An Adaptive Energy Recovery Braking System for Electric Vehicles

Abstract

The present invention provides an adaptive energy recovery braking system for electric vehicles, belonging to the technical field of electric vehicles, including: a judgment module: collecting real-time electric vehicle state data and road condition data to judge whether the energy recovery condition is satisfied; a first determination module: obtaining the driving behavior data of the user and the battery state data of the electric vehicle to determine a dynamic energy recovery strategy; a storage module: collecting the braking energy of the electric vehicle based on the dynamic energy recovery strategy and converting it into electric energy for storage in the electric vehicle battery; a display module: displaying the energy recovery status through a user interface and providing personalized braking options according to the battery saturation state. It solves the problem that the adaptive energy recovery braking system of electric vehicles usually adopts a fixed strategy and cannot be dynamically adjusted according to real-time driving conditions, vehicle states and driver behaviors, resulting in low energy recovery efficiency, affecting the driving experience of users and the battery life.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicles, and particularly to an adaptive energy recovery braking system for electric vehicles. Background Art

[0002] With the popularization of electric vehicles, the energy recovery braking system has become a key technology to improve energy utilization efficiency.

[0003] However, the adaptive energy recovery braking systems of traditional electric vehicles usually adopt fixed strategies and cannot be dynamically adjusted according to real-time driving conditions, vehicle states, and driver behaviors, resulting in low energy recovery efficiency and affecting the driving experience of users and the battery life. Summary of the Invention

[0004] The present invention provides an adaptive energy recovery braking system for electric vehicles to solve the defect that the adaptive energy recovery braking systems of electric vehicles in the prior art usually adopt fixed strategies and cannot be dynamically adjusted according to real-time driving conditions, vehicle states, and driver behaviors, resulting in low energy recovery efficiency and affecting the driving experience of users and the battery life.

[0005] The present invention provides an adaptive energy recovery braking system for electric vehicles, including:

[0006] A judgment module: collecting electric vehicle state data and road condition data in real time, and judging whether the energy recovery condition is met according to the electric vehicle state data and the road condition data;

[0007] A first determination module: obtaining the driving behavior data of the user and the battery state data of the electric vehicle, and determining a dynamic energy recovery strategy according to the driving behavior data and the battery state data;

[0008] A storage module: collecting the braking energy of the electric vehicle based on the dynamic energy recovery strategy and converting it into electric energy to be stored in the electric vehicle battery;

[0009] A display module: displaying the energy recovery state through a user interface and providing personalized braking options according to the battery saturation state.

[0010] According to the adaptive energy recovery braking system for electric vehicles provided by the present invention, the judgment module includes:

[0011] A first collection unit: obtaining the type of the electric vehicle, determining the corresponding sensor of the electric vehicle according to the type of the electric vehicle, and collecting the electric vehicle state data in real time according to the sensor;

[0012] A second collection unit: obtaining the driving path of the electric vehicle, and collecting the road condition data in real time according to the driving path and in combination with an external traffic information network;

[0013] The first determination unit: analyzes the electric vehicle state data and road condition data, and determines the states of multiple parts of the electric vehicle and the specific road conditions according to the analysis results;

[0014] The judgment unit: judges whether the energy recovery condition is met according to the states of multiple parts of the electric vehicle and the specific road conditions.

[0015] According to an adaptive energy recovery braking system for an electric vehicle provided by the present invention, the judgment module further includes:

[0016] The first acquisition unit: after collecting road condition data in real time according to the driving route and combining with an external traffic information network, obtains multiple driving routes through a navigation system, and obtains real-time data of multiple driving routes according to a third-party traffic data platform;

[0017] The first recognition unit: obtains the data types of the real-time data of multiple driving routes, and recognizes the real-time road conditions of different sections of multiple driving routes according to the data types;

[0018] The prediction unit: predicts the road condition changes of different sections in a future period of time based on the real-time road conditions of different sections of the multiple driving routes based on a road condition prediction model;

[0019] The first dynamic adjustment unit: dynamically adjusts the driving route according to the road condition changes, and recalculates and updates the estimated arrival time according to the adjusted route and the real-time road conditions;

[0020] The push unit: pushes road condition information and route adjustment suggestions to the user in different forms.

