Power distribution control method, device and equipment of new energy automobile and storage medium

Abstract

The invention provides a power distribution control method and control device for a new energy automobile, equipment and a storage medium. The power distribution control method comprises the following steps that vehicle state information, vehicle power consumption information, driving road information and external environment information are monitored in real time; according to the current vehicle state information and the current external environment information, priority weights are dynamically distributed to all electrical systems of the vehicle; establishing a vehicle electricity consumption demand prediction model according to the vehicle power consumption information, the current vehicle speed and the driving road information, and predicting the electricity consumption demand of the vehicle in a future specific time period; and according to the current battery available power consumption, the predicted power consumption demand, the current total available power of the vehicle and the priority weight of each electrical system, distributing power to each electrical system. According to the control method disclosed by the invention, the allocation strategy can be dynamically adjusted according to the real-time data and the vehicle state; a distribution decision is optimized by utilizing prediction modeling and a feedback mechanism, and the resource utilization efficiency is improved; electric power can be utilized to the greatest extent, and a key system always obtains enough electric power.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and in particular, to a power distribution control method, a control device, a computer device, and a computer storage medium for a new energy vehicle. Background Art

[0002] The vehicle power distribution strategy is one of the core technologies for the overall vehicle control of new energy vehicles such as pure electric vehicles, fuel cell vehicles, and hybrid vehicles. The power-consuming electrical systems of new energy vehicles usually include a power system, a safety system, an assisted driving system, a body electronics system, an entertainment system, etc. These systems play different roles during the operation of the vehicle. Therefore, it is necessary to reasonably allocate the energy consumption of each electrical system, so that the vehicle can achieve the best balance of power performance, safety, comfort, and economy.

[0003] Currently, the power energy distribution of new energy vehicles mainly adopts the following common strategies: 1. Static distribution. During the vehicle design stage, a fixed power distribution ratio is determined in advance according to the power requirements of the electrical systems. For example, the power system may be allocated 60% of the power, the body functions are allocated 20%, and the rest is allocated to other systems; 2. Distribution based on preset rules, and a set of predefined rules (such as "if the battery level is below 30%, turn off non-essential systems") are used to determine the power distribution.

[0004] Although the above strategies have played a role in specific scenarios, with the complication of the usage scenarios of new energy vehicles (such as autonomous driving, extreme weather, long-distance driving), their limitations have become increasingly prominent. The main disadvantages include lack of flexibility, low efficiency, and poor adaptability. These defects may cause key systems (such as brakes and steering) not to receive sufficient power in some cases; the waste or improper allocation of power resources shortens the driving range; and the user comfort experience is poor. Summary of the Invention

[0005] In view of the above problems in the prior art, on the one hand, an embodiment of the present disclosure provides a power distribution control method for a new energy vehicle, including the following steps:

[0006] Real-time monitor vehicle status information, vehicle power consumption information, driving road information, and external environment information;

[0007] Dynamically allocate priority weights to each electrical system of the vehicle according to the current vehicle status information and the current external environment information;

[0008] Establish a vehicle power consumption demand prediction model according to the vehicle power consumption information, the current vehicle speed, and the driving road information, and predict the power consumption demand D of the vehicle within a specific future period T future ;

[0009] According to the available power E of the current battery available 、predicted power consumption demand D future 、the total available power P of the current vehicle total and the priority weights of each electrical system to allocate power to each electrical system.

[0010] Optionally, the vehicle state information includes battery power information, driving mode information, vehicle speed information, vehicle charging mode information, and vehicle emergency fault information; the external environment information includes external temperature information, external light information, and weather condition information.

[0011] Optionally, the dynamically allocating priority weights to each electrical system of the vehicle according to the current vehicle state information and the current external environment information includes

[0012] determining the inherent safety factor S of each electrical system according to the electrical system category of each electrical system and the first preset rule i 、inherent comfort factor C i and inherent energy efficiency factor E i ;

[0013] determining the vehicle working scenario according to the vehicle state information and the external environment information, and determining the safety weight W of each electrical system according to the vehicle working scenario and the second preset rule S 、comfort weight W C and energy efficiency weight W e ;

[0014] determining the priority weight of each electrical system according to the safety weight W S 、the comfort weight W C 、the energy efficiency weight W e 、the inherent safety factor S i 、the inherent comfort factor C i and the inherent energy efficiency factor E i corresponding to each electrical system.

[0015] Optionally, the first preset rule includes a pre-stored electrical system category, a first correspondence, a preset inherent safety factor, a preset inherent comfort factor, and a preset inherent energy efficiency factor, and the first correspondence is used to represent the one-to-three correspondence between the pre-stored electrical system category and the preset inherent safety factor, the preset inherent comfort factor, and the preset inherent energy efficiency factor.

[0016] Optionally, the second preset rule includes a pre-stored vehicle working scenario, a second correspondence, a preset safety weight, a preset comfort weight, and a preset energy efficiency weight, and the second correspondence is used to represent the one-to-three correspondence between the pre-stored vehicle working scenario and the preset safety weight, the preset comfort weight, and the preset energy efficiency weight.

