Learning human-understandable models for the health assessment of Li-ion batteries via Multi-Objective Genetic Programming.

The health of automotive Li-ion batteries depends on different side reactions on the electrodes that may degrade the cells, thereby reducing their useable capacity and sometimes producing catastrophic failures with serious economic and safety implications. In this paper, a method of detection and prognosis of battery deterioration is proposed in which an intelligent soft sensor is able to synthesize human-understandable health indicators from sequences of voltages, currents and temperatures streamed via on-vehicle sensors. This soft sensor is based on a dynamic model optimizing three different criteria obtained by means of multi-objective grammatical evolution. Different survival selection strategies suitable for this problem are discussed and compared.