[0021] According to an adaptive energy recovery braking system for an electric vehicle provided by the present invention, the first determination module includes:

[0022] The second recognition unit: obtains standardized driving behavior data of the user through an on-board diagnostic system protocol, analyzes the driving behavior data, and recognizes the driving mode and driving scenario of the user according to the analysis results;

[0023] The evaluation unit: obtains the battery state data of the electric vehicle through the battery management system of the electric vehicle, evaluates the health state of the battery according to the battery state data, and obtains an evaluation result;

[0024] The second dynamic adjustment unit: determines a dynamic energy recovery strategy according to the driving behavior data and the battery state data, and dynamically adjusts the energy recovery intensity, response time, and energy distribution ratio in combination with the driving mode, driving scenario, and evaluation result.

[0025] An adaptive energy recovery braking system for an electric vehicle provided by the present invention, a second recognition unit, comprising:

[0026] A first acquisition subunit: analyze the driving behavior data, and obtain the user's speed preference and driving style according to the analysis result;

[0027] A second acquisition subunit: obtain the average vehicle speed, braking pedal depression depth and frequency of the electric vehicle according to the user's speed preference, and obtain the speed characteristics of the electric vehicle according to the average vehicle speed, braking pedal depression depth and frequency;

[0028] A determination subunit: obtain the lane change frequency, hard acceleration and hard braking times of the electric vehicle according to the user's driving style, and determine the operation characteristics of the electric vehicle according to the lane change frequency, hard acceleration and hard braking times;

[0029] A classification subunit: classify the speed characteristics and operation characteristics according to a machine learning algorithm, and identify the user's driving mode according to the classification result.

[0030] An adaptive energy recovery braking system for an electric vehicle provided by the present invention, a storage module, comprising:

[0031] A third dynamic adjustment unit: dynamically adjust the intensity and mode of energy recovery based on a dynamic energy recovery strategy and in combination with the vehicle's real-time working conditions;

[0032] A conversion unit: collect and convert the braking energy of the electric vehicle into electric energy according to the adjusted intensity and mode of energy recovery;

[0033] A storage unit: store the electric energy into the electric vehicle battery through a power converter.

[0034] An adaptive energy recovery braking system for an electric vehicle provided by the present invention, a display module, comprising:

[0035] A second acquisition unit: obtain the energy recovery progress and recovery mode status of the electric vehicle, and display the energy recovery progress and recovery mode status in real time through a user interface;

[0036] An optimization unit: provide braking options in different modes according to the battery saturation state and in combination with user requirements, and optimize the energy recovery efficiency according to the braking options in different modes and in combination with the driving scenario and requirements.

[0037] An adaptive energy recovery braking system for an electric vehicle provided by the present invention, further comprising:

[0038] A second determination module: obtain the operating parameters of the electric vehicle in each driving mode, and determine the dynamic feedback torque of the drive motor according to the operating parameters;

[0039] The third determination module: determines the electricity consumption intensity based on the dynamic feedback torque, and determines the theoretical level coefficient of electric energy recovery according to the electricity consumption intensity;

[0040] The fourth determination module: determines the energy recovery intensity and energy recovery smoothness of the electric vehicle in each driving mode according to the theoretical level coefficient of electric energy recovery;

[0041] The fifth determination module: determines the energy recovery logic of the electric vehicle in each driving mode based on the energy recovery intensity and energy recovery smoothness;

[0042] The first acquisition module: obtains the energy recovery condition parameters of the electric vehicle in each driving mode according to the energy recovery logic;

[0043] The sixth determination module: determines the pre-driving state parameters of energy recovery of the electric vehicle in each driving mode according to the energy recovery condition parameters;

[0044] The seventh determination module: determines the energy recovery characteristics of the electric vehicle in each driving mode based on the pre-driving state parameters, and determines the energy recovery attribute according to the energy recovery characteristics. The energy recovery attribute includes: gentle increase, gentle decrease, gradient increase, and gradient decrease;

[0045] The sorting module: determines the energy recovery rate of the electric vehicle in each driving mode according to the energy recovery attribute, and performs priority sorting on each driving mode of the electric vehicle based on the energy recovery rate;

[0046] The screening module: screens out the normal energy recovery driving mode and the abnormal energy recovery driving mode according to the sorting result.

