Vehicle low-voltage energy consumption optimization method, vehicle, equipment and product

Abstract

The invention provides a vehicle low-voltage energy consumption optimization method, a vehicle, equipment and a product, and the method can determine at least one target scene according to the life cycle and the vehicle use cycle of the vehicle, determines an initial optimization target and a first function with optimizable energy consumption for each target scene, and carries out the optimization of the vehicle low-voltage energy consumption. Only when the optimized energy consumption data corresponding to the first optimized energy consumption strategy obtained through optimization in the current energy consumption analysis stage can meet the initial optimization target, entering the next energy consumption analysis stage, otherwise, adjusting the initial optimization target and sequentially entering each energy consumption analysis stage again to execute strategy optimization and target comparison; therefore, it is guaranteed that the multifunctional first optimization energy consumption strategy for the target scene can meet the initial optimization target in different stages, and through multi-stage strategy analysis and iteration and synchronous iteration of the initial optimization target when the target is not met, while all-scene multifunctional integrated low-voltage energy consumption optimization is effectively achieved, the optimization efficiency is improved, and the optimization cost is reduced. And the minimization of low-voltage energy consumption after optimization is also ensured.

Description

Technical Field

[0001] The present disclosure relates to the field of vehicle energy consumption technology, and in particular to a vehicle low-voltage energy consumption optimization method, vehicle, equipment and product. Background Art

[0002] As vehicle intelligence gradually develops towards intelligentization, the proportion of low-voltage energy consumption of vehicles in the total vehicle energy consumption continues to increase. The high low-voltage energy consumption of vehicles may affect the normal operation of the vehicle.

[0003] In order to optimize the low-voltage energy consumption of the vehicle, it is often necessary to calculate how to optimize the energy consumption under each function of the vehicle separately, and there is room for refined operations. Summary of the invention

[0004] The embodiments of the present disclosure at least provide a vehicle low-voltage energy consumption optimization method, vehicle, equipment and product.

[0005] The present disclosure provides a method for optimizing low-voltage energy consumption of a vehicle, the method comprising: Determine at least one target scenario according to the life cycle and the use cycle of the vehicle; each of the target scenarios corresponds to a plurality of functions; For each of the target scenarios, determining an initial optimization target corresponding to the target scenario, and classifying the multiple functions by energy consumption according to the attribute information of each function to obtain a first function, where the first function refers to a function whose energy consumption can be optimized; For each of the target scenarios, historical energy consumption data and historical energy consumption strategies of each of the first functions are obtained, and based on the historical energy consumption data of each of the first functions, the historical energy consumption strategies are optimized in each energy consumption analysis stage to obtain first optimized energy consumption strategies corresponding to each energy consumption analysis stage; For the current energy consumption analysis stage under each of the target scenarios, based on the first optimized energy consumption strategy, the optimized energy consumption data is determined, the optimized energy consumption data is compared with the initial optimization target, and when the optimized energy consumption data cannot meet the initial optimization target, the initial optimization target is adjusted, and the historical energy consumption data based on each of the first functions is returned to, and the historical energy consumption strategy is optimized in each energy consumption analysis stage in turn to obtain the first optimized energy consumption strategy corresponding to each energy consumption analysis stage; When the optimized energy consumption data meets the initial optimization target, the next energy consumption analysis stage is used as the current energy consumption analysis stage, and the current energy consumption analysis stage for each target scenario is entered. Based on the first optimized energy consumption strategy, the optimized energy consumption data is determined, and the optimized energy consumption data is compared with the initial optimization target until the optimized energy consumption data in the last energy consumption analysis stage meets the initial optimization target, and the target first optimized energy consumption strategy corresponding to each target scenario is obtained.

[0006] In an optional implementation manner, determining at least one target scenario according to the life cycle and the vehicle usage cycle of the vehicle includes: Determine multiple life cycle scenarios based on the vehicle’s life cycle; Determining whether each of the life cycle scenarios is relevant to the user; In the case where the life cycle scenario is not relevant to the user, using the life cycle scenario as the target scenario; In the case where the life cycle scenario is related to the user, the target scenario is determined according to the vehicle usage cycle.

[0007] In an optional implementation manner, when the life cycle scenario is related to the user, determining the target scenario according to the vehicle usage cycle includes: In the case where the life cycle scenario is related to the user, determining a plurality of vehicle usage cycle scenarios under the life cycle scenario according to the vehicle usage cycle; For each of the vehicle usage cycle scenarios, multiple atomic scenarios are determined according to functional requirements corresponding to the vehicle usage cycle scenarios, and each of the atomic scenarios corresponds to a function; According to the user's car use needs, a combination of multiple atomic scenarios is determined as the target scenario.

[0008] In an optional implementation, the vehicle usage cycle scenarios include a vehicle sleep scenario, a vehicle semi-activated scenario, a vehicle wait-to-start scenario, and a vehicle driving scenario; the vehicle sleep scenario refers to when the user leaves the vehicle and all functions of the vehicle are not activated, the vehicle semi-activated scenario refers to when the user leaves the vehicle and some functions of the vehicle are activated and some functions are not activated, and the vehicle wait-to-start scenario refers to when the user enters the vehicle and the vehicle is not started.

[0009] In an optional implementation manner, the energy consumption analysis stages sequentially include an energy efficiency index analysis stage, a vehicle pre-mass production energy consumption data analysis stage, and a vehicle post-mass production energy consumption data analysis stage; The step of optimizing the historical energy consumption strategy in each energy consumption analysis stage in turn to obtain the first optimized energy consumption strategy corresponding to each energy consumption analysis stage respectively includes: In the energy efficiency index analysis stage, when the energy efficiency index is predicted to be the minimum index, based on the theoretical energy consumption data of the first function, the historical energy consumption strategy is optimized to obtain a first optimized energy consumption strategy corresponding to the energy efficiency index analysis stage; the energy efficiency index is used to indicate the proportional relationship between energy consumption cost and raw material cost; In the energy consumption data analysis stage before mass production of the vehicle, experimental energy consumption data of the first function is obtained, and based on the experimental energy consumption data of the first function, the historical energy consumption strategy is optimized to obtain a first optimized energy consumption strategy corresponding to the energy consumption data analysis stage before mass production of the vehicle; In the energy consumption data analysis stage after mass production of the vehicle, user energy consumption data of the first function is obtained, and based on the user energy consumption data of the first function, the historical energy consumption strategy is optimized to obtain the first optimized energy consumption strategy corresponding to the energy consumption data analysis stage after mass production of the vehicle.

[0010] In an optional implementation manner, when the life cycle scenario is related to a user, the method further includes: determining a second function associated with the user from among the first functions; Acquire user usage data corresponding to the second function; The user usage data is input into a pre-trained usage optimization model to obtain a second optimized energy consumption strategy corresponding to the second function.

[0011] In an optional implementation manner, after obtaining the target first optimization energy consumption strategy corresponding to the target scenario, the method further includes: Acquire a mapping relationship between a scene and an optimized energy consumption strategy, wherein the mapping relationship between the scene and the optimized energy consumption strategy includes a target first optimized energy consumption strategy corresponding to each target scene; Detecting a current scene in which the vehicle is located; According to the mapping relationship, a target optimization energy consumption strategy corresponding to the current scenario is determined, and the vehicle is controlled to execute the target optimization energy consumption strategy.

[0012] The present disclosure also provides a vehicle, including a controller, wherein the controller includes: a memory for storing machine-readable instructions; and A processor is used to call and execute the machine-readable instructions stored in the memory to implement the steps in any possible implementation of the above-mentioned vehicle low-voltage energy consumption optimization method.

[0013] An embodiment of the present disclosure also provides an electronic device, including: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the machine-readable instructions are executed by the processor to perform steps in any possible implementation of the above-mentioned vehicle low-voltage energy consumption optimization method.

[0014] The disclosed embodiment also provides a computer program product, on which a computer program is stored. When the computer program is executed by a processor, the computer program executes the steps in any possible implementation of the above-mentioned vehicle low-voltage energy consumption optimization method.

