Performance of Recursive Least Squares Algorithm Configurations for Online Parameter Identification of Induction Machines in an Automotive Environment

The recursive least squares (RLS) algorithm for online parameter identification (OPI) of induction machines (IMs) has a high potential to serve as a basis for an innovative electric vehicle diagnosis concept. Commonly used for control parameter tuning, this approach is established in numerous industrial applications. However, in the automotive environment, special machine designs are used, and the highly dynamic operation takes place in a wide speed and load range. This results in fast transient parameter behavior, which is challenging in terms of OPI. Therefore, the algorithm performance must be rated in our field of application with suitable dynamic test profiles. In this work, we compare several algorithm extensions to a novel RLS algorithm with multiple variable forgetting factors for IMs. The algorithms are analyzed regarding their handling of the associated transient parameter behavior. In addition, different identification model structures are considered to deal with the dynamic speed operation and the associated transient iron losses. Special attention is given to the real-time performance of the overall identification algorithms as this is a major requirement for implementation in automotive embedded systems. For validation, both simulation and experimental results are presented, and associated configuration recommendations are provided.