A Method, System and Storage Medium for Energy Recovery of a New Energy Vehicle

Abstract

The present invention discloses a method, system and storage medium for energy recovery of new energy vehicles, which relates to the technical field of monitoring and analysis, and includes a vehicle stop position recognition module, a vehicle braking scheme generation module, a coasting energy recovery unit, a braking energy recovery unit, a mechanical energy collection unit, a mechanical energy conversion unit, a part state analysis module, and a part life prediction module; this method, system and storage medium for energy recovery of new energy vehicles can, through the setting of the vehicle stop position recognition module, analyze a suitable parking position in real time according to the status information of the vehicle ahead, avoiding the situation that the vehicle energy recovery operation does not conform to the standard operation due to the artificial selection of the parking position. At the same time, through the setting of the part state analysis module and the part life prediction module, the state of the parts and the part life can be analyzed during the vehicle energy recovery, avoiding the aggravation of the part loss of new energy vehicles due to the overlong energy recovery process.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring and analysis, and particularly relates to a method, a system and a storage medium for energy recovery of new energy vehicles. Background Art

[0002] Regenerative braking energy is one of the important technologies for new energy vehicles. In general internal combustion engine vehicles, when the vehicle decelerates or brakes, the kinetic energy of the vehicle is converted into heat energy through the braking system and released into the atmosphere. In new energy vehicles, this wasted kinetic energy can be converted into electrical energy through regenerative braking energy technology and stored in the battery, and further converted into driving energy. Regenerative braking energy varies according to the working mode of new energy vehicles. The traditional braking method of vehicles is mainly mechanical braking or friction braking. The braking process will consume part of the kinetic energy, and most of the energy will be converted into heat energy and dissipated, resulting in great energy waste. When an electric vehicle brakes, based on the reversibility of the drive motor, it can be promptly switched from the driving state to the power generation state. By reasonably using regenerative braking energy, the energy generated during the braking process can be utilized and transmitted back to the battery system to achieve a good energy recovery effect. However, in the prior art, in the process of regenerative braking energy, the battery is charged and discharged through an energy control system, and the large current during the regenerative braking energy process will impact the power battery, resulting in a shortened service life of the battery.

[0003] Chinese Patent with application number 202410552475.5 discloses a new energy vehicle energy recovery system, which relates to the technical field of energy recovery. The following scheme is proposed, including a coasting energy recovery unit, a braking energy recovery unit, a mechanical energy collection unit, a mechanical energy conversion unit, a heat energy acquisition unit and a heat energy conversion unit. The coasting energy recovery unit is used to convert the dynamic energy generated when the wheels of a new energy vehicle coast during a non-fueling state into mechanical energy, and send the converted mechanical energy to the mechanical energy collection unit. The present invention can not only analyze the energy generated during the braking process to achieve a good energy recovery effect, but also effectively alleviate the current impact on the energy recovery and vehicle startup links through the supercapacitor module in the electric energy recovery storage unit, and try to avoid the impact of high instantaneous power on the power battery of new energy vehicles, effectively ensuring the energy recovery effect of new energy vehicles and the protection effect on the batteries of new energy vehicles.

[0004] This invention realizes the energy recovery effect of new energy vehicles and the protection effect on the batteries of new energy vehicles. However, since all parts are still in working state during the energy recovery process of new energy vehicles, the longer the energy recovery process, the more serious the component loss of new energy vehicles. Therefore, the component loss of new energy vehicles needs to be considered during the energy recovery process. Summary of the Invention

[0005] The object of the present invention is to provide a method, a system and a storage medium for energy recovery of new energy vehicles, so as to solve the above deficiencies in the prior art.