[0017] Optionally, the preset vehicle working scenarios include: low battery scenario, autonomous driving scenario, normal driving scenario, high-speed driving scenario, emergency fault scenario, charging scenario, and bad weather scenario.

[0018] Optionally, when determining the vehicle working scenario based on the vehicle status information and the external environment information, it is determined with the highest priority whether the vehicle is in an emergency fault scenario. If so, the corresponding safety weight W S , comfort weight W C and energy efficiency weight W e are determined.

[0019] Optionally, when determining the vehicle working scenario based on the vehicle status information and the external environment information, if it is determined that the vehicle is not in an emergency fault scenario, it is then determined whether the vehicle is in a low battery scenario. If so, the corresponding safety weight W S , comfort weight W C and energy efficiency weight W e are determined.

[0020] Optionally, establishing a vehicle power consumption demand prediction model according to the vehicle power consumption information, the current vehicle speed, and the driving road information, and predicting the power consumption demand of the vehicle within a specific future period includes:

[0021] Determining the average total power P of the vehicle in a specific first historical period according to the vehicle power consumption information avg and the load power adjustment factor k load ;

[0022] Determining the vehicle speed adjustment factor k according to the current vehicle speed v ;

[0023] Determining the road condition adjustment factor k according to the driving road information r ;

[0024] Predicting the power consumption demand D of the vehicle within a specific future period T according to the average total power P of the vehicle in the specific first historical period avg , the load power adjustment factor k load , the vehicle speed adjustment factor k v and the road condition adjustment factor k r future

[0025] Optionally, according to the current available battery power E available , the predicted power consumption demand D of the vehicle within a specific future period T future , and the current total available power P of the vehicle total ​​Allocating power to each electrical system according to the priority weights of each electrical system includes

[0026] According to the predicted power consumption demand D within a specific future time period T of the vehicle future Adjusting the current total available power P of the vehicle total And

[0027] Allocating power to each electrical system according to the adjusted total available power P total,adjusted And the priority weights of each electrical system.

[0028] Optionally, the adjusting the current total available power P of the vehicle according to the predicted power consumption demand D within a specific future time period T of the vehicle future Includes total Determining a power distribution adjustment factor α according to the power consumption demand D within a specific future time period T of the vehicle

[0029] And the current available battery power E future And available Adjusting the current total available power P of the vehicle according to the power distribution adjustment factor α to obtain the adjusted total available power P

[0030] And total Obtaining the adjusted total available power P total,adjusted .

[0031] Optionally, the allocating power to each electrical system according to the adjusted total available power P total,adjusted And the priority weights of each electrical system includes calculating the power allocated to each electrical system according to the following formula:

[0032] P alloc,i =P i ×P total,adjusted / ∑Pi, where P alloc,i Is the power allocated to the i-th electrical system, and P i Is the priority weight of the i-th electrical system.

[0033] Optionally, it further includes that if it is determined that the actual total power consumption P per second actual Is greater than the predicted total power consumption P per second pred,total , reallocating power to the electrical system with the lowest priority weight.

[0034] Optionally, the adjustment value for reallocating power to the electrical system with the lowest priority weight is calculated according to the following formula:

[0035] ΔP i_low =-K×E×(1 - P i_low ), where ΔP i_lowis the power adjustment value of the electrical system with the lowest dynamically allocated priority weight, K is an empirical coefficient, and E is the actual total power consumption P per second actual and the predicted total power consumption P per second pred,total The difference, P i_low is the priority weight of the electrical system with the lowest priority weight.

[0036] On the other hand, the present disclosure provides a power distribution control device for a new energy vehicle, including

[0037] a data information processing module that monitors vehicle status information, vehicle power consumption information, driving road information, and external environment information in real time;

[0038] a priority weight allocation module that dynamically allocates priority weights to each electrical system of the vehicle according to the current vehicle status information and the current external environment information;

[0039] a vehicle power consumption estimation module that establishes a vehicle power consumption demand prediction model based on the vehicle power consumption information, the current vehicle speed, and the driving road information, and predicts the power consumption demand D of the vehicle within a specific future time period T future ;

[0040] a power optimization distribution module that distributes power to each electrical system according to the current available battery power E available , the predicted power consumption demand D future , the current total available power P of the vehicle total and the priority weights of each electrical system.

[0041] In the third aspect of the embodiments of the present disclosure, a computer device is provided, including a memory, a processor, and a computer program stored on the memory. When the computer program is run by the processor, it executes the instructions of the method according to any one of the above embodiments.

[0042] In the fourth aspect of this article, a computer storage medium is provided, on which a computer program is stored. When the computer program is run by the processor of a computer device, it executes the instructions of the method according to any one of the above embodiments.

