Vehicle control method and device, vehicle, chip and storage medium

Abstract

The application provides a vehicle control method and device, a vehicle, a chip and a storage medium, and relates to the field of vehicle control.The method comprises the following steps: determining predicted energy consumption of a plurality of first road sections in navigation data of a vehicle according to environment data of an environment in which the vehicle is located and the navigation data of the vehicle; and controlling a range extender of the vehicle according to the predicted energy consumption of the plurality of first road sections, road condition information and vehicle state information. Thus, compared with a traditional method that only relies on historical driving data, the application can effectively avoid control errors caused by generalization of historical experience, so that the control strategy of the range extender is more suitable for the actual driving conditions of the vehicle, not only achieving accurate adaptation of energy supply and significantly improving energy utilization efficiency, but also effectively prolonging the cruising range of the vehicle.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and more particularly to a vehicle control method, device, vehicle, chip, and storage medium. Background Technology

[0002] In related technologies, the operating parameters of the range extender (such as speed and output power) are predicted based on the vehicle's historical driving data, and the range extender is controlled based on the predicted operating parameters to optimize energy management and extend the vehicle's driving range.

[0003] However, the above methods mainly rely on historical driving experience. When faced with dynamically changing driving environments, the limitations of historical data can easily lead to deviations in control strategies, which in turn affect energy utilization efficiency and may even cause unnecessary fuel consumption or waste of electricity. Summary of the Invention

[0004] This application proposes a vehicle control method, apparatus, vehicle, chip, and storage medium to at least partially solve one of the technical problems in the related art.

[0005] One embodiment of this application proposes a vehicle control method, including:

[0006] Based on environmental data of the vehicle's location and the vehicle's navigation data, the predicted energy consumption of the vehicle for multiple first road segments in the navigation data is determined.

[0007] The range extender of the vehicle is controlled based on the predicted energy consumption and road condition information of the multiple first road segments, as well as the vehicle status information.

[0008] Another embodiment of this application provides a vehicle control device, including:

[0009] The determination module is used to determine the predicted energy consumption of the vehicle in multiple first road segments in the navigation data based on environmental data of the vehicle's environment and the vehicle's navigation data.

[0010] The control module is used to control the range extender of the vehicle based on the predicted energy consumption and road condition information of the plurality of first road segments, as well as the vehicle status information.

[0011] In another aspect of this application, a vehicle is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to implement the vehicle control method as described in the preceding aspect.

[0012] Another aspect of this application provides a chip including an interface circuit and a processing circuit coupled to each other, the interface circuit being used to input or output signals, and the processing circuit being configured to perform the vehicle control method as described in the foregoing aspect.

[0013] In another aspect, this application provides a non-transitory computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the vehicle control method as described in the foregoing aspect.

[0014] Another aspect of this application provides a computer program product having a computer program stored thereon, which, when executed by a processor, implements the vehicle control method as described in the foregoing aspect.

[0015] The vehicle control method, device, vehicle, chip, and storage medium proposed in this application combine environmental data and navigation data of the actual environment in which the vehicle is located to predict the predicted energy consumption of the vehicle in multiple road segments in the future. Based on the predicted energy consumption of multiple road segments, real-time road condition information, and vehicle status information, the range extender is dynamically and precisely controlled. Compared with the traditional method that relies solely on historical driving data, this application can effectively avoid control errors caused by the generalization of historical experience, making the control strategy of the range extender more in line with the actual driving conditions of the vehicle. This not only achieves precise matching of energy supply and significantly improves energy utilization efficiency, but also effectively extends the vehicle's driving range.

[0016] Additional aspects and advantages of this application 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 this application. Attached Figure Description

[0017] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0018] Figure 1 A schematic flowchart of a vehicle control method provided for an exemplary embodiment of this application;

[0019] Figure 2 A schematic flowchart of another vehicle control method provided for an exemplary embodiment of this application;

[0020] Figure 3 A schematic flowchart of yet another vehicle control method provided for an exemplary embodiment of this application;

[0021] Figure 4 A schematic flowchart of another vehicle control method provided for an exemplary embodiment of this application;

[0022] Figure 5A schematic flowchart of another vehicle control method provided for an exemplary embodiment of this application;

[0023] Figure 6 A control principle for a range extender provided for an exemplary embodiment of this application;

[0024] Figure 7 Another control principle for a range extender provided for exemplary embodiments of this application;

[0025] Figure 8 Another control principle for a range extender provided for exemplary embodiments of this application;

[0026] Figure 9 A schematic diagram of online control logic for a range extender provided for an exemplary embodiment of this application;

[0027] Figure 10 A schematic diagram of the structure of a vehicle control device provided for an exemplary embodiment of this application;

[0028] Figure 11 A block diagram illustrating a vehicle according to an exemplary embodiment;

[0029] Figure 12 This is a schematic diagram of the structure of a chip proposed in an exemplary embodiment of this application. Detailed Implementation

[0030] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0031] In view of at least one of the problems existing in the above-mentioned related technologies, this application proposes a vehicle control method, device, vehicle, chip and storage medium.

[0032] The vehicle control method, apparatus, vehicle, chip, and storage medium according to embodiments of this application are described below with reference to the accompanying drawings.

[0033] Figure 1 This is a schematic flowchart of a vehicle control method provided for an exemplary embodiment of this application.

[0034] It should be noted that the vehicle control method of this application embodiment can be applied to a vehicle control device. In some possible embodiments, the vehicle control device can be configured in a vehicle or a chip so that the vehicle or chip can perform vehicle control functions. Additionally, in some possible embodiments, the vehicle control device can also be software within the vehicle.

[0035] In any embodiment of this application, the chip can be integrated into a vehicle. The chip includes a Central Processing Unit (CPU), an Image Signal Processing (ISP), an Application-Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Field-Programmable Gate Array (FPGA), a System-on-a-Chip (SoC), a Reduced Instruction Set Computer (RISC), etc., which will not be listed individually here.

[0036] like Figure 1 As shown, the vehicle control method may include the following steps S101 to S102:

[0037] Step S101: Based on the environmental data of the vehicle's location and the vehicle's navigation data, determine the predicted energy consumption of the vehicle in multiple first road segments in the navigation data.

[0038] The environmental data includes, but is not limited to, temperature and altitude, with temperature including, but not limited to, the ambient temperature inside the vehicle and the ambient temperature outside the vehicle.

