Abstract:

In order for the battery management system in an electric vehicle to function properly, accurate and robust indication of the energy state of the lithium-ion batteries is necessary. This paper implements battery remaining available energy prediction and state of charge (SOC) estimation against testing temperature uncertainties as well as inaccurate initial SOC values. A double-scale particle filtering method has been developed to estimate or predict the system state and parameters on two different time scales. The developed method considers the slow time-varying characteristics of the battery parameter set and the quick time-varying characteristics of the battery state set. In order to select the preferred battery model, the Akaike information criterion is used to make a tradeoff between the model prediction accuracy and complexity. To validate the developed double-scale particle filtering method, two different kinds of lithium-ion batteries were tested at three temperatures. The experimental results show that, with 20% initial SOC deviation, the maximum remaining available energy prediction and SOC estimation errors are both within 2% even when the wrong temperature is indicated. In this case, the developed double-scale PF method is expected to be robust in practice.

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