As a conventional transportation modality, bicycles have been gradually electrified to meet the desire for convenient and green commuting patterns, especially in developed urban areas. The electric bicycle battery pack and its management system are core elements that determine key performance metrics such as electric range and output power. With respect to electric bicycle applications, focused research on the battery, its management system, and performance has received less attention compared to other energy storage applications.
In this thesis, a well-developed conversion kit produced by BionX is studied. A data collecting system is first installed to record both mechanical and electrical data, such as speed, power and voltage; this enables defining two standard riding cycles at different riding conditions. Two benchmarking tests are performed to investigate the battery life in pure electric mode and at different threshold levels of optimal assistance.
A novel quadratic programming based fitting algorithm is derived and applied in both time and frequency domain parameter identification tests. The proposed algorithm is able to fit single/multiple pulses by applying a masking vector. Sensitivity study and experimental results show the high robustness and fast computation time of the approach compared to existing and commonly used methods, such as fmincon. The comparison between hybrid power pulse characterization (HPPC) and electrochemical impedance spectrum (EIS) tests are performed in terms of extracted internal resistance.
A second-order RC battery model is developed using parameters extracted from HPPC tests. The model is validated by experimental riding cycles and used to generate the reference SOC profiles that are employed in a SOC estimation study. Four estimation strategies, including extended Kalman Filter (EKF), Sigma point Kalman Filter (SPKF), Cubature Kalman Filter (CKF), and joint extended Kalman Filter (JEKF), are compared systematically in terms of accuracy, robustness and computation complexity. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/20293 |
| Date | January 2016 |
| Creators | Wang, Weizhong |
| Contributors | Emadi, Ali, Malysz, Pawel, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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