[0006] To achieve the above object, the present invention provides the following technical solution: A method for energy recovery of new energy vehicles, the method comprising the following working steps:

[0007] S1, based on computer vision and deep learning models, judge the driving state of the vehicle ahead. When the judgment result of the driving state of the vehicle ahead is decelerating, predict the stopping position of the vehicle ahead through a motion state prediction model, and judge the stopping position of the current vehicle based on the predicted stopping position of the vehicle ahead and the safe driving distance of the vehicle. When the judgment result of the driving state of the vehicle ahead is that the vehicle has stopped, judge the stopping position of the current vehicle based on the stopping position of the vehicle ahead and the safe driving distance of the vehicle;

[0008] S2, based on the stopping position of the current vehicle identified in step S1, and simulate all possible braking schemes when the current vehicle travels to the stopping process of the current vehicle through a vehicle braking process simulation model;

[0009] S3, analyze the energy recovery efficiency and energy recovery amount in different braking schemes in step S2 through a data analysis model, preset a data selection threshold, and select a braking scheme with a data analysis result higher than the preset selection threshold based on a data screening model;

[0010] S4, analyze the state of vehicle parts in the vehicle braking scheme screened in step S3 based on a state analysis model;

[0011] S5, based on the analysis result of the working state of the vehicle energy recovery parts of the part state analysis module, and predict the predicted life of vehicle parts in the energy recovery state through a part life prediction model;

[0012] S6, integrate the predicted life of vehicle parts and the vehicle braking scheme through a data integration model, and select a vehicle braking scheme with the highest predicted life of vehicle parts among all vehicle braking schemes through a data selection unit;

[0013] S7, the vehicle brakes according to the vehicle braking scheme selected in step S6;

[0014] S8, perform mechanical energy conversion processing on the dynamic energy generated when the wheels slide, and send the converted mechanical energy to the mechanical energy collection unit, convert the dynamic energy generated by the braking distance into mechanical energy, promptly convert from the driving state to the power generation state, and send the converted mechanical energy to the mechanical energy collection unit;

[0015] S9. The mechanical energy collection unit centrally and uniformly collects and processes the mechanical energy generated by the coasting energy recovery unit and the braking energy recovery unit, and at the same time sends the collected mechanical energy to the mechanical energy conversion unit. The mechanical energy conversion unit converts all the dynamic energy generated during mechanical motion collected through the mechanical energy collection unit into charging electric energy with an energy value equal to the electric energy that can be absorbed by the new energy vehicle battery.

[0016] Furthermore, the prediction of the service life of automotive parts specifically includes the following working steps:

[0017] For the part life degradation amount xi(t)~N(μit, σ 2 t) at any conditional moment, the probability density function of the degradation amount x i (t) at any moment is obtained:

[0018]

[0019] The service life of a part refers to the time when the sensitive parameter reaches the failure threshold during the performance degradation process, which is represented by T L . Among them, L is the failure threshold determined artificially, and the probability that the degradation amount reaches the failure threshold can be written as:

[0020] P(x i (t)≥L)=P(x i (t)≥L|T L ≤t)P(T L ≤t)+P(x i (t)≥L|T L >t)P(T L >t),

[0021] And when xi(TL)=L, P(x i (t)≥L)=P(x i (t)<L), and the life distribution function of the part T L can be obtained as:

[0022]

[0023] It can be written as:

[0024]

[0025] The reliability function can be obtained as:

[0026]

[0027] Among them, σ is the variance of the parameter change at each moment, and σ is used as a constant.

[0028] A new energy vehicle energy recovery system, including a vehicle stop position recognition module, a vehicle braking scheme generation module, a coasting energy recovery unit, a braking energy recovery unit, a mechanical energy collection unit, a mechanical energy conversion unit, a part state analysis module, and a part life prediction module:

[0029] The vehicle stop position recognition module judges the driving state of the vehicle ahead based on computer vision and a deep learning model. The driving state includes accelerating, decelerating, and vehicle stop. When the driving state of the vehicle ahead is judged to be decelerating, the stop position of the vehicle ahead is predicted through a motion state prediction model, and the stop position of the current vehicle is judged based on the predicted stop position of the vehicle ahead and the safe driving distance of the vehicle. When the driving state of the vehicle ahead is judged to be vehicle stop, the stop position of the current vehicle is judged based on the stop position of the vehicle ahead and the safe driving distance of the vehicle;

