New energy automobile energy recovery method and system and storage medium

Abstract

The invention discloses a new energy automobile energy recovery method and system and a storage medium, and relates to the technical field of monitoring analysis. Comprising an automobile stopping position recognition module, an automobile 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 part state analysis module and a part service life prediction module. According to the energy recovery method and system for the new energy automobile and the storage medium, the appropriate parking position can be analyzed in real time according to the state information of the front automobile through the arrangement of the automobile parking position recognition module, and the situation that the energy recovery operation of the automobile is inconsistent with the standard operation due to manual selection of the parking position is avoided; and meanwhile, by arranging a part state analysis module and a part service life prediction module, the state of the part and the service life of the part can be analyzed during automobile energy recovery, and the situation that part loss of the new energy vehicle is aggravated due to the too long energy recovery process is avoided.

Description

Technical Field

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

[0002] Braking energy recovery is one of the important technologies of new energy vehicles. In general internal combustion engine vehicles, when the vehicle slows down and brakes, the vehicle's kinetic energy 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 and stored in the battery through braking energy recovery technology, and further converted into driving energy. Braking energy recovery varies according to the working mode of new energy vehicles. Traditional automobile braking methods are 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, causing great energy waste. When braking, electric vehicles can be converted from the driving state to the power generation state in time based on the reversibility of the drive motor. Reasonable use of braking energy recovery can utilize the energy generated during braking and transmit it back to the battery system to achieve a good energy recovery effect. However, in the prior art, in braking energy recovery, the battery is charged and discharged by the energy control system. During the braking energy recovery process, the large current will impact the power battery, resulting in a shortened battery life.

[0003] The Chinese patent with application number 202410552475.5 discloses a new energy vehicle energy recovery system, which relates to the field of energy recovery technology. The following scheme is proposed, including a sliding energy recovery unit, a braking energy recovery unit, a mechanical energy collection unit, a mechanical energy conversion unit, a thermal energy collection unit and a thermal energy conversion unit. The sliding energy recovery unit is used to convert the dynamic energy generated by the wheels when the new energy vehicle is in 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 mitigate the current impact on the energy recovery and vehicle start-up 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, effectively ensuring the energy recovery effect of new energy vehicles and the protection effect of new energy vehicle batteries.

[0004] This invention achieves the effect of energy recovery for new energy vehicles and the effect of protecting new energy vehicle batteries. However, since the various parts of the new energy vehicle are still in a working state during the energy recovery process, the longer the energy recovery process is, the more serious the wear and tear of the parts of the new energy vehicle will be. Therefore, the wear and tear of the parts of the new energy vehicle needs to be considered during the energy recovery process. Summary of the invention

[0005] The purpose of the present invention is to provide a new energy vehicle energy recovery method, system and storage medium to solve the above-mentioned deficiencies in the prior art.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a new energy vehicle energy recovery method, the method comprising the following working steps:

[0007] S1, judging the driving state of the vehicle ahead based on computer vision and deep learning models, predicting the stopping position of the vehicle ahead through a motion state prediction model when the driving state judgment result of the vehicle ahead is deceleration, judging 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, and judging the stopping position of the current vehicle based on the stopping position of the vehicle ahead and the safe driving distance of the vehicle when the driving state judgment result of the vehicle ahead is stopping;

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

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

[0010] S4, analyzing the states of vehicle parts in the automobile braking solution screened in step S3 based on the state analysis model;

[0011] S5, based on the working state analysis result of the automobile energy recovery parts by the parts state analysis module, and predicting the predicted life of the automobile parts in the energy recovery state through the parts life prediction model;

[0012] S6, integrating the predicted life of the automobile parts with the automobile braking scheme through the data integration model, and selecting the automobile braking scheme with the highest predicted life of the automobile parts among all automobile braking schemes through the data selection unit;

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

[0014] S8, converting the dynamic energy generated by the wheel sliding into mechanical energy, and sending the converted mechanical energy to the mechanical energy collection unit, converting the dynamic energy generated by the braking distance into mechanical energy, timely converting the driving state into the power generation state, and sending 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] Further, 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 . Here, 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] Where σ is the variance of the parameter change at each moment, and σ is a constant.

