An Algorithm for an Online Electrochemical Impedance Spectroscopy and Battery Parameter Estimation: Development, Verification and Validation

Abstract More advanced battery diagnostic approaches are required for a safer and more reliable operation of today's and future battery technologies. For the purpose of evaluating a battery's internal conditions, Electrochemical impedance spectroscopy has proven to be a powerful tool, but nowadays it is only used in laboratory setups. It could provide valuable information about the battery's internal states, if instead it is applied online in an actual battery application. Therefore, we have developed an efficient algorithm, which is designed to run on a battery management system continuously carrying out measurements of the electrochemical impedance by iteratively evaluating measurements of battery current and voltage. Furthermore, the algorithm adapts the parameters of an equivalent circuit model to best match the battery's impedance, hence providing characteristic measures of a battery's internal conditions. The algorithm is implemented in a generic form, which severs as a baseline that can be adjusted to more specific requirements and circumstances in corresponding battery applications. The scope of this work focuses on an introduction of the generic algorithm as well as a proof-of-concept. The later was carried out in two steps. First, the algorithm was run against a battery model. Subsequently, the algorithm was validated utilizing measurements from a real battery cell. In this case, laboratory electrochemical impedance spectroscopy measurements served as reference. The algorithm was able to estimate impedance with a high accuracy in both tests. A high accuracy was also achieved for the parameter estimation; however, its accuracy decreases with large superposed DC current rates.