A strategy of electrical energy management for internal combustion engine vehicle based on driving cycle recognition and electrical load perception

Wang, Yuefei; Zhang, Nan; Wu, Ye; Liu, Baijun; Wu, Yuan

doi:10.1177/1687814018809236

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…is model processes information through a large number of neurons with nonlinear mapping ability. In the network, neurons are organized in the form of hierarchical structure [15][16][17][18], and the processing units on each layer are connected with neurons on other layers in a weighted way. Using error back propagation, a three-layer forward neural network model is obtained, with the specific structure as shown in Figure 1.…”

Section: Design Of Fault Diagnosis Structure Of Electrical Equipment ...mentioning

confidence: 99%

Neural Network Based on Health Monitoring Electrical Equipment Fault and Biomedical Diagnosis

Zhang

Lyu

2022

Computational Intelligence and Neuroscience

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In order to improve the accuracy of electrical equipment failure diagnosis and keep electrical equipment operating safely and efficiently, this paper proposes to design an electrical equipment failure diagnosis system based on a neural network, analyze the faults of electrical equipment and their causes, and establish knowledge base according to relevant data and expert judgment. The fault knowledge base was introduced into the neural network operation structure, and the fault diagnosis results were classified step by step through multiple subnetworks. In data preprocessing, in order to avoid the redundancy of primary fault information features, the principal component heuristic attribute reduction algorithm was used to select the fault data samples optimally. The neural network learning algorithm is used to calculate the forward direction and error rate of the initial error data, and the reliability function is used to optimize the initial weight threshold of the neural network, propagating the error backwards and high. Experimental results show that adding attribute reduction improves error classification performance, avoids the problem of local minima through neural network operation, and has fewer iteration steps, lower average error, and higher accuracy of fault diagnosis, reaching 95.6%.

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Section: Design Of Fault Diagnosis Structure Of Electrical Equipment ...mentioning

confidence: 99%

Neural Network Based on Health Monitoring Electrical Equipment Fault and Biomedical Diagnosis

Zhang

Lyu

2022

Computational Intelligence and Neuroscience

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“…The authors [19] designed a power management system based on the bus structure strategy to obtain refined control of vehicle energy. The authors used improved learning vector quantization neural networks and gradient optimization to achieve their goals.…”

Section: Dcr (Driving Cycle Recognition) and Elp (Electrical Load Per...mentioning

confidence: 99%

Optimization Approaches and Techniques for Automotive Alternators: Review Study

et al. 2022

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Optimization and improvement of the electrical system are applied to cope with the increased demand for electrical power in the vehicular system; they must be carried out in many ways to ensure that the vehicles are provided with the necessary electricity for their performance work electrical equipment. This paper reviews the various optimization approaches for the alternator used in automotive applications and discusses the advantages and disadvantages of each method. The optimization is achieved to the field excitation current that influences the alternator output voltage, and the other techniques designed a controller to optimize the output power of the alternator using power electronic converters. The most suitable approaches are those approaches that use real-time optimization and self-optimization methods. Combining the above two methods can achieve the best results, higher efficiency, stable performance, and a large amount of power produced by the alternator.

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