[0028] A new energy vehicle energy recovery system includes a vehicle stop 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 component state analysis module, and a component life prediction module:

[0029] The automobile stop position recognition module determines the driving state of the vehicle ahead based on computer vision and deep learning models, and the driving state includes accelerating driving, decelerating driving and stopping of the vehicle. When the driving state judgment result of the vehicle ahead is decelerating driving, the stopping position of the vehicle ahead is predicted by the motion state prediction model, and the stopping position of the current vehicle is determined based on the predicted stopping position of the vehicle ahead and the safe driving distance of the vehicle. When the driving state judgment result of the vehicle ahead is stopping, the stopping position of the current vehicle is determined based on the stopping position of the vehicle ahead and the safe driving distance of the vehicle.

[0030] The vehicle braking scheme generating module is used to simulate all possible braking schemes when the current vehicle is traveling to the current vehicle stopping process based on the current vehicle stopping position identified by the vehicle stopping position identifying module through the vehicle braking process simulation model, and analyze the energy recovery efficiency and energy recovery amount in different braking schemes through the data analysis model, preset the data selection threshold, and select the braking scheme whose data analysis result is higher than the preset selection threshold based on the data screening model;

[0031] The sliding energy recovery unit is used to convert the dynamic energy generated by the wheels during sliding into mechanical energy, and send the converted mechanical energy to the mechanical energy collection unit;

[0032] The braking energy recovery unit is used to convert the dynamic energy generated by the braking distance into mechanical energy by analyzing the braking intensity, battery charge state and vehicle speed, and timely convert the driving state into 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 and uniformly collect and process the mechanical energy generated by the sliding 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 the dynamic energy generated by 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;

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

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

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

[0038] Furthermore, the vehicle braking scheme generating module includes:

[0039] A vehicle braking process simulation module, the vehicle braking process simulation module is used to simulate all possible braking schemes from the current vehicle running to the current vehicle stopping process through a vehicle braking process simulation model, wherein the vehicle braking process simulation model includes a vehicle dynamics model, a braking system model and a control system model;

[0040] A data analysis module, 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] A threshold input module, wherein the threshold input module is used to input a preset selection threshold;

[0042] A data screening module, the data screening module is used to select a braking scheme whose result in the data analysis module is higher than a selection threshold preset by the threshold input module based on the data screening model;

[0043] A data integration model, wherein the data integration model is used to screen the completed braking scheme based on the data screening module;

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

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

[0046] A parts image analysis module, which is used to analyze in real time the changes in the posture of parts of the vehicle during the energy recovery process;

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

[0048] A component force change detection module, the component force change detection module is used to detect the force change status of vehicle components under the condition of vehicle motion posture change based on the force analysis model, the force change status of vehicle components includes vehicle component force compression, vehicle component force no change and vehicle component force reduction;

[0049] The part state change detection module is based on the signal force change state of the vehicle parts in the energy recovery state obtained in the part force change detection module, and through the data restoration model, the signal force state of the vehicle parts in the energy recovery state obtained in the part force change detection module is restored to the initial state of the automobile parts in the no-stress state. The change of the part state can be obtained through the data comparison model.

[0050] 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.

[0051] Compared with the prior art, the present invention provides a new energy vehicle energy recovery method, system and storage medium. Through the setting of the vehicle stop position recognition module, the appropriate parking position can be analyzed in real time according to the status information of the vehicle in front, thereby avoiding the vehicle energy recovery operation being inconsistent with the standard operation due to the artificial selection of the parking position. At the same time, through the setting of the part status 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, thereby avoiding the aggravation of the parts loss of the new energy vehicle due to the too long energy recovery process. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0053] Figure 1 A system structure block diagram provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0055] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0056] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. 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 below 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, the purpose of providing these embodiments is to make the present disclosure thorough and complete and to enable those skilled in the art to fully understand the scope of the present disclosure.