[0030] The vehicle braking scheme generation module is used to, based on the stop position of the current vehicle recognized by the vehicle stop position recognition module, simulate all possible braking schemes when the current vehicle travels to the stop process of the current vehicle through a vehicle braking process simulation model, analyze the energy recovery efficiency and energy recovery amount in different braking schemes through a data analysis model, preset a data selection threshold, and select a braking scheme with a data analysis result higher than the preset selection threshold based on a data screening model;

[0031] The coasting energy recovery unit is used to convert the dynamic energy generated when the wheels coast into mechanical energy and send the converted mechanical energy to the mechanical energy collection unit;

[0032] The braking energy recovery unit is used to analyze the braking intensity, battery state of charge, and vehicle speed, convert the dynamic energy generated by the braking distance into mechanical energy, promptly switch from the driving state to the power generation state, and send the converted mechanical energy to the mechanical energy collection unit;

[0033] The mechanical energy collection unit is used to centrally collect and process the mechanical energy generated by the coasting energy recovery unit and the braking energy recovery unit, and at the same time send the collected mechanical energy to the mechanical energy conversion unit;

[0034] The mechanical energy conversion unit is used to convert all the dynamic energy generated during mechanical motion collected by the mechanical energy collection unit into charging electric energy with an energy value equal to the electric energy that can be absorbed by the new energy vehicle battery;

[0035] The part state analysis module is used to analyze the state of the vehicle parts in the vehicle braking scheme generated by the vehicle braking scheme generation module based on a state analysis model;

[0036] The part life prediction module is used to analyze the working state of automotive energy recovery parts based on the results of the part state analysis module, and predict the predicted life of automotive parts in the energy recovery state through the part life prediction model;

[0037] The automotive braking scheme generation module is also used to predict the life of automotive parts based on the predicted life of automotive parts predicted by the part life prediction module, and integrate the predicted life of automotive parts with the automotive braking scheme through the data integration model, and select the automotive braking scheme with the highest predicted life of automotive parts among all automotive braking schemes through the data selection unit.

[0038] Furthermore, the automotive braking scheme generation module includes:

[0039] The vehicle braking process simulation module is used to simulate all possible braking schemes when the current vehicle travels to a stop through the vehicle braking process simulation model, where the vehicle braking process simulation model includes a vehicle dynamics model, a braking system model, and a control system model;

[0040] The data analysis module is used to analyze the energy recovery efficiency and energy recovery amount in different braking schemes generated by the vehicle braking process simulation module based on the data analysis model;

[0041] The threshold input module is used to input a preset selection threshold;

[0042] The data screening module is used to select braking schemes in the data analysis module whose results are higher than the selection threshold preset by the threshold input module based on the data screening model;

[0043] The data integration model is used to integrate the braking schemes completed by the data screening module;

[0044] The data selection module is used to select the automotive braking scheme with the highest predicted life of automotive parts among all automotive braking schemes based on the data selection model.

[0045] Furthermore, the part state analysis module includes:

[0046] The part image analysis module is used to analyze the attitude change of parts during the energy recovery process of the vehicle in real time;

[0047] The part shape change detection module is used to analyze the shape change of vehicle parts under the condition of vehicle motion attitude change based on the vehicle part distribution model;

[0048] A component force change detection module, which is used to detect the force change condition of vehicle components under the condition of vehicle motion posture change based on a force analysis model. The force change condition of the vehicle components includes that the vehicle components are under force compression, the force on the vehicle components remains unchanged, and the force on the vehicle components decreases.

[0049] A component state change detection module, based on the signal force change state of vehicle components in the energy recovery state obtained in the component force change detection module, and through a data restoration model, restores the signal force state of vehicle components in the energy recovery state obtained in the component force change detection module to the initial state of vehicle components in the non-force state. The change in the component state can be obtained through a data comparison model.