[0043] According to the power distribution control method and control device for a new energy vehicle according to the embodiments of the present disclosure, first, priority weights are dynamically allocated to each electrical system of the vehicle according to the current vehicle status information and the current external environment information. After that, a vehicle power consumption demand prediction model is established based on the vehicle power consumption information, the current vehicle speed, and the driving road information, and the power consumption demand D of the vehicle within a specific future time period T is predicted future , and then according to the current available battery power E available , the predicted power consumption demand D future , the current total available power P of the vehicle totalPower is allocated to each electrical system based on the priority weights of each electrical system. Thus, the allocation strategy can be dynamically adjusted according to real-time data (such as battery status, vehicle speed, environmental conditions) and vehicle status; the allocation decision is optimized using predictive modeling and feedback mechanisms to improve resource utilization efficiency; power can be maximally utilized to ensure that critical systems always receive sufficient power; and various driving scenarios and environmental changes, such as low battery, autonomous driving, or adverse weather, can be accommodated. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] To more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0045] Figure 1 It is a flowchart of a power allocation control method for a new energy vehicle according to a preferred embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] The following describes the preferred embodiments of the present disclosure in conjunction with the accompanying drawings of the specification. It should be understood that the preferred embodiments described herein are only for the purpose of illustrating and explaining the present disclosure and are not intended to limit the present disclosure. And without conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other.

[0047] As Figure 1 shown, the embodiments of the present disclosure provide a power allocation control method for a new energy vehicle, including the following steps:

[0048] Real-time monitor vehicle status information, vehicle power consumption information, driving road information, and external environment information;

[0049] Dynamically allocate priority weights to each electrical system of the vehicle according to the current vehicle status information and the current external environment information;

[0050] Establish a vehicle power consumption demand prediction model based on the vehicle power consumption information, the current vehicle speed, and the driving road information to predict the power consumption demand D of the vehicle within a specific future time period T future ;

[0051] Allocate power to each electrical system according to the current available battery power E available , the predicted power consumption demand D future , the current total available power P of the vehicle total and the priority weights of each electrical system (the power allocated in this step can be understood as the predicted value of the power allocation).

[0052] The above-mentioned vehicle status information, vehicle power consumption information, driving road information and external environment information can be collected from the body control module (BCM) through sensors and the vehicle bus to ensure low latency and high efficiency.

[0053] Specifically, vehicle status information may include battery power information, driving mode information, vehicle speed information, vehicle charging mode information and vehicle emergency fault information; external environment information may include external temperature information, external light information and weather condition information (rain, fog, strong wind and other weather information).

[0054] The vehicle power consumption information may include a total power consumption sequence continuously recorded over the past time, such as a total power consumption sequence of the past 60 minutes [P t-60 ,P t-59 ,…,P t-1 ], the power consumption sequence of each or specific electrical system continuously recorded in the past time, the current power consumption of each electrical system, etc.

[0055] The driving road information may include navigation data, the actual distance d between the vehicle and the next charging station, the slope of the road section, the road condition level, etc.

[0056] More specifically, the battery power information may include, for example, the current battery power SOC, battery temperature, battery voltage, and battery rated power capacity C battery ; Driving mode information may include the vehicle's automatic driving mode, manual driving mode, semi-automatic driving mode, etc.; vehicle speed information may include current vehicle speed, acceleration and other information; vehicle charging mode information may include information on whether the vehicle is in charging mode; vehicle emergency fault information may include information on whether emergency collision signals, manual emergency distress signals, remote emergency trigger signals and other signals are received.

[0057] Preferably, dynamically allocating priority weights to various electrical systems of the vehicle according to current vehicle state information and current external environment information includes:

[0058] Determine the inherent safety factor S of each electrical system according to the electrical system category of each electrical system and the first preset rule i , inherent comfort factor C i and inherent energy efficiency factor E i ;

[0059] Determine the vehicle working scenario according to the vehicle state information and the external environment information, and determine the safety weight W of each electrical system according to the vehicle working scenario and the second preset rule S , comfort weight W C and energy efficiency weight W e ;

[0060] Determine the priority weight of each electrical system according to the safety weight W corresponding to each electrical system S , comfort weight W C , energy efficiency weight W e , inherent safety factor S i , inherent comfort factor C i and inherent energy efficiency factor E i Specifically, the first preset rule includes a pre-stored electrical system category, a first correspondence, a preset inherent safety factor, a preset inherent comfort factor, and a preset inherent energy efficiency factor. The first correspondence is used to represent the one-to-three correspondence between the pre-stored electrical system category and the preset inherent safety factor, the preset inherent comfort factor, and the preset inherent energy efficiency factor.

[0061] Specifically, the second preset rule includes a pre-stored vehicle working scenario, a second correspondence, a preset safety weight, a preset comfort weight, and a preset energy efficiency weight. The second correspondence is used to represent the one-to-three correspondence between the pre-stored vehicle working scenario and the preset safety weight, the preset comfort weight, and the preset energy efficiency weight.