[0039] The navigation data includes the navigation route from the navigation start point to the navigation destination, the ETA (Estimated Time of Arrival) for the vehicle to reach the navigation destination, etc. The ETA is related to the congestion level of the navigation route; for example, the more congested the navigation route, the longer the ETA.

[0040] It should be noted that, in order to improve the user's driving experience, congested or poorly constructed sections of road can be filtered out during the navigation route planning stage to shorten the ETA (Electronic Time To Arrival).

[0041] Energy consumption includes electricity consumption (electricity consumption) and fuel consumption.

[0042] In this embodiment of the application, the predicted energy consumption of the vehicle in multiple first segments of the navigation path in the navigation data can be predicted by combining the environmental data of the actual environment in which the vehicle is located and the vehicle's navigation data.

[0043] Step S102: Control the vehicle's range extender based on the predicted energy consumption and road condition information of multiple first road segments, as well as vehicle status information.

[0044] The road condition information includes, but is not limited to: congestion, rapid acceleration, cruise control, gradient angle, and maximum speed v_max (i.e., speed limit).

[0045] The vehicle status information includes, but is not limited to: battery state of charge (SOC), fuel level, and driving mode.

[0046] In this embodiment of the application, the predicted energy consumption and road condition information of the vehicle in multiple first road segments, as well as the vehicle status information, can be comprehensively evaluated to assess the energy demand of the vehicle in the future driving path, and the range extender can be dynamically adjusted accordingly.

[0047] The vehicle control method of this application combines environmental data and navigation data of the actual environment in which the vehicle is located to predict the predicted energy consumption of the vehicle in multiple road segments in the future. Based on the predicted energy consumption of multiple road segments, real-time road condition information, and vehicle status information, the range extender is dynamically and precisely controlled. Compared with the traditional method that relies solely on historical driving data, this application can effectively avoid control errors caused by the generalization of historical experience, making the control strategy of the range extender more in line with the actual driving conditions of the vehicle. This not only achieves precise matching of energy supply and significantly improves energy utilization efficiency, but also effectively extends the vehicle's driving range.

[0048] As one possible implementation method, Figure 2 A schematic flowchart of another vehicle control method provided for an exemplary embodiment of this application.

[0049] It should be noted that the vehicle control method can be executed alone, or it can be executed together with any embodiment or possible implementation in the embodiments of this application, or it can be executed together with any technical solution in the related technology. The embodiments of this application do not limit this.

[0050] like Figure 2 As shown, the vehicle control method may include the following steps S201 to S203:

[0051] Step S201: Determine the initial energy consumption of the vehicle in the multiple first road segments based on the length information of the vehicle in the navigation data and the basic energy consumption of the vehicle.

[0052] Among them, basic energy consumption is used to characterize the energy consumption required by a vehicle per unit path length.

[0053] It should be noted that the explanations of navigation data and energy consumption in the foregoing embodiments also apply to this embodiment, and will not be repeated here.

[0054] In this embodiment of the application, the initial energy consumption of the vehicle in multiple first road segments can be calculated or predicted based on the length information of multiple first road segments in the navigation path of the vehicle in the navigation data and the basic energy consumption of the vehicle.

[0055] The initial energy consumption of each road segment is positively correlated with the length of that road segment (i.e., path length), and the initial energy consumption of each road segment is also positively correlated with the vehicle's basic energy consumption.

[0056] As an example, the initial energy consumption of a vehicle on a given first road segment can be determined by multiplying the length of that segment with its base energy consumption. For instance, let L be the length of a path marking a first road segment. link The vehicle's basic energy consumption is k base The initial energy consumption of the first section = k base *L link .

[0057] Step S202: Based on the environmental data of the vehicle's location, the initial energy consumption of multiple first road segments is corrected to obtain the predicted energy consumption of multiple first road segments.

[0058] In this embodiment of the application, the initial energy consumption of multiple first road segments can be corrected based on environmental data of the actual environment in which the vehicle is located, so as to obtain the predicted energy consumption of multiple first road segments.

[0059] In any embodiment of this application, a correction coefficient can be determined based on environmental data, and the initial energy consumption of multiple first road segments can be corrected using the correction coefficient to obtain the predicted energy consumption of multiple first road segments.

[0060] As an example, environmental data includes at least one of temperature and altitude. In this application, a correction coefficient can be determined based on at least one of temperature and altitude. For instance, an environmental temperature deviation can be determined based on the temperature in the environmental data, and an altitude change can be determined based on the altitude in the environmental data. Thus, a correction coefficient can be determined based on the environmental temperature deviation and the altitude change. Therefore, in this application, the initial energy consumption of multiple first road segments can be corrected based on this correction coefficient to obtain the predicted energy consumption of multiple first road segments.

[0061] For example, taking energy consumption as electricity consumption, the predicted energy consumption E for each first road segment can be calculated using the following formula. link :

[0062] E link =k base ·L link ·(1+k temp ΔT+k grad Δh); (1)

[0063] Among them, L link It is the path length of a certain first segment, in kilometers (km); k base This is the base power consumption, for example, k. base = 15 kWh / 100km, where kWh is the abbreviation for kilowatt-hour; ΔT is the ambient temperature deviation, in degrees Celsius (°C); k temp The set temperature compensation coefficient, such as k temp =0.015 / ℃; Δh is the change in altitude, in meters (m); k grad It is the set slope compensation coefficient, such as k grad =1.2% / 100m.

[0064] Step S203: Control the vehicle's range extender based on the predicted energy consumption and road condition information of multiple first road segments, as well as vehicle status information.

[0065] It should be noted that the explanation of step S203 can be found in the relevant description in any embodiment of this application, and will not be repeated here.

[0066] The vehicle control method of this application integrates the length information of multiple first road segments and the basic energy consumption of the vehicle to calculate the initial energy consumption of the vehicle in multiple first road segments. Based on the environmental data of the actual environment in which the vehicle is located, the initial energy consumption of multiple first road segments is corrected to obtain the predicted energy consumption of multiple first road segments. This can improve the rationality and reliability of the energy consumption prediction of the vehicle in each first road segment.

[0067] As one possible implementation method, Figure 3 A schematic flowchart of yet another vehicle control method provided for an exemplary embodiment of this application.

