Adaptive brake energy recovery method, system and vehicle

Abstract

The application relates to the technical field of intelligent automobile control, and provides a self-adaptive brake energy recovery method, a system and a vehicle.The method comprises the following steps: obtaining target vehicle information and driving behavior data within a target time; confirming the start and end positions of a single trip based on the target vehicle information and corresponding behavior time to obtain an operation scene characteristic matrix of the single trip; extracting target driving behavior data in the single trip based on the operation scene characteristic matrix of the single trip to obtain a driving habit characteristic matrix; performing brake energy recovery optimization in stages by using a pre-constructed brake energy recovery optimization function according to the operation scene characteristic matrix of the single trip and the driving habit characteristic matrix to obtain a target brake energy recovery strategy; and performing energy recovery according to the target brake energy recovery strategy.The application can realize more reasonable and accurate brake energy recovery, and achieve optimal whole-vehicle brake energy recovery effect under various scene working conditions and driving habits of drivers.

Description

Technical Field

[0001] This invention relates to the field of automotive intelligent control technology, and in particular to an adaptive braking energy recovery method, system, and vehicle. Background Technology

[0002] New energy vehicles, with their significant energy-saving advantages, have seen rapid development and application. Improving their driving range has become a key research focus. Regenerative braking technology can recover some of the braking energy lost through friction during braking, improving the overall vehicle energy efficiency, extending driving range, and also reducing the thermal load on the brakes, thus enhancing driving safety and fuel economy.

[0003] Regenerative braking is a crucial technology and a key feature of modern new energy vehicles. In conventional internal combustion engine vehicles, the vehicle's kinetic energy is converted into heat energy and released into the atmosphere during deceleration and braking. In new energy vehicles, this wasted kinetic energy can be converted into electrical energy through regenerative braking technology and stored in the battery, further converted into driving energy. For example, when the vehicle starts or accelerates and requires increased driving force, the electric motor provides auxiliary power to the engine, making efficient use of electrical energy. Especially under urban driving conditions with frequent braking and starting, research shows that effectively recovering braking energy can reduce energy consumption by approximately 15% and extend the driving range of new energy vehicles by 10% to 30%.

[0004] In existing technologies, braking energy recovery control is mostly based on fixed values ​​determined by looking up vehicle speed. For example, the braking energy recovery curve of a new energy dump truck is a single fixed calibration value. This means that even under different operating conditions or driver habits, the braking energy recovery intensity is the same, which leads to unreasonable and inaccurate braking energy recovery control and poor overall vehicle energy efficiency optimization. Summary of the Invention

[0005] This invention provides an adaptive braking energy recovery method, system, and vehicle to address the shortcomings of unreasonable and inaccurate braking energy recovery control in the prior art, achieving more reasonable and accurate braking energy recovery and reaching the optimal vehicle braking energy recovery effect under various operating conditions and driver driving habits.

[0006] This invention provides an adaptive braking energy recovery method, comprising:

[0007] Within a target time period, acquire target vehicle information and driving behavior data; the target vehicle information includes at least altitude information, location information, slope information, vehicle weight information, and time information, and the driving behavior data includes at least vehicle speed information, brake pedal opening information, and accelerator pedal information;

[0008] Based on the target vehicle information and the corresponding behavior time, the start and end positions of a single trip are determined to obtain the operational scenario feature matrix of the single trip;

[0009] Based on the operational scenario feature matrix of the single trip, target driving behavior data within the single trip is extracted to obtain a driving habit feature matrix.

[0010] Based on the single-trip operation scenario feature matrix and the driving habit feature matrix, brake energy recovery is optimized in stages using a pre-constructed brake energy recovery optimization function to obtain the target brake energy recovery strategy.

[0011] Energy recovery is performed according to the target braking energy recovery strategy.

[0012] According to an adaptive braking energy recovery method provided by the present invention, within a target time period, target vehicle information and driving behavior data are acquired, and then the method further includes:

[0013] An operational scenario matrix is ​​constructed based on the target vehicle information;

[0014] The operation scenario matrix consists of an altitude information vector in the first row, a location information vector in the second row, a slope information vector in the third row, a vehicle weight information vector in the fourth row, and a time vector in the fifth row.

[0015] According to an adaptive braking energy recovery method provided by the present invention, the start and end positions of a single trip are determined based on the target vehicle information and the corresponding behavior time, so as to obtain the operational scenario feature matrix of the single trip, specifically including:

[0016] Extract the target feature points of each row of data in the operation scenario matrix, compare the target feature points of the first four rows of data in the operation scenario matrix, and confirm the start and end positions of the single journey.

[0017] Construct an operational scenario feature matrix for the single route based on the start and end positions of the single route and the target feature points;

[0018] The single-trip operation scenario feature matrix consists of an altitude information vector in the first row, a relative position information vector in the second row, a slope information vector in the third row, a vehicle weight information vector in the fourth row, and a relative time vector in the fifth row.

