Vehicle energy management method and device and vehicle

Abstract

The invention relates to the technical field of vehicle energy management, and discloses a vehicle energy management method and device and a vehicle, and the method comprises the steps: determining a target correction coefficient according to the user energy consumption data of the vehicle under the condition that the navigation of the vehicle is started, and correcting a preset SOC threshold according to the target correction coefficient to obtain a corrected SOC threshold, adjusting the corrected SOC threshold value according to the navigation information to obtain a target SOC threshold value, and enabling the vehicle to perform energy management based on the target SOC threshold value; under the condition that navigation of the vehicle is not started, real-time working condition characteristics of the vehicle are obtained, a target energy management strategy matched with the real-time working condition characteristics is obtained, and energy management is conducted on the vehicle according to the target energy management strategy. According to the method, flexible energy management can be carried out on the vehicle according to the actual driving condition of a user, so that the start-stop control of the engine is more reasonable, the loss caused by excessive energy conversion is avoided, and the energy consumption of the whole vehicle is greatly reduced.

Description

Technical Field

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

[0002] Hybrid vehicles are vehicles with two power sources: an engine and a battery. These two sources enable various driving modes. Related technologies manage energy by controlling the engine's start and stop to switch between driving modes. However, these technologies suffer from frequent engine start-stop cycles or inappropriate start-stop timing, resulting in excessive energy conversion losses and increased overall vehicle energy consumption. Summary of the Invention

[0003] This application provides a vehicle energy management method, device, and vehicle, which solves the technical problem that frequent engine start-stop or unreasonable start-stop timing causes excessive energy conversion loss, resulting in increased vehicle energy consumption. This application can flexibly manage the vehicle's energy according to the user's actual driving situation, so as to make the engine start-stop control more reasonable, avoid the loss caused by excessive energy conversion, and greatly reduce the vehicle's energy consumption.

[0004] To achieve the above objectives, the main technical solutions adopted in this application include:

[0005] In a first aspect, embodiments of this application provide a vehicle energy management method, the method comprising:

[0006] When the vehicle is navigating, a target correction coefficient is determined based on the user energy consumption data of the vehicle, and a preset SOC threshold is corrected based on the target correction coefficient to obtain a corrected SOC threshold. The corrected SOC threshold is then adjusted based on navigation information to obtain a target SOC threshold, so that the vehicle performs energy management based on the target SOC threshold.

[0007] When the vehicle is not using navigation, the real-time operating condition characteristics of the vehicle are obtained, and a target energy management strategy matching the real-time operating condition characteristics is obtained, so as to manage the energy of the vehicle according to the target energy management strategy.

[0008] The energy management method provided in this embodiment, when navigation is enabled, determines a target correction coefficient based on user energy consumption data to adjust the preset SOC threshold. This allows for flexible adjustment of the target SOC threshold based on the user's daily energy consumption, such as charging habits and pure electric usage, making the adjusted target SOC threshold more consistent with the user's actual energy consumption habits and charging conditions under actual road conditions. When navigation is disabled, real-time operating condition characteristics are acquired and matched with a target energy management strategy. This strategy then enables reasonable energy management based on the current real-time operating conditions. Compared with related technologies, this embodiment fully utilizes the pure electric performance of hybrid vehicles, reduces unnecessary engine start-stop cycles and energy conversion losses, significantly lowers overall vehicle energy consumption, not only meeting the vehicle's low-energy consumption requirements but also extending the engine's equipment lifespan.

[0009] Optionally, in some embodiments of this application, the user energy consumption data includes the number of effective charging times of the vehicle within a preset statistical period, the selection of pure electric drive mode, and the driving mileage in pure electric drive mode.

[0010] The step of determining the target correction coefficient based on the user energy consumption data of the vehicle includes:

[0011] The first correction coefficient is determined based on the effective number of charging cycles and the pure electric drive mode selection.

[0012] The second correction coefficient is determined based on the driving range in the pure electric drive mode;

[0013] The target correction coefficient is determined by multiplying the first correction coefficient and the second correction coefficient.

[0014] By utilizing multiple dimensions such as the number of effective charging cycles, pure electric drive mode selection, and mileage in pure electric drive mode within a preset statistical period, the target correction coefficient can be determined. This can accurately reflect the user's charging habits and pure electric driving conditions, thereby more accurately correcting the preset SOC threshold. The adjusted target SOC threshold better matches the user's actual energy consumption characteristics, which helps reduce the start-stop frequency and energy conversion loss of the engine or motor, reduce overall vehicle energy consumption, and improve fuel economy.

[0015] Optionally, in some embodiments of this application, adjusting the modified SOC threshold based on navigation information to obtain the target SOC threshold includes:

[0016] The driving route is obtained based on the navigation information, and the charging equipment status along the driving route is determined.

[0017] If the charging device condition meets the preset conditions, the corrected SOC threshold is adjusted to be lowered, so as to obtain the target SOC threshold by using the adjustment result;

[0018] If the charging device does not meet the preset conditions, the corrected SOC threshold will be used as the target SOC threshold.

