Vehicle energy recovery method, storage medium and vehicle

Abstract

The invention discloses a vehicle energy recovery method, a storage medium and a vehicle, and relates to the technical field of vehicle parameter measurement, and the method comprises the steps: obtaining vehicle condition information, vehicle weight information, gradient information, driving habit information and working condition information of the vehicle; performing fuzzy processing on the gradient information, the driving habit information and the working condition information, and aggregating fuzzy parameters after fuzzy processing based on a preset fuzzy rule to obtain aggregated fuzzy parameters; defuzzifying the aggregated blur parameter to determine a desired deceleration; determining driving habit parameters based on a first preset rule and the driving habit information; determining working condition parameters based on a second preset rule and the working condition information; the compensation torque is determined according to the expected deceleration, the vehicle weight information, the gradient information, the driving habit parameters and the working condition parameters; and energy recovery is braked according to the compensation torque. According to the technical scheme, corresponding calculation rules are formulated according to different parameters. And finally, integrating a plurality of calculation results, and making a more suitable recovery control strategy for the vehicle.

Description

Technical Field

[0001] This invention relates to the field of vehicle parameter measurement technology, and in particular to vehicle energy recovery methods, storage media, and vehicles. Background Technology

[0002] The task of the energy recovery system of electric vehicles is to accurately calculate the optimal energy recovery value under the current operating conditions based on the current vehicle operating status. The quality of the current energy recovery control strategy directly affects the amount of energy consumption, and improving energy utilization is of utmost importance.

[0003] Current technologies only consider the vehicle's own parameters to determine the optimal energy recovery value or formulate a recovery control strategy for electric vehicles. This approach relies on a single control factor, lacks adaptability, and fails to account for the impact of external factors during vehicle operation on the energy recovery value. Consequently, the formulated recovery control strategies are unreasonable, resulting in unsatisfactory energy recovery performance. Summary of the Invention

[0004] The main objective of this invention is to propose a vehicle energy recovery method, which aims to solve the technical problem that the existing technology does not consider the impact of external factors on the energy recovery value during vehicle operation, resulting in unreasonable recovery control strategies and unsatisfactory energy recovery effects.

[0005] To achieve the above objectives, the present invention proposes a vehicle energy recovery method, which includes: Acquire vehicle driving data, including vehicle condition information, vehicle weight information, slope information, driving habit information, and operating condition information; After fuzzing the slope information, driving habit information, and working condition information, the fuzzy parameters after fuzzing are aggregated based on preset fuzzy rules to obtain aggregated fuzzy parameters. The aggregated fuzzy parameters are defuzzified to determine the desired deceleration; Driving habit parameters are determined based on the first preset rule and the driving habit information. The operating parameters are determined based on the second preset rule and the operating condition information. The compensation torque is determined based on the desired deceleration, vehicle weight information, slope information, driving habit parameters, and operating condition parameters. Energy is recovered through braking based on the compensated torque.

[0006] In one embodiment, the step of performing fuzzy processing on the slope information, the driving habit information, and the operating condition information, and then aggregating the fuzzy parameters based on preset fuzzy rules to obtain aggregated fuzzy parameters includes: The slope information, driving habit information, and working condition information are fuzzified by the membership function to obtain the corresponding fuzzy slope information, fuzzy driving habit information, and fuzzy working condition information respectively. Based on the preset fuzzy rules, the desired deceleration is evaluated according to the fuzzy slope information, the fuzzy driving habit information, and the fuzzy operating condition information respectively; The evaluation results of each desired deceleration are aggregated, and the aggregated fuzzy parameters are output.

[0007] In one embodiment, determining the driving habit parameters based on the first preset rule and the driving habit information includes: Extract at least one first feature value from the driving habit information, wherein the first feature value includes a first sub-feature, and the first sub-feature includes at least one of the following: average deceleration, frequency of rapid acceleration, average accelerator pedal opening, rate of change of pedal opening, frequency of brake pedal operation, or historical travel. The scoring rule corresponding to the first sub-feature is determined according to the first preset rule; Calculate the sub-score corresponding to the first sub-feature according to the scoring rule corresponding to the first sub-feature; Calculate the score of the first feature value based on the sub-score corresponding to the first sub-feature; The total score is calculated based on the weight coefficient corresponding to the first feature value and the score corresponding to the first feature value. The driving habit parameters are determined based on the total score.