[0047] Compared with the prior art, the beneficial effects of the present application are as follows:

[0048] Determine the dynamic energy recovery strategy through the energy recovery conditions and in combination with the user's driving behavior data and the battery state data of the electric vehicle, and convert it into electric energy and store it in the vehicle battery. Avoid using a fixed strategy, and can dynamically adjust the energy recovery strategy according to real-time driving conditions, vehicle status, and driver behavior, improve the energy recovery efficiency, and ensure the user's driving experience and battery life. Description of the Drawings

[0049] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings.

[0050] Figure 1 is a schematic structural diagram of an adaptive energy recovery braking system for an electric vehicle provided by an embodiment of the present invention;

[0051] Figure 2 is a schematic structural diagram of a judgment module provided by an embodiment of the present invention. Specific Embodiment

[0052] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts shall fall within the protection scope of the present invention.

[0053] Embodiment 1:

[0054] An adaptive energy recovery braking system for an electric vehicle provided by an embodiment of the present invention, as Figure 1 shown, the system mainly includes the following modules:

[0055] Judgment module: Real-time collect the state data of the electric vehicle and the road condition data, and judge whether the energy recovery condition is satisfied according to the state data of the electric vehicle and the road condition data;

[0056] First determination module: Obtain the driving behavior data of the user and the battery state data of the electric vehicle, and determine the dynamic energy recovery strategy according to the driving behavior data and the battery state data;

[0057] Storage module: Collect the braking energy of the electric vehicle based on the dynamic energy recovery strategy and convert it into electric energy for storage in the electric vehicle battery;

[0058] Display module: Display the energy recovery status through the user interface and provide personalized braking options according to the battery saturation status.

[0059] In this embodiment, the state data of the electric vehicle includes: vehicle speed, acceleration, battery state, brake pedal position.

[0060] In this embodiment, the road condition data may be: uphill distance, downhill distance.

[0061] In this embodiment, the energy recovery condition generally refers to the condition that when the electric vehicle decelerates or brakes, the kinetic energy of the electric vehicle can be converted into electric energy and stored in the battery

[0062] The beneficial effects of the above technical solution are as follows: By determining the dynamic energy recovery strategy based on the energy recovery conditions in combination with the driving behavior data of the user and the battery state data of the electric vehicle, and converting it into electrical energy to be stored in the vehicle battery, the adoption of a fixed strategy is avoided. The energy recovery strategy can be dynamically adjusted according to real-time driving conditions, vehicle status, and driver behavior, improving the energy recovery efficiency and ensuring the driving experience of the user and the battery life.

[0063] Embodiment 2:

[0064] Based on Embodiment 1, the judgment module of the present invention embodiment, as Figure 2 shown, includes:

[0065] The first acquisition unit: Obtain the type of the electric vehicle, determine the corresponding sensors for the electric vehicle according to the type of the electric vehicle, and collect the electric vehicle status data in real time according to the sensors;

[0066] The second acquisition unit: Obtain the driving path of the electric vehicle, and collect the road condition data in real time according to the driving path and in combination with the external traffic information network;

[0067] The first determination unit: Analyze the electric vehicle status data and the road condition data, and determine the status of multiple parts of the electric vehicle and the specific road surface conditions according to the analysis results;

[0068] The judgment unit: Judge whether the energy recovery conditions are met according to the status of multiple parts of the electric vehicle and the specific road surface conditions.

[0069] In this embodiment, it is judged whether the energy recovery conditions are met according to the vehicle status data and the road condition data. For example:

[0070] Judgment conditions for vehicle speed and acceleration:

[0071] If the vehicle is decelerating and the vehicle speed is moderate, the energy recovery conditions may be met.

[0072] If the vehicle is in an accelerating or constant speed state, the energy recovery conditions are not met.

