Accelerating the transition to cobalt-free batteries: a hybrid model for LiFePO4/graphite chemistry

Abstract

The increased adoption of lithium-iron-phosphate batteries, in response to the need to reduce the battery manufacturing process’s dependence on scarce minerals and create a resilient and ethical supply chain, comes with many challenges. The design of an effective and high-performing battery management system (BMS) for such technology is one of those challenges. In this work, a physics-based model describing the two-phase transition operation of an iron-phosphate positive electrode—in a graphite anode battery—is integrated with a machine-learning model to capture the hysteresis and path-dependent behavior during transient operation. The machine-learning component of the proposed “hybrid” model is built upon the knowledge of the electrochemical internal states of the battery during charge and discharge operation over several driving profiles. The hybrid model is experimentally validated over 15 h of driving, and it is shown that the machine-learning component is responsible for a small percentage of the total battery behavior (i.e., it compensates for voltage hysteresis). The proposed modeling strategy can be used for battery performance analysis, synthetic data generation, and the development of reduced-order models for BMS design.