Abstract Accurate online remaining useful life (RUL) estimation for lithium-ion batteries depends on reliable capacity-degradation modelling from measurable signals. This paper proposes an improved whale optimisation algorithm-optimised physics-informed neural network (IWOA-PINN) to address noisy incremental-capacity curves, degradation-inconsistent estimates, and hyperparameter sensitivity. A second-order RC equivalent-circuit model reconstructs the Q – V relationship, from which peak height, peak voltage, time at peak voltage, and time to reach the maximum surface temperature (MATT) are extracted. A lightweight capacity regressor is regularised by a training-only PH- Q prior and a soft adjacent-cycle monotonicity penalty, while IWOA selects the hidden-layer size, training epochs, learning rate, and two loss weights. RUL is mapped from the estimated capacity trajectory and the end-of-life threshold. Experiments on a public Xi’an Jiaotong University dataset and a laboratory-built platform show capacity-estimation RMSE around 0.01 Ah and mean RUL errors of 2.0–3.0 cycles under the tested fixed and random-walk partial-discharge protocols. The reported RUL errors reflect rolling end-of-life localisation as cycle-wise measurements become available, rather than single-origin open-loop forecasting. Under a unified comparison protocol, IWOA-PINN achieves the lowest mean RMSE values on B12 and B22, at 0.0084 and 0.0062 Ah, respectively. Its mean RUL errors are 3.0 and 1.1 cycles, with the former comparable to the strongest baseline and the latter the lowest among the compared models.

Data-driven fault diagnosis is an important approach for gearbox health management. In practical industrial scenarios, however, fault samples are usually scarce while normal samples are relatively abundant, resulting in severe class imbalance and degraded diagnostic performance. To alleviate this problem, a gated attention-enhanced improved conditional diffusion model is proposed for generating high-quality 1D vibration fault signals under small-sample conditions. Fault-type labels are embedded into the diffusion process to enable class-conditional generation of fault samples. Meanwhile, a gated attention-enhanced 1D U-Net denoising network is constructed to improve the extraction of long-range dependencies and salient fault features from vibration signals. In addition, a joint loss function incorporating spectral skewness is introduced to enhance time-domain reconstruction accuracy and frequency-domain fidelity simultaneously. Experiments on two planetary gearbox datasets show that the proposed method achieves better performance than representative generative models in terms of distribution similarity and waveform consistency. The generated samples are further used to balance the training set, leading to improved downstream diagnostic performance across ResNet-, ShuffleNetV2-, and MobileNetV3-based classifiers.

Intelligent fault diagnosis methods based on deep learning have made significant progress in the field of planetary gearbox fault diagnosis. However, in practical industrial scenarios, existing diagnostic models face challenges of excessive complexity that hinder their deployment on resource-constrained hardware platforms for real-time equipment monitoring. Although lightweight models typically employ fewer parameters and simplified architectures, they demonstrate insufficient diagnostic accuracy when confronted with intense noise in engineering practice. To address these issues, this paper proposes EPA-RepViT, a lightweight model with enhanced noise robustness. Firstly, an efficient parallel attention module is designed, combining channel attention and spatial attention mechanisms to enhance the model’s ability to perceive important channels and regions. Secondly, a concise stem module is developed by decoupling the dual-branch downsampling from channel expansion, thereby improving the stem’s representational capacity while increasing the model’s inference speed. Finally, the RepViT model is adapted into a one-dimensional form and pruned, and integrated with the aforementioned modules to achieve end-to-end intelligent fault diagnosis for planetary gearboxes. Experimental results on two planetary gearbox fault datasets demonstrate that the proposed model achieves superior diagnostic accuracy and operational efficiency compared to representative lightweight models, along with enhanced noise robustness.

To address the challenges of difficult feature extraction from gearbox fault signals and low accuracy in fault diagnosis, this paper presents a novel diagnostic model that integrates Variational Mode Decomposition (VMD) optimized by the Chaotic Hippopotamus Optimization algorithm (CHO) with a Bidirectional Gated Recurrent Unit (BiGRU) neural network. Firstly, CHO is used to optimize the number of modal decomposition and the penalty factor of VMD, using a composite index that combines the ratio of permutation entropy (PE) to mutual information entropy (MIE) as the fitness function. Then, the optimized VMD method decomposes the signal, and nine statistical features are extracted from the optimal Intrinsic Mode Function (IMF)components to form feature vectors. Finally, the feature vectors are input into the BiGRU for fault recognition. Experimental results demonstrate that the proposed model integrating CHO-VMD-BiGRU, can accurately extract fault features and outperform other algorithms, showing promising practical application value.