[0019] According to an adaptive braking energy recovery method provided by the present invention, target driving behavior data within the single trip is extracted based on the operational scenario feature matrix of the single trip to obtain a driving habit feature matrix, specifically including:

[0020] Based on the relative time vector of the single-trip operation scenario feature matrix, target driving behavior data within the single-trip at the corresponding time is extracted;

[0021] The driving habit feature matrix is ​​constructed based on the target driving behavior data within the single trip.

[0022] The driving habit feature matrix consists of a first row of vehicle speed information vector, a second row of brake pedal opening information vector, a third row of accelerator pedal information vector, a fourth row of relative position information vector, and a fifth row of relative time vector.

[0023] According to the adaptive braking energy recovery method provided by the present invention, the braking energy recovery value within a single trip is the overall objective, the braking energy recovery intensity parameters at different locations are the control variables, and a braking energy recovery optimization function is constructed in combination with preset constraints; the constraints include at least a coupling function of the operating scenario feature matrix of the single trip and the driving habit feature matrix.

[0024] According to an adaptive braking energy recovery method provided by the present invention, braking energy recovery is optimized in stages using a pre-constructed braking energy recovery optimization function based on the single-trip operation scenario feature matrix and the driving habit feature matrix, to obtain a target braking energy recovery strategy. Specifically, the method includes:

[0025] In each braking energy recovery stage of the single trip, the braking energy recovery intensity parameter is calculated in real time using the coupling function of the current stage based on the operating scenario feature matrix and the driving habit feature matrix of the single trip. The braking energy recovery strategy is determined based on the braking energy recovery intensity parameter, and the braking energy recovery value is obtained.

[0026] By comparing the braking energy recovery values ​​at different braking energy recovery stages, the optimal braking energy recovery value and its corresponding braking energy recovery intensity parameter are selected as the target braking energy recovery strategy.

[0027] According to an adaptive braking energy recovery method provided by the present invention, braking energy recovery is optimized in stages using a pre-constructed braking energy recovery optimization function based on the single-trip operation scenario feature matrix and the driving habit feature matrix to obtain a target braking energy recovery strategy. The method further includes:

[0028] The amount of electricity recovered by the target braking energy recovery strategy and the original braking energy recovery strategy are compared. If the amount of electricity recovered by the target braking energy recovery strategy is greater than the amount of electricity recovered by the original braking energy recovery strategy, the target braking energy recovery strategy is fixed for the current operating scenario and driver. Otherwise, the original braking energy recovery strategy is used as the target braking energy recovery strategy.

[0029] The present invention also provides an adaptive braking energy recovery system, comprising:

[0030] The information acquisition module is used to acquire target vehicle information and driving behavior data within a target time period; the target vehicle information includes at least altitude information, location information, slope information, vehicle weight information and time information, and the driving behavior data includes at least vehicle speed information, brake pedal opening information and accelerator pedal information.

[0031] The operation scenario module is used to determine the start and end positions of a single trip based on the target vehicle information and the corresponding behavior time, so as to obtain the operation scenario feature matrix of the single trip.

[0032] The driving habit module is used to extract target driving behavior data within the single trip based on the operational scenario feature matrix of the single trip, so as to obtain the driving habit feature matrix.

[0033] The recovery strategy module is used to optimize braking energy recovery in stages using a pre-built braking energy recovery optimization function based on the single-trip operation scenario feature matrix and the driving habit feature matrix, so as to obtain the target braking energy recovery strategy.

[0034] An energy recovery module is used to recover energy according to the target braking energy recovery strategy.

[0035] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of any of the adaptive braking energy recovery methods described above.

[0036] The present invention also provides a vehicle including an adaptive braking energy recovery system or electronic device as described in any of the above embodiments.

[0037] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the adaptive braking energy recovery method as described above.

[0038] The adaptive braking energy recovery method, system, and vehicle provided by this invention acquire target vehicle information and driving behavior data within a target time period. The target vehicle information includes at least altitude, location, slope, vehicle weight, and time information, while the driving behavior data includes at least vehicle speed, brake pedal opening, and accelerator pedal information. Based on the target vehicle information and corresponding behavior time, the start and end positions of a single trip are determined to obtain an operational scenario feature matrix for the single trip. Based on the operational scenario feature matrix of the single trip, target driving behavior data within the single trip is extracted to obtain a driving habit feature matrix. According to the operational scenario feature matrix of the single trip and the driving habit feature matrix, braking energy recovery is optimized in stages using a pre-constructed braking energy recovery optimization function to obtain a target braking energy recovery strategy. Energy recovery is performed according to the target braking energy recovery strategy. This invention collects data on operational scenarios and user driving habits, extracts operational scenario features, constructs user driving habit profiles, and optimizes the braking energy recovery curve at each node based on the current operational scenario features and driving habit profiles to obtain the target energy recovery strategy. This achieves more reasonable and accurate braking energy recovery, resulting in the optimal vehicle braking energy recovery effect under various operational scenarios and driver driving habits. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0040] Figure 1 This is one of the flowcharts of the adaptive braking energy recovery method provided by the present invention;

[0041] Figure 2 This is the second flowchart of the adaptive braking energy recovery method provided by the present invention;

[0042] Figure 3 This is a schematic diagram of the adaptive braking energy recovery system provided by the present invention;

[0043] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0045] The following is combined Figures 1 to 4 The present invention describes an adaptive braking energy recovery method, system, and vehicle.