[0058] In the absence of conflict, the various embodiments of the present disclosure and the various features therein 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 used to describe specific embodiments and are not intended to limit the present disclosure. As used herein, the singular forms "a", "an" 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 "comprising" and / or "made of" are used in this specification, the presence of the features, wholes, steps, operations, elements and / or components is specified, but the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof is not excluded.

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

[0062] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those 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 explicitly defined as such herein.

[0063] See also Figure 1 , a new energy vehicle energy recovery method, the method comprises the following working steps:

[0064] S1, judging the driving state of the vehicle ahead based on computer vision and deep learning models, predicting the stopping position of the vehicle ahead through a motion state prediction model when the driving state judgment result of the vehicle ahead is deceleration, judging 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, and judging the stopping position of the current vehicle based on the stopping position of the vehicle ahead and the safe driving distance of the vehicle when the driving state judgment result of the vehicle ahead is stopping;

[0065] S2, based on the current vehicle stop position identified in step S1, and simulating all possible braking schemes when the current vehicle travels to the current vehicle stop process through a vehicle braking process simulation model;

[0066] S3, analyzing the energy recovery efficiency and energy recovery amount in different braking schemes in step S2 through a data analysis model, presetting a data selection threshold, and selecting a braking scheme whose data analysis result is higher than the preset selection threshold based on the data screening model;

[0067] S4, analyzing the states of vehicle parts in the automobile braking solution screened in step S3 based on the state analysis model;

[0068] S5, based on the working state analysis result of the automobile energy recovery parts by the parts state analysis module, and predicting the predicted life of the automobile parts in the energy recovery state through the parts 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 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 electrical energy with an energy value equal to the electrical 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] You can write:

[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 sliding energy recovery unit, a braking energy recovery unit, a mechanical energy collection unit, a mechanical energy conversion unit, a component state analysis module, and a component life prediction module:

[0086] The vehicle stop position recognition module determines the driving state of the vehicle ahead based on computer vision and deep learning models. The driving state includes accelerating, decelerating and stopping. When the driving state of the vehicle ahead is decelerating, the stopping position of the vehicle ahead is predicted by the motion state prediction model. The stopping position of the current vehicle is determined based on the predicted stopping position of the vehicle ahead and the safe driving distance of the vehicle. When the driving state of the vehicle ahead is stopped, the stopping position of the current vehicle is determined based on the stopping position of the vehicle ahead and the safe driving distance of the vehicle.

[0087] The vehicle braking scheme generation module is used to simulate all possible braking schemes when the current vehicle is traveling to the current vehicle stopping process based on the current vehicle stopping position identified by the vehicle stopping position identification module, and analyze the energy recovery efficiency and energy recovery amount in different braking schemes through the data analysis model, preset the data selection threshold, and select the braking scheme whose data analysis result is higher than the preset selection threshold based on the data screening model;

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

[0089] The braking energy recovery unit is used to convert the dynamic energy generated by the braking distance into mechanical energy by analyzing the braking intensity, battery charge state and vehicle speed, and timely convert the driving state into 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 and uniformly collect and process the mechanical energy generated by the sliding 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 by 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;

[0092] The parts state analysis module is used to analyze the states of vehicle parts in the automobile braking solution generated by the automobile braking solution generation module based on the state analysis model;

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

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

[0095] The vehicle braking scheme generation module includes:

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

[0097] A data analysis module, 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;

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

[0099] A data screening module, the data screening module is used to select a braking scheme whose result in the data analysis module is higher than a selection threshold preset by the threshold input module based on the data screening model;

[0100] A data integration model, which is used to screen the completed braking scheme based on the data screening module;

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

[0102] The part status analysis module includes:

[0103] Parts image analysis module: The parts image analysis module is used to analyze the changes in the posture of parts of the vehicle during energy recovery in real time;

[0104] A part shape change detection module is used to analyze the shape change of vehicle parts under the condition of vehicle motion posture change based on the vehicle parts distribution model;

[0105] A parts force change detection module is used to detect the force change of vehicle parts under the condition of vehicle motion posture change based on the force analysis model. The force change of vehicle parts includes vehicle parts being compressed, vehicle parts being unchanged, and vehicle parts being reduced in force.