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The state of health (SOH) of lithium-ion batteries is a critical parameter of the battery management system. An accurate estimation of it can improve the battery's overall life. The data-driven method has the problems of low long-term prediction accuracy and difficult feature recognition. Therefore, this paper proposes a SOH estimation method based on the convolutional gated recurrent neural network with the self-attention mechanism. Firstly, the convolutional neural network (CNN) is applied to the input data, and its convolution operation is used to extract important local features. Then, the self-attention mechanism is added later to give greater weight to the more important features in the training process. Finally, the gated recurrent unit (GRU) recurrent neural network extracts the long-term correlation from the weighted output data. The experimental data of lithium-ion batteries from three sources are used to verify the correctness and effectiveness of this method. Experiments were based on whether the self-attention mechanism is present, different prediction starting points and ambient temperature, and analyzed and compared with the common machine learning methods. The experimental results show that the method exhibits good estimation accuracy, with a maximum mean absolute error below 1.78% and a maximum root mean square error below 2.3%.

As an important performance indicator of battery management systems, lithium-ion battery state of health (SOH) information is crucial to ensure battery safety and extend battery lifetime. Aiming at the problems of feature extraction difficulty, low accuracy of long-term prediction, and poor parallel computing capability of general data-driven methods, this paper proposes a SOH estimation method for lithium-ion batteries based on incremental capacity analysis (ICA) and Transformer. First, the original incremental capacity (IC) curve of the battery is extracted based on the ICA method, and the original IC curve is processed using the dual filtering method of moving average smoothing filter plus Gaussian smoothing filter, which in turn extracts the peak features of the curve. Then, the Transformer network model based on the multi-head attention mechanism is built. Finally, the extracted peak features of the IC curve are used as model inputs, and the Transformer model is utilized to realize the SOH estimation of lithium-ion batteries. In this paper, experiments based on different input features, prediction starting points, and ambient temperatures are conducted using experimental data of lithium-ion batteries from three sources and analyzed in comparison with commonly used machine learning methods. The experimental results show that the SOH estimation method proposed in this paper has higher long-term prediction accuracy and better temperature adaptability than commonly used machine learning methods.

因机载28V串联锂电池组内部早期故障难以被发现,且会持续恶化,严重时还会导致飞行事故发生.研发一种新型机载锂电池组早期故障预警方法,融合了基于改进样本熵及改进相关系数的锂电池早期故障预警方法的优点,能及时找出串联锂电池组内发生早期故障的单体电池且能够准确识别早期故障类型.实验结果表明:在DST工况下,该方法与基于改进样本熵的电池组早期故障预警方法相比,预警误报率降低90.48％,并能够准确地识别被注入的早期故障电池单体序号及早期故障类别.

利用超声波进行锂离子电池荷电状态(State of Charge,SOC)估计已经得到了初步验证.针对现有超声SOC估计中存在样本量小、 实验工况单一、 模型建立不完善等问题,提出了一种基于交叉验证高斯过程回归的锂电池SOC估计方法.将锂电池在不同温度、 不同工况下进行实验,对得到的数据进行初步分析;进一步对单一温度下的数据进行分析,建立基于联合幅值飞行时间的交叉验证高斯过程回归模型(PT-RPG-PR);对于在不同温度下实验得到的数据,增加温度因素构建新的模型(T-PT-RPGPR).实验结果表明,在复杂工况下,将超声信息与高斯过程结合建立模型进行锂电池SOC估计具有较高的估计精度.

文章首先从整体规划与方案设计、混合式教学平台建设、教学案例设计与实施、教学组织与评价四个方面论述了模拟电子技术课程混合式教学实践,然后对模拟电子技术课程混合式教学实践效果进行了分析.

针对前期新冠疫情防控的要求和"数字信号处理器(DSP)"课程实验教学的特点,提出了基于线上和线下相结合的混合教学DSP实验教学模式.要求学生通过网络学习,充分利用线上的资源,以时间换空间的方式,获取课程所必须的预先知识;根据学校疫情防控需要,合理安排学生的线下实验,并能够实际操作实验平台,获得感性知识,保障了实验教学的质量.