[0046] like Figure 1 As shown, an adaptive braking energy recovery method includes:

[0047] Step 110: Within the target time period, acquire target vehicle information and driving behavior data; the target vehicle information includes at least altitude information, location information, slope information, vehicle weight information and time information, and the driving behavior data includes at least vehicle speed information, brake pedal opening information and accelerator pedal information.

[0048] Target vehicle information and driving behavior data are acquired through onboard information equipment, including but not limited to GPS and related onboard sensors. The purpose of acquiring target vehicle information and driving behavior data is to confirm the operating conditions of the vehicle within a single trip. The vehicles involved are those equipped with regenerative braking functionality. In embodiments of this invention, the type of vehicle is not limited, including new energy passenger vehicles, new energy commercial vehicles, and new energy commercial vehicles such as new energy work machinery, such as new energy dump trucks, new energy aerial ladder trucks, new energy fire trucks, new energy mixer trucks, and other engineering vehicles.

[0049] Step 120: Based on the target vehicle information and the corresponding behavior time, confirm the start and end positions of a single trip to obtain the operational scenario feature matrix of the single trip.

[0050] Since the data features are the same within a certain threshold, the start and end positions of a single trip can be determined by using the obtained target vehicle information and its corresponding behavior time, and an operational scenario feature matrix can be constructed.

[0051] Step 130: Extract target driving behavior data within the single trip based on the single trip's operational scenario feature matrix to obtain a driving habit feature matrix.

[0052] After confirming the feature matrix of a single-trip operation scenario, the driver's driving behavior data within the current single-trip is identified as the target driving behavior data based on the time feature vector of the operation scenario feature matrix, and the driving habit feature matrix is ​​extracted.

[0053] The adaptive regenerative braking method provided by this invention identifies operating scenarios and driving habits based solely on the vehicle's current operating parameters and the driver's driving behavior. It requires fewer and readily available parameters, and the identification method is based on the vehicle's operating mechanism, making it simple and efficient. The determination of operating scenario conditions mainly relies on information such as the vehicle's current altitude, gradient, vehicle weight, and location. Driver habit identification primarily relies on information such as vehicle speed, brake pedal opening, and accelerator pedal opening.

[0054] Step 140: Based on the single-trip operation scenario feature matrix and the driving habit feature matrix, brake energy recovery is optimized in stages using a pre-built brake energy recovery optimization function to obtain the target brake energy recovery strategy.

[0055] Based on the confirmed operational scenario feature matrix and driving habit feature matrix, the braking energy recovery strategy is optimized node by node. Specifically, a single journey is divided into nodes according to different stages of braking energy recovery, and optimization is performed for each stage, selecting the optimal strategy as the target braking energy recovery strategy. During the optimization process, a pre-constructed braking energy recovery optimization function is used. It is important to note that the braking energy recovery optimization function is constructed with the braking energy recovery SOC within a single journey as the overall objective, and the braking recovery intensity at different locations as control variables.

[0056] Furthermore, the target braking energy recovery strategy is based on the operating scenario represented by the operating scenario feature matrix and the driver behavior represented by the driving habit feature matrix. The target braking energy recovery strategy will also adaptively learn and adjust according to different operating scenarios and driver behavior.

[0057] Step 150: Perform energy recovery according to the target braking energy recovery strategy.

[0058] Once the target braking energy recovery strategy is obtained, energy recovery is carried out using the target braking energy recovery strategy for the same operating scenario and driver.

[0059] This invention optimizes the braking energy recovery curve based on the characteristics of the operating scenario and the driving habits of the user, and comprehensively considers the optimization of the braking energy recovery strategy under specific operating scenarios and driving habits, and adaptively adjusts the braking energy recovery strategy.

[0060] In one embodiment, within a target time period, target vehicle information and driving behavior data are acquired, followed by:

[0061] An operational scenario matrix is ​​constructed based on the target vehicle information;

[0062] The operation scenario matrix consists of an altitude information vector in the first row, a location information vector in the second row, a slope information vector in the third row, a vehicle weight information vector in the fourth row, and a time vector in the fifth row.

[0063] Specifically, after obtaining target vehicle information and driving behavior data, the target vehicle information can be stored in a matrix according to categories to obtain an operation scenario matrix.

[0064] In this embodiment, an operational scenario matrix is ​​constructed based on the target vehicle information, such as a matrix. :

[0065]

[0066] The operation scenario matrix consists of an altitude information vector in the first row, a location information vector in the second row, a slope information vector in the third row, a vehicle weight information vector in the fourth row, and a time vector in the fifth row.

