![]() used the artificial neural network to estimate short-circuit current and then predicted the maximum temperature increase as well as internal and surface temperature distribution of the faulty cell based on a 3D electro-thermal coupling model. ![]() proposed an approach relying on the long short-term memory neural network, in conjunction with an alteration to the walk-forward technique, to accurately estimate battery surface temperature for thermal fault diagnosis. devised a robust studentized outlier sample method to select the principle feature components derived from cross-cell monitoring data. To achieve sensitive battery anomaly detection, Schmid et al. downsized the dimensions of randomly selected features of time-domain statistics with principal component analysis and the refined features are fed into a relevance vector machine to make diagnostic decisions on battery faults. exploited the grid search support vector machine using features extracted by a modified signal covariance matrix, whereby battery fault states can be identified timely and efficiently. Machine learning techniques were also extensively explored in battery fault diagnosis benefitting from the competent capability in nonlinear characteristic approximating and automatic decision making. proposed a thermal runaway prediction scheme based on the big data and information entropy, and the location of thermal faults in the battery pack can be accurately located. simulated the battery charging and discharging process in a vibration environment to observe voltage fluctuations and then used the entropy transfer to realize the detection of connection faults. realized battery failure detection by evaluating the local deviation of observed data using a local outlier factor based on the Grubbs criterion. proposed a model-based switching method to estimate the abnormality of cell resistance for internal short-circuit detection.ĭata-driven diagnostic methods try to obtain potential fault features and patterns by directly analyzing system-running data without the requirement of accurate analytical models or the understating of complicated fault mechanisms. Although physical coupling models have improved reliability and accuracy, they consume mass simulation and computing resources and thus are only suitable for online real-time applications. These methods rely too much on the model’s accuracy, and most of the models are affected by noise, interference, and unmolded characteristics. estimated and modeled the parameters of the battery with the machine learning technique to achieve accurate fault diagnosis. detected and evaluated thermal failure levels based on a one-dimensional temperature field model and a partial differential equation observer. proposed an energy transfer image method to quantify the reaction of battery materials, which makes the chain reaction mechanism of thermal runaway and internal short-circuit fault clearer. ![]() To describe the thermal runaway process, Ouyang et al. Model-based diagnostic methods estimate battery state of health by establishing a physical characteristic model or identifying the residuals between measured and model parameters. The success rates of fault isolation are 82% and 81%, and the success rates of fault grading are 98% and 90%, by ANN and mRVM, respectively. Experimental results show that the proposed method can effectively detect and locate different faults using the extracted fault features mRVM is better than ANN in thermal fault diagnosis, while the overall diagnosis performance of ANN is better than mRVM. Physical injection of external and internal short circuits, thermal damage, and loose connection failure is carried out to collect real fault data for model training and method validation. Finally, the artificial neural network (ANN) and multi-classification relevance vector machine (mRVM) are employed to classify and evaluate fault mode and fault degree, respectively. Secondly, the wavelet packet decomposition is applied to the coefficient series to obtain fault-related features from wavelet sub-bands, and the most representative characteristic principal components are extracted. First, the cross-cell voltages of multiple cells are preprocessed using an improved recursive Pearson correlation coefficient to capture the abnormal electrical signals. In this work, an intelligent fault diagnosis scheme for series-connected battery packs based on wavelet characteristics of battery voltage correlations is designed. As the installed energy storage stations increase year by year, the safety of energy storage batteries has attracted the attention of industry and academia.
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