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Li-ion battery has attracted more and more attention as it is a promising storage device which has long service life, higher energy, and power density. However, battery ageing always occurs during operation and leads to performance degradation and system fault which not only causes inconvenience, but also risks serious consequences such as thermal...
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... Many affordable systems do not implement features such as thermal runaway detection, reverse polarity protection, or short-circuit shutdown mechanisms. A literature survey [10] highlights that over 60% of low-cost battery-based systems reported performance degradation or failure due to inadequate safety monitoring. Integration of fault alerting mechanisms, particularly for short-circuit, overcurrent, and deep discharge conditions, is often overlooked in DIY or consumer-friendly systems. ...
Inverter-based energy storage systems are widely used in residential and small-scale commercial setups, yet users often remain unaware of the remaining backup duration and real-time battery health. This paper presents a cost-effective, Arduino-based embedded system that addresses this gap through two key contributions: (1) a real-time dynamic estimation of remaining battery backup time based on actual current load conditions, and (2) integrated safety monitoring for overvoltage, undervoltage, and overcurrent faults. The system leverages voltage divider circuits and ACS712 current sensors interfaced with an Arduino Uno to continuously acquire battery and load parameters. A backup time estimation algorithm processes these values to display predicted runtime on a 16x2 LCD, while fault conditions are indicated through buzzer alerts. The practical prototype was implemented using a 12V lead-acid battery under varying load profiles, and performance was validated across multiple discharge cycles. The design emphasizes low-cost components, ease of deployment, and scalability for broader adoption in energy-constrained regions. This work offers a viable solution to enhance user awareness and safety in decentralized energy storage systems without reliance on expensive battery management units. Key Words: Battery Backup Estimation, Smart Energy Storage, Arduino Monitoring, Battery Management System, Real-Time Load Monitoring
... • manageable computational effort (Alhanouti et al., 2016;Wu et al., 2015;Sidhu et al., 2015) • applicable for a wide time range (ms-h) • parameterizable for commercial cells from measurement data • scalable via virtual interconnection to modules Due to the required fine time resolution of the upstream system models, the computational effort is of special interest. The application of a proven battery model structure also enables parameterization using standard test procedures. ...
The electrical power system is facing an increasing share of distributed generation from renewable energy sources compared to conventional power plants with declining system inertia. Thus, ancillary services such as frequency containment by instantaneous frequency reserve has to be provided by new market players. Battery energy storage systems (BESS) offer rapid response capabilities, making them a favorable choice for enhancing power system stability. However, a wide variety of battery types are available, requiring careful selection based on specific applications. Full system simulations are essential for the delineation of the requirements for batteries to be able to provide instantaneous back-up.
This paper examines the system aspects of battery energy storage systems consisting of a converter powered by a battery. In order to investigate the battery system requirements from a power system perspective, a new holistic system model has been developed that includes detailed representations of the dynamic power system, the converter and the battery model. Employing the converter control technique of a virtual synchronous generator (VSG) the so parameterized BESS acts from grid viewpoint like a conventional synchronous generator providing system inertia. The validation of the converter and battery models, together with their parameterization, has been carried out through comparable experimental setups. Different scenarios have been developed and simulated to identify the parameters of the virtual synchronous machine for optimized frequency stabilization and to determine the corresponding load on the converter and battery. The results obtained, such as the weekly energy throughput of 1 P_nom hour based on real grid data, provide quantitative indicators for the design and optimization of BESS for use as VSG for the first time. The results were used to identify suitable battery system configurations for providing instantaneous back-up. Further analysis of the power demand characteristics reveals that only 4 % of the BESS capacity is utilized for instantaneous reserve, providing options for multi-use business concepts.
... Despite the advancements in LIBs that enhance their longevity, factors such as environmental conditions and usage patterns are key determinants of their lifespan. LIBs are particularly susceptible to reduced lifespan due to high rates of charging and discharging, especially under extreme ambient temperatures [4]. Operating these batteries within a specific temperature range, from −20 • C to +60 • C, is crucial to prevent damage, and optimal performance is observed when the cell temperature is maintained around +20 • C [5]. ...
