System identification-based lead-acid battery online monitoring system for electric vehicles

A system identification-based model for the online monitoring of batteries for electric vehicles (EVs) is presented. This algorithm uses a combination of battery voltage and current measurements plus battery data sheet information to implement model-based estimation of the stored energy, also referred to as state-of-charge (SOC), and power capability, also referred to as state-of-function (SOF), for deep-cycle batteries. This online monitoring scheme has been implemented for a bank of deep-cycle lead-acid batteries and experimental laboratory tests using simulated driving cycles have yielded promising results. In addition, actual road data from an EV powered by these same batteries has been analyzed with the proposed model to demonstrate the system's usefulness in determining the battery state-of-health (SOH). Finally, the limitation of the use of a linear model for battery terminal voltage behavior is discussed.

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