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This paper proposes an approach to estimate the state of health of DC-DC converters that feed the electrical system of an electric vehicle. They have an important role in providing a smooth and rectified DC voltage to the electric machine. Thus, it is important to diagnose the actual status and predict the future performance of the converter and sp...
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... this end, a random search algorithm was applied, where 100 neural networks were trained for 10 epochs each. Table 1 presents the boundaries of the search algorithm and the tuned values. Once the hyper-parameters have been tuned, the CNN-LSTM neural network is trained based on the strategy detailed in the previous section. ...Citations
... LSTM also performs better to predict acceleration using camera images in comparison with other neural network models and decision-tree-based ensemble methods [17]. Therefore, in the proposed work, LSTM-NN (Long Short Term Neural Network) is selected for the prediction of the velocity as it gives high accuracy for time series prediction [18] [19]. ...
... LSTM also performs better to predict acceleration using camera images in comparison with other neural network models and decision-tree-based ensemble methods [17]. Therefore, in the proposed work, LSTM-NN (Long Short Term Neural Network) is selected for the prediction of the velocity as it gives high accuracy for time series prediction [18] [19]. ...
p>This research introduces an innovative approach to enhance energy management in urban lightweight electric fleet vehicles by leveraging Hybrid Energy Storage Systems (HESS) based on user driving velocity profiles. Long Short-Term Memory (LSTM) network is used to predict vehicle energy consumption by forecasting velocity profiles based on historical data. The LSTM-derived energy predictions are then utilized as inputs for the Energy Management System (EMS) of the HESS. A HESS, combining batteries and supercapacitors, offers a promising solution to urban lightweight electric fleet vehicles due to its ability to harness the strengths of both high energy and high power density storage technologies. Through accurate vehicle power demand anticipation enabled by LSTM's temporal analysis of urban driving conditions, the HESS model optimizes energy flow within the system, contributing to improved energy efficiency, reduced range anxiety, smoother user driving experience and improved energy recovery from braking. The created LSTM neural network is able to predict the velocity of 21 drive cycles with good accuracy, having a maximum Root Mean Square Error (RMSE) of 1.94. This prediction capability is then used for forecasting the energy consumption of the vehicle. The developed HESS model was able to split the predicted energy consumption signal into low-frequency components for batteries and high-frequency components for supercapacitors, with an error of 3\% between the required power and the delivered power. This research lays the foundation for a user-centric solution that holds significant potential to enhance energy management in lightweight electric fleet vehicles employing hybrid energy storage systems.
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... Up to now, there has been little research on using the CNN-LSTM model to monitor the wear state of brake pads. However, R. D. Gabriel et al. [19] used a CNN-LSTM model to complete the remaining useful life monitoring of an electric vehicle bidirectional converter, and the model could fully mine the effective features of bidirectional converter data. The research results indicated that the CNN-LSTM model has higher robustness and prediction accuracy, which can improve the safety and reliability of the system, proving that it is also feasible to use the CNN-LSTM model to monitor the wear state of brake pads. ...
As the core component of the automobile braking system, brake pads have a complex structure and high failure rate. Their accurate and effective state monitoring can help to evaluate the safety performance of brake pads and avoid accidents caused by brake failure. The wear process of automobile brake pads is a gradual, nonlinear, and non-stationary time-varying system, and it is difficult to extract its features. Therefore, this paper proposes a CNN-LSTM brake pad wear state monitoring method. This method uses a Convolutional Neural Network (CNN) to complete the deep mining of brake pad wear characteristics and realize data dimensionality reduction, and a Long Short-Term Memory (LSTM) network to capture the time dependence of the brake pad wear sequence, so as to construct the nonlinear mapping relationship between brake pad wear characteristics and brake pad wear values. At the same time, the artificial Gorilla Troops Optimization (GTO) algorithm is used to perform multi-objective optimization of the network structure parameters in the CNN-LSTM model, and its powerful global search ability improves the monitoring effect of the brake pad wear status. The results show that the CNN-LSTM model based on GTO multi-objective optimization can effectively monitor the wear state of brake pads, and its coefficient of determination R2 value is 0.9944, the root mean square error RMSE value is 0.0023, and the mean absolute error MAE value is 0.0017. Compared with the BP model, CNN model, LSTM model, and CNN-LSTM model, the value of the coefficient of determination R2 is the closest to 1, which is increased by 8.29%, 5.52%, 4.47%, 3.30%, respectively, which can more effectively realize the monitoring and intelligent early warning of the brake pad wear state.
