Self-adaptive energy recovery braking system for electric automobile

Abstract

The invention provides a self-adaptive energy recovery braking system for an electric automobile, which belongs to the technical field of electric automobiles and comprises a judgment module, a control module, a braking module and an energy recovery module, and the judgment module is used for collecting state data and road condition data of the electric automobile in real time and judging whether energy recovery conditions are met; the first determination module is used for acquiring driving behavior data of a user and battery state data of the electric vehicle and determining a dynamic energy recovery strategy; the storage module is used for collecting braking energy of the electric vehicle based on a dynamic energy recovery strategy, converting the braking energy into electric energy and storing the electric energy into a battery of the electric vehicle; and the display module is used for displaying the energy recovery state through a user interface and providing personalized braking options according to the battery saturation state. The problems that a self-adaptive energy recovery braking system of an electric vehicle generally adopts a fixed strategy and cannot be dynamically adjusted according to real-time driving conditions, vehicle states and driver behaviors, so that the energy recovery efficiency is not high, and the driving experience of a user and the service life of a battery are affected are solved.

Description

Technical Field

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

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

[0003] However, the adaptive energy recovery braking system of traditional electric vehicles usually adopts a fixed strategy and cannot be dynamically adjusted according to real-time driving conditions, vehicle status and driver behavior, resulting in low energy recovery efficiency, affecting the user's driving experience and battery life. Summary of the invention

[0004] The present invention provides an adaptive energy recovery braking system for electric vehicles, so as to solve the defect that the adaptive energy recovery braking system of electric vehicles in the prior art usually adopts a fixed strategy and cannot be dynamically adjusted according to real-time driving conditions, vehicle status and driver behavior, resulting in low energy recovery efficiency, affecting the user's driving experience and battery life.

[0005] The present invention provides an adaptive energy recovery braking system for an electric vehicle, comprising: Judgment module: collects electric vehicle status data and road condition data in real time, and judges whether energy recovery conditions are met based on the electric vehicle status data and road condition data; The first determination module: obtains the user's driving behavior data and the battery status data of the electric vehicle, and determines the dynamic energy recovery strategy according to the driving behavior data and the battery status data; Storage module: Based on the dynamic energy recovery strategy, the braking energy of the electric vehicle is collected and converted into electrical energy and stored in the electric vehicle battery; Display module: displays the energy recovery status through the user interface and provides personalized braking options based on the battery saturation status.

[0006] According to the present invention, an adaptive energy recovery braking system for an electric vehicle, a judgment module, includes: The first acquisition unit is used to obtain the type of electric vehicle, determine the sensor corresponding to the electric vehicle according to the type of the electric vehicle, and collect the state data of the electric vehicle in real time according to the sensor; The second collection unit is used to obtain the driving path of the electric vehicle, and to collect road condition data in real time according to the driving path and in combination with an external traffic information network; A first determination unit: analyzing the electric vehicle state data and the road condition data, and determining the states of multiple parts of the electric vehicle and the specific conditions of the road surface according to the analysis results; A judgment unit is used to judge whether the energy recovery condition is met according to the status of the multiple parts of the electric vehicle and the specific conditions of the road surface.

[0007] According to an adaptive energy recovery braking system for an electric vehicle provided by the present invention, the judgment module further includes: A first acquisition unit: after collecting road condition data in real time based on the driving route and in combination with an external traffic information network, acquires multiple driving routes through a navigation system, and acquires real-time data of the multiple driving routes based on a third-party traffic data platform; A first identification unit is configured to obtain data types of real-time data of a plurality of driving routes, and to identify real-time road conditions of different sections of the plurality of driving routes according to the data types; Prediction unit: predicting the changes of road conditions of different sections within a period of time in the future based on the road condition prediction model according to the real-time road conditions of different sections of the multiple driving routes; A first dynamic adjustment unit: dynamically adjusts the driving route according to the change in road conditions, and recalculates and updates the estimated arrival time according to the adjusted route and real-time road conditions; Push unit: Push traffic information and route adjustment suggestions to users in different forms.

