An electric vehicle energy recovery method and related apparatus

Abstract

This invention discloses an energy recovery method and related equipment for electric vehicles, including steps such as detecting the motion information of a target vehicle relative to the vehicle itself, determining the recovery intensity based on the motion information, and executing energy recovery for the vehicle itself based on the recovery intensity. This invention enables the energy recovery module to automatically trigger energy recovery for the vehicle upon the appearance of a target vehicle, thus achieving automatic triggering of the energy recovery process. Furthermore, it dynamically adjusts the intensity of energy recovery based on the motion information of the target vehicle relative to the vehicle itself, achieving automatic adaptation in the energy recovery process. This is beneficial for improving energy utilization and extending driving range. This invention has wide applications in the field of automotive technology.

Description

Technical Field

[0001] This invention relates to the field of automotive technology, and in particular to a method and related equipment for energy recovery in electric vehicles. Background Technology

[0002] To improve the efficiency of electrical energy utilization and thus enhance driving range, energy recovery technology is typically applied to electric vehicles, including pure electric vehicles and hybrid electric vehicles. Energy recovery in electric vehicles is often referred to as kinetic energy recovery. This involves controlling the electric motor to operate in generator mode when the vehicle decelerates, converting the vehicle's kinetic energy into electrical energy to recharge the battery, thus preventing electrical energy from being wasted as heat.

[0003] Currently, there are two main types of energy recovery strategies used in electric vehicles: one is coasting energy recovery when the accelerator and brake are not pressed in D / R gear; the other is braking energy recovery when the brake is pressed in D / R gear. Generally, coasting energy recovery is controlled by the VCU (Vehicle Control Unit) to control the magnitude of the recovery torque, which is then sent to the MCU (Microcontroller Unit) for electric braking recovery by the motor. Braking energy recovery, on the other hand, is controlled by the IBS (Infrared Braking System) to distribute electric braking and mechanical braking based on the braking deceleration requested by the brake pedal depth.

[0004] The current energy recovery strategy for electric vehicles involves looking up a coasting recovery torque based on the current vehicle speed when the accelerator is released and there is no braking. The drive motor of the car is then controlled to work in generator mode to recover coasting energy at the intensity corresponding to the coasting recovery torque. This energy recovery strategy does not monitor the external driving environment and makes it difficult to balance braking accuracy and comfort. Summary of the Invention

[0005] In view of at least one of the above-mentioned technical problems, the purpose of this invention is to provide an energy recovery method and related equipment for electric vehicles.

[0006] On one hand, embodiments of the present invention include a method for energy recovery in electric vehicles, the method comprising the following steps: Identify the target vehicle ahead; Detect the motion information of the target vehicle relative to the vehicle itself; Based on the motion information, determine the recovery intensity; Energy recovery of this vehicle shall be performed according to the stated recovery intensity.

[0007] Furthermore, detecting the motion information of the target vehicle relative to the vehicle itself includes: Real-time detection is performed on the target vehicle ahead to obtain real-time relative longitudinal distance information, real-time relative lateral distance information, and real-time relative speed information; The real-time relative longitudinal distance information, the real-time relative lateral distance information, and the real-time relative velocity information are used as the motion information.

[0008] Further, determining the recovery intensity based on the motion information includes: Detect the vehicle model information of the vehicle preceding the target; Based on the vehicle model information, determine the longitudinal distance threshold, the lateral distance threshold, and the speed threshold; Calculate the difference between the longitudinal distance threshold and the real-time relative longitudinal distance information to obtain a first difference; Calculate the difference between the lateral distance threshold and the real-time relative lateral distance information to obtain a second difference; Calculate the difference between the real-time relative velocity information and the velocity threshold to obtain a third difference; The recovery intensity is determined based on the first difference, the second difference, and the third difference.

[0009] Further, determining the recovery intensity based on the motion information includes: Based on the motion information, determine the probability of a successful overtaking event. The recovery intensity is determined in a positive correlation with the probability of the successful overtaking event.

[0010] Furthermore, determining the probability of a successful overtaking event based on the motion information includes: The probability of an overtaking event is determined based on the real-time relative longitudinal distance information and the real-time relative speed information. Based on the real-time relative lateral distance information, the probability of an overtaking interference event occurring is determined; The probability of a successful overtaking event is determined based on the probability of the overtaking behavior event and the probability of the overtaking interference event.

[0011] Further, determining the probability of an overtaking event based on the real-time relative longitudinal distance information and the real-time relative speed information includes: Iterate through multiple target moments; For any of the target times, the estimated meeting time corresponding to the target time is determined based on the real-time relative longitudinal distance information and the real-time relative speed information corresponding to the target time. For any target time, the estimated meeting times corresponding to the target time itself and each target time before the target time are statistically analyzed to obtain the fluctuation level corresponding to the target time. Based on the fluctuation level, the probability of the overtaking behavior event corresponding to the target time is positively correlated and determined.

