CN-122008884-A - New energy automobile energy recovery self-learning method

Abstract

The invention relates to the field of whole vehicle control of new energy automobiles, in particular to a new energy automobile energy recovery self-learning method, which comprises the steps of S1, acquiring a vehicle weight signal, a vehicle speed signal and a brake pedal opening signal of a vehicle in real time based on a torque determining step, selecting a basic recovery torque MAP corresponding to the current vehicle weight tonnage according to the vehicle weight signal, checking a table in the selected basic recovery torque MAP based on the vehicle speed signal and the brake pedal opening signal, determining a basic recovery torque value, S2, performing gradient correction, acquiring the current road gradient signal of the vehicle in real time, S3, performing self-learning adjustment, and monitoring the opening of an accelerator pedal and the duration thereof in real time under the condition that the vehicle is in a downhill slope and an energy recovery function is activated, thereby solving the problems that the influence of the vehicle weight and the gradient and the single fault coping strategy are not considered at the present stage.

Inventors

• ZHANG LONGCONG
• LIU DI
• SONG QI
• XU LICHENG
• ZHANG SHUANG
• DING MINGFENG
• LU JIFEI

Assignees

• 浙江飞碟汽车制造有限公司
• 山东五征集团有限公司

Dates

Publication Date

20260512

Application Date

20260403

Claims (8)

1. 1. The self-learning method for recovering the energy of the new energy automobile is applied to the whole automobile controller and is characterized by comprising the following specific steps: Step S1, acquiring a vehicle weight signal, a vehicle speed signal and a brake pedal opening signal of a vehicle in real time based on a torque determining step, selecting a basic recovery torque MAP corresponding to the current vehicle weight tonnage according to the vehicle weight signal, and performing table lookup in the selected basic recovery torque MAP based on the vehicle speed signal and the brake pedal opening signal to determine a basic recovery torque value; s2, slope correction is carried out, and a current road slope signal of the vehicle is obtained in real time; And step S3, performing self-learning adjustment, namely monitoring the opening degree of the accelerator pedal and the duration time of the accelerator pedal in real time under the condition that the vehicle is in a downhill slope and the energy recovery function is activated, and if the accelerator pedal opening degree is detected to continuously exceed the set opening degree threshold value for a preset time, judging that the driver has acceleration intention, triggering a self-learning mechanism, and performing weight-reducing adjustment on the currently used basic recovery torque MAP.
2. 2. The method for self-learning energy recovery of a new energy vehicle of claim 1 further comprising failsafe monitoring, wherein said failsafe monitoring is performed continuously for said weight and grade signals, and wherein when said weight or grade signals are determined to be invalid or out of a reasonable range, steps S1 and S2 are ignored, and a predetermined, lower strength safety recovery torque value is used.
3. 3. The method of claim 1, wherein in step S3, all torque values in the basic recovered torque MAP are attenuated according to a fixed ratio to generate and apply a new self-learning recovered torque MAP.
4. 4. The method for self-learning energy recovery of a new energy automobile according to claim 2, wherein the safe recovery torque value is a fixed value, and the strength of the safe recovery torque value is lower than the recovery torque under normal working conditions.
5. 5. The method for self-learning energy recovery of a new energy vehicle according to claim 1, wherein the step S2 compares a gradient signal with a preset gradient threshold, multiplies the basic recovery torque value by a strengthening coefficient greater than 1 to obtain a first correction torque if the gradient signal indicates that the vehicle is on a downhill slope and the gradient value is greater than a first positive threshold, and multiplies the basic recovery torque value by a weakening coefficient smaller than 1 to obtain the first correction torque if the gradient signal indicates that the vehicle is on an uphill slope and the gradient value is greater than a second positive threshold.
6. 6. The method for recovering and self-learning the energy of the new energy automobile according to claim 1, wherein the whole automobile controller detects the speed and the steering wheel angle signal in real time, adopts the speed and the steering wheel angle input, takes the correction coefficient as the output MAP, obtains a coefficient smaller than 1 when the speed is higher and the steering wheel angle is larger, preferentially ensures safety when the automobile is driven at a high speed and turns at a larger speed, actively reduces the recovery torque, avoids unsafe phenomena such as tail flicking, and enhances the driving safety of the automobile.
7. 7. The method for self-learning new energy automobile energy recovery according to claim 4, wherein the basic recovery torque MAP is related to the intensity of the driving modes of the whole automobile, the driving modes comprise an ECO mode, a NORMAL mode and a SPORT mode, the trend of the energy recovery basic MAP corresponding to the three driving modes is gradually reduced, and a torque gradient change strategy is adopted when a driver switches the driving modes so as to avoid abrupt change of the energy recovery torque.
8. 8. The method of claim 5, wherein in step S2, the weakening coefficient is configured to ensure that the vehicle does not cause serious speed loss due to energy recovery when the vehicle is on an uphill slope, and to maintain smoothness of uphill slope driving.