[0019] Based on navigation information, the availability of charging facilities along the driving route is determined, and the corrected State of Charge (SOC) threshold is adjusted accordingly to better match vehicle energy management with actual driving scenarios. When charging facilities meet preset conditions, i.e., charging is relatively convenient, the corrected SOC threshold is lowered to obtain the target SOC threshold, thereby prioritizing pure electric driving, avoiding unnecessary engine start-stop cycles, reducing energy conversion losses, and lowering overall vehicle energy consumption.

[0020] Optionally, in some embodiments of this application, obtaining the real-time operating characteristics of the vehicle and obtaining a target energy management strategy matching the real-time operating characteristics includes:

[0021] The vehicle's driving data within a preset time period is acquired, and features are extracted from the driving data to obtain the real-time operating condition features.

[0022] Obtain typical operating condition characteristics corresponding to multiple preset operating conditions from the operating condition database, wherein the operating condition database stores the multiple preset operating conditions and the candidate energy management strategies corresponding to each preset operating condition.

[0023] Calculate the similarity between the real-time operating condition features and the typical operating condition features, and select the candidate energy management strategy corresponding to the preset operating condition with the highest similarity as the target energy management strategy.

[0024] By acquiring driving data over a preset time period and extracting features, the real-time driving status of the vehicle can be accurately depicted, providing an accurate basis for subsequent strategy matching. Utilizing preset operating conditions and corresponding candidate energy management strategies stored in the operating condition database, the vehicle can select an energy management strategy based on a large amount of historical data. Furthermore, by calculating the similarity between real-time operating condition features and typical operating condition features, the target energy management strategy is determined. This allows for the rapid selection of a target energy management strategy that matches the real-time operating conditions. The target energy management strategy then rationally controls the start-stop of the electric motor, reducing frequent engine start-stop cycles, effectively lowering overall vehicle energy consumption, improving vehicle energy utilization efficiency and fuel economy, and making vehicle energy management more aligned with actual driving needs.

[0025] Optionally, in some embodiments of this application, the candidate energy management strategy is determined in the following manner:

[0026] Obtain the sampled vehicle speed corresponding to each preset working condition and the final battery charge at the end of the preset working condition;

[0027] Based on the sampled vehicle speed and the final battery charge, the optimal planning calculation is performed on the battery charge of the vehicle at each sampling time under each preset operating condition, so as to obtain the candidate energy management strategy based on the calculation results.

[0028] By using the sampled vehicle speed and final battery charge corresponding to the preset operating conditions, the optimal planning calculation of the motor torque demand and battery charge is performed, thereby deriving the relatively optimal candidate energy management strategy under the preset operating conditions. This is beneficial for the subsequent rational planning of battery charge consumption and storage based on the candidate energy management strategy, optimizing the energy utilization efficiency of the vehicle under various preset operating conditions, reducing energy loss caused by frequent engine start-stop, and thus improving the vehicle's fuel economy and overall performance.

[0029] Optionally, in some embodiments of this application, the step of performing optimal planning calculations on the battery charge of the vehicle at each sampling time under each of the preset operating conditions based on the sampled vehicle speed and the final battery charge, so as to obtain the candidate energy management strategy based on the calculation results, includes:

[0030] Construct an objective function based on the vehicle's overall energy consumption;

[0031] While satisfying the objective function minimization, the optimal battery charge at each sampling time under each preset working condition is solved in reverse based on the sampled vehicle speed and the final battery charge.

[0032] The candidate energy management strategy is obtained based on the solution of the optimal battery capacity.

[0033] By inversely solving for the optimal battery charge while minimizing overall vehicle energy consumption, the vehicle's speed changes and final charge demand during driving can be fully considered. The resulting candidate energy management strategy can ensure that the battery charge is reasonably allocated under global operating conditions, realize reasonable engine start-stop, reduce frequent engine start-stop, effectively reduce overall vehicle energy consumption, improve vehicle energy utilization efficiency and fuel economy, and make vehicle energy management more in line with actual driving needs.

[0034] Optionally, in some embodiments of this application, the step of managing the vehicle's energy according to the target energy management strategy includes:

[0035] Obtain the optimal battery capacity corresponding to the target energy management strategy;

[0036] Energy management is achieved by controlling the vehicle's engine start / stop state based on the optimal battery charge level.

[0037] When the vehicle is not using navigation, the engine start-stop state is controlled by the target energy management strategy corresponding to the preset operating conditions, thereby achieving reasonable energy management, reducing frequent engine start-stop, effectively reducing vehicle energy consumption, improving vehicle energy utilization efficiency and fuel economy, and making vehicle energy management more in line with actual driving needs.

[0038] Optionally, in some embodiments of this application, the real-time operating condition characteristics include at least one of the vehicle's average speed, average acceleration, average deceleration, maximum speed, speed standard deviation, and acceleration standard deviation.