[0012] Further, determining the probability of an overtaking interference event occurring based on the real-time relative lateral distance information includes: For any target time, a lateral distance time series is formed by acquiring multiple real-time relative lateral distance information corresponding to the target time itself and each target time before the target time. The predicted value of the expected meeting time corresponding to the target time is predicted based on the lateral distance time series to obtain the predicted relative lateral distance information. Based on the predicted relative lateral distance information, the probability of the overtaking interference event corresponding to the target time is determined in a negative correlation.

[0013] Further, the step of performing energy recovery of the vehicle based on the recovery intensity includes: Determine the initial target electric braking torque based on the vehicle's real-time driving speed; The initial target electric braking torque is checked based on the recovery intensity to obtain the checked target electric braking torque; Based on the target electric braking torque, the vehicle's energy recovery module is controlled to perform braking.

[0014] On the other hand, embodiments of the present invention also include a computer device, including a memory and a processor, the memory for storing at least one program, and the processor for loading at least one program to execute the electric vehicle energy recovery method of the embodiments.

[0015] On the other hand, embodiments of the present invention also include a computer-readable storage medium storing a processor-executable program, which, when executed by a processor, is used to perform the electric vehicle energy recovery method in the embodiments.

[0016] The beneficial effects of the present invention are as follows: the electric vehicle energy recovery method in the embodiments can automatically trigger the energy recovery module to work and recover energy from the vehicle when the target vehicle appears, thereby realizing the automatic triggering of the energy recovery process. Moreover, it can dynamically adjust the intensity of energy recovery from the vehicle according to the motion information of the target vehicle relative to the vehicle, thereby realizing the automatic adaptation of the energy recovery process, which is conducive to improving the utilization rate of electric energy and improving the driving range performance. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of a vehicle system to which the electric vehicle energy recovery method can be applied in the embodiment. Figure 2 This is a schematic diagram of the vehicle and the target vehicle in front, as shown in the embodiment. Figure 3 This is a schematic diagram of the steps of the electric vehicle energy recovery method in the embodiment; Figure 4 This is a schematic diagram illustrating the principle of obtaining predicted relative lateral distance information in the embodiment. Detailed Implementation

[0018] In this embodiment, the electric vehicle energy recovery method can be applied to... Figure 1 The car system shown.

[0019] Reference Figure 1 The vehicle system includes a detection module, a control module, and an energy recovery module. The detection module includes sensors such as lidar, millimeter-wave radar, or visible light cameras. This module can detect a specific preceding vehicle (target vehicle) to obtain its motion information relative to the current vehicle. For example, referring to… Figure 2 When installed Figure 1 The illustrated automotive system shows the vehicle itself traveling on the road, while the detection module detects the target vehicle in front, obtaining real-time relative lateral distance information. d horizontal Real-time relative longitudinal distance information d vertal and real-time relative velocity information volecity Motion information, including real-time relative lateral distance information. d horizontal This represents the lateral component of the distance between the target vehicle and the current vehicle (perpendicular to the road), providing real-time relative longitudinal distance information. d vertal This represents the longitudinal component of the distance between the target vehicle and the vehicle ahead (parallel to the direction of road extension), and is the real-time relative speed information. volecity It represents the longitudinal component of the speed of the target vehicle relative to the vehicle itself (with the direction from the target vehicle to the vehicle itself being positive).

[0020] In this embodiment, the detection module can be set t 1. t 2. t 3…… t i ...and other multiple detection moments, at the detection moment t 1. Detect the target vehicle in front and obtain the corresponding real-time relative lateral distance information. d horizontal_1 Real-time relative longitudinal distance information d vertal_1 and real-time relative velocity information volecity _1 At the time of detection t 2. Detect the target vehicle in front and obtain the corresponding real-time relative lateral distance information. d horizontal_2 Real-time relative longitudinal distance information d vertal_2 and real-time relative velocity information volecity _2 ...at the time of detection ti The target vehicle in front is detected to obtain the corresponding real-time relative lateral distance information. d horizontal_i Real-time relative longitudinal distance information d vertal_i and real-time relative velocity information volecity _i ...Whenever the detection module detects motion information at a detection moment, it immediately sends the motion information to the control module.

[0021] In this embodiment, a component with data processing and control functions can be used as a control module.

