Lithium-ion battery pack as one of pioneer for energy storage system which has complexity issues about degradation life and it gave some impact on characterization of Lithium-ion battery pack, especially Internal Resistance (IR). IR is essential parameter of Lithium ion battery pack which is related to energy efficiency, power performance, physical life and degradation of Lithium-ion battery pack. Again, the IR is dangling on material of constructive, physical and electrochemical condition. As the consequences, the IR of Lithium-ion battery pack changes with the discharge or charge current rates, State of Health (SOH), State of Charge (SOC) and temperature.  All of the characteristics are critical for the precise functioning of the Electric Bike, Electric Vehicle (EV) and Battery Management System (BMS). The IR of Lithium-ion battery pack is non-linear system and complex which show the behavior of Lithium-ion battery pack such as resistive, capacitive and inductive.

In this study to obtain the value of Internal Resistance, it applied an experimental method which known as Hybrid Pulse Power Characterization (HPPC) Test. The HPPC Test is used to decide dynamic power qualification of voltage range using test profile integrates during charge and discharge pulse until 500 cycles. Additionally, the data from the HPPC Test for instance, the ohmic resistance and polarization resistance as the function of capacity is able to determine voltage response time during discharge, rest and discharge. These data of HPPC Test can be calculated and estimate internal resistance during subsequent life testing and to develop Lithium-ion battery pack performance models for Electric Bike and vehicle systems analysis.

The experiment results will be utilized to build up a model based on data driven method which has higer universality and attainability. The data driven will be provide by BMS. In general, Lithium-ion battery pack manufacturer supplies the battery product with a BMS to consumer. The BMS can communicate with the user interface to report Lithium-ion battery pack status, experimental data and state parameters which consist of current, voltage, temperature and capacity. The data driven method is committed to use the Lithium ion battery pack cycle state to achieve battery life prediction. The method will be focus on experimental data and state parameters model for predicting degradation life of Lithium-ion battery pack based on Internal Resistance behavior in every cycle using Supervised Machine Learning, Support Vector Regression (SVR)

*Keywords:* *Internal Resistance,* *Hybrid Pulse Power Characterization (HPPC) Test,* *Lithium-ion battery packs, Support Vector Regression*

 


Internal Resistance Based Assessment Model for the Degradation of Li-Ion Battery Pack

Division / Sponsors: 
Aerospace, Bioengineering, Applied Mechanics, Design Engineering Division, Advanced Energy Systems, Manufacturing Engineering Division, Noise Control and Acoustics, Nondestructive Evaluation, Diagnosis, and Prognosis (NDPD), Nuclear Engineering Division, Process Industries Division, Fluids Engineering, Heat Transfer, Materials, Microelectromechanical Systems, Safety Engineering and Risk Analysis, Computers & Information in Engineering


Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2020/volume/84478

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