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The reliability of battery fault diagnosis depends on an accurate estimation of the state of charge and battery characterizing parameters. This paper presents a fault diagnosis method based on an adaptive unscented Kalman filter to diagnose the parameter bias faults for a Li-ion battery in real time. The first-order equivalent circuit model and rel...
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... model is constructed using several components, including an open circuit voltage source ocv V , an ohmic resistance 0 R , and one RC network p p R C . The schematic of the ECM is plotted in Figure 1. The ECM differential equations can be expressed as ...
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... we cooled it down to 5 °C and maintained the temperature until 3600 s. The terminal voltage profile is shown in Figure 9. Seen from Figure 10a, the estimated value 0 R by using each of the two algorithms-AUKF and UKF-converges to 0.7 mΩ before 1300 s. Compared with the real value of 0.61 mΩ, the relative error is 14.3%. ...
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... estimated value by using each of the two algorithms converges to 19 s with a relative error of 25% before 1300 s. Being the same as the diagnosis results of the slow-varying-type fault, the AUKF converges around 18s while the UKF is still divergent, and the final relative errors of AUKF and UKF are 16.9% and 372%, respectively, as shown in Figure 10b. For the abrupt-type fault, the distinct residual comparison between the AUKF and the UKF is presented in Figure 10c. ...
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... the same as the diagnosis results of the slow-varying-type fault, the AUKF converges around 18s while the UKF is still divergent, and the final relative errors of AUKF and UKF are 16.9% and 372%, respectively, as shown in Figure 10b. For the abrupt-type fault, the distinct residual comparison between the AUKF and the UKF is presented in Figure 10c. It indicates residual increases abruptly after 1300 s. ...
Citations
... These faults can cause major performance and safety issues, and hence they need to be identified as early as possible [50]. Various model-based methods currently used in the BMS utilize battery models and algorithms such as sliding mode observer [51], adaptive unscented Kalman filter [52], and structural analysis and sequential residual generator [53] to estimate parameters or residuals to detect battery faults. However, using these methods, many fault features are not reflected in the early stage of system failure. ...
Energy storage plays an important role in the adoption of renewable energy to help solve climate change problems. Lithium-ion batteries (LIBs) are an excellent solution for energy storage due to their properties. In order to ensure the safety and efficient operation of LIB systems, battery management systems (BMSs) are required. The current design and functionality of BMSs suffer from a few critical drawbacks including low computational capability and limited data storage. Recently, there has been some effort in researching and developing smart BMSs utilizing the cloud platform. A cloud-based BMS would be able to solve the problems of computational capability and data storage in the current BMSs. It would also lead to more accurate and reliable battery algorithms and allow the development of other complex BMS functions. This study reviews the concept and design of cloud-based smart BMSs and provides some perspectives on their functionality and usability as well as their benefits for future battery applications. The potential division between the local and cloud functions of smart BMSs is also discussed. Cloud-based smart BMSs are expected to improve the reliability and overall performance of LIB systems, contributing to the mass adoption of renewable energy.
... Fault detection filter is a class of diagnostic observer, which can be used for state estimation as well as diagnostic purposes. 66,72 Kalman filter (KF), 73,74 and some modified KF methods have been successfully applied for diagnostic purposes in the practical application of engineered systems, for example, extended KF (EKF), 75 unscented KF (UKF), 76 and PF. 77 The parity space technique is one of the strategies to construct a model-based fault diagnosis. ...
Diagnostics and prognostics have a significant role in the reliability enhancement of systems and are focused topics of active research. Engineered systems are becoming more complex and are subjected to miscellaneous failure modes that impact adversely their performability. This ever‐increasing complexity makes fault diagnostics and prognostics challenging for the system‐level functions. A significant number of successes have been achieved and acknowledged in some review papers; however, these reviews rarely focused on application to complex engineered systems nor provided a systematic review of diverse techniques and approaches to address the related challenges. To bridge the gap, this paper first presents a review to systematically cover the general concepts and recent development of various diagnostics and prognostics approaches, along with their strengths and shortcomings for the application of diverse engineered systems. Afterwards, given the characteristics of complex systems, the applicability of different techniques and methods that are capable to address the features of complex systems are reviewed and discussed, and some of the recent achievements in the literature are introduced. Finally, the unaddressed challenges are discussed by taking into account the characteristics of automotive systems as an example of complex systems. In addition, future development and potential research trends are offered to address those challenges. Consequently, this review provides a systematic view of the state‐of‐the‐art and case studies with a reference value for scholars and practitioners.
... Computing MSO subsystems not only allows to use previously developed residual based schemes like observers [62]- [64], Kalman filter [65], [66] or parity equations [67]- [69] but also reduces the complexity of the fault diagnosis. ...
