13-099 Battery Parameters/SOC/SOH Co-estimation Algorithm

December 20, 2016

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While battery technology is growing very fast to provide cells with higher power and energy densities,
reliability and cost-efficiency of utilizing the batteries depends on the smart performance of battery
management system (BMS). Advanced Diagnosis, Automation, and Control (ADAC) Lab at the
North Carolina State University is one of the leading research groups in developing algorithms to
make the BMS performance smarter, especially in the rapidly evolving applications of smart grid and
electric vehicles (EVs). With more than 20 years of experience in control, diagnosis and prognosis,
ADAC has been able to design online adaptive co-estimation algorithms to identify and estimate the
parameters, state of charge (SOC) and state of health (SOH) of the battery considering a general
model to represent the battery dynamics. Similar existing BMS products in the market do not provide
high accuracy standards for recent applications in addition to being tailored for specific battery
chemistry and applications. The co-estimation algorithm enhances the accuracy of estimation and
flexibility of the algorithm to be adapted for different battery types by updating the parameters of the
battery model based on the online terminal current, voltage and temperature. Moreover, the
algorithm considers the statistical characteristics of the application to estimate the SOH and predict
the Remaining Useful Life (RUL) and end of life (EOL) of the battery.

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