[0015] The vehicle low-voltage energy consumption optimization method, vehicle, equipment and product provided by the embodiments of the present disclosure can determine at least one target scenario corresponding to multiple functions according to the life cycle and vehicle usage cycle of the vehicle, set an initial optimization target for each target scenario, and distinguish the first function whose energy consumption can be optimized among the multiple functions corresponding to each target scenario; based on the historical energy consumption data of each first function, optimize the historical energy consumption strategy of the first function in each energy consumption analysis stage in turn to obtain the first optimized energy consumption strategy corresponding to each energy consumption analysis stage; in each energy consumption analysis stage, if the optimized energy consumption data corresponding to the first optimized energy consumption strategy cannot meet the initial optimization target, adjust the initial optimization target, and re-optimize the historical energy consumption strategy in each energy consumption analysis stage in turn; if the optimized energy consumption data can meet the initial optimization target, enter the next energy consumption analysis stage, perform strategy optimization again and compare the optimized energy consumption data with the initial optimization target, until the optimized energy consumption data in the last energy consumption analysis stage meets the initial optimization target, and determine the first optimized energy consumption strategy corresponding to the last energy consumption analysis stage as the target first optimized energy consumption strategy corresponding to the target scenario.

[0016] In this way, since the target scenario corresponds to multiple functions and multiple energy consumption analysis stages are set, the next energy consumption analysis stage will only be entered when the optimized energy consumption data corresponding to the current energy consumption analysis stage can meet the initial optimization target. Otherwise, the initial optimization target is adjusted and each energy consumption analysis stage is re-entered in turn to perform strategy optimization and target comparison, so as to ensure that the multifunctional first optimized energy consumption strategy for the target scenario can meet the initial optimization target at different stages. Through multi-stage strategy analysis and iteration, as well as the synchronous iteration of the initial optimization target when the target is not met, while effectively realizing the overall low-voltage energy consumption optimization of the full-scene multifunctionality, it also ensures the minimization of the optimized low-voltage energy consumption, which helps to improve the matching degree of the optimized energy consumption strategy with the vehicle, and improve the comprehensiveness and integrity of the low-voltage energy consumption optimization. Further, it can effectively reduce the proportion of the vehicle's low-voltage energy consumption in the total vehicle energy consumption, thereby ensuring the normal operation of the vehicle.

[0017] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and are not intended to limit the technical solutions of the present disclosure.

[0018] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following is a brief introduction to the drawings required for use in the embodiments. The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used together with the specification to illustrate the technical solutions of the present disclosure. It should be understood that the following drawings only illustrate certain embodiments of the present disclosure and should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can also be obtained based on these drawings without creative work.

[0020] Figure 1 A flow chart of a vehicle low-voltage energy consumption optimization method provided by an embodiment of the present disclosure is shown; Figure 2 A schematic diagram showing the life cycle of a vehicle provided by an embodiment of the present disclosure is shown; Figure 3 A schematic diagram showing a vehicle usage cycle provided by an embodiment of the present disclosure; Figure 4 A schematic diagram of a target scene provided by an embodiment of the present disclosure is shown; Figure 5 A schematic diagram of various energy consumption analysis stages provided by an embodiment of the present disclosure is shown; Figure 6 One of the schematic diagrams of a vehicle low-voltage energy consumption optimization device provided by an embodiment of the present disclosure is shown; Figure 7 A second schematic diagram of a vehicle low-voltage energy consumption optimization device provided by an embodiment of the present disclosure is shown; Figure 8 A schematic structural diagram of an electronic device provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical scheme and advantages of the embodiments of the present disclosure clearer, the technical scheme in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all of the embodiments. The components of the embodiments of the present disclosure generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present disclosure provided in the drawings is not intended to limit the scope of the present disclosure for protection, but merely represents the selected embodiments of the present disclosure. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present disclosure.

[0022] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0023] The term "and / or" herein only describes an association relationship, indicating that three relationships may exist. For example, A and / or B may represent the following three situations: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C may represent including any one or more elements selected from the set consisting of A, B, and C.

[0024] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0025] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0026] As an optional but non-limiting implementation, in response to receiving an active request from the user, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device, etc.

[0027] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet the relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0028] Research has found that in order to optimize the low-voltage energy consumption of a vehicle, it is often necessary to calculate how to reduce energy consumption under each function of the vehicle separately. For example, for the door control function, it is necessary to calculate how to optimize the energy consumption of the door control separately, and for the window control function, it is necessary to calculate how to optimize the energy consumption of the window control separately. In addition, the calculation process is relatively simple, and most of the calculations are based on theoretical data. The optimization strategy obtained in this way can only meet the theoretical energy consumption reduction, and it is difficult to guarantee that it meets the optimization needs after the actual operation of the vehicle. The optimization effect is not good, and there is room for refined operation.

[0029] Based on the above research, the present disclosure provides a vehicle low-voltage energy consumption optimization method, which can determine the target scenarios corresponding to multiple functions, and can achieve comprehensive low-voltage energy consumption optimization for all scenarios and multiple functions. It can also effectively achieve low-voltage energy consumption optimization through multi-stage strategy analysis and iteration, as well as synchronous iteration of optimization targets, while also ensuring that the optimized low-voltage energy consumption is minimized.

[0030] To facilitate understanding of this embodiment, a vehicle low-voltage energy consumption optimization method disclosed in the embodiment of the present disclosure is first introduced in detail. The execution subject of the vehicle low-voltage energy consumption optimization method provided in the embodiment of the present disclosure is generally an electronic device with certain computing capabilities, and the electronic device can be a server or a terminal device or other processing device. If it is a server, the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, big data and artificial intelligence platforms. . If it is a terminal device, the terminal device can be a user equipment (UserEquipment, UE), a mobile device, a terminal, a computing device, and a vehicle-mounted device. If it is other processing devices, other processing devices can be devices including processors and memories, which are not limited here. In some possible implementations, the vehicle low-voltage energy consumption optimization method can be implemented by a processor calling computer-readable instructions stored in a memory.

[0031] It should be noted that low-voltage energy consumption and high-voltage energy consumption refer to two different voltage levels inside the vehicle, which are used for different functions respectively.

[0032] Low-voltage energy consumption mainly refers to the electric energy consumed by the low-voltage system of the vehicle during operation. The low-voltage system uses the power provided by the low-voltage battery. Here, the low-voltage battery is, for example, 12V, 24V, 36V, 48V, etc. The low-voltage system is used to provide stable power support for many electrical and electronic devices in the vehicle to ensure the normal operation of the vehicle. These devices include but are not limited to lighting systems, audio systems, instrument panels, etc. In addition to controlling these devices, the low-voltage system is also used to interact with the high-voltage system.

[0033] High-voltage energy consumption mainly refers to the electrical energy consumed by the vehicle's high-voltage system during operation. The high-voltage system uses a circuit with a voltage to the ground exceeding 250V, usually between 300V and 1000V, and is mainly powered by a power battery to drive the vehicle's motor and provide power.

[0034] The disclosed embodiments mainly optimize the energy consumption of the vehicle's low-voltage energy consumption function, which can effectively reduce the vehicle's low-voltage energy consumption and ensure the normal operation of the vehicle.

[0035] A vehicle low-voltage energy consumption optimization method provided by an embodiment of the present disclosure is described below in conjunction with the accompanying drawings.

[0036] See also Figure 1 As shown, it is a flow chart of a vehicle low-voltage energy consumption optimization method provided by an embodiment of the present disclosure, such as Figure 1 As shown in , the vehicle low-voltage energy consumption optimization method provided by the embodiment of the present disclosure includes steps S101 to S105, wherein: S101: Determine at least one target scenario according to the life cycle and usage cycle of a vehicle; each of the target scenarios corresponds to a plurality of functions.

[0037] In this step, the vehicle can be sliced ​​and decomposed according to its life cycle and usage cycle, and the scenes corresponding to a single time point or a single time period can be extracted, and at least one target scene can be formed from the user experience dimension.

[0038] In some possible implementations, determining at least one target scenario according to the life cycle and the vehicle usage cycle of the vehicle includes: Determine multiple life cycle scenarios based on the vehicle’s life cycle; Determining whether each of the life cycle scenarios is relevant to the user; In the case where the life cycle scenario is not relevant to the user, using the life cycle scenario as the target scenario; In the case where the life cycle scenario is related to the user, the target scenario is determined according to the vehicle usage cycle.

[0039] In the above steps, the life cycle of the vehicle refers to the entire cycle of the vehicle from production, sales, use to scrapping, and multiple life cycle scenarios can be split according to the life cycle of the vehicle.

