Date of Award
Doctor of Philosophy (PhD)
Dr. Zoran Filipi, Committee Chair
Dr. Andrej Ivanco, Committee Co-Chair, Research Advisor
Dr. Simona Onori
Dr. Srikanth Pilla
While being a competitive candidate for energy storage systems in automotive applications, lithium-ion battery still needs to overcome fundamental compromises regarding energy density, power density, lifetime, costs and safety concerns. A significant breakthrough can be expected by understanding the real-world customer usage patterns and leveraging this knowledge to develop an optimized battery design and control. However, the challenges of filtering through massive real-world driving data and identifying the features relevant to the real-world battery operations still remain. This dissertation aims to bridge this gap by linking vehicle drive cycles to battery cell duty cycles, which enables quantifying the impacts of real-world variability on battery performance. In addition to performance and efficiency considerations, the methodology enables battery aging analysis in the context of optimal design and control of hybrid electric vehicles. This will facilitate design decisions that ensure adequate performance over the life span of the vehicle with considerations of the battery health objective. The novelty of this work lies in a more accurate method of synthetizing representative real-world drive cycles with a new algorithm to classify road and an innovative quantitative metric of driver style. A modified 48V mild hybrid vehicle model was built to relate the real-world drive cycles all the way to the battery cell duty cycles and to validate the impacts from driver aggressiveness on both the fuel efficiency and the battery loads. The cell duty cycles were further analyzed in frequency domain to synthesize characteristic cell test profiles representative of driver styles and road conditions. A battery cell cycle aging experiment was carried out using the synthesized test profiles. Results validate the positive correlation between driver aggressiveness and cell degradation, and further allow parameter identification of cell electro-chemical model. Modeling effort was extended to generate insights regarding the aging mechanisms, and calibrate a semi-empirical aging model. These tools will enable the inclusion of road conditions and driver styles into the development of battery pack design and propulsion system control hence improving the design assumption fidelity and real-world representativeness of the modeling approach.
Liu, Zifan, "Battery Aging Studies Based on Real-World Driving" (2017). All Dissertations. 1921.