[0067] In one embodiment, the start and end locations of a single trip are determined based on the target vehicle information and the corresponding behavior time to obtain the operational scenario feature matrix of the single trip, specifically including:

[0068] Extract the target feature points of each row of data in the operation scenario matrix, compare the target feature points of the first four rows of data in the operation scenario matrix, and confirm the start and end positions of the single journey.

[0069] Construct an operational scenario feature matrix for the single route based on the start and end positions of the single route and the target feature points;

[0070] The single-trip operation scenario feature matrix consists of an altitude information vector in the first row, a relative position information vector in the second row, a slope information vector in the third row, a vehicle weight information vector in the fourth row, and a relative time vector in the fifth row.

[0071] After obtaining the operational scenario matrix, since the data features are the same within a certain threshold, feature points are extracted from each row of the operational scenario matrix. That is, after the vehicle starts, when the vehicle speed is detected to be greater than the threshold and the location is greater than a certain mileage, starting from the current data point, the column vector corresponding to the current data is selected as the feature point vector, and then compared sequentially with P based on the location information. i For each feature point vector after a certain threshold, calculate the difference vector between them. The feature point vector within this difference vector is the feature point vector, and this feature point is extracted. It's important to note that during the extraction process, obviously abnormal data can be deleted.

[0072] By comparing the information of four feature points other than the behavior time vector parameter, the start and end positions of a single trip are confirmed, and then the operational scenario feature matrix within the current single trip is constructed. After extracting feature points, the feature point extraction and comparison are restarted. When the next feature point vector is confirmed, the position information of the two feature point vectors is compared. If the error is within a certain threshold, the current data segment is determined to be single-trip data information.

[0073] In this embodiment, an operational scenario feature matrix is ​​constructed as follows: :

[0074]

[0075] The operation scenario feature matrix consists of an altitude information vector in the first row, a relative position information vector in the second row, a slope information vector in the third row, a vehicle weight information vector in the fourth row, and a relative time vector in the fifth row.

[0076] It is important to note that the content of each row in the operational scenario feature matrix is ​​extracted from the corresponding row of the operational scenario matrix. , The number of columns in the operational scenario feature matrix. This represents the number of columns in the operation scenario matrix. The relative position information vector of the operation scenario feature matrix is ​​obtained by comparing the start and end positions of a single trip with the position information vector of the operation scenario matrix. Correspondingly, the relative time vector of the operation scenario feature matrix is ​​obtained by comparing the corresponding behavior time of the start and end positions of a single trip with the behavior time vector of the operation scenario matrix. In addition, the altitude information vector, slope information vector, and vehicle weight information vector of the operation scenario feature matrix are all information vectors obtained after extracting feature points from the operation scenario matrix.

[0077] This invention automatically identifies operational scenarios and conditions, extracts and constructs operational scenario features for use in subsequent braking energy recovery strategy formulation and adaptive adjustment.

[0078] In one embodiment, target driving behavior data within the single trip is extracted based on the operational scenario feature matrix of the single trip to obtain a driving habit feature matrix, specifically including:

[0079] Based on the relative time vector of the single-trip operation scenario feature matrix, target driving behavior data within the single-trip at the corresponding time is extracted;

[0080] The driving habit feature matrix is ​​constructed based on the target driving behavior data within the single trip.

[0081] The driving habit feature matrix consists of a first row of vehicle speed information vector, a second row of brake pedal opening information vector, a third row of accelerator pedal information vector, a fourth row of relative position information vector, and a fifth row of relative time vector.

[0082] After confirming the feature matrix of the current single-trip operation scenario, the driver's driving behavior data within the current single trip is identified based on the relative time vector, and a driving habit feature matrix is ​​extracted. Further, based on the relative time vector of the operation scenario feature matrix and the corresponding behavior times at the start and end positions of the single trip, the driver's driving behavior data within the single trip is extracted to obtain the driving habit feature matrix. It should be noted that during the extraction process, obviously abnormal data can be deleted. This invention learns driver driving habits and constructs a user driving habit profile for subsequent braking energy recovery strategy formulation and adaptive adjustment.

[0083] In this embodiment, based on the operational scenario feature matrix The time feature vector is used to identify driver behavior data within the current single trip, and a driving habit feature matrix is ​​extracted. :

[0084]

[0085] The driving habit feature matrix consists of five rows: the first row contains vehicle speed information vectors, the second row contains brake pedal opening information vectors, the third row contains accelerator pedal information vectors, the fourth row contains relative position information vectors, and the fifth row contains relative time vectors. It's important to note that the time feature vector OpC... 5c = OpD 5d In other words, the fifth row of the driving habit feature matrix is ​​the same as the relative time vector in the fifth row of the operation scenario feature matrix.

[0086] In one embodiment, the braking energy recovery value within the single trip is taken as the overall objective, and the braking energy recovery intensity parameters at different locations are taken as control variables. A braking energy recovery optimization function is constructed in combination with preset constraints. The constraints include at least a coupling function of the operating scenario feature matrix of the single trip and the driving habit feature matrix.