[0039] Secondly, embodiments of this application provide a vehicle energy management device, the device comprising:

[0040] The first management module is used to determine a target correction coefficient based on the user energy consumption data of the vehicle when the vehicle is navigating, and to correct a preset SOC threshold based on the target correction coefficient to obtain a corrected SOC threshold. The corrected SOC threshold is then adjusted based on navigation information to obtain a target SOC threshold, so that the vehicle performs energy management based on the target SOC threshold.

[0041] The second management module is used to acquire the real-time operating condition characteristics of the vehicle when the vehicle is not using navigation, and to acquire a target energy management strategy that matches the real-time operating condition characteristics, so as to manage the energy of the vehicle according to the target energy management strategy.

[0042] The energy management device proposed in this application, when navigation is activated, determines a target correction coefficient based on user energy consumption data to adjust the preset SOC threshold. This allows for flexible adjustment of the target SOC threshold based on the user's daily energy consumption, such as charging habits and pure electric usage, making the adjusted target SOC threshold more consistent with the user's actual energy consumption habits and charging conditions under actual road conditions. When navigation is not activated, real-time operating condition characteristics are acquired and matched with a target energy management strategy. This strategy enables reasonable energy management based on the current real-time operating conditions. Compared with related technologies, this application embodiment can fully utilize the pure electric performance of hybrid vehicles, reduce unnecessary engine start-stop and energy conversion losses, significantly reduce overall vehicle energy consumption, not only meeting the vehicle's low energy consumption requirements but also extending the engine's equipment lifespan.

[0043] Thirdly, embodiments of this application provide a vehicle including a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the energy management method described in the above embodiments.

[0044] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer instructions, which are used to cause a vehicle to perform the energy management method described in the above embodiments. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0046] Figure 1 This is one of the flowcharts illustrating a vehicle energy management method proposed in this application.

[0047] Figure 2 This is a second schematic flowchart of a vehicle energy management method proposed in this application.

[0048] Figure 3 This is the third flowchart illustrating a vehicle energy management method proposed in this application.

[0049] Figure 4 This is the fourth flowchart illustrating a vehicle energy management method proposed in this application.

[0050] Figure 5 This is the fifth flowchart illustrating a vehicle energy management method proposed in this application.

[0051] Figure 6 This is a schematic diagram of the structure of a vehicle energy management device according to an embodiment of this application;

[0052] Figure 7 This is a schematic diagram of the structure of a computer device proposed in an embodiment of this application. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0054] A hybrid vehicle is a vehicle with two drive sources: an engine and a battery. These two drive sources can achieve a variety of different driving modes. During vehicle driving, the engine can be started alone to achieve fuel driving mode, the battery can be started alone to achieve pure electric driving mode, or both the engine and the battery can be started simultaneously to achieve hybrid driving mode.

[0055] Related technologies can manage energy by controlling the start and stop of the engine to achieve switching between driving modes. For example, in some application scenarios, the technology controls the start and stop of the engine based on conditions such as vehicle speed, a pre-set SOC threshold, and the driver's power demand. If the vehicle speed and the driver's power demand meet certain conditions, and the power battery's charge is lower than the pre-set SOC threshold, the engine is started instead of using the power battery as the driving source.

[0056] However, this control method may lead to frequent engine start-stop, failing to fully utilize the vehicle's pure electric performance, resulting in excessive energy conversion losses and increased overall vehicle energy consumption.

[0057] The energy management methods provided in this manual can be applied to hybrid vehicles with multiple drive sources.

[0058] According to an embodiment of this application, an embodiment of a vehicle energy management method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0059] Figure 1 This is one of the flowcharts of an energy management method according to an embodiment of this application, such as... Figure 1 As shown, the process includes the following steps:

[0060] Step S1: When the vehicle navigation is turned on, determine the target correction coefficient based on the vehicle's user energy consumption data, and correct the preset SOC threshold according to the target correction coefficient to obtain the corrected SOC threshold. Adjust the corrected SOC threshold according to the navigation information to obtain the target SOC threshold, so that the vehicle can perform energy management based on the target SOC threshold.

[0061] Specifically, vehicle user energy consumption data reflects user driving habits and charging convenience. If user driving habits indicate frequent charging, the preset SOC threshold can be appropriately lowered to obtain a corrected SOC threshold, maximizing the use of the battery as a power source. Furthermore, if navigation is activated while driving, the navigation information can promptly determine the charging convenience along the vehicle's route. If charging is convenient, the corrected SOC threshold can be further lowered to obtain a target SOC threshold. Thus, managing vehicle energy through the target SOC threshold is more adaptable to real-world driving scenarios.

[0062] Step S3: Without navigation enabled, obtain the vehicle's real-time operating condition characteristics and a target energy management strategy that matches the real-time operating condition characteristics, so as to manage the vehicle's energy according to the target energy management strategy.

