A vehicle energy management method, electronic device and vehicle

Abstract

The present application provides a vehicle energy management method, electronic device and vehicle. In the scenario that the vehicle navigates to a target station, the charging and discharging behavior of the vehicle at the target station is predicted according to the historical charging and discharging data of the vehicle. The problem that the charging and discharging behavior of the vehicle at the passing station or the destination cannot be predicted in the related art is solved.

Description

Technical Field

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

[0002] Hybrid vehicles can use either gasoline or electricity as an energy source. With the increasing popularity of intelligent technology, new energy vehicles can not only be externally charged through charging, but also operate through range extenders or discharge directly from the battery, thus providing electricity as a power supply device and meeting the needs of a large number of plug-in devices.

[0003] In related technologies, competitors have introduced a smart hybrid (Beta) function. When this function is enabled, it will be activated when a charging station is manually added along the way or when the navigation destination is set to a charging station. After the function is activated, the navigation page will display the status of "Smart hybrid in operation". At this time, the vehicle will automatically allocate fuel and electricity consumption and deplete the battery before reaching the charging station to achieve deep battery discharge.

[0004] However, the aforementioned technology activates the Beta function based on the designated charging stations. If the user's navigation is not to a charging station, it cannot predict the vehicle's charging and discharging behavior at transit points or the destination. When the vehicle is charging at a transit point or destination, the battery may not be fully utilized before fuel consumption increases. Therefore, determining the vehicle's charging and discharging behavior at transit points or the destination is currently the primary technical problem to be solved. Summary of the Invention

[0005] This invention aims to address at least one of the technical problems existing in the prior art. To this end, this invention proposes a vehicle energy management method, electronic device, and vehicle. This solution predicts the vehicle's charging and discharging behavior at the target station based on historical charging and discharging data during navigation. This solution can predict the vehicle's charging and discharging behavior at the target station based on historical charging and discharging data, thereby enabling the vehicle to further allocate fuel and electricity consumption according to the charging and discharging behavior at the target station, reducing fuel consumption and extending the lifespan of the vehicle's powertrain.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] In a first aspect, this application provides a vehicle energy management method, including: predicting the vehicle's charging and discharging behavior at the target station based on the vehicle's historical charging and discharging data when the vehicle is navigating to a target station.

[0008] In some embodiments of this application, the method further includes: controlling the vehicle to perform an intelligent fuel-electricity distribution strategy based at least on the vehicle's target SOC; and / or, controlling the vehicle to perform a preheating strategy based at least on the charging and discharging behavior of the target station.

[0009] In some embodiments of this application, controlling the vehicle to execute an intelligent fuel-electricity distribution strategy based at least on the charging and discharging behavior of the target station includes: determining the target SOC of the vehicle upon arrival at the target station based at least on the charging and discharging behavior of the target station; and controlling the vehicle to execute the intelligent fuel-electricity distribution strategy based at least on the target SOC of the vehicle.

[0010] In some embodiments of this application, determining the target SOC of the vehicle upon arrival at the target station based at least on the charging and discharging behavior of the target station includes: if the target station is a charging activity, determining the target SOC of the target station based on the road type leading to the target station; or, if the target station is a discharging activity, determining the target SOC of the target station based on the predicted discharge amount.

[0011] In some embodiments of this application, determining the target SOC of the target station based on the road type leading to the target station includes: if the road type leading to the target station is a highway or expressway, setting the target SOC of the target station to a first set value; or, if the road type leading to the target station is a congested road or urban road, setting the target SOC of the target station to a second set value.

[0012] In some embodiments of this application, determining the target SOC of the target site based on the predicted discharge amount includes: substituting the predicted discharge amount into a first formula to calculate the target SOC of the target site; the first formula is used to represent the correlation between the target SOC of the target site and the discharge amount and battery capacity.

[0013] In some embodiments of this application, controlling the vehicle to execute a preheating strategy based at least on the charging and discharging behavior of the target station includes: controlling the vehicle to execute a battery preheating strategy based on the charging and discharging behavior of the target station and the distance from the vehicle to the target station.

[0014] In some embodiments of this application, controlling the vehicle to perform a battery preheating strategy includes: heating the power battery through the on-board energy system so that the battery temperature is within the target charging temperature range at the start of the charging and discharging behavior.

[0015] In some embodiments of this application, the method further includes identifying the charging and discharging behavior of the vehicle based on at least one of the charging signal of the charging gun, the vehicle's charge level before power-off and after power-on, and the discharge signal of the external device.