[0087] Specifically, taking the state of charge (SOC) recovered during braking energy recovery within a single stroke as the overall objective, and the intensity of braking recovery at different locations as control variables, the following optimization function is established:

[0088]

[0089] Where n is the number of stages of regenerative braking in a single trip; X is the regenerative braking intensity parameter vector; f(X) is the SOC of the i-th stage of regenerative braking within a single trip; O(x) j ) is a coupling function based on the operational scenario feature matrix and driving behavior habits, and the coupling function O(x) jThe main meaning is to adjust the braking intensity recovery parameter set according to factors such as gradient, vehicle speed, and brake pedal depth during each segment of braking energy recovery within a single journey.

[0090] In one embodiment, based on the single-trip operation scenario feature matrix and the driving habit feature matrix, braking energy recovery is optimized in stages using a pre-built braking energy recovery optimization function to obtain a target braking energy recovery strategy, specifically including:

[0091] In each braking energy recovery stage of the single trip, the braking energy recovery intensity parameter is calculated in real time using the coupling function of the current stage based on the operating scenario feature matrix and the driving habit feature matrix of the single trip. The braking energy recovery strategy is determined based on the braking energy recovery intensity parameter, and the braking energy recovery value is obtained.

[0092] By comparing the braking energy recovery values ​​at different braking energy recovery stages, the optimal braking energy recovery value and its corresponding braking energy recovery intensity parameter are selected as the target braking energy recovery strategy.

[0093] Specifically, for each single trip, in each braking energy recovery stage of the single trip, through the coupling function O(x) j The braking energy recovery intensity parameter X is updated in real time, and the braking energy recovery value E corresponding to each set of X is obtained. soc The optimal energy recovery value and its corresponding control parameters are determined through iterative comparison, thus obtaining the target braking energy recovery strategy.

[0094] Furthermore, the present invention also includes: calculating the percentage improvement in the effectiveness of the braking energy recovery strategy, and selecting the braking energy recovery intensity parameter corresponding to the braking energy recovery value where the percentage improvement in the effectiveness of the braking energy recovery strategy reaches a preset target as the target braking energy recovery strategy. The adaptive braking energy recovery method provided by the present invention includes an operational scenario based on vehicle operating mechanisms, driver driving habit identification, and an adaptive optimization method for the braking energy recovery strategy that comprehensively considers the above two factors. Specifically, it includes collecting operational scenario conditions and driver driving habit data, extracting operational scenario features, analyzing driver driving habits and their corresponding braking energy recovery data for specific operational scenario conditions, and constructing a user driving habit profile after excluding abnormal data. Based on the current operational scenario features and driving habit profile, the braking energy recovery curve is optimized node by node using a pre-constructed braking energy recovery optimization function. It self-learns and analyzes whether the percentage improvement in the effectiveness of the optimized braking energy recovery strategy reaches a preset target, and adaptively adjusts the braking energy recovery strategy.

[0095] In one embodiment, based on the single-trip operation scenario feature matrix and the driving habit feature matrix, brake energy recovery is optimized in stages using a pre-built brake energy recovery optimization function to obtain a target brake energy recovery strategy, and then the process further includes:

[0096] The amount of electricity recovered by the target braking energy recovery strategy and the original braking energy recovery strategy are compared. If the amount of electricity recovered by the target braking energy recovery strategy is greater than the amount of electricity recovered by the original braking energy recovery strategy, the target braking energy recovery strategy is fixed for the current operating scenario and driver. Otherwise, the original braking energy recovery strategy is used as the target braking energy recovery strategy.

[0097] After obtaining the target regenerative braking strategy, it is compared with the original strategy to determine the difference in the amount of electricity recovered. If the target strategy recovers more electricity, it is adopted and implemented in the current operating scenario and driving habits. Otherwise, the original strategy is kept unchanged for the next round of strategy learning and optimization. This invention adaptively adjusts the regenerative braking strategy based on vehicle operating mechanisms, improving the vehicle's economy and braking comfort under different scenarios and driving styles.

[0098] The adaptive braking energy recovery method provided by this invention acquires target vehicle information and driving behavior data within a target time period. The target vehicle information includes at least altitude, location, slope, vehicle weight, and time information, while the driving behavior data includes at least vehicle speed, brake pedal opening, and accelerator pedal information. Based on the target vehicle information and corresponding behavior time, the start and end positions of a single trip are determined to obtain an operational scenario feature matrix for the single trip. Based on the operational scenario feature matrix of the single trip, target driving behavior data within the single trip is extracted to obtain a driving habit feature matrix. According to the operational scenario feature matrix of the single trip and the driving habit feature matrix, braking energy recovery is optimized in stages using a pre-constructed braking energy recovery optimization function to obtain a target braking energy recovery strategy. Energy recovery is performed according to the target braking energy recovery strategy. This invention collects data on operational scenarios and user driving habits, extracts operational scenario features, constructs user driving habit profiles, and optimizes the braking energy recovery curve at each node based on the current operational scenario features and driving habit profiles to obtain the target energy recovery strategy. This achieves more reasonable and accurate braking energy recovery, resulting in the optimal vehicle braking energy recovery effect under various operational scenarios and driver driving habits.