[0073] Judgment conditions for road condition data:

[0074] If the vehicle is on a downhill section and the slope is moderate, the energy recovery conditions may be met.

[0075] If the vehicle is on an uphill section or the slope is too steep, the energy recovery conditions are not met.

[0076] The beneficial effects of the above technical solution are as follows: By collecting the state data of the electric vehicle and the road condition data in real time according to the sensors and the external traffic information network, and determining whether the energy recovery condition is met, the credibility of the data can be improved and the accuracy of the data can be ensured. At the same time, determining whether the energy recovery condition is met can maximize the recovery of energy and improve the energy recovery efficiency.

[0077] Embodiment 3:

[0078] Based on Embodiment 2, the judgment module of the present invention embodiment further includes:

[0079] The first acquisition unit: After collecting the road condition data in real time according to the driving route and in combination with the external traffic information network, obtain multiple driving routes through the navigation system, and obtain the real-time data of the multiple driving routes according to the third-party traffic data platform;

[0080] The first recognition unit: Obtain the data types of the real-time data of the multiple driving routes, and identify the real-time road conditions of different sections of the multiple driving routes according to the data types;

[0081] The prediction unit: Predict the road condition changes of different sections within a future period of time based on the real-time road conditions of different sections of the multiple driving routes based on the road condition prediction model;

[0082] The first dynamic adjustment unit: Dynamically adjust the driving route according to the road condition changes, and recalculate and update the estimated arrival time according to the adjusted route and the real-time road conditions;

[0083] The push unit: Push the road condition information and route adjustment suggestions to the user in different forms.

[0084] In this embodiment, the third-party traffic data platform may be: Amap, Baidu Map.

[0085] In this embodiment, the driving route includes: the starting point, the ending point and the passing points.

[0086] In this embodiment, the data types include:

[0087] Traffic flow data: including the road congestion degree, average vehicle speed.

[0088] Event data: such as traffic accidents, road construction, temporary road closures.

[0089] Weather data: such as the impact of rain, snow, haze, etc. on the road conditions.

[0090] Traffic signal data: such as traffic light status, traffic control information.

[0091] In this embodiment, the road condition change refers to the congestion trend during peak hours.

[0092] In this embodiment, the different forms include: voice, text, or image.

[0093] The beneficial effects of the above technical solution are as follows: By identifying the real-time road conditions of different sections based on the data types of the real-time data of multiple driving routes, and predicting the changes in the road conditions of different sections within a certain period of time in the future based on the road condition prediction model, the driving route can be adjusted, which can reduce the braking demand of the vehicle during driving. By anticipating the road conditions in advance and adjusting the vehicle speed, the vehicle can drive in a more stable state, reduce the frequency of sudden braking, thereby reducing energy waste, and at the same time improve the efficiency of braking energy recovery.

[0094] Embodiment 4:

[0095] Based on Embodiment 3, the first determination module of the embodiment of the present invention includes:

[0096] The second recognition unit: Obtain the standardized driving behavior data of the user through the on-board diagnostic system protocol, analyze the driving behavior data, and identify the driving mode and driving scenario of the user according to the analysis results;

[0097] The evaluation unit: Obtain the battery state data of the electric vehicle through the battery management system of the electric vehicle, evaluate the health state of the battery according to the battery state data, and obtain the evaluation result;

[0098] The second dynamic adjustment unit: Determine the dynamic energy recovery strategy according to the driving behavior data and the battery state data, and dynamically adjust the energy recovery intensity, response time, and energy distribution ratio in combination with the driving mode, driving scenario, and evaluation result.

[0099] In this embodiment, the driving behavior data refers to a data set that reflects the driver's operation behavior, vehicle motion state, and road environment, etc., collected through various sensors and devices during the vehicle's driving process, including: driver operation data, steering angle, shift operation, acceleration, and deceleration.

[0100] In this embodiment, the battery state data of the electric vehicle is information that reflects the battery health status, performance, and usage status, including: battery power, battery voltage, and battery temperature.