[0063] Specifically, the vehicle's real-time operating characteristics reflect the actual driving conditions of the vehicle, thereby obtaining a target energy management strategy that matches the real-time operating characteristics. This target energy management strategy represents the energy management situation under typical operating conditions that match the real-time operating conditions. The target energy management strategy plans the optimal battery charge of the power battery for the vehicle under the entire operating condition, so that the vehicle can have a lower energy consumption performance when driving according to the optimal battery charge planned by the target energy management strategy during the energy management process.

[0064] The energy management method provided in this embodiment, when navigation is enabled, determines a target correction coefficient based on user energy consumption data to adjust the preset SOC threshold. This allows for flexible adjustment of the target SOC threshold based on the user's daily energy consumption, such as charging habits and pure electric usage, making the adjusted target SOC threshold more consistent with the user's actual energy consumption habits and charging conditions under actual road conditions. When navigation is disabled, real-time operating condition characteristics are acquired and matched with a target energy management strategy. This strategy then enables reasonable energy management based on the current real-time operating conditions. Compared with related technologies, this embodiment fully utilizes the pure electric performance of hybrid vehicles, reduces unnecessary engine start-stop cycles and energy conversion losses, significantly lowers overall vehicle energy consumption, not only meeting the vehicle's low-energy consumption requirements but also extending the engine's equipment lifespan.

[0065] In some embodiments of this application, the aforementioned user energy consumption data includes the number of effective charging times of the vehicle within a preset statistical period, the selection of pure electric drive mode, and the driving mileage in pure electric drive mode.

[0066] Specifically, this application embodiment utilizes background big data statistical analysis to determine the number of effective charging times for a user within a certain period (e.g., a statistical period of one week). The definition of the number of effective charging times is as follows:

[0067] The system detects the charging gun connection signal or the vehicle's charging status. Based on the detected signal, it obtains the vehicle's plug-in status and records the state when it changes from "vehicle is charging" to "charging stopped." If the charging duration exceeds a preset time, it is counted as one valid charging count. This avoids interference from charging malfunctions in the statistics of valid charging counts. The backend system can record the number of valid charging counts (C) within a weekly statistical period. The number of valid charging counts (C) is used to characterize the user's charging habits; a higher C value indicates more frequent charging.

[0068] The pure electric drive mode selection represents the proportion of time users spend in pure electric drive mode within the statistical period. A higher proportion indicates a greater inclination to use the battery as the driving source. Furthermore, the driving range in pure electric drive mode also provides another dimension of user preference for battery power. Therefore, a reasonable SOC threshold can be adjusted to reduce unnecessary engine start-stop cycles and meet user needs for different drive modes.

[0069] This application embodiment utilizes multiple dimensions, such as the number of effective charging cycles, pure electric drive mode selection, and driving mileage in pure electric drive mode within a preset statistical period, to ensure accurate assessment of user charging habits and pure electric driving conditions.

[0070] Figure 2 A second flowchart of an energy management method according to an embodiment of this application is shown, as follows: Figure 2 As shown, step S1 above may include the following steps:

[0071] Step S11: Determine the first correction coefficient based on the number of effective charging cycles and the pure electric drive mode selection.

[0072] Specifically, the first correction coefficient α is determined by the number of effective charging times and the proportion of time the user selects the pure electric driving mode to be used in the statistical period. The values ​​of the first correction coefficient α are shown in Table 1.

[0073] Table 1

[0074]

[0075] Step S13: Determine the second correction coefficient based on the driving range in pure electric drive mode.

[0076] Specifically, the second correction coefficient β is determined by the proportion of the vehicle's mileage in pure electric drive mode to the total mileage in the statistical period. The values ​​of the second correction coefficient β are shown in Table 2.

[0077] Table 2

[0078]

[0079] Step S15: Determine the target correction coefficient by multiplying the first correction coefficient and the second correction coefficient, i.e., the target correction coefficient δ = α * β.

[0080] Furthermore, in some embodiments of this application, the product of the preset SOC threshold and the target correction coefficient δ is used as the corrected SOC threshold.

[0081] By utilizing multiple dimensions such as the number of effective charging cycles, pure electric drive mode selection, and mileage in pure electric drive mode within a preset statistical period, the target correction coefficient can be determined. This can accurately reflect the user's charging habits and pure electric driving conditions, thereby more accurately correcting the preset SOC threshold. The adjusted target SOC threshold better matches the user's actual energy consumption characteristics, which helps reduce the start-stop frequency and energy conversion loss of the engine or motor, reduce overall vehicle energy consumption, and improve fuel economy.

[0082] Figure 3 The third flowchart of the energy management method according to an embodiment of this application is shown, as follows: Figure 3 As shown, step S1 above may further include the following steps:

[0083] Step S17: Obtain the driving route based on the navigation information and determine the charging equipment status along the driving route.

[0084] Specifically, in this embodiment of the application, the navigation system is used to obtain the corresponding navigation information. The navigation information is used to obtain the specific driving route from the starting point to the destination. The charging facility database built into the navigation system is used in conjunction with the geographical location information of the driving route to query the distribution of charging equipment along the route and around the destination, including the number of charging piles and their distance from the driving route, so as to comprehensively determine the charging equipment situation of the driving route.

