Energy consumption control method, device and equipment of hybrid vehicle, storage medium and product

Abstract

The invention relates to an energy consumption control method, device and equipment of a hybrid vehicle, a storage medium and a product. The method comprises the following steps: acquiring current energy consumption data, current navigation information, current energy data and user preference information of a vehicle driven by a user, and identifying current road condition information of a current driving route of the vehicle of the user; based on the user preference information of the user, a preferred energy supply and consumption mode and energy supplement limitation information of the user are identified, and an energy supply and consumption control strategy of the vehicle is generated through the preferred energy supply and consumption mode of the user; and generating an energy supply and consumption control scheme of the vehicle and energy supplement prompt information of the user based on the energy supply and consumption control strategy of the vehicle. By adopting the method, the energy consumption supply decision accuracy of the hybrid vehicle can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent driving and energy supply analysis of hybrid vehicles, in particular to an energy consumption control method, device, equipment, storage medium and product of a hybrid vehicle. BACKGROUND

[0002] With the popularity of hybrid vehicles (such as PHEV-plug-in hybrid vehicles, EREV-range-extended electric vehicles, etc.), users' demand for energy economy (oil / electricity cost) and driving convenience is increasing. However, the existing hybrid vehicles have weak technical research on energy optimization, charging decision, etc., which often leads to poor user experience and high vehicle travel cost. Therefore, how to improve the accuracy of energy consumption and supply decision of hybrid vehicles is the current research focus.

[0003] The prior art only focuses on energy consumption information and uses preset energy remaining amount reminder strategy and preset energy switching limit standard to control energy consumption and provide energy supply suggestions, which results in large actual vehicle energy consumption, high cost, and poor user experience, thereby leading to low accuracy of energy consumption and supply decision of hybrid vehicles. SUMMARY

[0004] Therefore, it is necessary to provide an energy consumption control method, device, equipment, storage medium and product of a hybrid vehicle to solve the above technical problems.

[0005] In a first aspect, the present application provides an energy consumption control method of a hybrid vehicle, comprising:

[0006] obtaining current energy consumption data of a user driving a vehicle, current navigation information of the vehicle, current energy data, and user preference information of the user, and identifying current road condition information of a current driving route of the user's vehicle based on the current navigation information of the user driving the vehicle;

[0007] identifying a preferred energy supply and consumption mode of the user and energy supplement limit information of the vehicle based on the user preference information of the user, and generating an energy supply and consumption control strategy of the vehicle through the preferred energy supply and consumption mode of the user based on the current road condition information of the current driving route, the energy supplement limit information of the vehicle, the current energy data, and the current energy consumption data of the vehicle;

[0008] generating an energy supply and consumption control scheme of the vehicle and energy supplement prompt information of the user based on the energy supply and consumption control strategy of the vehicle.

[0009] Optionally, the step of identifying the current road condition information of the current driving route of the user's vehicle based on the current navigation information of the user driving the vehicle comprises:

[0010] Based on the current navigation information of the vehicle driven by the user, identifying route data of the current driving route of the vehicle and real-time traffic data of the current driving route;

[0011] Based on the route data of the current driving route of the vehicle and the real-time traffic condition data of the current driving route, the route features of each road section area of ​​the current driving route and the traffic condition features of each road section area are extracted through a feature extraction network;

[0012] The route characteristics of each road section area and the road condition characteristics of each road section area are used as the current road condition information of the current driving route of the user vehicle.

[0013] Optionally, before generating the energy supply and consumption control strategy for the vehicle based on the current road condition information of the current driving route, the energy replenishment restriction information of the vehicle, the current energy data, and the current energy consumption data of the vehicle and using the user's preferred energy supply and consumption mode, the method further includes:

[0014] Based on the energy replenishment restriction information, identifying a maximum energy replenishment time and a maximum energy replenishment cost of the vehicle, and based on the maximum energy replenishment time and the maximum energy replenishment cost of the vehicle, identifying an energy replenishment logic for the vehicle and an energy replenishment feasibility determination strategy for the vehicle;

[0015] Based on the user's preferred energy supply and consumption pattern, the energy supply and consumption logic of the vehicle and the energy supply and consumption objective function of the vehicle are identified.

[0016] Optionally, before generating the energy supply and consumption control strategy for the vehicle based on the current road condition information of the current driving route, the energy replenishment restriction information of the vehicle, the current energy data, and the current energy consumption data of the vehicle and using the user's preferred energy supply and consumption mode, the method further includes:

[0017] Based on the route characteristics of each road section area and the road condition characteristics of each road section area, identifying the estimated driving time of each road section area and the driving characteristics of each vehicle in each road section area;

[0018] Based on the current energy data, identifying the location information of the target road section area where each energy supply point is located, the supply type corresponding to each energy supply point, and the real-time supply data corresponding to each energy supply point;

[0019] Based on the supply type corresponding to each of the energy supply points and the real-time supply data corresponding to each of the energy supply points, the supply cost distribution information of each energy supply point and the current supply waiting time information of each energy supply point are identified.

[0020] Optionally, the generating of the vehicle's energy supply and consumption control strategy based on the current road condition information of the current driving route, the vehicle's energy replenishment limit information, the current energy data, and the vehicle's current energy consumption data, and using the user's preferred energy supply and consumption mode, includes:

[0021] Based on the estimated driving time of each road section area and the driving characteristics of each vehicle in each road section area, the energy supply and consumption logic of the vehicle and the energy supply and consumption target function of the vehicle are used to generate the energy supply and consumption pattern of the vehicle in each road section area, and based on the energy supply and consumption pattern of each road section area and the current energy consumption data of the vehicle, the remaining energy information of the vehicle in each road section area is predicted;

[0022] Based on the remaining energy information of the vehicle in each of the road sections, the distribution information of the recharging cost of each energy recharging point, the current recharging time information of each energy recharging point, and the location information of the target road section where each energy recharging point is located, according to the energy recharging logic of the vehicle and the energy recharging feasibility judgment strategy of the vehicle, the target energy recharging point of the vehicle is identified;

[0023] The energy supply and consumption mode of the vehicle in each of the road sections, the remaining energy information of the vehicle in each of the road sections, and the target energy replenishment point of the vehicle are used as the energy supply and consumption control strategy of the vehicle.

[0024] Optionally, the generating of the vehicle's energy supply and consumption control plan and the user's energy replenishment prompt information based on the vehicle's energy supply and consumption control strategy includes:

[0025] Based on the remaining energy information of the vehicle in each of the road sections and the target energy replenishment point of the vehicle, generating energy replenishment reminder content for the user and the energy replenishment reminder location for the user through a reminder information generation strategy, and using the energy replenishment reminder content for the user and the energy replenishment reminder location for the user as the energy replenishment reminder information for the user;

[0026] Based on the energy supply and consumption mode of the vehicle in each of the road sections, energy supply and consumption control instructions for each of the road sections are generated, and the energy supply and consumption control instructions for all of the road sections are used as the energy supply and consumption control plan for the vehicle.

[0027] In a second aspect, the present application further provides an energy consumption control device for a hybrid vehicle, comprising:

[0028] an acquisition module, configured to acquire current energy consumption data of a vehicle driven by a user, current navigation information of the vehicle, current energy data, and user preference information of the user, and identify current road condition information of a current route of the vehicle driven by the user based on the current navigation information of the vehicle driven by the user;

[0029] an identification module for identifying the user's preferred energy supply and consumption pattern and the vehicle's energy replenishment restriction information based on the user's user preference information, and generating an energy supply and consumption control strategy for the vehicle based on the user's preferred energy supply and consumption pattern and current road condition information of the current travel route, the vehicle's energy replenishment restriction information, the current energy data, and the vehicle's current energy consumption data;

[0030] A generating module is used to generate the energy supply and consumption control scheme of the vehicle and the energy replenishment prompt information of the user based on the energy supply and consumption control strategy of the vehicle.

