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Influence of Driving Patterns and Optimal Robust PowertrainCombined Design and Control on Plug-in Vehicle Cost, Life CycleEmissions, Component Sizing, and Battery Stress

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  1. Tez No: 665539
  2. Yazar: ORKUN KARABSAŞOĞLU
  3. Danışmanlar: DR. CHAİR: JEREMY J. MİCHALEK
  4. Tez Türü: Doktora
  5. Konular: Makine Mühendisliği, Mechanical Engineering
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2013
  8. Dil: İngilizce
  9. Üniversite: Carnegie Mellon University
  10. Enstitü: Yurtdışı Enstitü
  11. Ana Bilim Dalı: Makine Mühendisliği Ana Bilim Dalı
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: 139

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Özet (Çeviri)

Plug-in Hybrid Electric Vehicle (PHEV) powertrain design and control is a challenging task for a few reasons: (1) their efficient powertrain architectures with multiple energy sources and regenerative braking capabilities introduce multiple degrees of freedom for design and energy management (control) decisions, which are coupled. Vehicle design includes multiple coupled decisions in itself, such as battery, motor, and engine sizing, while controllers are developed to achieve efficiency. Design and control is usually done sequentially which lead to suboptimal results. (2) Powertrain components are sized to follow driving patterns which dictates the road demand however they vary significantly from driver and driver, day to day and trip to trip. Still the vehicle should be designed robustly for the market. (3) Each design and control decision introduces a cost component which is a nonlinear function of the vehicle life so all design and control decisions should be evaluated from life cycle perspective (For example, energy storage systems are expensive but allows for the use of potentially cheap electricity). The first goal of this thesis is to determine the impact of driving patterns on the life cycle benefits of plug-in hybrid electric vehicles. For this purpose, I design different powertrains similar to the ones in the vehicle market and simulate them under different driving patterns. Then 4 I build a quantitative framework to model life cycle cost and emissions of vehicles. Using the proposed framework, I compare conventional, hybrid, plug-in hybrid vehicles with different battery sizes, and battery electric vehicles under various scenarios. This goal aims to answer the questions: How much do driving patterns affect the life cycle benefits of PHEVs? Which powertrain does perform better under which driving pattern? How much does the vehicles allelectric range change due to driving patterns? Results indicate that under the urban NYC driving cycle, hybrid and plug-in vehicles can cut life cycle emissions by 60% and reduce costs up to 20% relative to conventional vehicles (CVs). In contrast, under highway test conditions (HWFET) electrified vehicles offer only marginal emissions reductions at higher costs. NYC conditions with frequent stops triple life cycle emissions and increase costs of conventional vehicles by 30%, while aggressive driving (US06) reduces the all-electric range of plug-in vehicles by up to 45% compared to milder test cycles like HWFET. Hybrid vehicles are more robust to the variation in driving patterns. I discuss policy implications. The second goal of this thesis is to create a holistic framework for the optimal design and control of energy systems, accounting for the interactions between design and control, and making each design and control decision to achieve the maximum benefits for the entire life cycle of the system. Having determined the impact of driving patterns for PHEVs from the first goal, the aforementioned framework is applied to the co-design of PHEV powertrains for the minimum life cycle cost for a real world driving cycle. Firstly, a quasi-static backward looking parallel PHEV model is developed with its dynamic model counterpart for the performance computing which is the acceleration time from 0-60 mph. We account for design and control interactions by performing a parametric study over the vehicle design space, optimizing the controller for each design. The vehicle design space - consisting of engine, motor and battery 5 size variables - is discretized and searched exhaustively using an iteratively refined grid resolution to minimize lifetime cost. Ultimately, the system is optimized to determine the optimal vehicle designs that minimize life cycle cost. Intelligent system level control of the powertrain has the potential to downsize the expensive powertrain components such as battery, motor and engine. This goal aims to answer the questions: How to account for the design and control interactions during vehicle powertrain design? What is the life cycle cost-minimizing PHEV parallel powertrain design? How important is system level control? What are the implications of intelligent control on powertrain design and cost? Results indicate that using optimal control with perfect information (dynamic programming) provides a 5% smaller battery pack than using a simultaneously optimized rule-based controller for a particular 22-mile realworld driving cycle, creating an upper bound for the potential benefits of predictive controllers. The third goal of this thesis is to study the effects of intelligent system level combined with supercapacitors to reduce battery stress and extend battery life thus reducing lifetime cost. For this purpose I integrate energy-dense batteries with power-dense supercapacitors in battery electric vehicles(BEVs) to reduce battery stress and increase battery life for the variation of real world driving patterns and elevation profiles I have discussed in the first research question. We globally optimize the energy management strategy of supercapacitor-battery systems in EVs for real world driving conditions. We minimize battery current squared as a degradation factor as peak-leveling and Joule heating accelerate battery degradation in EVs. This goal aims to answer the questions: Can I reduce battery stress to extend its life with supercapacitors and smart control for real world driving conditions? Can I reduce battery peak power requirement which might lead to smaller batteries. Results indicate that by integrating a 50 Wh supercapacitor and 6 an optimal controller, we achieve over 60% reduction in battery current-squared losses using dynamic programming. Improved efficiency and reduced battery size can help to make plug-in vehicles more competitive, while reduced battery use can improve battery life, reducing life cycle cost and environmental issues associated with battery production. This research develops a robust co-design framework for energy systems with applications on PHEV powertrains by integrating vehicle design, control and economics, identifies optimal vehicle powertrain designs under a distribution of driving patterns, examines the potential component downsizing due to intelligent system level control, compares different powertrain technologies over various driving patterns for life cycle cost and emissions, and models the integration of supercapacitors with batteries to extend battery life and reduce battery stress, battery size, and lifetime cost.

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