The use of electric vehicles has been rapidly increasing due to their environmental benefits and advancements in battery technology. However, one of the significant challenges faced by EVs is to optimise battery performance and their operational life extension1. As the demand for EVs is increasing in our daily lives, the protection issues are also increasing. So, there is a need for a fault detection system that can detect these faults intelligently and proactively to anticipate issues before they escalate. Such fault detection systems improve not only the safety and dependability of EVs, but also promote the overall sustainability2. A Battery Management System (BMS) is a method used to control and regulate power during the battery’s charging and discharging phases. It safeguards the cells by observing battery capacity, computing subsidiary data, and helping to balance the battery properly3. The BMS identifies voltage, temperature, or current abnormalities, prompting the alarm system to protect the battery pack. These abnormalities are caused by the faults that occur in the battery. Different types of faults can occur during battery operation, including overcharging, over-discharging, and thermal issues4. Overcharging is a fault that can occur in a battery when it is charged beyond its recommended voltage limit.
This can happen for various reasons, such as charging the battery beyond the manufacturer’s specified voltage, faulty charging equipment, and extended charging time. In extreme cases, this may increase the temperature of the battery and the battery can catch fire or explode. Hence, it is essential to monitor the charging process carefully and to use reliable chargers to avoid overcharging5. A fault in the battery can also occur due to over discharging, which happens when the battery is discharged beyond its recommended voltage or capacity limits. This can be caused by prolonged usage without recharging, improper storage, or a faulty battery management system. Over discharging can lead to reduced capacity and performance. It is essential to monitor and prevent over-discharging to ensure the longevity and safety of the battery. Moreover, thermal fault in a battery occurs when there is an abnormal increase in temperature, leading to potential damage or failure6. Several reasons contribute to thermal faults in batteries, such as overcharging, over-discharging, external heat sources, manufacturing defects, and mechanical damage. Monitoring and controlling the temperature during battery operation is essential to prevent thermal faults. Proper cooling systems, temperature sensors, and battery management systems are crucial in maintaining safe operating conditions and preventing thermal incidents.
There are various types of electrical faults that can occur in lithium-ion batteries, as referred to in Table 1, and they differ in how easily they can be detected. Faults such as overcharging and over-discharging are generally easy to identify, as they produce clear and measurable changes in the battery’s behaviour. In contrast, faults like short circuits, voltage imbalances, and (under-over) temperature due to excessive heat or cold are more difficult to detect. These types of faults often develop gradually or arise under specific conditions, making real-time identification challenging. This underscores the importance of the IFD system to detect such hidden faults early, ensuring the safety, performance, and longevity of the battery.
An IFD system for EV batteries is designed to monitor and analyse battery behaviour in real time to detect potential issues before they lead to serious failures. By using advanced machine learning algorithms and data collected from onboard sensors—such as voltage, current, and temperature the system can identify unusual patterns that indicate early signs of battery faults. These intelligent models learn from both historical and live data, allowing them to differentiate between normal battery fluctuations and actual problems, such as cell imbalance, thermal anomalies, or capacity degradation. This proactive approach not only improves safety and extends battery life but also supports predictive maintenance by alerting users or service systems before a fault becomes critical. As EV adoption grows, such smart battery monitoring systems are becoming essential for ensuring reliable performance, reducing downtime, and building trust in electric mobility.
Literature survey
Various researchers are working on fault detection of batteries, like Abada et al.7, who discussed the hardware safety approach of batteries, mainly focused on the modelling and testing of batteries under different conditions. The modelling of the battery provided a thermal runaway for a pack to integrate the ageing of the battery effect, which helps to assess the battery functionality and desired safety aspects of the battery.
Similarly, Jiang et al.8 provided a hardware system for future safe direction for charging systems by comparing onboard connected batteries, cell energy density and efficiency with the long life cycles. Meissner et al.9 presented battery monitoring and energy management, which predicted the future power of batteries by generating a data set for complete charging periodically with well-defined SOC and the data generation on the battery with different test benches. Chen et al.10 employed the RC equivalent circuit model hardware setup and a swarm optimisation technique to ascertain battery parameters. Additionally, a two-layer model was utilised based on an external short circuit and a fault diagnosis approach to estimate defects in a battery. Similarly, Wang et al.11 put forth a modified Shannon entropy based on cell voltage data. A security management technique was also provided, which used Z-score normalisation. Zhu et al.12 proposed an ML approach for determining the temperature of a battery at a specific time to mitigate the risk of thermal runaway, along with the decomposition of temperature with reversible and irreversible heat with battery operation components. However, the voltage parameter was missing in this research. Chen et al.13 proposed a method for voltage problem identification in lithium-ion batteries utilising the Local Outlier Factor algorithm. The LOF was utilised to measure the degree of deviation of parameters from their neighbouring values. Consequently, faults were identified and evaluated using an outlier filter based on the Grubbs criterion14,15. Additionally, Zhao et al.16 carried out a study combining simulation and experimental analysis of external and internal short circuits. A modified electrochemical-thermal coupling model was used to predict temperature variations; however, it did not indicate active fault status.