[0031] Optionally, the acquisition module is specifically configured to:

[0032] Based on the current navigation information of the vehicle driven by the user, identifying route data of the current driving route of the vehicle and real-time traffic data of the current driving route;

[0033] Based on the route data of the current driving route of the vehicle and the real-time traffic condition data of the current driving route, the route features of each road section area of ​​the current driving route and the traffic condition features of each road section area are extracted through a feature extraction network;

[0034] The route characteristics of each road section area and the road condition characteristics of each road section area are used as the current road condition information of the current driving route of the user vehicle.

[0035] Optionally, the device further includes:

[0036] a replenishment identification module, configured to identify a maximum energy replenishment time and a maximum energy replenishment cost of the vehicle based on the energy replenishment restriction information, and to identify an energy replenishment logic for the vehicle and an energy replenishment feasibility determination strategy for the vehicle based on the maximum energy replenishment time and the maximum energy replenishment cost of the vehicle;

[0037] The supply and consumption identification module is used to identify the energy supply and consumption logic of the vehicle and the energy supply and consumption target function of the vehicle based on the user's preferred energy supply and consumption mode.

[0038] Optionally, the device further includes:

[0039] A driving feature recognition module is used to identify the estimated driving time of each road section area and the driving characteristics of each vehicle in each road section area based on the route characteristics of each road section area and the road condition characteristics of each road section area;

[0040] An energy recharge point identification module is used to identify, based on the current energy data, the location information of the target road section area where each energy recharge point is located, the recharge type corresponding to each energy recharge point, and the real-time recharge data corresponding to each energy recharge point;

[0041] Based on the supply type corresponding to each of the energy supply points and the real-time supply data corresponding to each of the energy supply points, the supply cost distribution information of each energy supply point and the current supply waiting time information of each energy supply point are identified.

[0042] Optionally, the identification module is specifically configured to:

[0043] Based on the estimated driving time of each road section area and the driving characteristics of each vehicle in each road section area, the energy supply and consumption logic of the vehicle and the energy supply and consumption target function of the vehicle are used to generate the energy supply and consumption pattern of the vehicle in each road section area, and based on the energy supply and consumption pattern of each road section area and the current energy consumption data of the vehicle, the remaining energy information of the vehicle in each road section area is predicted;

[0044] Based on the remaining energy information of the vehicle in each of the road sections, the distribution information of the recharging cost of each energy recharging point, the current recharging time information of each energy recharging point, and the location information of the target road section where each energy recharging point is located, according to the energy recharging logic of the vehicle and the energy recharging feasibility judgment strategy of the vehicle, the target energy recharging point of the vehicle is identified;

[0045] The energy supply and consumption mode of the vehicle in each of the road sections, the remaining energy information of the vehicle in each of the road sections, and the target energy replenishment point of the vehicle are used as the energy supply and consumption control strategy of the vehicle.

[0046] Optionally, the generating module is specifically configured to:

[0047] Based on the remaining energy information of the vehicle in each of the road sections and the target energy replenishment point of the vehicle, generating energy replenishment reminder content for the user and the energy replenishment reminder location for the user through a reminder information generation strategy, and using the energy replenishment reminder content for the user and the energy replenishment reminder location for the user as the energy replenishment reminder information for the user;

[0048] Based on the energy supply and consumption mode of the vehicle in each of the road sections, energy supply and consumption control instructions for each of the road sections are generated, and the energy supply and consumption control instructions for all of the road sections are used as the energy supply and consumption control plan for the vehicle.

[0049] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0050] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any one of the methods in the first aspect.

[0051] In a fifth aspect, the present application provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0052] The above-mentioned energy consumption control method, device, equipment, storage medium and product of the hybrid vehicle obtain the current energy consumption data of the user driving the vehicle, the current navigation information of the vehicle, the current energy data, and the user preference information of the user, and identify the current road condition information of the current driving route of the user's vehicle based on the current navigation information of the user driving the vehicle; identify the user's preferred energy supply and consumption mode and the energy replenishment restriction information of the vehicle based on the user's user preference information, and generate the vehicle's energy supply and consumption control strategy through the user's preferred energy supply and consumption mode based on the current road condition information of the current driving route, the energy replenishment restriction information of the vehicle, the current energy data, and the current energy consumption data of the vehicle; generate the vehicle's energy supply and consumption control plan and the user's energy replenishment prompt information based on the vehicle's energy supply and consumption control strategy. This solution first comprehensively considers energy consumption (e.g., 15kWh / 100km vs. 6L / 100km) and energy prices (e.g., 1.5 yuan / kWh electricity vs. 8 yuan / L oil), and calculates equivalent costs in real time to ensure the lowest actual cost mode is selected. Secondly, it comprehensively considers the impact of charging time on the total journey (e.g., if a user needs to arrive in 1 hour, charging for 30 minutes is not feasible) and the cost-effectiveness of charging (e.g., charging becomes more expensive when electricity prices are too high), thus avoiding the waste of time or money caused by blind charging. Finally, this solution considers individual user needs (e.g., business users need to arrive quickly, while home users are more concerned about cost) and adjusts weights to achieve a deep integration of mode switching and user needs. Therefore, this solution intelligently generates energy consumption control plans for hybrid vehicles and provides user energy recharge reminders by comprehensively integrating energy cost optimization, energy recharge time constraints, and user preferences, thereby improving the accuracy of energy supply decisions for hybrid vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 is a flow chart of an energy consumption control method for a hybrid vehicle in one embodiment;

[0055] Figure 2 A schematic diagram of a process for generating an energy supply and consumption control strategy in one embodiment;

[0056] Figure 3A flowchart of an example of energy consumption control of a hybrid vehicle in an embodiment;

[0057] Figure 4 A structural block diagram of an energy consumption control device of a hybrid vehicle in an embodiment;

[0058] Figure 5 An internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0059] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not intended to limit the present application.

[0060] The energy consumption control method of a hybrid vehicle provided by the embodiments of the present application can be applied in the application environment of energy consumption control of a hybrid vehicle. The method can be applied in a terminal, which can be, but is not limited to, various personal computers, notebook computers, etc.

[0061] In an exemplary embodiment, as shown in Figure 1 An energy consumption control method of a hybrid vehicle is provided, and the method is described by taking the terminal as an example, which includes the following steps S101 to S103. In the method, the following steps are included.

[0062] In step S101, current energy consumption data of a user driving a vehicle, current navigation information of the vehicle, current energy data, and user preference information of the user are obtained, and based on the current navigation information of the user driving the vehicle, current road condition information of a current driving route of the vehicle of the user is identified.

[0063] In this embodiment, the terminal obtains data information of different data types by real-time transmission of data through multiple devices / platforms, obtains current energy consumption data of the user driving the vehicle, current navigation information of the vehicle, current energy data, and user preference information of the user. Specifically, the data types include road section information: vehicle navigation system (navigation of road section congestion index, slope, speed limit, highway / urban road section ratio, etc.), charging station data: vehicle networking platform (position of charging station within 5km near the navigation road section, current electricity fee (yuan / kWh), real-time electricity price of charging station obtained through the vehicle networking platform or the electricity price (yuan / kWh) predicted based on historical data when arriving, number of queued vehicles, charging power (kW), etc.), real-time oil price: network acquisition (oil product label price that the vehicle can add in the area involved in the navigation road section, yuan / L), vehicle energy consumption data: corresponding signal values collected by vehicle sensors (current SOC (%), historical electricity consumption (kWh / 100km), historical oil consumption (L / 100km), battery capacity (kWh), etc.), user preference: user preferred HMI input (time priority / fee priority / comprehensive mode, or custom maximum charging time (minutes), maximum charging fee (yuan), etc.), etc., wherein the current navigation information is used to represent the route data of the route driven by the user's vehicle. Then, the terminal identifies the current road condition information of the current driving route of the user's vehicle based on the current navigation information of the user's driving vehicle. The current road condition information includes but is not limited to the route characteristics of each road section area and the road condition characteristics of each road section area. The division strategy of each road section area is to divide the road area corresponding to the same road grade (i.e. highway, national road, provincial road, county road), same congestion index, same speed limit value, or same slope data to obtain the area range. The route characteristics include but are not limited to slope characteristics, curve characteristics, road grade characteristics, etc., and the road condition information includes but is not limited to current road speed limit characteristics, current road congestion degree, and current road condition complexity characteristics. The specific identification process will be described in detail later.