[0062] Intuitively speaking, the following formula is used to determine the priority weight of each electrical system:

[0063] P

[0064] P i =W S ×S i +W C ×C i +W e ×E i , where P i is the priority weight of the i-th electrical system, S i is the inherent safety factor of the i-th electrical system (0 < S i < 1), C i is the inherent comfort factor of the i-th electrical system (0 < C i < 1), E i is the inherent energy efficiency factor of the i-th electrical system (0 < E i < 1); W S is the safety weight, W C is the comfort weight, W e is the energy efficiency weight, W S , W C and W e satisfy W S +W C +W e = 1.

[0065] The inherent safety factor Si, the inherent comfort factor Ci, and the inherent energy efficiency factor Ei are pre-set. These factors are the inherent properties of each electrical system and are determined according to the functions and characteristics of the system during the system design phase.

[0066] Take the braking electrical system as an example:

[0067] Si = 1.0: The braking system is crucial for vehicle safety, so the inherent safety factor Si is set to the maximum value of 1.0.

[0068] Ci = 0.0: The function of the braking system has little to do with user comfort, so the inherent comfort factor Ci is set to 0.0.

[0069] Ei = 0.7: The energy efficiency of the braking electrical system is average because its design prioritizes quick response and reliability rather than power consumption efficiency. Through actual testing or simulation, therefore, the inherent energy efficiency factor Ei is set to the medium level of 0.7.

[0070] The electrical systems in a vehicle usually include a power system, a safety system, an assisted driving system, a body electronics system, an entertainment system, etc. The power system usually includes a battery management system, a motor drive system, a charging system, etc. Next, Table 1 is given as an example to present the first preset rule for determining the inherent factors of each electrical system.

[0071] Table 1

[0072]

[0073]

[0074] It can be understood that once a specific electrical system (such as the braking electrical system) is determined, its corresponding Si, Ci, and Ei values are fixed accordingly. These factors are quantitative representations of the inherent properties of the system, are pre-assigned during the vehicle design phase, and are used as constants during the algorithm operation.

[0075] Therefore, it can be further understood that after the inherent comfort factor Ci, the inherent safety factor Si, and the inherent energy efficiency factor Ei are determined, by dynamically adjusting the safety weight W S , the comfort weight W C , and the energy efficiency weight W e , the priority weights are dynamically allocated to each electrical system.

[0076] Next, Table 2 is given as an example to present the second preset rule for determining the safety weight W S , the comfort weight W C , and the energy efficiency weight W e .

[0077] Table 2

[0078]

[0079]

[0080] As shown in Table 2, in this embodiment, the pre-stored vehicle working scenarios may include: low battery scenario, autonomous driving scenario, normal driving scenario, highway driving scenario, emergency failure scenario, charging scenario, and bad weather scenario.

[0081] In some embodiments, when determining the vehicle working scenario based on the vehicle status information and external environment information, it is determined with the highest priority whether the vehicle is in an emergency failure scenario. If so, the corresponding safety weight W S 、comfort weight W C and energy efficiency weight W e are determined.

[0082] It can be understood that, as shown in Table 2, if it is determined that the current vehicle is in an emergency failure scenario, according to the second preset rule, the weights are set as:

[0083] W s = 1.0; W c = 0.0; W e = 0.0.

[0084] In some embodiments, when determining the vehicle working scenario based on the vehicle status information and external environment information, if it is determined that the vehicle is not in an emergency failure scenario, it is then determined whether the vehicle is in a low battery scenario. If so, the corresponding safety weight W s 、comfort weight W c and energy efficiency weight W e are determined.

[0085] It can be understood that, as shown in Table 2, if the emergency failure scenario is excluded and it is determined that the current vehicle is in a low battery scenario, according to the second preset rule, the weights are set as:

[0086] W s = 0.7; W c = 0.1; W e = 0.2.

[0087] It can be understood that the priorities of the remaining vehicle working scenarios are lower than those of the emergency failure scenario and the low battery scenario, and no further priorities are set. After determining the vehicle working scenario, the corresponding safety weight W s 、comfort weight W c and energy efficiency weight W e are determined accordingly.

[0088] Thus, according to the first preset rule and the second preset rule, the priority weights of each electrical system can be dynamically adjusted.

[0089] In some embodiments, a vehicle power consumption demand prediction model is established based on vehicle power consumption information, current vehicle speed, and driving road information. Predicting the vehicle's power consumption demand for a specific future period includes:

[0090] Determining the average total power P of the vehicle during a specific first historical period according to the vehicle power consumption information avg and the load power adjustment factor k load ;

[0091] Determining the vehicle speed adjustment factor k according to the current vehicle speed v ;

[0092] Determining the road condition adjustment factor k according to the driving road information r ;

[0093] Predicting the vehicle's power consumption demand D for a specific future period T according to the average total power P of the vehicle during a specific first historical period avg , the load power adjustment factor k load , the vehicle speed adjustment factor k v and the road condition adjustment factor k r future .

[0094] Specifically, the average total power P of the vehicle during a specific first historical period avg In this embodiment, it can be the average total power consumption in the past 10 minutes (the average of the total power consumption of each electrical system). It can be understood that the specific first period is a 10-minute historical period. For example, P avg = 4100W.

