A study of a flux switching linear generator with a novel speed amplified mechanism and its optimization for the maximum power output and minimum cogging force of wave energy conversion

Liu, Zhenwei; Han, Xue; Khatri, Pooja; Ding, Sheng; Wang, Xu

doi:10.1016/j.ymssp.2021.108413

Cited by 9 publications

(2 citation statements)

References 18 publications

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“…As shown in Table 5, ANOVA was carried out to assess the adequacy and validity of the above models. For ANOVA, the F-value and p-value are typically used to confirm the statistical significance of the model [38]. From Table 5, it can be seen that the F-values of the specific energy consumption and loss rate are 33.66 and 22.03, and the p-values are all less than 0.001, demonstrating the reliability of both models.…”

Section: Rsm Modeling and Analysismentioning

confidence: 97%

Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach

Xue,

Fu,

et al. 2023

Agriculture

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Green forage maize harvesters face challenges such as high soil humidity and soft soil in the field, mismatched working parameters, and poor reliability and adaptability. These challenges often result in header blockage, significant harvest loss, and increased energy consumption. Traditional testing and statistical analysis methods used in most existing studies are limited by complex test processes, their time-consuming nature, high costs, and poor prediction accuracy. To address these problems, a test bench was constructed to analyze the effects of forward speed, cutting height, number of rows, and their interactions on specific energy consumption and harvest loss of the green forage maize (GFM) header. A combined response surface method (RSM)–artificial neural network (ANN) approach is proposed for modeling and predicting the performance parameters of the header. The optimal conditions were determined by optimizing the specific energy consumption and loss rate. The optimal combination parameters are a forward speed of 1.6 km/h, a cutting height of 167 mm, and a number of rows of 4. However, RSM–ANN has larger R2 values and lower root mean square errors (RMSE) and mean square errors (MSE) compared to RSM. Specifically, the R2 of the RSM–ANN model for specific energy consumption and loss rate a 0.9925 and 0.9906, MSE are 0.00001775 and 0.004558, and RMSE are 0.004214 and 0.006752, respectively. The results show that the combined RSM–ANN method has higher precision and accuracy and can better predict and optimize the header performance. This study overcomes the limitations of traditional methods and has the potential to provide data and method references for the design, optimization, prediction, and intelligent diagnosis of faults in the operational parameters of agricultural machinery.

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Section: Rsm Modeling and Analysismentioning

confidence: 97%

Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach

Xue,

Fu,

et al. 2023

Agriculture

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“…It could not be provided by regenerative braking because the rotation of the shaft in the braking process decreased. High flux concentrations can be created by chamfering the ends of the stator feet [18]. Another way is to increase the rotational speed of the drive shaft by giving the gear ratio.…”

Section: Experimental Components 31 Powertrainmentioning

confidence: 99%

Optimizing regenerative braking on electric vehicles using a model-based algorithm in the antilock braking system

Budijono

Sutantra

Pramono

2023

IJPEDS

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<span lang="EN-US">The regenerative braking effectiveness of electric vehicles (EVs), with <br /> 8-25% range, requires designers to produce better braking systems. The antilock braking system (ABS) was chosen because it offers various advantages, such as enhanced safety considerations, vehicle maneuverability, and so on. The measurement findings revealed that ABS took longer to stop the wheels with the same wheel rotation speed. Because of the lesser differentiation of magnetic flux to time, it created lower induced emf in the generator. ABS 50 Hz performance was 19.5% at 4500 pm, whereas hydraulic brake performance was 21% at the same speed. ABS used model-based algorithms (MBAs) to boost the friction frequency with the wheels from 10 to 50 Hz. As the frequency increased, the ABS graph approached the hydraulic graph, and the ABS performance improved. Although ABS loses to hydraulics in stopping wheel rotation, it gains in saved energy and battery temperature. Longer wheel stop-times allow the rotational kinetic energy of the wheel more time to be converted into electricity.</span>

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Vibration energy harvesting system based on track energy-recycling technology for heavy-duty freight railroads

Zhang

Pan

et al. 2022

Applied Energy

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A study of a flux switching linear generator with a novel speed amplified mechanism and its optimization for the maximum power output and minimum cogging force of wave energy conversion

Cited by 9 publications

References 18 publications

Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach

Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach

Optimizing regenerative braking on electric vehicles using a model-based algorithm in the antilock braking system

Vibration energy harvesting system based on track energy-recycling technology for heavy-duty freight railroads

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