[0064] In step S102, based on the user preference information of the user, the preferred energy supply and consumption mode of the user and the energy supply restriction information of the vehicle are identified, and based on the current road condition information of the current driving route, the energy supply restriction information of the vehicle, the current energy data, and the current energy consumption data of the vehicle, the energy supply and consumption control strategy of the vehicle is generated through the preferred energy supply and consumption mode of the user.

[0065] In this embodiment, the terminal identifies the user's preferred energy supply and consumption mode and the vehicle's energy replenishment limit information based on the user's user preference information. Based on the current road condition information for the current route, the vehicle's energy replenishment limit information, current energy data, and the vehicle's current energy consumption data, the terminal generates a vehicle energy supply and consumption control strategy based on the user's preferred energy supply and consumption mode. These preferred energy supply and consumption modes include, but are not limited to, a time-priority mode (which prioritizes minimizing total travel time (travel time + charging time) with energy cost as a secondary consideration); a cost-priority mode (which prioritizes minimizing energy cost (fuel + charging costs) with time as a secondary consideration); and a comprehensive mode (which balances time and cost, aiming for a comprehensive optimization of both) (minimizing the deviation between time and cost). The energy replenishment limit information can be a user-defined maximum charging time (minutes): the maximum allowed charging time set by the user (e.g., ≤20 minutes for business users and ≤60 minutes for home users). The terminal then optimizes decisions based on this constraint. Customized Maximum Charging Fee (CNY): Users independently set the maximum allowable charging fee (e.g., ≤30 CNY for budget-conscious users), and the terminal optimizes decisions based on this constraint. The vehicle's energy supply and consumption control strategy includes: the vehicle's energy supply and consumption method in each road section, the vehicle's remaining energy information in each road section, and the vehicle's target energy refueling point. The energy supply and consumption method characterizes the vehicle's driving mode in that road section (one or more combinations of pure gasoline, pure electric, hybrid, and extended-range modes). The vehicle's target energy refueling point is the target charging station or refueling station for the vehicle, located along the current route. The specific identification process and subsequent explanations will be provided in detail.

[0066] Step S103 : generating an energy supply and consumption control plan for the vehicle and energy replenishment prompt information for the user based on the energy supply and consumption control strategy for the vehicle.

[0067] In this embodiment, the terminal generates a vehicle energy supply and consumption control plan and user energy recharge reminder information based on the vehicle's energy supply and consumption control strategy. The vehicle energy supply and consumption control plan includes energy supply and consumption control instructions for the vehicle in various road sections and areas. These energy supply and consumption control instructions are used to control changes in the vehicle's energy supply mode and are instruction information for controlling changes in the vehicle's energy supply mode. The energy recharge reminder information includes energy recharge reminder content (battery level, fuel level, or both) and the energy recharge reminder location for the user. The specific generation process will be described in detail later.

[0068] Based on the above scheme, first, by comprehensively considering the energy consumption (such as electricity consumption 15 kWh / 100 km vs. oil consumption 6 L / 100 km) and energy price (such as electricity price 1.5 yuan / kWh vs. oil price 8 yuan / L), and real-time calculation of equivalent cost, ensure the selection of "actual cost lowest" mode. Secondly, the influence of charging time on total trip (such as user needs 1 hour to arrive, charging time 30 minutes is not feasible) or charging cost is not cost-effective (such as high electricity price, charging is more expensive) is evaluated, and the waste of time or cost caused by blind charging is avoided. Finally, this scheme considers the individual needs of users (such as business users need to arrive quickly, and home users pay more attention to cost) and adjusts the weight to realize the deep integration of mode switching and user needs. Therefore, this scheme optimizes energy consumption cost, energy supply time constraint, and user preference, intelligently generates hybrid vehicle energy consumption control scheme and user energy supply prompt information, thereby improving the accuracy of energy consumption supply decision for hybrid vehicles.

[0069] Optionally, based on the current navigation information of the user driving the vehicle, the current road condition information of the current driving route of the user's vehicle is identified, including: based on the current navigation information of the user driving the vehicle, identifying the line data of the current driving route of the vehicle and the real-time road condition data of the current driving route; based on the line data of the current driving route of the vehicle and the real-time road condition data of the current driving route, extracting the route features of each road section area and the road condition features of each road section area through a feature extraction network; the route features of each road section area and the road condition features of each road section area are used as the current road condition information of the current driving route of the user's vehicle.

[0070] In this embodiment, the terminal identifies the line data of the current driving route of the vehicle and the real-time road condition data of the current driving route based on the current navigation information of the user driving the vehicle. The line data is the road structure data corresponding to the current driving route of the vehicle, which is used to represent the uphill, downhill, turning, road grade and other information of the current driving route. The real-time road condition data of the current driving route is the congestion degree of the vehicle, the road speed limit, and the road condition complexity (types of motor vehicles, non-motor vehicles, pedestrians, and the elderly, the sick, and the disabled, the more types included, the higher the road condition complexity).

[0071] Then, based on the route data of the vehicle's current route and the real-time traffic data of the current route, the terminal uses a feature extraction network to extract route features and traffic characteristics of each section area of ​​the current route. Specifically, the terminal divides the current route into sections using a pre-set route area division strategy. Then, based on the sub-route data of each section area and the sub-real-time traffic data of each section area, the terminal uses a feature extraction network to perform feature extraction on the sub-route data to obtain route features for the section area and on the sub-real-time traffic data to obtain traffic characteristics for the section area. The feature extraction network is a convolutional neural network based on deep learning.

[0072] Finally, the terminal uses the route characteristics of each road section area and the road condition characteristics of each road section area as the current road condition information of the current driving route of the user's vehicle.

[0073] Based on the above solution, the comprehensiveness of the analysis of the vehicle's current driving route is improved by first identifying the real-time traffic data and line data of the current driving route, and then identifying the route characteristics and road condition characteristics by road section area.

[0074] Optionally, before generating the vehicle's energy supply and consumption control strategy based on the current road condition information of the current driving route, the vehicle's energy replenishment restriction information, current energy data, and the vehicle's current energy consumption data, through the user's preferred energy supply and consumption pattern, it also includes: based on the energy replenishment restriction information, identifying the vehicle's maximum energy replenishment time and the vehicle's maximum energy replenishment cost, and based on the vehicle's maximum energy replenishment time and the vehicle's maximum energy replenishment cost, identifying the vehicle's energy replenishment logic and the vehicle's energy replenishment feasibility judgment strategy; based on the user's preferred energy supply and consumption pattern, identifying the vehicle's energy supply and consumption logic and the vehicle's energy supply and consumption objective function.

[0075] In this embodiment, the terminal identifies the vehicle's maximum energy replenishment time and maximum energy replenishment cost based on the energy replenishment restriction information. Based on the maximum energy replenishment time and maximum energy replenishment cost, the terminal also identifies the vehicle's energy replenishment logic and energy replenishment feasibility determination strategy. The energy replenishment feasibility determination strategy corresponding to the vehicle's maximum energy replenishment time considers only charging stations where the total charging time (queue time + charging time) is ≤ a user-set value. The energy replenishment feasibility determination strategy corresponding to the vehicle's maximum energy replenishment cost considers only charging stations where the total charging cost (target SOC increment * battery capacity * electricity cost) is ≤ a user-set value. The energy replenishment logic corresponding to the vehicle's maximum energy replenishment time is as follows: if no qualifying charging stations are available, a mode that does not require charging or relies on fuel (such as extended-range mode or pure fuel mode) is prioritized. The energy supply and consumption objective function is to minimize cost within time constraints (or vice versa, incorporating other user preferences). The energy replenishment logic corresponding to the maximum energy replenishment cost of the vehicle is: if there is no qualified charging station, the mode that does not require charging or has low charging costs (such as pure oil / extended range mode) is given priority, and the energy supply and consumption objective function is to minimize time under the cost constraint (or vice versa, combined with other user preferences).