[0008] In one embodiment, calculating the sub-score corresponding to the first sub-feature according to the scoring rule corresponding to the first sub-feature includes: When the first sub-feature is of one type, the sub-score of the first sub-feature is used as the score of the corresponding first feature value; When there is more than one type of the first sub-feature, the sub-scores corresponding to all the first sub-features are summed and the sum is used as the score of the corresponding first feature value.

[0009] In one embodiment, determining the operating parameters based on the second preset rule and the operating condition information includes: Extract at least one second feature value from the operating condition information, wherein the second feature value includes a second sub-feature, and the second sub-feature includes at least one of the following: average vehicle speed, steering wheel angle, acceleration variance, acceleration pedal opening signal, braking signal, or gear signal; The preset threshold corresponding to the second sub-feature is determined according to the second preset rule; The second sub-feature is compared with the corresponding preset threshold, and the comparison result is used to determine whether the second sub-feature meets the threshold condition. When the second sub-feature meets the threshold condition, the operating condition parameter is determined based on the second feature value.

[0010] In one embodiment, comparing the second sub-feature with the corresponding preset threshold and determining whether the second sub-feature meets the threshold condition based on the comparison result includes: Obtain the priority of the second sub-feature; According to the priority, the second sub-feature of each class is compared with the corresponding preset threshold in turn, and the second sub-feature is judged to meet the threshold condition based on the comparison result.

[0011] In one embodiment, determining the compensation torque based on the desired deceleration, the vehicle weight information, the gradient information, the driving habit parameters, and the operating condition parameters includes: Based on the dynamic model, the required torque is calculated according to the desired deceleration and the vehicle weight information; The compensation torque is calculated based on the required torque, the gradient information, the driving habit parameters, and the operating condition parameters.

[0012] In one embodiment, after calculating the compensation torque based on the required torque, the slope information, the driving habit parameters, and the operating condition parameters, the vehicle energy recovery method further includes: Obtain the maximum allowable torque of the battery and the maximum allowable torque of the motor; The compensation torque, the maximum allowable torque of the battery, and the maximum allowable torque of the motor are proportionally divided into their magnitudes. Based on the comparison results, the minimum value among the compensated torque, the maximum allowable torque of the battery, and the maximum allowable torque of the motor is output as the final torque, and the value of the compensated torque is updated to the final torque.

[0013] In addition, to solve the above problems, the present invention also proposes a computer-readable storage medium storing a vehicle energy recovery program, which, when executed by a processor, implements the steps of the vehicle energy recovery method as described above.

[0014] Furthermore, to address the aforementioned problems, the present invention also proposes a vehicle comprising: The controller includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program performs the above-described vehicle energy recovery method.

[0015] The vehicle energy recovery method of this invention, in addition to collecting the vehicle's own condition and weight information, also collects external conditions such as slope information, driving habit information, and operating condition information. Furthermore, it formulates corresponding calculation rules for different parameters. Finally, it integrates multiple calculation results, thereby improving calculation accuracy and reliability, formulating a more suitable recovery control strategy for the vehicle, and improving energy recovery efficiency. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0017] Figure 1 This is a schematic flowchart of a vehicle energy recovery method provided in an embodiment of the present invention.

[0018] Figure 2 This is a schematic flowchart of a vehicle energy recovery method provided in another embodiment of the present invention.

[0019] Figure 3 This is a schematic flowchart of a vehicle energy recovery method provided in another embodiment of the present invention.

[0020] Figure 4 This is a schematic flowchart of a vehicle energy recovery method provided in another embodiment of the present invention.

[0021] Figure 5 This is a schematic flowchart of a vehicle energy recovery method provided in another embodiment of the present invention.

