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The effective enhancement of braking energy recovery and safety in electric vehicles can be achieved by accurately identifying the driver's braking intention and developing a corresponding regenerative braking control strategy based on different braking intentions. This paper presents a categorization of braking conditions into mild, moderate, and emergency levels, followed by the construction of a test system for recognizing braking intentions. Multiple sets of braking conditions are tested under various initial speeds to obtain parameters for recognizing the driver's intended action during braking. Through feature selection using random forest, acceleration, brake pedal displacement, and brake pedal force are identified as key parameters for recognizing the driver's intended action during braking. Subsequently, an AdaBoost-based model is established for recognizing the driver's intended action during braking. Experimental data is used to validate this model offline and compare it with various other models for recognition of driving intentions during brakes. The results show that the braking intention recognition model based on AdaBoost has a high recognition accuracy.
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