An Adaptive Recursive Multi-step Prediction Method for Required Torque of Electromechanical Hybrid Transmission System

Abstract

The invention discloses a self-adaptive recursive multi-step prediction method of demand torque for a mechanical-electrical compound drive system, relating to a self-adaptive recursive multi-step prediction method. Based on an externally-input auto-regression model (ARX), the self-adaptive recursive multi-step prediction method is applied to achieve online multi-step real-time prediction of the mechanical-electrical compound drive system.In a prediction algorithm, an original driver's accelerator pedal signal and an actual demand torque signal obtained by signal conversion are utilized as input of a model to achieve direct prediction of demand torque. Therefore, accumulative error of prediction is reduced. Meanwhile, two weight coefficients are introduced to ensure accuracy and self adaptation of the prediction algorithm.The self-adaptive recursive multi-step prediction method of demand torque for the mechanical-electrical compound drive system has following beneficial effects: introduced self-adaptive weight coefficients help achieve on-line update of the predication model; and while ensuring real-time performance and accuracy of the prediction algorithm, online prediction of demand torque information of the mechanical-electrical compound drive system is achieved.

Description

technical field [0001] The invention relates to an adaptive recursive multi-step prediction method, in particular to an adaptive recursive multi-step prediction method of the demand torque of an electromechanical compound transmission system. Background technique [0002] The electromechanical compound transmission system provides driving torque according to the driver's pedal information. When the vehicle's operating state changes, due to the hysteresis of the engine response, the motor usually provides excess torque to ensure the power of the vehicle. When the vehicle is cold started, especially when the temperature of the battery is relatively low, excessive motor torque may cause the discharge current of the battery to exceed the maximum allowable current, thereby reducing the service life of the battery. If the demanded torque of the vehicle can be predicted, the output power of the engine is controlled based on the predicted demanded torque, which can effectively impro...

AI Technical Summary

Problems solved by technology

The main problem in information prediction is: under the premise of ensuring the prediction accuracy, use an algorithm to apply as little effective historical data as possible, reduce the calculation amount of prediction and estimation, and realize real-time prediction of information at the same time

However, the problem with this multi-step forecasting algorithm is that the predicted data is used as the input of the next forecast, which will cause the accumulation of errors. When the number of forecast steps is large, the error will increase significantly.

However, the shortcomings of poor adaptability of the algorithm have not been improved accordingly

[0004] Through the analysis and comparison of the current forecasting algorithms, there are mainly the following shortcomings: (1) the application of forecasted data in multi-step forecasting iteratively causes the accumulation of errors, which leads to poor accuracy of the forecasting algorithm; (2) the forecasting process involves matrix The computational complexity of the calculation and solution is very large, which makes the real-time performance of the prediction algorithm poor, and it is difficult to realize online application; (3) the prediction algorithm uses a fixed prediction model. When the characteristics of the system change, the prediction algorithm is not self-adaptive and cannot be timely adjustments to the forecasting model, resulting in poor forecasting accuracy

However, for the electromechanical compound transmission system where the driving environment is constantly changing, its torque demand information is a kind of rapidly changing and complex information, so when the above prediction methods are used to predict the torque demand information of the system, the prediction effect obtained is not good. ideal

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