[0016] In some embodiments of this application, the method further includes: storing the charging and discharging behavior of the vehicle at different locations.

[0017] In some embodiments of this application, the method further includes: if the target station for vehicle navigation includes a charging station, then the vehicle will charge at the target station.

[0018] In some embodiments of this application, predicting the vehicle's charging and discharging behavior at the target station based on the vehicle's historical charging and discharging data includes: if the target station for the vehicle navigation does not include a charging station, inputting the vehicle's historical charging and discharging data into a trained charging and discharging prediction model to output the vehicle's charging and discharging behavior at the target station.

[0019] In some embodiments of this application, the method further includes: when it is predicted that the vehicle will charge or discharge at the target station, popping up an information box on the vehicle interface, the information box being used to ask the user whether to charge or discharge; and determining whether to execute an energy management strategy based on the user's input information in the information box.

[0020] In some embodiments of this application, the method further includes: setting a first switch and / or a second switch on the vehicle interface, wherein the first switch is used to instruct the user to select whether to enable the energy management strategy, and the second switch is used to instruct the user to select whether to enable the charging and discharging behavior prediction.

[0021] Secondly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, causes the computer to perform the method described in the first aspect.

[0022] Thirdly, this application provides a computer program product that stores instructions which, when executed by a computer, cause the computer to perform the method described in the first aspect.

[0023] Fourthly, this application provides an electronic device, comprising: a memory having a computer program stored thereon; and a processor for executing the computer program in the memory to implement the method as described in the first aspect.

[0024] Fifthly, this application provides a vehicle comprising: an electronic device as described in the fourth aspect; or, a processor configured to perform the method as described in the first aspect.

[0025] The advantages and control methods of the vehicle and electronic equipment compared to the prior art are the same, and will not be elaborated here.

[0026] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments 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.

[0028] To gain a more complete understanding of this application and its beneficial effects, the following description will be provided in conjunction with the accompanying drawings, wherein the same reference numerals in the following description denote the same parts.

[0029] Figure 1 This is a schematic flowchart of a vehicle energy management method according to an embodiment of the present invention;

[0030] Figure 2 This is a schematic diagram of the charge / discharge prediction method provided in an embodiment of the present invention;

[0031] Figure 3 This is a schematic flowchart of another vehicle energy management method provided according to an embodiment of the present invention;

[0032] Figure 4 This is a diagram of an energy management framework based on charging and discharging behavior provided according to an embodiment of the present invention;

[0033] Figure 5 This is a schematic diagram of an energy management process based on charging and discharging behavior according to an embodiment of the present invention;

[0034] Figure 6 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. Detailed Implementation

[0035] 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 a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the protection scope of this application.

[0036] In related technologies, competitors have introduced a smart hybrid (Beta) function. When this function is enabled, it will be activated when a charging station is manually added along the way or when the navigation destination is set to a charging station. After the function is activated, the navigation page will display the status of "Smart hybrid in operation". At this time, the vehicle will automatically allocate fuel and electricity consumption and deplete the battery before reaching the charging station to achieve deep battery discharge.

[0037] However, the aforementioned technology activates the Beta function based on designated charging stations, which cannot predict the vehicle's charging and discharging behavior at transit stations or the destination. When the vehicle is charging at transit stations or the destination, the battery may not be fully utilized before fuel consumption increases. Therefore, determining the vehicle's charging and discharging behavior at transit stations or the destination is currently the primary technical problem to be solved.

[0038] To address this, the present invention proposes a vehicle energy management method, electronic device, and vehicle. This solution utilizes the vehicle's historical charging and discharging data. This solution can predict the vehicle's charging and discharging behavior at a target station based on this historical data, thereby enabling the vehicle to further allocate fuel and electricity consumption according to the charging and discharging behavior at the target station, reducing fuel consumption and extending the lifespan of the vehicle's powertrain.

[0039] The present invention will now be described in further detail with reference to the embodiments.

[0040] like Figure 1 The diagram shown is a flowchart illustrating a vehicle energy management method according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0041] 101. In the scenario of navigating a vehicle to a target station, predict the vehicle's charging and discharging behavior at the target station based on the vehicle's historical charging and discharging data.

[0042] For example, the target stations mentioned above include the vehicle's waypoints and destinations. The charging and discharging behavior mentioned above includes at least one of charging or discharging behavior.

[0043] In the above embodiments, in the scenario of vehicle navigation to a target station, by analyzing historical charging and discharging data, the charging and discharging behavior of the vehicle at the target station can be predicted more accurately, thereby improving the efficiency and intelligence level of energy management.