[0076] Then, the terminal identifies the vehicle's energy supply and consumption logic and the vehicle's energy supply and consumption objective function based on the user's preferred energy supply and consumption pattern.

[0077] Specifically,

[0078] 1) Time priority mode:

[0079] Definition: Minimizing the total travel time (driving time + charging time) is the core goal, with energy costs as a secondary consideration.

[0080] logic:

[0081] Energy supply and consumption objective function: objective function = 0.7*time index + 0.3*cost index;

[0082] Energy supply and consumption objective function weights: time index (driving time + charging time) weight 0.7, energy cost index (fuel cost + charging cost) weight 0.3;

[0083] Mode selection logic: Prioritize the mode with the shortest driving time (e.g., direct fuel driving on highways to avoid charging time). Charging is only considered when the charging time is ≤ the user's acceptable time and can significantly shorten subsequent driving time (e.g., after charging, the remaining driving time can be covered by pure electric power to avoid frequent mode switching).

[0084] Charging decision: Prioritize the charging station with the shortest queue time and the highest charging power (even if the electricity price is higher) to ensure that the total journey time is minimized.

[0085] 2) Cost-priority model:

[0086] Definition: Minimizing energy costs (fuel and charging costs) is the core goal, with time as a secondary consideration.

[0087] logic:

[0088] Energy supply and consumption objective function: objective function = 0.7*cost index + 0.3*time index;

[0089] Energy supply and consumption objective function weights: cost index weight 0.7, time index weight 0.3;

[0090] Mode selection logic: Prioritize the mode with the lowest energy cost equivalent (such as pure electric mode and extended-range mode in low electricity price scenarios). Charging is only considered when the charging cost is ≤ the user's acceptable cost and can significantly reduce subsequent energy costs.

[0091] Charging decision: Prioritize the charging station with the lowest electricity rate (even if the waiting time is long) to achieve the optimal total cost by "trading time for cost".

[0092] 3) Comprehensive mode:

[0093] Definition: Balancing time and cost, with the goal of optimizing both (minimizing the deviation between time and cost).

[0094] logic:

[0095] Energy supply and consumption objective function: 0.5*time index + 0.5*cost index, where time index = (actual time - shortest time) / shortest time, cost index = (actual cost - minimum cost) / minimum cost;

[0096] Mode selection logic: Find the "Pareto optimal" point between time and cost (e.g., choose a charging station with a medium electricity price and medium charging time to avoid extreme waste of time or cost);

[0097] Charging decision: Simultaneously evaluate the charging time and cost constraints, and select the charging station with the smallest sum of time and cost deviations.

[0098] Based on the above solution, by considering the personalized needs of users (such as business users need to arrive quickly, and home users are more concerned about costs) and combining weight adjustment, a deep integration of mode switching and user needs is achieved. By introducing a two-dimensional evaluation of "time constraints" and "cost constraints", the waste of time or costs caused by blind charging is avoided, and the charging timeliness and cost optimization effect are improved.

[0099] Optionally, based on the current road condition information of the current driving route, the vehicle's energy replenishment limit information, current energy data, and the vehicle's current energy consumption data, before generating the vehicle's energy supply and consumption control strategy through the user's preferred energy supply and consumption mode, it also includes: based on the route characteristics of each road section area and the road condition characteristics of each road section area, identifying the estimated driving time of each road section area and the driving characteristics of each vehicle in each road section area; based on the current energy data, identifying the location information of the target road section area where each energy supply point is located, the supply type corresponding to each energy supply point, and the real-time supply data corresponding to each energy supply point; based on the supply type corresponding to each energy supply point and the real-time supply data corresponding to each energy supply point, identifying the supply cost distribution information of each energy supply point and the current supply waiting time information of each energy supply point.

[0100] In this embodiment, the terminal identifies the estimated travel time for each road section and the driving characteristics of each vehicle in each road section based on the route characteristics and road condition characteristics of each road section. The terminal inputs the route characteristics, road condition characteristics, and road length of each road section into a pre-configured travel time prediction model to obtain the estimated travel time for each road section. The travel time prediction model is a convolutional neural network based on an attention mechanism. The prediction process can be transmitted from the terminal to a server, where it is processed by high-speed computing. The driving characteristics of each vehicle are used to characterize the vehicle's driving speed characteristics (speed variation range, speed variation frequency, average speed) and energy consumption characteristics for that road section. For example, when a vehicle is traveling on a congested road, the frequent starting and braking causes large changes in speed, resulting in high energy consumption. When traveling on a highway, the speed is stable and the vehicle does not need to change lanes or speeds frequently. However, the driving speed is too high, resulting in moderate energy consumption. When traveling on a non-congested national highway, the speed change is low, the vehicle does not need to change lanes frequently, and the driving speed is moderate, resulting in low energy consumption.

[0101] Then, based on the current energy data, the terminal identifies the location information of the target road section where each energy supply point is located, the supply type corresponding to each energy supply point, and the real-time supply data corresponding to each energy supply point. The real-time supply data includes real-time electricity prices / oil prices, real-time vehicle waiting times, real-time charging / refueling rates, etc. Based on the supply type corresponding to each energy supply point and the real-time supply data corresponding to each energy supply point, the terminal identifies the supply cost distribution information of each energy supply point and the current supply waiting time information of each energy supply point. The supply waiting time information is used to represent the estimated total charging time (i.e., the waiting time in line for energy replenishment) for vehicles currently charging and waiting to be charged at each energy supply point. The total charging time changes in real time as the vehicle travels. The supply type includes, but is not limited to, a power supply type and a fuel supply type. This replenishment cost distribution information is obtained using the following method: the terminal first uses a forecasting and analysis network to predict the electricity price / oil price for the energy replenishment point in the future based on the historical electricity price / oil price at the energy replenishment point. The terminal then chronologically sorts the electricity price / oil price in the future time periods to obtain the replenishment cost distribution information. The forecasting and analysis network is a linear trend prediction network.

[0102] Based on the above solution, through a comprehensive, comprehensive, and multi-angle analysis of the road section characteristics and current energy data of the vehicle, the estimated driving time of each road section area, the driving characteristics of each vehicle in each road section area, the supply cost distribution information of each energy supply point, and the current supply waiting time information of each energy supply point are identified. This improves the accuracy of the dynamic energy consumption analysis of road driving and the comprehensiveness of the supply cost analysis of each energy supply point.

[0103] Optionally, based on the current road condition information of the current driving route, the vehicle's energy replenishment restriction information, current energy data, and the vehicle's current energy consumption data, the vehicle's energy supply and consumption control strategy is generated through the user's preferred energy supply and consumption mode, including: based on the estimated driving time of each road section area and the driving characteristics of each vehicle in each road section area, the vehicle's energy supply and consumption logic and the vehicle's energy supply and consumption target function are used to generate the vehicle's energy supply and consumption method in each road section area, and based on the energy supply and consumption method in each road section area and the vehicle's current energy consumption data, the vehicle's remaining energy information in each road section area is predicted; based on the vehicle's remaining energy information in each road section area, the supply cost distribution information of each energy supply point, the current supply waiting time information of each energy supply point, and the location information of the target road section area where each energy supply point is located, the vehicle's target energy replenishment point is identified according to the vehicle's energy replenishment logic and the vehicle's energy replenishment feasibility judgment strategy; the vehicle's energy supply and consumption method in each road section area, the vehicle's remaining energy information in each road section area, and the vehicle's target energy replenishment point are used as the vehicle's energy supply and consumption control strategy.

