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Application of Convex Optimization Techniques for Intelligent Electric Vehicles : 지능형 전기자동차를 위한 최적화 기법의 적용
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 서승우 | - |
dc.contributor.author | 최믿음 | - |
dc.date.accessioned | 2017-07-13T07:06:55Z | - |
dc.date.available | 2017-07-13T07:06:55Z | - |
dc.date.issued | 2015-02 | - |
dc.identifier.other | 000000024782 | - |
dc.identifier.uri | https://hdl.handle.net/10371/119049 | - |
dc.description | 학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 2. 서승우. | - |
dc.description.abstract | Batteries are often damaged by a high peak power and a steep variation of
the power since it has a relatively low power density. In order to reduce battery damage, the battery/super-capacitor (SC) hybrid energy storage system (HESS) has been utilized since the SC can act as a buffer against large magnitudes and rapid fluctuations in power. The major objective regarding the battery/SC HESS is to minimize the magnitude/variation of the battery power and the power loss. To achieve the objective, I formulate optimization problems to provide the optimal HESS power using given load operation profiles. In addition, I propose an algorithm using a barrier method and a Multiplicative Increase Additive Decrease method for providing a feasible optimal solution for energy management in HESS. The battery/SC HESS can be effectively utilized for Electric Vehicles (EVs) because high peak power or rapid charging/discharging occur frequently in driving situations. However, the optimization method proposed in the second chapter cannot be adopted for EVs because it is difficult to obtain the future driving profile in advance. To calculate the optimal power of the battery/SC without the future profiles, I propose a method for computing the reference voltage of the SC based on the characteristic of power-train and the vehicle dynamics. In addition, I formulate the real-time optimization problem that minimizes the magnitude/variation of the battery power and the power loss simultaneously. To improve the power control for the battery/SC HESS in EVs, it is necessary to know the future motor power in advance. They can be derived from the future speed/acceleration of the vehicle through the method proposed in the third chapter if the future speed/acceleration can be predicted. Fortunately, there are many prediction techniques such as car following models, path planning algorithms and model predictive schemes, which are based on results of target tracking. Therefore, the driving environments, e.g., moving objects, should be accurately estimated. To improve the multi-target estimation accuracy even if there are many false detections, I propose a robust multi-target tracking scheme based on the GMPHD filter. The proposed scheme includes the processing step of evaluating multiple states/measurements which is designed to overcome the weight under/overestimation problem. Furthermore, it includes the step of generating the birth intensity for the next iteration using the measurements not associated with any tracked states. I also show that the proposed method can be extended to nonlinear Gaussian models. | - |
dc.description.tableofcontents | 1 Introduction 1
1.1 Background and Motivations . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Contributions and Outline of the Dissertation . . . . . . . . . . . . . 3 1.2.1 EnergyManagement Optimization in a Battery/Supercapacitor Hybrid Energy Storage System . . . . . . . . . . . . . . . . . 3 1.2.2 Real-time Optimization for Power Management Systems of a Battery/Supercapacitor Hybrid Energy Storage System in Electric Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.3 Robust Multi-Target Tracking Scheme against False Detections based on Gaussian Mixture Probability Hypothesis Density Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 EnergyManagement Optimization in a Battery/Supercapacitor Hy- brid Energy Storage System 7 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Active Hybrid Energy Storage Systems . . . . . . . . . . . . . . . . . 11 2.2.1 A Review of Active Hybrid Energy Storage Systems . . . . . 13 2.2.2 Considered HESS Topology . . . . . . . . . . . . . . . . . . . 14 2.3 HESS Energy Management Optimization . . . . . . . . . . . . . . . 15 2.3.1 Notations and Assumptions . . . . . . . . . . . . . . . . . . . 15 2.3.2 Minimization of Magnitude/Fluctuation of Battery Power . . 17 2.3.3 Minimization of the Power Loss . . . . . . . . . . . . . . . . . 21 2.3.4 Minimization of the Dual Objective Functions . . . . . . . . . 22 2.4 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.4.1 Computation by Solver . . . . . . . . . . . . . . . . . . . . . 24 2.4.2 Parameter Adjustment Algorithm . . . . . . . . . . . . . . . 24 2.4.3 Analysis of the Total Number of Iterations in the Algorithm . 26 2.5 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.5.1 Review of Previous Approach . . . . . . . . . . . . . . . . . . 29 2.5.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . 32 2.5.3 Adjustment of the Boundary Parameters in the Algorithm . . 33 2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 2.7 Proof of 2nd constraint in P2 . . . . . . . . . . . . . . . . . . . . . . 35 3 Real-time Optimization for Power Management Systems of a Bat- tery/Supercapacitor Hybrid Energy Storage System in Electric Ve- hicles 36 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.2 System Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.2.1 Powertrain Model . . . . . . . . . . . . . . . . . . . . . . . . 40 3.2.2 Regenerative Braking System . . . . . . . . . . . . . . . . . . 43 3.2.3 Battery/SC Hybrid Energy Storage Systems . . . . . . . . . . 45 3.3 Power Control System for HESS . . . . . . . . . . . . . . . . . . . . 47 3.3.1 Computation of SC Reference Voltage . . . . . . . . . . . . . 49 3.3.2 Computation of the optimal SC power . . . . . . . . . . . . . 51 3.4 Simulation Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4 Robust Multi-Target Tracking Scheme based on Gaussian Mixture Probability Hypothesis Density Filter 69 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.2 Background and Motivation . . . . . . . . . . . . . . . . . . . . . . . 73 4.2.1 Prediction of Future Driving Profile . . . . . . . . . . . . . . 73 4.2.2 Brief Overview of The Conventional GM-PHD Filter . . . . . 74 4.2.3 Problems of the GM-PHD Filter . . . . . . . . . . . . . . . . 77 4.3 The Proposed Robust GM-PHD Filter . . . . . . . . . . . . . . . . . 83 4.3.1 Target Prediction and PHD Update Component Construction 85 4.3.2 State and Measurement Evaluation . . . . . . . . . . . . . . . 86 4.3.3 PHD Updating and Merging . . . . . . . . . . . . . . . . . . 89 4.3.4 Duplication Check . . . . . . . . . . . . . . . . . . . . . . . . 91 4.3.5 Birth Intensity Generation for the Next Iteration . . . . . . . 91 4.4 Nonlinear Gaussian Model Extension . . . . . . . . . . . . . . . . . 92 4.5 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5 Conclusion and Future Work 110 | - |
dc.format | application/pdf | - |
dc.format.extent | 3404230 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | Optimal power control | - |
dc.subject | Battery/Super-capacitor | - |
dc.subject | Electric vehicle | - |
dc.subject | Intelligent vehicle | - |
dc.subject | Multi target tracking | - |
dc.subject | Probability Hypothesis Density filter | - |
dc.subject.ddc | 621 | - |
dc.title | Application of Convex Optimization Techniques for Intelligent Electric Vehicles | - |
dc.title.alternative | 지능형 전기자동차를 위한 최적화 기법의 적용 | - |
dc.type | Thesis | - |
dc.contributor.AlternativeAuthor | Mid-Eum Choi | - |
dc.description.degree | Doctor | - |
dc.citation.pages | v,120 | - |
dc.contributor.affiliation | 공과대학 전기·컴퓨터공학부 | - |
dc.date.awarded | 2015-02 | - |
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