# A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges
## 4 Fault Diagnostic Algorithms for the Li-Ion Battery System
### Internal Battery Fault Diagnosis
#### 4.1.2 Non-Model-Based Methods  
Non-model-based methods include signal processing and knowledge-based methods. These methods primarily rely on battery data collection, although still using battery modeling to an extent. They can improve fault diagnostic accuracy but might require a large amount of fault data, which is often not available, or have very high computational cost, which is impractical for usage in the BMS. Several recent works implement signal processing for Li-ion battery fault diagnosis. [PERSON] et al. [58] obtained the remaining charging capacity, which can increase due to extra charge depletion caused by micro-short circuiting, from charging cell voltage curve transformation. The micro-short circuit fault was then identified from the remaining charging capacities between adjacent charges, which was used to obtain the leakage current of the shorted cell. The correlation coefficient was used in [59] to detect the initial stage of short circuits by capturing the off-trend voltage drop and reflect the drop variation in the correlation coefficient. Another work [60] proposed the use of an interclass correlation coefficient to analyze battery short circuit fault also by capturing the off-trend voltage drop. Both methods using the correlation coefficient employ moving windows to prolong the fault memory and do not need extra hardware design or model adjustment. However, they can be subject to measurement noises. Entropy theory fault diagnosis has also been studied recently. Authors in [61] implemented the Shannon entropy and the Z-score method to detect any abnormality in the battery temperature, as well as predicting the time and location of the fault, to prevent thermal runaway. [PERSON] et al. [62; 63] proposed the use of a modified Shannon entropy with the Z-score method to capture abnormality in cell voltage, and predict the time and location of the voltage fault occurrence. The entropy-based methods are effective in detecting battery faults, but the computational cost increases with the desired diagnostic precision. Knowledge-based is another type of non-model-based fault diagnostic method. In some earlier works, [PERSON] et al. [64] implemented an expert system, including a rule-based method and a probabilistic method, to detect an overdischarge fault, where the rules for failure are an unusual increase in temperature and unusual decrease in voltage. This method is easy to implement, but the threshold for the rules is very difficult to obtain for different applications and cell chemistries. In [65], the authors characterized external short circuit faults through three criteria, which are voltage variation, current variation, and temperature change rate, and constructed thresholds for these criteria to detect external short circuit faults. This method is simple to implement, but it is unreliable as Li-ion cells vary in characteristics, and more experiments would need to be conducted to validate the set thresholds. In [66], the authors used fuzzy logic to analyze temperature, SOC, and voltage residuals from an electrochemical model to detect overcharge, overdischarge, and accelerated degradation. Later works explore the application of machine learning in Li-ion battery fault diagnosis. The Random forests classifier was used in [67] to detect the electrolyte leakage behavior that occurs during an external short circuit fault. The two root-mean-square-error indicators for an external short circuit fault in this study are higher maximum temperature rise and lower discharge capacity. The result of the leakage condition is obtained through training a large amount of data using the classifier. The normal and faulty cells can then be classified effectively, and external short circuit fault can be diagnosed accurately. [PERSON] et al. [68] established a battery fault diagnostic model through the combination of the 3\(\sigma\) multi-level screening strategy and the machine learning algorithm.