[0044] As an optional implementation, the above method further includes: A1, identifying the charging and discharging behavior of the vehicle based on at least one of the charging signal of the plug, the vehicle's charge level before power-off and after power-on, and the discharge signal of the external device.

[0045] For example, the system identifies charging and discharging behavior through multi-source signal fusion: it can be determined based on the identified charging gun signal; specifically: during power-on, if a "charging gun insertion" signal is detected in the OBD bus (e.g., CAN ID: 0x1F0, Data: 0x01) and the BMS reports a charging current > threshold 1 (e.g., 1A), it is determined to be a charging behavior; it can also be determined based on the vehicle's SOC before power-off and SOC after power-on; specifically: if the vehicle's infotainment system is not operating, SOC1 before power-off and SOC2 after power-on can be recorded. If SOC1-SOC2 > threshold 2 (e.g., 5%), it is inferred that the vehicle has performed a discharging behavior at that location. If SOC2-SOC1 > threshold 3 (e.g., 2%), it is considered that the vehicle has performed a charging behavior at that location; it can also be determined based on the discharge signal of an external device; specifically: if an external device discharge signal is detected (e.g., V2L relay closed, load current > 2A), or when an external discharge device is connected and the discharge power is not 0, it is directly marked as a discharging behavior. This mechanism covers user behavior in non-standard charging station scenarios such as parking lots, campsites, private garages, and shopping malls, avoiding misjudgments. For example, if a user gets out of their car in an underground parking garage without unplugging the charging gun, but the vehicle enters V2L mode to supply power to a refrigerator, the system accurately identifies this as a discharge behavior through dual confirmation of "no charging gun plugged in + discharge current," which can be used to train the model.

[0046] Furthermore, the method also includes storing the charging and discharging behavior of the vehicle at different locations.

[0047] For example, after identifying the vehicle's charging and discharging behavior at different locations using the content of A1 mentioned above, a table can be created inside the vehicle's infotainment system to store this behavior. For instance, a user might have installed a private charging station at home, used a friend's private charging station, or visited a shopping mall not marked as a charging station. When the vehicle has charged or discharged in these locations, but not limited to these, the charging and discharging prediction model can predict the current charging and discharging behavior based on this stored historical data when the vehicle returns to these locations. The more data accumulated, the more accurate the prediction. Location information is crucial, and in addition to location, it also includes charging and discharging behavior, charging and discharging power, etc.

[0048] As an optional implementation, the above method also includes: 101a1, if the target station for vehicle navigation includes a charging station, then the vehicle will charge at the target station.

[0049] For example, in step 101 above, predicting the vehicle's charging and discharging behavior at the target station based on the vehicle's historical charging and discharging data specifically includes the following: 101a2. If the target station for vehicle navigation does not include a charging station, then the vehicle's historical charging and discharging data is input into the trained charging and discharging prediction model to output the vehicle's charging and discharging behavior at the target station.

[0050] Among them, the aforementioned charge and discharge prediction model includes a neural network model.

[0051] For example, the determination of whether the target station for vehicle navigation includes a charging station can be based on the name of the target station or on the POI tag corresponding to the target station. For instance, if the target station is "XXX charging station", then the target station itself is determined to be a charging station; if the target station is "XXX" and the corresponding POI tag is "charging station", then the target station is also determined to be a charging station.

[0052] For example, the POI tags mentioned above are point of interest tags for a certain location by the map provider. For example, charging stations, convenience stores, hospitals, gas stations, etc. are all POI tags. A certain location may have multiple POI tags or no POI tags. This invention only needs to determine whether the POI tag transmitted by the map is a charging station.

[0053] In the above embodiments, if the POI tag of the user's navigation waypoint or destination is a charging station, no prediction is needed; the system directly determines that the user will charge at the waypoint or destination, thereby improving the system's response speed. If the POI tag of the user's navigation waypoint or destination is not a charging station, charging and discharging behavior prediction is performed. The prediction method involves an embedded neural network. The input is the user's historical charging and discharging behavior and navigation time near the location, and the output is the vehicle's predicted charging and discharging action and charging and discharging power for the current time. If the vehicle has recently engaged in multiple charging and discharging behaviors near the location within a similar time period, the neural network will predict that the vehicle is highly likely to exhibit similar charging and discharging behaviors within a similar time period.

[0054] like Figure 2 The diagram shows a flowchart of the charging and discharging prediction method provided by this invention. Specifically, historical vehicle information (including location, charging and discharging behavior, and charging and discharging power, etc.) is input into a trained neural network model, and the prediction results output charging and discharging behavior, charging and discharging power, etc.