[0095] Specifically, determining the load power adjustment factor k according to the vehicle power consumption information load may include: determining the load power adjustment factor k according to the current power P of a specific electrical system load and the average power P of the specific electrical system during a specific second historical period base load .

[0096] In this embodiment, the current power P of the specific electrical system load is the current power of the air conditioning system, and the average power P of the specific electrical system during a specific second historical period base is the average power of the air conditioning system in the past 60 minutes. For example, P load_空调 = 1000W; P base_空调 = 4000W.

[0097] The load power adjustment factor k load is determined according to the following formula: k load = 1 + P load / P base。In this embodiment, k load = 1 + 1000 / 4000 = 1.25.

[0098] In some embodiments, the vehicle speed adjustment factor k is determined according to the current vehicle speed v including normalizing the current vehicle speed V to obtain the normalized vehicle speed value V normal and inputting the normalized vehicle speed value V normal into a preset vehicle speed adjustment factor function for calculation to obtain the vehicle speed adjustment factor k v .

[0099] k v is determined according to the following preset vehicle speed adjustment factor function:

[0100] k v = 1 + y × V normal , where y is an empirical coefficient. In this embodiment, y = 0.5, which is an empirical value obtained through experiments or simulations.

[0101] In this embodiment, it is assumed that the current vehicle speed is 60 km / h; the maximum designed vehicle speed V max = 200 km / h, V normal = V / V max = 0.3. Then k v = 1 + 0.5 × 0.3 = 1.15.

[0102] In some embodiments, the road condition adjustment factor k is determined according to the driving road information r including normalizing the actual distance d from the current vehicle position to the next charging station to obtain the normalized distance value d normal , normalizing the current road section slope slope to obtain the normalized slope value slope_normal, and normalizing the current road condition level road_condition to obtain the normalized road condition value road_condition_normal; inputting the normalized distance value dnormal, the normalized slope value slope_normal, and the normalized road condition value road_condition_normal into a preset road condition adjustment factor function to obtain the road condition adjustment factor k r .

[0103] That is, k r is determined according to the following formula:

[0104] k r = 1 + k1 × d normal+k2×slope_normal + k3×road_condition_normal, where k1 is the first preset adjustment coefficient, k2 is the second preset adjustment coefficient, and k3 is the third preset adjustment coefficient. In this embodiment, k1 = 0.2; k2 = 0.3; k3 = 0.1.

[0105] More specifically, d normal According to d normal = d / d max It is calculated that, where d is the actual distance from the current vehicle position to the next charging station, and d max is the full - charge driving range of the vehicle;

[0106] slope_normal is calculated according to slope_normal = slope / slope_max, where slope is the slope of the current road section and slope_max is the maximum designed slope of the vehicle;

[0107] road_condition_normal is calculated according to road_condition_normal = (road_condition - road_condition_min) / (road_condition_max - road_condition_min), where road_condition is the current road condition level, road_condition_min is the minimum road condition level, and road_condition_max is the maximum road condition level.

[0108] In this embodiment, d = 20km; d max = 400km. d normal = d / d max = 0.05, indicating that the current distance accounts for 5% of the driving range.

[0109] In this embodiment, slope = 5°; slope_max = 30°. slope_normal = slope / slope_max ≈ 0.1667.

[0110] In this embodiment, the road condition level is represented by numbers and divided into five levels from 1 - 5 (for example, 1 represents flat and 5 represents bumpy); road_condition = 3; road_condition_min = 1; road_condition_max = 5. road_condition_normal = (3 - 1) / (5 - 1) = 2 / 4 = 0.5.

[0111] Therefore, k r= 1 + 0.2×0.05 + 0.3×0.167 + 0.1×0.5

[0112] = 1 + 0.01 + 0.0501 + 0.05

[0113] ≈1.1101。

[0114] In summary, the electricity consumption demand of the vehicle within a specific future time period T is calculated by the following formula:

[0115] D future = P avg ×k v ×k load ×k r ×T / 60,

[0116] In the formula, D future is the electricity consumption demand of the vehicle within the future T time period, in watt-hours; P avg is the average total power of the vehicle in a specific first historical time period, in watts; k v is the vehicle speed adjustment factor, k load is the load power adjustment factor, k r is the road condition adjustment factor, and T is the duration of the predicted electricity consumption demand, in minutes.

[0117] Therefore, in this embodiment, D future = P avg ×k v ×k load ×k r ×T / 60 = 4100×1.15×1.25×1.1101×5 / 60 = 545.22 wh.

[0118] In some embodiments, allocating power to each electrical system according to the available electricity E of the current battery available , the predicted electricity consumption demand D of the vehicle within a specific future time period T future , the total available power P of the current vehicle total and the priority weights of each electrical system includes,

[0119] Adjusting the total available power P of the current vehicle according to the predicted electricity consumption demand D of the vehicle within a specific future time period T future Allocating power to each electrical system according to the adjusted total available power P total and the priority weights of each electrical system.

