State of charge estimation of LiFePO4 batteries based on online parameter identification

• RLS and UKF are elaborately combined to form an integral RLS-UKF algorithm.
• Model parameters of LiFePO4 battery are identified online for SOC estimation.
• SOC for each cell within the battery pack is correctly estimated.
• The inconsistency of working performance among different cells is well recognized.

With the research object of LiFePO4 battery, this paper aims to correctly estimate the battery state of charge (SOC) by constructing a comprehensive SOC estimation strategy. Firstly, recursive least square (RLS) algorithm is adopted to realize online parameter identification of the equivalent battery model; and then an elaborate combination of RLS and Unscented Kalman Filter (UKF) is established, thus the battery model parameters used in UKF are actually obtained recursively by RLS; finally, SOC can be estimated by UKF. This strategy has an obvious adaptability due to the adoption of online parameter identification, so it is also called adaptive SOC estimation technique. Experimental results show that sometimes battery model parameters of different cells can be much different even though terminal voltages of these cells are very close or same when they are under resting state, and this inconsistency among LiFePO4 batteries is captured by the RLS-UKF strategy presented in this paper; and of course battery SOC can also be correctly estimated by using the continuously updated model parameters.