The rapid growth in the battery electric vehicle (BEV) market has brought lithium-ion battery (LIB) packs to the forefront due to their superior power and energy density properties. However, LIBs are highly susceptible to environmental factors, operating conditions, and manufacturing inconsistencies and operate within a narrow safety operating window. Battery faults pose significant risks, including potentially catastrophic thermal runaway, that can be initiated even by small faults, propagating further into a chain reaction cascade of failures. Aiming to improve the safety of such battery packs, this article presents the developed autoencoder-based fault detection method. The method, enhanced by computational intelligence and machine learning, is a result of extensive research into optical liquid detection systems (OLDSs) for immersion-cooled battery packs, where optical rather than electrical signals are used inside high-voltage areas. The performance was evaluated using recorded real-life datasets under faultless states and under simulated fault states through specific model performance indicators as well as detection performance indicators.
... By training the model on the training set, the number of neurons and the number of network layers can be determined. If the number selection is inappropriate, the model will have problems such as over-fitting and gradient explosion [34]. This means that the model can not learn the relationship between data perfectly. ...
The lithium battery is an essential component of electric cars; prompt and accurate problem detection is vital in guaranteeing electric cars’ safe and dependable functioning and addressing the limitations of Back Propagation (BP) neural networks in terms of vanishing gradients and inability to effectively capture dependencies in time series, and the limitations of Long-Short Term Memory (LSTM) neural network models in terms of risk of overfitting. A method based on LSTM-BP is put forward for power battery fault diagnosis to improve the accuracy of lithium battery fault diagnosis. First, a lithium battery model is constructed based on the second-order RC equivalent circuit and the electro-thermal coupling model, and various lithium battery failures are simulated to examine the fault characteristics. Then, the lithium battery charging and discharging experiments collect, clean, and process the battery data. By constructing a neural network LSTM-BP model, we verified the superiority and accuracy of the LSTM-BP neural network model by comparing the LSTM model and BP model vertically and by comparing the Recurrent Neural Network (RNN) model, the Gated Recurrent Unit (GRU) model, and the Residual Neural Network (ResNet) model of a more advanced architecture horizontally. Finally, the lithium battery fault diagnosis process is summarized through the threshold quantitative criteria, and different faults are diagnosed and analyzed. The results show that the LSTM-BP neural network not only overcomes the limitations of the LSTM neural network and BP neural network but also improves the ability to process sequence data and reduces the risk of overfitting.
... These models can be effectively applied to estimate SOH and RUL with reduced computational demands, rendering them more suitable for real-time applications [29][30][31]. For example, models that combine empirical capacity degradation curves with simplified mathematical formulations have demonstrated reliable performance in real-world scenarios [2,[25][26][27]32]. Nonetheless, semi-empirical models often lack the precision needed to capture underlying electrochemical processes or to predict performance under extreme conditions. ...
... Nonetheless, semi-empirical models often lack the precision needed to capture underlying electrochemical processes or to predict performance under extreme conditions. This limitation reduces their applicability in scenarios requiring detailed degradation analysis [26,27,32]. ...
... However, their simplified approach creates challenges in capturing a battery's complex physical and chemical processes such as ion diffusion and reaction rates. This oversimplification can result in inaccuracies under highrate charging/discharging conditions or extreme temperatures [2,26,32]. ...
Lithium-ion (Li-ion) batteries have become essential in modern industries and domestic applications due to their high energy density and efficiency. However, they experience gradual degradation over time, which presents significant challenges in maintaining optimal battery performance and increases the risk of unexpected system failures. To ensure the reliability and longevity of Li-ion batteries in applications, various methods have been proposed for battery health monitoring and remaining useful life (RUL) prediction. This paper provides a comprehensive review and analysis of the primary approaches employed for battery health monitoring and RUL estimation under the categories of model-based, data-driven, and hybrid methods. Generally speaking, model-based methods use physical or electrochemical models to simulate battery behaviour, which offers valuable insights into the principles that govern battery degradation. Data-driven techniques leverage historical data, AI, and machine learning algorithms to identify degradation trends and predict RUL, which can provide flexible and adaptive solutions. Hybrid approaches integrate multiple methods to enhance predictive accuracy by combining the physical insights of model-based methods with the statistical and analytical strengths of data-driven techniques. This paper thoroughly evaluates these methodologies, focusing on recent advancements along with their respective strengths and limitations. By consolidating current findings and highlighting potential pathways for advancement, this review paper serves as a foundational resource for researchers and practitioners working to advance battery health monitoring and RUL prediction methods across both academic and industrial fields.