[0068] It should be noted that the vehicle control method can be executed alone, or it can be executed together with any embodiment or possible implementation in the embodiments of this application, or it can be executed together with any technical solution in the related technology. The embodiments of this application do not limit this.

[0069] like Figure 3 As shown, the vehicle control method may include the following steps S301 to S303:

[0070] Step S301: Based on the environmental data of the vehicle's location and the vehicle's navigation data, determine the predicted energy consumption of the vehicle in multiple first road segments in the navigation data.

[0071] It should be noted that the explanation of step S301 can be found in the relevant description in any embodiment of this application, and will not be repeated here.

[0072] Step S302: Based on the predicted energy consumption and road condition information of multiple first road segments, as well as vehicle status information, determine the target SOC and target control action of the vehicle in multiple first road segments; wherein, the target control action is used to indicate the control parameters of the range extender.

[0073] It should be noted that the explanations of road condition information and vehicle status information in the aforementioned embodiments also apply to this embodiment, and will not be repeated here.

[0074] The control parameters include, but are not limited to, power and speed.

[0075] The target SOC can be determined from the vehicle's state space, which includes multiple candidate SOCs. For example, the SOC of the vehicle battery can be pre-divided into equal intervals to obtain the state space. For instance, the battery's SOC can be divided into 20 discrete candidate SOCs at 5% intervals to form the state space X.

[0076] The target control action can be determined from the vehicle's action space, which includes multiple candidate control actions. For example, multiple candidate control actions of the range extender can be pre-assigned based on path characteristics, thereby forming the action space.

[0077] In this embodiment of the application, the predicted energy consumption and road condition information of multiple first road segments, as well as vehicle status information, can be combined to determine the target SOC and target control actions of the vehicle in multiple first road segments.

[0078] In any embodiment of this application, the target SOC and target control action for each first road segment can be determined using the following steps A to C:

[0079] Step A: Based on the predicted energy consumption and road condition information of multiple first road segments, as well as vehicle status information, determine or predict the consumption cost of the vehicle under multiple candidate combinations in any one of the multiple first road segments.

[0080] The candidate combination includes a candidate SOC in the state space and a candidate control action in the action space.

[0081] Among these costs are, but are not limited to: fuel consumption costs, and the costs associated with NVH (noise, vibration, and harshness).

[0082] In this embodiment of the application, reverse dynamic programming can be performed based on the predicted energy consumption and road condition information of multiple first road segments, as well as vehicle status information, to obtain the consumption cost of vehicles in each first road segment under multiple candidate combinations.

[0083] Inverse dynamic programming refers to: the SOC constraints at the navigation endpoint (such as the final SOC). final Under the condition of ≥15%, the optimal control action (referred to as target control action in this application) and target SOC of each first segment are calculated segment by segment starting from the navigation endpoint.

[0084] In any embodiment of this application, for any candidate combination among multiple candidate combinations, the fuel consumption cost when the range extender takes the candidate control action of the candidate combination under the candidate SOC of the vehicle in the first road segment, and the impact cost of the range extender taking the candidate control action of the candidate combination on NVH can be determined or predicted based on vehicle status information and energy consumption curve. Thus, in this application, the consumption cost of the vehicle using the candidate combination in the first road segment can be determined based on the road condition information, fuel consumption cost and NVH impact cost of the first road segment.

[0085] Among them, the energy consumption curve is obtained by curve fitting of the predicted energy consumption of multiple first road sections.

[0086] Among them, the consumption cost of the vehicle using the candidate combination is positively correlated with the fuel consumption cost, and the consumption cost of the vehicle using the candidate combination is also positively correlated with the impact cost of NVH.

[0087] The fuel consumption cost of the vehicle when the range extender takes different control actions under different SOC states can be pre-calibrated or predicted in real time. For example, relevant algorithms or deep learning technology can be used to predict the fuel consumption cost of the vehicle when the range extender takes different control actions under each SOC state. This application does not limit this.

[0088] Similarly, the cost of the impact of different control actions of the range extender on NVH can be obtained in advance or predicted in real time, and this application does not limit this.

[0089] As an example, based on the road condition information of the first road segment mentioned above, a road condition sensitivity weight (or NVH penalty coefficient) can be determined for that first road segment. Then, based on this road condition sensitivity weight, the fuel consumption cost and NVH impact cost of the vehicle using the aforementioned candidate combination can be fused to obtain the consumption cost of the vehicle using that candidate combination in the first road segment. For example, the consumption cost can be calculated based on the value function V = min(fuel consumption cost + λ·NVH impact cost), where λ is determined based on the road condition sensitivity weight.

[0090] For example, the candidate SOC in the candidate combination is labeled as x. tThe candidate control action is u t Therefore, based on the value function V = min(fuel consumption cost + λ·NVH impact cost), the following formula can be used to calculate the consumption cost of the vehicle using this candidate combination:

[0091]

[0092] Among them, J fuel (x t ,u t ) indicates that x is in SOC state t Take control action u t fuel consumption cost at that time; J NVH (u t ) indicates the control action u t The cost of NVH impact; β(s) is the road condition sensitivity weight, which varies depending on different road condition information s (congestion, cruising, rapid acceleration). For example, β(s) = 0.5 for congestion, β(s) = 0.2 for cruising, and β(s) = 1.0 for rapid acceleration. Its function is to adjust the weight of NVH impact cost in the total consumption cost according to road condition information, to achieve comfort compensation, and to avoid charging operations during rapid acceleration or congestion; f(x t ,u t () is the state transition function, which describes the state transition in state x. t Take control action u t Then, how does the SOC state transition to the next SOC state?

[0093] In summary, by employing targeted road condition sensitivity weights for road condition information such as cruising, congestion, and rapid acceleration, and by integrating fuel consumption costs and NVH impact costs, the rationality and reliability of vehicle consumption cost calculations for each road segment can be improved.

[0094] Step B: Determine the target combination from the multiple candidate combinations based on the vehicle's consumption costs under multiple candidate combinations.

[0095] For example, the candidate combination with the lowest cost can be selected as the target combination.

[0096] Step C: Based on the target combination, determine the target SOC and target control actions of the vehicle in the first road segment mentioned above.

[0097] For example, the candidate SOC in the target combination can be used as the target SOC of the vehicle in the first road segment, and the candidate control action in the target combination can be used as the target control action of the vehicle in the first road segment.

