Hybrid vehicle energy management apparatus and method

Abstract

The application discloses a hybrid vehicle energy management device, which comprises a driving style recognition module, a typical road condition recognition module and an energy management adaptive module; the driving style recognition module is used for acquiring driving style reference data in a preset time period, inputting the driving style reference data into a driving style fuzzy recognition model, and acquiring a current driving style output by the driving style fuzzy recognition model; the typical road condition recognition module is used for acquiring driving characteristic parameters of a vehicle in a preset time period, calculating driving characteristic similarities corresponding to various typical road conditions according to the driving characteristic parameters and various corresponding driving characteristic parameters of the various typical road conditions, and taking a typical road condition corresponding to a maximum driving characteristic similarity as a current road condition; and the energy management adaptive module is used for acquiring the current driving style and the current road condition, formulating an electric quantity balance point according to the current driving style and the current road condition, and selecting a proper vehicle driving mode according to a relationship between a current residual electric quantity and the electric quantity balance point.

Description

Technical Field

[0001] This application relates to the field of hybrid power control technology, and in particular to a hybrid vehicle energy management device and method. Background Technology

[0002] The vehicle drive system of a hybrid electric vehicle consists of two or more power drive systems working together. The hybrid electric vehicle can select an appropriate energy management strategy according to the actual driving conditions of the vehicle, and control the power drive systems to provide driving energy to the vehicle independently or jointly. In order to ensure that the hybrid electric vehicle has excellent performance, the selection of energy management strategy is crucial.

[0003] In existing technologies, vehicle energy management strategies are typically determined based on the driver's actual driving needs and the vehicle's current driving status information. However, this method ignores the impact of driver behavior differences and real-time road conditions on vehicle energy demand, which may lead to situations where the vehicle is in congested traffic and has low remaining battery power, resulting in direct requests for engine power or charging.

[0004] In summary, there is an urgent need for a capability management and control strategy that can comprehensively consider driver driving style and real-time road conditions. Summary of the Invention

[0005] To address the aforementioned technical issues, this application provides a hybrid electric vehicle energy management device and method that comprehensively considers the impact of differences in driving style and road conditions on the vehicle's battery balance strategy.

[0006] The embodiments of this application disclose the following technical solutions:

[0007] In a first aspect, embodiments of this application provide a hybrid electric vehicle energy management device, the device comprising: a driving style recognition module, a typical road condition recognition module, and an energy management adaptive module;

[0008] The driving style recognition module is used to acquire driving style reference data within a preset time period; input the driving style reference data into the driving style fuzzy recognition model, and acquire the current driving style output by the driving style fuzzy recognition model; the driving style fuzzy recognition model is used to determine the driver's driving style based on fuzzy pattern recognition according to the input driving style reference data.

[0009] The typical road condition recognition module is used to obtain the driving characteristic parameters of vehicles within a preset time period; calculate the driving characteristic similarity between the current road condition and various typical road conditions based on the driving characteristic parameters and the driving characteristic parameters corresponding to each typical road condition; and take the typical road condition corresponding to the maximum driving characteristic similarity as the current road condition.

[0010] The energy management adaptive module is used to obtain the current driving style and the current road conditions; determine the battery balance point based on the current driving style and the current road conditions; and select the vehicle driving mode based on the relationship between the current remaining battery power and the battery balance point.

[0011] Optionally, the driving style reference data includes: the average speed, average acceleration, acceleration variance, cumulative braking frequency, and road slope within the preset time period;

[0012] The driving style pattern recognition model is specifically used to determine whether a driver's driving style is gentle, normal, or aggressive based on fuzzy pattern recognition, according to the input driving style reference data.

[0013] The driving characteristic parameters include: the average speed, standard deviation of speed, average acceleration, average deceleration, and idle time ratio within a preset time period;

[0014] Typical road conditions include: congested road conditions, smooth road conditions, and expressway road conditions.

[0015] Optionally, the device further includes: a typical road condition construction module;

[0016] The typical road condition construction module is used to divide kinematic segments using the vehicle idling state as a node; perform principal component analysis on the vehicle operating state feature parameters corresponding to each kinematic segment, and use clustering methods to determine the driving feature parameters corresponding to each type of typical road condition; and send the driving feature parameters corresponding to each type of typical road condition to the typical road condition recognition module so that the typical road condition recognition module stores the driving feature parameters corresponding to each type of typical road condition.

[0017] Optionally, the typical road condition recognition module is specifically used for:

[0018] Calculate the cosine similarity between the driving characteristic parameters and the driving characteristic parameters corresponding to each typical road condition; take the typical road condition corresponding to the maximum cosine similarity as the current road condition.