[0099] The present invention also includes a specific embodiment of energy recovery using the adaptive braking energy recovery method provided by the present invention, such as... Figure 2 As shown, it includes the following steps:

[0100] S1: Use GPS information to identify vehicle location and travel information to obtain target vehicle information.

[0101] S2: Combine driving behavior data to analyze road conditions and driver operations, determine the current operating scenario and driver habits, and obtain the operating scenario feature matrix and driving habit feature matrix.

[0102] S3: Adjust the intensity of brake energy recovery and obtain the target brake energy recovery strategy based on the operational scenario feature matrix and driving habit feature matrix.

[0103] S4: The driver presses the brake pedal, and brake energy is recovered using the target brake energy recovery strategy.

[0104] S5: Calculate the amount of regenerated braking energy for the current single trip using the BMS-SOC of braking energy recovery.

[0105] S6: If the current strategy recovers more electricity than the original strategy, proceed to step S7; if the current strategy recovers less electricity than the original strategy, proceed to step S3.

[0106] S7: Current braking energy recovery curve for this operating scenario and under fixed driver conditions.

[0107] The adaptive braking energy recovery system provided by this invention is described below. The adaptive braking energy recovery system described below can be referred to in correspondence with the adaptive braking energy recovery method described above. For example... Figure 3 As shown, the adaptive braking energy recovery system of the present invention includes at least:

[0108] The information acquisition module 310 is used to acquire target vehicle information and driving behavior data within a target time period; the target vehicle information includes at least altitude information, location information, slope information, vehicle weight information and time information, and the driving behavior data includes at least vehicle speed information, brake pedal opening information and accelerator pedal information.

[0109] The operation scenario module 320 is used to determine the start and end positions of a single trip based on the target vehicle information and the corresponding behavior time, so as to obtain the operation scenario feature matrix of the single trip.

[0110] The driving habit module 330 is used to extract target driving behavior data within the single trip based on the single trip's operational scenario feature matrix, so as to obtain a driving habit feature matrix.

[0111] The recovery strategy module 340 is used to optimize braking energy recovery in stages based on the single-trip operation scenario feature matrix and the driving habit feature matrix, using a pre-built braking energy recovery optimization function, so as to obtain the target braking energy recovery strategy.

[0112] The energy recovery module 350 is used to recover energy according to the target braking energy recovery strategy.

[0113] In one embodiment, within the target time period, the information acquisition module 310 is further used for:

[0114] An operational scenario matrix is ​​constructed based on the target vehicle information;

[0115] The operation scenario matrix consists of an altitude information vector in the first row, a location information vector in the second row, a slope information vector in the third row, a vehicle weight information vector in the fourth row, and a time vector in the fifth row.

[0116] In one embodiment, the operation scenario module 320 is specifically used for:

[0117] Extract the target feature points of each row of data in the operation scenario matrix, compare the target feature points of the first four rows of data in the operation scenario matrix, and confirm the start and end positions of the single journey.

[0118] Construct an operational scenario feature matrix for the single route based on the start and end positions of the single route and the target feature points;

[0119] The single-trip operation scenario feature matrix consists of an altitude information vector in the first row, a relative position information vector in the second row, a slope information vector in the third row, a vehicle weight information vector in the fourth row, and a relative time vector in the fifth row.

[0120] In one embodiment, the driving habit module 330 is specifically used for:

[0121] Based on the relative time vector of the single-trip operation scenario feature matrix, target driving behavior data within the single-trip at the corresponding time is extracted;

[0122] The driving habit feature matrix is ​​constructed based on the target driving behavior data within the single trip.

[0123] The driving habit feature matrix consists of a first row of vehicle speed information vector, a second row of brake pedal opening information vector, a third row of accelerator pedal information vector, a fourth row of relative position information vector, and a fifth row of relative time vector.

[0124] In one embodiment, the braking energy recovery value within the single trip is taken as the overall objective, and the braking energy recovery intensity parameters at different locations are taken as control variables. A braking energy recovery optimization function is constructed in combination with preset constraints. The constraints include at least a coupling function of the operating scenario feature matrix of the single trip and the driving habit feature matrix.

[0125] In one embodiment, the recycling strategy module 340 is specifically used for:

[0126] In each braking energy recovery stage of the single trip, the braking energy recovery intensity parameter is calculated in real time using the coupling function of the current stage based on the operating scenario feature matrix and the driving habit feature matrix of the single trip. The braking energy recovery strategy is determined based on the braking energy recovery intensity parameter, and the braking energy recovery value is obtained.

[0127] By comparing the braking energy recovery values ​​at different braking energy recovery stages, the optimal braking energy recovery value and its corresponding braking energy recovery intensity parameter are selected as the target braking energy recovery strategy.

[0128] In one embodiment, the recycling strategy module 340 further includes:

[0129] The amount of electricity recovered by the target braking energy recovery strategy and the original braking energy recovery strategy are compared. If the amount of electricity recovered by the target braking energy recovery strategy is greater than the amount of electricity recovered by the original braking energy recovery strategy, the target braking energy recovery strategy is fixed for the current operating scenario and driver. Otherwise, the original braking energy recovery strategy is used as the target braking energy recovery strategy.

