CN117175077A

CN117175077A - Method and apparatus for controlling cooling of vehicle battery, and computer-readable storage medium

Info

Publication number: CN117175077A
Application number: CN202311226727.7A
Authority: CN
Inventors: 王燕; 刘建康; 刘元治; 王德平; 牛超凡; 李坤远; 霍云龙; 刘力源; 车显达
Original assignee: FAW Group Corp
Current assignee: FAW Group Corp
Priority date: 2023-09-21
Filing date: 2023-09-21
Publication date: 2023-12-05

Abstract

The invention discloses a cooling control method, a cooling control device and a computer readable storage medium for a vehicle battery. The method relates to the field of intelligent automobiles, and comprises the following steps: acquiring historical big data of a vehicle running on a current path, and battery temperature and battery attribute of the vehicle, wherein the historical big data are historical road condition data and historical running data of the vehicle running on the current path, and the battery temperature is the current temperature of the battery; based on the historical big data, predicting to obtain a predicted speed value of the vehicle running on the current path; determining a temperature change curve of the battery based on the battery attribute and the predicted speed value; vehicle battery cooling is controlled based on the temperature profile and the battery temperature. The invention solves the technical problem of poor safety of the battery caused by overheat of the battery.

Description

Method and apparatus for controlling cooling of vehicle battery, and computer-readable storage medium

Technical Field

The present invention relates to the field of intelligent automobiles, and in particular, to a cooling control method and apparatus for a vehicle battery, and a computer readable storage medium.

Background

At present, a new energy automobile is powered by a power battery, and the power battery works for a long time to overheat the battery, so that the safety of the automobile is affected. Therefore, the battery cooling technology is an important research topic of the electric automobile technology, and the battery cooling technology is continuously advancing and innovating to adapt to the development requirement of the electric automobile and ensure the safety and performance of the battery. However, the conventional battery cooling technology adjusts the operation of the cooling system according to the real-time temperature and the real-time state of the battery, and the battery still has the condition of overheat temperature, resulting in poor battery safety.

In view of the above problems, no effective solution has been proposed at present.

Disclosure of Invention

The embodiment of the invention provides a cooling control method, a cooling control device and a computer readable storage medium for a vehicle battery, which are used for at least solving the technical problem of poor battery safety caused by overheating of the battery.

According to an aspect of an embodiment of the present invention, there is provided a cooling control method of a vehicle battery, including: acquiring historical big data of a vehicle running on a current path, and battery temperature and battery attribute of the vehicle, wherein the historical big data are historical road condition data and historical running data of the vehicle running on the current path, and the battery temperature is the current temperature of the battery; based on the historical big data, predicting to obtain a predicted speed value of the vehicle running on the current path; determining a temperature change curve of the battery based on the battery attribute and the predicted speed value; vehicle battery cooling is controlled based on the temperature profile and the battery temperature.

Optionally, based on the historical big data, predicting a predicted speed value of the vehicle running on the current path includes: acquiring preset big data of a plurality of vehicles running on a current path, wherein the preset big data are road condition data and running data of the plurality of vehicles running on the current path; based on the historical big data, constructing a state transition prediction model, wherein the state transition prediction model is used for determining the state transition probability of the vehicle at the next moment; and based on preset big data, predicting by using a state transition prediction model to obtain a predicted speed value.

Optionally, constructing the state transition prediction model based on the historical big data includes: performing discrete processing on the historical running speed in the historical big data according to the preset speed discrete interval value to obtain a plurality of limited speed values; based on a plurality of limited vehicle speed values, constructing a state transition probability matrix, wherein the state transition probability matrix is a matrix formed by the probability that the vehicle speed of the vehicle is transited from any one of the plurality of limited vehicle speed values to another vehicle speed value, and the other vehicle speed value is a vehicle speed value except any one of the plurality of limited vehicle speed values; and constructing a state transition prediction model based on the state transition probability matrix and the historical driving speed.

