Module Voltage and Resistance Estimation of Battery-Integrated Cascaded Converters Through Output Sensors Only for EV Applications

Modular reconfigurable batteries are gaining significant traction in the electromobility sector, mainly due to better scalability to large capacities and the falling cost of electronic components. Despite their advantages over hard-wired battery packs, modular systems rely on a high number of complex and expensive sensing and monitoring circuits, which challenge their cost-effectiveness. This article proposes an effective method to estimate the parameters of each individual battery module without any direct measurement at the module level. The proposed algorithm uses the output voltage and current of the load combined with exact knowledge of the connection states of the modules to estimate the open-circuit voltage (OCV), ohmic resistance, and polarization resistance according to the equivalent circuit representation of each battery module. We demonstrate the performance of the method combined with a Kalman filter (KF) through simulations for both high- and low-power systems as well as through experiments on a scaled laboratory setup. Beyond KFs, the technique can also be combined with other iteration-based estimators following the provided flowcharts. Based on simulations and experiments, the proposed method estimates the OCV and equivalent resistance of batteries with errors lower than 1% and 4%, respectively. Furthermore, the method can calculate the ohmic and polarization resistances with a maximum error of <0.015 in the low-power systems and <0.2 m in the high-power systems.

Duke Scholars

Author Stefan M Goetz Psychiatry & Behavioral Sciences, Behavioral Medicine & Neur ...