Battery state estimation with a self-evolving electrochemical ageing model

The parameters for most battery state estimation techniques are usually calibrated during the development stage before vehicle production. However different usages of battery lead to different ageing processes, and end up with different model parameters. As a result, an estimation algorithm based on pre-calibrated parameters may not generate accurate estimates. This paper presents a new advanced methodology to address this challenge. First, we employ a comprehensive electrochemical model for lead acid batteries that were developed recently. The battery model explicitly characterizes the electric and thermal behavior of the battery, as well as the evolution of major battery failure modes. Second, we apply a two-time-scale model-based estimation method, where the micro time-scale algorithm estimates battery SOC and SOH, and the macro time-scale algorithm tracks the changes of the battery being monitored and updates the parameters of the battery model accordingly. The algorithms at both time scales use particle filter, a sequential Monte-Carlo estimation technique. Several datasets based on real vehicle driving profiles are used to validate the proposed algorithms. The experiment results show that the proposed estimation scheme is effective, due to both the careful selection of battery model and the tailored filtering technique.