... Data-driven methods do not require the structure and fault mechanism of the converter, including statistical methods and machine learning methods. Some data-driven methods build degradation models for fault indicators based on statistical or machine learning models, such as [14], [15]. [14] combines convolutional neural network and long short-term memory neural network to predict C and ESR values of electrolytic capacitors based on degradation data collected from accelerated aging tests. ...
... Some data-driven methods build degradation models for fault indicators based on statistical or machine learning models, such as [14], [15]. [14] combines convolutional neural network and long short-term memory neural network to predict C and ESR values of electrolytic capacitors based on degradation data collected from accelerated aging tests. [15] utilizes long short-term memory neural network to provide an efficiency time series prediction model which can reflect the law of power efficiency degradation. ...
... Then, the LSTM extracts the temporal feature relationships between historical time points for multi-step wind power prediction. Rojas-Dueñas et al. [34] propose a method to detect the health status of electric vehicle power supply converters by training a CNN+LSTM model to predict the remaining service life of the device and the fault diagnosis of the device. In [35], a one-dimensional CNN+LSTM network structure is used for short-time traffic flow prediction, and the effectiveness of the algorithm is verified by conducting experiments on collected real data. ...
Anomaly perception of infrared point targets has high application value in many fields, such as maritime surveillance, airspace surveillance, and early warning systems. This kind of abnormality includes the explosion of the target, the separation between stages, the disintegration caused by the abnormal strike, etc. By extracting the radiation characteristics of continuous frame targets, it is possible to analyze and warn the target state in time. Most anomaly detection methods adopt traditional outlier detection, which has the problems of poor accuracy and a high false alarm rate. Driven by data, this paper proposes a new network structure, called AC-LSTM, which combines Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM), and embeds the Periodic Time Series Data Attention module (PTSA). The network can better extract the spatial and temporal characteristics of one-dimensional time series data, and the PTSA module can consider the periodic characteristics of the target in the process of continuous movement, and focus on abnormal data. In addition, this paper also proposes a new time series data enhancement method, which slices and re-amplifies the long time series data. This method significantly improves the accuracy of anomaly detection. Through a large number of experiments, AC-LSTM has achieved higher scores on our collected datasets than other methods.
... The concept was utilized in the aging, with experimental investigation showing that an escape of electrolytes from the capacitor would result in a decrease in the capacitance value and an increase in the ESR value. Also, several papers have addressed the fault diagnosis of capacitors using offline techniques [21][22][23]. ...
... The increasing evaporation of the electrolyte is the leading cause of degradation in most AEC, which increases the value of equivalent series resistance (ESR) and decreases the value of the capacitance (C) concerning the time of usage. Degradation can either lead to power loss, unstable output voltage, or shutdown of the entire system (SMPS) [23]. ...
Recent research has seen an interest in the condition monitoring (CM) approach for aluminium electrolytic capacitors (AEC), which are present in switched-mode power supplies and other power electronics equipment. From various literature reviews conducted and from a failure mode effect analysis (FMEA) standpoint, the most critical and prone to fault component with the highest percentage is mostly capacitors. Due to its long-lasting ability (endurance), CM offers a better paradigm for AEC due to its application. However, owing to severe conditions (over-voltage, mechanical stress, high temperature) that could occur during use, they (capacitors) could be exposed to early breakdown and overall shutdown of the SMPS. This study considered accelerated life testing (electrical stress and long-term frequency testing) for the component due to its endurance in thousands of hours. We have set up the experiment test bench to monitor the critical electrical parameters: dissipation factor (D), equivalent series resistance (ESR), quality factor (Q), and impedance (Z), which would serve as a health indicator (HI) for the evaluation of the AECs. Time-domain features were extracted from the measured data, and the best features were selected using the correlation-based technique.