[0019] Optionally, the capability management adaptive module is specifically used for:

[0020] When the current driving style is smooth and the current road condition is congested, the battery balance point is set as the first battery balance point.

[0021] When the current driving style is normal and the current road condition is congested, the battery balance point is set as the first battery balance point;

[0022] When the current driving style is aggressive and the current road condition is congested, the battery balance point is set as the first battery balance point.

[0023] When the current driving style is smooth and the current road condition is unobstructed, the battery balance point is set as the first battery balance point;

[0024] When the current driving style is normal and the current road condition is smooth, the battery balance point is set as the second battery balance point;

[0025] When the current driving style is aggressive and the current road condition is unobstructed, the battery balance point is set as the third battery balance point.

[0026] When the current driving style is gentle and the current road condition is fast, the battery balance point is set as the second battery balance point.

[0027] When the current driving style is normal and the current road condition is fast, the battery balance point is set as the second battery balance point;

[0028] When the current driving style is aggressive and the current road condition is fast, the battery balance point is set as the third battery balance point.

[0029] The remaining power at the first power balance point is less than the remaining power at the second power balance point, and the remaining power at the second power balance point is less than the remaining power at the third power balance point.

[0030] Optionally, the first power balance point is 21% of the remaining power; the second power balance point is 30% of the remaining power; and the third power balance point is 40% of the remaining power.

[0031] Optionally, the capability management adaptive module is specifically used for:

[0032] Determine whether the vehicle's current remaining battery power is less than the battery balance point;

[0033] If it is less than, then select the drive mode of engine driving and charging;

[0034] Conversely, it determines whether the vehicle's driving torque is greater than the motor's maximum torque; if it is, it adopts a combined engine and motor driving mode; otherwise, it adopts a pure electric motor driving mode.

[0035] Secondly, embodiments of this application provide a hybrid electric vehicle energy management method, the method comprising:

[0036] The driving style recognition module acquires driving style reference data within a preset time period; inputs the driving style reference data into the driving style fuzzy recognition model, and obtains the current driving style output by the driving style fuzzy recognition model; the driving style fuzzy recognition model is used to determine the driver's driving style based on fuzzy pattern recognition according to the input driving style reference data.

[0037] The typical road condition recognition module acquires the driving characteristic parameters of vehicles within a preset time period; based on the driving characteristic parameters and the driving characteristic parameters corresponding to each typical road condition, it calculates the driving characteristic similarity between the current road condition and each typical road condition; and takes the typical road condition corresponding to the maximum driving characteristic similarity as the current road condition.

[0038] The energy management adaptive module obtains the current driving style and the current road conditions; determines the battery balance point based on the current driving style and the current road conditions; and selects the vehicle driving mode based on the relationship between the current remaining battery power and the battery balance point.

[0039] Optionally, the method further includes:

[0040] The typical road condition construction module divides kinematic segments using the vehicle idling state as a node; it performs principal component analysis on the vehicle operating state characteristic parameters corresponding to each kinematic segment, and uses clustering methods to determine the driving characteristic parameters corresponding to each typical road condition; it sends the driving characteristic parameters corresponding to each typical road condition to the typical road condition recognition module so that the typical road condition recognition module can store the driving characteristic parameters corresponding to each typical road condition.

[0041] Optionally, determining the battery balance point based on the current driving style and the current road conditions includes:

[0042] When the current driving style is smooth and the current road condition is congested, the battery balance point is set as the first battery balance point.

[0043] When the current driving style is normal and the current road condition is congested, the battery balance point is set as the first battery balance point;

[0044] When the current driving style is aggressive and the current road condition is congested, the battery balance point is set as the first battery balance point.

[0045] When the current driving style is smooth and the current road condition is unobstructed, the battery balance point is set as the first battery balance point;

[0046] When the current driving style is normal and the current road condition is smooth, the battery balance point is set as the second battery balance point;

[0047] When the current driving style is aggressive and the current road condition is unobstructed, the battery balance point is set as the third battery balance point.

[0048] When the current driving style is gentle and the current road condition is fast, the battery balance point is set as the second battery balance point.

[0049] When the current driving style is normal and the current road condition is fast, the battery balance point is set as the second battery balance point;

[0050] When the current driving style is aggressive and the current road condition is fast, the battery balance point is set as the third battery balance point.

[0051] The remaining power at the first power balance point is less than the remaining power at the second power balance point, and the remaining power at the second power balance point is less than the remaining power at the third power balance point.

