  Monitoring the state of health for Li-ion batteries is crucial in the battery
management system (BMS), which helps end-users use batteries efficiently and
safely. Battery state of health can be monitored by identifying parameters of
battery models using various algorithms. Due to the low computation power of
BMS and time-varying parameters, it is very important to develop an online
algorithm with low computational cost. Among various methods, Equivalent
circuit model (ECM) -based recursive least squares (RLS) parameter
identification is well suited for such difficult BMS environments. However, one
well-known critical problem of RLS is that it is very likely to be numerically
unstable unless the measured inputs make enough excitation of the battery
models. In this work, A new version of RLS, which is called condition memory
recursive least squares (CMRLS) is developed for the Li-ion battery parameter
identification to solve such problems and to take advantage of RLS at the same
time by varying forgetting factor according to condition numbers. In CMRLS,
exact condition numbers are monitored with simple computations using recursive
relations between RLS variables. The performance of CMRLS is compared with the
original RLS through Li-ion battery simulations. It is shown that CMRLS
identifies Li-ion battery parameters about 100 times accurately than RLS in
terms of mean absolute error.