[0098] In summary, based on the consumption cost of vehicles under multiple candidate combinations in each first road segment, the target combination is determined from the multiple candidate combinations, and the target SOC and target control cost of the vehicle in that first road segment are determined accordingly. This enables precise matching of energy supply for vehicles in each first road segment and improves energy utilization efficiency.

[0099] Step S303: Control the range extender according to the target SOC and target control actions of multiple first road segments.

[0100] In this embodiment of the application, the range extender can be controlled in a targeted manner for each first segment based on the target SOC and target control action of each first segment.

[0101] The vehicle control method of this application, based on the predicted energy consumption of each first road segment, real-time road condition information, and vehicle status information, determines the target SOC and the target control action of the range extender for each road segment. This achieves refined and segmented planning of the energy management strategy, ensuring not only the reasonable distribution of battery power across different road segments, avoiding premature depletion or ineffective power generation, but also dynamically adjusting the control parameters of the range extender to improve the spatiotemporal matching accuracy of energy utilization. In other words, it makes the control of the range extender more precise and predictable, effectively extending the vehicle's driving range while improving the driving experience and overall vehicle energy efficiency.

[0102] As one possible implementation method, Figure 4 A schematic flowchart of another vehicle control method provided for an exemplary embodiment of this application.

[0103] It should be noted that the vehicle control method can be executed alone, or it can be executed together with any embodiment or possible implementation in the embodiments of this application, or it can be executed together with any technical solution in the related technology. The embodiments of this application do not limit this.

[0104] like Figure 4 As shown, the vehicle control method may include the following steps S401 to S404:

[0105] Step S401: Based on the environmental data of the vehicle's environment and the vehicle's navigation data, determine the predicted energy consumption of the vehicle in multiple first road segments in the navigation data.

[0106] Step S402: Based on the predicted energy consumption and road condition information of multiple first road segments, as well as vehicle status information, determine the target SOC and target control action of the vehicle in multiple first road segments; wherein, the target control action is used to indicate the control parameters of the range extender.

[0107] It should be noted that the explanations of steps S401 to S402 can be found in the relevant descriptions in any embodiment of this application, and will not be repeated here.

[0108] In any embodiment of this application, during the reverse dynamic planning of the target SOC and target control action of each first road segment, the target SOC and target control action of multiple first road segments can be adjusted according to the set constraints; wherein, the set constraints include at least one of the vehicle's battery power constraints, charging time constraints, and path deviation constraints.

[0109] For example, the power constraints include: the final SOC (State of Charge) for each first segment. final ≥15%.

[0110] For example, the power replenishment duration constraint includes: the duration of a single power replenishment is less than or equal to the set duration. Taking a set duration of 5 minutes (300 seconds) as an example, the power replenishment duration constraint includes: ∑Δt charge ≤300s.

[0111] For example, the path deviation constraint includes: the actual driving path P actual With navigation planning path P plan The distance deviation between them is less than or equal to a set threshold, or the ratio of the above distance deviation to the current travel segment is less than or equal to a set proportion (e.g., 10%).

[0112] In summary, adjusting the target SOC and target control actions of multiple first road segments based on at least one safety constraint can improve the feasibility of control strategies (including target SOC and target control actions).

[0113] Step S403: Based on the vehicle's actual SOC, the target SOC of the vehicle in the current driving segment, and the control parameters indicated by the target control action, determine the target control parameters of the range extender in the current driving segment.

[0114] In any embodiment of this application, if the actual SOC of the vehicle is not less than the set SOC threshold, the SOC deviation between the target SOC and the actual SOC of the current driving segment can be further determined, and the control parameters indicated by the target control action of the current driving segment can be corrected according to the SOC deviation to obtain the target control parameters of the range extender in the current driving segment.

[0115] In any embodiment of this application, when the actual SOC of the vehicle is less than the set SOC threshold, the set control parameters can be used as the target control parameters for the range extender in the current driving segment.

[0116] As an example, taking power as the control parameter and setting the SOC threshold to 10%, the target power P of the range extender on the current driving segment can be calculated using the following formula. req :

[0117]

[0118] Among them, P opt It is the power indicated by the target control action for the current driving segment; K p It is the power gradient slope, such as K. p = 5kW / s; e(t) is the target SOC (SOC) of the vehicle's current travel segment. target ) and actual SOC (SOC) real The SOC deviation between t and t, i.e., e(t) = SOC target -SOC real ;P emerg The power required to force the range extender to start in the emergency mode.

[0119] In summary, this method can calculate the target control parameters of the range extender based on the vehicle's actual SOC on the current driving segment, thereby improving the rationality and accuracy of the target control parameter calculation.

[0120] Step S404: Control the range extender based on the target control parameters.

[0121] In this embodiment of the application, the range extender can be controlled specifically for the current driving segment based on the target control parameters of the range extender in the current driving segment.

[0122] The vehicle control method of this application embodiment calculates the target control parameters of the range extender in the current driving segment based on the vehicle's actual SOC in the current driving segment, the vehicle's target SOC in the current driving segment, and the control parameters indicated by the target control action, and controls the range extender accordingly, which can improve the effectiveness and rationality of the range extender control.

[0123] As one possible implementation method, Figure 5 A schematic flowchart of another vehicle control method provided for an exemplary embodiment of this application.

[0124] It should be noted that the vehicle control method can be executed alone, or it can be executed together with any embodiment or possible implementation in the embodiments of this application, or it can be executed together with any technical solution in the related technology. The embodiments of this application do not limit this.

[0125] like Figure 5As shown, the vehicle control method may include the following steps S501 to S507:

[0126] Step S501: Based on the environmental data of the vehicle's environment and the vehicle's navigation data, determine the predicted energy consumption of the vehicle in multiple first road segments in the navigation data.

[0127] Step S502: Control the vehicle's range extender based on the predicted energy consumption and road condition information of multiple first road segments, as well as vehicle status information.

[0128] It should be noted that the explanations of steps S501 to S502 can be found in the relevant descriptions in any embodiment of this application, and will not be repeated here.

[0129] Step S503: Determine whether the set policy update conditions are met. If not, return to step S502. If yes, proceed to step S504.

[0130] The policy update conditions are preset. For example, the policy update conditions include, but are not limited to, at least one of the following: the path deviation between the vehicle's current driving segment and the first segment is greater than a preset distance threshold; the change in the vehicle's ETA to the navigation destination is greater than a preset change threshold; the rate of change of the vehicle's ETA to the navigation destination is greater than a preset proportion threshold.