[0104] In this embodiment, the terminal generates the vehicle's energy supply and consumption pattern for each road section based on the estimated driving time and driving characteristics of each vehicle in each road section, using the vehicle's energy supply and consumption logic and the vehicle's energy supply and consumption target function. Furthermore, based on the energy supply and consumption pattern for each road section and the vehicle's current energy consumption data, the terminal predicts the remaining energy information for each road section. Specifically, the energy supply and consumption pattern is the actual energy consumption equivalent corresponding to the vehicle's energy supply mode (including pure electric energy consumption equivalent, pure oil energy consumption equivalent, hybrid energy consumption equivalent, and extended-range energy consumption equivalent, etc.). The energy supply mode includes, but is not limited to, a pure electric mode, a pure oil mode, an electric hybrid mode, and an extended-range mode.

[0105] Specifically, for example, take the four modes of pure electric, pure oil, electric hybrid, and extended range.

[0106] Function: Converts the energy consumption corresponding to the amount of oil / electricity into a unified "yuan / kilometer" equivalent, i.e. C, to quantify the economy of different modes.

[0107] in,

[0108] C_elec: energy consumption equivalent in pure electric mode;

[0109] C_fuel: energy consumption equivalent in pure fuel mode;

[0110] C_hybrid: energy consumption equivalent in electric hybrid mode;

[0111] C_range: Energy consumption equivalent in extended range mode.

[0112] α is the proportion of electric motor drive in the electric hybrid mode, which is dynamically adjusted by the current vehicle speed: α = 0.8 when the vehicle speed is ≤ 60 km / h (low speed in urban areas), α = 0.5 when the vehicle speed is 60 km / h and ≤ 80 km / h (freeways), and α = 0.2 when the vehicle speed is > 80 km / h (freeways). (The load effect can be adjusted using a correction factor; this article assumes a stable load.) K is the range-extended equivalent coefficient.

[0113] The extended-range mode is driven by a two-stage energy conversion process: fuel-generated electricity → electric motor drive. Its fuel economy is significantly better than the traditional pure oil mode (direct engine drive). The determination of K must strictly follow the energy conversion efficiency chain:

[0114] 1. Energy conversion efficiency benchmark (industry measured data):

[0115] Pure oil mode: Fuel chemical energy → mechanical energy, average thermal efficiency β_pure oil = 38% (average value for mainstream fuel vehicle operating conditions);

[0116] Extended range mode:

[0117] Fuel chemical energy → electrical energy (range extender power generation efficiency β_1 = 85%);

[0118] Electrical energy → mechanical energy (motor drive efficiency β_2 = 90%);

[0119] Overall efficiency: β_extended range = β_1 * β_2 = 85%*90% = 76.5%.

[0120] 2. Derivation of the theoretical energy consumption ratio: Under the same mechanical energy output, fuel consumption is inversely proportional to system efficiency:

[0121] ;

[0122] Conclusion: The theoretically calculated fuel consumption of the extended-range mode is only 49.7% of that of the pure oil mode.

[0123] 3. Actual working condition correction and final determination of K value:

[0124] Considering that the range extender must take into account constraints such as NVH and transient response, and cannot continuously operate in the peak efficiency range, combined with the actual energy consumption performance of benchmark models in the industry, the fuel consumption ratio of the extended-range mode compared to the pure oil mode is definitely higher than 49.7%;

[0125] According to actual measured data, the fuel consumption in extended-range mode is about 6-9.7 liters per 100 kilometers, while the fuel consumption of fuel SUVs of the same level generally exceeds 11 liters. The corresponding range of F_extended-range / F_fuel is 54.5~88.2%.

[0126] The value of K in this article is based on: ① considering the principle of engineering conservatism, and ② referring to the actual measured energy efficiency data of mainstream models in the industry, so K=0.8.

[0127] Formula 1: Pure electric mode equivalent cost (yuan / km), ;

[0128] Formula 2: Equivalent cost of pure oil mode (yuan / km), ;

[0129] Formula 3: Equivalent cost of hybrid mode (yuan / km), C_hybrid = α*C_elec + (1-α)*C_fuel;

[0130] Formula 4: Equivalent cost of extended-range mode (yuan / km), C_range=C_fuel*K.

[0131] The terminal combines the estimated driving time of each road section area and the driving characteristics of each vehicle in each road section area, and uses the above formulas 1 to 4 to identify the cost consumption corresponding to different energy supply and consumption methods, and uses the energy supply and consumption method corresponding to the lowest cost consumption of each road section as the energy supply and consumption method of each road section area.

[0132] Then, if Figure 2 As shown, the terminal identifies the target energy replenishment point of the vehicle according to the vehicle's energy replenishment logic and the vehicle's energy replenishment feasibility judgment strategy through user preference recognition and multi-objective optimization calculation methods at the decision-making layer, based on the vehicle's energy remaining information in each road section, the supply cost distribution information of each energy supply point, the current supply waiting time information of each energy supply point, and the location information of the target road section where each energy supply point is located.

[0133] Finally, the terminal uses the vehicle's energy supply and consumption mode in each road section, the vehicle's remaining energy information in each road section, and the vehicle's target energy replenishment point as the vehicle's energy supply and consumption control strategy.

[0134] Based on the above scheme, a multi-objective optimization function is constructed according to the preferences set by the user (time priority, cost priority, comprehensive mode, etc.). Then, by combining the multi-objective optimization function, the energy supply and consumption control strategy of the user's currently selected mode is generated, which can not only meet the user's actual preference needs, but also improve the energy supply and consumption cost optimization effect of the vehicle in different modes.

[0135] Optionally, based on the vehicle's energy supply and consumption control strategy, the vehicle's energy supply and consumption control plan and the user's energy replenishment prompt information are generated, including: based on the vehicle's energy remaining information in each road section area and the vehicle's target energy replenishment point, the user's energy replenishment prompt content and the user's energy replenishment prompt location point are generated through the prompt information generation strategy, and the user's energy replenishment prompt content and the user's energy replenishment prompt location point are used as the user's energy replenishment prompt information; based on the vehicle's energy supply and consumption mode in each road section area, energy supply and consumption control instructions for each road section area are generated, and the energy supply and consumption control instructions for all road sections area are used as the vehicle's energy supply and consumption control plan.

[0136] In this embodiment, the terminal generates the energy supplement prompt content of the user and the energy supplement prompt position point of the user based on the vehicle energy remaining information of each road section area and the target energy supplement point of the vehicle through a prompt information generation strategy, and takes the energy supplement prompt content of the user and the energy supplement prompt position point of the user as the energy supplement prompt information of the user. The prompt information generation strategy is to identify the remaining value of the electric quantity and the remaining value of the oil quantity of the vehicle at the target energy supplement point based on the vehicle energy remaining information of each road section area and the target energy supplement point of the vehicle, and identify the position point where the prompt electric quantity remaining value threshold value is located and the position point where the prompt oil quantity remaining value threshold value is located in the vehicle energy remaining information of each road section area through the preset prompt electric quantity remaining value threshold value and the preset prompt oil quantity remaining value threshold value, and take the position point where the prompt electric quantity remaining value threshold value is located and the position point where the prompt oil quantity remaining value threshold value is located as the energy supplement prompt position point of the user, and take the remaining value of the electric quantity and the remaining value of the oil quantity of the target energy supplement point as the energy supplement prompt content of the user.

[0137] The terminal generates the energy supply and consumption control instructions of each road section area based on the energy supply and consumption mode of the vehicle in each road section area through a preset instruction generation strategy, and takes the energy supply and consumption control instructions of all road sections as the energy supply and consumption control scheme of the vehicle.

[0138] Based on the above scheme, the energy supply and consumption control instructions of different road sections and the energy supplement prompt information of the user are generated, which improves the accuracy of the energy consumption supply decision of the hybrid vehicle.