[0040] For example, please refer to Figure 2 , Figure 2 A schematic diagram of the life cycle of a vehicle provided by an embodiment of the present disclosure. Figure 2 As shown in the figure, the life cycle of a vehicle starts from manufacturing in the factory, followed by transportation logistics, delivery to users, and then user use or store display. During the manufacturing, user use and store display period, firmware over-the-air (FOTA) can be upgraded, such as Figure 2 As shown in the , FOTA upgrades can be performed during manufacturing (i.e., production line FOTA), after delivery to users, and during store display. The specific content of FOTA upgrades includes but is not limited to remotely repairing software defects, providing new functions, optimizing vehicle performance, adjusting vehicle configuration, etc. When users use the vehicle, the vehicle will eventually be scrapped due to airbag detonation, thermal runaway, etc.

[0041] In this example, based on the life cycle of the vehicle, multiple life cycle scenarios can be determined, including factory mode scenarios, transportation mode scenarios, vehicle use mode scenarios, show car mode scenarios, FOTA mode scenarios, collision mode scenarios, and thermal runaway scenarios.

[0042] When multiple life cycle scenarios are determined, it can be determined whether each of the life cycle scenarios is relevant to the user. Specifically, it can be determined whether each of the life cycle scenarios involves the user's operation experience.

[0043] In the case that the life cycle scenario is not relevant to the user, the life cycle scenario is used as the target scenario.

[0044] Continuing with the above examples, the factory mode scenario, transportation mode scenario, show car mode scenario, FOTA mode scenario, collision mode scenario and thermal runaway scenario are all irrelevant to the user. Each life cycle scenario can be used as the target scenario according to the scenario design requirements.

[0045] In the case where the life cycle scenario is related to the user, the target scenario is determined according to the vehicle usage cycle.

[0046] It can be understood that if the life cycle scenario is related to the user, then the life cycle scenario is essentially the scenario in which the user uses the vehicle, that is, the vehicle usage mode scenario in the above example. Therefore, the life cycle scenario can be further subdivided according to the vehicle usage cycle to determine the target scenario.

[0047] In this way, the target scenarios for low-voltage energy consumption control can be customized according to the vehicle's life cycle and usage cycle, so that the low-voltage energy consumption scenarios that meet user needs can be optimized in a targeted manner, providing personalized optimization strategies and improving the user's optimization experience.

[0048] In some possible implementations, when the life cycle scenario is related to the user, determining the target scenario according to the vehicle usage cycle includes: In the case where the life cycle scenario is related to the user, determining a plurality of vehicle usage cycle scenarios under the life cycle scenario according to the vehicle usage cycle; For each of the vehicle usage cycle scenarios, multiple atomic scenarios are determined according to functional requirements corresponding to the vehicle usage cycle scenarios, and each of the atomic scenarios corresponds to a function; According to the user's car use needs, a combination of multiple atomic scenarios is determined as the target scenario.

[0049] In the above steps, if the life cycle scenario is related to the user, multiple vehicle usage cycle scenarios can be divided from the life cycle scenario related to the user according to the vehicle usage cycle.

[0050] Here, you can also refer to Figure 3 , Figure 3 A schematic diagram of a vehicle usage cycle provided by an embodiment of the present disclosure. Figure 3 As shown in , the vehicle usage cycle scenarios include a vehicle dormant scenario, a vehicle semi-activated scenario, a vehicle waiting to be started scenario, and a vehicle driving scenario.

[0051] Among them, the vehicle dormant scenario refers to the user leaving the vehicle and all functions of the vehicle are not activated, the vehicle semi-activated scenario refers to the user leaving the vehicle and some functions of the vehicle are activated and some functions are not activated, and the vehicle waiting to start scenario refers to the user entering the vehicle and the vehicle is not started.

[0052] It can be understood that the vehicle sleep scene is a scene in which the user leaves the vehicle and all functions of the vehicle are not activated, the vehicle semi-activated scene is a scene in which the user leaves the vehicle and some functions of the vehicle are activated and some functions are not activated, the vehicle waiting to start scene is a scene in which the user enters the vehicle and the vehicle is not started, and the vehicle driving scene is a scene in which the user drives the vehicle.

[0053] Exemplarily, in the semi-activated vehicle scenario, the basic safety functions of the vehicle and related functions for user interaction with the vehicle are activated, and other functions are not activated, such as alarm, remote control, unlocking doors, etc.

[0054] In this way, according to the vehicle usage cycle, the vehicle dormant scenario, vehicle semi-activated scenario, vehicle waiting to start scenario and vehicle driving scenario can be divided from the user-related life cycle, so as to better determine the target scenario and improve the rationality and effectiveness of the scenario definition.

[0055] For each vehicle usage cycle scenario, multiple atomic scenarios can be determined according to the functional requirements corresponding to the vehicle usage cycle scenario.

[0056] Here, each of the atomic scenes corresponds to a function, and each of the atomic scenes corresponds to a single time point or a single time period.

[0057] Exemplarily, for the vehicle sleep scenario, the atomic scenarios that can be determined include but are not limited to whole vehicle sleep, whole vehicle sleep and maintaining the battery energy control module (BECM) working for 1 hour after power-off, etc.

[0058] For the semi-activation scenario of the vehicle, the atomic scenarios that can be determined include but are not limited to FOTA, Feature on Demand (FOD), local diagnosis, remote diagnosis, remote data acquisition, 12V power delay control, steering gear receiving a specific signal (service interface) to provide assistance, intelligent power replenishment, anti-theft alarm, Home Automated Valet Parking assist (HAVP) valet parking, one-click summoning, sentry mode-perception, sentry mode-event, high-voltage power-on, outside vehicle voice, air conditioning / thermal management post-operation, etc.

[0059] For the vehicle dormant scenario or the vehicle semi-activated scenario, the atomic scenarios that can be determined include but are not limited to the following: the electronic parking brake (EPB) can still be used for parking after the vehicle is turned off; if the EPB is faulty when the vehicle is turned off, it needs to be lit for at least 10 seconds; the braking ability must be available when the vehicle is dormant; the vehicle can be awakened by braking to start the vehicle; the inside / outside door button is pressed; the tailgate switch is pressed; the tailgate switch is pressed; the electric door position changes; the tailgate position changes / tailgate lock status changes; the charging port cover position changes; the side door lock status changes; the central lock status changes; the keyless entry system (Passive Entry, PE) unlocks; Bluetooth sneaks away; the Bluetooth key approaches the vehicle to welcome guests; the Bluetooth key automatically unlocks; the Bluetooth button remote control access control system (Remote Keyless Active Entry, RKE) control - unlocking / tailgate / charging port cover / window control, Bluetooth button RKE active control - unlocking linkage door opening and closing function, Bluetooth button RKE active control - car search horn and flashing light function, Bluetooth button RKE active control - Auto Parking Assist (APA) / Remote Parking Assist (RPA), Bluetooth key wake-up vehicle synchronization, Near Field Communication (NFC) card unlocking, Bluetooth key sharing / revocation and physical key addition / disablement / remote update, charging pile obtains gun insertion status, RKE sets the maximum charging state of charge (SOC), Telematics Service Provider (Telematics Service Provider, TSP) remote vehicle control - unlock / tailgate / charging port cover / window control / one-button start, TSP remote vehicle control - unlock and link door opening and closing function, TSP remote vehicle control - find car, honk and flash light function, TSP remote refresh vehicle condition (inside and outside temperature, tire pressure, etc.), TSP remote start charging / stop charging / set max SOC, TSP remote control air conditioning / defrost / air purification / refrigerator, TSP remote control fragrance, TSP remote seat heating and ventilation / steering wheel heating, TSP control battery preheating / extreme low temperature battery self-protection, TSP remote parking photo / real-time video viewing and audio call, appointment real-time clock (RTC) timed wake-up, tire pressure monitoring system (TPMS) function, thermal runaway, high-voltage charging, rainy day window closing, wiper washing, front hatch switch status change, etc.

[0060] For the vehicle to be started scenario, the atomic scenarios that can be determined include but are not limited to the standard driving (C_Normal) mode, C super energy-saving mode, etc.

[0061] For the vehicle driving scenario, the atomic scenarios that can be determined include but are not limited to driving_Normal (LT) mode, driving_super energy-saving mode, etc.

[0062] When multiple atomic scenes are determined, a combination of the multiple atomic scenes can be determined as the target scene based on the user's vehicle use needs.

[0063] Here, the user's car use demand includes the user's car use process within a period of time. Correspondingly, the target scenario is, for example, the user leaving the car and locking it for 24 hours before getting in the car, the user leaving the car and locking it for 48 hours before getting in the car, the user leaving the car and locking it for one month before getting in the car, the user charging, etc.