[0022] In this embodiment, refer to Figure 1 The energy recovery module can specifically be a motor mechanically connected to the vehicle's wheels. This motor can operate in either electric motor or generator mode. In electric motor mode, it receives electrical energy from the power battery and converts it into mechanical energy to drive the wheels, thus giving the vehicle kinetic energy. In generator mode, it applies braking torque to the wheels, converting the wheels' kinetic energy into electrical energy and charging it back into the power battery or a dedicated high-capacity capacitor or other energy storage device, thereby reducing the vehicle's overall kinetic energy. The greater the braking torque applied to the wheels by the energy recovery module, the greater the power of the module in converting the vehicle's kinetic energy into electrical energy, thus achieving a higher recovery intensity.

[0023] In this embodiment, when the motor is operating in motor mode, the motor can be discharged in parallel by a large-capacity capacitor and a power battery. This allows the motor to simultaneously obtain electrical energy provided by the power battery discharge and the previously recovered electrical energy released by the large-capacity capacitor. This enables the motor to obtain electrical energy exceeding the rated power of the power battery in a short period of time, achieving more abundant acceleration performance.

[0024] In this embodiment, the control module can control the detection module and the energy recovery module to execute the electric vehicle energy recovery method. (Refer to...) Figure 3 The energy recovery method for electric vehicles includes the following steps: S1. Identify the target vehicle ahead; S2. Detect the motion information of the target vehicle relative to this vehicle; S3. Determine the recovery intensity based on the motion information; S4. Perform energy recovery for this vehicle based on the recovery intensity.

[0025] In step S1, the control module can control the detection module to detect the area in front of the vehicle and identify the vehicle within the detection range of the detection module as the target vehicle. If multiple vehicles are detected, the vehicle closest to the target vehicle, such as the vehicle with real-time relative longitudinal distance information, can be selected.d vertal The smallest vehicle in front is identified as the target vehicle.

[0026] In step S2, the detection module will... t 1. t 2. t 3…… t i ...and other real-time relative lateral distance information detected at each detection time. d horizontal_1 , d horizontal_2 ... d horizontal_i ...real-time relative longitudinal distance information d vertal_1 , d vertal_2 ... d vertal_i ...and real-time relative velocity information volecity _1 , volecity _2 ... volecity _i ...and other motion information are sent to the control module.

[0027] In this embodiment, at the detection time t i The detected motion information is the real-time relative lateral distance information. d horizontal_i Real-time relative longitudinal distance information d vertal_i and real-time relative velocity information volecity _i Let's take an example to illustrate.

[0028] In this embodiment, when performing step S3, which is to determine the recovery intensity based on the motion information, the following steps can be performed: S301A. Detects the vehicle model information of the vehicle ahead of the target; S302A. Based on vehicle model information, determine the longitudinal distance threshold, lateral distance threshold, and speed threshold; S303A. Calculate the difference between the longitudinal distance threshold and the real-time relative longitudinal distance information to obtain the first difference; S304A. Calculate the difference between the lateral distance threshold and the real-time relative lateral distance information to obtain the second difference; S305A. Calculate the difference between the real-time relative speed information and the speed threshold to obtain the third difference; S306A. Determine the recovery intensity based on the first difference, the second difference, and the third difference.

[0029] Steps S301A-S306A are the first execution method of step S3.

[0030] In step S301A, the vehicle type information of the target vehicle can be detected by the detection module. Specifically, the target vehicle can be identified as a small car (e.g., a car with fewer than 9 seats), a medium-sized car (e.g., a car with more than 9 seats or fewer than 20 seats), or a large car (e.g., a car with more than 20 seats or a height exceeding 3 meters). The control module locally stores a mapping table between vehicle type information and longitudinal distance thresholds, lateral distance thresholds, and speed thresholds. Specifically, the form of this mapping table is shown in Table 1.

[0031] Table 1

[0032] Referring to Table 1, the larger the vehicle model of the target preceding vehicle indicated by the vehicle model information, the larger the longitudinal distance threshold should be set. d vertical_theshold Larger lateral distance threshold d horizontal_theshold and smaller speed threshold volecity threshold .

[0033] In step S302A, the longitudinal distance threshold can be determined based on Table 1 and the vehicle model information. d vertical_theshold Horizontal distance threshold d horizontal_theshold and speed threshold volecity threshold .

[0034] In steps S303A-S305A, the formula can be used. Δ1= d vertical_theshold - d vertal_i if d vertal_i ≤ d vertical_theshold , or Δ1=0 Δ2= d horizontal_theshold - d horizontal_i if d horizontal_i ≤ d horizontal_theshold , or Δ2=0 Δ3= volecity _i - volecity threshold if volecity _i≥ volecity threshold , or Δ3=0 (1) The first difference Δ1, the second difference Δ2, and the third difference Δ3 are calculated. The above formula guarantees that all three differences Δ1, Δ2, and Δ3 are non-negative.