[0055] For example, the system continuously collects and stores charging and discharging behavior data of the vehicle over the past 12 months through on-board storage, including the timestamp of each charging and discharging event, precise geographical location (latitude and longitude error ≤ 50 meters), duration, SOC start and end values, ambient temperature, road type (highway / city / congested), whether it is associated with a fixed POI (such as charging station, supermarket, company, parking lot), whether it triggers an external discharge V2L signal, and power-on / off operation sequence, etc. After the above data is anonymized, it is input into a multimodal neural network model. This model consists of an LSTM network encoding time series features, a graph neural network embedding POI semantic categories, and a fully connected layer fusing environmental variables (temperature, humidity, altitude) and driving habits (average vehicle speed, acceleration variance), and outputs the charging probability and discharging probability for each point of interest and destination. For example, if historical data shows that a user charges their car at the "XX Commercial Center Underground Parking Lot" every Wednesday from 18:00 to 19:00, and the POI tag for that location is "Charging Station," then when the system detects that the user's journey passes through that coordinate and the time matches, it directly assigns a charging behavior confidence level of ≥95%. If the target station is "XX Shopping Mall" and historical records show that charging behavior occurred at that location, when the vehicle travels to that location again, the charging / discharging prediction model infers that the user will charge at that station based on historical records. This mechanism overcomes the shortcomings of traditional static strategies that rely solely on navigation endpoints, achieving accurate modeling of dynamic charging / discharging intentions across multiple transit points, and providing a decision-making basis for subsequent energy allocation and thermal management.

[0056] As an optional implementation method, such as Figure 3 As shown, the above method also includes: 102, controlling the vehicle to execute an intelligent fuel-electricity distribution strategy based at least on the charging and discharging behavior of the target station.

[0057] For example, step 102 above includes the following: 102a, determining the target SOC of the vehicle upon arrival at the target station based at least on the charging and discharging behavior of the target station; 102b, controlling the vehicle to execute an intelligent fuel-electricity distribution strategy based at least on the vehicle's target SOC.

[0058] In the above embodiments, when the system predicts that the target station will be a charging location, it activates the dynamic programming energy management module. Using minimizing total fuel consumption and battery wear as the objective function, it combines real-time traffic flow data (Gaode / Baidu API), slope maps, air conditioning load prediction, and battery health status (SOH) to solve for the optimal SOC trajectory. If charging is predicted at a waypoint or destination, the SOC at that point or destination is planned to be minimized to save fuel. If discharging is predicted at a waypoint or destination, the SOC at that point or destination is maintained at a higher level, where the higher level is predicted based on the vehicle's historical discharge volume at the current location. Based on the target SOC at the waypoint or destination, traffic flow during the journey, environment, and vehicle status, the dynamic programming algorithm intelligently calculates the vehicle's operating mode, engine power, and battery power throughout the journey, aiming to minimize equivalent fuel consumption, to achieve optimal system efficiency and the most fuel-efficient fuel-electricity distribution, thereby achieving energy conservation and emission reduction.

[0059] For example, step 102a above specifically includes the following: 102a1, if the target station is a charging activity, determine the target SOC of the target station according to the road type leading to the target station; or, 102a2, if the target station is a discharging activity, determine the target SOC of the target station according to the predicted discharge amount.

[0060] Furthermore, in step 102a1 above, determining the target SOC of the target station based on the road type leading to the target station includes the following: 102a11, if the road type leading to the target station is a highway or expressway, the target SOC of the target station is planned as a first set value; or, 102a12, if the road type leading to the target station is a congested road or urban road, the target SOC of the target station is planned as a second set value.

[0061] Specifically, to ensure the vehicle's power economy and battery retention performance, the battery charge must be maintained above a certain level to guarantee battery discharge power, thus ensuring power, economy, and NVH performance. When the target station is for charging, this invention can determine the target SOC value based on the road type and traffic flow speed of the route or destination. The value used is a preset value, where: when the destination station is a highway or expressway, the target SOC value = equilibrium point - constant 1. When the destination is a city or congested road, the target SOC = equilibrium point - constant 2. Constant 1 and constant 2 are calibration values, and different values ​​are used for different vehicle models.