[0022] Figure 6 This is a schematic flowchart of a vehicle energy recovery method provided in another embodiment of the present invention.

[0023] Figure 7 The diagram shown is a structural block diagram of a vehicle provided in an embodiment of this application.

[0024] Attached icon number 10. Vehicle; 101. Processor; 102. Memory; 103. Input device; 104. Output device.

[0025] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0027] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a specific posture. If the specific posture changes, the directional indicators will also change accordingly.

[0028] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the use of "and / or" or "and / or" throughout the text includes three parallel solutions. For example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0029] This invention proposes a vehicle energy recovery method. In this embodiment, the vehicle is divided into four layers, each processed sequentially: the input layer, the strategy decision layer, the torque coordination and compensation layer, and the execution layer.

[0030] Please see Figure 1 The vehicle energy recovery method includes the following steps: Step S10: Obtain vehicle driving data, including vehicle condition information, vehicle weight information, slope information, driving habit information, and operating condition information; The input layer comprises a CAN bus, a slope sensor, an IMU (Inertial Measurement Unit), a motor controller, a battery management system (BMS), a navigation system, and a driver operation signal system. The input layer collects, preprocesses, and verifies raw signals from various vehicle sensors, converting them into parameters with clear physical meaning that can be directly used by subsequent algorithm modules. This includes, but is not limited to, driving data such as vehicle condition information, vehicle weight information, slope information, driving habit information, and operating condition information.

[0031] Step S20: After fuzzing the slope information, driving habit information and working condition information, aggregate the fuzzy parameters after fuzzing based on the preset fuzzy rules to obtain aggregated fuzzy parameters. Step S30: Defuzzify the aggregated fuzzy parameters to determine the desired deceleration; The input layer transmits the collected vehicle condition information, vehicle weight information, slope information, driving habit information, and operating condition information to the strategy decision layer. Based on preset fuzzy rules, the strategy decision layer first fuzzifies the slope information, driving habit information, and operating condition information, and then performs inference and evaluation on each data information.

[0032] The pre-defined fuzzy rules are used to fuzzify specific problems and then solve them according to certain rules. For example, to reduce vehicle energy consumption, the pre-defined fuzzy rules take fuzzified slope information as input and output the corresponding expected deceleration according to the rules. Since the dimensions and types of various data information are different, the fuzzed data information is aggregated after reasoning and evaluation. This aggregation method considers the influence of all factors, that is, it unifies all opinions based on the reasoning and evaluation results to arrive at an overall solution. Finally, the aggregated data information is defuzzified to obtain the expected deceleration.

[0033] Step S40: Determine driving habit parameters based on the first preset rule and driving habit information; Step S50: Determine the operating parameters based on the second preset rule and operating condition information; Driving habit parameters and operating condition parameters are used to calculate the compensation torque. Driving habit parameters determine the driver's driving style, while operating condition parameters identify the current road conditions. During the calculation of the compensation torque, adjustments are made based on the driver's driving style and current road conditions to ensure that the vehicle better matches the driver's driving style under different road conditions during energy recovery through compensation torque.

[0034] Specifically, driving habit parameters are based on a first preset rule, while operating condition parameters are based on a second preset rule. The first and second preset rules can be derived from various preset scoring functions, which are typically non-linear mapping functions. The specific form of the function can be determined by those skilled in the art based on the characteristics of the vehicle platform, through expert experience calibration or machine learning training.

[0035] For example, under the influence of the scoring function, the original feature values ​​of input driving habit information are mapped to a unified scoring range, thereby obtaining the corresponding driving habit parameters. The mapping relationship of the scoring function follows the principle of "critical threshold amplification." That is, when the feature value is near the critical threshold reflecting a qualitative change in driving style, the scoring function provides high sensitivity, and changes in the feature value cause significant changes in the score. At the same time, it follows the principle of "diminishing marginal returns." When the feature value is too high or too low, the sensitivity of the scoring function decreases, and the output tends to saturate, in order to avoid a single feature excessively dominating the final decision.