[0035] The first difference Δ1 calculated by the above formula represents the degree of proximity of the vehicle to the target vehicle in the longitudinal direction. The smaller the first difference Δ1, the closer the vehicle is. The second difference Δ2 represents the degree of proximity of the vehicle to the target vehicle in the lateral direction. The smaller the second difference Δ2, the closer the vehicle is. The third difference Δ3 represents the speed at which the target vehicle moves toward the vehicle in the longitudinal direction. The larger the third difference Δ3, the greater the speed at which the vehicle approaches the target vehicle.

[0036] Based on the above principle, in step S306A, the recovery intensity can be determined such that the recovery intensity is negatively correlated with the first difference Δ1 and the second difference Δ2, and positively correlated with the third difference Δ3. That is, the closer the vehicle is to the target vehicle in front and the greater the speed at which it approaches, the more the control module will control the energy recovery module to recover energy with a greater recovery intensity.

[0037] Specifically, when executing step S306A, the control module can, according to the formula... Z=Zscore(Δ3)-Zscore(Δ1)-Zscore(Δ2) (2) The calculation is performed, where Zscore() represents Z-score normalization. The resulting Z represents the Z-score normalized value of the desired recovery intensity. Therefore, the inverse operation of Z-score normalization yields the recovery intensity. intensity recyle ,Right now intensity recyle =Zscore -1 (Z) (3) Zscore -1 () indicates the inverse operation of Z-score normalization.

[0038] The results obtained by formulas (1)-(3) above intensity recyle It is negatively correlated with the first difference Δ1 and the second difference Δ2, and positively correlated with the third difference Δ3, thereby realizing "real-time relative longitudinal distance information" between the vehicle and the target vehicle. d vertal_i Less than the vertical distance threshold dvertical_theshold Real-time relative lateral distance information d horizontal_i Less than the horizontal distance threshold d horizontal_theshold "and real-time relative speed information" volecity _i greater than the speed threshold volecity threshold "The recycling intensity is triggered when at least one of these three events occurs." intensity recyle The closer the vehicle is to the target vehicle in front, and the higher the speed at which they approach, the greater the recovery intensity calculated by the control module. intensity recyle This allows the energy recovery module to recover energy at a higher recovery intensity.

[0039] In this embodiment, the recovery intensity is obtained after step S3 is performed. intensity recyle Next, the control module executes step S4, which is to perform the energy recovery step of the vehicle based on the recovery intensity. Specifically, the following steps can be executed: S401. Determine the initial target electric braking torque based on the vehicle's real-time driving speed; S402. Verify the initial target electric braking torque based on the recovery intensity to obtain the verified target electric braking torque; S403. Control the vehicle's energy recovery module to perform braking based on the target electric braking torque.

[0040] Due to the recovery intensity in this embodiment intensity recyle It is based on the detection time t i The recovery intensity is calculated from the detected motion information. intensity recyle At any moment t i To control the energy recovery intensity achieved by the energy recovery module, in step S401, the control module can call the vehicle's speed sensor to detect the vehicle's speed at a given time. t i Real-time driving speed v .

[0041] In step S401, the control module can determine the initial target electric braking torque according to the correspondence shown in Table 2. T initial .

[0042] Table 2

[0043] The initial target electric braking torque is determined by executing step S401. T initial Then, when performing step S402, the formula can be used. T verified = T initial × intensity recyle (4) Calculations are performed to obtain the target electric braking torque for verification. T verified .

[0044] In step S403, the control module controls the energy recovery module at a specific time. t i Apply electric braking torque equal to the target torque to the wheels. T verified The braking torque causes the wheel, which is subjected to the braking torque, to transfer kinetic energy to the energy recovery module, which then converts the kinetic energy into electrical energy.

[0045] In this embodiment, by executing steps S1-S4, and selecting to execute steps S301A-S306A during step S3, it is possible to achieve "real-time relative longitudinal distance information" between the vehicle and the target vehicle when there is a target vehicle in front of the vehicle. d vertal_i Less than the vertical distance threshold d vertical_theshold Real-time relative lateral distance information d horizontal_i Less than the horizontal distance threshold d horizontal_theshold "and real-time relative speed information" volecity _i greater than the speed threshold volecity threshold "When at least one of these three events occurs, the energy recovery module is triggered to recover energy for the vehicle. The closer the vehicle is to the target vehicle and the higher the speed at which they approach, the greater the energy recovery intensity calculated by the control module." intensity recyleThis allows the energy recovery module to recover energy at a higher intensity. Therefore, by executing steps S1-S4 based on steps S301A-S306A, the appearance of the target vehicle in front automatically triggers the energy recovery module to recover energy from the vehicle, thus achieving automatic triggering of the energy recovery process. Furthermore, it dynamically adjusts the intensity of energy recovery based on the motion information of the target vehicle relative to the vehicle, achieving automatic adaptation of the energy recovery process. This contrasts with methods requiring the driver to trigger the energy recovery process and using a fixed recovery intensity throughout the entire process. Compared with existing technologies, by executing steps S1-S4, the intensity of energy recovery for the vehicle can be adapted to the condition of the target vehicle. For example, the closer the vehicle is to the target vehicle, the greater the intensity of energy recovery. This can not only recover excess kinetic energy corresponding to the condition of the target vehicle (such as kinetic energy that would make the vehicle too close to the target vehicle and face danger) into electrical energy in a timely manner, which is beneficial to improving the utilization rate of electrical energy and improving range performance, but also allow the vehicle to retain the necessary kinetic energy corresponding to the condition of the target vehicle (such as kinetic energy that allows the vehicle to keep moving forward), thereby ensuring the vehicle's operation.