[0062] The aforementioned balance point is a key control threshold in battery energy management, used to define the management range of battery charge. Different balance point values ​​are set for different operating conditions, and different vehicle models also have different balance point values ​​to adjust the vehicle's energy distribution strategy. For example, the balance point for some models is 15. When the vehicle is driven with a low battery for a long time, the battery charge will remain around 15%. The setting of the balance point is mainly related to the capability of the vehicle's powertrain and the overall vehicle resistance. Hybrid vehicles require both the battery and engine to work simultaneously under high-demand conditions. The battery charge must be above a certain value to ensure the vehicle's power. The balance point is the most reasonable value that balances power and fuel economy, calibrated and verified for different vehicle models and powertrain configurations.

[0063] In the above embodiments, when the target station is for charging, the system sets the SOC threshold differently based on road type: if it is a highway or expressway (such as the G4 Beijing-Hong Kong-Macau Expressway or urban express ring road), the electric drive efficiency is high and energy consumption is stable during high-speed cruising, so the system sets the target SOC to a first set value (e.g., 25%) to reserve enough energy to cope with low-speed congestion at exit ramps; if it is a congested road section or urban road (such as main roads during morning and evening rush hours or around schools), the electric drive efficiency drops sharply due to frequent starts and stops, so the system raises the target SOC to a second set value (e.g., 40%) to ensure that the vehicle can rely entirely on pure electric power in low-speed areas, avoiding inefficient power generation by the range extender under low load. This set value is calibrated based on real vehicle road test data and matches the characteristics of the vehicle's powertrain system to ensure optimal energy consumption without sacrificing driving experience.

[0064] Furthermore, in step 102a2 above, determining the target SOC of the target site based on the predicted discharge amount includes the following: substituting the predicted discharge amount into the first formula to calculate the target SOC of the target site; the first formula is used to express the correlation between the target SOC of the target site and the discharge amount and battery capacity.

[0065] For example, the first formula above can be: Target SOC = Balance Point - (Predicted Discharge / Battery Rated Capacity) × 100%. The predicted discharge is output from the user's historical behavior model and dynamically calculated based on the power of the external device and the expected usage time. For instance, if a user historically averages 2.2 kWh of discharge using a V2L device at "XX Campsite," and connects an electric cooker (2.0 kW) and sets its usage time to 2.5 hours, then the predicted discharge is 5.0 kWh. If the balance point is 15% and the battery capacity is 70 kWh, then the target SOC = 15% - (5.0 / 70) × 100% ≈ 8%. The system uses this value as a hard constraint for energy management, ensuring that the SOC does not fall below this value at the end of the discharge, preventing battery voltage drops due to over-discharge and affecting the triggering of BMS safety policies.

[0066] Furthermore, after obtaining the target SOC at the waypoints or destination, based on road type, traffic flow information, environmental information, etc., and according to the vehicle status and the target SOC at the waypoints or destination, a dynamic programming algorithm (DP) is used to calculate the optimal SOC curve and target operating mode (series, parallel, pure electric) instructions for the journey. These instructions are then sent to relevant components for control (engine, clutch, power battery, etc.). Detailed implementation processes can be found in other publicly available technical documents and will not be elaborated upon here.

[0067] As an optional implementation method, such as Figure 3 As shown, the above method also includes: 103, controlling the vehicle to execute a preheating strategy based at least on the charging and discharging behavior of the target site.

[0068] For example, when the system predicts that charging will occur at the target site, it automatically activates the battery preheating subsystem. This subsystem is controlled collaboratively by the onboard heat pump, PTC heater, and battery liquid cooling circuit. Based on the predicted charging time and distance, it initiates the heating process in advance, ensuring that the battery temperature reaches the target range within a certain time before charging or discharging begins. This strategy significantly shortens charging time, improves charging efficiency, reduces the risk of lithium dendrite precipitation during high-current charging, reduces user charging waiting time, and extends battery cycle life. For instance, if it is predicted that the battery will arrive at the charging station in 30 minutes and the current battery temperature is 5°C, the system will activate the liquid cooling circuit and the PTC heater, continuously heating at 1.2kW to raise the battery temperature to 15°C within 25 minutes, meeting the fast-charging temperature requirements.

[0069] For example, step 103 above may include the following: 103a, controlling the vehicle to execute a battery preheating strategy based on the charging and discharging behavior of the target site and the distance from the vehicle to the target site.

