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Hydrogen internal combustion engine ignition correct time calibration optimization system based on L-M neural network and optimization method thereof

An ignition timing and neural network technology, which is applied in the field of hydrogen internal combustion engine ignition timing calibration and optimization system, can solve problems such as heavy experimental workload, and achieve the effects of reducing manual experimental operation process, small calibration error and fast speed.

Inactive Publication Date: 2016-11-02
NORTH CHINA UNIV OF WATER RESOURCES & ELECTRIC POWER
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AI Technical Summary

Problems solved by technology

Optimizing the ignition timing of hydrogen internal combustion engines often requires a lot of experimental calibration work. The best ignition timing is obtained through multiple tests in each different working condition. Therefore, when the working condition parameters (such as speed, load, cooling water temperature, etc.) or need to improve the control accuracy, the experimental workload will be very heavy

Method used

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  • Hydrogen internal combustion engine ignition correct time calibration optimization system based on L-M neural network and optimization method thereof
  • Hydrogen internal combustion engine ignition correct time calibration optimization system based on L-M neural network and optimization method thereof
  • Hydrogen internal combustion engine ignition correct time calibration optimization system based on L-M neural network and optimization method thereof

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Embodiment 1

[0036] Embodiment one, see figure 1 As shown, a hydrogen internal combustion engine ignition timing calibration optimization system based on L-M neural network, including data acquisition module, signal conditioning module, control unit, power supply module, clock module, storage module, execution unit, interface circuit and monitoring module, data The acquisition module collects various working condition parameters, and transmits the collected working condition parameters to the signal conditioning module. The signal conditioning module, power supply module, clock module, storage module, execution unit, and monitoring module are respectively connected to the control unit through the interface circuit. The control unit includes a data processing unit and an ignition timing optimization control module. The data processing unit uses the L-M optimization algorithm to perform neural network optimization training based on the parameters of each working condition received by the cont...

Embodiment 2

[0039] Embodiment two, see Figure 1~3 As shown, an optimization method for calibration of ignition timing of hydrogen internal combustion engines based on L-M neural network is an optimization method based on the calibration optimization system for ignition timing of hydrogen internal combustion engines based on L-M neural network described in Embodiment 1, which specifically includes Follow the steps below:

[0040] Step 1. Carry out the ignition timing calibration test of the hydrogen internal combustion engine, select the working condition parameters under different working conditions, conduct tests on different working conditions, and obtain the best ignition advance angle for the corresponding working conditions, as the calibration data, select part of the calibration data The data is used as a training sample, and the other part is used as a test sample;

[0041] Step 2. Establish an ignition timing optimization model, first establish a neural network structure model, ...

Embodiment 3

[0048] Embodiment three, see Figure 1~4 As shown, an optimization method for calibration of ignition timing of hydrogen internal combustion engines based on L-M neural network is an optimization method based on the calibration optimization system for ignition timing of hydrogen internal combustion engines based on L-M neural network described in Embodiment 1, which specifically includes Follow the steps below:

[0049] Step 1. Carry out the ignition timing calibration test of the hydrogen internal combustion engine, select the working condition parameters under different working conditions, conduct tests on different working conditions, and obtain the best ignition advance angle for the corresponding working conditions, as the calibration data, select part of the calibration data The data is used as a training sample, and the other part is used as a test sample;

[0050] Step 2. Establish an ignition timing optimization model, first establish a neural network structure model...

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Abstract

The invention relates to a hydrogen internal combustion engine ignition correct time calibration optimization system based on an L-M neural network and an optimization method thereof. The system comprises a data acquisition module, a signal conditioning module, a control unit, a power supply module, a clock module, a storage module, a performing unit, an interface circuit and a monitoring module. The control unit comprises a data processing unit and an ignition correct time optimization control module. The data processing unit performs neural network optimization training by utilizing an L-M optimizing algorithm according to all the condition parameters received by the control unit so as to obtain the optimal ignition correct time of the current condition and transmit the optimal ignition correct time to the ignition correct time optimization control module. The ignition correct time optimization control module transmits the received optimal ignition correct time to an ignition performer. Hydrogen internal combustion engine overall condition optimization control is accurately and rapidly performed based on the L-M neural network so that the error of ignition correct time calibration is low, speed is high, the ideal prediction effect can be achieved, and the system and the method thereof have important meaning for the experimental research of the hydrogen internal combustion engine.

Description

technical field [0001] The invention relates to the technical field of hydrogen fuel engine ignition timing optimization, in particular to a hydrogen internal combustion engine ignition timing calibration optimization system and an optimization method based on an L-M neural network. Background technique [0002] In recent years, my country's auto industry has shown a prosperous scene. According to the latest statistics from the China Association of Automobile Manufacturers, my country's auto production and sales exceeded 24.5 million in 2015, a record high in global history. Ranked first in the world for 7 consecutive years, production and sales increased by 3.3% and 4.7% respectively over the previous year, showing a steady growth overall. The rapid development of the automobile industry has made environmental issues more prominent. Today, with the gradual deterioration of the ecological environment and the gradual shortage of energy sources, environmental protection, ener...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 王丽君刘源翟昱尧张晨杨振中赵亚楠
Owner NORTH CHINA UNIV OF WATER RESOURCES & ELECTRIC POWER
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