Quantitative Diagnosis of Internal Short Circuit Fault for Lithium-Ion Battery Based on Unknown Input Observer

Internal short circuit (ISC) is a huge obstacle to the safe functioning of battery. In the early stage of ISC, the fault features are slight which results the difficulty of diagnosing ISC fault quickly and accurately. Therefore, this paper proposes the unknown input observer (UIO) to achieve the quick and accurate ISC fault diagnosis. The SC current is regarded as the unknown input, which is estimated using the UIO. The ISC fault is then detected based on whether the SC current exceeds the preset threshold. Furthermore, the SC resistance is calculated to quantify the fault severity. The performance of the approach is verified through an experiment under Dynamic Stress Test (DST) condition with four series-connected cells. The fault diagnosis results indicate that the approach is capable of diagnosing fault quickly and accurately. The root mean square errors are <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.418\Omega$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.861\Omega$</tex> for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$6\Omega$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$20\Omega$</tex> ISC faults, respectively.