scholarly journals A Fault Diagnosis and Prognosis Method for Lithium-Ion Batteries Based on a Nonlinear Autoregressive Exogenous Neural Network and Boxplot

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1714
Author(s):  
Yan Qiu ◽  
Jing Sun ◽  
Yunlong Shang ◽  
Dongchang Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Early Warning ◽  
Lithium Ion ◽  
Diagnosis And Prognosis ◽  
Fault Diagnosis And Prognosis ◽  
Nonlinear Autoregressive ◽  
Narx Neural Network

The frequent occurrence of electric vehicle fire accidents reveals the safety hazards of batteries. When a battery fails, its symmetry is broken, which results in a rapid degradation of its safety performance and poses a great threat to electric vehicles. Therefore, accurate battery fault diagnoses and prognoses are the key to ensuring the safe and durable operation of electric vehicles. Thus, in this paper, we propose a new fault diagnosis and prognosis method for lithium-ion batteries based on a nonlinear autoregressive exogenous (NARX) neural network and boxplot for the first time. Firstly, experiments are conducted under different temperature conditions to guarantee the diversity of the data of lithium-ion batteries and then to ensure the accuracy of the fault diagnosis and prognosis at different working temperatures. Based on the collected voltage and current data, the NARX neural network is then used to accurately predict the future battery voltage. A boxplot is then used for the battery fault diagnosis and early warning based on the predicted voltage. Finally, the experimental results (in a new dataset) and a comparative study with a back propagation (BP) neural network not only validate the high precision, all-climate applicability, strong robustness and superiority of the proposed NARX model but also verify the fault diagnosis and early warning ability of the boxplot. In summary, the proposed fault diagnosis and prognosis approach is promising in real electric vehicle applications.

scholarly journals A Hybrid Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 19175-19186
Author(s):  
Jiuchun Jiang ◽  
Xinwei Cong ◽  
Shuowei Li ◽  
Caiping Zhang ◽  
Weige Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Diagnosis Method

SOH estimation method for lithium-ion batteries based on nonlinear autoregressive neural network model with exogenous input

2021 ◽  
pp. 1-28
Author(s):  
Yanbo Che ◽  
Yibin Cai ◽  
Hongfeng Li ◽  
Yushu Liu ◽  
Mingda Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Equivalent Circuit ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Circuit Model ◽  
Variable Order ◽  
Nonlinear Correlation ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

Abstract The working state of lithium-ion batteries must be estimated accurately and efficiently in the battery management system. Building a model is the most prevalent way of predicting the battery's working state. Based on the variable order equivalent circuit model, this paper examines the attenuation curve of battery capacity with the number of cycles. It identifies the order of the equivalent circuit model using Bayesian Information Criterion (BIC). Based on the correlation between capacity and resistance, the paper concludes that there is a nonlinear correlation between model parameters and state of health (SOH). The nonlinear autoregressive neural network with exogenous input (NARX) is used to fit the nonlinear correlation for capacity regeneration. Then, the self-adaptive weight particle swarm optimization (SWPSO) method is suggested to train the neural network. Finally, single-battery and multi-battery tests are planned to validate the accuracy of the SWPSO-NARX estimate of SOH. The experimental findings indicate that the SOH estimate effect is significant.

Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network

2021 ◽  
Vol 282 ◽  
pp. 116159 ◽  
Author(s):  
Sahar Khaleghi ◽  
Danial Karimi ◽  
S. Hamidreza Beheshti ◽  
Md. Sazzad Hosen ◽  
Hamidreza Behi ◽  
...  
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Nonlinear Autoregressive ◽  
Health Diagnosis ◽  
Autoregressive Neural Network

scholarly journals A Comprehensive Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1221
Author(s):  
Xinwei Cong ◽  
Caiping Zhang ◽  
Jiuchun Jiang ◽  
Weige Zhang ◽  
Yan Jiang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Anomaly Detection ◽  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Dominant Role ◽  
Lithium Ion ◽  
Window Size ◽  
Operational Reliability ◽  
Signal Features ◽  
Diagnosis Method

To enhance the operational reliability and safety of electric vehicles (EVs), big data platforms for EV supervision are rapidly developing, which makes a large quantity of battery data available for fault diagnosis. Since fault types related to lithium-ion batteries play a dominant role, a comprehensive fault diagnosis method is proposed in this paper, in pursuit of an accurate early fault diagnosis method based on voltage signals from battery cells. The proposed method for battery fault diagnosis mainly includes three parts: variational mode decomposition in the signal analysis part to separate the inconsistency of cell states, critical representative signal feature extraction by using a generalized dimensionless indicator construction formula and effective anomaly detection by sparsity-based clustering. The signal features of the majority of signal-based battery fault detection studies are found to be particular cases with a specific set of parameter values of the proposed indicator construction formula. With the sensitivity and stability balanced by appropriate moving-window size selection, the proposed signal-based method is validated to be capable of earlier anomaly detection, false-alarm reduction, and anomalous performance identification, compared with traditional approaches, based on actual pre-fault operating data from three different situations.

scholarly journals A Fault Diagnosis Method for Lithium-Ion Battery Packs Using Improved RBF Neural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Jia Wang ◽  
Shenglong Zhang ◽  
Xia Hu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Rbf Neural Network ◽  
Lithium Ion ◽  
Rbf Neural Networks ◽  
Battery Packs ◽  
Diagnosis Method

With the increasing demand for electric vehicles, the high voltage safety of electric vehicles has attracted significant attention. More than 30% of electric vehicle accidents are caused by the battery system; hence, it is vital to investigate the fault diagnosis method of lithium-ion battery packs. The fault types of lithium-ion battery packs for electric vehicles are complex, and the treatment is cumbersome. This paper presents a fault diagnosis method for the electric vehicle power battery using the improved radial basis function (RBF) neural network. First, the fault information of lithium-ion battery packs was collected using battery test equipment, and the fault levels were then determined. Subsequently, the improved RBF neural networks were employed to identify the fault of the lithium-ion battery pack system using the experimental data. The diagnosis test results showed that the improved RBF neural networks could effectively identify the fault diagnosis information of the lithium-ion battery packs, and the diagnosis accuracy was about 100%.

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