[0008] According to an adaptive energy recovery braking system for an electric vehicle provided by the present invention, a first determination module includes: A second identification unit: obtaining standardized user driving behavior data through an on-board diagnostic system protocol, analyzing the driving behavior data, and identifying the user's driving mode and driving scene according to the analysis results; Evaluation unit: obtains battery status data of the electric vehicle through the battery management system of the electric vehicle, evaluates the health status of the battery according to the battery status data, and obtains the evaluation result; The second dynamic adjustment unit determines a dynamic energy recovery strategy according to the driving behavior data and the battery status data, and dynamically adjusts the energy recovery strength, response time and energy distribution ratio in combination with the driving mode, driving scenario and evaluation results.

[0009] According to an adaptive energy recovery braking system for an electric vehicle provided by the present invention, the second identification unit includes: A first acquisition subunit: analyzing the driving behavior data, and acquiring the user's speed preference and driving style according to the analysis results; A second acquisition subunit: acquiring an average speed, brake pedal depression depth and frequency of the electric vehicle according to the speed preference of the user, and acquiring a speed feature of the electric vehicle according to the average speed, brake pedal depression depth and frequency; A determination subunit: obtaining a lane changing frequency, a number of sudden accelerations and sudden brakings of the electric vehicle according to the driving style of the user, and determining an operation characteristic of the electric vehicle according to the lane changing frequency, the number of sudden accelerations and sudden brakings; Classification subunit: classifies the speed characteristics and operation characteristics according to a machine learning algorithm, and identifies the user's driving mode according to the classification results.

[0010] According to the present invention, an adaptive energy recovery braking system for an electric vehicle, a storage module, includes: A 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; Conversion unit: collects the braking energy of the electric vehicle and converts it into electrical energy according to the adjusted intensity and mode of energy recovery; Storage unit: stores the electrical energy into the electric vehicle battery through a power converter.

[0011] According to the present invention, an adaptive energy recovery braking system for an electric vehicle, a display module, includes: A second acquisition unit is used to acquire the energy recovery progress and recovery mode status of the electric vehicle, and to display the energy recovery progress and recovery mode status in real time through a user interface; Optimization unit: provides different modes of braking options according to the battery saturation status and combined with user needs, and optimizes energy recovery efficiency according to different modes of braking options and combined with driving scenarios and needs.

[0012] An adaptive energy recovery braking system for an electric vehicle provided by the present invention further includes: The second determination module: obtains the operating parameters of the electric vehicle in each driving mode, and determines the power feedback torque of the drive motor according to the operating parameters; The third determination module: determines the power intensity based on the power feedback torque, and determines the power recovery theoretical level coefficient according to the power intensity; The fourth determination module: determining the energy recovery intensity and energy recovery smoothness of the electric vehicle in each driving mode according to the electric energy recovery theoretical level coefficient; The fifth determination module is used to determine the energy recovery logic of the electric vehicle in each driving mode based on the energy recovery intensity and the energy recovery smoothness; The first acquisition module is used to acquire energy recovery condition parameters of the electric vehicle in various driving modes according to the energy recovery logic; A sixth determination module: determining energy recovery pre-driving state parameters of the electric vehicle in each driving mode according to the energy recovery condition parameters; A seventh determination module: determining the energy recovery characteristics of the electric vehicle in each driving mode based on the preceding driving state parameter, and determining the energy recovery attribute according to the energy recovery characteristics, wherein the energy recovery attribute includes: gently increasing, gently decreasing, and gradually increasing and gradually decreasing; Sorting module: determining the energy recovery rate of the electric vehicle in each driving mode according to the energy recovery attribute, and prioritizing each driving mode of the electric vehicle based on the energy recovery rate; Screening module: Screen out normal energy recovery driving mode and abnormal energy recovery driving mode according to the sorting results.

