Battery state-of-charge estimation prototype using EMF voltage prediction

A reliable knowledge of cell parameters like the state-of-charge (SoC) is essential for the optimization of battery-powered applications. Usually, during relaxation (the phase of no or low loads) the SoC is determined based on the measurement of the battery´s electro-motive force (EMF). To obtain a reliable measurment, it is required that the battery voltage transient is in a well-relaxed state, which is rarely reached in practice (e.g. due to periodic discharge activities). In this paper, a predictive methodology is presented which is able to forecast the EMF and therewith the SoC already during a not well-relaxed state of the voltage transient. A nonlinear relaxation voltage model is reformulated such that the problem can be treated as a linear least squares estimation problem. Based on this estimation, the performance is evaluated with respect to the following aspects: prediction time, current rate influence, SoC influence, cell-to-cell deviation, or rather aging and temperature effects. Experimental results are presented for a fixed-point implementation of the estimation scheme on a CY8CKIT-050 PSOC5 programmable system on chip. For validation, measurements of 2.25Ah Sanyo UR18650A lithium cells have been used. It is shown that the presented approach offers an improved re-initialization methodology for the Coulomb counting method, and that it clearly outperforms the usual EMF-measurement based SoC determination method.