[0036] Step S60: Determine the compensation torque based on the desired deceleration, vehicle weight information, slope information, driving habit parameters, and operating condition parameters; Specifically, the compensation torque calculation process is as follows: the torque coordination and compensation layer is used to receive the desired deceleration, vehicle weight information, slope information, driving habit parameters, and operating condition parameters.

[0037] Based on the vehicle dynamics model, the desired deceleration and vehicle weight information are converted into a basic torque requirement. Specifically, the basic torque requirement satisfies the following relationship: ; Among them, T req Based on the required torque, m is the vehicle weight information, r is the rolling radius of the vehicle wheel hub, and G desired The desired deceleration.

[0038] By incorporating driver habit parameters and real-time operating condition parameters, the base torque demand is proportionally adjusted, and a slope and gravity compensation term is added to obtain the compensated torque. Specifically, the compensated torque is obtained by correcting the vehicle's base torque through the product of driving habit parameters, operating condition parameters, and the base torque demand. The vehicle's base torque is calculated based on the vehicle's wheel hub rolling radius, its own weight, and the slope in which the vehicle is situated.

[0039] Step S70: Recover energy by braking according to the compensated torque.

[0040] The execution layer is used to control the vehicle's drive motor based on the calculated compensation torque, so that it can accurately generate a torque that is equal in magnitude and opposite in direction to the command value, i.e., braking torque, thereby realizing energy recovery and reducing energy consumption.

[0041] To improve the accuracy of the compensation torque calculation, in this embodiment, the driving data can also include more dimensions of vehicle parameter information, so that more factors are considered when aggregating at the strategy decision layer, making the overall solution more reasonable.

[0042] In one embodiment of the present invention, please refer to Figure 2 Step S20 includes: Step S21: Use membership functions to fuzzify the slope information, driving habit information, and working condition information respectively, so as to obtain the corresponding fuzzy slope information, fuzzy driving habit information, and fuzzy working condition information respectively; Under the influence of the membership function, the precise input values ​​of slope information, driving habit information, and operating condition information are fuzzified respectively. The membership function is the degree of conformity in the fuzzy concept within fuzzy rules. For example, after fuzzifying the slope information through the membership function, the membership degree is 100% when the fuzzy slope information is within a certain specific range, and gradually decreases according to the deviation value when it exceeds that specific range.

[0043] Step S22: Based on preset fuzzy rules, evaluate the desired deceleration according to fuzzy slope information, fuzzy driving habit information, and fuzzy operating condition information respectively; Pre-defined fuzzy rules are typically rules developed by those skilled in the art based on experience, usually with the aim of reducing vehicle energy consumption. For example, if a driver's energy consumption is low during driving, their driving habits are used as a template. Under this template, when the gradient changes, assuming an uphill section, the expected deceleration is assessed by referring to the changing patterns of their driving habits; or, based on driving habits under different operating conditions, such as slow starts or coasting in congested traffic, the expected deceleration is assessed. Based on the pre-defined fuzzy rules and the membership inference evaluation results of fuzzy gradient information, fuzzy driving habit information, and fuzzy operating condition information, all opinions are unified to derive an overall solution, and aggregated fuzzy parameters are output. Finally, the aggregated fuzzy parameters are defuzzified to derive the most suitable expected deceleration.

[0044] Step S23: Aggregate the evaluation results of each desired deceleration and output the aggregated fuzzy parameters.

[0045] Specifically, the desired deceleration is obtained by aggregating fuzzy parameters after aggregating fuzzy driving habit information, fuzzy operating condition information, and fuzzy slope information.

[0046] In one embodiment of the present invention, please refer to Figure 3 Step S40 includes: Step S41: Extract at least one first feature value from the driving habit information; The driving habit information includes at least one first feature value, and the first feature value includes at least one first sub-feature. The first sub-feature can be average deceleration, frequency of rapid acceleration, average accelerator pedal opening, pedal opening change rate, brake pedal operation frequency, or historical travel.

