# A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges
## 1 Introduction  
Lithium-ion (Li-ion) batteries play a significant role in daily applications due to their important advantages over other energy storage technologies, such as high energy and power density, long lifespan, and low self-discharge performance factors under improper temperatures [1]. Li-ion batteries have gained a significant amount of attention in recent years, showing promise as an energy storage source in electric vehicles (EVs) due to the aforementioned advantages [2]. They are also widely used in many electronics and stationary applications. Although Li-ion batteries have had reported accidents causing public concern, the advent of safety features over time has decreased the associated risk factors and improved battery operation [3; 4]. Li-ion batteries have gone through many modifications to improve their safety, and system management is required to guarantee the performance and reliability of these batteries [1]. A key component in monitoring the battery health and safety is the battery management system (BMS). Some of its main functions are data acquisition, state of charge (SOC) and state of health (SOH) estimation, cell balancing, charge management, and thermal management. An important function of the BMS is the diagnosis of faults which can come from extreme operating conditions, manufacturing flaws, or battery aging [5]. Many factors affect the Li-ion battery operation, such as collision and shock, vibration, deformation, metallic lithium plating, formation of a solid electrolyte interphase (SEI) layer, formation of lithium dendrite, etc. [1; 6]. Some common external battery faults are sensor faults, including temperature, voltage and current sensor faults, as well as cell connection and cooling system faults. There are also internal battery faults that are caused by the above factors and external battery faults. Some common internal battery faults are overcharge, overdischarge, internal and external short circuit, overheating, accelerated degradation, and thermal runaway. These battery faults lead to potentially hazardous consequences, such as an increase in temperature and pressure, which could increase the risk ofcombustion and explosion [7]. The consequences of Li-ion battery faults can be minimized or eliminated by the BMS, as it prevents the battery from functioning outside its safe operational range, and also detects faults using various fault diagnostic methods [8]. It is an indispensable component in the Li-ion battery system since it can handle failures safely by going into failure mode, providing a safer environment for the users of Li-ion battery applications. Since fault diagnosis is a crucial part of Li-ion battery advancements, various methods for fault diagnosis have been studied and developed. Many fault diagnostic algorithms have been proposed, which can be categorized into model-based and non-model-based methods. Model-based methods include parameter estimation, state estimation, parity space, and structural analysis. Non-model-based methods include signal processing and knowledge-based methods [1; 9]. Fault diagnosis research in other fields has shown that the most effective approach is often a combination of more than one method [9]. [PERSON] et al. [8] briefly presented fault diagnosis as one of the key issues for Li-ion battery management in electric vehicles. In [10], a review of sensor fault diagnosis for Li-ion battery systems was provided, but other types of faults were not discussed. [PERSON] et al. [1] conducted a review on fault mechanism and diagnosis for Li-ion battery, but there have been many new developments in the field since then. Researchers have made significant progress in understanding the mechanism and operation of Li-ion batteries, and with that, many innovations in battery fault diagnosis have emerged. Therefore, there is a need to identify the current progress and future direction of Li-ion battery fault diagnosis research.