17th edition)

osu1534506777487814
415
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This open access ETD is published by The Ohio State University and OhioLINK.