[0101] In this embodiment, the dynamic energy recovery strategy refers to that the electric vehicle dynamically adjusts the intensity and timing of energy recovery according to the real-time driving state of the vehicle to achieve a balance between energy recovery efficiency and driving comfort.

[0102] The beneficial effects of the above technical solution are as follows: By obtaining the driving behavior data of the user and the battery state data of the electric vehicle, a dynamic energy recovery strategy can be determined, enabling more precise adjustment of the recovery intensity. At the same time, the battery state data can help the system avoid energy recovery under unsuitable conditions and protect the battery by adjusting the recovery strategy.

[0103] Embodiment 5:

[0104] Based on Embodiment 4, the second recognition unit of the embodiment of the present invention includes:

[0105] The first acquisition subunit: Analyze the driving behavior data and obtain the speed preference and driving style of the user according to the analysis results;

[0106] The second acquisition subunit: Obtain the average vehicle speed, the depression depth and frequency of the brake pedal of the electric vehicle according to the speed preference of the user, and obtain the speed characteristics of the electric vehicle according to the average vehicle speed, the depression depth and frequency of the brake pedal;

[0107] The determination subunit: Obtain the lane change frequency, the number of rapid accelerations and rapid brakings of the electric vehicle according to the driving style of the user, and determine the operation characteristics of the electric vehicle according to the lane change frequency, the number of rapid accelerations and rapid brakings;

[0108] The classification subunit: Classify the speed characteristics and operation characteristics according to the machine learning algorithm, and identify the driving mode of the user according to the classification results.

[0109] In this embodiment, the driving behavior data refers to a data set that reflects the driver's operation behavior, the vehicle's motion state, and the road environment, etc., collected by various sensors and devices during the vehicle's driving, including: driver operation data, steering angle, gear shifting operation, acceleration, and deceleration.

[0110] In this embodiment, the speed preference includes: the average vehicle speed, the depression depth and frequency of the brake pedal of the electric vehicle.

[0111] In this embodiment, the driving style includes: the lane change frequency, the number of rapid accelerations and rapid brakings of the electric vehicle.

[0112] In this embodiment, the depression depth of the brake pedal refers to the degree to which the pedal is depressed when the driver steps on the brake pedal, usually expressed as a percentage or an absolute distance.

[0113] In this embodiment, obtaining the average vehicle speed of the electric vehicle according to the speed preference of the user includes:

[0114] Obtain the speed preference range of the user and the real-time vehicle speed, and obtain the average vehicle speed of the electric vehicle according to the speed preference range and the real-time vehicle speed:

[0115] ; wherein, represents the average vehicle speed of the electric vehicle, represents the time period, represents the real-time vehicle speed of the electric vehicle, represents the midpoint of the user's speed preference range, is calculated, represents the standard deviation, which is the width controlling the weight distribution, and is calculated by is calculated.

[0116] The beneficial effects of the above technical solution are as follows: analyzing the driving behavior data, obtaining the speed characteristics and operation characteristics during driving and classifying them, thereby identifying the user's driving mode, the intelligent braking system can dynamically adjust the braking force according to the driver's intention and driving style, reduce the suddenness of braking caused by differences in driving habits, provide a smooth braking experience. At the same time, the system can respond quickly and provide the maximum braking force, significantly shortening the braking distance and reducing the collision risk.

[0117] Embodiment 6:

[0118] Based on Embodiment 5, the storage module of the embodiment of the present invention includes:

[0119] The third dynamic adjustment unit: dynamically adjusts the intensity and mode of energy recovery based on the dynamic energy recovery strategy and in combination with the real-time working conditions of the vehicle;

[0120] The conversion unit: collects and converts the braking energy of the electric vehicle into electric energy according to the adjusted intensity and mode of energy recovery;

[0121] The storage unit: stores the electric energy into the electric vehicle battery through a power converter.

[0122] In this embodiment, the dynamic energy recovery strategy means that the electric vehicle dynamically adjusts the intensity and timing of energy recovery according to the real-time driving state of the vehicle to achieve a balance between energy recovery efficiency and driving comfort.