[0085] Step S19: If the charging equipment condition meets the preset conditions, the corrected SOC threshold is adjusted to be lowered so as to obtain the target SOC threshold using the adjustment result; if the charging equipment condition does not meet the preset conditions, the corrected SOC threshold is used as the target SOC threshold.

[0086] Specifically, in some embodiments of this application, the preset conditions are typically set to ensure that charging equipment is sufficient and readily available. For example, there are at least N available charging piles within a certain range around the destination, or there are suitable charging piles at regular intervals along the driving route. When the charging equipment situation meets the preset conditions, it indicates that charging is relatively convenient during the vehicle's journey or after reaching the destination. In this case, appropriately lowering the corrected SOC threshold allows the vehicle to use pure electric drive mode more frequently during driving, reducing unnecessary engine start-stop frequencies and thus lowering overall vehicle energy consumption.

[0087] When the charging equipment does not meet the preset conditions, the corrected SOC threshold is maintained as the target SOC threshold. This ensures that the vehicle has sufficient power reserves to cope with various road conditions when charging is inconvenient, ensuring normal vehicle operation and avoiding inconvenience caused by insufficient power.

[0088] Based on navigation information, the availability of charging facilities along the driving route is determined, and the corrected State of Charge (SOC) threshold is adjusted accordingly to better match vehicle energy management with actual driving scenarios. When charging facilities meet preset conditions, i.e., charging is relatively convenient, the corrected SOC threshold is lowered to obtain the target SOC threshold, thereby prioritizing pure electric driving, avoiding unnecessary engine start-stop cycles, reducing energy conversion losses, and lowering overall vehicle energy consumption.

[0089] Figure 4 A flowchart of the energy management method according to an embodiment of this application is shown in Figure 4. Figure 4 As shown, step S3 above may include the following steps:

[0090] Step S31: Obtain the vehicle's driving data within a preset time period, and extract features from the driving data to obtain real-time operating condition features.

[0091] Specifically, the aforementioned real-time operating condition characteristics include at least one of the following: average speed, average acceleration, average deceleration, maximum speed, speed standard deviation, and acceleration standard deviation.

[0092] Step S33: Obtain typical operating condition characteristics corresponding to multiple preset operating conditions from the operating condition database. The operating condition database stores multiple preset operating conditions and candidate energy management strategies corresponding to each preset operating condition.

[0093] Specifically, the operating condition database stores multiple preset operating conditions, including but not limited to severe urban congestion, moderate urban congestion, light urban congestion, urban elevated expressway, long-distance highway, and suburban conditions.

[0094] Each preset operating condition corresponds to a candidate energy management strategy, which includes state variables and control variables. The state variable is the battery charge at each sampling time under the preset operating condition, and the control variable is the motor torque demand at each sampling time. Specifically, the candidate energy management strategy represents the optimal battery charge and optimal motor torque demand planned for the vehicle at each sampling time under the preset operating condition, so as to minimize the vehicle's overall energy consumption under that preset operating condition.

[0095] In this way, the optimal battery charge and optimal motor torque requirements can be determined under each preset operating condition through the candidate energy management strategy. This allows the vehicle to adapt to the corresponding preset operating conditions and reasonably control the start and stop of the engine or motor to reduce energy conversion losses and help reduce the overall vehicle energy consumption.

[0096] Step S35: Calculate the similarity between real-time operating condition features and typical operating condition features, and take the candidate energy management strategy corresponding to the preset operating condition with the highest similarity as the target energy management strategy.

[0097] Specifically, by calculating the similarity between real-time operating condition characteristics and typical operating condition characteristics, the preset operating condition that best matches the current real-time operating condition is found among multiple preset operating conditions.

[0098] In some embodiments of this application, similarity calculation methods such as Euclidean distance are used to quantitatively compare real-time operating condition characteristics and typical operating condition characteristics of each preset operating condition. Taking Euclidean distance as an example, firstly, the typical operating condition characteristics of the preset operating condition within the time period Δt are obtained. Assuming the current time is t, the real-time operating condition characteristics of the real-time operating condition between t-ΔT and t are obtained. In order to use the real-time operating condition characteristics within the time period Δt before the current time, the operating condition type within the future time period Δt, i.e., between t and t+ΔT, is predicted.

[0099] Secondly, to eliminate the influence of the feature parameter dimensions on the recognition results, the typical working condition features and real-time working condition features are normalized. Then, based on the following formula (1), the similarity σ(C) between the real-time working condition and the i-th preset working condition is calculated. i ,X):

[0100]

[0101] In the formula, C i X(k) represents the kth typical working condition feature of the i-th preset working condition, X(k) represents the kth real-time working condition feature of the real-time working condition, and m represents the total number of features.

[0102] This leads to the working condition identification result σ(C) i X) is shown in the following formula (2):

[0103] δ(C i ,X)=max{σ(C1,X),σ(C2,X),…,σ(C n Formula (2)

[0104] In the formula, n is the total number of preset working conditions.