[0070] In the above embodiments, the timing and power of the preheating strategy are controlled by both distance and time constraints. For example, if the distance to the target station is ≤ threshold 1 (e.g., 10km) and the estimated arrival time is ≥ time 1 (e.g., 15 minutes), then medium-power heating (e.g., 1.0–1.5kW) is activated; if the distance is > 10km and the estimated arrival time is ≥ time 2 (e.g., 30 minutes), then high-power heating (e.g., 1.5–2.0kW) is activated, combined with a heat pump to recover waste heat from the motor; if the distance is < threshold 3 (e.g., 5km) or the estimated arrival time is < time 2 (e.g., 10 minutes), then only low-power heat preservation (e.g., 0.5kW) is activated to maintain the current temperature. For example, when driving on a highway, 18km from the charging station, with an estimated arrival time of 22 minutes, the system determines it to be in "high-power heating" mode, activates heat pump + PTC combined heating, and simultaneously turns off the air conditioning, prioritizing all energy for battery warming to ensure the battery temperature reaches 18°C ​​upon arrival, meeting the 80kW fast charging temperature zone requirements.

[0071] Further optionally, the above step 103a, controlling the vehicle to execute the battery preheating strategy, specifically includes the following: heating the power battery through the on-board energy system so that the battery temperature is within the target charging temperature range at the start of charging and discharging.

[0072] For example, the target charging temperature range mentioned above can be 10℃–25℃. This range is determined based on experimental data of battery electrochemical characteristics. Within this range, lithium-ion migration rate is high, internal resistance is low, and charging acceptance is optimal. The system monitors the battery module temperature distribution in real time through the BMS and can adopt a zoned heating strategy: if the temperature at the front end of the battery pack is detected to be low (e.g., 8℃), while the temperature at the back end has reached the standard (20℃), the front end module is heated independently first to avoid overall overheating. During the heating process, the system simultaneously shuts down unnecessary loads (such as seat heating and the large screen of the entertainment system) to ensure that energy is concentrated on the battery. One minute before charging begins, the system sends a "Battery preheating complete, safe for fast charging" signal to the charging pile to achieve V2G communication coordination. This strategy can shorten charging time and improve charging efficiency in low-temperature environments (-5℃).

[0073] As an optional implementation, the above method also includes: B1, when it is predicted that the vehicle will charge or discharge at the target station, a pop-up information box appears on the vehicle interface, which asks the user whether to charge or discharge; B2, based on the information entered by the user in the information box, it is determined whether to execute the energy management strategy.

[0074] For example, when the system predicts that the target station will be for charging or discharging, the vehicle's infotainment system will display a semi-transparent information box on the central control screen, stating: "You are expected to arrive at [XX Charging Station] in 15 minutes. The system will reserve 35% SOC and preheat the battery. Confirm charging?" or "You are expected to arrive at [XX Campsite] in 20 minutes. The system will reserve 42% SOC for V2L discharge. Confirm discharging?" The user can choose "Confirm," "Cancel," or "Custom." If "Confirm" is selected, the system immediately locks the energy management strategy; if "Cancel" is selected, the prediction strategy is turned off, and the default fuel-electric distribution is restored; if "Custom" is selected, a slider interface will pop up, allowing the user to manually adjust the target SOC (e.g., from 35% to 45%), and the system will recalculate the energy trajectory accordingly.

[0075] In the above embodiments, the user is given control through the above-described interaction mechanism, which avoids energy waste caused by system misjudgment and improves the human-machine collaboration experience.

[0076] As an optional implementation, the above method also includes: setting a first switch and / or a second switch on the vehicle's infotainment interface. The first switch is used to instruct the user whether to enable the energy management strategy, and the second switch is used to instruct the user whether to enable charging and discharging behavior prediction.

[0077] For example, the vehicle's infotainment system provides two independent physical buttons (or virtual switches) in the "Energy Settings" menu: the first switch is "Intelligent Energy Management," which is enabled by default and controls whether to execute SOC planning and fuel-electricity distribution strategies; the second switch is "Charging and Discharging Behavior Prediction," which is enabled by default and controls whether to enable historical data and neural network prediction modules. If the user turns off the first switch, the system only executes basic energy recovery and engine start-stop strategies and does not perform target SOC planning; if the second switch is turned off, the system only performs static SOC settings based on the navigation destination and ignores waypoint behavior prediction.

[0078] In the above embodiments, this design caters to different user preferences: for example, long-distance drivers can turn off the predictive switch to simplify operation, while urban commuters can turn on both switches to maximize energy savings. The switch status is synchronously stored in the cloud account, achieving consistency across multiple vehicles and platforms.

[0079] The vehicle energy management method of the present invention will be described below based on specific examples.

