Data-driven short circuit resistance estimation in battery safety issues

Abstract

Developing precise and fast methods for short circuit detection is crucial for preventing or mitigating the risk of safety issues of lithium-ion batteries (LIBs). In this paper, we developed a Convolutional Neural Networks (CNN) based model that can quickly and precisely predict the short circuit resistance of LIB cells during various working conditions. Cycling tests of cells with an external short circuit (ESC) are produced to obtain the database and generate the training/ testing samples. The samples are sequences of voltage, current, charging capacity, charging energy, total charging capacity, total charging energy with a length of 120 s and frequency of 1 Hz, and their corresponding short circuit resistances. A big database with ∼ 6 × 105 samples are generated, covering various short circuit resistances (47 ∼ 470 Ω), current loading modes (Constant current-constant voltage (CC-CV) and drive cycle), and electrochemical states (cycle numbers from 1 to 300). Results show that the average relative absolute error of five random sample splits is 6.75 %±2.8 %. Further parametric analysis indicates the accuracy estimation benefits from the appropriate model setups: the optimized input sequence length (∼120 s), feature selection (at least one total capacity-related variable), and rational model design, using multiple layers with different kernel sizes. This work highlights the capabilities of machine learning algorithms and data-driven methodologies in real-time safety risk prediction for batteries.