[0105] In some embodiments of this application, based on similarity σ(C) iThe maximum value of X) determines the preset operating condition that best matches the real-time operating condition, and then the candidate energy management strategy corresponding to the best-matching preset operating condition is taken as the target energy management strategy.

[0106] In other embodiments of this application, a neural network model can also be used to learn candidate energy management strategies corresponding to various preset operating conditions. Specifically, each candidate energy management strategy is generated offline for the corresponding preset operating condition. During the training phase, the neural network model is trained using each preset operating condition and its corresponding candidate energy management strategy as the training set. The trained neural network model can output the candidate energy management strategy corresponding to the preset operating condition that matches the real-time operating condition characteristics.

[0107] By acquiring driving data over a preset time period and extracting features, the real-time driving status of the vehicle can be accurately depicted, providing a precise basis for subsequent strategy matching. Utilizing preset operating conditions and corresponding candidate energy management strategies stored in the operating condition database, the vehicle can select an energy management strategy based on a large amount of historical data. Furthermore, by calculating the similarity between real-time operating condition features and typical operating condition features, the target energy management strategy is determined. This allows for the rapid selection of a target energy management strategy that matches the real-time operating condition, thereby optimizing engine control, reducing unnecessary engine start-stop cycles, effectively reducing overall vehicle energy consumption, improving vehicle energy utilization efficiency and fuel economy, and making vehicle energy management more aligned with actual driving needs.

[0108] Furthermore, in some embodiments of this application, the candidate energy management strategy is determined in the following manner:

[0109] Obtain the sampling vehicle speed corresponding to each preset working condition and the final battery charge at the end of the preset working condition.

[0110] Specifically, historical vehicle speeds under preset operating conditions are sampled based on a preset sampling frequency to obtain sampled vehicle speeds. For example, the vehicle speed is recorded every 10 seconds within a time period Δt to maintain consistency between real-time operating conditions and preset operating conditions on a time scale. In addition, the final battery charge is recorded at the end of the preset operating condition.

[0111] Based on the sampled vehicle speed and the final battery charge, the optimal planning calculation is performed on the battery charge of the vehicle at each sampling time under each preset operating condition, so as to obtain the candidate energy management strategy based on the calculation results.

[0112] Specifically, the candidate energy management strategies corresponding to each preset operating condition include the optimal battery charge at each sampling time in the Δt time period, indicating that when the vehicle performs energy management according to the optimal battery charge planned by the candidate energy management strategy under the preset operating conditions, it can meet the requirement of minimizing vehicle energy consumption.

[0113] By using the sampled vehicle speed and final battery charge corresponding to the preset operating conditions, the optimal planning calculation of the motor torque demand and battery charge is performed, thereby deriving the relatively optimal candidate energy management strategy under the preset operating conditions. This is beneficial for the subsequent rational planning of battery charge consumption and storage based on the candidate energy management strategy, optimizing the energy utilization efficiency of the vehicle under various preset operating conditions, reducing energy loss caused by frequent start-stop of the engine or motor, and thus improving the vehicle's fuel economy and overall performance.

[0114] The following section further explains the process of calculating the optimal planning algorithm described above:

[0115] First, an objective function is constructed based on the vehicle's overall energy consumption. Then, while minimizing the objective function, the optimal battery charge at each sampling time under each preset operating condition is solved in reverse based on the sampled vehicle speed and the final battery charge.

[0116] Specifically, in some embodiments of this application, the objective function is constructed using the sum of the vehicle's battery energy consumption and fuel energy consumption.

[0117] Assuming the final battery charge at the end of the preset operating condition is 30%, meaning the battery charge at the last sampling time is 30%, the battery charge is calculated backwards from the vehicle speed at the previous sampling time, combined with the vehicle dynamics model and the battery charge / discharge model. This backwards calculates how much battery charge should be consumed between the previous and last sampling times, minimizing the objective function while meeting the vehicle power demand reflected by the sampling speed. For example, if the backward calculation shows that 5% of the charge should be consumed, then the optimal battery charge at the previous sampling time is 35%, and the corresponding optimal motor torque demand is planned based on this 5% charge consumption. The optimal battery charge for other sampling times is calculated similarly.

[0118] In this way, through the above reverse solution process, the optimal battery charge and the corresponding optimal motor torque at each sampling time under various preset operating conditions can be determined, thereby deriving a candidate energy management strategy that minimizes the sum of vehicle battery energy consumption and fuel energy consumption under specific preset operating conditions.

[0119] Therefore, it can be seen that by inversely solving for the optimal battery charge while minimizing the overall vehicle energy consumption, the changes in vehicle speed and the final charge demand during driving can be fully considered. The resulting candidate energy management strategy can ensure that the battery charge is reasonably allocated under global operating conditions.