[0050] A new energy vehicle energy recovery storage medium, on which a new energy vehicle energy recovery program is stored. When the new energy vehicle energy recovery program is executed by a processor, a new energy vehicle energy recovery method is implemented.

[0051] Compared with the prior art, a new energy vehicle energy recovery method, system and storage medium provided by the present invention can analyze a suitable parking position in real time according to the status information of the vehicle ahead through the setting of the vehicle stop position recognition module, avoiding the inconsistency between the vehicle energy recovery operation and the standard operation caused by the artificial selection of the parking position. At the same time, through the setting of the component state analysis module and the component life prediction module, the state of the components and the component life can be analyzed during the vehicle energy recovery, avoiding the aggravation of the component loss of new energy vehicles due to the too long energy recovery process. Description of the Drawings

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings.

[0053] Figure 1 It is a system structure block diagram provided by an embodiment of the present invention. Detailed Embodiments

[0054] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the following will further introduce the present invention in detail with reference to the drawings.

[0055] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the present invention.

[0056] In addition, the terms "first" and "second" are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined. In addition, the terms "mounted", "connected", and "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0057] Example embodiments will be described more fully hereinafter with reference to the accompanying drawings, but the example embodiments may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of this disclosure to those skilled in the art.

[0058] In the case of no conflict, the various embodiments of the present disclosure and the features in the embodiments may be combined with each other.

[0059] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0060] The terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure. As used herein, the singular forms "a" and "the" are also intended to include the plural forms unless the context clearly indicates otherwise. It will also be understood that when the terms "comprises" and / or "consists of" are used in this specification, it specifies the presence of the described features, wholes, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or groups thereof.

[0061] The embodiments described herein may be described with reference to plan views and / or cross-sectional views by means of the ideal schematic diagrams of the present disclosure. Therefore, the example illustrations can be modified according to manufacturing techniques and / or tolerances. Accordingly, the embodiments are not limited to the embodiments shown in the drawings, but include modifications of configurations formed based on manufacturing processes. Therefore, the regions illustrated in the drawings have schematic attributes, and the shapes of the regions shown in the drawings illustrate the specific shapes of the regions of the components, but are not intended to be restrictive.

[0062] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0063] Please refer to Figure 1 , a method for energy recovery of a new energy vehicle, the method comprising the following working steps:

[0064] S1. Based on computer vision and a deep learning model, judge the driving state of the vehicle ahead. When the judgment result of the driving state of the vehicle ahead is decelerating, predict the stopping position of the vehicle ahead through a motion state prediction model, and judge the stopping position of the current vehicle based on the predicted stopping position of the vehicle ahead and the safe driving distance of the vehicle. When the judgment result of the driving state of the vehicle ahead is that the vehicle has stopped, judge the stopping position of the current vehicle based on the stopping position of the vehicle ahead and the safe driving distance of the vehicle;

[0065] S2. Based on the stopping position of the current vehicle identified in step S1, and simulate all possible braking schemes when the current vehicle travels to the process of stopping the current vehicle through a vehicle braking process simulation model;

[0066] S3. Analyze the energy recovery efficiency and the amount of energy recovered in different braking schemes in step S2 through a data analysis model, preset a data selection threshold, and select the braking schemes with data analysis results higher than the preset selection threshold based on a data screening model;

[0067] S4. Analyze the states of the vehicle parts in the vehicle braking schemes completed in step S3 based on a state analysis model;

[0068] S5. Based on the analysis results of the working states of the vehicle energy recovery parts of the part state analysis module, and predict the predicted life of the vehicle parts in the energy recovery state through a part life prediction model;

[0069] S6. Integrate the predicted life of automotive parts with the automotive braking scheme through the data integration model, and select, via the data selection unit, the automotive braking scheme with the highest predicted life of automotive parts among all automotive braking schemes.

[0070] S7. The vehicle brakes according to the automotive braking scheme selected in step S6.