Description

New energy automobile energy recovery self-learning method Technical Field The invention relates to the field of energy recovery of new energy automobiles, in particular to a self-learning method for energy recovery of a new energy automobile. Background With the popularization of new energy automobiles, improving energy utilization efficiency has become one of the key technologies. The braking energy recovery system can effectively prolong the endurance mileage of the vehicle by converting the kinetic energy of the vehicle during braking or sliding into electric energy and storing the electric energy. Currently, most vehicle braking energy recovery strategies look-up table based on vehicle speed and brake pedal opening (or deceleration request) to determine a fixed recovery torque, however, such strategies suffer from the following drawbacks: 1. The influence of the vehicle weight is not considered, the inertia of the vehicle is different in the no-load state and the full-load state, the same recovery torque can cause completely different deceleration experiences, comfortable and consistent braking experiences cannot be provided under various load conditions, and the optimization of recovery efficiency is also influenced. 2. On the slope, the influence of the gradient is not considered, and the fixed recovery torque strategy can bring bad experience and even potential safety hazards, and when the vehicle descends, the vehicle is accelerated due to insufficient recovery strength, the driver is required to frequently and deeply step on mechanical braking, the energy recovery opportunity is wasted, and when the vehicle ascends, the vehicle is possibly stalled too early due to the excessively strong recovery torque, and the driving smoothness and safety are influenced. 3. The fault coping strategy is single, when signals of key sensors (such as a weight sensor and a gradient sensor) fail, the system can only report errors or exit the energy recovery function, and the energy efficiency of the vehicle and the robustness of the system are reduced. 4. Lacking intelligent adaptability, the existing system cannot be dynamically adjusted according to the real-time intention of a driver, and in the downhill recovery process, if the driver has an accelerating intention, the system cannot intelligently reduce the recovery strength to meet the driving requirement. 5. When the steering is performed under a certain condition of an attachment coefficient, the steering force is insufficient, and unsafe phenomena such as tail flick and the like are possibly caused. In summary, a self-learning method for recovering energy of a new energy automobile is proposed to solve the problems in the prior art. Disclosure of Invention The invention aims to provide a self-learning method for recovering energy of a new energy automobile, which can dynamically adjust energy recovery torque according to real-time automobile weight, road gradient and driver intention, thereby realizing the unification of recovery efficiency, driving safety and comfort. The new energy automobile energy recovery self-learning method is applied to a whole automobile controller and comprises the following specific steps: Step S1, acquiring a vehicle weight signal, a vehicle speed signal and a brake pedal opening signal of a vehicle in real time based on a torque determining step, selecting a basic recovery torque MAP corresponding to the current vehicle weight tonnage according to the vehicle weight signal, and performing table lookup in the selected basic recovery torque MAP based on the vehicle speed signal and the brake pedal opening signal to determine a basic recovery torque value; s2, slope correction is carried out, and a current road slope signal of the vehicle is obtained in real time; And step S3, performing self-learning adjustment, namely monitoring the opening degree of the accelerator pedal and the duration time of the accelerator pedal in real time under the condition that the vehicle is in a downhill slope and the energy recovery function is activated, and if the accelerator pedal opening degree is detected to continuously exceed the set opening degree threshold value for a preset time, judging that the driver has acceleration intention, triggering a self-learning mechanism, and performing weight-reducing adjustment on the currently used basic recovery torque MAP. And when judging that the vehicle weight signal or the gradient signal fails or exceeds a reasonable range, ignoring the steps S1 and S2, and adopting a preset safety recovery torque value with lower intensity level. Further defined, in the step S3, all torque values in the basic recovery torque MAP are attenuated according to a fixed ratio, so as to generate and apply a new self-learning recovery torque MAP. Further defined, the safe recovery torque value is a fixed value, and the strength of the safe recovery torque value is lower than the recovery torque under the normal working condit