# A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges
## 4 Fault Diagnostic Algorithms for the Li-Ion Battery System
### Current Progress and Future Challenges of Li-Ion Battery Fault Diagnosis  
In summary, the fault diagnostic algorithms that were discussed have made certain progress on improving Li-ion battery safety, but they still have some limitations in real-life applications. A summary of all the reviewed algorithms is shown in Table 1. Model-based methods can quickly detect and isolate a fault in real-time but require high modeling accuracy. Therefore, further research needs to be conducted to reach a better understanding of the internal battery operation to develop a precise, but not overly complicated, battery model. In addition, the trade-off between robustness and sensitivity of the diagnostic approach, which depends on the fault thresholds, should also be considered. Non-model-based methods can avoid the difficult requirements for battery modeling but still have some drawbacks. Signal processing methods have good dynamic performance but are sensitive to measurement noises and are not able to detect early faults reliably. Simple knowledge-based methods, such as expert systems, require effective rules to precisely detect faults, which is challenging as some battery faults are still not fully understood. Complex knowledge-based methods, such as machine learning, have high accuracy and compatibility with a nonlinear system, such as the Li-ion battery, but the training process is time-consuming and requires a large amount of data. Overall, even though model-based methods are more common, many difficulties still exist in improving battery model accuracy, especially throughout the entire lifespan of the battery. Non-model-based methods, particularly data-driven methods, can have a crucial role in predicting battery behavior as it degrades and aiding the model development process. Therefore, the most effective approach for Li-ion battery fault diagnosis should be a combination of both model-based and non-model-based methods. There are several challenges that all of these fault diagnostic methods face. First, because some faults have similar effects on the battery, it is difficult to isolate each fault accurately to provide appropriate responses. Current methods often assume that the other components in the system are operating normally to avoid the need to isolate and identify the detected faults. Future research should focus on the development of fault identification methods after successful fault detection. Another challenging task is the determination of effective fault thresholds for early and accurate detection, due to the lack of understanding of fault behavior. Furthermore, a better understanding of battery fault behavior is needed to develop fault simulation tools, because producing a real physical fault can be impractical, costly, and unsafe. Therefore, more studies on the behavior of faults need to be conducted through well-designed experiments to collect data for modeling and simulation purposes. Finally, the BMS computational capability needs to be enhanced to accommodate more complex algorithms that can improve fault diagnostic accuracy significantly. This can be achieved by future hardware development in the BMS, as well as the utilization of cloud-based technologies for battery condition monitoring.