[0013] Compared with the prior art, the present invention has the following beneficial effects: The dynamic energy recovery strategy is determined by energy recovery conditions and combined with the user's driving behavior data and the battery status data of the electric vehicle, and converted into electrical energy and stored in the vehicle battery, avoiding the use of fixed strategies. The energy recovery strategy can be dynamically adjusted according to real-time driving conditions, vehicle status and driver behavior, thereby improving energy recovery efficiency and ensuring the user's driving experience and battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] 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; Figure 2 It is a structural diagram of a judgment module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Embodiment 1: An adaptive energy recovery braking system for an electric vehicle is provided in an embodiment of the present invention. Figure 1 As shown, the system mainly includes the following modules: Judgment module: collects electric vehicle status data and road condition data in real time, and judges whether energy recovery conditions are met based on the electric vehicle status data and road condition data; The first determination module: obtains the user's driving behavior data and the battery status data of the electric vehicle, and determines the dynamic energy recovery strategy according to the driving behavior data and the battery status data; Storage module: Based on the dynamic energy recovery strategy, the braking energy of the electric vehicle is collected and converted into electrical energy and stored in the electric vehicle battery; Display module: displays the energy recovery status through the user interface and provides personalized braking options based on the battery saturation status.

[0018] In this embodiment, the electric vehicle status data includes: vehicle speed, acceleration, battery status, and brake pedal position.

[0019] In this embodiment, the road condition data may be: uphill distance, downhill distance.

[0020] In this embodiment, the energy recovery condition generally refers to the condition that the kinetic energy of the electric vehicle can be converted into electrical energy and stored in the battery when the electric vehicle is decelerating or braking. The beneficial effects of the above technical solution are: determining a dynamic energy recovery strategy through energy recovery conditions and combining the user's driving behavior data and the battery status data of the electric vehicle, and converting it into electrical energy and storing it in the car battery, avoiding the use of a fixed strategy. The energy recovery strategy can be dynamically adjusted according to real-time driving conditions, vehicle status and driver behavior, thereby improving energy recovery efficiency and ensuring the user's driving experience and battery life.

[0021] Embodiment 2: Based on Example 1, the judgment module of the embodiment of the present invention is as follows: Figure 2 As shown, including: The first acquisition unit is used to obtain the type of electric vehicle, determine the sensor corresponding to the electric vehicle according to the type of the electric vehicle, and collect the state data of the electric vehicle in real time according to the sensor; The second collection unit is used to obtain the driving path of the electric vehicle, and to collect road condition data in real time according to the driving path and in combination with an external traffic information network; A first determination unit: analyzing the electric vehicle state data and the road condition data, and determining the states of multiple parts of the electric vehicle and the specific conditions of the road surface according to the analysis results; A judgment unit is used to judge whether the energy recovery condition is met according to the status of the multiple parts of the electric vehicle and the specific conditions of the road surface.

[0022] In this embodiment, it is determined whether the energy recovery conditions are met based on the vehicle status data and the road condition data, for example: Vehicle speed and acceleration judgment conditions: If the vehicle is decelerating and the speed is moderate, the conditions for energy recovery may be met.

[0023] If the vehicle is accelerating or moving at a constant speed, the energy recovery conditions are not met.

[0024] Judgment conditions of road condition data: If the vehicle is on a downhill road and the gradient is moderate, the conditions for energy recuperation may be met.

[0025] If the vehicle is on an uphill section or the slope is too steep, the energy recovery conditions are not met.

[0026] The beneficial effects of the above technical solution are: collecting electric vehicle status data and road condition data in real time based on sensors and external traffic information networks, and judging whether energy recovery conditions are met, which can improve the credibility of data and ensure data accuracy. At the same time, judging whether energy recovery conditions are met can maximize energy recovery and improve energy recovery efficiency.

[0027] Embodiment 3: Based on Example 2, the judgment module in this embodiment of the present invention further includes: A first acquisition unit: after collecting road condition data in real time based on the driving route and in combination with an external traffic information network, acquires multiple driving routes through a navigation system, and acquires real-time data of the multiple driving routes based on a third-party traffic data platform; A first identification unit is configured to obtain data types of real-time data of a plurality of driving routes, and to identify real-time road conditions of different sections of the plurality of driving routes according to the data types; Prediction unit: predicting the changes of road conditions of different sections within a period of time in the future based on the road condition prediction model according to the real-time road conditions of different sections of the multiple driving routes; A first dynamic adjustment unit: dynamically adjusts the driving route according to the change in road conditions, and recalculates and updates the estimated arrival time according to the adjusted route and real-time road conditions; Push unit: Push traffic information and route adjustment suggestions to users in different forms.