[0052] As can be seen from the above technical solutions, the hybrid vehicle energy management device provided in this application includes a driving style recognition module, a typical road condition recognition module, and an energy management adaptive module. The driving style recognition module acquires driving style reference data within a preset time period, inputs the acquired driving style reference data into a driving style fuzzy recognition model, and acquires the current driving style output by the driving style fuzzy recognition model. The typical road condition recognition module acquires the vehicle's driving characteristic parameters within a preset time period, then calculates the driving characteristic similarity of each typical road condition based on these driving characteristic parameters and the corresponding driving characteristic parameters for various typical road conditions. The typical road condition with the highest driving characteristic similarity is then used as the current road condition. The energy management adaptive module acquires the current driving style determined by the driving style recognition module and the current road condition determined by the typical road condition recognition module. It then determines the battery balance point based on the current driving style and the current road condition, and selects a suitable vehicle drive mode based on the relationship between the current remaining battery power and the battery balance point.

[0053] This hybrid electric vehicle energy management device uses a driving style recognition module to identify the driver's driving style in real time and a typical road condition recognition module to identify the vehicle's driving conditions in real time. Based on the current driving style determined by the driving style recognition module and the current road conditions determined by the typical road condition recognition module, it adaptively formulates an energy balance point that can be used to determine the vehicle's energy management control strategy. This enables the online real-time configuration of the hybrid electric vehicle's energy management control strategy and effectively improves the fuel economy of the hybrid electric vehicle. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of this application 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 only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a schematic diagram of the structure of the hybrid electric vehicle energy management device provided in the embodiments of this application;

[0056] Figure 2 This is a flowchart illustrating the hybrid electric vehicle energy management method provided in an embodiment of this application. Detailed Implementation

[0057] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0058] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0059] In existing technologies, the impact of differences in driving style and real-time road conditions on vehicle energy demand is ignored when determining energy management control strategies, which can easily lead to poor fuel economy in the final determined vehicle drive mode.

[0060] To address the technical problems existing in the prior art, this application provides a hybrid electric vehicle energy management device that can comprehensively consider the impact of differences in driving style and road conditions on the overall vehicle battery balance measurement, thereby improving the fuel economy of hybrid electric vehicles.

[0061] The core technical concept of the hybrid vehicle energy management device provided in this application is introduced below:

[0062] The hybrid electric vehicle energy management device provided in this application includes: a driving style recognition module, a typical road condition recognition module, and an energy management adaptive module. The driving style recognition module acquires driving style reference data within a preset time period, inputs the acquired driving style reference data into a driving style fuzzy recognition model, and obtains the current driving style output by the driving style fuzzy recognition model. The typical road condition recognition module acquires the vehicle's driving characteristic parameters within a preset time period, then calculates the driving characteristic similarity of each typical road condition based on these driving characteristic parameters and the corresponding driving characteristic parameters for various typical road conditions. The typical road condition with the highest driving characteristic similarity is then used as the current road condition. The energy management adaptive module acquires the current driving style determined by the driving style recognition module and the current road condition determined by the typical road condition recognition module. It then determines a battery balance point based on the current driving style and current road condition, and selects a suitable vehicle drive mode based on the relationship between the current remaining battery power and the battery balance point.

[0063] The aforementioned hybrid electric vehicle energy management device uses a driving style recognition module to identify the driver's driving style in real time and a typical road condition recognition module to identify the vehicle's driving conditions in real time. Based on the current driving style determined by the driving style recognition module and the current road condition determined by the typical road condition recognition module, it adaptively formulates an energy balance point that can be used to determine the vehicle's energy management control strategy. This enables the online real-time configuration of the hybrid electric vehicle's energy management control strategy and effectively improves the fuel economy of the hybrid electric vehicle.

[0064] The hybrid vehicle energy management device provided in this application is described below through embodiments:

[0065] See Figure 1 , Figure 1 This is a schematic diagram of the structure of a hybrid electric vehicle energy management device provided in an embodiment of this application. Figure 1 As shown, the device includes: a driving style recognition module 101, a typical road condition recognition module 102, and an energy management adaptive module 103.

[0066] The driving style recognition module 101 is used to acquire driving style reference data for a preset time period; input the driving style reference data into the driving style fuzzy recognition model, and acquire the current driving style output by the driving style fuzzy recognition model; the driving style fuzzy recognition model is used to determine the driver's driving style based on fuzzy pattern recognition according to the input driving style reference data.

[0067] Different driving styles have different demands on the overall vehicle power. A power balancing strategy that does not match the driver's driving style will have a certain impact on the driving performance and fuel economy of hybrid vehicles. Therefore, it is important to fully consider the driver's current driving style when determining the energy management control strategy.

[0068] It should be understood that if the driving style is determined to be more aggressive, the battery balance point can be increased accordingly to meet the driver's demand for the power of the hybrid vehicle; conversely, if the driving style is determined to be more relaxed, the battery balance point can be decreased accordingly to meet the vehicle's fuel economy.