... In recent years, fault detection of EVs batteries has been a very important research topic [5]. After reviewing the research and discussions of many scholars on the fault detection aspects of lithium-ion batteries, among the current research methods, model-based methods, knowledge-based methods, and data-driven methods are still the more advanced methods [6][7][8]. ...
Accurate and reliable fault detection is essential for the safe operation of electric vehicles. Support vector data description (SVDD) has been widely used in the field of fault detection. However, constructing the hypersphere boundary only describes the distribution of unlabeled samples, while the distribution of faulty samples cannot be effectively described and easily misses detecting faulty data due to the imbalance of sample distribution. Meanwhile, selecting parameters is critical to the detection performance, and empirical parameterization is generally time-consuming and laborious and may not result in finding the optimal parameters. Therefore, this paper proposes a semi-supervised data-driven method based on which the SVDD algorithm is improved and achieves excellent fault detection performance. By incorporating faulty samples into the underlying SVDD model, training deals better with the problem of missing detection of faulty samples caused by the imbalance in the distribution of abnormal samples, and the hypersphere boundary is modified to classify the samples more accurately. The Bayesian Optimization NSVDD (BO-NSVDD) model was constructed to quickly and accurately optimize hyperparameter combinations. In the experiments, electric vehicle operation data with four common fault types are used to evaluate the performance with other five models, and the results show that the BO-NSVDD model presents superior detection performance for each type of fault data, especially in the imperceptible early and minor faults, which has seen very obvious advantages. Finally, the strong robustness of the proposed method is verified by adding different intensities of noise in the dataset.
... This approach enables researchers to estimate battery lifespan and performance more rapidly and efficiently. [85] Multiple cell lithium-ion battery system electric fault online diagnostics ...
Electric Vehicles (EVs) are a rapidly growing segment in India’s automotive sector, with an expected 70% growth by 2030. Lithium-ion (Li-ion) rechargeable batteries are favoured because of their high efficiency in power and energy delivery, along with fast charging, long lifespan, low self-discharge, and environmental friendliness. However, as a crucial subsystem in EVs, batteries are susceptible to faults arising from various factors. Li-ion battery faults can be categorized as internal or external. Internal faults stem from over-charging, over-discharging, overheating, acceleration and degradation processes, short circuits, and thermal runaway. External faults are caused by sensor malfunctions, cooling system failures, and cell connection problems. A Battery Management System (BMS) plays an essential role in regulating battery operation, monitoring its health status, and implementing fault diagnostic techniques. Fault diagnostic algorithms running on the BMS enable early or post-fault detection and control measures to minimize the consequences of faults, thereby ensuring battery safety and reliability. This paper reviews various internal and external battery fault diagnosis methods. In addition to battery fault detection, this work conducts a comparative analysis of optimization techniques for fault diagnosis, including Fuzzy Clustering, Long Short-Term Memory, Support Vector Machines, and Particle Swarm Optimization.
... The anomaly detection process of a battery system covers four key aspects: fault identification, fault isolation, fault estimation, and fault tolerance measures [6]. The flowchart of the lithium-ion battery system fault diagnosis is shown in Figure 1. ...
... Therefore, the development of diagnostic techniques capable of rapidly and precisely detecting battery pack failures in electric vehicles is essential to improve safety standards and bolster consumer confidence in this eco-friendly, low-carbon mode of transportation [5]. The anomaly detection process of a battery system covers four key aspects: fault identification, fault isolation, fault estimation, and fault tolerance measures [6]. The flowchart of the lithium-ion battery system fault diagnosis is shown in Figure 1. ...
... In the second step, the gray wolves engage in a population search by gradually surrounding their prey. This behavior is mathematically modeled and can be expressed using Equations (6) and (7): (8) and (9): ...
The research of electric vehicle power battery fault diagnosis technology is turning to machine learning methods. However, during operation, the time of occurrence of faults is much smaller than the normal driving time, resulting in too small a proportion of fault data as well as a single fault characteristic in the collected data. This has hindered the research progress in this field. To address this problem, this paper proposes a data enhancement method using Least Squares Generative Adversarial Networks (LSGAN). The method consists of training the original power battery fault dataset using LSGAN models to generate diverse sample data representing various fault states. The augmented dataset is then used to develop a fault diagnosis framework called LSGAN-RF-GWO, which combines a random forest (RF) model with a Gray Wolf Optimization (GWO) model for effective fault diagnosis. The performance of the framework is evaluated on the original and enhanced datasets and compared with other commonly used models such as Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Naïve Bayes (NB). The results show that the proposed fault diagnosis scheme improves the evaluation metrics and accuracy level, proving that the LSGAN-RF-GWO framework can utilize limited data resources to effectively diagnose power battery faults.
