Abstract
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 algorithm applied to experimental data. Performance of the Li-S cell is also tested based on urban dynamometer driving schedule (UDDS). 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 demonstrated that the set of parameters changes 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 might not be applicable for it. 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
Highlights
The results show that Li-S cell's ohmic charge resistance (OCR) is a bit more than ohmic discharge resistance (ODR) at very high SOC
Li-S cell equivalent circuit network (ECN) model parameterisation was performed under different conditions using the prediction-error minimisation (PEM) identification algorithm
Various experimental tests were conducted on a 3.4 Ah Li-S cell including discharge pulse test, mixed charge–discharge pulse test and a test based on electric vehicles (EVs) power demand on urban dynamometer driving schedule (UDDS)
Summary
There are many studies in the literature focusing on development of battery modelling and estimation techniques for various battery types. The proposed techniques in the literature have been mostly applied for lead-acid, NiMH and Li-ion batteries, there is no similar study focusing on Li-S battery since it is a roughly new technology and it is still under development. Based on an observability analysis performed in this study, it is shown that the existing battery state estimation methods might not be applicable for Li-S battery chemistry because of its unique features. The NiMH battery chemistry is selected as it is a mature battery technology which has been the subject of many previous studies It is ‘safe’, and suitable for an experimental laboratory environment.
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