[0071] S8. Convert the dynamic energy generated during wheel skidding into mechanical energy, and send the converted mechanical energy to the mechanical energy collection unit. Convert the dynamic energy generated by the braking distance into mechanical energy, promptly switch from the driving state to the power generation state, and send the converted mechanical energy to the mechanical energy collection unit.

[0072] S9. The mechanical energy collection unit centrally and uniformly collects and processes the mechanical energy generated by the skidding energy recovery unit and the braking energy recovery unit, and simultaneously sends the collected mechanical energy to the mechanical energy conversion unit. The mechanical energy conversion unit converts all the dynamic energy generated during mechanical movement collected by the mechanical energy collection unit into charging electric energy with an energy value equal to the electric energy that can be absorbed by the new energy vehicle battery.

[0073] The prediction of the life of automotive parts specifically includes the following working steps:

[0074] For the part life degradation amount xi(t)~N(μit, σ 2 t) at any conditional moment, obtain the probability density function of the degradation amount x i (t) at any moment:

[0075]

[0076] The life of a part refers to the time when the sensitive parameter reaches the failure threshold during the performance degradation process, denoted by T L . Among them, L is the failure threshold determined artificially, and the probability that the degradation amount reaches the failure threshold can be written as:

[0077] P(x i (t)≥L)=P(x i (t)≥L|T L ≤t)P(T L ≤t)+P(x i (t)≥L|T L >t)P(T L >t),

[0078] And when xi(TL)=L, P(x i (t)≥L)=P(x i (t)<L), and the life distribution function of the part T L can be obtained as:

[0079]

[0080] It can be written as:

[0081]

[0082] The reliability function can be obtained as:

[0083]

[0084] where σ is the variance of the parameter change at each moment, and σ is a constant.

[0085] A new energy vehicle energy recovery system includes a vehicle stop position recognition module, a vehicle braking scheme generation module, a coasting energy recovery unit, a braking energy recovery unit, a mechanical energy collection unit, a mechanical energy conversion unit, a part state analysis module, and a part life prediction module:

[0086] The vehicle stop position recognition module judges the driving state of the vehicle ahead based on computer vision and a deep learning model. The driving state includes accelerating, decelerating, and vehicle stop. When the driving state judgment result of the vehicle ahead is decelerating, the stop position of the vehicle ahead is predicted through a motion state prediction model, and the stop position of the current vehicle is judged based on the predicted stop position of the vehicle ahead and the safe driving distance of the vehicle. When the driving state judgment result of the vehicle ahead is vehicle stop, the stop position of the current vehicle is judged based on the stop position of the vehicle ahead and the safe driving distance of the vehicle;

[0087] The vehicle braking scheme generation module is used to identify the stop position of the current vehicle based on the vehicle stop position recognition module, simulate all possible braking schemes when the current vehicle travels to the stop process of the current vehicle through a vehicle braking process simulation model, analyze the energy recovery efficiency and energy recovery amount in different braking schemes through a data analysis model, preset a data selection threshold, and select the braking scheme with the data analysis result higher than the preset selection threshold based on a data screening model;

[0088] The coasting energy recovery unit is used to convert the dynamic energy generated when the wheels coast into mechanical energy and send the converted mechanical energy to the mechanical energy collection unit;

[0089] The braking energy recovery unit is used to analyze the braking intensity, state of charge of the battery, and vehicle speed, convert the dynamic energy generated by the braking distance into mechanical energy, promptly convert from the driving state to the power generation state, and send the converted mechanical energy to the mechanical energy collection unit;

[0090] The mechanical energy collection unit is used to centrally collect and process the mechanical energy generated by the coasting energy recovery unit and the braking energy recovery unit, and at the same time send the collected mechanical energy to the mechanical energy conversion unit;

[0091] The mechanical energy conversion unit is used to convert the dynamic energy generated during all mechanical motions collected by the mechanical energy collection unit into charging electric energy with an energy value equal to the electric energy that can be absorbed by the new energy vehicle battery;

[0092] The part status analysis module is used to analyze the status of vehicle parts in the vehicle braking scheme generated by the vehicle braking scheme generation module based on the status analysis model;

[0093] The part life prediction module is used to predict the predicted life of vehicle parts in the energy recovery state based on the analysis results of the working status of vehicle energy recovery parts by the part status analysis module and through the part life prediction model;

[0094] The vehicle braking scheme generation module is also used to predict the predicted life of vehicle parts based on the part life prediction module, and integrate the predicted life of vehicle parts with the vehicle braking scheme through the data integration model, and select the vehicle braking scheme with the highest predicted life of vehicle parts among all vehicle braking schemes through the data selection unit.