[0064] For example, please refer to Figure 4 , Figure 4 Schematic diagram of a target scenario provided by an embodiment of the present disclosure. In this example, the target scenario is that the user leaves the car and locks it for 24 hours before getting back in the car. Figure 4 As shown in the figure, the device before the user enters the vehicle after leaving the vehicle and locking it goes through the atomic scenarios of high voltage maintenance, sleep, remote viewing, sleep, power replenishment, sleep, and Bluetooth stealing.

[0065] In this way, when the life cycle scenario is related to the user, multiple vehicle use cycle scenarios can be split out under the life cycle scenario, and multiple atomic scenarios corresponding to single functions can be determined under each vehicle use cycle scenario. Starting from the user experience dimension, a target scenario is formed by superimposing a series of atomic scenarios, so that low-voltage energy consumption scenarios that exist for user needs can be optimized in a targeted manner, personalized optimization strategies can be provided, and the user's optimization experience can be enhanced.

[0066] S102: For each of the target scenarios, determine an initial optimization target corresponding to the target scenario, and classify the multiple functions by energy consumption according to attribute information of each function to obtain a first function, where the first function refers to a function whose energy consumption can be optimized.

[0067] Here, the initial optimization target is the target under the energy consumption dimension.

[0068] Optionally, the initialization target set by the user may be obtained. Alternatively, the initial optimization target matching the target scenario may be automatically generated based on the overall energy consumption data and low-voltage energy consumption data of the vehicle.

[0069] In some possible implementations, the target in other dimensions (set by the user or automatically generated) can be determined first, and then converted into the target in the energy consumption dimension as the initial optimization target, which effectively improves the suitability of the initial optimization target.

[0070] Among them, the other dimensions include but are not limited to user experience dimension, display dimension, etc.

[0071] For example, taking the target scenario of a user leaving the car and locking it for 24 hours before getting back in the car, the goal under the user experience dimension can be first determined as: 24h mileage display shows a loss of 0 km, and then the goal is converted into the goal under the energy consumption dimension: low-voltage static endurance loss <0.5 km / day (0.07 kWh), to obtain the initial optimization goal.

[0072] In this step, for each of the target scenes, energy consumption of multiple functions corresponding to the target scene is classified according to attribute information of each function, and the first function and other functions except the first function are divided. The first function refers to a function whose energy consumption can be optimized, and the other functions except the first function refer to functions whose energy consumption cannot be optimized.

[0073] Here, the attribute information indicates whether the energy consumption of the corresponding function is fixed or variable. It can be understood that if the energy consumption is fixed, the corresponding function is a function whose energy consumption cannot be optimized; if the energy consumption is variable, the corresponding function is a function whose energy consumption can be optimized.

[0074] For example, taking the target scenario where the user leaves the car and locks it for 24 hours before getting in the car again, the functions corresponding to this target scenario include high voltage maintenance, sleep, remote viewing, power replenishment, and Bluetooth sneaking. Among them, the static current loss corresponding to sleep is 0.015kWh. The minimum energy consumption corresponding to power replenishment is 0.026kWh, and the corresponding maximum energy consumption is 0.037kWh; at the minimum energy consumption, the corresponding energy consumption strategy is to replenish power once every 2.4 days for vehicles with a SOC of 98%-20%; at the maximum energy consumption, the corresponding energy consumption strategy is to replenish power once every 1.7 days for vehicles with a SOC of 98%-50%; however, at the minimum energy consumption, collision safety may not be met, and thermal runaway may occur.

[0075] Therefore, it is determined that high voltage maintenance, remote viewing and Bluetooth sneaking are the first functions in the target scenario, and sleep and power replenishment are other functions in the target scenario except the first function.

[0076] S103: For each of the target scenarios, the historical energy consumption data and the historical energy consumption strategy of each of the first functions are obtained, and based on the historical energy consumption data of each of the first functions, the historical energy consumption strategy is optimized in each energy consumption analysis stage in turn to obtain the first optimized energy consumption strategy corresponding to each energy consumption analysis stage.

[0077] In this step, when the historical energy consumption data and historical energy consumption strategies of each of the first functions are obtained, the historical energy consumption strategies of each of the first functions can be optimized in turn in each energy consumption analysis stage according to the optimization direction of reducing low-voltage energy consumption based on the historical energy consumption data of each of the first functions, so as to obtain the first optimized energy consumption strategies corresponding to each energy consumption analysis stage.

[0078] The historical energy consumption data includes the energy consumption loss of the first function, and the historical energy consumption strategy includes the execution mode of the first function.

[0079] For example, taking the target scenario of the user leaving the car and locking it for 24 hours before getting in the car, the first functions under this target scenario are high voltage maintenance, remote viewing, and Bluetooth sneaking. Within 24 hours, the historical energy consumption data and historical energy consumption strategies of each of the first functions are: the historical energy consumption strategy for high voltage maintenance is to maintain 0.06kWh, and the historical energy consumption data is 0.06kWh; the historical energy consumption strategy for remote viewing is 5 times, 0.17h each time, the power is set to 40W, and the historical energy consumption data is 40W×0.17h×5=0.034kWh; the historical energy consumption strategy for Bluetooth sneaking is an average of 1.5 times / day, 0.25h each time, the current is set to 25A, and the historical energy consumption data is 25A×0.25h×1.5×14V=0.13125kWh.

[0080] In this example, the design constraints that need to be considered are: recharging generally wakes up the continuous damping control (CDC) and the automated-driving control unit (ACU), thereby requesting water cooling and causing greater energy consumption. Here, the ACU wakes up for 11 minutes and requests water cooling after waking up; the CDC wakes up for 30 seconds and does not request water cooling after waking up. Within 24 hours, for recharging, its historical energy consumption strategy is to recharge once every 6 hours. Under the ideal single state, the current is 17A in the first 15 minutes and 8A in the last 20 minutes. Its historical energy consumption data is (17A×0.25h+8A×0.35h)×4×14V=0.3948kWh. Within 24 hours, for hibernation, its historical energy consumption strategy is to set the theoretical hibernation current to 50mA, and the measured hibernation current is 47.9mA. Its historical energy consumption data is 0.05A×(24h-2.4h)×12.5V≈0.014kWh.

[0081] Therefore, within 24 hours, the total energy consumption data is less than 0.64kWh under ideal conditions. Based on the calculation of 7km of driving range per kilowatt-hour, the static loss per day is approximately 4.48km of driving range.

[0082] When determining the historical energy consumption data and historical energy consumption strategy of each of the first functions, the historical energy consumption strategy can be optimized in each energy consumption analysis stage based on the historical energy consumption data of each of the first functions to obtain the first optimized energy consumption strategy corresponding to each energy consumption analysis stage.

[0083] Here, you can also refer to Figure 5 , Figure 5 A schematic diagram of various energy consumption analysis stages provided by an embodiment of the present disclosure. Figure 5 As shown in , the various energy consumption analysis stages sequentially include an energy efficiency index analysis stage, a vehicle pre-mass production energy consumption data analysis stage, and a vehicle post-mass production energy consumption data analysis stage.

[0084] Specifically, the optimizing the historical energy consumption strategy in each energy consumption analysis stage in turn to obtain the first optimized energy consumption strategy corresponding to each energy consumption analysis stage respectively includes: In the energy efficiency index analysis stage, when the energy efficiency index is predicted to be the minimum index, based on the theoretical energy consumption data of the first function, the historical energy consumption strategy is optimized to obtain a first optimized energy consumption strategy corresponding to the energy efficiency index analysis stage; the energy efficiency index is used to indicate the proportional relationship between energy consumption cost and raw material cost; In the energy consumption data analysis stage before mass production of the vehicle, experimental energy consumption data of the first function is obtained, and based on the experimental energy consumption data of the first function, the historical energy consumption strategy is optimized to obtain a first optimized energy consumption strategy corresponding to the energy consumption data analysis stage before mass production of the vehicle; In the energy consumption data analysis stage after mass production of the vehicle, user energy consumption data of the first function is obtained, and based on the user energy consumption data of the first function, the historical energy consumption strategy is optimized to obtain the first optimized energy consumption strategy corresponding to the energy consumption data analysis stage after mass production of the vehicle.

[0085] In the above steps, in the energy efficiency index analysis stage, the vehicle is in the research and development design stage and vehicle production has not yet started. Therefore, the theoretical energy consumption data of the first function is used to optimize the strategy to obtain the first optimized energy consumption strategy corresponding to the energy efficiency index analysis stage.

