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With electric vehicles (EVs) being widely accepted as a clean technology to solve carbon emissions in modern transportation, lithium-ion batteries (LIBs) have emerged as the dominant energy storage medium in EVs due to their superior properties, like high energy density, long lifespan, and low self-discharge. Performing real-time condition monitoring of LIBs, especially accurately estimating the state of charge (SOC) and state of health (SOH), is crucial to keep the LIBs work under safe state and maximize their performance. However, due to the non-linear dynamics caused by the electrochemical characteristics in LIBs, the accurate estimations of SOC and SOH are still challenging and many technologies have been developed to solve this challenge. This paper reviews and discusses the state-of-the-art online SOC and SOH evaluation technologies published within the recent five years in view of their advantages and limitations. As SOC and SOH are strongly correlated, the joint estimation methods are specifically reviewed and discussed. Based on the investigation, this study eventually summarizes the key issues and suggests future work in the real-time battery management technology. It is believed that this review will provide valuable support for future academic research and commercial applications.

Broken rotor bar (BRB) is a common fault in induction motors (IM), which results in devastating consequences in the stator and reduces system reliability if not detected in a timely manner. An effective monitoring method of BRB is desired to guarantee production efficiency and service life of IM. Currently, most of the detection methods face low recognition accuracy among different BRB fault types, hence the accurate and reliable diagnosis of BRB faults is still a challenging task. Vibration signal contains substantial information reflecting the real working conditions of IM. Therefore, this paper presents an optimized application of cyclic modulation spectrum (CMS) based on vibration analysis to improve the BRB diagnosis, in which the spectral coherence and enhanced envelop spectrum (EES) is used to extract fault features more clearly. The STFT is a critical step in CMS analysis, therefore it is essential to determine the appropriate window function utilized in STFT according to BRB fault signatures in IM. Finally, the experimental analysis under a wide range of working conditions reveals the CMS with selected Kaiser (β = 3) window function can provide more accurate diagnostic information for different BRB severity compared to the popular envelope analysis.

Cyclostationary analysis has been strongly recognized as an effective demodulation tool in identifying fault features of rotating machinery based on vibration signature analysis. This study improves two current mature cyclostationary approaches, cyclic modulation spectrum (CMS) and fast spectral correlation (Fast-SC), combined with the novel frequency-domain application of Teager Kaiser energy operator (TKEO). They can enhance fault feature identification with the lower computational burden. Firstly, the raw vibration signal is transformed into the time-frequency domain through the short-time Fourier transform (STFT) to realize the conversion of the multi-carrier signal to a multiple signal-carrier signal. Secondly, the TKEO is utilized to enhance the fault feature by taking full advantage of demodulating the mono-component. Finally, the spectral coherence and enhanced envelope spectrum (EES) are calculated to effectively exhibit fault features. The superiority of the proposed methods is successfully validated by the simulation study and diagnosing the broken rotor bar (BRB) and bearing outer race faults of induction motors (IMs) under various operating conditions.

Broken rotor bar (BRB) faults are one of the most common faults in induction motors (IM). One or more broken bars can reduce the performance and efficiency of the IM and hence waste the electrical power and decrease the reliability of the whole mechanical system. This paper proposes an effective fault diagnosis method using the Teager–Kaiser energy operator (TKEO) for BRB faults detection based on the motor current signal analysis (MCSA). The TKEO is investigated and applied to remove the main supply component of the motor current for accurate fault feature extraction, especially for an IM operating at low load with low slip. Through sensing the estimation of the instantaneous amplitude (IA) and instantaneous frequency (IF) of the motor current signal using TKEO, the fault characteristic frequencies can be enhanced and extracted for the accurate detection of BRB fault severities under different operating conditions. The proposed method has been validated by simulation and experimental studies that tested the IMs with different BRB fault severities to consider the effectiveness of the proposed method. The obtained results are compared with those obtained using the conventional envelope analysis methods and showed that the proposed method provides more accurate fault diagnosis results and can distinguish the BRB fault types and severities effectively, especially for operating conditions with low loads.

Induction motors (IMs) are widely used in many manufacturing processes and industrial applications. The harsh work environment, long-time enduring, and overloads mean that it is subjected to broken rotor bar (BRB) faults. The vibration signal of IMs with BRB faults consists of the reliable modulation information used for fault diagnosis. Cyclostationary analysis has been found to be effective in identifying and extracting fault feature. The estimators of cyclic modulation spectrum (CMS) and fast spectral correlation (FSC) based on the short-time fourier transform (STFT) have higher cyclic frequency resolution, which has proven efficient in demodulating second order cyclostationary (CS2) signals. However, these two estimators have limitations of processing the maximum cyclic frequency &alpha, max that is smaller than Fs/2 (Fs is the sampling frequency) according to Nyquist&rsquo, s Theorem. In addition, they have lower carrier frequency resolution due to the fixed window size used in STFT. In order to resolve the initial shortcomings of the CMS and FSC methods, in this paper, we extended the analysis of CMS algorithm based on the continuous wavelet transform (CWT), which enlarged the maximum cyclic frequency range to Fs/2 and provides higher carrier frequency resolution because the CWT has the advantage of multi-resolution analysis. The reliability and applicability of the proposed method for fault components localization were validated by CS2 simulation signals. Compared to CMS and FSC methods, the proposed approach shows better performance by analyzing vibration signals between healthy motor and faulty motor with one BRB fault under 0%, 20%, 40%, and 80% load conditions.

Induction motors (IMs) play an essential role in the field of various industrial applications. Long-time service and tough working situations make IMs become prone to a broken rotor bar (BRB) that is one of the major causes of IMs faults. Hence, the continuous condition monitoring of BRB faults demands a computationally efficient and accurate signal diagnosis technique. The advantage of high reliability and wide applicability in condition monitoring and fault diagnosis based on vibration signature analysis results in an improved cyclic modulation spectrum (CMS), which is one of the cyclic spectral analysis algorithms. CMS is proposed in this paper for the detection and identification of BRB faults in IMs at a steady-state operation based on a vibration signature analysis. The application of CMS is based on the short-time Fourier transform (STFT) and the improved CMS approach is attributed to the optimization of STFT. The optimal window is selected to improve the accuracy for identifying the BRB fault types and severities. The appropriate window length and step size are optimized based on the selected window function to receive a better calculation benefit through simulation and experimental analysis. Compared to other estimators, the improved CMS method provides better fault detectability results by analyzing vertical vibration signatures of a healthy motor, and damaged motors with 1 BRB and 2 BRBs under 0%, 20%, 40%, 60%, and 80% load conditions. Both synthetic and experimental investigations demonstrate the proposed methodology can significantly reduce computational costs and identify the BRB fault types and severities effectively.