[0046] In this embodiment, when performing step S3, which is to determine the recovery intensity based on the motion information, the following steps can be performed: S301B. Determine the probability of a successful overtaking event based on motion information; S302B. The recovery intensity is determined negatively correlated with the probability of a successful overtaking event.

[0047] Steps S301B-S302B are the second execution method of step S3.

[0048] In this embodiment, the i-th detection time is used as the basis for the following steps. t i As an example of the target time, the current time is the target time, i.e., the detection time. t i Therefore, it has been detected so far. d horizontal_1 , d horizontal_2 ... d horizontal_i i real-time relative lateral distance information d vertal_1 , d vertal_2 ... d vertal_i i real-time relative longitudinal distance information, and volecity _1 , volecity _2 ... volecity _iThe i real-time relative velocity information, i real-time relative lateral distance information, real-time relative longitudinal distance information, and real-time relative velocity information constitute the motion information in step S301B.

[0049] In this embodiment, the probability of a successful overtaking event to be determined in step S301B is... P A This refers to the target time, i.e., the detection time. t i The observed probability of the event "Event A: This vehicle successfully overtakes the target vehicle" is determined when event A is considered to be the simultaneous occurrence of two events: "Event B: This vehicle initiates overtaking of the target vehicle" and "Event C: This vehicle is not interfered with by the target vehicle during the overtaking process." Since this embodiment obtains the probability of the successful overtaking event... P A The purpose is to estimate the approximate probability of event A occurring, with a relatively large tolerance for the estimation range. Therefore, events B and C can be considered independent events, and the influence of other events on event A can be ignored. Thus, the probability of the successful overtaking event to be determined in step S301B is... P A , and at the target time, i.e., the detection time t i The probability of the observed event B occurring P B And at the target time, i.e., the detection time t i The probability of the observed event C occurring P C satisfy: P A = P B × P C (4) In formula (4), P B The probability of an overtaking incident occurring. P C The probability of the overtaking interference event can be calculated by the probability of the opposite event of event C, "Event D: The vehicle was interfered with by the target vehicle while overtaking it." P D Determined, satisfies: P C =1- P D (5) Based on the principles of the above formulas (4)-(5), when executing step S301B, the probability of the overtaking event can be determined according to the motion information. P B Probability of overtaking interference events P D Then, by combining formulas (4) and (5), the probability of a successful overtaking event can be calculated. P A ,Right now P A = P B ×(1- P D ) (6) In this embodiment, the target time is the detection time. t i The probability of the observed event B occurring is the probability of the overtaking action event occurring. P B It can be calculated through the following steps: ① Regarding the detection time t i At this target moment, its corresponding real-time relative longitudinal distance information is calculated using the following formula. d vertal_i Real-time relative velocity information volecity _i Zhi Shang: period i = d vertal_i / volecity _i (7) Calculated according to formula (7) period i This indicates that if this vehicle continues to maintain the inspection schedule... t i Real-time relative velocity information at this target moment volecity _i The time it takes for the vehicle to encounter the target vehicle in the longitudinal direction during travel, i.e., the detection time. t i The estimated meeting time corresponding to this target time; ②Since the target time is arbitrarily selected, it is based on the detection time. t i The estimated meeting time corresponding to this target time period i The same principle applies, and detection times can be used separately. t i Previoust 1. t 2…… t i-1 At each detection time such as t the predicted meeting time corresponding to 1 period 1. Detection time t the predicted meeting time corresponding to 2 period 2……Detection time t i-1 the corresponding predicted meeting time period i-1 , thus obtaining period 1. period 2……<00SO548> i-1 and other multiple predicted meeting times; ③ For period 1. period 2…… period i-1 , period i and other multiple predicted meeting times, calculate their Normalized Standard Deviation (NSD) as the probability of the overtaking behavior event occurring corresponding to this target time t i of this target time. P <00001SO8>.

