A Master of Science thesis in Electrical Engineering by Daniyal Ali entitled, "Adaptive Estimation of Li-ion Battery Model Parameters," submitted in May 2016. Thesis advisor is Dr. Shayok Mukhopadhyay and thesis co-advisor is Dr. Habib-ur Rehman. Soft and hard copy available.
This work presents a novel application of a high gain adaptive observer-based technique for Lithium-ion (Li-ion) battery modeling. The model used in this work was originally developed by Chen and Mora. However, in Chen and Mora's original work, the parameters required for the battery model were estimated through intensive experimentation. In contrast, this work presents an adaptive observer for estimating the battery model parameters. This results in the reduction of experimental effort required to estimate battery
model parameters. The selected model (Chen and Mora's model) requires twenty one parameters to accurately model a Li-ion battery. This work initially proposes three variations of a high gain adaptive observer-based technique to adaptively tune fifteen of the required parameters accurately. The remaining six parameters related to the shape of the no-load electromotive-force (EMF) curve are obtained via a voltage relaxation test. Based on observations made during simulations of the above proposed techniques, an improved estimation technique is proposed in the latter half of this document, and experimental results validating the proposed technique are presented. Experiments show that the model obtained through this technique is independent of the magnitude and type of load. The improved parameter estimation technique is justified using rigorous mathematical analysis. The proposed improved technique can be used either online or offline for estimating battery model parameters. This may be valuable for automatically updating battery models parameters on-board future smart vehicles in real time.