[0047] Step S42: Determine the scoring rule corresponding to the first sub-feature according to the first preset rule; The first set of predefined rules includes multiple scoring rules, with one scoring rule corresponding to each type of sub-feature. The scoring rule is also known as the scoring function, which can be a linear or non-linear mapping function, and its mapping relationship follows the principles of "diminishing marginal returns" or "critical threshold amplification." That is, when the feature value is near a critical threshold reflecting a qualitative change in driving style, the scoring function provides high sensitivity, with a large change in score caused by a unit change in feature value; when the feature value is too high or too low, the sensitivity of the scoring function decreases, and the output tends to saturate, to avoid a single feature excessively dominating the final decision. For example, for "average accelerator pedal opening," the deeper the accelerator pedal is pressed, the higher the corresponding score, but after a certain point, further increasing the pedal depth will result in a slower increase in score. This more reasonably reflects the driver's true intentions.

[0048] Step S43: Calculate the sub-score corresponding to the first sub-feature according to the scoring rule corresponding to the first sub-feature; Step S44: Calculate the score of the first feature value based on the sub-score corresponding to the first sub-feature; The first sub-feature is the average deceleration mean(a) negative Taking (a) as an example, the scoring function for average deceleration is expressed as f1(mean(a)). negative And the sub-components of the average deceleration satisfy the following relationship: ; Among them, Score dec The score for the deceleration behavior is the sub-score corresponding to the average deceleration.

[0049] Step S45: Calculate the total score based on the weight coefficient corresponding to the first feature value and the score corresponding to the first feature value; It is understandable that driving habit information may include one or more feature values, and when the first feature value also includes an acceleration behavior score... acc Then, weighting coefficients are assigned to the deceleration behavior score and the acceleration behavior score respectively, and the total score of driving habit information is calculated. The total score satisfies the following relationship: ; Where W1 is the weighting coefficient for deceleration behavior score, W2 is the weighting coefficient for acceleration behavior score, and Total score This is the total score.

[0050] It should be noted that when the driving habit information only includes average deceleration, the weighting coefficient for the deceleration behavior score is 1, which means the score of the first feature is the Score. dec The score.

[0051] Step S46: Determine driving habit parameters based on the total score.

[0052] Finally, the driving habit information is categorized based on the total score, which determines the type of driving habit parameters. In this embodiment, there are three types: Aggressive, Normal, and Eco. The driving habit parameters satisfy the following mapping relationship: .

[0053] K habit For driving habit parameters, when the score of the first feature value is less than 0.8, it indicates that the driving style is energy-saving; when it is greater than 0.8 and less than 1.2, it indicates that the driving style is normal; and when it is greater than 1.2, it indicates that the driving style is aggressive.

[0054] Further, please refer to Figure 4 Step S43 includes: Step S431: When the first sub-feature has only one type, the sub-score of the first sub-feature is used as the score of the corresponding first feature value; The first feature value may include one or more sub-features. For example, in the deceleration behavior score, the first feature value may only include the average deceleration as the first feature value. Therefore, when calculating the score of the first feature value, it is only necessary to calculate the average deceleration sub-score.

[0055] Step S432: When there is more than one type of the first sub-feature, sum the sub-scores corresponding to all the first sub-features and use the sum as the score of the corresponding first feature value.

[0056] In the acceleration behavior score, the first feature value can include two first sub-features: the frequency of rapid acceleration (count, ΔAPPS / Δt) and the average accelerator pedal opening (mean, APPS). In this case, the scoring rule for the frequency of rapid acceleration is obtained from the first preset rule and denoted as f2(mean(APPS)), and the scoring rule for the average accelerator pedal opening is denoted as f3(count(ΔAPPS / Δt)). The acceleration behavior score is the sum of the scores for the frequency of rapid acceleration and the average accelerator pedal opening.

[0057] It is understandable that the more first sub-features included, the more the score of the first feature can be summed up as the scores of the sub-features.

[0058] Further, please refer to Figure 5 Step S50 includes: Step S51: Extract at least one second feature value from the operating condition information; The operating condition information includes at least one second feature value, and the second feature value includes at least one second sub-feature, which is the average vehicle speed, steering wheel angle, acceleration variance, acceleration pedal opening signal, braking signal, or gear signal.