[0131] Taking a distance threshold of 300m and a proportion threshold of 10% as an example, local or global strategy replanning can be triggered whenever the path deviation is greater than 300m and / or the ETA change rate is greater than 10%.

[0132] Step S504: Perform route planning based on the navigation destination and the vehicle's current location in the navigation data to obtain updated navigation data.

[0133] In this embodiment of the application, a path planning algorithm can be used to plan the path between the navigation destination and the current position of the vehicle to obtain updated navigation data.

[0134] Step S505: Based on the updated navigation data and environmental data of the vehicle's environment, determine the predicted energy consumption of the vehicle in at least one second road segment in the updated navigation data.

[0135] It should be noted that the implementation principle of step S505 is similar to that of step S101, and will not be elaborated here.

[0136] It should also be noted that if the second road segment overlaps with the first road segment, the predicted energy consumption of the non-overlapping second road segment can be predicted only to reduce the overhead of computing resources.

[0137] Step S506: Based on the predicted energy consumption and road condition information of the second road segment, as well as the actual state information of the vehicle, determine the target SOC and target control action of the second road segment.

[0138] It should be noted that the implementation principle of step S506 is similar to that of step S302, and will not be elaborated here.

[0139] Step S507: Control the range extender according to the target SOC and target control action of the second segment.

[0140] It should be noted that the implementation principle of step S507 is similar to that of step S303, and will not be elaborated here.

[0141] The vehicle control method of this application, when the set policy update conditions are met, triggers local or global policy replanning, which can make the control of the range extender more in line with the actual driving conditions of the vehicle and further improve energy utilization efficiency.

[0142] In any embodiment of this application, multiple candidate control actions of the range extender pre-allocated based on path characteristics, discretized candidate SOCs (e.g., divided into 20 discrete SOC states according to a 5% step size), road segment information and road condition information (including gradient angle, speed limit, etc.) in high-precision navigation data, real-time power consumption, ETA, and SOC buffer are incorporated into a unified range extender optimization control architecture to improve energy utilization efficiency.

[0143] The navigation data has been optimized to avoid congested or rapidly accelerating sections of road.

[0144] As an example, the control principle of a range extender can be as follows: Figure 6 , 7 As shown in Figure 8, the main steps include:

[0145] 1. Data Fusion: Synchronize multi-source data (including high-precision navigation data, vehicle status information, and environmental data), and perform time alignment and validity verification on the multi-source data. For example, the sampling period of the multi-source data is 500 milliseconds (ms).

[0146] 2. An energy consumption prediction model is used to predict energy consumption in segments based on navigation data and environmental data. This yields the predicted energy consumption for multiple road segments in the navigation data. Curve fitting is then performed on the predicted energy consumption for these multiple road segments to obtain an energy consumption curve. For example, taking electricity consumption as an example, the energy consumption curve can also be called the electricity consumption curve.

[0147] As an example, taking energy consumption as electricity consumption, the above formula (1) can be used to calculate the predicted electricity consumption of each road segment by combining the basic electricity consumption, temperature compensation coefficient, and slope compensation coefficient.

[0148] 3. A dynamic programming optimizer is used to perform inverse dynamic rules based on energy consumption curves, vehicle status information, and road condition information of multiple road segments to obtain the target SOC for each road segment and the target control action of the range extender.

[0149] As an example, inverse dynamic programming can be performed based on formula (2) to obtain the consumption cost V of vehicles in each road segment under each combination of candidate SOC and candidate control action, and construct a consumption cost matrix accordingly. The candidate SOC and candidate control action in the combination corresponding to the minimum consumption cost in the consumption cost matrix are taken as the target SOC and target control action of the road segment.

[0150] Among them, inverse dynamic programming: the SOC constraints at the navigation endpoint (such as SOC) final Under the condition of ≥15%, the optimal control action (referred to as the target control action in this application) is calculated segment by segment starting from the navigation endpoint, and the value function is V = min(fuel consumption cost + λ·NVH impact cost); where λ is determined based on the road condition sensitivity weight determined by the road condition information.

[0151] 4. Safety verification to ensure the feasibility of the strategy, including minimum power limit, recharging time and path deviation constraints.

[0152] For example, SOC final ≥15% (i.e., SOC buffer ≥15%, emergency mode trigger line); ∑Δt charge ≤300s; ||P actual -P plan ||≤10%.

[0153] 5. Range extender control: The range extender is controlled using the above formula (3).

[0154] As an example, the online control logic of a range extender can be as follows: Figure 9 As shown, the main steps include:

[0155] a. Based on the actual SOC of the vehicle, query the pre-calculated optimal strategy table (including the target SOC and target control action of the vehicle in each road segment) and the above formula (3) to obtain the optimal control parameters of the range extender in the current driving segment, and output the control command of the range extender accordingly.

[0156] b. When the mileage displayed by the odometer increases by 2km, calculate whether the path deviation is greater than the set distance threshold (e.g., 300m) or whether the rate of change of ETA is greater than the set percentage threshold (e.g., 10%). If so, trigger local replanning.

[0157] c. Use rolling horizontal control (or predictive horizontal control, or RHC) to update subsequent policies.

[0158] In summary, the solution provided in this application has at least the following advantages: Reduced energy consumption: Actual tests show that it can reduce fuel consumption by 8-12%; Improved comfort: Actual tests show that it can reduce unnecessary range extender starts by 63%; Enhanced safety: Dynamically maintains battery buffer to avoid battery depletion caused by sudden road conditions.

[0159] To achieve the above embodiments, this application also proposes a vehicle control device.

[0160] Figure 10 This is a schematic diagram of the structure of a vehicle control device provided for an exemplary embodiment of this application.

[0161] like Figure 10 As shown, the vehicle control device 1000 may include a determination module 1010 and a control module 1020.

[0162] The determining module 1010 is used to determine the predicted energy consumption of the vehicle in multiple first road segments in the navigation data based on the environmental data of the vehicle's environment and the vehicle's navigation data.

[0163] The control module 1020 is used to control the vehicle's range extender based on the predicted energy consumption and road condition information of multiple first road segments, as well as vehicle status information.