[0069] The driving style recognition module in the hybrid electric vehicle energy management device provided in this application needs to obtain driving style reference data within a preset time period when determining the driver's current driving style. The driving style reference data may specifically include the average speed, average acceleration, acceleration variance, cumulative braking frequency, and road slope within the preset time period.

[0070] When acquiring driving style reference data, the driving style recognition module obtains the driving speed of the hybrid vehicle within a preset time period and calculates the average speed, average acceleration, and acceleration variance accordingly. The driving style recognition module also obtains the brake pedal signal within the preset time period to calculate the cumulative braking frequency. Finally, the driving style recognition module uses the Geographic Information System (GIS) on the hybrid vehicle to determine the road slope within the preset time period.

[0071] It should be understood that the preset time period can be set according to actual needs, and no specific limitation is made here.

[0072] Then, the driving style recognition module inputs the acquired driving style reference data into the driving style fuzzy recognition model. This driving style fuzzy recognition model takes into account the influence of each driving style reference data on the driving style and optimizes the weight corresponding to each driving style reference data accordingly. It establishes a corresponding membership function for each driving style reference data. Then, using a fuzzy rule base established based on expert experience, the driver's current driving style is determined according to the membership function corresponding to the driving style reference data.

[0073] It should be understood that the fuzzy rule base is used to store fuzzy rules corresponding to various driving styles. The membership function corresponding to the driving style reference data is matched with the various fuzzy rules stored in the fuzzy rule base to determine the driving style corresponding to the most matching fuzzy rule as the current driving style.

[0074] It should be noted that the fuzzy rules stored in the fuzzy rule base can specifically include: fuzzy rules determined based on driving style reference data corresponding to a smooth driving style, fuzzy rules determined based on driving style reference data corresponding to a normal driving style, and fuzzy rules determined based on driving style reference data corresponding to an aggressive driving style. Correspondingly, the driving styles determined based on the driving style fuzzy recognition model including this fuzzy rule base specifically include: smooth, normal, and aggressive.

[0075] It should be understood that in practical applications, the fuzzy rule base can also store fuzzy rules corresponding to other driving styles. Accordingly, the driving style finally determined by the fuzzy recognition model of driving style can also be other types; no limitation is made here on the types of driving styles that the fuzzy recognition model of driving style can determine.

[0076] The typical road condition recognition module 102 is used to obtain the driving characteristic parameters of vehicles within a preset time period; calculate the driving characteristic similarity between the current road condition and various typical road conditions based on the driving characteristic parameters and the driving characteristic parameters corresponding to each typical road condition; and take the typical road condition corresponding to the maximum driving characteristic similarity as the current road condition.

[0077] From the perspective of driving conditions, effective identification of driving conditions helps to coordinate the weight of various performance indicators of the control strategy, avoiding a decrease in fuel economy due to excessive remaining battery power in hybrid vehicles, or insufficient vehicle power due to excessively low remaining battery power.

[0078] It should be understood that if the current road conditions are congested, the battery balance point can be appropriately lowered to ensure the vehicle's fuel economy; conversely, if the current road conditions are smooth, the battery balance point can be appropriately raised so that the vehicle can enter the charging mode earlier, thereby ensuring that there is enough remaining battery power to maintain the vehicle in pure electric mode when entering congested road conditions, thus improving the vehicle's fuel economy.

[0079] When the typical road condition recognition module is working, it collects vehicle speed information in real time, and then calculates driving characteristic parameters based on the speed information collected within a preset time period.

[0080] The driving characteristic parameters may specifically include: the average speed, standard deviation of speed, average acceleration, average deceleration, and idle time ratio within a preset time period.

[0081] It should be understood that in practical applications, the preset time period can be set according to actual needs, and no specific limitation is made here; in addition, the driving characteristic parameters can also include other parameters that can reflect driving characteristics, and no specific limitation is made here either.

[0082] Furthermore, the calculated driving characteristic parameters are used to form an eigenvalue matrix x1 = (x 11 ,x 12 ,……,x 1n The similarity of the driving features between the eigenvalue matrix and the eigenvalue matrix composed of driving feature parameters of various typical road conditions can be calculated. Specifically, the cosine similarity between the eigenvalue matrix and the eigenvalue matrix corresponding to typical road conditions can be calculated.

[0083] Assume the eigenvalue matrix composed of driving characteristic parameters of typical road conditions is x2=(x 21 ,x 22 ,……,x 2n If the cosine similarity is such that the formula for calculating the cosine similarity is as follows:

[0084]

[0085] Here, cosθ represents the cosine similarity between the current road condition and a typical road condition. It should be understood that different typical road conditions correspond to different eigenvalue matrices.

