To address the problem of low efficiency in estimating the state of health (SOH) of lithium-ion batteries, a method based on the maximal information coefficient (MIC) algorithm and the back propagation (BP) neural net...
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To address the problem of low efficiency in estimating the state of health (SOH) of lithium-ion batteries, a method based on the maximal information coefficient (MIC) algorithm and the back propagation (BP) neural network optimized by the golden jack optimization (GJO) algorithm is proposed in this study. Firstly, six aging features of SOH were extracted from the University of Maryland's lithium-ion battery aging test data, and three high-quality aging features were selected using the MIC algorithm;then, the GJO algorithm is selected to optimize the initial weights and thresholds of the BP neural network to eliminate the problem of overfitting in the BP neural network;finally, GJO-BP was compared with BP neural networks optimized by genetic algorithm (GA) and simulated annealing (SA) algorithm. The results showed that after optimization using the MIC algorithm, the average error (MAE) of the model decreased by 31.37% compared to before optimization for aging characteristics;the reduction in MAE for GJO-BP compared to BP is 18.57% and 22.85% higher than that for GA-BP and SA-BP, respectively, while the convergence speed of GJO-BP is 50% faster than that of SA-BP. High-efficiency lithium battery SOH estimation can be achieved.
Aiming at the problem that the vibration signals of rolling bearings in high-speed rail traction motors are often affected by noise when they are in a fault state, which makes it very difficult to extract the fault fe...
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Aiming at the problem that the vibration signals of rolling bearings in high-speed rail traction motors are often affected by noise when they are in a fault state, which makes it very difficult to extract the fault features during fault diagnosis and causes obstruction in fault classification. The article proposes a rolling bearing fault diagnosis based on optimized variational mode decomposition (VMD) combined with signal features and an improved convolutional neural network (CNN). The goldenjackaloptimization (GJO) algorithm is employed to optimize the key parameters of the VMD, enabling effective signal decomposition. The decomposed signals are then filtered and reconstructed using criteria based on kurtosis and interrelationship measures. The time-domain features of the reconstructed signals are computed, and the feature vectors are constructed, which are used as inputs to the deep learning network;the CNN combined with the support vector machine (SVM) network model is used for the extraction of the features and the classification of the faults. The experimental results show that the method can effectively extract fault features in noise-covered signals, and the accuracy is also significantly improved compared with traditional methods.
The wheel-rail contact force is a crucial indicator for ensuring the secure operation of a heavy-load train. However, obtaining the real-time wheel-rail contact force of a heavy-load train is a challenging task as, du...
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The wheel-rail contact force is a crucial indicator for ensuring the secure operation of a heavy-load train. However, obtaining the real-time wheel-rail contact force of a heavy-load train is a challenging task as, due to safety considerations, it is not possible to install instrumented wheelsets on heavy-load trains. This work presents a novel approach to quantify the wheel-rail contact force of a heavy-load train, utilizing a cooperative calibration methodology. First, a ground measurement platform for the wheel-rail contact force of a heavy-load train is constructed on a selected rail section. The railway inspection car's wheel-rail contact force measurement system is fine-tuned using a multilayer perceptron calibration approach, and the ground platform then uses the results to fine-tune the railway inspection car's examined wheelset. Second, based on actual measured data on the wheel-rail contact force of a heavy-load train, and using the golden jackal optimization algorithm, the multilayer perceptron correction approach is employed to create a data relationship mapping model. This model correlates the corrected data on the wheel-rail contact force obtained from the railway inspection car with the wheel-rail contact force of a heavy-haul train with an axle load of 25 tons, and the precision of the mapping is guaranteed. Finally, by combining the wheel-rail contact force correction method for the railway inspection car and the contact force mapping model between the railway inspection car and the heavy-load train, collaborative calibration of the wheel-rail contact force of the heavy-load train is realized. The experimental results under two working conditions show that this method can realize high-precision, real-time measurement of the wheel-rail contact force of a heavy-load train. For the working condition of a straight-line section, the calibration error was within 1.593 kN, and the MAPE was 0.105%;for the working condition of a curved-line section, the cali
The accuracy of tool life prediction will directly affect the overall efficiency of the dicing saw. Since the tool life of dicing saw is easily affected by different working conditions and the material of the tools th...
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The accuracy of tool life prediction will directly affect the overall efficiency of the dicing saw. Since the tool life of dicing saw is easily affected by different working conditions and the material of the tools themselves, it is difficult to establish an accurate tool life prediction model. Therefore, this paper proposes a prediction model based on adaptive goldenjackaloptimization (AGJO) gated recurrent unit (GRU) to improve the accuracy of tool life prediction of dicing saw. Specifically, the standard goldenjackaloptimization (GJO) algorithm suffers from slow convergence in the early stage, low convergence accuracy in the late stage, and easy to fall into local optimization. In this paper, a nonlinear convergence factor and an adaptive weighting factor are introduced to improve the standard GJO algorithm. The AGJO algorithm is then compared with other optimizationalgorithms such as the GJO algorithm using benchmark functions. Secondly, AGJO is used to optimize the hyperparameters of GRU, and the AGJO-GRU tool life prediction model is constructed. Finally, the effectiveness of the AGJO-GRU prediction model was verified using actual data from the ADT-8230 dicing saw. The experimental results show that the method proposed in this paper can effectively predict the tool life of dicing saws. Compared with the GJO-GRU prediction model, the accuracy of the proposed AGJO-GRU prediction model is improved by 1.96%, and the root mean square error decreased by 27.04%, which has better prediction ability.
The construction of a power system including renewable energy has become the direction of development for the power industry as a result of the "carbon peaking and carbon neutrality" targets. Yet because the...
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The construction of a power system including renewable energy has become the direction of development for the power industry as a result of the "carbon peaking and carbon neutrality" targets. Yet because the electricity market (EM), carbon market (CM), and green certificate market (GCM) have traditionally operated independently, with little interaction among them. To explore the interaction and correlation among the three markets, this paper analyzes the trading patterns and mutual influencing factors of the EM, CM and GCM and proposes the optimal decision-making model of "carbon-electricity-certificate" integration of multiple markets based on the decision-making behavior of power producers in each market. Finally, the golden jackal optimization algorithm (GJO) is used to solve the problem under the condition of network security. The simulation results show that the integration of multiple markets is more conducive to promoting the consumption of renewable energy source (RES), and also verify the feasibility and effectiveness of GJO in solving the optimal decision-making problem of power producers in EM.
The state of charge (SOC) is a characteristic parameter that indicates the remaining capacity of electric vehicle batteries. It plays a significant role in determining driving range, ensuring operational safety, and e...
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The state of charge (SOC) is a characteristic parameter that indicates the remaining capacity of electric vehicle batteries. It plays a significant role in determining driving range, ensuring operational safety, and extending the service life of battery energy storage systems. Accurate SOC estimation can ensure the safety and reliability of vehicles. To tackle the challenge of precise SOC estimation in complex environments, this study introduces an improved forgetting factor recursive least squares (IFFRLS) method, which integrates the goldenjackaloptimization (GJO) algorithm with the traditional FFRLS method. This integration is grounded in the formulation of a lithium battery state equation derived from a second-order RC equivalent circuit model. Additionally, the research utilizes the interactive multiple model unscented Kalman filter (IMMUKF) algorithm for SOC estimation, with experimental validation conducted under various conditions, including hybrid pulse power characterization (HPPC), urban dynamometer driving schedule (UDDS), and real underwater scenarios. The experimental results demonstrate that the SOC estimation method of lithium batteries based on IFFRLS-IMMUKF exhibits high accuracy and a favorable temperature applicability range.
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