[0130] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other via the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute an adaptive braking energy recovery method, which includes:

[0131] Within a target time period, acquire target vehicle information and driving behavior data; the target vehicle information includes at least altitude information, location information, slope information, vehicle weight information, and time information, and the driving behavior data includes at least vehicle speed information, brake pedal opening information, and accelerator pedal information;

[0132] Based on the target vehicle information and the corresponding behavior time, the start and end positions of a single trip are determined to obtain the operational scenario feature matrix of the single trip;

[0133] Based on the operational scenario feature matrix of the single trip, target driving behavior data within the single trip is extracted to obtain a driving habit feature matrix.

[0134] Based on the single-trip operation scenario feature matrix and the driving habit feature matrix, brake energy recovery is optimized in stages using a pre-constructed brake energy recovery optimization function to obtain the target brake energy recovery strategy.

[0135] Energy recovery is performed according to the target braking energy recovery strategy.

[0136] The present invention also provides a vehicle including an adaptive braking energy recovery system or electronic device as described in any of the above embodiments.

[0137] In this embodiment, the vehicle is a vehicle with regenerative braking function, such as a new energy passenger car or a new energy commercial vehicle. New energy commercial vehicles include new energy construction machinery, such as new energy dump trucks, new energy aerial ladder trucks, new energy fire trucks, new energy mixer trucks, and other engineering vehicles.

[0138] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0139] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer is able to execute the adaptive braking energy recovery method provided by the above methods, the method comprising:

[0140] Within a target time period, acquire target vehicle information and driving behavior data; the target vehicle information includes at least altitude information, location information, slope information, vehicle weight information, and time information, and the driving behavior data includes at least vehicle speed information, brake pedal opening information, and accelerator pedal information;

[0141] Based on the target vehicle information and the corresponding behavior time, the start and end positions of a single trip are determined to obtain the operational scenario feature matrix of the single trip;

[0142] Based on the operational scenario feature matrix of the single trip, target driving behavior data within the single trip is extracted to obtain a driving habit feature matrix.

[0143] Based on the single-trip operation scenario feature matrix and the driving habit feature matrix, brake energy recovery is optimized in stages using a pre-constructed brake energy recovery optimization function to obtain the target brake energy recovery strategy.

[0144] Energy recovery is performed according to the target braking energy recovery strategy.

[0145] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the adaptive braking energy recovery methods provided above, the method comprising:

[0146] Within a target time period, acquire target vehicle information and driving behavior data; the target vehicle information includes at least altitude information, location information, slope information, vehicle weight information, and time information, and the driving behavior data includes at least vehicle speed information, brake pedal opening information, and accelerator pedal information;

[0147] Based on the target vehicle information and the corresponding behavior time, the start and end positions of a single trip are determined to obtain the operational scenario feature matrix of the single trip;

[0148] Based on the operational scenario feature matrix of the single trip, target driving behavior data within the single trip is extracted to obtain a driving habit feature matrix.

[0149] Based on the single-trip operation scenario feature matrix and the driving habit feature matrix, brake energy recovery is optimized in stages using a pre-constructed brake energy recovery optimization function to obtain the target brake energy recovery strategy.

[0150] Energy recovery is performed according to the target braking energy recovery strategy.

[0151] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0152] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An adaptive braking energy recovery method, characterized in that, include: Acquire target vehicle information and driving behavior data within the target timeframe; The target vehicle information includes at least altitude information, location information, slope information, vehicle weight information, and time information; the driving behavior data includes at least vehicle speed information, brake pedal opening information, and accelerator pedal information. Based on the target vehicle information and the corresponding behavior time, the start and end positions of a single trip are determined to obtain the operation scenario feature matrix of the single trip; wherein, the operation scenario feature matrix of the single trip includes a first row of altitude information vector, a second row of relative position information vector, a third row of slope information vector, a fourth row of vehicle weight information vector, and a fifth row of relative time vector. Based on the single-trip operation scenario feature matrix, target driving behavior data within the single trip is extracted to obtain a driving habit feature matrix; wherein, the driving habit feature matrix includes a first row of vehicle speed information vector, a second row of brake pedal opening information vector, a third row of accelerator pedal information vector, a fourth row of relative position information vector, and a fifth row of relative time vector. With the braking energy recovery value within the single trip as the overall objective, and the braking energy recovery intensity parameters at different locations as control variables, a braking energy recovery optimization function is constructed in conjunction with preset constraints; the constraints include at least a coupling function of the operational scenario feature matrix and the driving habit feature matrix of the single trip. Based on the single-trip operation scenario feature matrix and the driving habit feature matrix, the braking energy recovery is optimized in stages using a pre-constructed braking energy recovery optimization function to obtain the target braking energy recovery strategy. Based on the single-trip operation scenario feature matrix and the driving habit feature matrix, braking energy recovery is optimized in stages using a pre-constructed braking energy recovery optimization function to obtain the target braking energy recovery strategy, specifically including: In each braking energy recovery stage of the single trip, the braking energy recovery intensity parameter is calculated in real time using the coupling function of the current stage based on the operating scenario feature matrix and the driving habit feature matrix of the single trip. The braking energy recovery strategy is determined based on the braking energy recovery intensity parameter, and the braking energy recovery value is obtained. By comparing the braking energy recovery values ​​at different braking energy recovery stages, the optimal braking energy recovery value and its corresponding braking energy recovery intensity parameter are selected as the target braking energy recovery strategy. Energy recovery is performed according to the target braking energy recovery strategy.