[0086] The greater the similarity in driving characteristics between the current road condition and a typical road condition, the greater the likelihood that the current road condition is a typical road condition; conversely, the smaller the similarity in driving characteristics between the current road condition and a typical road condition, the less likely that the current road condition is a typical road condition. Therefore, the maximum value of the driving characteristic similarity is selected, and the typical road condition corresponding to the maximum value of the driving characteristic similarity is taken as the current road condition.

[0087] It should be noted that the similarity of driving features corresponding to the above typical road conditions is determined by the typical road condition construction module. This typical road condition construction module is used to divide kinematic segments using the vehicle idling state as a node; it performs principal component analysis on the vehicle operating state feature parameters corresponding to each kinematic segment, and uses clustering methods to determine the driving feature parameters corresponding to each typical road condition; then, it sends the driving feature parameters corresponding to each typical road condition to the typical road condition recognition module, so that the typical road condition recognition module can store the driving feature parameters corresponding to each typical road condition.

[0088] Specifically, the vehicle information unit (T-Box) data acquisition device collects vehicle-related parameters such as speed, engine speed, engine torque, and motor speed and torque during vehicle operation. These parameters are then transmitted via network to a typical road condition construction module, which runs on a central server system. Based on the acquired vehicle-related parameters, the typical road condition construction module divides the vehicle into kinematic segments, using the vehicle's idling state as a node. It then selects vehicle operating state characteristic parameters from the acquired parameters, including travel distance, speed, average speed, average acceleration, and acceleration variance. Principal component analysis is performed on the vehicle operating state characteristic parameters corresponding to each kinematic segment. Based on this, a clustering method is used to determine the driving characteristic parameters corresponding to various typical road conditions. Finally, the determined driving characteristic parameters for each typical road condition are sent to a typical road condition recognition module, which then determines the current road condition based on these parameters.

[0089] It should be noted that, in the technical solution provided in this application, the typical road conditions constructed by the typical road condition construction module mainly include: congested road conditions, smooth road conditions, and express road conditions; of course, in practical applications, driving characteristic parameters corresponding to other typical road conditions can also be constructed according to actual needs.

[0090] The energy management adaptive module 103 is used to obtain the current driving style determined by the driving style recognition module 101 and the current road condition described by the typical road condition recognition module 102; determine the battery balance point based on the current driving style and the current road condition; and select the vehicle driving mode based on the relationship between the current remaining battery power and the battery balance point.

[0091] When determining the energy management control strategy, different State of Charge (SOC) range constraints are set based on the current driving style and road conditions. For driving styles that emphasize power and in good road conditions, the SOC balance point can be set to a higher value to cope with frequent rapid acceleration. For driving styles that emphasize economy and in poor road conditions, the SOC balance point can be set to a lower value to ensure both vehicle power and fuel economy.

[0092] In specific implementation, when the current driving style is smooth and the current road condition is congested, the battery balance point is set as the first battery balance point; when the current driving style is normal and the current road condition is congested, the battery balance point is set as the second battery balance point; when the current driving style is aggressive and the current road condition is congested, the battery balance point is set as the third battery balance point; when the current driving style is smooth and the current road condition is unobstructed, the battery balance point is set as the fourth battery balance point; when the current driving style is normal and the current road condition is unobstructed, the battery balance point is set as the fifth battery balance point; when the current driving style is aggressive and the current road condition is unobstructed, the battery balance point is set as the sixth battery balance point; when the current driving style is aggressive and the current road condition is unobstructed, the battery balance point is set as the seventh battery balance point; when the current driving style is aggressive and the current road condition is unobstructed, the battery balance point is set as the eighth battery balance point; when the current driving style is aggressive and the current road condition is unobstructed, the battery balance point is set as the ninth battery balance point; when the current driving style is aggressive and the current road condition is unobstructed, the battery balance point is set as the eleventh ... When driving in a high-intensity mode and the current road condition is smooth, the battery balance point is set as the third battery balance point; when driving in a gentle mode and the current road condition is fast, the battery balance point is set as the second battery balance point; when driving in a normal mode and the current road condition is fast, the battery balance point is set as the second battery balance point; when driving in an aggressive mode and the current road condition is fast, the battery balance point is set as the third battery balance point. The remaining battery level at the first battery balance point is less than the remaining battery level at the second battery balance point, and the remaining battery level at the second battery balance point is less than the remaining battery level at the third battery balance point.

[0093] In one possible implementation, the first power balance point is 21% of the remaining power; the second power balance point is 30% of the remaining power; and the third power balance point is 40% of the remaining power. The method for determining the power balance point is shown in Table 1.