[0086] Here, the energy efficiency index is used to indicate the proportional relationship between energy consumption cost and raw material cost. In some possible implementations, the energy efficiency index may be an energy efficiency index. Specifically, the energy efficiency index is used to indicate the ratio between the energy consumption cost of a component used to perform the first function and its raw material cost. If the energy consumption cost of a component used to perform the first function is much higher than its raw material cost, then the energy efficiency index of this component is high, which means that there may be problems such as overly complex design and inefficient manufacturing process, resulting in unnecessary high costs.

[0087] In the disclosed embodiment, in the energy efficiency index analysis stage, an extreme design approach is adopted. If the ratio between the energy consumption cost and the raw material cost of each component is 1, then the energy efficiency index is the minimum index at this time, and it can be considered that the theoretical design at this time is better. Therefore, when the energy efficiency index is predicted to be the minimum index (1), strategy optimization is performed based on the theoretical energy consumption data of the first function, which helps to improve the optimization effect and reduce resource consumption in subsequent production of vehicles.

[0088] For example, taking the target scenario of a user leaving the car and locking it for 24 hours before getting back in the car, in the energy efficiency index analysis stage, the design limitations that need to be considered are: Bluetooth unleashing generally requires waking up CCU_CD and CCU_AD, and in this example the vehicle requires water cooling, and whether the vehicle is on high voltage is handled according to the high voltage treatment.

[0089] When the energy efficiency index is predicted to be the minimum index, the historical energy consumption strategy is optimized based on the theoretical energy consumption data of the first function, and the first optimized energy consumption strategies of each of the first functions corresponding to the energy efficiency index analysis stage are: within 24 hours, the first optimized energy consumption strategy for high voltage maintenance is to maintain 0.067kWh; the first optimized energy consumption strategy for remote viewing is 5 times, each time for 0.17h, and the power is set to 60.7W, and the electronic control units (ECUs) involved include NAD, CCU_CD_MCU, CCU_CD_SOC, LCU_L, and LCU_R; the first optimized energy consumption strategy for Bluetooth sneaking is an average of 1.5 times / day, each time for 0.25h, and the power is set to 267W, and the ECUs involved include CCU_CD_MCU, CCU_CD_SOC, CCU_AD_MCU, CCU_AD_SOC, BNCM, NAD, and DRM×4.

[0090] During the vehicle energy consumption data analysis phase before mass production, it is in the vehicle testing phase, so the experimental energy consumption data of the first function can be obtained through experiments, and based on the experimental energy consumption data of the first function, the historical energy consumption strategy is optimized to obtain the first optimized energy consumption strategy corresponding to the vehicle energy consumption data analysis phase before mass production.

[0091] In the stage of analyzing the energy consumption data after mass production of the vehicle, the vehicle has been delivered to the user for use, so the actual energy consumption data of the user after actually using the vehicle (i.e., the user energy consumption data of the first function) can be collected, and based on the user energy consumption data of the first function, the historical energy consumption strategy is optimized to obtain the first optimized energy consumption strategy corresponding to the stage of analyzing the energy consumption data after mass production of the vehicle.

[0092] In this way, by setting different stages, multi-stage strategy analysis and optimization can be carried out, effectively improving the comprehensiveness and stability of strategy optimization and ensuring the matching degree between the optimization strategy and the vehicle.

[0093] S104: For the current energy consumption analysis stage under each of the target scenarios, based on the first optimized energy consumption strategy, determine the optimized energy consumption data, compare the optimized energy consumption data with the initial optimization target, and if the optimized energy consumption data cannot meet the initial optimization target, adjust the initial optimization target, and return to the historical energy consumption data based on each of the first functions, and optimize the historical energy consumption strategy in each energy consumption analysis stage in turn to obtain the first optimized energy consumption strategy corresponding to each energy consumption analysis stage.

[0094] In this step, in each energy consumption analysis stage, the predicted optimized energy consumption data is determined when the first optimized energy consumption strategy corresponding to the current energy consumption analysis stage is executed. When the optimized energy consumption data cannot meet the initial optimization target, it means that it is difficult to meet the initial optimization target. Therefore, the initial optimization target can be adjusted to reduce the difficulty of achieving the initial optimization target, and the first energy consumption analysis stage in each energy consumption analysis stage (the energy efficiency index analysis stage in the embodiment of the present disclosure) is returned to perform strategy optimization, and the optimized energy consumption data corresponding to the first optimized energy consumption strategy corresponding to the first energy consumption analysis stage obtained by optimization is compared with the adjusted initial optimization target.

[0095] Continuing with the above example, taking the target scenario of the user leaving the car and locking it for 24 hours before getting in the car again, in the energy efficiency index analysis stage, the first optimized energy consumption strategies for each of the first functions corresponding to the energy efficiency index analysis stage are: within 24 hours, the optimized energy consumption strategy for high voltage maintenance is to maintain 0.067kWh; the optimized energy consumption strategy for remote viewing is 5 times, each time for 0.17h, and the set power is 60.7W; the optimized energy consumption strategy for Bluetooth sneaking is an average of 1.5 times / day, each time for 0.25h, and the set power is 267W.

[0096] Correspondingly, the optimized energy consumption data of each of the first functions corresponding to the energy efficiency index analysis stage are: the optimized energy consumption data for high voltage maintenance is 0.067kWh; the optimized energy consumption data for remote viewing is 60.7W×0.17h×5=0.052kWh; the optimized energy consumption data for Bluetooth sneaking is 267W×0.25h×1.5=0.1kWh.

[0097] Within 24 hours, for charging, the first optimized energy consumption strategy corresponding to the energy efficiency index analysis stage is triggered 1 / 1.7 times, and the triggering number is calculated as follows: (main battery SOC threshold at the end of charging (98%) - main battery SOC threshold at the start of charging (50%)) ÷ (average static current loss + remote viewing loss) ≈ 1.7 times, and the average charging time is set to 0.5h. The ECUs involved include CCU_CD_MCU, VCU, NAD, LCU_L, and LCU_R. The low-voltage power is set to 125W, and the optimized energy consumption data is (125W×0.5h) / 1.7=0.037kWh. Within 24 hours, for sleep, the first optimized energy consumption strategy corresponding to the energy efficiency index analysis stage is that the theoretical sleep current is set to 50mA, and the optimized energy consumption data is 0.05A×24h×12.5V=0.015kWh.

[0098] Therefore, within 24 hours, the total optimized energy consumption data corresponding to the energy efficiency index analysis stage is: without considering remote wake-up and Bluetooth sneaking, the total optimized energy consumption data is less than 0.052kWh in an ideal state, and the static loss is about 0.364km per day; considering remote wake-up and Bluetooth sneaking, the total optimized energy consumption data is less than 0.271kWh in an ideal state, and the static loss is about 1.9km per day. In actual applications, the energy consumption of remote wake-up and Bluetooth sneaking needs to be taken into account. Therefore, in this example, in the energy efficiency index analysis stage, the optimized energy consumption data cannot meet the initial optimization target, and then the initial optimization target is adjusted, and the historical energy consumption data based on each of the first functions is returned to, and the historical energy consumption strategy is optimized in each energy analysis stage to obtain the first optimized energy consumption strategy corresponding to each energy analysis stage.

[0099] For example, see again Figure 5 In each energy consumption analysis stage, the historical energy consumption strategy is optimized to obtain the first optimized energy consumption strategy and the corresponding optimized energy consumption data corresponding to each energy consumption analysis stage, and the optimized energy consumption data is compared with the initial optimization target. If the optimized energy consumption data cannot meet the initial optimization target, the initial optimization target is adjusted and returned to the energy efficiency index analysis stage, and the strategy is optimized again in each energy consumption analysis stage in turn.

[0100] S105: When the optimized energy consumption data meets the initial optimization target, the next energy consumption analysis stage is used as the current energy consumption analysis stage, and the current energy consumption analysis stage for each target scenario is entered. Based on the first optimized energy consumption strategy, the optimized energy consumption data is determined, and the optimized energy consumption data is compared with the initial optimization target until the optimized energy consumption data in the last energy consumption analysis stage meets the initial optimization target, and the target first optimized energy consumption strategy corresponding to each target scenario is obtained.

