Onboard Impedance Diagnostics Method of Li-ion Traction Batteries using Pseudo-Random Binary Sequence
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Environmental and economic reasons have lead automotive companies towards the direction of EVs and HEVs. Stricter emission legislations along with the consumer needs for more cost-efficient and environmental friendly vehicles have increased immensely the amount of hybrid and electric vehicles available in the market. It is essential though for Li-ion batteries, the main propulsion force of EVs and HEVs, to be able to read the battery characteristics in a high accuracy manner, predict life expectancy and behaviour and act accordingly. The following thesis constitutes a concept study of a battery diagnostics method. The method is based on the notion of a pseudo-random binary signal used as the current input and from its voltage response, the impedance is used for the estimation of parameters such as the state of charge and more. The feasibility of the PRBS method at a battery cell has been examined through various tests, both in an experimental manner at the lab but also in a simulation manner. The method is compared for validation against the electrochemical impedance spectroscopy method which is being used as a reference. For both the experimental and the simulation examinations, the PRBS method has been validated and proven to work. No matter the change in the parameters of the system, the method behaves in a similar manner as in the reference EIS method.
The level of detail in the research and the performed experiments is what makes the significance of the results of high importance. The method in all ways has been proven to work in the concept study and based on the findings, if implemented on an EV’s or HEV’s electric drive line and the same functionality is observed, be used as a diagnostics method of the battery of the vehicle.
Place, publisher, year, edition, pages
2015. , 84 p.
Li-ion batteries, hybrid vehicles, EIS, PRBS, state of charge, state of health, battery diagnostics
Other Mechanical Engineering
IdentifiersURN: urn:nbn:se:liu:diva-118970ISRN: LiTH-ISY-EX--15/4872--SEOAI: oai:DiVA.org:liu-118970DiVA: diva2:818024
Subject / course