Abstract
The global electric vehicle (EV) is expanding enormously, foreseeing a 17.4% increase of compound annual growth rate (CAGR) by the end of 2027 [1], the lithium-ion battery is considered as most widely used battery in EV, the accurate and reliable diagnostic and prognostic of battery state guarantees the safe operation of EV and are crucial for durable electric vehicles. Research focusing on lithium-ion battery life degradation has grown more importance in recent years. In this study, a model built for state of health (SoH) estimation for LTO-anode based lithium-ion battery is presented. First, the electrochemical impedance spectroscopy (EIS) is used to study the deterioration in battery performance, measurements such as charge transfer resistance and ohmic resistance are analyzed for different operation conditions and selected as key characteristic parameters for the model. Then, the model based on backpropagation neural network (BPNN) along with the characteristic parameters is trained and validated with a real-life driving profile. The model shows a relatively accurate estimationof SoH with a mean square error (MSE) of 0.002.
| Date of Award | 2021 |
|---|---|
| Original language | English |
| Supervisor | Md Sazzad Hosen (Co-promotor) & Maitane Berecibar (Promotor) |