[0123] In this embodiment, the real-time working conditions of the vehicle refer to the current operating state and environmental conditions during the vehicle's driving, including: vehicle speed, battery state, driving intention.

[0124] In this embodiment, the power converter is an electronic device for converting electric energy from one form to another.

[0125] The beneficial effects of the above technical solution are as follows: By means of the dynamic energy recovery strategy, the energy of the electric vehicle is collected and converted into electric energy for storage, optimizing the overall vehicle energy management, reducing the waste of energy during braking. At the same time, it can increase the driving range of the electric vehicle and improve the reliability of the braking system.

[0126] Embodiment 7:

[0127] Based on Embodiment 6, the display module of the embodiment of the present invention includes:

[0128] The second acquisition unit: acquires the energy recovery progress and recovery mode status of the electric vehicle, and displays the energy recovery progress and recovery mode status in real time through the user interface;

[0129] The optimization unit: provides braking options in different modes according to the battery saturation state and in combination with user requirements, and optimizes the energy recovery efficiency according to the braking options in different modes and in combination with the driving scenario and requirements.

[0130] In this embodiment, the energy recovery mode status of the electric vehicle refers to the working state and characteristics of the energy recovery system when the vehicle is in different driving modes, including: the starting conditions of energy recovery, the recovery intensity, the recovery timing, and the recovery efficiency.

[0131] The beneficial effects of the above technical solution are as follows: By displaying the energy recovery status through the user interface and providing personalized braking options according to the battery saturation state, it can ensure that users can understand the current state of the electric vehicle in real time, improving the driving experience. At the same time, according to the battery saturation state, the energy recovery intensity can be dynamically adjusted to ensure the maximum energy recovery efficiency.

[0132] Embodiment 8:

[0133] Based on Embodiment 7, the embodiment of the present invention further includes:

[0134] The second determination module: acquires the operating parameters of the electric vehicle in each driving mode, and determines the dynamic feedback torque of the drive motor according to the operating parameters;

[0135] The third determination module: determines the power consumption intensity based on the dynamic feedback torque, and determines the theoretical level coefficient of electric energy recovery according to the power consumption intensity;

[0136] The fourth determination module: determines the energy recovery intensity and energy recovery smoothness of the electric vehicle in each driving mode according to the theoretical level coefficient of electric energy recovery;

[0137] The fifth determination module: determines the energy recovery logic of the electric vehicle in each driving mode based on the energy recovery intensity and energy recovery smoothness;

[0138] The first acquisition module: Obtain the energy recovery condition parameters of the electric vehicle in various driving modes according to the energy recovery logic;

[0139] The sixth determination module: Determine the pre-driving state parameters of the electric vehicle for energy recovery in various driving modes according to the energy recovery condition parameters;

[0140] The seventh determination module: Determine the energy recovery characteristics of the electric vehicle in various driving modes based on the pre-driving state parameters, and determine the energy recovery attributes according to the energy recovery characteristics. The energy recovery attributes include: gentle increase, gentle decrease, gradient increase, and gradient decrease;

[0141] The sorting module: Determine the energy recovery rate of the electric vehicle in various driving modes according to the energy recovery attributes, and perform priority sorting on various driving modes of the electric vehicle based on the energy recovery rate;

[0142] The screening module: Screen out the normal energy recovery driving mode and the abnormal energy recovery driving mode according to the sorting result.

[0143] In this embodiment, the operating parameters in various driving modes include: power, power, energy recovery intensity, and steering assist.

[0144] In this embodiment, the dynamic feedback torque of the drive motor refers to the ability of the motor to dynamically adjust the output torque during operation according to the actual operating state and control requirements of the vehicle.

[0145] In this embodiment, the theoretical level coefficient of electric energy recovery refers to the theoretical efficiency or level of the electric vehicle recovering energy through the braking energy recovery system under ideal conditions.

[0146] In this embodiment, the energy recovery intensity refers to the magnitude of the ability of the electric vehicle to convert the kinetic energy of the vehicle into electric energy and recover it into the battery through the motor during coasting or braking.

