Abstract:
Batteries are an important category of electrochemical storage technologies that can store
electrical energy for remote on-demand consumption. Lithium-Ion Cells are one such
technology, that are quickly replacing older batteries like Nickel and Lead Acid Batteries
in different applications such as Portable and Smart Devices. They are also being touted
as the best option for several future applications such as Hybrid Electric Vehicles, Power
Banks, and Alternative Storage, due to their climate friendly nature and several other
advantages. However, these cells have a State of Charge and State of Health sensitive
operation. These Two key parameters (State of Charge and State of Health) are used to
study Cell performance under different loading conditions, and their estimation is the
primary focus of this thesis. This thesis proposes an indigenous method of SOC and SOH
estimation on a cell level, based on the influence of cell internal impedance on a Pseudo
Random Binary Sequence and the derivation of a Piecewise Linear Model through
Machine Learning. A hardware model is prepared to verify the method and demonstrate
its working. Results of this estimation technique are comparable to other popular PRBS
methods but with added simplicity due to less data clustering and quick results.