Conventional automotive battery systems consisting of a large number of battery cells pose a variety of challenges in terms of safety, reliability, lifetime and energy efficiency. Reconfigurable Battery Systems (RBS) are a promising solution to these issues of conventional battery systems. However, the large number of components in RBS also increases the fault probability. To meet this challenge on the way to fault tolerance, this work addresses fault isolation in an RBS, which comprises two switches per cell. Based on an electro-thermal model a structural analysis is performed and a sensor set with optimal fault isolation properties is found. Since the system consists of many equations, a novel algorithm is introduced to efficiently calculate Minimal Structurally Overdetermined (MSO) subsystems for fault diagnosis. For each fault, the algorithm allows determining the MSO set that has the least number of equations. A complexity analysis of the algorithm reveals that the proposed algorithm is computationally significantly less expensive for systems with high redundancy, like RBS, than existing algorithms that compute all MSO sets. Since the algorithm considers the switch states, it is suitable for active fault isolation through switches. The application to the RBS shows that the electrical equations are prioritized over the thermal equations due to the model uncertainties.
... Liu et al. [16] combined AEKF (Adaptive Extended Kalman Filter) with a second-order equivalent circuit model to estimate the battery terminal voltage in real time; additionally, he used terminal voltage residuals to ascertain battery fault separation and detection. Zheng et al. [17] combined AEKF with a first-order equivalent circuit model in order to study battery failure due to changes in ohmic internal resistance and diffusion internal resistance. Xiong et al. [18] employed UKF (unscented Kalman filter) to estimate the SOC (State of Charge) for batteries with faults and used SOC residuals to diagnose battery faults. ...
... According to the functions f j k−1 (X j k−1 , k−1 ) and h j k (X j k , k ) under the model m j given by (17), a Tailor expansion was conducted, where the first-degree term and zero-degree term were reserved, and the following was obtained: ...
... At this point, each coefficient of the equation can be determined via comparison with (17). ...
In view of the problems of declined estimation and diagnostic accuracy, as well as diagnosis delay caused by the fixed model transformation probability of the interacting multiple model (IMM) fault diagnosis algorithm, an IMM algorithm based on low inertia noise reduction (LN-IMM) was presented in this paper. The proposed algorithm realized the multi-fault diagnosis of lithium-ion batteries in combination with strong tracking Kalman filter (STKF). In the non-model transformation stage, a transition probability correction function was constructed using the difference of the n-th order based on model probability to suppress the effect of noise on the estimation accuracy of the algorithm. In the model transformation stage, a model jump threshold was introduced in order to reduce inertia when the matched model was switched and realized quick model transformation. Accordingly, the final experiment proved that the LN-IMM-STKF algorithm efficiently completed the state estimation of lithium-ion batteries and improved the diagnostic accuracy of faults while reducing diagnosis delay and attaining accurate detection as well as rapid separation of battery fault information.
... The flow chart is shown in Fig. 9, where r is the generated residual (residual=measured valueestimated value), and J is the fault threshold. Currently, battery models that can be used include electrochemical model [100][101][102][103][104], equivalent circuit model [105][106][107][108][109], fractional order model [110][111][112] and coupling model (mechanical-electric-thermal coupling model [113], electric-thermal coupling model [114], electrochemical-thermal coupling model [115]). Among the above-mentioned various battery models, the electrochemical model can reflect the changes of various parameters inside the battery. ...
Due to the limited capacity and voltage of single battery cell, the battery system for electric vehicles often consists of hundreds or thousands of single cells in series and parallel connection. The inconsistency of individual cell in capacity, voltage, internal resistance, etc., and their coupling effects with aging make the battery system fail frequently, which brings great challenges to the safe and reliable operation of the battery system. This paper discusses the research progress of battery system faults and diagnosis from sensors, battery and components, and actuators: (1) the causes and influences of sensor fault, actuator fault, internal/external short circuit fault, overcharge/over-discharge fault, connection fault, inconsistency, insulation fault, thermal management system fault are analyzed; (2) the fault diagnosis methods and their application characteristics in up-to-date battery system fault research are discussed, and the research trends of battery system fault diagnosis are sorted out; (3) Further, the future challenges and potential research directions of battery system fault diagnosis driven by new technologies such as big data are discussed. Finally, the summarization of whole paper is presented.
... However, this approach could be implemented where temperature fault will not occur [32]. Recently, a multiple model-based fault diagnosis approach was implemented for LIBs to diagnose the overcharge and overdischarge with the application of a bank of extended Kalman filters (EKFs) [33,34]. The identification of a healthy model, overcharge model, and overdischarge model is necessary for this approach, and in each model, an EKF is used for state variable estimation. ...
In electric and hybrid electric aircraft the battery systems are usually composed of up to thousands of battery cells connected in series or parallel to provide the voltage and power/energy requirements. The inconsistent cells could affect the battery pack and its performance or even endanger electric and hybrid electric aircraft security; thus, the early fault diagnosis of the battery system is essential. A well-designed battery management system along with a set of reliable voltage and current sensors is required to properly measure and control the cells operational variables in a large battery pack. In this study, based on the battery working mechanism, a new, fast and robust fault diagnostic scheme is proposed for a Lithium-Ion Battery (LIB) pack that can be employed for applications such as electric and hybrid electric aircraft. In this method some faults such as the over-charge, over-discharge occurring in LIB packs can be detected and isolated, based on some predefined factors gained from the battery models in healthy, over-charge and over-discharge conditions. Finally, the effectiveness of the proposed fast fault diagnosis scheme is experimentally validated with LIBs under a typical flight cycle.