[0028] In this embodiment, the third-party traffic data platform may be: Amap and Baidu Maps.

[0029] In this embodiment, the driving path includes: a starting point, an end point and a passing point.

[0030] In this embodiment, the data types include: Traffic flow data: including road congestion level and average vehicle speed.

[0031] Event data: such as traffic accidents, road construction, and temporary road closures.

[0032] Weather data: such as the impact of rain, snow, fog and haze on road conditions.

[0033] Traffic signal data: such as traffic light status and traffic control information.

[0034] In this embodiment, the traffic condition change refers to the congestion trend during peak hours.

[0035] In this embodiment, different forms include: voice, text or image.

[0036] The beneficial effects of the above technical solution are: identifying the real-time road conditions of different sections according to the data type of the real-time data of multiple driving routes, and predicting the changes in road conditions of different sections in the future based on the road condition prediction model, so as to adjust the driving route, which can reduce the braking demand of the vehicle during driving. By predicting the road conditions in advance and adjusting the vehicle speed, the vehicle can travel in a more stable state, reduce the frequency of emergency braking and thus reduce energy waste, while improving the efficiency of braking energy recovery.

[0037] Embodiment 4: Based on Example 3, the first determination module in this embodiment of the present invention includes: A second identification unit: obtaining standardized user driving behavior data through an on-board diagnostic system protocol, analyzing the driving behavior data, and identifying the user's driving mode and driving scene according to the analysis results; Evaluation unit: obtains battery status data of the electric vehicle through the battery management system of the electric vehicle, evaluates the health status of the battery according to the battery status data, and obtains the evaluation result; The second dynamic adjustment unit determines a dynamic energy recovery strategy according to the driving behavior data and the battery status data, and dynamically adjusts the energy recovery strength, response time and energy distribution ratio in combination with the driving mode, driving scenario and evaluation results.

[0038] In this embodiment, driving behavior data refers to a data set that reflects information such as driver operating behavior, vehicle motion status, and road environment, collected through various sensors and devices during vehicle driving, including: driver operating data, steering angle, gear shifting operation, acceleration, and deceleration.

[0039] In this embodiment, the battery status data of the electric vehicle is information reflecting the health status, performance and usage status of the battery, including: battery power, battery voltage, and battery temperature.

[0040] 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 status of the vehicle to achieve a balance between energy recovery efficiency and driving comfort.

[0041] The beneficial effects of the above technical solution are: obtaining the user's driving behavior data and the battery status data of the electric vehicle to determine the dynamic energy recovery strategy, which can more accurately adjust the recovery intensity. At the same time, the battery status data can help the system avoid energy recovery under inappropriate conditions and protect the battery by adjusting the recovery strategy.

[0042] Embodiment 5: Based on Embodiment 4, the second identification unit of the embodiment of the present invention includes: A first acquisition subunit: analyzing the driving behavior data, and acquiring the user's speed preference and driving style according to the analysis results; A second acquisition subunit: acquiring an average speed, brake pedal depression depth and frequency of the electric vehicle according to the speed preference of the user, and acquiring a speed feature of the electric vehicle according to the average speed, brake pedal depression depth and frequency; A determination subunit: obtaining a lane changing frequency, a number of sudden accelerations and sudden brakings of the electric vehicle according to the driving style of the user, and determining an operation characteristic of the electric vehicle according to the lane changing frequency, the number of sudden accelerations and sudden brakings; Classification subunit: classifies the speed characteristics and operation characteristics according to a machine learning algorithm, and identifies the user's driving mode according to the classification results.

[0043] In this embodiment, driving behavior data refers to a data set that reflects information such as driver operating behavior, vehicle motion status, and road environment, collected through various sensors and devices during vehicle driving, including: driver operating data, steering angle, gear shifting operation, acceleration, and deceleration.

[0044] In this embodiment, the speed preference includes: the average speed of the electric vehicle, and the brake pedal depression depth and frequency.

[0045] In this embodiment, the driving style includes: the lane changing frequency, sudden acceleration and sudden braking times of the electric vehicle.

[0046] In this embodiment, the brake pedal depression depth refers to the degree to which the brake pedal is depressed when the driver depresses the brake pedal, and is usually expressed as a percentage or an absolute distance.