2. The adaptive braking energy recovery method according to claim 1, characterized in that, Within the target timeframe, acquire target vehicle information and driving behavior data, followed by: An operational scenario matrix is ​​constructed based on the target vehicle information; The operation scenario matrix consists of an altitude information vector in the first row, a location information vector in the second row, a slope information vector in the third row, a vehicle weight information vector in the fourth row, and a time vector in the fifth row.

3. The adaptive braking energy recovery method according to claim 2, characterized in that, Based on the target vehicle information and corresponding behavior time, the start and end positions of a single trip are determined to obtain the operational scenario feature matrix of the single trip, specifically including: Extract the target feature points of each row of data in the operation scenario matrix, compare the target feature points of the first four rows of data in the operation scenario matrix, and confirm the start and end positions of the single journey. The operational scenario feature matrix of the single route is constructed based on the start and end positions of the single route and the target feature points.

4. The adaptive braking energy recovery method according to claim 1, characterized in that, Based on the operational scenario feature matrix of the single trip, target driving behavior data within the single trip is extracted to obtain a driving habit feature matrix, specifically including: Based on the relative time vector of the single-trip operation scenario feature matrix, target driving behavior data within the single-trip at the corresponding time is extracted; The driving habit feature matrix is ​​constructed based on the target driving behavior data within the single trip.

5. The adaptive braking energy recovery method according to claim 1, characterized in that, Based on the single-trip operation scenario feature matrix and the driving habit feature matrix, braking energy recovery is optimized in stages using a pre-constructed braking energy recovery optimization function to obtain the target braking energy recovery strategy. This process further includes: The amount of electricity recovered by the target braking energy recovery strategy and the original braking energy recovery strategy are compared. If the amount of electricity recovered by the target braking energy recovery strategy is greater than the amount of electricity recovered by the original braking energy recovery strategy, the target braking energy recovery strategy is fixed for the current operating scenario and driver. Otherwise, the original braking energy recovery strategy is used as the target braking energy recovery strategy.

6. An adaptive braking energy recovery system, characterized in that, include: The information acquisition module is used to acquire target vehicle information and driving behavior data within a target time period. The target vehicle information includes at least altitude information, location information, slope information, vehicle weight information, and time information; the driving behavior data includes at least vehicle speed information, brake pedal opening information, and accelerator pedal information. The operation scenario module is used to determine the start and end positions of a single trip based on the target vehicle information and the corresponding behavior time, so as to obtain the operation scenario feature matrix of the single trip; wherein, the operation scenario feature matrix of the single trip includes a first row of altitude information vector, a second row of relative position information vector, a third row of slope information vector, a fourth row of vehicle weight information vector, and a fifth row of relative time vector. The driving habit module is used to extract target driving behavior data within the single trip based on the single trip's operational scenario feature matrix to obtain a driving habit feature matrix; wherein, the driving habit feature matrix includes a first row of vehicle speed information vector, a second row of brake pedal opening information vector, a third row of accelerator pedal information vector, a fourth row of relative position information vector, and a fifth row of relative time vector. The braking energy recovery strategy module is used to construct a braking energy recovery optimization function with the braking energy recovery value within the single trip as the overall objective and braking energy recovery intensity parameters at different locations as control variables, combined with preset constraints. The constraints include at least a coupling function between the operational scenario feature matrix and the driving habit feature matrix of the single trip. Based on the operational scenario feature matrix and the driving habit feature matrix of the single trip, braking energy recovery is optimized in stages using the pre-constructed braking energy recovery optimization function to obtain a target braking energy recovery strategy. Specifically, in each braking energy recovery stage of the single trip, the module calculates the braking energy recovery intensity parameter in real time using the coupling function of the current stage, based on the operational scenario feature matrix and the driving habit feature matrix of the single trip. It then determines the braking energy recovery strategy based on the braking energy recovery intensity parameter and obtains the braking energy recovery value. By comparing the braking energy recovery values ​​at different braking energy recovery stages, the optimal braking energy recovery value and its corresponding braking energy recovery intensity parameter are selected as the target braking energy recovery strategy. An energy recovery module is used to recover energy according to the target braking energy recovery strategy.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the adaptive braking energy recovery method as described in any one of claims 1 to 5.

8. A vehicle, characterized in that, This includes the adaptive braking energy recovery system as described in claim 6 or the electronic device as described in claim 7.

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