A neural network-based method with data preprocess for fault diagnosis of drive system in battery electric vehicles

As part of the increasing efforts toward the prevention and control of motor vehicle pollution, the Chinese government has practiced a range of policies to stimulate the purchase and use of battery electric vehicles (BEVs). Zhejiang Province, a key province in China, has proactively implemented and monitored an environmental protection plan. This study aims to contribute toward streamlining marketing and planning activities to introduce strategic policies that stimulate the purchase and use of BEVs. This study considers the nature of human behavior by extending the theory of planned behavior model to identify its predictors, as well as its non-linear relationship with customers’ purchase intention. To better understand the predictors, a substantial literature review was given to validate the hypotheses. A quantitative study using 382 surveys completed by customers in Zhejiang Province was conducted by integrating a structural equation model (SEM) and a neural network (NN). The initial analysis results from the SEM revealed five factors that have impacted the customers’ purchase intention of BEVs. In the second phase, the normalized importance among those five significant predictors was ranked using the NN. The findings have provided theoretical implications to scholars and academics, and managerial implications to enterprises, and are also helpful for decision makers to implement appropriate policies to promote the purchase intention of BEVs, thereby improving the air quality.

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Battery Fault Diagnosis for Electric Vehicles Based on Voltage Abnormality by Combining the Long Short-Term Memory Neural Network and the Equivalent Circuit Model

IEEE Transactions on Power Electronics ◽

10.1109/tpel.2020.3008194 ◽

2021 ◽

Vol 36 (2) ◽

pp. 1303-1315 ◽

Cited By ~ 1

Author(s):

Da Li ◽

Zhaosheng Zhang ◽

Peng Liu ◽

Zhenpo Wang ◽

Lei Zhang

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Equivalent Circuit ◽

Electric Vehicles ◽

Short Term Memory ◽

Equivalent Circuit Model ◽

Circuit Model ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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

Frontiers in Energy Research ◽

10.3389/fenrg.2021.702139 ◽

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

Symmetry ◽

10.3390/sym13091714 ◽

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.

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Switch Fault Diagnosis using S- Transform on Three Phase Inverter for BLDC Drive

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1014.0782s519 ◽

2019 ◽

Vol 8 (2S5) ◽

pp. 58-65

Keyword(s):

Fault Diagnosis ◽

Electric Vehicles ◽

Short Circuit ◽

Fault Analysis ◽

Drive System ◽

Transform Method ◽

Phase Inverter ◽

S Transform ◽

Three Phase ◽

Three Phase Inverter

BLDC drives are highly preferred for electric vehicles application because of its less maintenance, longer life, lower weight and reliability. Normally for electric vehicles the motors are powered by batteries, so three phase inverter is very important in driving the motor. The closed loop system is highly efficient in controlling the parameters of the drive but any fault occurring inside the system may lead to abnormalities which can damage the entire system. So due to this, the fault analysis on the BLDC drive system is very important. As three phase inverter is important in driving the motor, in this project we are going to perform fault diagnosis on three phase inverter in BLDC drive system. The commonly occurring faults in the switches are open circuit and short circuit faults. There are different methods for diagnosing switch open and circuit faults but S-Transform analysis proves to be the best method in providing better results compared to the conventional methods. So in this paper the fault diagnosis on three phase inverter is done using S-Transform method to accurately find where and which type of fault has occurred.

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