Regenerative braking control system and method of active electric vehicle

Abstract

The invention relates to a regenerative braking control system and method of an active electric vehicle. The regenerative braking control system comprises an information acquisition unit, an information interaction unit, an information parsing unit and a control unit, wherein the information acquisition unit is used for acquiring vehicle information, the information interaction unit is used for receiving feedback information of a driver to achieve person-vehicle information interaction, the information parsing unit is used for parsing the vehicle information acquired by the regenerative braking control system, the control unit is used for obtaining the braking intention of the driver according to the obtained parsed vehicle information and providing corresponding control parameters according to the braking intention to control the braking situation of an electric vehicle, the information acquisition unit and the information interaction unit are connected with the information parsing unit, and the information parsing unit is connected with the control unit.

Description

technical field [0001] The invention relates to an electric vehicle electric braking control device, in particular to the control and management of vehicle motor braking for energy recovery function, and specifically refers to an active electric vehicle regenerative braking control system and its method. Background technique [0002] The purpose of vehicle braking is to slow down or stop. Using the kinetic energy conversion of the vehicle under the condition of ensuring braking performance to optimize economy and braking performance is one of the technical indicators generally pursued by the electric vehicle industry. One of the typical characteristics of electric vehicles that distinguishes them from traditional internal combustion engine vehicles is the ability to use the electric powertrain to regenerate electric energy under braking, that is, the regenerative braking function of the motor converts the braking energy of the vehicle into electric energy and stores it in the...

AI Technical Summary

Problems solved by technology

[0005] (1) The driver's braking intention analysis is too simple, and it is impossible to obtain sufficient braking intention analysis resources through simple input information. The braking strategy is rough, random, and seriously lacking in refinement, resulting in low efficiency of regenerative braking energy recovery. Poor comfort, poor consistency of electric braking strength, and even interference affecting the safety of driving brakes;

[0006] (2) Many vehicles are not equipped with brake pedal depth sensors, and only have ordinary brake switch values. Because the driving intention cannot be analyzed, and the user's requirements for regenerative braking are not clear, so basically this type of vehicle cannot make a reasonable regenerative braking control;

[0007] (3) Many vehicles use a single vehicle controller for regenerative braking control. Very professional regenerative braking or electric braking control processing systems are generally not equipped, resulting in the inability to form an efficient braking system with the vehicle's ABS or other components. Dynamic performance optimization control, for example, during hybrid braking, due to changes in the physical characteristics of the mechanical brake and the environment, the corresponding relationship between the mechanical braking law and the brake pedal depth signal often deviates or changes greatly. The perception of this change, The adjustment of the electric braking law, the performance consistency evaluation and information notification of the hybrid braking system require a specialized control system;

[0008] (4) At this stage, the performance evaluation and information feedback functions of regenerative braking are relatively lacking, and it cannot provide effective information support for the driver or vehicle information system, such as the reasonable timing of using regenerative braking, regenerative braking behavior suggestions, Real-time information on the distance and orientation of obstacles in the direction of travel, real-time technical status of regenerative braking (such as braking torque, braking current, energy recovery, etc.), regenerative braking effect evaluation (difference from the ideal state), regenerative braking limit Conditions, fault information (such as drive system faults, abnormal distance sensing signals, other control signals normal or not, etc.) notification and countermeasures, etc.;

[0009] (5) The current common regenerative braking system does not deeply analyze the deep physical signal meaning of the input signal. In actual engineering, a large amount of vehicle information is rarely used to apply relevant vehicle control theoretical knowledge to guide the in-depth research and development of regenerative braking control technology. Such as driving condition analysis, road condition memory (road characteristics and GPS positioning information), vehicle parameter analysis (braking force distribution curve, tire pressure, wheel speed, vehicle inclination angle, vehicle acceleration, vehicle weight evaluation, driving resistance evaluation, braking ability Limit, calibration curve of mechanical braking force and brake pedal depth, braking strength classification, steering operation characteristics, etc.), dynamic analysis of vehicle distance (relative distance, relative speed, relative orientation, etc.);

[0010] (6) Regenerative braking control needs to form multi-level information communication and control with many systems on electric vehicles. Joint control can efficiently optimize the energy-saving effect of the whole vehicle, but currently such control ideas and applications are relatively lacking

[0011] (7) Lack of self-learning and adaptability

Domestic use of specialized regenerative braking control systems to optimize regenerative braking control of electric vehicles has a low popularity rate, especially the active regenerative braking control technology grafted on in-depth analysis of braking intentions

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