[0139] In one implementation scenario, taking the user from A to B (300km) as an example, the user selects the "cost priority" mode (cost weight 0.7, time weight 0.3), the total pure electric endurance of the vehicle is 120km (corresponding to SOC=100%, battery capacity 30kWh), the initial SOC=50%, the vehicle sets the minimum protection threshold to 10% (i.e. SOC<10% cannot be driven by the motor alone), so the current available SOC=50%-10%=40%, the available electric quantity=30kWhx40%=12kWh, the pure electric endurance=12kWh / (25kWh / 100km)=48km is calculated according to the electric consumption 25kWh / 100km.

[0140] Data collection:

[0141] Road section:

[0142] The first 50km of urban congestion (electric consumption 25kWh / 100km, electric hybrid mode motor proportion 80%),

[0143] The middle 200km of highway (fuel consumption 10L / 100km, electric hybrid mode motor proportion 20%),

[0144] The next 50 km of urban expressway (electric hybrid mode with 50% electric motor share);

[0145] Charging station data:

[0146] Charging station A (150 km): Electricity fee 2.0 yuan / kWh (high price), 3 cars in queue (waiting time 20 minutes), charging power 60kW;

[0147] Charging Station B (250km): Electricity fee is 1.0 yuan / kWh (low price), no queue, and charging power is 120kW.

[0148] Oil price: 8 yuan / L (high price);

[0149] User preference: Maximum charging time is 60 minutes (including queuing), maximum charging fee is 40 yuan; preference is "cost first" (cost weight 0.7).

[0150] Equivalent calculation:

[0151] Pure electric equivalent:

[0152] Home charging (initial capacity): electricity fee 0.5 yuan / kWh, C_elec=25*0.5 / 100 =0.125 yuan / km;

[0153] Charging station A (high electricity price): C_elec = 25*2 / 100 = 0.5 yuan / km;

[0154] Charging station B (low electricity price): C_elec = 25*1 / 100 = 0.25 yuan / km;

[0155] Pure fuel equivalent: C_fuel=8*10 / 100=0.8 yuan / km;

[0156] Electric hybrid equivalent:

[0157] α=80% in the first 50km low-speed section;

[0158] For home charging, C_hybrid = 0.8*0.125+0.2*0.8 = 0.26 yuan / km;

[0159] The middle 200km high-speed section α=20%;

[0160] Charging station A, C_hybrid=0.2*0.5+0.8*0.8=0.74 yuan / km;

[0161] Charging station B, C_hybrid = 0.2*0.25+0.8*0.8=0.69 yuan / km;

[0162] Extended-range equivalent: C_range=0.8*0.8=0.64 yuan / km.

[0163] Decision optimization:

[0164] The first 50km of urban congested road (0km-50km):

[0165] Candidate modes: pure electric (first 48km), electric hybrid (remaining 2km).

[0166] Indicator calculation:

[0167] Pure electric mode (first 48km): time = 48km / / 30km / h = 1.6h (assuming city speed of 30km / h), cost = 48km*0.125 yuan / km = 6 yuan;

[0168] Electric hybrid mode (2km remaining): time = 2km / 30km / h = 0.067h, cost = 2km*0.26 yuan / km = 0.52 yuan.

[0169] Objective function (cost first):

[0170] Since the pure electric mode is required for the first 48km (with the lowest cost), the hybrid mode has the lowest objective function and is selected directly for the remaining 2km, as the pure electric mode cannot be used. No further comparison is made here:

[0171] Time index = (0.0667h - shortest time 0.067h) / 0.067h = 0 (no shorter time);

[0172] Cost index = (0.52 yuan - minimum cost 0.52 yuan) / 0.52 yuan = 0;

[0173] Objective function = 0.7*0+0.3*0=0 (optimal).

[0174] Conclusion: Choose pure electric mode (first 48km) + hybrid mode (remaining 2km).

[0175] Therefore, for this section of the journey, the first 48km was in pure electric mode (cost 0.125 yuan / km*48km=6 yuan), and the remaining 2km was switched to hybrid mode (cost 0.26 yuan / km*2km=0.52 yuan). The total cost of this section was 6.52 yuan, and it took 0.067h.

[0176] The middle 200km expressway section (50km-250km):

[0177] Candidate modes: pure oil, hybrid (no power available for pure electric drive, α=20% on highway sections), and extended range.

[0178] Indicator calculation:

[0179] Pure oil mode: time = 200km / 100km / h = 2h (assuming high-speed speed of 100km / h), cost = 200km*0.8 yuan / km = 160 yuan;

[0180] Extended range mode: time = 200km / 100km / h = 2h (same as pure oil time), cost = 200km*0.64 yuan / km = 128 yuan;

[0181] Hybrid mode_Charging station A (α=20%): time = 2h, cost = 200km*0.69yuan / km = 138yuan (same as pure oil).

[0182] Hybrid mode_Charging station B (α=20%): time = 2h, cost = 200km*0.74yuan / km = 148yuan (same as pure oil).

[0183] Objective function (cost first):

[0184] Time index (pure oil / extended range / electric hybrid) = (2h - shortest time 2h) / 2h = 0;

[0185] Cost index (pure oil) = (160 yuan - minimum cost 128 yuan) / minimum cost 128 yuan = 0.25;

[0186] Cost index (extended range) = (128 yuan - minimum cost 128 yuan) / minimum cost 128 yuan = 0;

[0187] Cost index (electric hybrid_B station) = (138 yuan - minimum cost 128 yuan) / minimum cost 128 yuan = 0.078

[0188] Cost index (electric hybrid_station A) = (148 yuan - minimum cost 128 yuan) / minimum cost 128 yuan = 0.15625

[0189] Objective function (pure oil) = 0.7*0.25+0.30*0=0.175 (worst);

[0190] Objective function (extended range) = 0.7*0+0.3*0=0 (optimal).

[0191] Objective function (electric hybrid) = 0.7*0.078+0.3*0=0.054 (suboptimal);

[0192] Conclusion: Select the extended-range mode (the objective function value is the smallest).

[0193] Therefore, the extended range mode is selected for this section of the journey, the cost is 200*0.64 yuan / km=128 yuan, and the journey time is 2 hours;

[0194] The last 50km of the expressway section:

[0195] Candidate strategies: pure electric after charging (select charging station B), continue to extend the range without charging.

[0196] Indicator calculation:

[0197] Pure electric after charging (charging station B): Here, only the last 50 km of charging is considered, which makes it easier to compare cost and time indicators.

[0198] According to the scenario description, 12kWh and a pure electric range of 48km, so 12.5kWh is needed to travel 50km;

[0199] Charging time = 12.5kWh / 120kW*60 minutes = 6.25 minutes. Considering the time spent in the service area and looking for a charging station, which is 5 minutes, the total charging time is 11.25 minutes, or 0.1875 hours.

[0200] Charging cost = 12.5kWh*1 yuan / kWh = 12.5 yuan;

[0201] Driving time = 50km / 80km / h = 0.625h (expressway speed 80km / h);

[0202] Total time = 0.1875h + 0.625h = 0.8125h; total cost = 12.5 yuan;

[0203] Continue to extend the range without charging:

[0204] Driving time = 50km / 80km / h = 0.625h;

[0205] Cost = 50km*0.64 yuan / km = 32 yuan;

[0206] Total time = 0.625h; total cost = 32 yuan.

[0207] Objective function (cost first):

[0208] Time index (pure electricity after charging) = (0.8125h - shortest time 0.625h) / shortest time 0.625h = 0.3;

[0209] Time index (range extension without charging) = (0.625h - shortest time 0.625h) / shortest time 0.625h = 0;

[0210] Cost index (pure electricity after charging) = (12.5 yuan - minimum cost 12.5 yuan) / minimum cost 12.5 yuan = 0;

[0211] Cost index (without charging and range extension) = (32 yuan - minimum cost 12.5 yuan) / minimum cost 12.5 yuan = 1.56;

[0212] Objective function (pure electricity after charging) = 0.7*0+0.3*0.3=0.09;

[0213] Objective function (range extension without charging) = 0.7*1.56+0.3*0=1.092.

