| Abstract |
Kalman filtering is commonly used for state-of-charge (SOC) estimation for lithium-ion (Li-ion)batteries owing to its simplicity, computational efficiency, and relatively precise results. However,kalman filters depend on the Li-ion battery model. Several laboratory tests such as incremental cur-rent and dynamic stress tests are required to determine battery model parameters in model-based SOCestimation. These tests such as incremental current test and dynamic stress test are time-consumingand can take multiple days. A mathematical optimization along with a battery test method, whichdoes not need rest time for battery, are adopted to reduce the battery parameter identification time,drastically. A mathematical optimization stage is embedded prior to Kalman Filter based SOC esti-mation computing the battery open circuit voltage (OCV) and as well as an initial guess of the RCparameters of the battery equivalent circuit. Therefore, it reduces the required number of tests toone. Extensive numerical studies on a 2 Ah Lithium-ion cell verify the effectiveness of the proposedmethod by achieving a RMS error less than one percent. |