Optionally, constructing a state transition probability matrix based on a plurality of finite vehicle speed values includes: determining a vehicle speed transition number of the vehicle from a first vehicle speed value to a second vehicle speed value and a total transition number of the vehicle from the first vehicle speed value to other vehicle speed values based on the plurality of limited vehicle speed values, wherein the first vehicle speed value is a running vehicle speed of the vehicle at a first moment, the second vehicle speed value is a running vehicle speed of the vehicle at a next moment of the first moment, and the other vehicle speed values are vehicle speed values except the first vehicle speed value and the second vehicle speed value in the plurality of limited vehicle speed values; determining a state transition probability of the transition from the first vehicle speed value to the second vehicle speed value based on the number of vehicle speed transitions and the total number of transitions; based on the state transition probabilities, a state transition probability matrix of the vehicle is constructed.

Optionally, constructing a state transition prediction model based on the state transition probability matrix and the historical driving vehicle speed includes: vectorizing the historical driving speed to obtain an initial vehicle speed state vector, wherein the initial vehicle speed state vector is an initial vehicle speed vector without state transition; performing state transition on the initial vehicle speed state vector according to a preset step number to obtain a target state vector, wherein the target state vector is a vehicle speed vector subjected to state transition; and constructing a state transition prediction model of the vehicle based on the state transition probability matrix and the target state vector.

Optionally, determining a temperature change curve of the battery based on the battery attribute and the predicted speed value includes: acquiring historical ambient temperature of the battery; constructing a heat generation model of the battery based on the battery attribute and the predicted speed value; constructing a heat dissipation model of the battery based on the battery attribute and the historical environmental temperature; and determining a temperature change curve of the battery based on the heat generation model and the heat dissipation model.

Optionally, controlling the vehicle battery cooling based on the temperature profile and the battery temperature includes: determining a temperature variation difference of the vehicle based on the temperature variation curve and the battery temperature; determining a cooling strategy of the battery based on the temperature variation difference and the battery temperature; and controlling the cooling of the vehicle battery according to the cooling strategy.

Optionally, the method further comprises: acquiring the duration of the battery temperature in a preset temperature interval based on the battery temperature; determining a cooling strategy of the battery based on the battery temperature in response to the duration exceeding a preset duration; and controlling the cooling of the vehicle battery according to the cooling strategy.

According to another aspect of the embodiment of the present invention, there is also provided a cooling control device of a vehicle battery, including: the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring historical big data of a current path of a vehicle, battery temperature and battery attribute of the vehicle, wherein the historical big data are historical road condition data and historical driving data of the vehicle driving to the current path, and the battery temperature is the current temperature of the battery; the prediction module is used for predicting and obtaining the predicted speed of the vehicle running on the current path based on the historical big data; the determining module is used for determining a temperature change curve of the battery based on the battery attribute and the predicted speed value; and the control module is used for controlling the cooling of the vehicle battery based on the temperature change curve and the battery temperature.

According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium including a stored program, wherein the cooling control method of the vehicle battery of any one of the above embodiments is performed in a processor of a device in which the program is controlled to run.

According to another aspect of an embodiment of the present invention, there is also provided a vehicle including: one or more processors; a storage means for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors are caused to perform the cooling control method of the vehicle battery of any one of the above embodiments.

In the embodiment of the invention, historical big data of the vehicle running on the current path is obtained, and the battery temperature and the battery attribute of the vehicle are obtained; based on the historical big data, predicting to obtain a predicted speed value of the vehicle running on the current path; determining a temperature change curve of the battery based on the battery attribute and the predicted speed value; vehicle battery cooling is controlled based on the temperature profile and the battery temperature. It should be noted that, because the running speed of the vehicle affects the heat dissipation of the battery, the predicted speed value of the running of the vehicle on the current path is predicted by using the historical big data, and the temperature change curve of the battery is determined, so that the battery can be cooled in advance, the purpose of improving the safety of the battery is achieved, the technical effect of cooling the battery in advance is achieved, and the technical problem that the safety of the battery is poor due to overheat of the battery is solved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:

fig. 1 is a flowchart of a cooling control method of a vehicle battery according to an embodiment of the present application;

FIG. 2 is a schematic diagram of an alternative battery cooling implementation system according to an embodiment of the present application;

FIG. 3 is a schematic diagram of an alternative battery cooling control system according to an embodiment of the present application;

FIG. 4 is a flow chart of an alternative battery cooling control method according to an embodiment of the application;

fig. 5 is a schematic view of a cooling control device for a vehicle battery according to an embodiment of the present application.