[0214] Conclusion: Choose the pure electric mode after charging (the objective function value is the smallest).

[0215] Therefore, the pure electric mode is selected for this section of the journey, the cost is 50km*0.25 yuan / km=12.5 yuan, and the time taken is 0.8125h;

[0216] The total cost is 6.52+128+12.5=147.02 yuan, and the time taken is 2.8795 hours.

[0217] This application also provides an example of energy consumption control for a hybrid vehicle, such as Figure 3 As shown, the specific processing process includes the following steps:

[0218] Step S301, obtaining the current energy consumption data of the vehicle driven by the user, the current navigation information of the vehicle, the current energy data, and the user preference information of the user.

[0219] Step S302 , based on the current navigation information of the vehicle driven by the user, the route data of the current driving route of the vehicle and the real-time road condition data of the current driving route are identified.

[0220] Step S303 , based on the route data of the current driving route of the vehicle and the real-time road condition data of the current driving route, the route features and road condition features of each section area of ​​the current driving route are extracted through a feature extraction network.

[0221] Step S304 : taking the route characteristics of each road section area and the road condition characteristics of each road section area as the current road condition information of the current driving route of the user's vehicle.

[0222] Step S305: Based on the energy replenishment restriction information, identify the vehicle's maximum energy replenishment time and the vehicle's maximum energy replenishment cost, and based on the vehicle's maximum energy replenishment time and the vehicle's maximum energy replenishment cost, identify the vehicle's energy replenishment logic and the vehicle's energy replenishment feasibility judgment strategy.

[0223] Step S306 : Based on the user's preferred energy supply and consumption mode, identifying the vehicle's energy supply and consumption logic and the vehicle's energy supply and consumption target function.

[0224] Step S307 , based on the route characteristics and road condition characteristics of each road section area, the estimated driving time of each road section area and the driving characteristics of each vehicle in each road section area are identified.

[0225] Step S308 : Based on the current energy data, identify the location information of the target road section where each energy supply point is located, the supply type corresponding to each energy supply point, and the real-time supply data corresponding to each energy supply point.

[0226] Step S309: Based on the recharge type corresponding to each energy recharge point and the real-time recharge data corresponding to each energy recharge point, the recharge cost distribution information of each energy recharge point and the current recharge waiting time information of each energy recharge point are identified.

[0227] Step S310, based on the estimated driving time of each road section area and the driving characteristics of each vehicle in each road section area, the energy supply and consumption logic of the vehicle and the energy supply and consumption target function of the vehicle are used to generate the energy supply and consumption mode of the vehicle in each road section area, and based on the energy supply and consumption mode of each road section area and the current energy consumption data of the vehicle, the remaining energy information of the vehicle in each road section area is predicted.

[0228] Step S311: Based on the remaining energy information of the vehicle in each road section, the distribution information of the refueling cost of each energy refueling point, the current refueling waiting time information of each energy refueling point, and the location information of the target road section where each energy refueling point is located, the target energy refueling point of the vehicle is identified according to the vehicle's energy refueling logic and the vehicle's energy refueling feasibility judgment strategy.

[0229] In step S312, the energy supply and consumption mode of the vehicle in each road section, the remaining energy information of the vehicle in each road section, and the target energy replenishment point of the vehicle are used as the energy supply and consumption control strategy of the vehicle.

[0230] Step S313, based on the vehicle energy remaining information of each road section area and the vehicle's target energy replenishment point, the user's energy replenishment prompt content and the user's energy replenishment prompt location are generated through the prompt information generation strategy, and the user's energy replenishment prompt content and the user's energy replenishment prompt location are used as the user's energy replenishment prompt information.

[0231] Step S314 , based on the energy supply and consumption mode of the vehicle in each road section, generates energy supply and consumption control instructions for each road section, and uses the energy supply and consumption control instructions for all road sections as the energy supply and consumption control plan for the vehicle.

[0232] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0233] Based on the same inventive concept, embodiments of the present application also provide a hybrid vehicle energy consumption control device for implementing the aforementioned hybrid vehicle energy consumption control method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more hybrid vehicle energy consumption control device embodiments provided below can be found in the aforementioned limitations of the hybrid vehicle energy consumption control method and will not be further elaborated here.

[0234] In an exemplary embodiment, Figure 4 As shown, an energy consumption control device for a hybrid vehicle is provided, comprising: an acquisition module 410, an identification module 420 and a generation module 430, wherein:

[0235] an acquisition module 410 for acquiring current energy consumption data of a vehicle driven by a user, current navigation information of the vehicle, current energy data, and user preference information of the user, and identifying current road condition information of a current route of the vehicle driven by the user based on the current navigation information of the vehicle driven by the user;

[0236] an identification module 420 for identifying the user's preferred energy supply and consumption pattern and the vehicle's energy replenishment restriction information based on the user's user preference information, and generating an energy supply and consumption control strategy for the vehicle based on the user's preferred energy supply and consumption pattern and current road condition information of the current travel route, the vehicle's energy replenishment restriction information, the current energy data, and the vehicle's current energy consumption data;

[0237] The generating module 430 is configured to generate the energy supply and consumption control plan of the vehicle and the energy replenishment prompt information of the user based on the energy supply and consumption control strategy of the vehicle.

[0238] Optionally, the acquisition module 410 is specifically configured to:

[0239] identify line data of a current driving route of the vehicle, real-time road condition data of the current driving route based on current navigation information of the vehicle driving;

[0240] extract line features of each road section area of the current driving route, road condition features of each road section area of the current driving route based on the line data of the current driving route of the vehicle, the real-time road condition data of the current driving route through a feature extraction network;

[0241] use the line features of each road section area, the road condition features of each road section area as current road condition information of the current driving route of the vehicle.

[0242] Optionally, the apparatus further includes:

[0243] a supplementary identification module configured to identify maximum energy supplement time of the vehicle, maximum energy supplement cost of the vehicle based on the energy supplement restriction information, and identify energy supplement logic of the vehicle, energy supplement feasibility judgment strategy of the vehicle based on the maximum energy supplement time of the vehicle, the maximum energy supplement cost of the vehicle;

[0244] a supply and consumption identification module configured to identify energy supply and consumption logic of the vehicle, energy supply and consumption target function of the vehicle based on the preferred energy supply and consumption mode of the user.

[0245] Optionally, the apparatus further includes:

[0246] a driving feature identification module configured to identify estimated driving duration of each road section area, each vehicle driving feature of each road section area based on the line features of each road section area, the road condition features of each road section area.

[0247] an energy supply point identification module configured to identify location information of each target road section area where each energy supply point is located, a supply type corresponding to each energy supply point, real-time supply data corresponding to each energy supply point based on the current energy data.

[0248] identify supply cost distribution information of each energy supply point, current supply time information of each energy supply point based on the supply type corresponding to each energy supply point, the real-time supply data corresponding to each energy supply point.

[0249] Optionally, the identification module 420 is specifically configured to:

[0250] Based on the estimated driving time of each road section area and the driving characteristics of each vehicle in each road section area, the energy supply and consumption logic of the vehicle and the energy supply and consumption target function of the vehicle are used to generate the energy supply and consumption pattern of the vehicle in each road section area, and based on the energy supply and consumption pattern of each road section area and the current energy consumption data of the vehicle, the remaining energy information of the vehicle in each road section area is predicted;

[0251] Based on the remaining energy information of the vehicle in each of the road sections, the distribution information of the recharging cost of each energy recharging point, the current recharging time information of each energy recharging point, and the location information of the target road section where each energy recharging point is located, according to the energy recharging logic of the vehicle and the energy recharging feasibility judgment strategy of the vehicle, the target energy recharging point of the vehicle is identified;

[0252] The energy supply and consumption mode of the vehicle in each of the road sections, the remaining energy information of the vehicle in each of the road sections, and the target energy replenishment point of the vehicle are used as the energy supply and consumption control strategy of the vehicle.

