Estimation of battery internal resistance using built-in self-scaling method

This paper proposes the use of the built-in self-scaling (BS) method for the effective estimation of the internal resistance of lithium-ion batteries. The internal resistance is a measure of the battery's state-of-health and an important parameter to monitor, especially in safety-critical applications such as hybrid electric vehicle applications. The BS technique works by identifying the system's impulse response and then computing the resistance from this response. This approach makes use of a prior DC gain which capitalizes on the fact that the state-of-health changes slowly with time. The BS method can be utilized on the fly in real time, is passive, and has high accuracy which is invariant with respect to the battery dynamics. Simulation results show that the BS method reduces the mean square error by factors of 32, 69 and 20 compared with the series resistance, the least squares and data pieces, and the kernel-based techniques, respectively, in the absence of hysteresis. The corresponding values in the presence of hysteresis are 42, 62 and 21, respectively. Experimental results using a lithium nickel manganese cobalt oxide battery and a dynamic current profile based on the Federal Urban Driving Schedule further confirm the superiority of the proposed BS approach.