[0120] According to the adjusted total available power P total,adjusted and the priority weights of each electrical system to allocate power to each electrical system.

[0121] It can be understood that in this text, the electrical quantity is the total amount of energy (kWh / Wh), which represents the energy stored in the battery or the consumption over a period of time and is used to evaluate long-term sustainability. The electric power is the power (W), which represents the rate of energy use and is used for real-time allocation to each electrical system. The electrical quantity (E available and D future ) is used for future planning, and the electric power (P total and P alloc,i ) is used for current execution. In this disclosure, when calculating the electrical quantity and the electric power, the units only need to be unified.

[0122] Specifically, according to the predicted electricity consumption demand D within a specific future time period T of the vehicle future adjusting the current total available electric power P of the vehicle total may include,

[0123] determining a power distribution adjustment factor α according to the electricity consumption demand D within a specific future time period T of the vehicle future and the current available battery electrical quantity E available ;

[0124] adjusting the current total available electric power P of the vehicle according to the power distribution adjustment factor α total to obtain the adjusted total available electric power P total,adjusted .

[0125] Therefore, it may specifically include:

[0126] determining α = min(1, D future / E available ), where α is the power distribution adjustment factor, and E available is the current available battery electrical quantity (the data involved is obtained from the vehicle status information), and the unit is watt-hour or kilowatt-hour. This formula reflects the pressure of future demand on the current available energy. If (D future / E available ) > 1, it means that the future demand exceeds the current available energy, and at this time, α = 1 is taken; if α < 1, the pressure is smaller.

[0127] E available is calculated according to E available = SOC normal *C battery , where SOC normal is the value after normalizing the battery electrical quantity, and C battery is the rated battery electrical quantity capacity. In this embodiment, C battery = 50 kwh; SOC normal = 0.2, and E available = SOC normal *C battery = 10 kwh.

[0128] Therefore, assume D future = 8 kwh; α = min(1, 8 / 10) = 0.8.

[0129] Next, determine P total,adjusted = P total ×(1 - k×α), where P total,adjusted is the total available power after adjustment, in watts; P total is the current total available power of the vehicle (determined by the power management system), in watts; k is an empirical coefficient. In this embodiment, k = 0.5; P total = 6000W; α = min(1, 8 / 10) = 0.8. Therefore, P total,adjusted = 6000×(1 - 0.5×0.8) = 3600W.

[0130] More specifically, allocating power to each electrical system according to the total available power P total,adjusted after adjustment and the priority weights of each electrical system includes calculating the power allocated to each electrical system according to the following formula:

[0131] P alloc,i = P i ×P total,adjusted / ∑Pi, where P alloc,i is the power allocated to the i-th electrical system, and P i is the priority weight of the i-th electrical system.

[0132] Specifically, taking three electrical systems, namely the braking system P1 (power range 1000 - 1500W), the air conditioning system P2 (power range 0 - 1200W), and the audio system P3 (power range 0 - 500W) as an example, assuming the current vehicle is in a high-speed driving scenario, it can be determined according to Table 1 and Table 2 that:

[0133] P1 = 0.5*1 + 0.2*0 + 0.3*0.7 = 0.71;

[0134] P2 = 0.5*0.2 + 0.2*0.8 + 0.3*0.5 = 0.41;

[0135] P3 = 0.5*0 + 0.2*0.6 + 0.3*0.4 = 0.24;

[0136] ∑Pi = 0.71 + 0.41 + 0.24 = 1.36.

[0137] P alloc,1 = P1×P total,adjusted / ∑Pi = 0.71×3600 / 1.36 ≈ 1879W. Since it exceeds the upper limit of 1500W, therefore, take Palloc,1 = 1500 W.

[0138] P alloc,2 = P2 × P total,adjusted / ∑Pi = 0.41 × 3600 / 1.36 ≈ 1085 W, within the power range of the air - conditioning system. Therefore, take P alloc,2 = 1085 W.

[0139] P alloc,3 = P3 × P total,adjusted / ∑Pi = 0.24 × 3600 / 1.36 ≈ 635 W. Since it exceeds the upper limit of 500 W, therefore, P alloc,3 = 500 W.

[0140] Optionally, the method further includes that if it is determined that the actual total power consumption P actual per second is greater than the predicted total power consumption P pred,total per second, re - allocate the power for the electrical system with the lowest priority weight.

[0141] Optionally, the adjustment value for re - allocating the power for the electrical system with the lowest priority weight is calculated according to the following formula:

[0142] ΔP i_low = -K × E × (1 - P i_low ), where ΔP i_low is the power adjustment value for the electrical system with the lowest priority weight dynamically allocated, K is an empirical coefficient, E is the difference between the actual total power consumption P actual per second and the predicted total power consumption P pred,total per second, and P i_low is the priority weight of the electrical system with the lowest priority weight.

