Data for "Electric Vehicle Battery Parameter Identification and SOC Observability Analysis: NiMH and Li-S Case Studies"
Abbas Fotouhi
Daniel Auger
Karsten Propp
Stefano Longo
10.17862/cranfield.rd.5172334.v1
https://cord.cranfield.ac.uk/articles/dataset/Data_for_Electric_Vehicle_Battery_Parameter_Identification_and_SOC_Observability_Analysis_NiMH_and_Li-S_Case_Studies_/5172334
<p>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. <br></p><p><br></p><p>File created in MATLAB 2015a.</p>
2017-11-21 11:49:31
lithium-sulfur battery
pulse test data
Automotive Mechatronics