... The causes of battery deterioration at the anode and their consequences are depicted in Figure 3. Wu et al. [17] provided a comprehensive overview of the most recent research and progress in comprehending the mechanisms behind the aging of LiBs. By gaining insights into the origins and indications of battery malfunctions, this review encompasses the predominant factors that contribute to aging, along with the corresponding effects and results. ...
... The article concludes by summarizing recent advancements in diagnostic technologies for detecting battery malfunctions, accompanied by a fair assessment of their pros and cons. Ultimately, the paper proposes innovative strategies for diagnosing faults and outlines the enduring challenges that persist in this field [17]. Figure 3. Causes for battery ageing at anode and their effects [17]. ...
... Ultimately, the paper proposes innovative strategies for diagnosing faults and outlines the enduring challenges that persist in this field [17]. Figure 3. Causes for battery ageing at anode and their effects [17]. ...
The process of achieving balance among sequentially connected cells is crucial to prevent excessive charging or discharging, and it also improves the overall energy capacity. This article discusses various algorithms created for equalizing cell charge within a battery management system (BMS). Proper cell balancing is indispensable for upkeeping lithium-ion battery (LiB) packs. Within the BMS, identifying faults is of utmost importance. This encompasses detecting, isolating, and estimating faults. To prevent batteries from operating in unsafe ranges, it is vital to ensure the accurate functioning of current, voltage, and temperature sensors. Accurate fault diagnosis is pivotal for the optimal operation of battery management systems. In the context of electric vehicle battery management systems, precise measurement of current, voltage, and temperature is greatly relied upon to estimate the State of Charge (SOC) and overall battery health. Swiftly identifying early failures can mitigate safety hazards and minimize damage. Nevertheless, effectively pinpointing these initial failures using genuine operational data from electric vehicles remains a intricate task. This paper presents an analysis of different algorithms for detecting balancing-related faults, covering both methods based on models and those not reliant on models. The strengths and weaknesses of the evaluated algorithms, along with upcoming challenges in the realm of balancing and fault detection for LiBs, are also discussed in this document.
... Undervoltage signifies over-discharging of the battery system or internal short circuits. When an internal short circuit fault occurs in the power battery pack, with the generation of a large current, the temperature of the battery pack rises rapidly within a short period, leading to severe thermal runaway [6,7]. Some researchers have employed methods such as mechanical penetration, compression, implantation of shape memory alloys, and extreme temperature testing to obtain the dynamic characteristic parameters of internal short circuit faults. ...
Battery voltage is a pivotal parameter for evaluating battery health and safety. The precise prediction of battery voltage and the implementation of anomaly detection are imperative for ensuring the secure and dependable operation of battery systems. Nevertheless, during the actual operation of electric vehicles, battery performance is subject to the influence of the vehicle's operational state and battery characteristic parameters, introducing challenges to safety alerts. In order to address these challenges and achieve precise battery voltage prediction, this paper comprehensively considers the battery characteristics and driving behavior of electric vehicles in both charging and operational states. Mathematical processing, including averaging and variance calculation, is applied to the battery characteristic parameter data and driving behavior data. By integrating historical voltage data and employing a modified gradient boosting decision tree algorithm (GBDT), a fast and accurate online voltage prediction method is proposed. Hyperparameter optimization is employed to minimize prediction voltage errors. The accuracy and timeliness of the predictions are validated through a comprehensive evaluation and comparison of the forecasted voltages. To diagnose anomalies in battery voltage, the paper proposes a fault diagnosis method that combines the Isolation Forest and Boxplot techniques. Finally, utilizing authentic electric vehicle data for validation, the research underscores the capability of the proposed method to achieve accurate voltage predictions six minutes in advance and provide effective fault diagnosis. This investigation carries substantial practical implications for fortifying battery management and optimizing the performance of electric vehicles.