[0120] Figure 5 The fifth flowchart of the energy management method according to an embodiment of this application is shown, as follows: Figure 5 As shown, step S3 above may further include the following steps:

[0121] Step S37: Obtain the optimal battery capacity corresponding to the target energy management strategy.

[0122] Step S39: Control the engine start / stop state of the vehicle according to the optimal battery charge to perform energy management.

[0123] Specifically, in some embodiments of this application, the optimal battery charge corresponding to the target energy management strategy is used as the basis for controlling the engine start-stop.

[0124] It should be noted that, as can be seen from step S35 above, by calculating the similarity between the real-time operating condition features and the typical operating condition features, the matching status of the operating condition type and the preset operating condition within the future time period Δt can be determined. As can be seen from the step of determining the candidate energy management strategy, the candidate energy management strategy is determined based on the sampled vehicle speed and the final battery charge within the preset operating condition within the time period Δt. Therefore, the operating condition within the future time period Δt can correspond to the preset operating condition in terms of time length.

[0125] For a given moment within a future time interval Δt, under the target energy management strategy corresponding to the preset operating conditions, if the optimal battery charge at the sampling moment corresponding to that moment is 50%, and assuming the vehicle's actual battery charge at that moment is greater than or equal to 50%, then it means the current battery charge meets the optimal battery charge requirement of the target energy management strategy. In this case, the engine is stopped, and the vehicle adopts pure electric drive mode. If the vehicle's actual battery charge at that moment is less than 50%, it means the current battery charge cannot meet the optimal battery charge requirement of the target energy management strategy. In this case, the engine is started, and the vehicle adopts fuel drive mode, while simultaneously charging the power battery until the battery charge subsequently increases to a certain level. Then, the engine is stopped, and the vehicle adopts pure electric drive mode again.

[0126] Therefore, when the vehicle is not using navigation, the engine start-stop state is controlled by the target energy management strategy corresponding to the preset operating conditions, thereby achieving reasonable energy management, reducing frequent engine start-stop, effectively reducing vehicle energy consumption, improving vehicle energy utilization efficiency and fuel economy, and making vehicle energy management more in line with actual driving needs.

[0127] Accordingly, please refer to Figure 6 This application provides a vehicle energy management device, such as... Figure 6 As shown, the device includes:

[0128] The first management module 100 is used to determine a target correction coefficient based on the user energy consumption data of the vehicle when the vehicle is navigating, and to correct a preset SOC threshold based on the target correction coefficient to obtain a corrected SOC threshold. The corrected SOC threshold is then adjusted based on navigation information to obtain a target SOC threshold, so that the vehicle performs energy management based on the target SOC threshold. For details, please refer to step S1.

[0129] The second management module 200 is used to acquire the real-time operating condition characteristics of the vehicle when the vehicle is not using navigation, and to acquire a target energy management strategy that matches the real-time operating condition characteristics, so as to determine a second target SOC threshold according to the target energy management strategy, and to enable the vehicle to perform energy management based on the second target SOC threshold. For details, please refer to step S3.

[0130] In some embodiments of this application, the first management module 100 includes:

[0131] The first correction unit 101 is used to determine a first correction coefficient based on the effective number of charging cycles and the pure electric drive mode selection.

[0132] The second correction unit 102 is used to determine a second correction coefficient based on the driving mileage in the pure electric drive mode.

[0133] The target correction unit 103 is used to determine the target correction coefficient by multiplying the first correction coefficient and the second correction coefficient.

[0134] In some embodiments of this application, the first management module 100 further includes:

[0135] The charging equipment determination unit 104 is used to obtain the driving route based on the navigation information and determine the charging equipment status of the driving route.

[0136] The adjustment unit 105 is used to adjust the modified SOC threshold by reducing it if the charging device condition meets the preset conditions, so as to obtain the target SOC threshold by using the adjustment result; if the charging device condition does not meet the preset conditions, the modified SOC threshold is used as the target SOC threshold.

[0137] In some embodiments of this application, the second management module 200 includes:

[0138] The first feature acquisition unit 201 is used to acquire the vehicle's driving data within a preset time period and extract features from the driving data to obtain the real-time operating condition features.

[0139] The second feature acquisition unit 202 is used to acquire typical operating condition features corresponding to multiple preset operating conditions from the operating condition library, wherein the operating condition library stores the multiple preset operating conditions and candidate energy management strategies corresponding to each preset operating condition.

[0140] The operating condition matching unit 203 is used to calculate the similarity between the real-time operating condition features and the typical operating condition features, and to take the candidate energy management strategy corresponding to the preset operating condition with the highest similarity as the target energy management strategy.

[0141] In some embodiments of this application, the above-mentioned device further includes a strategy acquisition unit 300, which is used to acquire the sampling vehicle speed corresponding to each preset working condition and the final battery charge at the end of the preset working condition, and to perform optimal planning calculation on the battery charge of the vehicle at each sampling time under each preset working condition based on the sampling vehicle speed and the final battery charge, so as to obtain the candidate energy management strategy based on the calculation result.