[0101] In this step, in each energy consumption analysis stage, when the optimized energy consumption data predicted according to the first optimized energy consumption strategy can meet the initial optimization target, it means that the initial optimization target is set more reasonably, so the next energy consumption analysis stage can be entered to perform strategy optimization again, and the optimized energy consumption data corresponding to the first optimized energy consumption strategy obtained by optimization is compared with the initial optimization target, until the optimized energy consumption data in the last energy consumption analysis stage meets the initial optimization target, and the first optimized energy consumption strategy corresponding to the last energy consumption analysis stage is used as the target first optimized energy consumption strategy corresponding to the target scenario.

[0102] Here, if the initial optimization target is adjusted in a certain energy consumption analysis stage, the initial optimization target for comparison in the last energy consumption analysis stage is the adjusted initial optimization target.

[0103] For example, see again Figure 5 First, in the energy efficiency index analysis stage, strategy optimization and comparison between the optimized energy consumption data and the initial optimization target are performed. If the optimized energy consumption data meets the initial optimization target, the vehicle pre-mass production energy consumption data analysis stage is entered; in the vehicle pre-mass production energy consumption data analysis stage, strategy optimization and comparison between the optimized energy consumption data and the initial optimization target are performed again. If the optimized energy consumption data meets the initial optimization target, the vehicle post-mass production energy consumption data analysis stage is entered; in the vehicle post-mass production energy consumption data analysis stage, strategy optimization and comparison between the optimized energy consumption data and the initial optimization target are performed again. If the optimized energy consumption data meets the initial optimization target, the first optimized energy consumption strategy corresponding to the vehicle post-mass production energy consumption data analysis stage is determined as the target first optimized energy consumption strategy corresponding to the target scenario.

[0104] In some possible implementations, after obtaining the target first optimization energy consumption strategy corresponding to the target scenario, the method further includes: Acquire a mapping relationship between a scene and an optimized energy consumption strategy, wherein the mapping relationship between the scene and the optimized energy consumption strategy includes a target first optimized energy consumption strategy corresponding to each target scene; Detecting a current scene in which the vehicle is located; According to the mapping relationship, a target optimization energy consumption strategy corresponding to the current scenario is determined, and the vehicle is controlled to execute the target optimization energy consumption strategy.

[0105] In this step, after obtaining the target first optimization energy consumption strategy corresponding to each of the target scenes, the mapping relationship between the scene and the optimization energy consumption strategy can be determined. When it is necessary to optimize the energy consumption of the vehicle, the current scene in which the vehicle is located can be detected in real time, and the target scene consistent with the current scene can be determined by searching the mapping relationship. The target first optimization energy consumption strategy corresponding to the target scene consistent with the current scene indicated by the mapping relationship is determined as the target optimization energy consumption strategy corresponding to the current scene, and the vehicle is controlled to execute the target optimization energy consumption strategy.

[0106] In this way, by controlling the vehicle to execute the target optimization energy consumption strategy corresponding to the current scenario, the low-voltage energy consumption of the entire scenario can be effectively reduced. Further, the proportion of the vehicle's low-voltage energy consumption in the total vehicle energy consumption can be effectively reduced, thereby ensuring the normal operation of the vehicle.

[0107] In some possible implementations, when the life cycle scenario is related to a user, the method further includes: determining a second function associated with the user from among the first functions; Acquire user usage data corresponding to the second function; The user usage data is input into a pre-trained usage optimization model to obtain a second optimized energy consumption strategy corresponding to the second function.

[0108] In the above steps, if the life cycle scenario is related to the user, the determined target scenario must also be related to the user, so there must be a second function associated with the user among the multiple functions corresponding to the target scenario. It can then be detected whether there is a second function associated with the user in the first function, and if it is determined that there is, the second function in the first function is determined.

[0109] The second function is specifically a function of optimizing energy consumption in the first function that requires user operation.

[0110] Optionally, when the first function is determined, the second function may be determined according to a historical energy consumption strategy of the first function.

[0111] Alternatively, the second function may be identified in the process of optimizing the historical energy consumption strategy in each energy consumption analysis stage in combination with the analysis and optimization of the strategy.

[0112] Continuing with the above example, taking the target scenario of a user leaving the car and locking it for 24 hours before getting in the car again, the first function under this target scenario is high voltage maintenance, remote viewing, and Bluetooth sneaking. It can be determined that the second function is remote viewing and Bluetooth sneaking. Specifically, the energy consumption of remote viewing is related to the number of times the user views it, and the energy consumption of Bluetooth sneaking is related to the number of times and distance the user connects to Bluetooth.

[0113] Then, user usage data corresponding to the second function can be obtained; the user usage data is input into a pre-trained usage optimization model, and the usage optimization model can be processed according to an energy consumption optimization analysis strategy matching the user usage data to obtain a second optimized energy consumption strategy corresponding to the second function.

[0114] Among them, the second energy consumption optimization strategy corresponding to the second function includes an adjustment strategy for the user usage data and a predicted energy consumption optimization result under the execution of the adjustment strategy.

[0115] Here, the user usage data may be the acquired user actual usage data, wherein when a certain data cannot be acquired, a default value may be used.

[0116] Following the above example, taking the second function of remote viewing and Bluetooth sneaking as an example, the user usage data includes the user's parking location, the user's charging type, the type of remote operation per day, the number of Bluetooth connections per day, the number of times the vehicle is returned to the vehicle after leaving the vehicle and locking it per day, the time of returning to the vehicle after leaving the vehicle and locking it per day, and the number of times the vehicle is switched to D gear after switching from D gear to P gear. The pre-trained usage optimization model is processed according to the energy consumption optimization analysis strategy that if the number of times the vehicle is switched to D gear after switching from D gear to P gear is 0, the motor power-on maintenance time after engaging P gear is the minimum value of 10s, and the second optimized energy consumption strategy corresponding to the second function is output. Specifically, the second optimized energy consumption strategy includes the value of the number of sneaking and anti-playing times, the value of the remote wake-up keep-alive time, the value of the high voltage maintenance time after the remote wake-up part range is locked, the value of the motor power-on maintenance time after engaging P gear, and the value of energy consumption optimization.

[0117] It should be noted that the process of determining the second optimized energy consumption strategy corresponding to the second function is different from the process of determining the target first optimized energy consumption strategy corresponding to the target scenario. It can be understood that any target scenario can determine the corresponding target first optimized energy consumption strategy, among which only the function whose energy consumption can be optimized and associated with the user (that is, the second function) can obtain the second optimized energy consumption strategy corresponding to the function through the use of the optimization model.

[0118] In this way, the second function associated with the user in the first function can be modeled by analyzing the user's car usage habits. By using the optimization model, the user's usage data can be intelligently adjusted to optimize low-voltage energy consumption while taking into account the user's car usage habits. This can provide personalized optimization strategies and enhance the user's optimization experience.

[0119] Correspondingly, after obtaining the second energy consumption optimization strategy corresponding to the second function, the method further includes: Detecting a current execution function of the vehicle; and when the current execution function is the second function, controlling the vehicle to execute an optimized energy consumption strategy corresponding to the current execution function.

[0120] In this way, by controlling the vehicle to execute the optimized energy consumption strategy corresponding to the second function, it is possible to effectively achieve low-voltage energy consumption reduction in all scenarios while taking into account the user's driving habits. Furthermore, it is possible to effectively reduce the proportion of the vehicle's low-voltage energy consumption in the total vehicle energy consumption, thereby ensuring the normal operation of the vehicle.

[0121] The vehicle low-voltage energy consumption optimization method provided by the embodiment of the present disclosure, since the target scenario corresponds to multiple functions and multiple energy consumption analysis stages are set, only when the optimized energy consumption data corresponding to the first optimized energy consumption strategy corresponding to the current energy consumption analysis stage can meet the initial optimization target, will it enter the next energy consumption analysis stage, otherwise the initial optimization target is adjusted and each energy consumption analysis stage is re-entered in turn to perform strategy optimization and target comparison, so as to ensure that the multifunctional first optimized energy consumption strategy for the target scenario can meet the initial optimization target at different stages, through multi-stage strategy analysis and iteration, and synchronous iteration of the initial optimization target when the target is not met, while effectively realizing the overall low-voltage energy consumption optimization of the multifunctionality of the whole scenario, it also ensures the minimization of the optimized low-voltage energy consumption, which helps to improve the matching degree between the optimized energy consumption strategy and the vehicle, and improve the comprehensiveness and integrity of the low-voltage energy consumption optimization, and further, can effectively reduce the proportion of the vehicle's low-voltage energy consumption in the total vehicle energy consumption, thereby ensuring the normal operation of the vehicle.