[0094] Table 1

[0095]

[0096] When the remaining power at the power balance point is low, an energy allocation principle with a higher proportion of motor power can be adopted; when the remaining power at the power balance point is moderate, an energy allocation principle based on efficiency can be adopted; when the remaining power at the power balance point is high, an energy allocation principle with a higher proportion of engine power can be adopted.

[0097] In practice, it determines whether the vehicle's current remaining battery power is less than the specified battery balance point. If it is less, the engine-driven and charging mode is selected. Otherwise, if it is greater, it further determines whether the vehicle's driving torque is greater than the motor's maximum torque. If it is greater, the engine and motor combined driving mode is adopted, with the engine and motor jointly driving the vehicle, using the engine to compensate for the torque demand that the motor cannot meet. Otherwise, if the vehicle's driving torque is less than the motor's maximum torque, the pure electric motor driving mode is adopted.

[0098] The aforementioned hybrid electric vehicle energy management device uses a driving style recognition module to identify the driver's driving style in real time and a typical road condition recognition module to identify the vehicle's driving conditions in real time. Based on the current driving style determined by the driving style recognition module and the current road condition determined by the typical road condition recognition module, it adaptively formulates an energy balance point that can be used to determine the vehicle's energy management control strategy. This enables the online real-time configuration of the hybrid electric vehicle's energy management control strategy and effectively improves the fuel economy of the hybrid electric vehicle.

[0099] In addition to the hybrid electric vehicle energy management device described above, this application also provides a hybrid electric vehicle energy management method.

[0100] See Figure 2 , Figure 2 This is a flowchart illustrating the energy management method for hybrid electric vehicles provided in an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0101] Step 201: The driving style recognition module obtains driving style reference data within a preset time period; inputs the driving style reference data into the driving style fuzzy recognition model, and obtains the current driving style output by the driving style fuzzy recognition model; the driving style fuzzy recognition model is used to determine the driver's driving style based on fuzzy pattern recognition according to the input driving style reference data.

[0102] Step 202: The typical road condition recognition module obtains the driving characteristic parameters of the vehicle within a preset time period; based on the driving characteristic parameters and the driving characteristic parameters corresponding to each typical road condition, it calculates the driving characteristic similarity between the current road condition and each typical road condition; and takes the typical road condition corresponding to the maximum driving characteristic similarity as the current road condition.

[0103] Step 203: The energy management adaptive module obtains the current driving style and the current road conditions; determines the battery balance point based on the current driving style and the current road conditions; and selects the vehicle drive mode based on the relationship between the current remaining battery power and the battery balance point.

[0104] Optionally, the driving style reference data includes: the average speed, average acceleration, acceleration variance, cumulative braking frequency, and road slope within the preset time period;

[0105] The driving style pattern recognition model is specifically used to determine whether a driver's driving style is gentle, normal, or aggressive based on fuzzy pattern recognition, according to the input driving style reference data.

[0106] The driving characteristic parameters include: the average speed, standard deviation of speed, average acceleration, average deceleration, and idle time ratio within a preset time period;

[0107] Typical road conditions include: congested road conditions, smooth road conditions, and expressway road conditions.

[0108] Optionally, the method further includes:

[0109] The typical road condition construction module divides kinematic segments using the vehicle idling state as a node; it performs principal component analysis on the vehicle operating state characteristic parameters corresponding to each kinematic segment, and uses clustering methods to determine the driving characteristic parameters corresponding to each typical road condition; it sends the driving characteristic parameters corresponding to each typical road condition to the typical road condition recognition module so that the typical road condition recognition module can store the driving characteristic parameters corresponding to each typical road condition.

[0110] Optionally, the step of calculating the similarity of driving characteristics between the current road condition and various typical road conditions based on the driving characteristic parameters and the driving characteristic parameters corresponding to each typical road condition includes:

[0111] Calculate the cosine similarity between the driving characteristic parameters and the driving characteristic parameters corresponding to each typical road condition; take the typical road condition corresponding to the maximum cosine similarity as the current road condition.

[0112] Optionally, determining the battery balance point based on the current driving style and the current road conditions includes:

[0113] When the current driving style is smooth and the current road condition is congested, the battery balance point is set as the first battery balance point.

[0114] When the current driving style is normal and the current road condition is congested, the battery balance point is set as the first battery balance point;

[0115] When the current driving style is aggressive and the current road condition is congested, the battery balance point is set as the first battery balance point.

[0116] When the current driving style is smooth and the current road condition is unobstructed, the battery balance point is set as the first battery balance point;

[0117] When the current driving style is normal and the current road condition is smooth, the battery balance point is set as the second battery balance point;

[0118] When the current driving style is aggressive and the current road condition is unobstructed, the battery balance point is set as the third battery balance point.