[0050] When performing the above steps ① - ③, the value range of the calculated Normalized Standard Deviation is [0, 1]. Therefore, the Normalized Standard Deviation itself can be used as the probability of the overtaking behavior event occurring <00oso558> B .

[0051] In this embodiment, the principle of performing the above steps ① - ③ is as follows: period 1. period 2…… period i-1 , period i etc. respectively represent the time when the vehicle itself will meet the target leading vehicle longitudinally if it maintains a constant speed at <oososo565>1. t 2. t 3…… t i and other each detection time. Their Normalized Standard Deviation represents the degree of dispersion, that is, the degree of fluctuation of multiple predicted meeting times. The smaller the degree of fluctuation, the more consistent the multiple predicted meeting times are, corresponding to the smoother the relative speed change between the target leading vehicle and the vehicle itself longitudinally. Therefore, it can be judged that the overtaking intention of the vehicle itself is weaker. Therefore, for the detection timet i The probability of an overtaking event occurring at this target time. P B The smaller the value, the better; similarly, the greater the fluctuation, the more inconsistent the estimated passing times, corresponding to a more drastic change in the relative speed between the target vehicle and the vehicle ahead. Therefore, it can be judged that the vehicle ahead will want to get rid of the interference of such a drastic change in relative speed and will have a stronger overtaking intention. Therefore, the detection time can be adjusted accordingly. t i The probability of an overtaking event occurring at this target time. P B The larger the value, the better; therefore, by performing steps ①-③, the detection time can be calculated based on the motion information of the vehicle in front of the target. t i The probability of an overtaking event occurring at this target time. P B The specific size.

[0052] In this embodiment, the target time is the detection time. t i The probability of the observed event D occurring is the probability of the overtaking interference event occurring. P D It can be calculated through the following steps: ④ Regarding the detection time t i This target moment, obtaining itself and all previous target moments, is... t 1. t 2. t 3…… t i Real-time relative lateral distance information detected separately d horizontal_1 , d horizontal_2 ... d horizontal_i This forms a horizontal distance time series; ⑤Reference Figure 4 For the horizontal distance time series obtained in step ④ d horizontal_1 , d horizontal_2 ... d horizontal_i Run a time series prediction algorithm to predict the time at the detection point. t i The estimated meeting time is calculated using formula (7). period i The following moments t ' i =t i + period i The predicted value to obtain the predicted relative lateral distance information d predicted ; ⑥According to the predicted relative lateral distance information obtained in step ⑤ d predicted , determine the detection time in a negatively correlated manner t i The corresponding probability of an overtaking interference event occurring P D ; Specifically, a maximum distance value can be set d max (the road width in the current driving environment can be taken, such as 6m), determine the predicted relative lateral distance information d predicted in the interval [0, d max , set the predicted relative lateral distance information d predicted equal to d max Then map to obtain the probability of an overtaking interference event occurring P D is 0, and when the predicted relative lateral distance information d predicted is equal to 0, map to obtain the probability of an overtaking interference event occurring P D is 1, and according to the above boundary values, linearly map this position to obtain the probability of an overtaking interference event occurring P D .

[0053] In this embodiment, the principle of performing the above steps ④ - ⑥ is as follows: Refer to Figure 4 , d horizontal_1 , d horizontal_2 ……[[ID=​​​​​​​​​​​​​​​​​​​​​​​​period i The subsequent moment, that is, the moment when the vehicle is predicted to meet the target vehicle longitudinally, is used to predict the relative lateral distance information. d predicted This predicts the lateral distance between the current vehicle and the target vehicle when they meet longitudinally. Since the prediction indicates that the current vehicle and the target vehicle have already met longitudinally, it predicts the relative lateral distance information. d predicted The smaller the value, the greater the likelihood of a collision between the vehicle and the target vehicle, and the greater the interference from the target vehicle. This allows us to determine the probability of a larger overtaking interference event occurring. P D Similarly, predicting relative lateral distance information. d predicted The larger the value, the lower the probability of a collision between the vehicle and the target vehicle in front, and the less interference the target vehicle in front will cause to the vehicle in front. This allows us to determine a smaller probability of an overtaking interference event occurring. P D Therefore, by performing steps ④-⑥, the detection time can be calculated based on the motion information of the target vehicle ahead. t i The probability of an overtaking interference event occurring at this target time. P D The specific size.

[0054] In this embodiment, when executing step S301B, the probability of the overtaking event is obtained by executing steps ①-⑥. P B Probability of overtaking interference events P D Then, the probability of a successful overtaking event is calculated according to formula (6). P A In step S302B, the formula can be used... intensity recyle = P A (8) Determine the recycling intensity intensity recyle That is, the probability of a successful overtaking event. P A itself as recycling strength intensity recyle .