[0143] In this embodiment, assume K = 0.5, E = P actual - P pred,total = 3800 W - 3000 W = 800 W. The electrical system with the lowest priority weight is the audio system, and the initially allocated power is 500 W, and its priority weight P i_low is 0.2. Then ΔP i_low = -K × E × (1 - P i_low ) = -0.5 × 800 × (1 - 0.2) = -320 W. Then, the power P alloc,i_low，adjuested allocated to the audio system after re - adjustment is P alloc,i_low + ΔP i_low = 500 + (-320 W) = 180 W. The above - mentioned adjustment frequency can be set to once every 20 minutes.

[0144] On the other hand, the present disclosure provides a power distribution control device for a new - energy vehicle, including,

[0145] A data information processing module that monitors vehicle status information, vehicle power consumption information, driving road information, and external environment information in real time;

[0146] A priority weight allocation module that dynamically allocates priority weights to each electrical system of the vehicle according to the current vehicle status information and the current external environment information;

[0147] A vehicle power consumption prediction module that establishes a vehicle power consumption demand prediction model based on the vehicle power consumption information, the current vehicle speed, and the driving road information, and predicts the power consumption demand D of the vehicle within a specific future time period T future ;

[0148] A power optimization allocation module that allocates power to each electrical system according to the current available battery power E available , the predicted power consumption demand D future , the current total available power P of the vehicle total and the priority weights of each electrical system.

[0149] It can be understood that in addition to monitoring and storing data, the data information processing module can also be responsible for preprocessing the collected data, such as data normalization processing, etc.

[0150] In some embodiments, the power distribution control device of the new energy vehicle can be an intelligent power distribution box. It can be understood that the power distribution control methods of the above embodiments can be applied to the control module of the intelligent power distribution box.

[0151] The present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory. When the computer program is run by the processor, it executes the instructions of the method according to any one of the above embodiments.

[0152] The present invention also provides a computer storage medium, on which a computer program is stored. When the computer program is run by the processor of a computer device, it executes the instructions of the method according to any one of the above embodiments.

[0153] It should be understood that in various embodiments herein, the magnitudes of the serial numbers of the above processes do not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments herein.

[0154] It should also be understood that in the embodiments herein, the term "and / or" is merely a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0155] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of the examples have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this article.

[0156] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0157] In the several embodiments provided in this article, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed couplings, direct couplings, or communication connections to each other can be indirect couplings or communication connections through some interfaces, devices, or units, and can also be electrical, mechanical, or other forms of connection.

[0158] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of this article.

[0159] In addition, the functional units in the various embodiments of this article can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0160] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this article, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this article. The aforementioned storage medium includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0161] Specific embodiments are applied in this article to elaborate on the principles and implementation manners of this article. The description of the above embodiments is only used to help understand the method of this article and its core idea; at the same time, for those of ordinary skill in the art, according to the idea of this article, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to this article.

Claims

1. A power distribution control method for a new energy vehicle, characterized in that It includes the following steps: Real-time monitor vehicle status information, vehicle power consumption information, driving road information, and external environment information; Dynamically allocate priority weights to each electrical system of the vehicle according to the current vehicle status information and the current external environment information; Establish a vehicle power consumption demand prediction model based on the vehicle power consumption information, the current vehicle speed, and the driving road information to predict the power consumption demand D of the vehicle within a specific future time period T future ; According to the available battery power E at present available , the predicted power consumption demand D future , the total available vehicle power P at present total and the priority weights of each electrical system, power is allocated to each electrical system.

2. The power distribution control method according to claim 1, wherein The vehicle status information includes battery power information, driving mode information, vehicle speed information, vehicle charging mode information, and vehicle emergency fault information; the external environment information includes external temperature information, external light information, and weather condition information.

3. The power distribution control method according to claim 2, wherein The dynamically allocating priority weights to each electrical system of the vehicle according to the current vehicle status information and the current external environment information includes Determine the inherent safety factor S, inherent comfort factor C, and inherent energy efficiency factor E of each electrical system according to the electrical system category of each electrical system and the first preset rule i , inherent comfort factor C i , and inherent energy efficiency factor E i ; Determine the vehicle working scenario based on the vehicle state information and the external environment information, and determine the safety weight W, comfort weight W, and energy efficiency weight W of each electrical system according to the vehicle working scenario and the second preset rule S , comfort weight W C , and energy efficiency weight W e ; Determine the priority weights of each electrical system according to the safety weight W S corresponding to each electrical system, the comfort weight W C corresponding to each electrical system, the energy efficiency weight W e corresponding to each electrical system, the inherent safety factor S i corresponding to each electrical system, the inherent comfort factor C i corresponding to each electrical system, and the inherent energy efficiency factor E i corresponding to each electrical system.

4. The power distribution control method according to claim 3, wherein The first preset rule includes pre-stored electrical system categories, a first correspondence, a preset inherent safety factor, a preset inherent comfort factor, and a preset inherent energy efficiency factor. The first correspondence is used to represent the one-to-three correspondence between the pre-stored electrical system categories and the preset inherent safety factor, the preset inherent comfort factor, and the preset inherent energy efficiency factor.

