Cranfield Online Research Data (CORD)

sorry, we can't preview this file

test_data_2014_08_discharge_01C_cell7_30deg.mat (358.44 kB)

Data for "Electric Vehicle Battery Parameter Identification and SOC Observability Analysis: NiMH and Li-S Case Studies"

Download (358.44 kB)

In this study, battery model identification is performed to be applied in electric vehicle battery management systems. Two case studies are investigated: nickel-metal hydride (NiMH), which is a mature battery technology, and lithium-sulfur (Li-S), a promising next-generation technology. Equivalent circuit battery model parameterization is performed in both cases using the Prediction-Error Minimization (PEM) algorithm applied to experimental data. Performance of a Li-S cell is also tested based on urban dynamometer driving schedule (UDDS) and the proposed parameter identification framework is applied in this case as well. The identification results are then validated against the exact values of the battery parameters. The use of identified parameters for battery state-of-charge (SOC) estimation is also discussed. It is shown that the set of parameters needed can change with a different battery chemistry. In the case of NiMH, the battery open circuit voltage (OCV) is adequate for SOC estimation whereas Li-S battery SOC estimation is more challenging due to its unique features such as flat OCV-SOC curve. An observability analysis shows that Li-S battery SOC is not fully observable and the existing methods in the literature might not be applicable for a Li-S cell. Finally, the effect of temperature on the identification results and the observability are discussed by repeating the UDDS test at 5, 10, 20, 30, 40 and 50 degree Celsius.

File created in MATLAB 2015a.


This research was funded as part of the ‘Revolutionary Electric Vehicle Battery’ (REVB) project, co-funded by Innovate UK and EPSRC (TS/L000903/1 and EP/L505286/1), and the ‘Understanding Future Vehicles’ project funded by EPSRC (EP/I038586/1)


Usage metrics



    Ref. manager