... Recently, energy storage systems within microgrids have engaged an indispensable part [9,10]. Yet, on-grid electric vehicles (EVs) and renewables lend themselves as clean energy and flexible deployment energy storage system; however, the integration of electric vehicle to grid (V2G) is confronted by several challenges such as the stochastic nature of V2Gs and renewables, load demands requirements, battery wearing, and market prices. ...
In on-grid microgrids, electric vehicles (EVs) have to be efficiently scheduled for cost-effective electricity consumption and network operation. The stochastic nature of the involved parameters along with their large number and correlations make such scheduling a challenging task. This paper aims at identifying pertinent innovative solutions for reducing the relevant total costs of the on-grid EVs within hybrid microgrids. To optimally scale the EVs, a heuristic greedy approach is considered. Unlike most existing scheduling methodologies in the literature, the proposed greedy scheduler is model-free, training-free, and yet efficient. The proposed approach considers different factors such as the electricity price, on-grid EVs state of arrival and departure, and the total revenue to meet the load demands. The greedy-based approach behaves satisfactorily in terms of fulfilling its objective for the hybrid microgrid system, which is established of photovoltaic, wind turbine, and a local utility grid. Meanwhile, the on-grid EVs are being utilized as an energy storage exchange location. A real time hardware-in-the-loop experimentation is comprehensively conducted to maximize the earned profit. Through different uncertainty scenarios, the ability of the proposed greedy approach to obtain a global optimal solution is assessed. A data simulator was developed for the purposes of generating evaluation datasets, which captures uncertainties in the behaviors of the system’s parameters. The greedy-based strategy is considered applicable, scalable, and efficient in terms of total operating expenditures. Furthermore, as EVs penetration became more versatile, total expenses decreased significantly. Using simulated data of an effective operational duration of 500 years, the proposed approach succeeded in cutting down the energy consumption costs by about 50–85%, beating existing state-of-the-arts results. The proposed approach is proved to be tolerant to the large amounts of uncertainties that are involved in the system’s operational data.
A ship power system refers to an isolated grid consisting of power generation, electrical energy conversion, transmission, distribution, and consumption. As the heart of the ship’s route, the ship’s power station should try to avoid malfunction, and the malfunction should be quickly investigated and recovered. In this paper, a stand-alone system of a ship power station is built for simulation using the Simulink software platform, and the fault voltage and current parameters of each phase are selected as the source of the data set and input to the Long Short Term Memory (LSTM) neural network algorithm for training and diagnostic prediction of faults. By comparing the diagnostic results with those of Back Propagation (BP) neural network, the results show that LSTM has an accuracy of 98.334%, which can diagnose the failure modes of ship power stations more accurately and has higher explanatory power for the predicted data.
Landslide deformation is affected by its geological conditions and many environmental factors. So it has the characteristics of dynamic, nonlinear and unstable, which makes the prediction of landslide displacement difficult. In view of the above problems, this paper proposes a dynamic prediction model of landslide displacement based on the improvement of complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), approximate entropy (ApEn) and convolution long short-term memory (CNN-LSTM) neural network. Firstly, ICEEMDAN and ApEn are used to decompose the cumulative displacements into trend, periodic and random displacements. Then, the least square quintic polynomial function is used to fit the displacement of trend term, and the CNN-LSTM is used to predict the displacement of periodic term and random term. Finally, the displacement prediction results of trend term, periodic term and random term are superimposed to obtain the cumulative displacement prediction value. The proposed model has been verified in Bazimen landslide in the Three Gorges Reservoir area of China. The experimental results show that the model proposed in this paper can more effectively predict the displacement changes of landslides. As compared with long short-term memory (LSTM) neural network, gated recurrent unit (GRU) network model and back propagation (BP) neural network, CNN-LSTM neural network had higher prediction accuracy in predicting the periodic displacement, with the mean absolute percentage error (MAPE) reduced by 3.621%, 6.893% and 15.886% respectively, and the root mean square error (RMSE) reduced by 3.834 mm, 3.945 mm and 7.422 mm respectively. Conclusively, this model not only has high prediction accuracy but also is more stable, which can provide a new insight for practical landslide prevention and control engineering.