[0147] In this embodiment, the energy recovery smoothness refers to whether the braking or deceleration process of the vehicle is stable and comfortable during the energy recovery process of the electric vehicle, and whether the driving and riding experience is significantly affected.

[0148] In this embodiment, the energy recovery logic of the electric vehicle in different driving modes refers to that the vehicle adjusts the intensity, timing, and method of the energy recovery system according to the setting of the driving mode to achieve different driving experiences and energy utilization efficiencies.

[0149] In this embodiment, the pre-driving state parameters for energy recovery refer to a series of driving state conditions that the vehicle needs to meet before the energy recovery system of the electric vehicle is started, such as vehicle speed.

[0150] In this embodiment, the energy recovery characteristics under different driving modes refer to the differences in the working mode, intensity, timing, and recovery efficiency of the energy recovery system in various driving modes of an electric vehicle. For example:

[0151] The energy recovery characteristics of the economy mode include: high-intensity recovery, frequent start, and obvious deceleration feeling.

[0152] The energy recovery characteristics of the standard / comfort mode include: medium-intensity recovery, natural deceleration feeling.

[0153] In this embodiment, the energy recovery attributes include: gentle increase, gentle decrease, gradient increase, and gradient decrease.

[0154] The beneficial effects of the above technical solutions are: determining the energy recovery attributes according to the energy recovery characteristics, thereby determining the energy recovery rate of the electric vehicle in each driving mode, and sorting the driving modes by priority, which can accurately obtain the energy recovery driving modes in different states, can recover braking energy more efficiently, and improve the overall performance of the vehicle.

[0155] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solutions, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0156] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An adaptive energy recovery braking system for an electric vehicle, characterized in that, Including: Judgment module: Collecting electric vehicle status data and road condition data in real time, and judging whether the energy recovery condition is met according to the electric vehicle status data and road condition data; First determination module: Obtaining the driving behavior data of the user and the battery status data of the electric vehicle, and determining a dynamic energy recovery strategy according to the driving behavior data and the battery status data; Storage module: Collecting the braking energy of the electric vehicle based on the dynamic energy recovery strategy and converting it into electric energy to be stored in the electric vehicle battery; Display module: Displaying the energy recovery status through the user interface, and providing personalized braking options according to the battery saturation status; Among them, the first determination module includes: Second recognition unit: Obtaining the standardized driving behavior data of the user through the on-board diagnostic system protocol, analyzing the driving behavior data, and identifying the driving mode and driving scenario of the user according to the analysis results; Evaluation unit: Obtaining the battery status data of the electric vehicle through the battery management system of the electric vehicle, evaluating the health status of the battery according to the battery status data, and obtaining the evaluation result; Second dynamic adjustment unit: Determining a dynamic energy recovery strategy according to the driving behavior data and the battery status data, and dynamically adjusting the energy recovery intensity, response time and energy distribution ratio in combination with the driving mode, driving scenario and evaluation result; Among them, the second recognition unit includes: First acquisition subunit: Analyzing the driving behavior data and obtaining the speed preference and driving style of the user according to the analysis results; Second acquisition subunit: Obtaining the average vehicle speed, braking pedal depression depth and frequency of the electric vehicle according to the speed preference of the user, and obtaining the speed characteristics of the electric vehicle according to the average vehicle speed, braking pedal depression depth and frequency; Determination subunit: Obtaining the lane change frequency, hard acceleration and hard braking times of the electric vehicle according to the driving style of the user, and determining the operation characteristics of the electric vehicle according to the lane change frequency, hard acceleration and hard braking times; Classification subunit: Classifying the speed characteristics and operation characteristics according to the machine learning algorithm, and identifying the driving mode of the user according to the classification results; Among them, obtaining the average vehicle speed of the electric vehicle according to the speed preference of the user includes: Obtain the speed preference range of the user and the real-time vehicle speed, and obtain the average vehicle speed of the electric vehicle according to the speed preference range and the real-time vehicle speed: ; wherein, represents the average vehicle speed of the electric vehicle, represents the time period, represents the real-time vehicle speed of the electric vehicle, represents the midpoint of the user's speed preference range, is calculated, represents the standard deviation, which is the width of controlling the weight distribution, and is calculated by calculation; Among them, it further includes: Second determination module: Obtaining the operation parameters of the electric vehicle in each driving mode, and determining the power feedback torque of the drive motor according to the operation parameters; Third determination module: Determining the power consumption intensity based on the power feedback torque, and determining the theoretical level coefficient of electric energy recovery according to the power consumption intensity; Fourth determination module: Determining the energy recovery intensity and energy recovery smoothness of the electric vehicle in each driving mode according to the theoretical level coefficient of electric energy recovery; Fifth determination module: Determining the energy recovery logic of the electric vehicle in each driving mode based on the energy recovery intensity and energy recovery smoothness; First acquisition module: Obtaining the energy recovery condition parameters of the electric vehicle in each driving mode according to the energy recovery logic; Sixth determination module: Determining the energy recovery pre-driving state parameters of the electric vehicle in each driving mode according to the energy recovery condition parameters; The seventh determination module: Determine the energy recovery characteristics of the electric vehicle in each driving mode based on the pre-driving state parameters, and determine the energy recovery attribute according to the energy recovery characteristics. The energy recovery attribute includes: gentle increase, gentle decrease, gradient increase, and gradient decrease; The sorting module: Determine the energy recovery rate of the electric vehicle in each driving mode according to the energy recovery attribute, and perform priority sorting on each driving mode of the electric vehicle based on the energy recovery rate; The screening module: Screen out the normal energy recovery driving mode and the abnormal energy recovery driving mode according to the sorting result.