[0122] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0123] Based on the same inventive concept, a vehicle low-pressure energy consumption optimization device corresponding to the vehicle low-pressure energy consumption optimization method is also provided in the embodiment of the present disclosure. Since the principle of solving the problem by the vehicle low-pressure energy consumption optimization device in the embodiment of the present disclosure is similar to the above-mentioned vehicle low-pressure energy consumption optimization method in the embodiment of the present disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0124] See also Figure 6 and Figure 7 , Figure 6 This is one of the schematic diagrams of a vehicle low-voltage energy consumption optimization device provided by an embodiment of the present disclosure, Figure 7 This is a second schematic diagram of a vehicle low-voltage energy consumption optimization device provided by an embodiment of the present disclosure. Figure 6 As shown in , the vehicle low-voltage energy consumption optimization device 600 provided by the embodiment of the present disclosure includes: The scene determination module 610 is used to determine at least one target scene according to the life cycle and the vehicle usage cycle of the vehicle; each of the target scenes corresponds to multiple functions; The scenario configuration module 620 is used to determine, for each of the target scenarios, an initial optimization target corresponding to the target scenario, and classify the energy consumption of the multiple functions according to the attribute information of each function to obtain a first function, where the first function refers to a function whose energy consumption can be optimized; The strategy optimization module 630 is used to obtain the historical energy consumption data and the historical energy consumption strategy of each of the first functions for each of the target scenarios, and optimize the historical energy consumption strategy in each energy consumption analysis stage in turn based on the historical energy consumption data of each of the first functions to obtain the first optimized energy consumption strategy corresponding to each energy consumption analysis stage; The optimization iteration module 640 is used to determine the optimized energy consumption data based on the first optimized energy consumption strategy for the current energy consumption analysis stage under each of the target scenarios, compare the optimized energy consumption data with the initial optimization target, and adjust the initial optimization target when the optimized energy consumption data cannot meet the initial optimization target, and return to the historical energy consumption data based on each of the first functions, and optimize the historical energy consumption strategy in each energy consumption analysis stage to obtain the first optimized energy consumption strategy corresponding to each energy consumption analysis stage; The strategy determination module 650 is used to, when the optimized energy consumption data meets the initial optimization target, take the next energy consumption analysis stage as the current energy consumption analysis stage, and enter the current energy consumption analysis stage for each target scenario, determine the optimized energy consumption data based on the first optimized energy consumption strategy, and compare the optimized energy consumption data with the initial optimization target until the optimized energy consumption data in the last energy consumption analysis stage meets the initial optimization target, thereby obtaining the target first optimized energy consumption strategy corresponding to each target scenario.

[0125] In an optional implementation manner, the scene determination module 610 is specifically configured to: Determine multiple life cycle scenarios based on the vehicle’s life cycle; Determining whether each of the life cycle scenarios is relevant to the user; In the case where the life cycle scenario is not relevant to the user, using the life cycle scenario as the target scenario; In the case where the life cycle scenario is related to the user, the target scenario is determined according to the vehicle usage cycle.

[0126] In an optional implementation manner, the scenario determination module 610, when used to determine the target scenario according to the vehicle usage cycle of the vehicle when the life cycle scenario is related to the user, is specifically used to: In the case where the life cycle scenario is related to the user, determining a plurality of vehicle usage cycle scenarios under the life cycle scenario according to the vehicle usage cycle; For each of the vehicle usage cycle scenarios, multiple atomic scenarios are determined according to functional requirements corresponding to the vehicle usage cycle scenarios, and each of the atomic scenarios corresponds to a function; According to the user's car use needs, a combination of multiple atomic scenarios is determined as the target scenario.

[0127] In an optional implementation, the vehicle usage cycle scenarios include a vehicle sleep scenario, a vehicle semi-activated scenario, a vehicle wait-to-start scenario, and a vehicle driving scenario; the vehicle sleep scenario refers to when the user leaves the vehicle and all functions of the vehicle are not activated, the vehicle semi-activated scenario refers to when the user leaves the vehicle and some functions of the vehicle are activated and some functions are not activated, and the vehicle wait-to-start scenario refers to when the user enters the vehicle and the vehicle is not started.

[0128] In an optional implementation manner, the energy consumption analysis stages sequentially include an energy efficiency index analysis stage, a vehicle pre-mass production energy consumption data analysis stage, and a vehicle post-mass production energy consumption data analysis stage; When the strategy optimization module 630 and the optimization iteration module 640 are used to optimize the historical energy consumption strategy in each energy consumption analysis stage in turn to obtain the first optimized energy consumption strategy corresponding to each energy consumption analysis stage, they are specifically used to: In the energy efficiency index analysis stage, when the energy efficiency index is predicted to be the minimum index, based on the theoretical energy consumption data of the first function, the historical energy consumption strategy is optimized to obtain a first optimized energy consumption strategy corresponding to the energy efficiency index analysis stage; the energy efficiency index is used to indicate the proportional relationship between energy consumption cost and raw material cost; In the energy consumption data analysis stage before mass production of the vehicle, experimental energy consumption data of the first function is obtained, and based on the experimental energy consumption data of the first function, the historical energy consumption strategy is optimized to obtain a first optimized energy consumption strategy corresponding to the energy consumption data analysis stage before mass production of the vehicle; In the energy consumption data analysis stage after mass production of the vehicle, user energy consumption data of the first function is obtained, and based on the user energy consumption data of the first function, the historical energy consumption strategy is optimized to obtain the first optimized energy consumption strategy corresponding to the energy consumption data analysis stage after mass production of the vehicle.

[0129] In an optional implementation, Figure 7 As shown in , the vehicle low-voltage energy consumption optimization device 600 further includes a model output module 660, and the model output module 660 is specifically used for: determining a second function associated with the user from among the first functions; Acquire user usage data corresponding to the second function; The user usage data is input into a pre-trained usage optimization model to obtain a second optimized energy consumption strategy corresponding to the second function.

[0130] In an optional implementation, Figure 7 As shown in , the vehicle low-voltage energy consumption optimization device 600 further includes a strategy execution module 670, and the strategy execution module 670 is specifically used for: Acquire a mapping relationship between a scene and an optimized energy consumption strategy, wherein the mapping relationship between the scene and the optimized energy consumption strategy includes a target first optimized energy consumption strategy corresponding to each target scene; Detecting a current scene in which the vehicle is located; According to the mapping relationship, a target optimization energy consumption strategy corresponding to the current scenario is determined, and the vehicle is controlled to execute the target optimization energy consumption strategy.

[0131] For descriptions of the processing flow of each module in the device and the interaction flow between each module, reference may be made to the relevant descriptions in the above method embodiment, which will not be described in detail here.

[0132] The vehicle low-voltage energy consumption optimization device provided by the embodiment of the present disclosure, since the target scenario corresponds to multiple functions and multiple energy consumption analysis stages are set, only when the optimized energy consumption data corresponding to the first optimized energy consumption strategy corresponding to the current energy consumption analysis stage can meet the initial optimization target, it will enter the next energy consumption analysis stage, otherwise the initial optimization target is adjusted and the various energy consumption analysis stages are re-entered in sequence to perform strategy optimization and target comparison, so as to ensure that the multifunctional first optimized energy consumption strategy for the target scenario can meet the initial optimization target at different stages, through multi-stage strategy analysis and iteration, and synchronous iteration of the initial optimization target when the target is not met, while effectively realizing the overall low-voltage energy consumption optimization of the multifunctionality of the whole scenario, it also ensures the minimization of the optimized low-voltage energy consumption, which helps to improve the matching degree of the optimized energy consumption strategy with the vehicle, and improve the comprehensiveness and integrity of the low-voltage energy consumption optimization. Further, it can effectively reduce the proportion of the vehicle's low-voltage energy consumption in the total vehicle energy consumption, thereby ensuring the normal operation of the vehicle.

[0133] The disclosed embodiment also provides a vehicle, including a controller, the controller including: a memory for storing machine-readable instructions; and a processor for calling and executing the machine-readable instructions stored in the memory to implement the steps of the vehicle low-voltage energy consumption optimization method as described in the above method embodiment. The vehicle may be a vehicle with multiple functions, the multiple functions including functions that can optimize energy consumption and functions that cannot optimize energy consumption.