[0055] In this embodiment, the principle of executing steps S301B-S302B is as follows: the probability of a successful overtaking event obtained by executing step S301B. P A Indicates from the detection timet i The observed motion information supports the estimated probability of event A: "This vehicle successfully overtakes the target vehicle in front." However, the probability of a successful overtaking event is... P A It is based on the motion information of the target vehicle ahead, without considering the vehicle's own dynamics. For example, if a high probability of a successful overtaking event is calculated... P A (For example, 80%), but if the vehicle's power reserve is insufficient, it may be difficult for the vehicle to overtake the target vehicle in front, thus increasing the probability of a successful overtaking event. P A That is, the probability of a successful overtaking event. P A In reality, this is the target that the vehicle's powertrain needs to achieve; therefore, by executing steps S301B-S302B, the probability of a successful overtaking event is determined based on the motion information of the target vehicle ahead. P A For larger scales, set a higher recycling intensity. intensity recyle And the energy recovery module can be adjusted to recover energy intensity. intensity recyle The electrical energy recovered through energy recovery is stored in a large-capacity capacitor, a component independent of the power battery. When the vehicle actually overtakes a target vehicle, this stored energy can be released from the large-capacity capacitor as power assistance, supplementing the discharge of the power battery itself. In other words, before the actual overtaking maneuver, the energy recovery module utilizes the energy recovery intensity... intensity recyle The electrical energy recovered through energy recovery is released during actual overtaking, allowing the vehicle to supply power exceeding the rated discharge power of the battery for a short period. This provides the vehicle with more power to support a higher probability of successful overtaking. P A The realization of the event "Event A: This vehicle successfully overtakes the target vehicle in front" helps to ensure that this vehicle can overtake the target vehicle in front, and a high success rate overtaking process helps to ensure smooth and safe road traffic.

[0056] A computer program for executing the electric vehicle energy recovery method in this embodiment can be written into a computer device or storage medium. When the computer program is read out and run, the electric vehicle energy recovery method in this embodiment is executed, thereby achieving the same technical effect as the electric vehicle energy recovery method in the embodiment.

[0057] It should be noted that, unless otherwise specified, when a feature is referred to as "fixed" or "connected" to another feature, it can be directly fixed or connected to the other feature, or indirectly fixed or connected to the other feature. Furthermore, the descriptions of "upper," "lower," "left," and "right" used in this disclosure are only relative to the relative positional relationships of the components of this disclosure in the accompanying drawings. The singular forms "a," "an," and "the" used in this disclosure are also intended to include the plural forms, unless the context clearly indicates otherwise. Moreover, unless otherwise defined, all technical and scientific terms used in this embodiment have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this embodiment specification is only for describing particular embodiments and is not intended to limit the invention. The term "and / or" as used in this embodiment includes any combination of one or more of the associated listed items.

[0058] It should be understood that although the terms first, second, third, etc., may be used to describe various elements in this disclosure, these elements should not be limited to these terms. These terms are only used to distinguish elements of the same type from each other. For example, a first element may also be referred to as a second element without departing from the scope of this disclosure, and similarly, a second element may also be referred to as a first element. The use of any and all instances or exemplary language (“e.g.,” “such as,” etc.) provided in this embodiment is intended only to better illustrate embodiments of the invention and, unless otherwise required, does not impose a limitation on the scope of the invention.

[0059] It should be recognized that embodiments of the present invention can be implemented or carried out by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable storage medium. The method can be implemented using standard programming techniques—including a non-transitory computer-readable storage medium configured with a computer program, wherein such a storage medium causes the computer to operate in a specific and predefined manner—according to the methods and drawings described in the specific embodiments. Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with the computer system. However, if desired, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. Furthermore, for this purpose, the program can run on a programmed application-specific integrated circuit (ASIC).

[0060] Furthermore, the procedures described in this embodiment can be performed in any suitable order unless otherwise indicated by this embodiment or clearly contradicted by the context. The procedures (or variations and / or combinations thereof) described in this embodiment can be executed under the control of one or more computer systems configured with executable instructions, and can be implemented by hardware or a combination thereof as code (e.g., executable instructions, one or more computer programs, or one or more applications) that commonly executes on one or more processors. A computer program includes multiple instructions executable by one or more processors.

[0061] Furthermore, the method can be implemented in any suitable type of computing platform, including but not limited to personal computers, minicomputers, mainframes, workstations, networked or distributed computing environments, standalone or integrated computer platforms, or in communication with charged particle tools or other imaging devices, etc. Aspects of the invention can be implemented as machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optical read and / or write storage medium, RAM, ROM, etc., such that it is readable by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the processes described herein. Furthermore, the machine-readable code, or portions thereof, can be transmitted via wired or wireless networks. The invention of this embodiment includes these and other different types of non-transitory computer-readable storage media when such media comprises instructions or programs that implement the steps above in conjunction with a microprocessor or other data processor. When programmed according to the methods and techniques of the invention, the invention also includes the computer itself.