Detailed Description

In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

According to an embodiment of the present invention, there is provided an embodiment of a cooling control method of a vehicle battery, it being noted that the steps shown in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is shown in the flowchart, in some cases the steps shown or described may be performed in an order different from that herein.

Fig. 1 is a flowchart of a cooling control method of a vehicle battery according to an embodiment of the present invention, as shown in fig. 1, the method including the steps of:

step S102, historical big data of the vehicle running on the current path, battery temperature and battery attribute of the vehicle are obtained, wherein the historical big data are historical road condition data and historical running data of the vehicle running on the current path, and the battery temperature is the current temperature of the battery.

The vehicle described above may be a vehicle that is powered by a power battery, including but not limited to: pure electric vehicles and hybrid electric vehicles.

The current path may be a path for determining the vehicle to travel according to the origin and the destination, and may be determined according to the user's selection or may be determined according to the vehicle location since there may be a plurality of paths from the origin to the destination. If the vehicle is already in the running process, the current path determined by the vehicle positioning is mainly used.

The above-mentioned historical big data may be historical driving data and historical road condition data of a vehicle that has historically driven on the current path, including but not limited to: average vehicle speed, total distance, average acceleration, average power consumption, starting position, end position and external average environment temperature. The historical driving data may be data generated during driving of the vehicle on the current path. The historical road condition data may be road condition data of the road section at the current time historically.

The above-mentioned battery temperature may be a battery temperature detected in real time.

The battery properties described above may be the performance of the battery itself, including but not limited to: battery volume, battery specific heat capacity, battery mass.

In an alternative embodiment, the vehicle location is obtained in real time by a global positioning system (Global Positioning System, abbreviated as GPS) after the user inputs the origin and destination. And determining the rest road sections of the current path according to the vehicle positioning, and selecting historical big data about the rest road sections in the database. And determining the battery attribute according to the data stored in the system, and acquiring the battery temperature by using a temperature sensor in real time.

In another alternative embodiment, after receiving the origin and the destination input by the user, determining a plurality of routes capable of reaching the destination from the origin, calculating an estimated travel time, and transmitting the plurality of routes and the estimated travel time to the client or the central control screen, wherein the user selects a target route according to the plurality of routes provided by the system, and the target route may be a path selected by the user to travel. The GPS acquires vehicle positioning in real time so as to acquire historical big data of the current path in real time. The historical big data can be divided according to the time of weekends, holidays, four seasons, morning and evening time periods and the like, so that the historical big data related to the current path is selected according to the current driving time of the user. For example, when the current driving time of the user is 15 pm on the four weeks, the historical big data of the current path in the period from 15 pm to 17 pm in the week is selected. And meanwhile, determining the battery attribute according to the data stored in the system, and acquiring the battery temperature by using a temperature sensor in real time.

Step S104, based on the historical big data, a predicted speed value of the vehicle running on the current path is predicted.

The predicted speed value may be a value that predicts a speed at which the vehicle travels on the current path for a period of time in the future.

In an alternative embodiment, all the vehicle running speeds in the current path in the historical big data are obtained, and the running speeds are scattered by a neighbor method to obtain a plurality of limited speed values, wherein the plurality of limited speed values comprise a plurality of possible speeds and the occurrence times of each speed. The possible state transition probabilities of each vehicle speed are calculated, and a state transition matrix is determined based on the plurality of state transition probabilities. The state transition probability may be a probability of a vehicle speed change, and the state transition matrix may be a matrix composed of a plurality of state transition probabilities. Thereby constructing a state transition prediction model by using the historical big data and the state transition matrix. And further, acquiring preset big data of a plurality of vehicles on the current path, wherein the preset big data can be current running data and road condition data of the plurality of vehicles, including but not limited to: number of vehicles, average speed, average acceleration. Substituting the preset big data into a state transition prediction model to obtain a predicted speed value.