[0142] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0143] In this embodiment, the energy management device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0144] Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application, which is installed on a vehicle. Figure 7 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 7 Take a processor 10 as an example.

[0145] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0146] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0147] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0148] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0149] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0150] This application also provides a computer-readable storage medium. The methods described in this application can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the methods shown in the above embodiments are implemented.

[0151] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and all such modifications and variations fall within the scope defined by the appended claims.

[0152] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0153] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0154] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0155] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0156] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0157] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0158] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0159] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

[0160] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for energy management of a vehicle, characterized in that, The method includes: When the vehicle is navigating, a target correction coefficient is determined based on the user energy consumption data of the vehicle, and a preset SOC threshold is corrected based on the target correction coefficient to obtain a corrected SOC threshold. The corrected SOC threshold is then adjusted based on navigation information to obtain a target SOC threshold, so that the vehicle performs energy management based on the target SOC threshold. When the vehicle is not using navigation, the real-time operating condition characteristics of the vehicle are obtained, and a target energy management strategy matching the real-time operating condition characteristics is obtained, so as to manage the energy of the vehicle according to the target energy management strategy.

2. The method according to claim 1, characterized in that, The user energy consumption data includes the number of effective charging times of the vehicle within a preset statistical period, the selection of pure electric drive mode, and the driving mileage in pure electric drive mode. The step of determining the target correction coefficient based on the user energy consumption data of the vehicle includes: The first correction coefficient is determined based on the effective number of charging cycles and the pure electric drive mode selection. The second correction coefficient is determined based on the driving range in the pure electric drive mode; The target correction coefficient is determined by multiplying the first correction coefficient and the second correction coefficient.

3. The method according to claim 1, characterized in that, The step of adjusting the corrected SOC threshold based on navigation information to obtain the target SOC threshold includes: The driving route is obtained based on the navigation information, and the charging equipment status along the driving route is determined. If the charging device condition meets the preset conditions, the corrected SOC threshold is adjusted to be lowered, so as to obtain the target SOC threshold by using the adjustment result; If the charging device does not meet the preset conditions, the corrected SOC threshold will be used as the target SOC threshold.

4. The method according to claim 1, characterized in that, The step of acquiring the real-time operating characteristics of the vehicle and acquiring a target energy management strategy that matches the real-time operating characteristics includes: The vehicle's driving data within a preset time period is acquired, and features are extracted from the driving data to obtain the real-time operating condition features. Obtain typical operating condition characteristics corresponding to multiple preset operating conditions from the operating condition database, wherein the operating condition database stores the multiple preset operating conditions and the candidate energy management strategies corresponding to each preset operating condition. Calculate the similarity between the real-time operating condition features and the typical operating condition features, and select the candidate energy management strategy corresponding to the preset operating condition with the highest similarity as the target energy management strategy.

5. The method according to claim 4, characterized in that, The candidate energy management strategy is determined in the following manner: Obtain the sampled vehicle speed corresponding to each preset working condition and the final battery charge at the end of the preset working condition; Based on the sampled vehicle speed and the final battery charge, the optimal planning calculation is performed on the battery charge of the vehicle at each sampling time under each preset operating condition, so as to obtain the candidate energy management strategy based on the calculation results.

6. The method according to claim 5, characterized in that, The step of performing optimal planning calculations on the battery charge of the vehicle at each sampling time under each of the preset operating conditions based on the sampled vehicle speed and the final battery charge, and obtaining the candidate energy management strategy based on the calculation results, includes: Construct an objective function based on the vehicle's overall energy consumption; While satisfying the objective function minimization, the optimal battery charge at each sampling time under each preset working condition is solved in reverse based on the sampled vehicle speed and the final battery charge. The candidate energy management strategy is obtained based on the solution of the optimal battery capacity.

7. The method according to claim 6, characterized in that, The step of managing the vehicle's energy according to the target energy management strategy includes: Obtain the optimal battery capacity corresponding to the target energy management strategy; Energy management is achieved by controlling the vehicle's engine start / stop state based on the optimal battery charge level.

8. The method according to any one of claims 1 to 7, characterized in that, The real-time operating condition characteristics include at least one of the vehicle's average speed, average acceleration, average deceleration, maximum speed, speed standard deviation, and acceleration standard deviation.

9. An energy management device for a vehicle, characterized in that, The device includes: The first management module is used to determine a target correction coefficient based on the user energy consumption data of the vehicle when the vehicle is navigating, and to correct a preset SOC threshold based on the target correction coefficient to obtain a corrected SOC threshold. The corrected SOC threshold is then adjusted based on navigation information to obtain a target SOC threshold, so that the vehicle performs energy management based on the target SOC threshold. The second management module is used to acquire the real-time operating condition characteristics of the vehicle when the vehicle is not using navigation, and to acquire a target energy management strategy that matches the real-time operating condition characteristics, so as to manage the energy of the vehicle according to the target energy management strategy.

10. A vehicle, characterized in that, include: A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the energy management method of any one of claims 1 to 8.

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