[0253] Optionally, the generating module 430 is specifically configured to:

[0254] Based on the remaining energy information of the vehicle in each of the road sections and the target energy replenishment point of the vehicle, generating energy replenishment reminder content for the user and the energy replenishment reminder location for the user through a reminder information generation strategy, and using the energy replenishment reminder content for the user and the energy replenishment reminder location for the user as the energy replenishment reminder information for the user;

[0255] Based on the energy supply and consumption mode of the vehicle in each of the road sections, energy supply and consumption control instructions for each of the road sections are generated, and the energy supply and consumption control instructions for all of the road sections are used as the energy supply and consumption control plan for the vehicle.

[0256] Each module in the aforementioned hybrid vehicle energy consumption control device may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor within a computer device in the form of hardware, or may be stored in a computer device memory in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0257] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 5As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, while the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless means, and the wireless means may be implemented via Wi-Fi, a mobile cellular network, NFC (near-field communication), or other technologies. When executed by the processor, the computer program implements a method for controlling energy consumption of a hybrid vehicle. The display unit of the computer device is used to produce a visual image and may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0258] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0259] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements steps corresponding to the energy consumption control method of a hybrid vehicle when executing the computer program.

[0260] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the computer program implements the steps corresponding to the energy consumption control method of a hybrid vehicle.

[0261] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements steps corresponding to a method for controlling energy consumption of a hybrid vehicle.

[0262] An embodiment of the present application further provides a vehicle, the vehicle comprising at least one of the following:

[0263] The energy consumption control device for a hybrid vehicle as in the aforementioned embodiment;

[0264] The electronic device as in the aforementioned embodiment;

[0265] The computer-readable storage medium in the aforementioned embodiment;

[0266] As the computer program product in the aforementioned embodiment.

[0267] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0268] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0269] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0270] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for controlling energy consumption of a hybrid vehicle, characterized in that: The method comprises: obtaining current energy consumption data of a vehicle driven by a user, current navigation information of the vehicle, current energy data, and user preference information of the user, and identifying current road condition information of a current route of the vehicle driven by the user based on the current navigation information of the vehicle driven by the user; Based on the user preference information of the user, identifying the user's preferred energy supply and consumption pattern and the energy replenishment restriction information of the vehicle, and generating an energy supply and consumption control strategy for the vehicle based on the user's preferred energy supply and consumption pattern and current road condition information of the current travel route, the energy replenishment restriction information of the vehicle, the current energy data, and the current energy consumption data of the vehicle; Based on the energy supply and consumption control strategy of the vehicle, an energy supply and consumption control plan of the vehicle and energy replenishment prompt information of the user are generated.

2. The method according to claim 1, characterized in that The identifying, based on the current navigation information of the vehicle driven by the user, the current road condition information of the current driving route of the user's vehicle includes: Based on the current navigation information of the vehicle driven by the user, identifying route data of the current driving route of the vehicle and real-time traffic data of the current driving route; Based on the route data of the current driving route of the vehicle and the real-time traffic condition data of the current driving route, the route features of each road section area of ​​the current driving route and the traffic condition features of each road section area are extracted through a feature extraction network; The route characteristics of each road section area and the road condition characteristics of each road section area are used as the current road condition information of the current driving route of the user vehicle.

3. The method according to claim 2, characterized in that Before generating the energy supply and consumption control strategy for the vehicle based on the current road condition information of the current driving route, the energy replenishment restriction information of the vehicle, the current energy data, and the current energy consumption data of the vehicle and the user's preferred energy supply and consumption mode, the method further includes: Based on the energy replenishment restriction information, identifying a maximum energy replenishment time and a maximum energy replenishment cost of the vehicle, and based on the maximum energy replenishment time and the maximum energy replenishment cost of the vehicle, identifying an energy replenishment logic for the vehicle and an energy replenishment feasibility determination strategy for the vehicle; Based on the user's preferred energy supply and consumption mode, the energy supply and consumption logic of the vehicle and the energy supply and consumption objective function of the vehicle are determined.

4. The method according to claim 3, characterized in that Before generating the energy supply and consumption control strategy for the vehicle based on the current road condition information of the current driving route, the energy replenishment restriction information of the vehicle, the current energy data, and the current energy consumption data of the vehicle and the user's preferred energy supply and consumption mode, the method further includes: Based on the route characteristics of each road section area and the road condition characteristics of each road section area, identifying the estimated driving time of each road section area and the driving characteristics of each vehicle in each road section area; Based on the current energy data, identifying the location information of the target road section where each energy supply point is located, the supply type corresponding to each energy supply point, and the real-time supply data corresponding to each energy supply point; Based on the supply type corresponding to each of the energy supply points and the real-time supply data corresponding to each of the energy supply points, the supply cost distribution information of each energy supply point and the current supply waiting time information of each energy supply point are identified.

5. The method according to claim 4, characterized in that The generating of the vehicle's energy supply and consumption control strategy based on the current road condition information of the current driving route, the vehicle's energy replenishment restriction information, the current energy data, and the vehicle's current energy consumption data, and using the user's preferred energy supply and consumption mode, includes: Based on the estimated driving time of each road section area and the driving characteristics of each vehicle in each road section area, the energy supply and consumption logic of the vehicle and the energy supply and consumption target function of the vehicle are used to generate the energy supply and consumption pattern of the vehicle in each road section area, and based on the energy supply and consumption pattern of each road section area and the current energy consumption data of the vehicle, the remaining energy information of the vehicle in each road section area is predicted; Based on the remaining energy information of the vehicle in each of the road sections, the distribution information of the recharging cost of each energy recharging point, the current recharging time information of each energy recharging point, and the location information of the target road section where each energy recharging point is located, according to the energy recharging logic of the vehicle and the energy recharging feasibility judgment strategy of the vehicle, the target energy recharging point of the vehicle is identified; The energy supply and consumption mode of the vehicle in each of the road sections, the remaining energy information of the vehicle in each of the road sections, and the target energy replenishment point of the vehicle are used as the energy supply and consumption control strategy of the vehicle.

6. The method according to claim 5, characterized in that The generating of the vehicle's energy supply and consumption control plan and the user's energy replenishment prompt information based on the vehicle's energy supply and consumption control strategy includes: Based on the remaining energy information of the vehicle in each of the road sections and the target energy replenishment point of the vehicle, generating energy replenishment reminder content for the user and the energy replenishment reminder location for the user through a reminder information generation strategy, and using the energy replenishment reminder content for the user and the energy replenishment reminder location for the user as the energy replenishment reminder information for the user; Based on the energy supply and consumption mode of the vehicle in each of the road sections, energy supply and consumption control instructions for each of the road sections are generated, and the energy supply and consumption control instructions for all of the road sections are used as the energy supply and consumption control plan for the vehicle.

7. An energy consumption control device for a hybrid vehicle, characterized in that: The device comprises: an acquisition module, configured to acquire current energy consumption data of a vehicle driven by a user, current navigation information of the vehicle, current energy data, and user preference information of the user, and identify current road condition information of a current route of the vehicle driven by the user based on the current navigation information of the vehicle driven by the user; an identification module for identifying the user's preferred energy supply and consumption pattern and the vehicle's energy replenishment restriction information based on the user's user preference information, and generating an energy supply and consumption control strategy for the vehicle based on the user's preferred energy supply and consumption pattern and current road condition information of the current travel route, the vehicle's energy replenishment restriction information, the current energy data, and the vehicle's current energy consumption data; A generating module is used to generate the energy supply and consumption control scheme of the vehicle and the energy replenishment prompt information of the user based on the energy supply and consumption control strategy of the vehicle.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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