The state of charge(SOC) of lithium-ion battery is an essential parameter of battery management system. Accurate estimation of SOC is conducive to give full play to the capacity and performance of the battery. For the problems of selection of forgetting factor and poor robustness and susceptibility to the noise of extended Kalman filtering algorithm, this paper proposes a SOC estimation method for the lithium-ion battery based on adaptive extended Kalman filter using improved parameter identification. Firstly, the Thevenin equivalent circuit model is established and the recursive least squares with forgetting factor(FFRLS) method is used to achieve the parameter identification. Secondly, an evaluation factor is defined, and fuzzy control is used to realize the mapping between the evaluation factor and the correction value of forgetting factor, so as to realize the adaptive adjustment of forgetting factor. Finally, the noise adaptive algorithm is introduced into the extended Kalman filtering algorithm(AEKF) to estimate the SOC based on the identification results, which is applied to the parameter identification at the next time and executed circularly, so as to realize the accurate estimation of SOC. The experimental results show that the proposed method has good robustness and estimation accuracy compared with other filtering algorithms under different working conditions, state of health(SOH) and temperature.

In this paper, we present a fast and robust visual-inertial odometry (VIO) algorithm, which realizes the tight coupling of monocular visual odometry (VO) and low-cost inertial measurement unit (IMU). Aiming at the problem of tracking loss caused by the interference of moving objects or blurred images in the traditional VIO algorithm, a sparse optical flow method combining edge detection algorithms is proposed. In the image preprocessing, Laplace edge detection is performed on the original image first, and an area with the most texture is searched according to the sharpened image. When tracking feature points with the sparse optical flow method, the feature points within the searched area are used as the main tracking targets to reduce the impact because of some unclear part in the image on the visual front-end. The trust region dogleg method is used for non-linear optimization in the back-end of the VIO system. The effectiveness of the proposed VIO system is validated on the public data set named EuRoC MAV and compared with the advanced VIO algorithm in recent years. Experimental results show that the new VIO system proposed in this paper has good robustness and can be applied to complex scenes such as fast motion, lighting changes, lack of features, and image blur. Compared with the traditional VIO algorithm, the pose estimation speed of the proposed VIO algorithm can be improved by 10% or more.

The lithium-ion battery’s state of health (SOH) is one of the essential parameters of the battery management system. An accurate state of health estimation of the battery pack helps to improve the service life of the overall battery pack. Given the poor generalization ability of a single data-driven model during SOH online estimation and the lack of uncertainty expression ability in the estimation results, this paper proposes an online SOH estimation method of lithium-ion battery based on bat algorithm optimized relevance vector machine (BA-RVM) with dynamic integration. Firstly, we perform feature extraction and select equal voltage drop discharge time as an indirect health factor. Secondly, we establish the integration model, take the wavelet kernel relevance vector machine (RVM) as the sub-model, and use the bat algorithm (BA) to optimize its kernel parameters to improve the estimation accuracy of the sub-model. Then we use the online monitoring data to update the weights of the sub-models continuously and dynamically integrate the output of the sub-models to improve the accuracy of SOH online estimation further. Finally, the correctness and effectiveness of the method are verified based on battery data from NASA and compared with other data-driven methods. The experimental results show that compared with the method based on a single data-driven model, this method has higher accuracy and more vital generalization ability, and the estimation results have specific uncertainty expression ability.

机载28 V串联蓄电池组内的不一致性会减小其能量利用率及寿命,严重时会导致飞行事故发生.本文提出了一种基于新型均衡拓扑的锂电池组能量均衡方法,以改善串联电池组的不一致性.该均衡拓扑利用结构简单的隔离型DC-DC变换器及功率电阻器实现蓄电池组内任一单体电池的"削峰"或"填谷",相对于非集中式均衡拓扑减小了均衡元件的数量和系统体积.以荷电状态作为均衡变量,实现了机载28 V蓄电池组内各单体之间的能量快速转移.仿真及实验结果表明,所提出的新型均衡方法,相较于基于集中式DC-DC拓扑的均衡速度最高可提升13.3％,均衡后电压极差最低降低至32 mV,SOC极差低于0.4％,具有更快更好的均衡效果.