[0047] In this embodiment, obtaining the average speed of the electric vehicle according to the speed preference of the user includes: Get the user's speed preference range and the real-time vehicle speed, and obtaining the average speed of the electric vehicle according to the speed preference range and the real-time vehicle speed: ;in, represents the average speed of the electric vehicle, Indicates the time period, Indicates the real-time speed of the electric vehicle. represents the midpoint of the user's speed preference range, calculate, Represents the standard deviation, which controls the width of the weight distribution. calculate.

[0048] The beneficial effects of the above technical solution are: analyzing driving behavior data, obtaining and classifying speed characteristics and operation characteristics during driving, so as to identify 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 abrupt braking feeling caused by differences in driving habits, and provide a smooth braking experience. At the same time, the system can respond quickly and provide maximum braking force, significantly shortening the braking distance and reducing the risk of collision.

[0049] Embodiment 6: Based on Embodiment 5, the storage module of the embodiment of the present invention includes: A 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; Conversion unit: collects the braking energy of the electric vehicle and converts it into electrical energy according to the adjusted intensity and mode of energy recovery; Storage unit: stores the electrical energy into the electric vehicle battery through a power converter.

[0050] 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 status of the vehicle to achieve a balance between energy recovery efficiency and driving comfort.

[0051] In this embodiment, the real-time operating condition of the vehicle refers to the current operating state and environmental conditions of the vehicle during driving, including: vehicle speed, battery status, and driving intention.

[0052] In this embodiment, a power converter is an electronic device used to convert electrical energy from one form to another.

[0053] The beneficial effects of the above technical solution are: the energy of the electric vehicle is collected through a dynamic energy recovery strategy and converted into electrical energy for storage, which optimizes the energy management of the entire vehicle and reduces energy waste during braking. At the same time, it can increase the cruising range of the electric vehicle and improve the reliability of the braking system.

[0054] Embodiment 7: Based on Embodiment 6, the display module of the embodiment of the present invention includes: A second acquisition unit is used to acquire the energy recovery progress and recovery mode status of the electric vehicle, and to display the energy recovery progress and recovery mode status in real time through a user interface; Optimization unit: provides different modes of braking options according to the battery saturation status and combined with user needs, and optimizes energy recovery efficiency according to different modes of braking options and combined with driving scenarios and needs.

[0055] In this embodiment, the energy recovery mode state of the electric vehicle refers to the working state and characteristics of the energy recovery system of the vehicle in different driving modes, including: the start-up conditions, recovery intensity, recovery timing and recovery efficiency of energy recovery.

[0056] The beneficial effects of the above technical solution are: the energy recovery status is displayed through the user interface, and personalized braking options are provided according to the battery saturation status, which can ensure that the user understands the current status of the electric vehicle in real time and improve the driving experience. At the same time, the energy recovery intensity can be dynamically adjusted according to the battery saturation status to ensure maximum energy recovery efficiency.

[0057] Embodiment 8: Based on Example 7, the embodiment of the present invention further includes: The second determination module: obtains the operating parameters of the electric vehicle in each driving mode, and determines the power feedback torque of the drive motor according to the operating parameters; The third determination module: determines the power intensity based on the power feedback torque, and determines the power recovery theoretical level coefficient according to the power intensity; The fourth determination module: determining the energy recovery intensity and energy recovery smoothness of the electric vehicle in each driving mode according to the electric energy recovery theoretical level coefficient; The fifth determination module is used to determine the energy recovery logic of the electric vehicle in each driving mode based on the energy recovery intensity and the energy recovery smoothness; The first acquisition module is used to acquire energy recovery condition parameters of the electric vehicle in various driving modes according to the energy recovery logic; A sixth determination module: determining energy recovery pre-driving state parameters of the electric vehicle in each driving mode according to the energy recovery condition parameters; A seventh determination module: determining the energy recovery characteristics of the electric vehicle in each driving mode based on the preceding driving state parameter, and determining the energy recovery attribute according to the energy recovery characteristics, wherein the energy recovery attribute includes: gently increasing, gently decreasing, and gradually increasing and gradually decreasing; Sorting module: determining the energy recovery rate of the electric vehicle in each driving mode according to the energy recovery attribute, and prioritizing each driving mode of the electric vehicle based on the energy recovery rate; Screening module: Screen out normal energy recovery driving mode and abnormal energy recovery driving mode according to the sorting results.