[0062] A computer program can be applied to input data to perform the functions of this embodiment, thereby transforming the input data to generate output data stored in non-volatile memory. The output information can also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including specific visual depictions of physical and tangible objects generated on the display.

[0063] The above are merely preferred embodiments of the present invention. The present invention is not limited to the above-described embodiments. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention, as long as they achieve the technical effects of the present invention by the same means, should be included within the scope of protection of the present invention. Within the scope of protection of the present invention, the technical solutions and / or implementation methods can have various modifications and variations.

Claims

1. A method for energy recovery in electric vehicles, characterized in that, The electric vehicle energy recovery method includes: Identify the target vehicle ahead; Detect the motion information of the target vehicle relative to the vehicle itself; Based on the motion information, determine the recovery intensity; Energy recovery of this vehicle shall be performed according to the stated recovery intensity.

2. The method for energy recovery in electric vehicles according to claim 1, characterized in that, The detection of the motion information of the target vehicle relative to the vehicle itself includes: Real-time detection is performed on the target vehicle ahead to obtain real-time relative longitudinal distance information, real-time relative lateral distance information, and real-time relative speed information; The real-time relative longitudinal distance information, the real-time relative lateral distance information, and the real-time relative velocity information are used as the motion information.

3. The method for energy recovery in electric vehicles according to claim 2, characterized in that, Determining the recovery intensity based on the motion information includes: Detect the vehicle model information of the vehicle preceding the target; Based on the vehicle model information, determine the longitudinal distance threshold, the lateral distance threshold, and the speed threshold; Calculate the difference between the longitudinal distance threshold and the real-time relative longitudinal distance information to obtain a first difference; Calculate the difference between the lateral distance threshold and the real-time relative lateral distance information to obtain a second difference; Calculate the difference between the real-time relative velocity information and the velocity threshold to obtain a third difference; The recovery intensity is determined based on the first difference, the second difference, and the third difference.

4. The method for energy recovery in electric vehicles according to claim 2, characterized in that, Determining the recovery intensity based on the motion information includes: Based on the motion information, determine the probability of a successful overtaking event. The recovery intensity is determined in a positive correlation with the probability of the successful overtaking event.

5. The electric vehicle energy recovery method according to claim 4, characterized in that, Determining the probability of a successful overtaking event based on the motion information includes: The probability of an overtaking event is determined based on the real-time relative longitudinal distance information and the real-time relative speed information. Based on the real-time relative lateral distance information, the probability of an overtaking interference event occurring is determined; The probability of a successful overtaking event is determined based on the probability of the overtaking behavior event and the probability of the overtaking interference event.

6. The method for energy recovery in electric vehicles according to claim 5, characterized in that, The step of determining the probability of an overtaking event based on the real-time relative longitudinal distance information and the real-time relative speed information includes: Iterate through multiple target moments; For any of the target times, the estimated meeting time corresponding to the target time is determined based on the real-time relative longitudinal distance information and the real-time relative speed information corresponding to the target time. For any target time, the estimated meeting times corresponding to the target time itself and each target time before the target time are statistically analyzed to obtain the fluctuation level corresponding to the target time. Based on the fluctuation level, the probability of the overtaking behavior event corresponding to the target time is positively correlated and determined.

7. The method for energy recovery in electric vehicles according to claim 6, characterized in that, The step of determining the probability of an overtaking interference event occurring based on the real-time relative lateral distance information includes: For any target time, a lateral distance time series is formed by acquiring multiple real-time relative lateral distance information corresponding to the target time itself and each target time before the target time. The predicted value of the expected meeting time corresponding to the target time is predicted based on the lateral distance time series to obtain the predicted relative lateral distance information. Based on the predicted relative lateral distance information, the probability of the overtaking interference event corresponding to the target time is determined in a negative correlation.

8. The method for energy recovery in electric vehicles according to any one of claims 1-7, characterized in that, The step of performing energy recovery of the vehicle based on the recovery intensity includes: Determine the initial target electric braking torque based on the vehicle's real-time driving speed; The initial target electric braking torque is checked based on the recovery intensity to obtain the checked target electric braking torque; Based on the target electric braking torque, the vehicle's energy recovery module is controlled to perform braking.

9. A computer device, characterized in that, It includes a memory and a processor, the memory being used to store at least one program, and the processor being used to load at least one program to execute the electric vehicle energy recovery method according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, It contains a processor-executable program, which, when executed by the processor, is used to perform the electric vehicle energy recovery method according to any one of claims 1-8.

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