... Zhou Donghua et al. [3] proposed an extended Kalman filter with suboptimal fading factor, and introduced a suboptimal fading factor, which is used to weaken the influence of old data and improve the tracking performance of the filter. Chang Wen Zheng et al. [4] optimized the calculation steps of the suboptimal fading factor, and derived the equivalent formula of covariance and cross covariance to calculate the suboptimal fading factor. However, some equations lack the derivation source, and the result is debatable. ...
The UAV system is a typical closed-loop control system. Its good robustness can inhibit the fault signal, which poses certain difficulties for the detection of early or small amplitude faults. In this paper, a nonlinear longitudinal control system model of a class of UAVs is established, and a fault detection method based on the suboptimal fading unscented Kalman filter (SFUKF) is designed. Aiming at the common failure of actuators and sensors of the drone, this paper proves that the method realizes the fault detection of the airspeed tube blockage and the elevator part failure by simulation.
... Model-based methods utilize a battery model (e.g., electrochemical models [32,33], electrical circuit models [34], multiple models [35], and thermal models [36]) and estimate parameters and/or evaluate residuals which can be good indicators for battery faults. Model-based condition monitoring algorithm have been applied for the model-based fault diagnosis (e.g., a sliding mode observer (SMO) [37], an adaptive unscented KF [38], and a structural analysis and sequential residual generators [39]). Model-free methods extract fault symptoms from signals by using signal processing methods (e.g., wavelet transform [40] and Shannon entropy [41]) and using artificial intelligence [31] (e.g., fuzzy logic and artificial neural network). ...
Performance of the current battery management systems is limited by the on-board embedded systems as the number of battery cells increases in the large-scale lithium-ion (Li-ion) battery energy storage systems (BESSs). Moreover, an expensive supervisory control and data acquisition system is still required for maintenance of the large-scale BESSs. This paper proposes a new cloud-based battery condition monitoring and fault diagnosis platform for the large-scale Li-ion BESSs. The proposed cyber-physical platform incorporates the Internet of Things embedded in the battery modules and the cloud battery management platform. Multithreads of a condition monitoring algorithm and an outlier mining-based battery fault diagnosis algorithm are built in the cloud battery management platform (CBMP). The proposed cloud-based condition monitoring and fault diagnosis platform is validated by using a cyber-physical testbed and a computational cost analysis for the CBMP. Therefore, the proposed platform will support the on-board health monitoring and provide an intelligent and cost-effective maintenance of the large-scale Li-ion BESSs.
The power battery constitutes the fundamental component of new energy vehicles. Rapid and accurate fault diagnosis of power batteries can effectively improve the safety and power performance of the vehicle. In response to the issues of limited generalization ability and suboptimal diagnostic accuracy observed in traditional power battery fault diagnosis models, this study proposes a fault diagnosis method utilizing a Convolutional Block Attention Capsule Network (CBAM-CapsNet) based on a stacked sparse autoencoder (SSAE). The reconstructed dataset is initially input into the SSAE model. Layer-by-layer greedy learning using unsupervised learning is employed, combining unsupervised learning methods with parameter updating and local fine-tuning to enhance visualization capabilities. The CBAM is then integrated into the CapsNet, which not only mitigates the effect of noise on the SSAE but also improves the model’s ability to characterize power cell features, completing the fault diagnosis process. The experimental comparison results show that the proposed method can diagnose power battery failure modes with an accuracy of 96.86%, and various evaluation indexes are superior to CNN, CapsNet, CBAM-CapsNet, and other neural networks at accurately identifying fault types with higher diagnostic accuracy and robustness.
Estimating the state of charge (SOC), state of health (SOH), and core temperature under internal faults will significantly improve the battery management system’s (BMS’s) autonomy and accuracy in range prediction. This paper presents a neural network (NN) based state estimation scheme that can estimate the SOC, core temperature, and SOH under internal faults in lithium-ion batteries (LIBs). First, we propose a model-based internal fault detection scheme by employing a SOH-coupled electro-thermal-aging model (ETA) of the LIB. Then, a nonlinear observer is used to estimate the proposed SOH-coupled model’s healthy states for a residual generation. The fault diagnosis scheme compares the output voltage and surface temperature residuals against the designed adaptive threshold to detect thermal faults. The adaptive threshold effectively alleviates the false positives due to degradation and model uncertainties of the battery under no-fault conditions. Upon fault detection, we employ an additional NN-based observer in the second step to learn the faulty dynamics. A novel NN weight tuning algorithm is proposed using the measured voltage, surface temperature, and estimated healthy states. The convergence of the nonlinear and NN-based observer state estimation errors is proven using the Lyapunov theory. Finally, numerical simulation results are presented.