[0119] When the current driving style is gentle and the current road condition is fast, the battery balance point is set as the second battery balance point.

[0120] When the current driving style is normal and the current road condition is fast, the battery balance point is set as the second battery balance point;

[0121] When the current driving style is aggressive and the current road condition is fast, the battery balance point is set as the third battery balance point.

[0122] The remaining power at the first power balance point is less than the remaining power at the second power balance point, and the remaining power at the second power balance point is less than the remaining power at the third power balance point.

[0123] Optionally, the first power balance point is 21% of the remaining power; the second power balance point is 30% of the remaining power; and the third power balance point is 40% of the remaining power.

[0124] Optionally, selecting the vehicle drive mode based on the relationship between the current remaining battery power and the battery balance point includes:

[0125] Determine whether the vehicle's current remaining battery power is less than the battery balance point;

[0126] If it is less than, then select the drive mode of engine driving and charging;

[0127] Conversely, it determines whether the vehicle's driving torque is greater than the motor's maximum torque; if it is, it adopts a combined engine and motor driving mode; otherwise, it adopts a pure electric motor driving mode.

[0128] The aforementioned hybrid electric vehicle energy management method utilizes a driving style recognition module to identify the driver's driving style in real time and a typical road condition recognition module to identify the vehicle's driving conditions in real time. Based on the current driving style determined by the driving style recognition module and the current road conditions determined by the typical road condition recognition module, it adaptively formulates an energy balance point that can be used to determine the vehicle's energy management control strategy. This enables the online real-time configuration of the hybrid electric vehicle's energy management control strategy and effectively improves the fuel economy of the hybrid electric vehicle.

[0129] It should be noted that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for the device and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiments. The device and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and 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 solution according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0130] The above description is merely one specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A hybrid vehicle energy management device, characterized by comprising: The device comprises a driving style recognition module, a typical road condition recognition module, and an energy management adaptive module; The driving style recognition module is configured to acquire driving style reference data in a preset time period, input the driving style reference data into a driving style fuzzy recognition model, and acquire a current driving style output by the driving style fuzzy recognition model; the driving style fuzzy recognition model is configured to determine a driving style of a driver based on fuzzy pattern recognition according to the input driving style reference data; The typical road condition recognition module is configured to acquire driving feature parameters of a vehicle in a preset time period, calculate driving feature similarities between a current road condition and various typical road conditions according to the driving feature parameters and driving feature parameters corresponding to the various typical road conditions respectively, and take a typical road condition corresponding to a maximum driving feature similarity as the current road condition; The energy management adaptive module is configured to acquire the current driving style and the current road condition, formulate an electric quantity balance point according to the current driving style and the current road condition, take a higher electric quantity balance point if the current driving style is power-oriented and the road condition is good, take a lower electric quantity balance point if the current driving style is economy-oriented and the road condition is poor, and select a vehicle driving mode according to a relationship between a current residual electric quantity and the electric quantity balance point.

2. The apparatus of claim 1, wherein, The driving style reference data comprises a speed average value, an acceleration average value, an acceleration variance, a brake cumulative frequency, and a road slope in the preset time period; The driving style fuzzy recognition model is specifically configured to determine a driving style of a driver as gentle, ordinary, or intense based on fuzzy pattern recognition according to the input driving style reference data; The driving feature parameters comprise a speed average value, a speed standard deviation, an acceleration average value, a deceleration average value, and an idling time ratio in a preset time period; The typical road conditions comprise a congested road condition, a smooth road condition, and an expressway road condition.

3. The apparatus of claim 1, wherein, The device further comprises a typical road condition construction module; The typical road condition construction module is configured to divide kinematic segments by taking a vehicle idling state as a node, perform principal component analysis on vehicle operating state feature parameters corresponding to each kinematic segment, determine driving feature parameters corresponding to various typical road conditions respectively by using a clustering method, and send the driving feature parameters corresponding to the various typical road conditions respectively to the typical road condition recognition module, so that the typical road condition recognition module stores the driving feature parameters corresponding to the various typical road conditions respectively.

4. The apparatus of claim 1, wherein, The typical road condition recognition module is specifically configured to: Calculate cosine similarities between the driving feature parameters and the driving feature parameters corresponding to the various typical road conditions respectively, and take a typical road condition corresponding to a maximum cosine similarity as the current road condition.

