Online multi-fault detection and diagnosis for battery packs in electric vehicles

Kang, Yongzhe; Duan, Bin; Zhou, Zhongkai; Shang, Yunlong; Zhang, Chenghui

doi:10.1016/j.apenergy.2019.114170

Cited by 108 publications

(35 citation statements)

References 34 publications

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“…Although the temperature measured on the battery surface can infer the current operating state of the battery, the temperature has the problem of lag reaction. Other researchers compared the preset threshold value with voltage, current, and temperature rise rate [30] to identify the external connection faults of the battery. Because surface temperature is a nonlinear system determined by power, internal resistance, sensor position, and other influencing factors, fault diagnosis based on temperature alone may lead to misjudgment.…”

Section: Introductionmentioning

confidence: 99%

Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network

Yao

Xiao

et al. 2022

IEEE Access

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The battery system is one of the core technologies of the new energy electric vehicle, so the frequent occurrence of safety accidents seriously limits the large-scale promotion and application. An innovative extreme learning machine optimized by genetic algorithm (GA-ELM)-based method is proposed to estimate the current system status, which improves the accuracy and timeliness of fault identification. It is feasible in the application of electric vehicles. To ensure the effectiveness of the signal, the proposed method is adopted using the simple mean filter to clean the data with eliminate wrong points. After the variance analysis, covariance, a horizontal variance of the filtered data, a modified feature parameters matrix is presented. The dimension is reduced by principal component analysis to improve the engineering application ability. Furthermore, a comprehensive GA-ELM-based identification method is proposed to reduce the resulting identification error of extreme learning machines due to the initial value change. More importantly, the sensitivity and accuracy of different solutions are compared and verified, which shows the technique has great potential in battery fault diagnosis based on the voltage signal.

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Section: Introductionmentioning

confidence: 99%

Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network

Yao

Xiao

et al. 2022

IEEE Access

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“…The short-circuit current and internal resistance were estimated using the change in electric quantity difference and verified through experiments on the actual battery pack. Meanwhile, some studies started with fault data and conducted safety early warning and fault identification in Li-ion batteries through the rules of fault data (Gao et al, 2020;Kang et al, 2020;Naha et al, 2020). Battery fault analyses using new sensors have also been reported.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive early warning strategies based on consistency deviation of thermal–electrical characteristics for energy storage grid

Cui

Zhou

et al. 2021

iScience

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Lithium iron phosphate (LiFePO 4 ) batteries have been dominant in energy storage systems. However, it is difficult to estimate the state of charge (SOC) and safety early warning of the batteries. To solve these problems, this paper developed a multiple timescale comprehensive early warning strategy based on the consistency deviation of the electrical and thermal characteristics of LiFePO 4 batteries. The unscented Kalman filter method was employed to estimate the battery SOC. The established comprehensive early warning strategy was verified through fault-triggered experiments at different time scales with different equivalent resistances. The results show that the comprehensive early warning strategy can realize early warning for different timescale failures of LiFePO 4 batteries under different energy storage conditions. For more dangerous severe failures that can break the safety valve, safety early warning can be realized 15 min in advance. This study provides a reference to ensure safe and reliable operations of energy storage systems.

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“…In order to deal with the strong nonlinearity of the battery system and to find simple, efficient, easy to implement and robust fault diagnosis methods, data-driven-based methods have been widely explored and have become a hot spot for fault diagnosis research [27][28][29][30]. Kang et al [31] proposed an online multi-fault diagnosis method for a series battery pack based on an improved correlation coefficient method and a nonredundant crossed-style measurement circuit. Shang et al [32] proposed an early multi-fault diagnosis method for batteries based on modified sample entropy.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

Self Cite

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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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Online multi-fault detection and diagnosis for battery packs in electric vehicles

Cited by 108 publications

References 34 publications

Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network

Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network

Comprehensive early warning strategies based on consistency deviation of thermal–electrical characteristics for energy storage grid

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

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