Feng et al.17 investigated fault identification in large-format lithium batteries, specifically focusing on internal short-circuit faults. This paper aimed to utilise voltage and temperature responses to develop an algorithm capable of identifying the occurrence of faults and determining their particular position within the battery. Moreover, studies have been conducted on the modelling and analysis of battery short circuits; for example, Adnan et al.18 introduced a methodology that utilises a support vector machine (SVM) to include the diagnosis and prognostics of battery health. Similarly, Zhang et al.19 introduced a novel approach to the diagnosis of capacity problems online and in real time in parallel connected lithium ionhium ionhium ion battery groups. For a better approach, there is a need for more accurate diagnostic methods.
Further, the outlier analysis is a technique employed in fault diagnostics20,21,22. However, the majority of traditional statistical analyses conducted on electric vehicles are unable to identify the specific defective battery cells effectively. In addition, the currently employed approaches are incapable of identifying the emergence of faults because the voltages remain within the acceptable range. To overcome this, Hu et al.23 introduced a technique for estimating battery capacity using a combination of particle swarm optimisation and k-nearest neighbour regression. Yang et al.24 utilised an extreme learning machine (ELM) based thermal model to represent the thermal response of batteries during external short circuits. However, the aforementioned models possess limited applicability since they are solely suitable for diagnosing specific defects within specific operational circumstances. Fault detection was performed by comparing parameter values against predefined criteria. While these methods are easy to implement, they require multiple thresholds, posing challenges in accurately determining their values25. Furthermore, AI techniques, such as LS-SVM and LSTM neural networks, have been utilised for diagnosing issues in LIBs26. However, it is important to acknowledge that these methods rely on a large volume of historical data, which can be challenging to access and acquire.
Yuksek et al. emphasise the need for accurate SOH estimation to support efficient battery management. While traditional methods require large datasets and heavy processing, they propose a simple curve-fitting approach that delivers high accuracy with minimal data. Remarkably, using just 1% of the Oxford battery dataset, their model outperformed other methods based on RMSE, showing strong results with low cost and complexity27.
Lale shows that lithium-based batteries, while powerful, are sensitive to heat and charging speed. To tackle this, they propose an adaptive BMS that adjusts charging rates based on temperature. This smart approach boosts efficiency, extends battery life, and in simulations, improved energy output by 11.49% compared to standard methods, proving the value of temperature-aware charging28.
Yamacli highlights that battery safety and ageing are key challenges, especially in electric vehicles. Unlike most methods that focus on single cells, this study looks at series-connected batteries in real-world setups. Using deep learning with hybrid classification, and data from Oxford and CALCE, the model achieved 98.33% accuracy with low error, proving it’s both accurate and practical for real-time SOH monitoring29.
Shang et al. point out that as we move toward carbon neutrality, keeping lithium-ion batteries safe and reliable is crucial. They review three main ways to detect battery faults: data-driven, model-based, and threshold-based, each with its pros and cons. Their work offers a clear summary of fault types and the latest techniques, helping guide future research in better battery fault detection30.
The literature has discovered that few authors have used machine learning approaches for over-temperature, under-temperature, over-voltage and under-voltage protection. Consequently, there is a need for an intelligent fault detection (IFD) system that continuously oversees the battery’s status and establishes protective zones to prevent potential issues. This system alerts the user upon detecting hazardous conditions, enabling timely intervention to safeguard the battery from significant faults. This method aims to shield battery cells from potential harm and optimise safe operational ranges for batteries.
Contribution
The major contributions of this research are as follows:
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This approach uses real-world test data from LiFePO4 batteries to analyse fault patterns and inconsistencies within the battery pack under various operating conditions.
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For better optimisation, the study utilises two test benches for data generation, facilitating the identification of aberrant battery conditions.
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An IFD has been proposed to detect the faults in EVs proactively to improve reliability.
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By factoring in temperature and voltage, this model significantly reduces inaccuracies and enhances performance, resulting in shorter computational times while improving the accuracy of the estimation.
Novelty
The need for a fault detection approach of lithium-ion batteries defines the limits of voltage and temperature within which the battery can function safely. Recent research focuses on developing a novel approach that adjusts in real time based on battery health, usage patterns, and environmental conditions. By integrating machine learning, dynamic frameworks improve safety, extend battery life, and enable higher performance. This shift from static to data-driven for the fault detection approach represents a key innovation for applications such as electric vehicles and energy storage systems.
Organisation
“Proposed model intelligent fault detection system (IFD)” section discusses the proposed model with data generation and data preprocessing. The fault detection model is represented in “Intelligent fault detection model” section for safe operating areas. “Results and discussion” section discusses the results and discussion, and “Proposed model intelligent fault detection system (IFD)” section concludes the research findings.
