Health assessment of automotive batteries through computational intelligence-based soft sensors: An empirical study

Abstract

An empirical comparison of different intelligent soft sensors for obtaining the state of health of automotive rechargeable batteries is presented. Data streamed from on-vehicle sensors of current, voltage and temperature is processed through a selection of model-based observers of the state of health, including data-driven statistical models, first principle-based models, fuzzy observers and recurrent neural networks with different topologies. It is concluded that certain types of recurrent neural networks can outperform well established first-principle models and provide the supervisor with a prompt reading of the State of Health. The algorithms have been validated with automotive Li-FePO4 cells.