[0164] Furthermore, in one implementation of this application embodiment, the determining module 1010 is used to: determine the initial energy consumption of the vehicle in the multiple first road segments based on the length information of the multiple first road segments and the basic energy consumption of the vehicle; and correct the initial energy consumption of the multiple first road segments based on environmental data to obtain the predicted energy consumption of the multiple first road segments.

[0165] In one implementation of this application, the environmental data includes at least one of temperature and altitude. The determining module 1010 is used to: determine a correction coefficient based on at least one of temperature and altitude; and correct the initial energy consumption of multiple first road segments based on the correction coefficient to obtain the predicted energy consumption of multiple first road segments.

[0166] In one implementation of this application, the control module 1020 is used to: determine the target SOC and target control action of the vehicle in the multiple first road segments based on the predicted energy consumption and road condition information of multiple first road segments and the vehicle status information; wherein the target control action is used to indicate the control parameters of the range extender; and control the range extender according to the target SOC and target control action of the multiple first road segments.

[0167] In one implementation of this application, the control module 1020 is configured to: determine the energy consumption cost of a vehicle in any first road segment under multiple candidate combinations based on the predicted energy consumption and road condition information of multiple first road segments, as well as vehicle status information; wherein, the candidate combinations include a candidate SOC and a candidate control action; determine a target combination from the multiple candidate combinations based on the energy consumption cost of the vehicle under the multiple candidate combinations; and determine the target SOC and target control action of the vehicle in any first road segment based on the target combination.

[0168] In one implementation of this application, the control module 1020 is configured to: determine, based on vehicle status information and energy consumption curves, the fuel consumption cost when the range extender takes a candidate control action in any candidate combination under a candidate SOC in any candidate combination of any first road segment, and the impact cost of the range extender taking a candidate control action in any candidate combination on NVH; wherein, the energy consumption curve is obtained by curve fitting of the predicted energy consumption of multiple first road segments; and determine the consumption cost of the vehicle using any candidate combination in any first road segment based on the fuel consumption cost and impact cost, and based on the road condition information of any first road segment.

[0169] In one implementation of this application, the control module 1020 is configured to: determine the road condition sensitivity weight of any first road segment based on the road condition information of any first road segment; and fuse the fuel consumption cost and the impact cost based on the road condition sensitivity weight to obtain the consumption cost of a vehicle using any candidate combination in any first road segment.

[0170] In one implementation of this application, the vehicle control device 1000 further includes:

[0171] The adjustment module is used to adjust the target SOC and target control actions of multiple first road segments according to the set constraints; wherein the set constraints include at least one of the vehicle's battery power constraints, charging time constraints, and path deviation constraints.

[0172] In one implementation of this application, the control module 1020 is used to: determine the target control parameters of the range extender in the current driving segment based on the vehicle's actual SOC, the vehicle's target SOC in the current driving segment, and the control parameters indicated by the target control action; and control the range extender based on the target control parameters.

[0173] In one implementation of this application, the control module 1020 is configured to: determine the SOC deviation between the target SOC and the actual SOC of the current driving segment in response to the actual SOC not being less than a set SOC threshold; and correct the control parameters indicated by the target control action of the current driving segment based on the SOC deviation to obtain the target control parameters of the range extender in the current driving segment.

[0174] In one implementation of this application, the control module 1020 is configured to: in response to the actual SOC being less than a set SOC threshold, use a set control parameter as the target control parameter for the range extender in the current driving segment.

[0175] In one implementation of this application, the vehicle control device 1000 further includes:

[0176] The planning module is used to perform route planning based on the navigation destination and the vehicle's current location in the navigation data in response to the fulfillment of the set strategy update conditions, and to obtain the updated navigation data.

[0177] The determination module 1010 is also used to: determine the predicted energy consumption of the vehicle in at least one second road segment in the updated navigation data based on the updated navigation data and the environmental data of the vehicle's environment; and determine the target SOC and target control action of the second road segment based on the predicted energy consumption and road condition information of the second road segment and the actual state information of the vehicle.

[0178] The control module 1020 is also used to control the range extender based on the target SOC and target control action of the second segment.

[0179] In one implementation of this application, the policy update condition includes at least one of the following:

[0180] The path deviation between the vehicle's current travel segment and the first segment is greater than the set distance threshold;

[0181] The change in the ETA of the vehicle reaching the navigation destination is greater than the set change threshold.

[0182] The rate of change of the ETA when the vehicle reaches the navigation destination is greater than the set proportional threshold.

[0183] It should be noted that the foregoing explanation of the vehicle control method embodiment also applies to the vehicle control device of this embodiment, and will not be repeated here.

[0184] In the vehicle control device of this application embodiment, the predicted energy consumption of the vehicle in multiple road segments is predicted by combining environmental data and navigation data of the actual environment in which the vehicle is located. Based on the predicted energy consumption of multiple road segments, real-time road condition information and vehicle status information, the range extender is dynamically and precisely controlled. Compared with the traditional method that relies solely on historical driving data, this application can effectively avoid control errors caused by the generalization of historical experience, making the control strategy of the range extender more in line with the actual driving conditions of the vehicle. This not only achieves precise matching of energy supply and significantly improves energy utilization efficiency, but also effectively extends the vehicle's driving range.

[0185] To implement the above embodiments, this application also proposes a vehicle, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the vehicle control method as described in any of the foregoing embodiments.

[0186] Figure 11 This is a block diagram illustrating a vehicle 1100 according to an exemplary embodiment. For example, vehicle 1100 may be a hybrid vehicle, a non-hybrid vehicle, an electric vehicle, a fuel cell vehicle, or other types of vehicle. Vehicle 1100 may be an autonomous vehicle, a semi-autonomous vehicle, or a non-autonomous vehicle.

[0187] Reference Figure 11 The vehicle 1100 may include various subsystems, such as an infotainment system 1110, a perception system 1120, a decision control system 1130, a drive system 1140, and a computing platform 1150. The vehicle 1100 may also include more or fewer subsystems, and each subsystem may include multiple components. Furthermore, each subsystem and each component of the vehicle 1100 can be interconnected via wired or wireless means.

[0188] In some embodiments, the infotainment system 1110 may include a communication system, an entertainment system, and a navigation system, etc.

[0189] The perception system 1120 may include several types of sensors for sensing information about the environment surrounding the vehicle 1100. For example, the perception system 1120 may include a global positioning system (which may be a GPS system, a BeiDou system, or another positioning system), an inertial measurement unit (IMU), a lidar, a millimeter-wave radar, an ultrasonic radar, and a camera device.

