Self-adaptive prediction method for running temperature of power battery

Abstract

The invention provides a self-adaptive prediction method for the running temperature of a power battery. The self-adaptive prediction method comprises the following steps: during the dynamic charge-discharge process of a power battery, real data causing temperature variation of the power battery can be collected, current predicted value during the running of the power battery is obtained, the parameter original value of a temperature predictive model and the parameter original value of a heat production computation module are determined; coupled calculation to the temperature predicted value of the power battery and the heat production predicted value during the charge-discharge process can be performed; self-adaptive identification to the parameters of the temperature predictive model and the parameters of the heat production computation module can be performed so as to obtain modified temperature predictive model parameters and the heat production computation module parameters; the running temperature of the power battery can be calculated according to the modified temperature predictive model parameters and the heat production computation module parameters. The method is suitable for prediction to temperature variation of the power battery, caused by dynamic charging and discharging, temperature variation predicted values of the power battery can be given in real time, and state predicting precision of the power battery can be further improved.

Description

technical field [0001] The invention belongs to the field of battery management and relates to an adaptive prediction method for the operating temperature of a power battery. Background technique [0002] Compared with traditional fuel vehicles, electric vehicles have great advantages in terms of driving economy and environmental friendliness, but the performance of power batteries in electric vehicles is greatly affected by temperature, and battery parameters, voltage output and discharge efficiency are different under different temperatures. The difference makes the maximum power and remaining available energy of the battery different at different temperatures, which affects the power capability and mileage of the vehicle in actual vehicle use. Therefore, the impact of temperature must be considered in the battery management system. In addition to the performance change of the power battery caused by the difference in ambient temperature, the heat generated during the char...

AI Technical Summary

Problems solved by technology

[0003] However, at present, in the temperature prediction of the power battery charging and discharging process, the neural network method is mostly used to predict the battery temperature. This method needs to be trained based on a large amount of data, which is inconvenient to use, and this method is mainly used for battery temperature with a fixed current rate. It is also not conducive to the temperature prediction of the power battery during the normal driving of the real vehicle

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