[0095] The vehicle braking scheme generation module includes:

[0096] The vehicle braking process simulation module, which is used to simulate all possible braking schemes during the process from the current vehicle driving to the current vehicle stopping through the vehicle braking process simulation model, where the vehicle braking process simulation model includes a vehicle dynamics model, a braking system model, and a control system model;

[0097] The data analysis module, which is used to analyze the energy recovery efficiency and energy recovery amount in different braking schemes generated by the vehicle braking process simulation module based on the data analysis model;

[0098] The threshold input module, and the threshold input model is used to input a preset selection threshold;

[0099] The data screening module, which is used to select the braking schemes with results higher than the selection threshold preset by the threshold input module in the data analysis module based on the data screening model;

[0100] The data integration model, which is used to be based on the braking schemes screened by the data screening module;

[0101] The data selection module, which is used to select the vehicle braking scheme with the highest predicted life of vehicle parts among all vehicle braking schemes based on the data selection model.

[0102] The part status analysis module includes:

[0103] The part image analysis module is used to analyze the attitude change of parts in real time during the energy recovery process of the vehicle.

[0104] The part shape change detection module is used to analyze the shape change condition of vehicle parts under the condition of vehicle motion attitude change based on the vehicle part distribution model.

[0105] The part force change detection module is used to analyze the force change condition of vehicle parts under the condition of vehicle motion attitude change based on the force analysis model. The force change condition of vehicle parts includes vehicle parts being stressed and compressed, vehicle parts having no change in force, and vehicle parts having a decrease in force.

[0106] The part status change detection module is based on the signal force change state of vehicle parts in the energy recovery state obtained in the part force change detection module, and through the data reduction model, the signal force state of vehicle parts in the energy recovery state obtained in the part force change detection module is restored to the initial state of vehicle parts in the non-force state. The change in part status can be obtained through the data comparison model.

[0107] By setting the vehicle stop position recognition module, the appropriate stop position can be analyzed in real time according to the status information of the vehicle ahead, avoiding the situation that the vehicle energy recovery operation does not conform to the standard operation due to the artificial selection of the stop position. At the same time, by setting the part status analysis module and the part life prediction module, the status of parts and the part life can be analyzed during the energy recovery of the vehicle, avoiding the aggravation of part wear of new energy vehicles due to too long energy recovery process. At the same time, by integrating the status of parts and the part life with the braking scheme generated by the vehicle braking scheme generation module, the service life of vehicle parts can be guaranteed during the generation of the vehicle braking scheme, increasing the working years of the vehicle, and at the same time, it can avoid the overloading of vehicle parts caused by the driver in order to increase the energy recovery amount.

[0108] A new energy vehicle energy recovery storage medium stores a new energy vehicle energy recovery program. When the new energy vehicle energy recovery program is executed by a processor, a new energy vehicle energy recovery method is implemented.

[0109] Only some exemplary embodiments of the present invention have been described by way of illustration above. Undoubtedly, for those of ordinary skill in the art, without departing from the spirit and scope of the present invention, the described embodiments can be modified in various different ways. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the protection scope of the claims of the present invention.

