Abstract
An important issue towards cell individual battery monitoring is the employment of new sensor technologies and efficient algorithms to enlarge the diagnostic capabilities of battery systems. Especially for the onboard application in electric vehicles, impedance spectroscopy is one of the most promising methods to gather useful information about the battery. In this paper, a new framework for onboard impedance spectroscopy on cell level is developed, which incorporates a passive method without the need of an additional active signal generation. The derived estimator is based on the weighted overlapped segment averaging algorithm and is optimized for the application in a battery management system. The theoretical considerations are experimentally examined with a battery module consisting of parallel connected cells. Therefore, a battery test rig with high bandwidth is designed to enable a realistic replication of road driving scenarios. Statistical quality criterions for the online validation of impedance spectra using linear Kramers-Kronig tests are established and verified with corresponding experiments. A significant comparison with reference measurements based on the accuracy of estimated model parameters shows very good agreement. Next to model parameter estimation, diverse other application scenarios for the monitoring and diagnosis of battery systems based on impedance data are outlined.
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