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Monitoring the state of health for Li-ion batteries is crucial in the batterymanagement system (BMS), which helps end-users use batteries efficiently andsafely. Battery state of health can be monitored by identifying parameters ofbattery models using various algorithms. Due to the low computation power ofBMS and time-varying parameters, it is very important to develop an onlinealgorithm with low computational cost. Among various methods, Equivalentcircuit model (ECM) -based recursive least squares (RLS) parameteridentification is well suited for such difficult BMS environments. However, onewell-known critical problem of RLS is that it is very likely to be numericallyunstable unless the measured inputs make enough excitation of the batterymodels. In this work, A new version of RLS, which is called condition memoryrecursive least squares (CMRLS) is developed for the Li-ion battery parameteridentification to solve such problems and to take advantage of RLS at the sametime by varying forgetting factor according to condition numbers. In CMRLS,exact condition numbers are monitored with simple computations using recursiverelations between RLS variables. The performance of CMRLS is compared with theoriginal RLS through Li-ion battery simulations. It is shown that CMRLSidentifies Li-ion battery parameters about 100 times accurately than RLS interms of mean absolute error.

Low computational cost method for online parameter identification of Li-ion battery in battery management systems using matrix condition number