Claims

1. A method for energy recovery of a new energy vehicle, characterized in that: The method includes the following working steps: S1. Based on computer vision and a deep learning model, judge the driving state of the vehicle ahead. When the judgment result of the driving state of the vehicle ahead is decelerating, predict the stopping position of the vehicle ahead through a motion state prediction model, and judge the stopping position of the current vehicle based on the predicted stopping position of the vehicle ahead and the safe driving distance of the vehicle. When the judgment result of the driving state of the vehicle ahead is that the vehicle has stopped, judge the stopping position of the current vehicle based on the stopping position of the vehicle ahead and the safe driving distance of the vehicle; S2. Based on the stopping position of the current vehicle identified in step S1, simulate all possible braking schemes during the process of the current vehicle driving to the stopping position of the current vehicle through a vehicle braking process simulation model; S3. Analyze the energy recovery efficiency and energy recovery amount in different braking schemes in step S2 through a data analysis model, preset a data selection threshold, and select the braking schemes with data analysis results higher than the preset selection threshold based on a data screening model; S4. Analyze the states of vehicle parts in the vehicle braking schemes completed in step S3 based on a state analysis model; S5. Based on the analysis result of the working state of the vehicle energy recovery parts of the parts state analysis module, predict the predicted life of vehicle parts in the energy recovery state through a parts life prediction model; S6. Integrate the predicted life of vehicle parts with the vehicle braking schemes through a data integration model, and select the vehicle braking scheme with the highest predicted life of vehicle parts among all vehicle braking schemes through a data selection unit; S7. The vehicle brakes according to the vehicle braking scheme selected in step S6; S8. Perform mechanical energy conversion processing on the dynamic energy generated when the wheels slide, and send the converted mechanical energy to a mechanical energy collection unit. Convert the dynamic energy generated by the braking distance into mechanical energy, promptly convert from the driving state to the power generation state, and send the converted mechanical energy to the mechanical energy collection unit; S9. The mechanical energy collection unit centrally collects and processes the mechanical energy generated by the sliding energy recovery unit and the braking energy recovery unit, and at the same time sends the collected mechanical energy to a mechanical energy conversion unit. The mechanical energy conversion unit converts the dynamic energy generated during all mechanical movements collected by the mechanical energy collection unit into charging electric energy with an energy value equal to the electric energy that can be absorbed by the new energy vehicle battery.

2. A method for energy recovery of a new energy vehicle according to claim 1, characterized in that: For the above steps, the prediction of the life of vehicle parts specifically includes the following working steps: For the component life degradation amount x at any conditional moment i (t)~N(μ i t, σ 2 t), the probability density function of the degradation amount x i (t) at any moment is obtained as follows: The life of a part refers to the time when a sensitive parameter reaches the failure threshold during the performance degradation process, denoted by T L where L is the failure threshold determined artificially, and the probability that the degradation amount reaches the failure threshold can be written as: P(x i (t)≥L) = P(x i (t)≥L|T L ≤t)P(T L ≤t) + P(x i (t)≥L| T L>t)P(T L >t), And when xi(TL) = L, P(x i (t) ≥ L) = P(x i (t) < L), the life T of the part can be obtained L The distribution function is as follows: It can be written as: The reliability function can be obtained as: Where σ is the variance of the parameter change at each moment, and σ is a constant.