[0080] like Figure 4 The diagram shown illustrates a framework for energy management based on charge / discharge prediction provided in an embodiment of the present invention. Figure 4In the process of predicting vehicle charging and discharging behavior, it is necessary to determine whether the vehicle will not charge or discharge. If so, the energy management strategy will not be interfered with. If the vehicle is charging, it will perform deep discharge and preheat the battery in advance based on the distance to the charging station. If the vehicle is discharging, it will generate electricity efficiently during the journey to maintain the battery level at the destination.

[0081] Based on the above Figure 4 The following describes an intelligent management method based on the charging and discharging behavior of hybrid electric vehicles at navigation target stations, such as... Figure 5 As shown, the method includes:

[0082] S1. After the user starts navigation using the in-vehicle map, the map will transmit the waypoints and destination POI tags or GPS location information to the intelligent energy management module.

[0083] S2. The charging and discharging prediction model of the intelligent energy management module predicts the charging and discharging behavior of the vehicle at waypoints and destinations by using the vehicle's historical charging behavior at waypoints or destinations and POI tags.

[0084] S3. The intelligent energy management module calculates the target SOC of the waypoints and destination based on the predicted charging and discharging behavior. Based on the target SOC of the destination and the traffic flow and environmental information of the journey, it intelligently plans the SOC of the entire journey and intelligently allocates fuel and electricity.

[0085] S4. If it is predicted that the vehicle will need to charge at a waypoint or destination, the battery will be preheated intelligently based on the distance to the waypoint or destination, effectively improving the charging rate, reducing the user's charging waiting time, and extending the battery life.

[0086] like Figure 6 The above is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. The electronic device 600 includes a processor 601 with one or more processing cores, a memory 602 with one or more computer-readable storage media, and a computer program stored in the memory 602 and executable on the processor. The processor 601 and the memory 602 are electrically connected.

[0087] The processor 601 is the control center of the electronic device 600. It connects various parts of the electronic device 600 via various interfaces and lines. By running or loading software programs and / or units stored in the memory 602, and by calling data stored in the memory 602, it executes various functions and processes data of the electronic device 600, thereby providing overall monitoring of the electronic device 600. The processor 601 can be a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a Network Processor (NP), etc., and can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0088] In this embodiment of the application, the processor 601 in the electronic device 600 loads the computer program corresponding to the process of one or more applications into the memory 602 according to the method or steps of the above embodiment, and the processor 601 runs the applications stored in the memory 602 to execute the above method.

[0089] According to an embodiment of the present invention, an electronic device, by performing the above-described method, predicts the vehicle's charging and discharging behavior at the target station based on the vehicle's historical charging and discharging data in a scenario where the vehicle is navigating to the target station. This solution can predict the vehicle's charging and discharging behavior at the target station based on its historical charging and discharging data, thereby enabling the vehicle to further allocate fuel and electricity consumption according to the charging and discharging behavior at the target station, reducing fuel consumption and extending the lifespan of the vehicle's powertrain.

[0090] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, enables the computer to implement the vehicle control method described above. For example, the computer-readable storage medium may be the aforementioned memory including program instructions, which may be executed by a processor of an electronic device to implement or execute the methods, steps, and logic diagrams disclosed in the embodiments of this application.

[0091] This invention also provides a computer program product storing instructions that, when executed by a computer, cause the computer to implement the vehicle control method described above. For example, when executed by a computer, the instructions implement or execute the methods, steps, and logic diagrams disclosed in the embodiments of this application.

[0092] Embodiments of the present invention also provide a vehicle comprising the electronic equipment described above, or a processor, the processor being configured to perform the methods described above. The vehicle may be a plug-in hybrid electric vehicle (PHEV), a range-extended electric vehicle (EREV), or a new energy vehicle, etc., and this specification does not specifically limit it to any particular type.

[0093] According to embodiments of the present invention, a vehicle executes the above-described method via an electronic device or processor to predict the vehicle's charging and discharging behavior at a target station based on historical charging and discharging data, in a scenario where the vehicle is navigating to a target station. This solution can predict the vehicle's charging and discharging behavior at a target station based on historical charging and discharging data, thereby enabling the vehicle to further allocate fuel and electricity consumption according to the charging and discharging behavior at the target station, reducing fuel consumption and extending the lifespan of the vehicle's powertrain.

[0094] The above-described embodiments are only used to illustrate the technical solutions of applying the above methods to vehicles, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the method can also be used in motor vehicles, trains, and ships, etc., without causing the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0095] In one embodiment, the vehicle can be configured for fully or partially autonomous driving. For example, the vehicle can control itself while in autonomous driving mode, and can determine the current state of the vehicle and its surrounding environment through human intervention, determine the possible behaviors of at least one other vehicle in the surrounding environment, and determine the confidence level corresponding to the probability of that other vehicle performing a possible behavior, and control the vehicle based on the determined information. When the vehicle is in autonomous driving mode, it can be configured to operate without human interaction.