5. The apparatus of claim 2, wherein, The energy management adaptive module is specifically configured to: Formulate the electric quantity balance point as a first electric quantity balance point when the current driving style is gentle and the current road condition is a congested road condition; Formulate the electric quantity balance point as the first electric quantity balance point when the current driving style is ordinary and the current road condition is a congested road condition; When the current driving style is the intense type and the current road condition is the congested road condition, the electric quantity balance point is set as the first electric quantity balance point; When the current driving style is the gentle type and the current road condition is the smooth road condition, the electric quantity balance point is set as the first electric quantity balance point; When the current driving style is the ordinary type and the current road condition is the smooth road condition, the electric quantity balance point is set as the second electric quantity balance point; When the current driving style is the intense type and the current road condition is the smooth road condition, the electric quantity balance point is set as the third electric quantity balance point; When the current driving style is the gentle type and the current road condition is the expressway condition, the electric quantity balance point is set as the second electric quantity balance point; When the current driving style is the ordinary type and the current road condition is the expressway condition, the electric quantity balance point is set as the second electric quantity balance point; When the current driving style is the intense type and the current road condition is the expressway condition, the electric quantity balance point is set as the third electric quantity balance point. The first electric quantity balance point corresponds to the remaining electric quantity which is less than the second electric quantity balance point, and the second electric quantity balance point corresponds to the remaining electric quantity which is less than the third electric quantity balance point.

6. The apparatus of claim 5, wherein, The first electric quantity balance point is 21% of the remaining electric quantity, the second electric quantity balance point is 30% of the remaining electric quantity, and the third electric quantity balance point is 40% of the remaining electric quantity.

7. The apparatus of claim 1, wherein, The energy management adaptive module is specifically used for: judging whether the current remaining electric quantity of the vehicle is less than the electric quantity balance point; if yes, selecting the driving mode of engine driving and charging; otherwise, judging whether the driving torque of the vehicle is greater than the maximum torque of the motor; if yes, adopting the driving mode of engine and motor combination; otherwise, adopting the driving mode of pure motor.

8. A hybrid vehicle energy management method characterized by comprising: The method comprises: a driving style recognition module acquires driving style reference data in a preset time period; the driving style reference data is input into a driving style fuzzy recognition model, and a current driving style output by the driving style fuzzy recognition model is acquired; the driving style fuzzy recognition model is used for determining the driving style of a driver based on fuzzy pattern recognition according to the input driving style reference data; a typical road condition recognition module acquires driving characteristic parameters of the vehicle in a preset time period; the driving characteristic parameters and the respective driving characteristic parameters of various typical road conditions are used to calculate the driving characteristic similarity between the current road condition and various typical road conditions; the typical road condition corresponding to the maximum driving characteristic similarity is taken as the current road condition; an energy management adaptive module acquires the current driving style and the current road condition; an electric quantity balance point is set according to the current driving style and the current road condition; if the current driving style is power-oriented and the road condition is good, the set electric quantity balance point is higher; if the current driving style is economy-oriented and the road condition is poor, the set electric quantity balance point is lower; the relationship between the current remaining electric quantity and the electric quantity balance point is used to select the driving mode of the whole vehicle.

9. The method of claim 8, wherein, The method further comprises: The typical road condition construction module divides a kinematic segment with a vehicle idle state as a node; principal component analysis is performed on a vehicle running state characteristic parameter corresponding to each kinematic segment, and a clustering method is used to determine driving characteristic parameters corresponding to various typical road conditions; and the driving characteristic parameters corresponding to the various typical road conditions are sent to the typical road condition recognition module, so that the typical road condition recognition module stores the driving characteristic parameters corresponding to the various typical road conditions.

10. The method of claim 8, wherein, The power balance point is determined according to the current driving style and the current road condition, including: when the current driving style is gentle and the current road condition is a congested road condition, the power balance point is determined as a first power balance point; when the current driving style is ordinary and the current road condition is a congested road condition, the power balance point is determined as the first power balance point; when the current driving style is intense and the current road condition is a congested road condition, the power balance point is determined as the first power balance point; when the current driving style is gentle and the current road condition is a smooth road condition, the power balance point is determined as the first power balance point; when the current driving style is ordinary and the current road condition is a smooth road condition, the power balance point is determined as a second power balance point; when the current driving style is intense and the current road condition is a smooth road condition, the power balance point is determined as a third power balance point; when the current driving style is gentle and the current road condition is a fast road condition, the power balance point is determined as the second power balance point; when the current driving style is ordinary and the current road condition is a fast road condition, the power balance point is determined as the second power balance point; when the current driving style is intense and the current road condition is a fast road condition, the power balance point is determined as the third power balance point; the first power balance point corresponds to a remaining power smaller than a remaining power corresponding to the second power balance point, and the second power balance point corresponds to a remaining power smaller than a remaining power corresponding to the third power balance point.

Images