[0134] Corresponds to Figure 1 The vehicle low-voltage energy consumption optimization method in the present disclosure embodiment also provides an electronic device 800, such as Figure 8 As shown in FIG. 8 , it is a schematic diagram of the structure of an electronic device 800 provided in an embodiment of the present disclosure, including: Processor 810, memory 820, and bus 830. The memory 820 is used to store execution instructions, including internal memory 821 and external memory 822; the internal memory 821 is also called internal memory, which is used to temporarily store the operation data in the processor 810 and the data exchanged with the external memory 822 such as a hard disk. The processor 810 exchanges data with the external memory 822 through the internal memory 821.

[0135] In the embodiment of the present application, the memory 820 is specifically used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 810. That is, when the electronic device 800 is running, the processor 810 communicates with the memory 820 through the bus 830, or the processor 810 communicates with the memory 820 in other ways, so that the processor 810 executes the application code stored in the memory 820, and then executes the steps of the vehicle low-voltage energy consumption optimization method described in any of the aforementioned embodiments.

[0136] Among them, the memory 820 can be, but is not limited to, random access memory (Random Access Memory, RAM), read only memory (Read Only Memory, ROM), programmable read-only memory (Programmable Read-Only Memory, PROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable read-only memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.

[0137] The processor 810 may be an integrated circuit chip with signal processing capabilities. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present invention may be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0138] It is to be understood that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the electronic device 800. In other embodiments of the present application, the electronic device 800 may include more or fewer components than shown in the figure, or combine some components, or split some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0139] The embodiment of the present disclosure also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the vehicle low-voltage energy consumption optimization method described in the above method embodiment are executed. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0140] In addition, an embodiment of the present disclosure also provides a computer program product, which stores a computer program. When the computer program is executed by a processor, the steps of the vehicle low-voltage energy consumption optimization method provided in any of the above embodiments of the present disclosure are executed. For details, please refer to the above method embodiments, which will not be repeated here.

[0141] The computer program product may be implemented in hardware, software or a combination thereof. In an optional embodiment, the computer program product is embodied as a computer storage medium, which may be a volatile or non-volatile computer-readable storage medium. In another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK) and the like.

[0142] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the above-described equipment and devices can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the several embodiments provided in the present disclosure, it should be understood that the disclosed equipment, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0143] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0144] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0145] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present disclosure. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0146] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present disclosure, which are used to illustrate the technical solutions of the present disclosure, rather than to limit them. The protection scope of the present disclosure is not limited thereto. Although the present disclosure is described in detail with reference to the aforementioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the aforementioned embodiments within the technical scope disclosed in the present disclosure, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be based on the protection scope of the claims.

Claims

1. A method for optimizing vehicle low-voltage energy consumption, It is characterized in that The method comprises: Determine at least one target scenario according to the life cycle and the use cycle of the vehicle; each of the target scenarios corresponds to a plurality of functions; For each of the target scenarios, determining an initial optimization target corresponding to the target scenario, and classifying the multiple functions by energy consumption according to the attribute information of each function to obtain a first function, where the first function refers to a function whose energy consumption can be optimized; For each of the target scenarios, historical energy consumption data and historical energy consumption strategies of each of the first functions are obtained, and based on the historical energy consumption data of each of the first functions, the historical energy consumption strategies are optimized in each energy consumption analysis stage to obtain first optimized energy consumption strategies corresponding to each energy consumption analysis stage; For the current energy consumption analysis stage under each of the target scenarios, based on the first optimized energy consumption strategy, the optimized energy consumption data is determined, the optimized energy consumption data is compared with the initial optimization target, and when the optimized energy consumption data cannot meet the initial optimization target, the initial optimization target is adjusted, and the historical energy consumption data based on each of the first functions is returned to, and the historical energy consumption strategy is optimized in each energy consumption analysis stage in turn to obtain the first optimized energy consumption strategy corresponding to each energy consumption analysis stage; When the optimized energy consumption data meets the initial optimization target, the next energy consumption analysis stage is used as the current energy consumption analysis stage, and the current energy consumption analysis stage for each target scenario is entered. Based on the first optimized energy consumption strategy, the optimized energy consumption data is determined, and the optimized energy consumption data is compared with the initial optimization target until the optimized energy consumption data in the last energy consumption analysis stage meets the initial optimization target, and the target first optimized energy consumption strategy corresponding to each target scenario is obtained.

2. The method according to claim 1, It is characterized in that The determining of at least one target scenario according to the life cycle and the vehicle usage cycle of the vehicle includes: Determine multiple life cycle scenarios based on the vehicle’s life cycle; Determining whether each of the life cycle scenarios is relevant to the user; In the case where the life cycle scenario is not relevant to the user, using the life cycle scenario as the target scenario; In the case where the life cycle scenario is related to the user, the target scenario is determined according to the vehicle usage cycle.

3. The method according to claim 2, It is characterized in that When the life cycle scenario is related to the user, determining the target scenario according to the vehicle usage cycle includes: In the case where the life cycle scenario is related to the user, determining a plurality of vehicle usage cycle scenarios under the life cycle scenario according to the vehicle usage cycle; For each of the vehicle usage cycle scenarios, multiple atomic scenarios are determined according to functional requirements corresponding to the vehicle usage cycle scenarios, and each of the atomic scenarios corresponds to a function; According to the user's car use needs, a combination of multiple atomic scenarios is determined as the target scenario.

4. The method according to claim 3, It is characterized in that The vehicle usage cycle scenarios include a vehicle dormant scenario, a vehicle semi-activated scenario, a vehicle to-be-started scenario, and a vehicle driving scenario; the vehicle dormant scenario refers to a scenario in which the user leaves the vehicle and all functions of the vehicle are not activated, the vehicle semi-activated scenario refers to a scenario in which the user leaves the vehicle and some functions of the vehicle are activated and some functions are not activated, and the vehicle to-be-started scenario refers to a scenario in which the user enters the vehicle and the vehicle is not started.

5. The method according to claim 1, It is characterized in that The energy consumption analysis stages include, in sequence, an energy efficiency index analysis stage, a vehicle pre-production energy consumption data analysis stage, and a vehicle post-production energy consumption data analysis stage; The step of optimizing the historical energy consumption strategy in each energy consumption analysis stage in turn to obtain the first optimized energy consumption strategy corresponding to each energy consumption analysis stage respectively includes: In the energy efficiency index analysis stage, when the energy efficiency index is predicted to be a minimum index, based on the theoretical energy consumption data of the first function, the historical energy consumption strategy is optimized to obtain a first optimized energy consumption strategy corresponding to the energy efficiency index analysis stage; In the energy consumption data analysis stage before mass production of the vehicle, experimental energy consumption data of the first function is obtained, and based on the experimental energy consumption data of the first function, the historical energy consumption strategy is optimized to obtain a first optimized energy consumption strategy corresponding to the energy consumption data analysis stage before mass production of the vehicle; In the energy consumption data analysis stage after mass production of the vehicle, user energy consumption data of the first function is obtained, and based on the user energy consumption data of the first function, the historical energy consumption strategy is optimized to obtain the first optimized energy consumption strategy corresponding to the energy consumption data analysis stage after mass production of the vehicle.

6. The method according to claim 2, It is characterized in that In the case where the life cycle scenario is related to a user, the method further includes: determining a second function associated with the user from among the first functions; Acquire user usage data corresponding to the second function; The user usage data is input into a pre-trained usage optimization model to obtain a second optimized energy consumption strategy corresponding to the second function.

7. The method according to claim 1, It is characterized in that After obtaining the target first optimized energy consumption strategy corresponding to the target scenario, the method further includes: Acquire a mapping relationship between a scene and an optimized energy consumption strategy, wherein the mapping relationship between the scene and the optimized energy consumption strategy includes a target first optimized energy consumption strategy corresponding to each target scene; Detecting a current scene in which the vehicle is located; According to the mapping relationship, a target optimization energy consumption strategy corresponding to the current scenario is determined, and the vehicle is controlled to execute the target optimization energy consumption strategy.

8. A vehicle, It is characterized in that A controller is included, the controller comprising: a memory for storing machine-readable instructions; and A processor is used to call and execute the machine-readable instructions stored in the memory to implement the steps of the vehicle low-voltage energy consumption optimization method as described in any one of claims 1 to 7.

9. An electronic device, It is characterized in that include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the vehicle low-voltage energy consumption optimization method as described in any one of claims 1 to 7 are performed.

10. A computer program product, It is characterized in that The computer program product stores a computer program, and when the computer program is executed by a processor, the steps of the vehicle low-voltage energy consumption optimization method as claimed in any one of claims 1 to 7 are executed.

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