5. The power distribution control method according to claim 3, characterized in that The second preset rule includes pre-stored vehicle working scenarios, a second correspondence, a preset safety weight, a preset comfort weight, and a preset energy efficiency weight. The second correspondence is used to represent the one-to-three correspondence between the pre-stored vehicle working scenarios and the preset safety weight, the preset comfort weight, and the preset energy efficiency weight.

6. The power distribution control method according to claim 5, wherein The pre-stored vehicle working scenarios include: low battery power scenario, autonomous driving scenario, normal driving scenario, high-speed driving scenario, emergency fault scenario, charging scenario, and bad weather scenario.

7. The power distribution control method according to claim 6, wherein When determining the vehicle working scenario based on the vehicle state information and the external environment information, it is determined with the highest priority whether the vehicle is in an emergency failure scenario. If so, the corresponding safety weight W S , comfort weight W C and energy efficiency weight W e are determined.

8. The power distribution control method according to claim 7, wherein When determining the vehicle working scenario based on the vehicle state information and the external environment information, if it is determined that the vehicle is not in an emergency failure scenario, continue to determine whether the vehicle is in a low battery scenario. If so, determine the corresponding safety weight W S , comfort weight W C and energy efficiency weight W e .

9. The power distribution control method according to claim 1, characterized in that, The establishing a vehicle power consumption demand prediction model according to the vehicle power consumption information, the current vehicle speed, and the driving road information to predict the power consumption demand of the vehicle within a specific future period includes: Determine the average total power P of the vehicle during a specific historical first period based on the vehicle power consumption information avg and the load power adjustment factor k load ; Determine the vehicle speed adjustment factor k according to the current vehicle speed v ; Determine the road condition adjustment factor k according to the driving road information r ; Based on the average total power P during a specific first period of the vehicle's history avg 、the load power adjustment factor k load 、the vehicle speed adjustment factor k v and the road condition adjustment factor k r predict the electricity consumption demand D within a specific future period T of the vehicle future .

10. The power distribution control method according to claim 1, wherein According to the current available battery power E available , the predicted power consumption demand D within a specific future period T of the vehicle future , the current total available power P of the vehicle total and the priority weights of each electrical system to allocate power to each electrical system, including Based on the predicted electricity consumption demand D of the vehicle within a specific future time period T future adjust the current total available power P of the vehicle total to perform adjustment Allocate power to each electrical system according to the adjusted total available power P total,adjusted and the priority weights of each electrical system 11. The power distribution control method according to claim 10, wherein The predicted electricity consumption demand D of the vehicle within a specific future time period T according to the prediction future Adjusting the current total available power P of the vehicle total includes According to the electricity consumption demand D within a specific future time period T of the vehicle future and the current available battery power E available Determine the power distribution adjustment factor α; Adjust the current total available power P of the vehicle according to the power distribution adjustment factor α total to obtain the adjusted total available power P total,adjusted .

12. The power distribution control method according to claim 11, wherein Said according to the adjusted total available power P total,adjusted And the priority weights of each electrical system to allocate power to each electrical system, including calculating the power allocated to each electrical system according to the following formula: P alloc,i = P i × P total,adjusted / ∑Pi, where P alloc,i is the power allocated to the i-th electrical system, and P i is the priority weight of the i-th electrical system.

13. The power distribution control method according to claim 12, wherein It also includes, if the actual total power consumption per second P is determined actual Greater than the total power consumption P predicted per second pred,total , reallocate power to the electrical system with the lowest priority weight.

14. The power distribution control method according to claim 13, wherein The adjustment value for reallocating power to the electrical system with the lowest priority weight is calculated according to the following formula: ΔP i_low = -K × E × (1 - P i_low ), where ΔP i_low is the power adjustment value of the electrical system with the lowest dynamically allocated priority weight, K is an empirical coefficient, E is the difference between the actual total power consumption P actual per second and the predicted total power consumption P pred,total per second, and P i_low is the priority weight of the electrical system with the lowest priority weight.

15. A power distribution control device for a new energy vehicle, characterized in that, including A data information processing module that real-time monitors vehicle status information, vehicle power consumption information, driving road information, and external environment information; A priority weight allocation module that dynamically allocates priority weights to each electrical system of the vehicle according to the current vehicle status information and the current external environment information; The vehicle power consumption estimation module establishes a vehicle power consumption demand prediction model based on the vehicle power consumption information, the current vehicle speed, and the driving road information, and predicts the power consumption demand D of the vehicle within a specific future time period T. future ; The power optimization distribution module distributes power to each electrical system according to the current available battery power E available , the predicted power consumption demand D future , the current total available vehicle power P total and the priority weights of each electrical system 16. A computer device, comprising a memory, a processor, and a computer program stored on the memory, characterized in that, When the computer program is run by the processor, it executes the instructions of the method according to any one of claims 1 to 14.

17. A computer storage medium, on which a computer program is stored, characterized in that, When the computer program is run by the processor of the computer device, it executes the instructions of the method according to any one of claims 1 to 14.

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