[0190] The decision control system 1130 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

[0191] The drive system 1140 may include components that provide powered motion to the vehicle 1100. In one embodiment, the drive system 1140 may include an engine, an energy source, a transmission system, and wheels. The engine may be one or a combination of internal combustion engines, electric motors, and compressed air engines. The engine is capable of converting energy provided by the energy source into mechanical energy.

[0192] Some or all of the functions of vehicle 1100 are controlled by computing platform 1150. Computing platform 1150 may include at least one processor 1151 and memory 1152, and processor 1151 may execute instructions 1153 stored in memory 1152.

[0193] Processor 1151 can be any conventional processor, such as a commercially available CPU. Processors may also include graphics processing units (GPUs), field-programmable gate arrays (FPGAs), systems-on-chips (SoCs), application-specific integrated circuits (ASICs), or combinations thereof.

[0194] The memory 1152 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0195] In addition to instruction 1153, memory 1152 can also store data, such as road maps, route information, vehicle position, direction, speed, and other data. The data stored in memory 1152 can be used by computing platform 1150.

[0196] In this embodiment of the application, processor 1151 may execute instruction 1153 to complete all or part of the steps of any of the above method embodiments.

[0197] To implement the above embodiments, this application also proposes a chip, wherein the chip includes an interface circuit and a processing circuit coupled to each other. The interface circuit is used to input or output signals, and the processing circuit is configured to execute the vehicle control method provided in any of the foregoing embodiments.

[0198] Figure 12 This is a schematic diagram of the structure of a chip proposed in an exemplary embodiment of this application. See also... Figure 12 The diagram shown is a schematic representation of the structure of chip 1200, but it is not limited to this.

[0199] Chip 1200 includes processing circuit 1201, which is configured to execute any of the above vehicle control methods.

[0200] In some embodiments, chip 1200 further includes one or more interface circuits 1202. Optionally, the interface circuit 1202 is connected to memory 1203, and the interface circuit 1202 can be used to receive signals from memory 1203 or other devices, and the interface circuit 1202 can be used to send signals to memory 1203 or other devices. For example, the interface circuit 1202 can read instructions stored in memory 1203 and send the instructions to processing circuit 1201.

[0201] In some embodiments, the interface circuit 1202 performs at least one of the communication steps such as sending and / or receiving in the above method, while the processing circuit 1201 performs other steps.

[0202] In some embodiments, the terms interface circuit, interface, transceiver pin, transceiver, etc., can be used interchangeably.

[0203] In some embodiments, chip 1200 further includes one or more memories 1203 for storing instructions. Optionally, all or part of the memories 1203 may be located outside of chip 1200.

[0204] To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the vehicle control method as described in any of the foregoing method embodiments.

[0205] To implement the above embodiments, this application also proposes a computer program product having a computer program stored thereon, which, when executed by a processor, implements the vehicle control method as described in any of the foregoing method embodiments.

[0206] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. 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. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0207] Furthermore, 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. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0208] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0209] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and compact disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0210] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0211] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0212] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0213] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A vehicle control method, characterized in that, include: Based on environmental data of the vehicle's location and the vehicle's navigation data, the predicted energy consumption of the vehicle for multiple first road segments in the navigation data is determined. The range extender of the vehicle is controlled based on the predicted energy consumption and road condition information of the multiple first road segments, as well as the vehicle status information.

2. The method according to claim 1, characterized in that, The step of determining the predicted energy consumption of the vehicle for multiple first road segments in the navigation data based on environmental data of the vehicle's location and the vehicle's navigation data includes: Based on the length information of the multiple first road segments and the basic energy consumption of the vehicle, the initial energy consumption of the vehicle in the multiple first road segments is determined; Based on the environmental data, the initial energy consumption of the plurality of first road segments is corrected to obtain the predicted energy consumption of the plurality of first road segments.

3. The method according to claim 2, characterized in that, The environmental data includes at least one of temperature and altitude. The step of correcting the initial energy consumption of the plurality of first road segments based on the environmental data to obtain the predicted energy consumption of the plurality of first road segments includes: A correction factor is determined based on at least one of the temperature and the altitude; Based on the correction coefficient, the initial energy consumption of the plurality of first road segments is corrected to obtain the predicted energy consumption of the plurality of first road segments.

4. The method according to claim 1, characterized in that, The step of controlling the vehicle's range extender based on the predicted energy consumption and road condition information of the plurality of first road segments, as well as vehicle status information, includes: Based on the predicted energy consumption and road condition information of the multiple first road segments, as well as the vehicle status information, the target SOC and target control action of the vehicle in the multiple first road segments are determined; wherein, the target control action is used to indicate the control parameters of the range extender; The range extender is controlled based on the target SOC and target control actions of the multiple first road segments.

5. The method according to claim 4, characterized in that, The step of determining the target SOC and target control action of the vehicle in the plurality of first road segments based on the predicted energy consumption and road condition information of the plurality of first road segments, and the vehicle status information, includes: Based on the predicted energy consumption and road condition information of the multiple first road segments, as well as the vehicle status information, the consumption cost of the vehicle in any first road segment under multiple candidate combinations is determined; wherein, the candidate combination includes a candidate SOC and a candidate control action; Based on the consumption cost of the vehicle under multiple candidate combinations, a target combination is determined from the multiple candidate combinations; Based on the target combination, the target SOC and target control action of the vehicle in any first road segment are determined.

6. The method according to claim 5, characterized in that, The step of determining the energy consumption cost of the vehicle in any first road segment under multiple candidate combinations based on the predicted energy consumption and road condition information of the multiple first road segments, and the vehicle status information, includes: Based on the vehicle status information and energy consumption curve, determine the fuel consumption cost when the range extender takes the candidate control action in any candidate combination under the candidate SOC of the vehicle in any candidate combination in any first road segment, and the impact cost of the range extender taking the candidate control action in any candidate combination on NVH; wherein, the energy consumption curve is obtained by curve fitting the predicted energy consumption of the multiple first road segments. Based on the fuel consumption cost and the impact cost, and based on the road condition information of any first road segment, determine the consumption cost of the vehicle using any candidate combination in any first road segment.

