Whole vehicle load prediction method based on LSTM neural network

A technology of neural network and prediction method, which is applied in the direction of neural learning method, biological neural network model, prediction, etc., which can solve the problems of easy aging of sensors, slow convergence speed, increased maintenance cost, etc., so as to save later maintenance cost and improve prediction accuracy , The effect of saving manufacturing cost

Active Publication Date: 2019-08-20
兴民智通(武汉)汽车技术有限公司
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Problems solved by technology

[0004] Disadvantages: The shifting operation is not considered, and the estimation during the shifting process will lead to low estimation accuracy and slow convergence speed of the algorithm
[0006] Disadvantages: Additional sensors need to be installed on each vehicle. The price of an ordinary vehicle load sensor is more than RMB 10,000, which is expensive, and the sensor based on deformation is easy to age, which will increase the maintenance cost in the later period, and the calculation of load accuracy also have an impact

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  • Whole vehicle load prediction method based on LSTM neural network
  • Whole vehicle load prediction method based on LSTM neural network
  • Whole vehicle load prediction method based on LSTM neural network

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Embodiment Construction

[0033] The present invention will be further described below in conjunction with drawings and embodiments.

[0034] The present invention provides a kind of vehicle load forecasting method based on LSTM neural network, comprising the following steps:

[0035] (1) Collect vehicle data through the vehicle terminal (T-BOX), including time, vehicle speed, engine speed, clutch switch, accelerator pedal opening and brake switch, and torque or torque percentage;

[0036] (2) Integrate, clean and standardize the vehicle data to obtain training data;

[0037] This step is mainly to prepare the data. From the longitudinal dynamics formula:

[0038]

[0039] Among them, M represents the total mass of the car, v represents the driving speed of the car (data can be collected), acceleration T e Indicates the torque of the engine acting on the flywheel (data can be collected), Indicates the overall gear ratio, where r w Indicates the radius of the wheel (static value of the model)...

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Abstract

The invention provides a whole vehicle load prediction method based on an LSTM neural network. The method comprises the following steps of collecting vehicle data through a vehicle-mounted terminal; integrating vehicle data, and carrying out data cleaning and standardized processing to obtain training data; training through a long-term and short-term memory neural network model to obtain a load model; and inputting predicted data into the trained load model to predict and acquiring load data. By using the whole vehicle load prediction method based on the LSTM neural network, a big data calculating platform is used, based on an application of the neural network model in a prediction field, a longitudinal dynamic vehicle mass calculation equation is combined, and real-time and accurate prediction of the whole vehicle load can be realized.

Description

technical field [0001] The invention relates to a vehicle load prediction method based on an LSTM neural network, and belongs to the technical fields of Internet of Vehicles and logistics transportation. Background technique [0002] At present, there are two main methods of traditional load forecasting: [0003] Method 1: Based on the longitudinal dynamics, the weight of the vehicle is estimated by the recursive least squares method with the forgetting factor. [0004] Disadvantages: The shifting operation is not considered, and the estimation during the shifting process will lead to low estimation accuracy and slow convergence speed of the algorithm. Moreover, in reality, due to real-time changes in braking force, air resistance, rolling friction and other factors, it is difficult to obtain it accurately, which also affects the prediction accuracy to a certain extent. It is difficult to control the error range between the predicted result and the real value within 15%. ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G07C5/08G06Q10/04G06Q10/08G08G1/01G06N3/04G06N3/08
CPCG07C5/0808G06Q10/04G06Q10/083G08G1/0112G08G1/0125G06N3/08G06N3/044G06N3/045
Inventor 王平李荣成柳伟
Owner 兴民智通(武汉)汽车技术有限公司
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