[0106] The part state change detection module is based on the signal force change state of the vehicle parts in the energy recovery state obtained in the part force change detection module, and through the data restoration model, the signal force state of the vehicle parts in the energy recovery state obtained in the part force change detection module is restored to the initial state of the automobile parts in the no-stress state. The change of the part state can be obtained through the data comparison model.

[0107] Through the setting of the vehicle stopping position recognition module, the appropriate parking position can be analyzed in real time according to the status information of the vehicle in front, thereby avoiding the vehicle energy recovery operation being inconsistent with the standard operation due to the artificial selection of the parking position. At the same time, through the setting of the parts status analysis module and the parts life prediction module, the state of the parts and the life of the parts can be analyzed during the vehicle energy recovery, thereby avoiding the aggravation of the parts loss of new energy vehicles due to the long energy recovery process. At the same time, by integrating the state of the parts and the life of the parts with the braking plan generated by the vehicle braking plan generation module, the service life of the vehicle parts can be guaranteed when the vehicle braking plan is generated, the service life of the vehicle can be increased, and at the same time, it can avoid the driver's overload of vehicle parts caused by increasing the amount of energy recovery.

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

[0109] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A new energy vehicle energy recovery method, characterized in that: The method comprises the following working steps: S1, judging the driving state of the vehicle ahead based on computer vision and deep learning models, predicting the stopping position of the vehicle ahead through a motion state prediction model when the driving state judgment result of the vehicle ahead is deceleration, judging 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, and judging the stopping position of the current vehicle based on the stopping position of the vehicle ahead and the safe driving distance of the vehicle when the driving state judgment result of the vehicle ahead is stopping; S2, based on the current vehicle stop position identified in step S1, and simulating all possible braking schemes when the current vehicle travels to the current vehicle stop process through a vehicle braking process simulation model; S3, analyzing the energy recovery efficiency and energy recovery amount in different braking schemes in step S2 through a data analysis model, presetting a data selection threshold, and selecting a braking scheme whose data analysis result is higher than the preset selection threshold based on the data screening model; S4, analyzing the states of vehicle parts in the automobile braking solution screened in step S3 based on the state analysis model; S5, based on the working state analysis result of the automobile energy recovery parts by the parts state analysis module, and predicting the predicted life of the automobile parts in the energy recovery state through the parts life prediction model; S6, integrating the predicted life of the automobile parts with the automobile braking scheme through the data integration model, and selecting the automobile braking scheme with the highest predicted life of the automobile parts among all automobile braking schemes through the data selection unit; S7, the vehicle brakes according to the vehicle braking scheme selected in step S6; S8, converting the dynamic energy generated by the wheel sliding into mechanical energy, and sending the converted mechanical energy to the mechanical energy collection unit, converting the dynamic energy generated by the braking distance into mechanical energy, timely converting the driving state into the power generation state, and sending the converted mechanical energy to the mechanical energy collection unit; S9, the mechanical energy collection unit collects and processes the mechanical energy generated by the coasting energy recovery unit and the braking energy recovery unit in a centralized and unified manner, and sends the collected mechanical energy to the mechanical energy conversion unit. The mechanical energy conversion unit converts the dynamic energy generated by all mechanical movements collected by the mechanical energy collection unit into charging energy with an energy value equal to the electric energy that can be absorbed by the new energy vehicle battery.