2. The adaptive energy recovery braking system for an electric vehicle according to claim 1, wherein The judgment module, including: The first acquisition unit: Obtain the type of the electric vehicle, determine the corresponding sensor of the electric vehicle according to the type of the electric vehicle, and collect the electric vehicle state data in real time according to the sensor; The second acquisition unit: Obtain the driving path of the electric vehicle, and collect the road condition data in real time according to the driving path and in combination with the external traffic information network; The first determination unit: Analyze the electric vehicle state data and the road condition data, and determine the states of multiple parts of the electric vehicle and the specific road surface conditions according to the analysis result; The judgment unit: Judge whether the energy recovery condition is met according to the states of multiple parts of the electric vehicle and the specific road surface conditions.

3. The adaptive energy recovery braking system for an electric vehicle according to claim 2, characterized in that, The judgment module further includes: The first acquisition unit: After collecting the road condition data in real time according to the driving path and in combination with the external traffic information network, obtain multiple driving paths through the navigation system, and obtain the real-time data of the multiple driving paths according to the third-party traffic data platform; The first recognition unit: Obtain the data type of the real-time data of the multiple driving paths, and recognize the real-time road conditions of different sections of the multiple driving paths according to the data type; The prediction unit: Predict the road condition changes of different sections in the future period based on the real-time road conditions of different sections of the multiple driving paths based on the road condition prediction model; The first dynamic adjustment unit: Dynamically adjust the driving path according to the road condition changes, and recalculate and update the estimated arrival time according to the adjusted path and the real-time road conditions; The push unit: Push the road condition information and path adjustment suggestions to the user in different forms.

4. The adaptive energy recovery braking system for an electric vehicle according to claim 1, wherein The storage module, including: The third dynamic adjustment unit: Dynamically adjust the intensity and mode of energy recovery based on the dynamic energy recovery strategy and in combination with the real-time working conditions of the vehicle; The conversion unit: Collect and convert the braking energy of the electric vehicle into electric energy according to the adjusted intensity and mode of energy recovery; The storage unit: Store the electric energy into the electric vehicle battery through a power converter.

5. The adaptive energy recovery braking system for an electric vehicle according to claim 1, wherein The display module, including: The second acquisition unit: Obtain the energy recovery progress and the recovery mode status of the electric vehicle, and display the energy recovery progress and the recovery mode status in real time through the user interface; The optimization unit: Provide braking options in different modes according to the battery saturation state and in combination with the user's needs, and optimize the energy recovery efficiency according to the braking options in different modes and in combination with the driving scenario and needs.

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