Abstract
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.
Highlights
In recent years, electric vehicles powered by lithium-ion batteries have received widespread attention at home and abroad as the future of the automobile industry is developed in the direction of high efficiency and sustainability to solve problems such as the energy crisis and environment pollution [1,2,3,4]
Due to defects in the production and manufacturing process, abusive operation during the actual use process, the aging of the battery and the destruction of the symmetrical structure, each cell or related components may have various faults and this safety hazard is huge [11,12,13]. If these faults are not diagnosed and handled in a timely manner, the safety performance of the battery will be significantly reduced and thermal runaway may even appear in a few extreme cases, posing a serious threat to the normal operation of electric vehicles and the safety of drivers [14,15,16]
A standard nonlinear autoregressive exogenous (NARX) neural network is composed of an input layer, a hidden layer and an output layer as well as an input and output time delay
Summary
Electric vehicles powered by lithium-ion batteries have received widespread attention at home and abroad as the future of the automobile industry is developed in the direction of high efficiency and sustainability to solve problems such as the energy crisis and environment pollution [1,2,3,4]. Duan et al [20] calculated the evaluated values of twelve cells under three evaluation factors based on information entropy calculated the standard deviation of the evaluated values and compared the standard deviation with the set threshold to evaluate the degree of the battery pack inconsistency This kind of method has the advantages of simplicity and easy implementation but it is a great challenge to determine the appropriate thresholds in practical applications. Xiong et al [26] proposed a two-step ECM-based method for the diagnosis of an external short-circuit (ESC) fault in a battery pack. Hong et al [17] used a long short-term memory (LSTM) neural network for the multi-step voltage prediction of a battery system and combined this with the alarm threshold to evaluate the safety of the battery to determine whether the battery would fail. The temperature data could not well-reflect the electrical characteristics of the battery system and the correlation analysis showed a weak correlation between the temperature and voltage so the battery current was selected as the input of the NARX in this work
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