Online Milliohm Variation Detector for Assessing Battery Internal Resistance

This work introduces an online technique to detect variations at the milliohm-level of the equivalent internal resistance in a battery. This method is based on the voltage ripple generated by a DC-DC switching converter operating normally as its charger. It targets simple battery-associated hardware that measures only terminal voltage and output current, without additional excitation signals or specialized impedance instrumentation. An online dynamical model of the nominal converter–battery system replicates the baseline terminal voltage ripple generated under the same PWM input. The online-calculated covariance matrix for the measured and model-predicted ripple components is used to reveal variations in the internal resistance. Experimental validation, using a lead-acid battery together with a series-connected resistor network in the mΩ range to emulate an increase in internal resistance, showed smooth, monotonic dependencies between the proposed descriptors and resistance variations. This proof of concept suggests that converter-induced switching ripple could potentially support state-of-health estimation based on internal-resistance variations, using lightweight statistics compatible with typical charging hardware.