A dynamic Bayesian network parameter self-updating hub motor state identification method

A dynamic Bayesian, in-wheel motor technology, applied in character and pattern recognition, computer parts, instruments, etc., to achieve the effect of improving matching degree, accurate and timely recognition of operating status, and improving recognition accuracy

Active Publication Date: 2019-06-25
JIANGSU UNIV
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Problems solved by technology

However, this method still ignores that the state of each road section is constantly changing every day. As time goes ...

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  • A dynamic Bayesian network parameter self-updating hub motor state identification method
  • A dynamic Bayesian network parameter self-updating hub motor state identification method
  • A dynamic Bayesian network parameter self-updating hub motor state identification method

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Embodiment Construction

[0021] The technical solutions of the present invention will be further described in detail below in conjunction with the accompanying drawings and specific examples.

[0022] The present invention provides a method for on-line recognition of the running state of a hub motor with dynamic Bayesian network parameters self-updating, including three stages. The first stage is the establishment of a recognition model for the running state of a single hub motor; The state online recognition stage; the third stage is the dynamic Bayesian network parameter update stage based on the recognition model cloud training center.

[0023] Such as figure 1 As shown in , it is a flow chart of establishing a single in-wheel motor operating state identification model in the first stage, and its specific steps are as follows:

[0024] Step 1: Collect the operating status information of a single hub motor, and sort and classify the collected information with 2s as a time segment. It specifically ...

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Abstract

The invention discloses a dynamic Bayesian network parameter self-updating hub motor state identification method, which comprises the following steps of: establishing an initial data training set in asingle time slice and a Bayesian network model in the single time slice according to hub motor operation state information and high-sensitivity characteristic parameters, and establishing a single hub motor operation state identification model; using the high-sensitivity characteristic parameter, the road grade, the load level, and the vehicle speed grade as the input of the recognition model toidentify the running state of the hub motor online, and obtaining and uploading the recognition result to the recognition model cloud training center; performing parameter learning on the Bayesian network in a single time slice by the recognition model cloud training center to obtain brand-new conditional probability distribution of each network node, and updating the state transition probabilitydistribution between the two time slices by utilizing a new data set; The problem that the running state of the hub motor is inaccurately identified only by means of a single factor is solved, and theidentification accuracy is improved.

Description

technical field [0001] The invention relates to the field of state identification and fault intelligent diagnosis of wheel hub motors, in particular to an online identification method for wheel hub motor operating states with dynamic Bayesian network parameter self-updating. Background technique [0002] The in-wheel motor is a built-in motor in the wheel, which has the characteristics of flexible control, compact structure, high transmission efficiency, etc., and integrates multiple kinetic energy such as driving and braking. Using in-wheel motors to build a four-wheel distributed drive system for electric vehicles is considered to be the best choice for future electric vehicle drive systems. However, complex road conditions and changing vehicle driving conditions can easily affect the safe operation of in-wheel motors, which has become a key issue restricting the application and promotion of in-wheel motor drive technology. Therefore, in order to ensure the rapid developm...

Claims

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Application Information

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IPC IPC(8): G06K9/62
Inventor 李仲兴陈震宇薛红涛江洪
Owner JIANGSU UNIV
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