[0096] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0097] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," "optional example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0098] The embodiments, implementation methods, and related technical features of this application can be combined and substituted for each other without conflict.

[0099] The above are merely preferred embodiments of this application and are not intended to limit this application in any way. Although the descriptions of each embodiment in this application have different focuses, and the parts not described in detail in a certain embodiment can be referred to the relevant embodiments of other embodiments, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of this application without departing from the content of the technical solution of this application shall still fall within the scope of the technical solution of this application.

Claims

1. A vehicle energy management method, characterized by, include: In the scenario of navigating a vehicle to a target station, the charging and discharging behavior of the vehicle at the target station is predicted based on the vehicle's historical charging and discharging data.

2. The method of claim 1, wherein, The method further includes: controlling the vehicle to execute an intelligent fuel-electricity distribution strategy based at least on the charging and discharging behavior of the target station; And / or, at least based on the charging and discharging behavior of the target site, control the vehicle to perform a preheating strategy.

3. The method of claim 2, wherein, The step of controlling the vehicle to execute an intelligent fuel-electricity distribution strategy based at least on the charging and discharging behavior of the target station includes: The target SOC of the vehicle upon arrival at the target station is determined at least based on the charging and discharging behavior at the target station; The vehicle is controlled to execute an intelligent fuel-electricity distribution strategy based at least on the vehicle's target SOC.

4. The method of claim 3, wherein, Determining the target SOC of the vehicle upon arrival at the target station based at least on the charging and discharging behavior of the target station includes: If the target station is for charging activities, the target SOC of the target station is determined based on the road type leading to the target station; Alternatively, if the target site is discharging, the target SOC of the target site is determined based on the predicted discharge amount.

5. The method of claim 4, wherein, Determining the target SOC of the target station based on the road type leading to the target station includes: If the road type leading to the target station is a highway or expressway, the target SOC of the target station is planned as a first set value; Alternatively, if the road type leading to the target station is a congested section or urban road, the target SOC of the target station is planned as a second set value.

6. The method of claim 4, wherein, Determining the target SOC of the target site based on the predicted discharge amount includes: The predicted discharge amount is substituted into the first formula to calculate the target SOC of the target site; the first formula is used to express the correlation between the target SOC of the target site, the discharge amount, and the battery capacity.

7. The method according to claim 2, characterized in that, The step of controlling the vehicle to execute a preheating strategy based at least on the charging and discharging behavior of the target station includes: controlling the vehicle to execute a battery preheating strategy based on the charging and discharging behavior of the target station and the distance of the vehicle from the target station.

8. The method according to claim 7, characterized in that, The control of the vehicle to execute the battery preheating strategy includes: The power battery is heated by the on-board energy system so that the battery temperature is within the target charging temperature range at the start of the charging and discharging process.

9. The method according to claim 1, characterized in that, Also includes: The charging and discharging behavior of the vehicle is identified based on at least one of the following: the charging signal from the charging gun, the vehicle's charge level before power-off and after power-on, and the discharge signal from the external device.

10. The method according to claim 1, characterized in that, The method further includes: if the target station for vehicle navigation includes a charging station, then the vehicle will charge at the target station.

11. The method according to claim 1, characterized in that, The step of predicting the charging and discharging behavior of the vehicle at the target station based on the vehicle's historical charging and discharging data includes: If the target station for vehicle navigation does not include a charging station, the vehicle's historical charging and discharging data is input into the trained charging and discharging prediction model to output the vehicle's charging and discharging behavior at the target station this time.

12. The method according to claim 1, characterized in that, The method further includes: When it is predicted that the vehicle will be charging or discharging at the target station, an information box will pop up on the vehicle interface to ask the user whether they want to charge or discharge. Whether to execute an energy management strategy is determined based on the information entered by the user in the information box.

13. The method according to any one of claims 1-12, characterized in that, The method further includes: setting a first switch and / or a second switch on the vehicle interface, wherein the first switch is used to instruct the user to select whether to enable the energy management strategy, and the second switch is used to instruct the user to select whether to enable the charging and discharging behavior prediction.

14. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the method of any one of claims 1 to 13.

15. A vehicle, characterized in that, include: The electronic device according to claim 14; Alternatively, a processor, said processor being configured to perform the method of any one of claims 1-13.

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