Lithium-ion batteries are currently being widely used. Accurately predicting their remaining useful life (RUL) is essential for battery management systems (BMS) and rationally planning the battery usage. There exist problems such as battery capacity regeneration and randomness caused by single time prediction and parameter settings. This paper proposes a hybrid algorithm that combines the broad learning system (BLS) with the relevance vector machine (RVM). First, use the empirical mode decomposition (EMD) to extract the features of the used data. Then input the training data into the BLS network and set different prediction starting points, and the corresponding prediction data is output. All prediction data is formed into a matrix to train the RVM. The RVM is used as the prediction layer of the hybrid model. Eventually, the RVM's output is the RUL prediction of the hybrid model. In this paper, the proposed method is experimentally validated using Li-ion battery experimental data from three sources, and its accuracy is compared with several common machine learning algorithms. Experimental results show that BLS-RVM has higher prediction accuracy, stronger long-term prediction, and generalization capabilities, and its root mean square error is about 0.01. The algorithm proposed in this paper for multiple training and prediction followed by fusion of the results broadens the research horizon of lithium-ion battery life hybrid methods for prediction.

SiC功率管器件广泛应用在航空领域的电能变换、配电等场合,其健康状况十分重要.在SiC器件的健康监测应用中,导通电阻的检测是一项十分重要的技术.为了能够简单准确地得到碳化硅(SiC)MOSFET功率器件的导通电阻,本文提出了一种基于神经网络的SiC MOSFET器件导通电阻估测方法.本文搭建SiC MOSFET导通电阻测试电路仿真和物理试验平台,并使用BP神经网络(BP neural networks,BPNN)对不同温度、不同栅极电压以及不同漏极电流下SiC MOSFET器件的导通电阻数据进行详细描述.最后,对基于BPNN的SiC MOSFET导通电阻估测方法进行效果验证.结果表明,该方法具有精度高和泛化能力强的优点,能够实现SiC MOSFET器件导通电阻的有效估测.

Accurately predicting the remaining useful life of lithium-ion batteries is critical to battery health management systems. Aiming at the problems of low long-term prediction accuracy, unstable model output, and difficult key parameter selection, this paper proposes a self-adaptive differential evolution optimized monotonic echo state network prediction method. First, we analyze the life decay characteristics of Li-ion batteries and select appropriate indirect health indicators to replace the capacity based on the partial correlation coefficient analysis. Then use the self-adaptive differential evolution algorithm to optimize the free parameters of the monotonic echo state network to maintain the monotonic relationship between input and output. Finally, the remaining useful life indirect prediction model is established. This paper uses NASA Li-ion battery experimental data and independent experimental data to verify the feasibility, followed by the different starting points experiments and cut-off voltage experiments. The accuracy of the proposed method is compared with other commonly used artificial intelligence prediction algorithms. Experimental results prove that this method has high prediction accuracy and stable output.

As an emergency and auxiliary power source for aircraft, lithium (Li)-ion batteries are important components of aerospace power systems. The Remaining Useful Life (RUL) prediction of Li-ion batteries is a key technology to ensure the reliable operation of aviation power systems. Particle Filter (PF) is an effective method to predict the RUL of Li-ion batteries because of its uncertainty representation and management ability. However, there are problems that particle weights cannot be updated in the prediction stage and particles degradation. To settle these issues, an innovative technique of F-distribution PF and Kernel Smoothing (FPFKS) algorithm is proposed. In the prediction stage, the weights of the particles are dynamically updated by the F kernel instead of being fixed all the time. Meanwhile, a first-order independent Markov capacity degradation model is established. Moreover, the kernel smoothing algorithm is integrated into PF, so that the variance of the parameters of capacity degradation model keeps invariant. Experiments based on NASA battery data sets show that FPFKS can be excellently applied to RUL prediction of Li-ion batteries.

In BeiDou satellite receiver, a fast BeiDou signal acquisition algorithm based on coherent down-sampling was proposed to solve the problem that the large amount of data to be processed by the Fourier transform affects the acquisition speed of satellite signals in the traditional parallel frequency acquisition algorithm. On the basis of FPGA+DSP (field-programmable gate arrays+digital signal processors), the coherent down-sampling module was added to the traditional parallel frequency acquisition algorithm, and after the carrier and pseudo random code were stripped, the sampling frequency was reduced to decrease the number of points to be processed in the Fourier transform, and then a three-dimensional search for the satellite signal was performed. Theoretical analysis shows that the algorithm can reduce the amount of calculation by more than 80%; the experiments on the actual BeiDou signal indicate that the average acquisition time of each star is 9.95 ms, and the memory resource consumption is reduced by 42% compared with the traditional parallel frequency acquisition algorithm. This algorithm can effectively improve the acquisition speed while saving resources, which can provide reference for further improving the acquistition performance of the software receiver.