[0059] Step S52: Determine the preset threshold corresponding to the second sub-feature according to the second preset rule; Step S53: Compare the second sub-feature with the corresponding preset threshold, and determine whether the second sub-feature meets the threshold condition based on the comparison result; When determining whether a vehicle is in congestion based on operating condition information, a maximum speed threshold V1 and a maximum acceleration variance threshold are set in the second preset rule. The second feature value then includes two sub-features: average speed and acceleration variance. If the average speed is less than the maximum speed threshold V1 and the acceleration variance is greater than the maximum acceleration variance threshold, the vehicle is determined to be in congestion. The time period for acceleration variance is the same as the time period for average speed.

[0060] When it is necessary to determine whether a vehicle is on a highway based on operating condition information, a minimum vehicle speed threshold V2 and accelerator pedal opening stability are set in the second preset rule. The second feature value includes two sub-features: average vehicle speed and accelerator pedal opening signal. If the average vehicle speed is greater than the minimum vehicle speed threshold V2, and the accelerator pedal opening signal is stable (meaning the accelerator pedal opening signal remains relatively stable within the average vehicle speed period without any jitter), then the vehicle is determined to be on a highway.

[0061] When it is necessary to determine whether a vehicle is in a cornering situation based on operating condition information, a minimum steering wheel angle threshold is set in the second preset rule. Then, the second feature value only includes the steering wheel angle. When the steering wheel angle is greater than the low steering wheel angle threshold, the vehicle is determined to be in a cornering situation.

[0062] Step S54: When the second sub-feature meets the threshold condition, determine the operating parameters based on the second feature value.

[0063] Furthermore, in this embodiment, braking signals or gear signals can be collected to identify and address more complex road conditions. The operating parameters satisfy the following mapping relationship: When the road condition is judged to be congested, the corresponding operating parameter is 1.2; when the road condition is judged to be on a highway, the corresponding operating parameter is 1.0; when the road condition is judged to be on a curved road, the corresponding operating parameter is 0.8.

[0064] To avoid misjudgments, different secondary feature values ​​can be assigned priorities during the process of identifying the vehicle's current road condition based on operational information. These priorities are then used to sort the values, comparing each secondary feature value with its corresponding preset threshold, and determining whether the secondary feature meets the threshold condition based on the comparison result. In other words, prioritization allows for consideration of whether the vehicle is in congested traffic, highway conditions, etc. For example, the risk coefficient of the road condition can be used for sorting, prioritizing the determination of whether the vehicle is in a highway condition, thereby avoiding safety hazards caused by misjudgments and improving safety.

[0065] Further, please refer to Figure 6 Step S60 includes: Step S61: Based on the dynamic model, calculate the required torque according to the desired deceleration and vehicle weight information; Based on the vehicle dynamics model, the desired deceleration and vehicle weight information are converted into a basic torque requirement. Specifically, the basic torque requirement satisfies the following relationship: ; Among them, T req Based on the required torque, m is the vehicle weight information, r is the rolling radius of the vehicle wheel hub, and G desired The desired deceleration.

[0066] Step S62: Calculate the compensation torque based on the required torque, slope information, driving habit parameters, and operating condition parameters; By incorporating driver habit parameters and real-time operating parameters, the basic torque requirement is proportionally adjusted, and a gradient gravity compensation term is added to obtain the compensated torque.

[0067] Step S63: Obtain the maximum allowable torque of the battery and the maximum allowable torque of the motor; Step S64: Ratio the compensation torque, the maximum allowable torque of the battery, and the maximum allowable torque of the motor; When the compensation torque T is calculated compensated Subsequently, to protect the battery and motor from damage, the maximum allowable torque T of the battery should be obtained. batt and the maximum allowable torque T of the motor motor T batt And T motor T is a real-time known quantity, and is affected by external factors such as temperature and battery power. batt And T motor The values ​​differ. Therefore, the compensated torque is compared with the maximum allowable torque of the battery and the maximum allowable torque of the motor, and the final torque T is output through a safety arbitration function. final .