[0058] In this embodiment, the operating parameters in each driving mode include: power, power, energy recovery intensity, and steering assist.

[0059] In this embodiment, the power feedback torque of the drive motor refers to the ability of the motor to dynamically adjust the output torque according to the actual operating state and control requirements of the vehicle during operation.

[0060] In this embodiment, the electric energy recovery theoretical level coefficient refers to the theoretical efficiency or level of energy recovery by the electric vehicle through the braking energy recovery system under ideal conditions.

[0061] In this embodiment, the energy recovery intensity refers to the ability of the electric vehicle to convert the vehicle's kinetic energy into electrical energy through the motor and recover it into the battery during coasting or braking.

[0062] In this embodiment, energy recovery smoothness refers to whether the braking or deceleration process of the electric vehicle is smooth and comfortable during the energy recovery process, and whether the driving and riding experience is significantly affected.

[0063] In this embodiment, the energy recovery logic of the electric vehicle in different driving modes refers to the vehicle adjusting the intensity, timing and method of the energy recovery system according to the setting of the driving mode to achieve different driving experience and energy utilization efficiency.

[0064] In this embodiment, the energy recovery pre-driving state parameter refers 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.

[0065] In this embodiment, the energy recovery characteristics in different driving modes refer to the differences in the working mode, intensity, timing and recovery efficiency of the energy recovery system of the electric vehicle in various driving modes, such as: The energy recovery characteristics of the economic mode include: high-intensity recovery, frequent starts, and obvious deceleration feeling.

[0066] The energy recovery characteristics of Standard / Comfort mode include: medium intensity recovery and natural deceleration feeling.

[0067] In this embodiment, the energy recovery properties include: gently increasing, gently decreasing, gradient increasing, and gradient decreasing.

[0068] The beneficial effects of the above technical solution are: determining the energy recovery properties according to the energy recovery characteristics, thereby determining the energy recovery rate of the electric vehicle in each driving mode, and prioritizing the driving modes, being able to accurately obtain the energy recovery driving modes in different states, being able to recover braking energy more efficiently, and improving the overall performance of the vehicle.

[0069] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for 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.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions 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: include: Judgment module: collects electric vehicle status data and road condition data in real time, and judges whether energy recovery conditions are met based on the electric vehicle status data and road condition data; The first determination module: obtains the user's driving behavior data and the battery status data of the electric vehicle, and determines the dynamic energy recovery strategy according to the driving behavior data and the battery status data; Storage module: Based on the dynamic energy recovery strategy, the braking energy of the electric vehicle is collected and converted into electrical energy and stored in the electric vehicle battery; Display module: displays the energy recovery status through the user interface and provides personalized braking options based on the battery saturation status.

2. The adaptive energy recovery braking system for electric vehicles according to claim 1, characterized in that: The judgment module includes: The first acquisition unit is used to obtain the type of electric vehicle, determine the sensor corresponding to the electric vehicle according to the type of the electric vehicle, and collect the state data of the electric vehicle in real time according to the sensor; The second collection unit is used to obtain the driving path of the electric vehicle, and to collect road condition data in real time according to the driving path and in combination with an external traffic information network; A first determination unit: analyzing the electric vehicle state data and the road condition data, and determining the states of multiple parts of the electric vehicle and the specific conditions of the road surface according to the analysis results; A judgment unit is used to judge whether the energy recovery condition is met according to the status of the multiple parts of the electric vehicle and the specific conditions of the road surface.

3. The adaptive energy recovery braking system for electric vehicles according to claim 2, characterized in that: The judging module further includes: A first acquisition unit: after collecting road condition data in real time based on the driving route and in combination with an external traffic information network, acquires multiple driving routes through a navigation system, and acquires real-time data of the multiple driving routes based on a third-party traffic data platform; A first identification unit is configured to obtain data types of real-time data of a plurality of driving routes, and to identify real-time road conditions of different sections of the plurality of driving routes according to the data types; Prediction unit: predicting the changes of road conditions of different sections within a period of time in the future based on the road condition prediction model according to the real-time road conditions of different sections of the multiple driving routes; A first dynamic adjustment unit: dynamically adjusts the driving route according to the change in road conditions, and recalculates and updates the estimated arrival time according to the adjusted route and real-time road conditions; Push unit: Push traffic information and route adjustment suggestions to users in different forms.