7. The method according to claim 6, characterized in that, The step of determining the consumption cost of the vehicle using any candidate combination in any first road segment based on the fuel consumption cost, the impact cost, and the road condition information of any first road segment includes: Based on the road condition information of any first road segment, determine the road condition sensitivity weight of any first road segment; Based on the road condition sensitivity weight, the fuel consumption cost and the impact cost are fused to obtain the consumption cost of the vehicle using any candidate combination in any first road segment.

8. The method according to claim 4, characterized in that, The method further includes: Based on the set constraints, the target SOC and target control actions of the multiple first road segments are adjusted; The set constraints include at least one of the vehicle's battery power constraints, charging time constraints, and path deviation constraints.

9. The method according to claim 4, characterized in that, The step of controlling the range extender based on the target SOC and target control actions of the plurality of first road segments includes: Based on the actual SOC of the vehicle, the target SOC of the vehicle in the current driving segment, and the control parameters indicated by the target control action, the target control parameters of the range extender in the current driving segment are determined. The range extender is controlled based on the target control parameters.

10. The method according to claim 9, characterized in that, The step of determining the target control parameters of the range extender for the current driving segment based on the vehicle's actual SOC, the vehicle's target SOC for the current driving segment, and the control parameters indicated by the target control action includes: In response to the fact that the actual SOC is not less than the set SOC threshold, the SOC deviation between the target SOC and the actual SOC of the current driving segment is determined; Based on the SOC deviation, the control parameters indicated by the target control action of the current driving segment are corrected to obtain the target control parameters of the range extender in the current driving segment.

11. The method according to claim 10, characterized in that, The step of determining the target control parameters of the range extender in the current driving segment based on the vehicle's actual SOC, the vehicle's target SOC in the current driving segment, and the control parameters indicated by the target control action further includes: In response to the actual SOC being less than the set SOC threshold, a set control parameter is used as the target control parameter for the range extender in the current driving segment.

12. The method according to any one of claims 1-11, characterized in that, The method further includes: In response to the fulfillment of the set policy update conditions, route planning is performed based on the navigation destination in the navigation data and the current position of the vehicle to obtain updated navigation data; Based on the updated navigation data and the environmental data of the vehicle's environment, determine the predicted energy consumption of the vehicle in at least one second road segment in the updated navigation data; Based on the predicted energy consumption and road condition information of the second road segment, as well as the actual state information of the vehicle, the target SOC and target control action of the second road segment are determined. The range extender is controlled according to the target SOC and target control action of the second road segment.

13. The method according to claim 12, characterized in that, The policy update conditions include at least one of the following: The path deviation between the vehicle's current travel segment and the first travel segment is greater than the set distance threshold. The change in the ETA of the vehicle to the navigation destination is greater than the set change threshold. The rate of change of the ETA of the vehicle reaching the navigation destination is greater than a set proportional threshold.

14. A vehicle control device, characterized in that, include: The determination module is used to determine the predicted energy consumption of the vehicle in multiple first road segments in the navigation data based on environmental data of the vehicle's environment and the vehicle's navigation data. The control module is used to control the range extender of the vehicle based on the predicted energy consumption and road condition information of the plurality of first road segments, as well as the vehicle status information.

15. The apparatus according to claim 14, characterized in that, The determining module is used for: Based on the length information of the multiple first road segments and the basic energy consumption of the vehicle, the initial energy consumption of the vehicle in the multiple first road segments is determined; Based on the environmental data, the initial energy consumption of the plurality of first road segments is corrected to obtain the predicted energy consumption of the plurality of first road segments.

16. The apparatus according to claim 14, characterized in that, The control module is used for: Based on the predicted energy consumption and road condition information of the multiple first road segments, as well as the vehicle status information, the target SOC and target control action of the vehicle in the multiple first road segments are determined; wherein, the target control action is used to indicate the control parameters of the range extender; The range extender is controlled based on the target SOC and target control actions of the multiple first road segments.

17. The apparatus according to claim 16, characterized in that, The control module is used for: Based on the predicted energy consumption and road condition information of the multiple first road segments, as well as the vehicle status information, the consumption cost of the vehicle in any first road segment under multiple candidate combinations is determined; wherein, the candidate combination includes a candidate SOC and a candidate control action; Based on the consumption cost of the vehicle under multiple candidate combinations, a target combination is determined from the multiple candidate combinations; Based on the target combination, the target SOC and target control action of the vehicle in any first road segment are determined.

18. The apparatus according to claim 17, characterized in that, The control module is used for: Based on the vehicle status information and energy consumption curve, determine the fuel consumption cost when the range extender takes the candidate control action in any candidate combination under the candidate SOC of the vehicle in any candidate combination in any first road segment, and the impact cost of the range extender taking the candidate control action in any candidate combination on NVH; wherein, the energy consumption curve is obtained by curve fitting the predicted energy consumption of the multiple first road segments. Based on the fuel consumption cost and the impact cost, and based on the road condition information of any first road segment, determine the consumption cost of the vehicle using any candidate combination in any first road segment.

19. The apparatus according to claim 18, characterized in that, The control module is used for: Based on the road condition information of any first road segment, determine the road condition sensitivity weight of any first road segment; Based on the road condition sensitivity weight, the fuel consumption cost and the impact cost are fused to obtain the consumption cost of the vehicle using any candidate combination in any first road segment.

20. The apparatus according to claim 16, characterized in that, The device further includes: The adjustment module is used to adjust the target SOC and target control actions of the plurality of first road segments according to the set constraints. The set constraints include at least one of the vehicle's battery power constraints, charging time constraints, and path deviation constraints.

21. The apparatus according to claim 16, characterized in that, The control module is used for: Based on the actual SOC of the vehicle, the target SOC of the vehicle in the current driving segment, and the control parameters indicated by the target control action, the target control parameters of the range extender in the current driving segment are determined. The range extender is controlled based on the target control parameters.

22. A vehicle, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured as follows: The steps of implementing the method as described in any one of claims 1 to 13.

23. A non-transitory computer-readable storage medium having computer program instructions stored thereon, characterized in that, When executed by a processor, the program instructions implement the steps of the method described in any one of claims 1 to 13.

24. A chip, characterized in that, The chip includes an interface circuit and a processing circuit that are coupled to each other. The interface circuit is used to input or output signals, and the processing circuit is used to implement the method of any one of claims 1 to 13.

25. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 13.

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