2. A new energy vehicle energy recovery method according to claim 1, characterized in that: The steps of predicting the life of automobile parts specifically include the following working steps: For the degradation of component life x under any condition at any time i (t)~N(μ i t,σ 2 t), we can get the degradation amount x at any time i The probability density function of (t) is: The life of a component refers to the time it takes for a sensitive parameter to reach the failure threshold during the performance degradation process, expressed as T. L It is expressed as: L is the artificially determined failure threshold, and the probability that the degradation 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: You can write: 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 a new energy vehicle energy recovery method according to any one of claims 1-2, characterized in that: It includes vehicle stop position recognition module, vehicle braking scheme generation module, sliding energy recovery unit, braking energy recovery unit, mechanical energy collection unit, mechanical energy conversion unit, part status analysis module, and part life prediction module: The automobile stop position recognition module determines the driving state of the vehicle ahead based on computer vision and deep learning models, and the driving state includes accelerating driving, decelerating driving and stopping of the vehicle. When the driving state judgment result of the vehicle ahead is decelerating driving, the stopping position of the vehicle ahead is predicted by the motion state prediction model, and the stopping position of the current vehicle is determined based on the predicted stopping position of the vehicle ahead and the safe driving distance of the vehicle. When the driving state judgment result of the vehicle ahead is stopping, the stopping position of the current vehicle is determined based on the stopping position of the vehicle ahead and the safe driving distance of the vehicle. The vehicle braking scheme generating module is used to simulate all possible braking schemes when the current vehicle is traveling to the current vehicle stopping process based on the current vehicle stopping position identified by the vehicle stopping position identifying module through the vehicle braking process simulation model, and analyze the energy recovery efficiency and energy recovery amount in different braking schemes through the data analysis model, preset the data selection threshold, and select the braking scheme whose data analysis result is higher than the preset selection threshold based on the data screening model; The sliding energy recovery unit is used to convert the dynamic energy generated by the wheels during sliding into mechanical energy, and send the converted mechanical energy to the mechanical energy collection unit; The braking energy recovery unit is used to convert the dynamic energy generated by the braking distance into mechanical energy by analyzing the braking intensity, battery charge state and vehicle speed, and timely convert the driving state into 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 and uniformly collect and process the mechanical energy generated by the sliding 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 the dynamic energy generated by 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; The part state analysis module is used to analyze the state of vehicle parts in the automobile braking solution generated by the automobile braking solution generation module based on the state analysis model; The part life prediction module is used to predict the predicted life of the automobile parts in the energy recovery state based on the working state analysis results of the automobile energy recovery parts of the part state analysis module, and through the part life prediction model; The automobile braking scheme generation module is also used to integrate the predicted life of automobile parts with the automobile braking scheme based on the predicted life of automobile parts predicted by the parts life prediction module through a data integration model, and select the automobile braking scheme with the highest predicted life of automobile parts among all automobile braking schemes through a data selection unit.

4. The energy recovery system for new energy vehicles according to claim 3 is characterized in that: The automobile braking scheme generating module comprises: A vehicle braking process simulation module, the vehicle braking process simulation module is used to simulate all possible braking schemes from the current vehicle running to the current vehicle stopping process through a vehicle braking process simulation model, wherein the vehicle braking process simulation model includes a vehicle dynamics model, a braking system model and a control system model; A data analysis module, 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; A threshold input module, wherein the threshold input module is used to input a preset selection threshold; A data screening module, the data screening module is used to select a braking scheme whose result in the data analysis module is higher than a selection threshold preset by the threshold input module based on the data screening model; A data integration model, wherein the data integration model is used to screen the completed braking scheme based on the data screening module; A data selection module is used to select an automobile braking scheme with the highest predicted life of automobile parts among all automobile braking schemes based on a data selection model.

5. The energy recovery system for new energy vehicles according to claim 4, characterized in that: The part status analysis module comprises: A parts image analysis module, which is used to analyze in real time the changes in the posture of parts of the vehicle during the energy recovery process; A part shape change detection module, the part shape change detection module is used to analyze the shape change of vehicle parts under the condition of vehicle motion posture change based on the vehicle part distribution model; A component force change detection module, the component force change detection module is used to detect the force change status of vehicle components under the condition of vehicle motion posture change based on the force analysis model, the force change status of vehicle components includes vehicle component force compression, vehicle component force no change and vehicle component force reduction; The part state change detection module is based on the signal force change state of the vehicle parts in the energy recovery state obtained in the part force change detection module, and through the data restoration model, the signal force state of the vehicle parts in the energy recovery state obtained in the part force change detection module is restored to the initial state of the automobile parts in the no-stress state. The change of the part state can be obtained through the 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 the processor, a new energy vehicle energy recovery method as described in any one of claims 1-2 is implemented.

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