[0068] Step S65: Based on the comparison results, output the minimum value among the compensated torque, the maximum allowable torque of the battery, and the maximum allowable torque of the motor as the final torque, and update the value of the compensated torque to the final torque.

[0069] The secure arbitration function satisfies the following relationship: .

[0070] When the compensation torque is less than the maximum allowable torque of the battery and also less than the maximum allowable torque of the motor, the value of the compensation torque remains unchanged; when the maximum allowable torque of the battery is less than the compensation torque and also less than the maximum allowable torque of the motor, the value of the maximum allowable torque of the battery is replaced with the new compensation torque; when the maximum allowable torque of the motor is less than the maximum allowable torque of the battery and also less than the compensation torque, the value of the maximum allowable torque of the motor is replaced with the new compensation torque.

[0071] In addition, to solve the above problems, the present invention also proposes a computer-readable storage medium storing an adaptive vehicle energy recovery program, which, when executed by a processor, implements the steps of the vehicle energy recovery method as described above.

[0072] Computer-readable storage media may take the form of any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0073] In addition to the methods and devices described above, embodiments of this application may also be computer program products, which include computer program information. When the computer program information is run by a processor, it causes the processor to execute the steps of a vehicle energy recovery method according to various embodiments of this application.

[0074] Computer program products can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0075] In addition to collecting vehicle condition and weight information, the technical solution of this invention also collects external conditions such as slope, driving habits, and operating conditions. Furthermore, it formulates corresponding calculation rules for different parameters. Finally, it integrates multiple calculation results, thereby improving calculation accuracy and reliability, and developing a more suitable energy recovery control strategy for the vehicle, thus improving energy recovery efficiency.

[0076] In addition, to solve the above problems, the present invention also proposes a vehicle, the vehicle including a controller, the controller including a processor and a memory, the memory storing a computer program, and the processor executing the computer program to execute the above-mentioned vehicle energy recovery method.

[0077] like Figure 5 As shown, vehicle 10 includes one or more processors 101 and memory 102.

[0078] The processor 101 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the vehicle 10 to perform desired functions.

[0079] The memory 102 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 101 may execute the program instructions to implement a vehicle energy recovery method and / or other desired functions according to the various embodiments of this application described above.

[0080] In one example, vehicle 10 may also include input device 103 and output device 104, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0081] When the vehicle is a standalone device, the input device 103 can be a communication network connector for receiving the collected input signals from the first device and the second device.

[0082] In addition, the input device 103 may also include, for example, a keyboard, a mouse, etc.

[0083] The output device 104 can output various information to the outside, including determined distance information, direction information, etc. The output device 104 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0084] Of course, for the sake of simplicity, Figure 5 Only some of the components of the vehicle 10 relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the vehicle 10 may include any other suitable components depending on the specific application.

[0085] The above description is merely an exemplary embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention specification and drawings under the technical concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A method for recovering vehicle energy, characterized in that, The vehicle energy recovery method includes: Acquire vehicle driving data, including vehicle condition information, vehicle weight information, slope information, driving habit information, and operating condition information; After fuzzing the slope information, driving habit information, and working condition information, the fuzzy parameters after fuzzing are aggregated based on preset fuzzy rules to obtain aggregated fuzzy parameters. The aggregated fuzzy parameters are defuzzified to determine the desired deceleration; Driving habit parameters are determined based on the first preset rule and the driving habit information. The operating parameters are determined based on the second preset rule and the operating condition information. The compensation torque is determined based on the desired deceleration, vehicle weight information, slope information, driving habit parameters, and operating condition parameters. Energy is recovered through braking based on the compensated torque.