4. The adaptive energy recovery braking system for electric vehicles according to claim 1, characterized in that: The first determination module includes: A second identification unit: obtaining standardized user driving behavior data through an on-board diagnostic system protocol, analyzing the driving behavior data, and identifying the user's driving mode and driving scene according to the analysis results; Evaluation unit: obtains battery status data of the electric vehicle through the battery management system of the electric vehicle, evaluates the health status of the battery according to the battery status data, and obtains the evaluation result; The second dynamic adjustment unit determines a dynamic energy recovery strategy according to the driving behavior data and the battery status data, and dynamically adjusts the energy recovery strength, response time and energy distribution ratio in combination with the driving mode, driving scenario and evaluation results.

5. The adaptive energy recovery braking system for electric vehicles according to claim 1, characterized in that: The second identification unit comprises: A first acquisition subunit: analyzing the driving behavior data, and acquiring the user's speed preference and driving style according to the analysis results; A second acquisition subunit: acquiring an average speed, brake pedal depression depth and frequency of the electric vehicle according to the speed preference of the user, and acquiring a speed feature of the electric vehicle according to the average speed, brake pedal depression depth and frequency; A determination subunit: obtaining a lane changing frequency, a number of sudden accelerations and sudden brakings of the electric vehicle according to the driving style of the user, and determining an operation characteristic of the electric vehicle according to the lane changing frequency, the number of sudden accelerations and sudden brakings; Classification subunit: classifies the speed characteristics and operation characteristics according to a machine learning algorithm, and identifies the user's driving mode according to the classification results.

6. The adaptive energy recovery braking system for electric vehicles according to claim 1, characterized in that: Storage module, including: A 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; Conversion unit: collects the braking energy of the electric vehicle and converts it into electrical energy according to the adjusted intensity and mode of energy recovery; Storage unit: stores the electrical energy into the electric vehicle battery through a power converter.

7. The adaptive energy recovery braking system for electric vehicles according to claim 1, characterized in that: Display module, including: A second acquisition unit is used to acquire the energy recovery progress and recovery mode status of the electric vehicle, and to display the energy recovery progress and recovery mode status in real time through a user interface; Optimization unit: provides different modes of braking options according to the battery saturation status and combined with user needs, and optimizes energy recovery efficiency according to different modes of braking options and combined with driving scenarios and needs.

8. The adaptive energy recovery braking system for electric vehicles according to claim 1, characterized in that: Also includes: The second determination module: obtains the operating parameters of the electric vehicle in each driving mode, and determines the power feedback torque of the drive motor according to the operating parameters; The third determination module: determines the power intensity based on the power feedback torque, and determines the power recovery theoretical level coefficient according to the power intensity; The fourth determination module: determining the energy recovery intensity and energy recovery smoothness of the electric vehicle in each driving mode according to the energy recovery theoretical level coefficient; The fifth determination module is used to determine the energy recovery logic of the electric vehicle in each driving mode based on the energy recovery intensity and the energy recovery smoothness; The first acquisition module is used to acquire energy recovery condition parameters of the electric vehicle in various driving modes according to the energy recovery logic; A sixth determination module: determining energy recovery pre-driving state parameters of the electric vehicle in each driving mode according to the energy recovery condition parameters; A seventh determination module: determining the energy recovery characteristics of the electric vehicle in each driving mode based on the preceding driving state parameter, and determining the energy recovery attribute according to the energy recovery characteristics, wherein the energy recovery attribute includes: gently increasing, gently decreasing, and gradually increasing and gradually decreasing; Sorting module: determining the energy recovery rate of the electric vehicle in each driving mode according to the energy recovery attribute, and prioritizing each driving mode of the electric vehicle based on the energy recovery rate; Screening module: Screen out normal energy recovery driving mode and abnormal energy recovery driving mode according to the sorting results.

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