As a superior solution to the developing demand for energy storage, lithium-ion batteries play an important role in our daily lives. To ensure their safe and efficient usage, battery management systems (BMSs) are often integrated into the battery systems. Among other critical functionalities, BMSs provide information about the key states of the batteries under usage, including state of charge (SOC) and state of health (SOH). This paper proposes a data-driven approach for the joint online estimation of SOC and SOH utilizing multi-task learning (MTL) approaches, particularly highlighting cross-stitch units and cross-stitch networks. The proposed model is able to achieve an accurate estimation of SOC and SOH in online applications with optimized information sharing and multi-scale implementation. Comprehensive results on training and testing of the model are presented. Possible improvements for future work are also discussed in the paper.

Lithium-ion batteries (LIBs) are widely used in diverse applications, ranging from portable ones to stationary ones. The appropriate handling of the immense amount of spent batteries has, therefore, become significant. Whether recycled or repurposed for second-life applications, knowing their chemistry type can lead to higher efficiency. In this paper, we propose a novel machine learning-based approach for accurate chemistry identification of the electrode materials in LIBs based on their temperature dynamics under constant current cycling using gated recurrent unit (GRU) networks. Three different chemistry types, namely lithium nickel cobalt aluminium oxide cathode with silicon-doped graphite anode (NCA-GS), nickel cobalt aluminium oxide cathode with graphite anode (NCA-G), and lithium nickel manganese cobalt oxide cathode with graphite anode (NMC-G), were examined under four conditions, 0.2 C charge, 0.2 C discharge, 1 C charge, and 1 C discharge. Experimental results showed that the unique characteristics in the surface temperature measurement during the full charge or discharge of the different chemistry types can accurately carry out the classification task in both experimental setups, where the model is trained on data under different cycling conditions separately and jointly. Furthermore, experimental results show that the proposed approach for chemistry type identification based on temperature dynamics appears to be more universal than voltage characteristics. As the proposed approach has proven to be efficient in the chemistry identification of the electrode materials LIBs in most cases, we believe it can greatly benefit the recycling and second-life application of spent LIBs in real-life applications.