3. A new energy vehicle energy recovery system, which is applicable to the new energy vehicle energy recovery method described in any one of claims 1-2, and is characterized in that: It includes a vehicle stopping position recognition module, a vehicle braking scheme generation module, a sliding energy recovery unit, a braking energy recovery unit, a mechanical energy collection unit, a mechanical energy conversion unit, a parts state analysis module, and a parts life prediction module: The vehicle stop position recognition module determines the driving state of the vehicle ahead based on computer vision and a deep learning model. The driving state includes accelerating, decelerating, and stopping. When the driving state of the vehicle ahead is determined to be decelerating, the stop position of the vehicle ahead is predicted through a motion state prediction model. The stop position of the current vehicle is determined based on the predicted stop position of the vehicle ahead and the safe driving distance of the vehicle. When the driving state of the vehicle ahead is determined to be stopped, the stop position of the current vehicle is determined based on the stop position of the vehicle ahead and the safe driving distance of the vehicle; The vehicle braking scheme generation module is used to determine the stop position of the current vehicle recognized by the vehicle stop position recognition module, and simulate all possible braking schemes when the current vehicle travels to the stop process of the current vehicle through a vehicle braking process simulation model. The energy recovery efficiency and energy recovery amount in different braking schemes are analyzed through a data analysis model. A preset data selection threshold is set, and the braking schemes with data analysis results higher than the preset selection threshold are selected based on a data screening model; The coasting energy recovery unit is used to convert the dynamic energy generated when the wheels coast into mechanical energy, and send the converted mechanical energy to the mechanical energy collection unit; The braking energy recovery unit is used to analyze the braking intensity, state of charge of the battery, and vehicle speed, convert the dynamic energy generated by the braking distance into mechanical energy, promptly switch from the driving state to the power generation state, and send the converted mechanical energy to the mechanical energy collection unit; The mechanical energy collection unit is used to centrally collect and process the mechanical energy generated by the coasting energy recovery unit and the braking energy recovery unit, and at the same time send the collected mechanical energy to the mechanical energy conversion unit; The mechanical energy conversion unit is used to convert all the dynamic energy generated during mechanical motion collected by the mechanical energy collection unit into charging electric energy with an energy value equal to the electric energy that can be absorbed by the new energy vehicle battery; The part state analysis module is used to analyze the state of the vehicle parts in the vehicle braking scheme generated by the vehicle braking scheme generation module based on a state analysis model; The part life prediction module is used to predict the predicted life of the vehicle parts in the energy recovery state based on the analysis result of the working state of the vehicle energy recovery parts by the part state analysis module and through a part life prediction model; The vehicle braking scheme generation module is also used to predict the predicted life of the vehicle parts based on the part life prediction module, and integrate the predicted life of the vehicle parts with the vehicle braking scheme through a data integration model. The braking scheme with the highest predicted life of the vehicle parts among all vehicle braking schemes is selected through a data selection unit.

4. An energy recovery system for a new energy vehicle according to claim 3, characterized in that: The vehicle braking scheme generation module includes: A vehicle braking process simulation module, which is used to simulate all possible braking schemes when the current vehicle travels to the stop process of the current vehicle through a vehicle braking process simulation model. The vehicle braking process simulation model includes a vehicle dynamics model, a braking system model, and a control system model; A data analysis module, which is used to analyze the energy recovery efficiency and energy recovery amount in different braking schemes generated by the vehicle braking process simulation module based on a data analysis model; A threshold input module, where the threshold input model is used to input a preset selection threshold; A data screening module, which is used to select braking schemes with results higher than the preset selection threshold of the threshold input module in the data analysis module based on a data screening model; A data integration model, which is used for the braking schemes completed by screening through the data screening module; A data selection module, which is used to select the vehicle braking scheme with the highest predicted life of vehicle parts among all vehicle braking schemes based on a data selection model.

5. The energy recovery system for a new energy vehicle according to claim 4, wherein: The part status analysis module includes: A part image analysis module, which is used to analyze the attitude change of parts in real time during the energy recovery process of the vehicle; A part shape change detection module, which is used to analyze the shape change condition of vehicle parts under the condition of vehicle motion attitude change based on a vehicle part distribution model; A part force change detection module, which is used to analyze the force change condition of vehicle parts under the condition of vehicle motion attitude change based on a force analysis model. The force change condition of the vehicle parts includes vehicle parts being forced, vehicle parts having no force change, and vehicle parts having a reduced force; A part status change detection module, based on the signal force change state of vehicle parts in the energy recovery state obtained in the part force change detection module, and through a data restoration model, restores the signal force state of vehicle parts in the energy recovery state obtained in the part force change detection module to the initial state of vehicle parts in the non-force state. The change in part status can be obtained through a data comparison model.

6. A storage medium, characterized in that, The storage medium stores a new energy vehicle energy recovery program, and when the new energy vehicle energy recovery program is executed by a processor, it implements a new energy vehicle energy recovery method as described in any one of claims 1-2.

Images