Battery parameter identification with Pseudo Random Binary Sequence excitation (PRBS)

This paper extends previous work on the use of maximum length sequences as tools for parameter estimation within electrochemical batteries. An improved technique using a simplified monopolar current pulse excitation strategy allows identification of Randles’ model equivalent circuit values, which can be subsequently employed in state-of-charge and state-of-health algorithms. Within the approach the problems associated with establishing bulk capacitance over a short time period are avoided. Instead, using readily identifiable parameters (surface capacitance, series resistance and charge transfer resistance) comparison of parameters for new and aged batteries is carried out, and a potential indicator for State of Health described. Experimental results are presented to verify the proposed technique.

• PRBS battery parameter estimation methods present the battery state with equivalent circuit component parameters. • Frequency domain PRBS State of Charge (SoC) and State of Health (SoH) measurement techniques can advance the development of electric vehicles and renewable energy. • Non intrusive battery state evaluation is highly desirable in applications with dynamic charge/discharge regimes.