2. The vehicle energy recovery method as described in claim 1, characterized in that, After performing fuzzy processing on the slope information, driving habit information, and operating condition information, the fuzzy parameters are aggregated based on preset fuzzy rules to obtain aggregated fuzzy parameters, including: The slope information, driving habit information, and working condition information are fuzzified by the membership function to obtain the corresponding fuzzy slope information, fuzzy driving habit information, and fuzzy working condition information respectively. Based on the preset fuzzy rules, the desired deceleration is evaluated according to the fuzzy slope information, the fuzzy driving habit information, and the fuzzy operating condition information respectively; The evaluation results of each desired deceleration are aggregated, and the aggregated fuzzy parameters are output.

3. The vehicle energy recovery method as described in claim 1, characterized in that, The determination of driving habit parameters based on the first preset rule and the driving habit information includes: Extract at least one first feature value from the driving habit information, wherein the first feature value includes a first sub-feature, and the first sub-feature includes at least one of the following: average deceleration, frequency of rapid acceleration, average accelerator pedal opening, rate of change of pedal opening, frequency of brake pedal operation, or historical travel. The scoring rule corresponding to the first sub-feature is determined according to the first preset rule; Calculate the sub-score corresponding to the first sub-feature according to the scoring rule corresponding to the first sub-feature; Calculate the score of the first feature value based on the sub-score corresponding to the first sub-feature; The total score is calculated based on the weight coefficient corresponding to the first feature value and the score corresponding to the first feature value. The driving habit parameters are determined based on the total score.

4. The vehicle energy recovery method as described in claim 3, characterized in that, The step of calculating the sub-score corresponding to the first sub-feature according to the scoring rule corresponding to the first sub-feature includes: When the first sub-feature is of one type, the sub-score of the first sub-feature is used as the score of the corresponding first feature value; When there is more than one type of the first sub-feature, the sub-scores corresponding to all the first sub-features are summed and the sum is used as the score of the corresponding first feature value.

5. The vehicle energy recovery method as described in claim 1, characterized in that, The determination of operating parameters based on the second preset rule and the operating condition information includes: Extract at least one second feature value from the operating condition information, wherein the second feature value includes a second sub-feature, and the second sub-feature includes at least one of the following: average vehicle speed, steering wheel angle, acceleration variance, acceleration pedal opening signal, braking signal, or gear signal; The preset threshold corresponding to the second sub-feature is determined according to the second preset rule; The second sub-feature is compared with the corresponding preset threshold, and the comparison result is used to determine whether the second sub-feature meets the threshold condition. When the second sub-feature meets the threshold condition, the operating condition parameter is determined based on the second feature value.

6. The vehicle energy recovery method as described in claim 5, characterized in that, Comparing the second sub-feature with the corresponding preset threshold, and determining whether the second sub-feature meets the threshold condition based on the comparison result includes: Obtain the priority of the second sub-feature; According to the priority, the second sub-feature of each class is compared with the corresponding preset threshold in turn, and the second sub-feature is judged to meet the threshold condition based on the comparison result.

7. The vehicle energy recovery method as described in claim 1, characterized in that, The step of determining the compensation torque based on the desired deceleration, vehicle weight information, slope information, driving habit parameters, and operating condition parameters includes: Based on the dynamic model, the required torque is calculated according to the desired deceleration and the vehicle weight information; The compensation torque is calculated based on the required torque, the gradient information, the driving habit parameters, and the operating condition parameters.

8. The vehicle energy recovery method as described in claim 7, characterized in that, After calculating the compensation torque based on the required torque, the slope information, the driving habit parameters, and the operating condition parameters, the vehicle energy recovery method further includes: Obtain the maximum allowable torque of the battery and the maximum allowable torque of the motor; The compensation torque, the maximum allowable torque of the battery, and the maximum allowable torque of the motor are proportionally divided into their magnitudes. Based on the comparison results, the minimum value among the compensated torque, the maximum allowable torque of the battery, and the maximum allowable torque of the motor is output as the final torque, and the value of the compensated torque is updated to the final torque.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a vehicle energy recovery program, which, when executed by a processor, implements the steps of the vehicle energy recovery method as described in any one of claims 1 to 8.

10. A vehicle, characterized in that, The vehicles include: A controller, comprising a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to perform the vehicle energy recovery method according to any one of claims 1 to 8.

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