The historical big data includes the origin and the destination corresponding to each driving cycle, and the average speed, the average acceleration and the external average environmental temperature data of the corresponding road section of the vehicle can be determined according to the driving data of the vehicle. Wherein the route determined according to the originating point and the destination in the history big data contains the current path, and thus the originating point and the destination in the history big data may not coincide with the current destination and the originating point of the user. Table 1 is a historical data table, as shown in Table 1, including origin, destination, average speed, total distance, average acceleration, external average ambient temperature, average power consumption, where origin is denoted by A1, A2 … An, destination is denoted by B1, B2, …, bn, average speed corresponding to origin and destination road segments is denoted by Representation, corresponding toS for total distance of road section _total1 、S _total2 、…、S _totaln Representing the average acceleration of the corresponding road segment>Indicating the outside average ambient temperature of the corresponding road section>And (3) representing. Average power consumption EC of corresponding road section _avg1 、EC _avg2 、…、EC _avgn And (3) representing.

TABLE 1 historical big data sheet

Step S106, determining a temperature change curve of the battery based on the battery attribute and the predicted speed value.

The temperature change curve may be an image describing a change in the battery temperature over a period of time in the future, and may describe a trend in temperature change with rising, falling, stabilizing, or the like.

In an alternative embodiment, a historical ambient temperature is obtained, where the historical ambient temperature may be the ambient temperature around the battery when the vehicle is historically traveling on the current path. And determining the heat generation rate and the liquid cooling heat dissipation convection heat exchange area of the battery according to the predicted speed value, constructing a heat generation model according to the heat generation rate, the battery volume, the battery specific heat capacity and the battery quality of the battery, and determining how much heat the battery can generate in a future period of time, wherein the heat generation model can be a model for calculating the heat generation of the battery. And constructing a heat radiation model according to the liquid cooling heat radiation convection heat exchange coefficient, the liquid cooling heat radiation convection heat exchange area, the historical environment temperature, the specific heat capacity of the battery and the battery quality, wherein the heat radiation model can be a model for calculating heat radiation of the battery. And calculating a temperature change curve of the battery in a future period of time by using the heat generation model and the heat dissipation model.

In yet another alternative embodiment, a historical ambient temperature of the current path is obtained. And determining the heat generation rate and the liquid cooling heat dissipation convection heat exchange area of the battery according to the predicted speed value, so that the heat generation rate, the battery volume, the battery specific heat capacity and the battery quality of the battery are utilized to calculate the heat output in a future period. The heat dissipation of the battery for a period of time in the future is calculated by using the liquid cooling heat dissipation and convection heat exchange coefficient, the liquid cooling heat dissipation and convection heat exchange area, the historical ambient temperature, the specific heat capacity of the battery and the mass of the battery. And then calculates the difference between the generated heat and the emitted heat, thereby determining the temperature change curve of the battery.

It should be noted that when the vehicle speed is high, the battery needs to provide more energy to drive the vehicle, which may result in an increased chemical reaction inside the battery, thereby generating more heat. Therefore, the higher the vehicle speed, the higher the heat generation rate of the battery. And liquid cooling heat dissipation is a way of dissipating heat through circulation of cooling liquid, and the efficiency of convective heat transfer is related to the contact area between the cooling liquid and air. When the vehicle speed is lower, the air flow is slower, the contact area between the liquid cooling radiator and the air is smaller, and the heat exchange efficiency is relatively lower. And when the vehicle speed increases, the air flow speed also increases, and the contact area between the liquid cooling radiator and the air increases, thereby increasing the heat exchange efficiency. Therefore, the heat generation rate and the liquid cooling heat dissipation convection heat exchange area of the battery are required to be determined according to the predicted speed value, so that heat dissipation of the battery can be better achieved.

Step S108, controlling cooling of the vehicle battery based on the temperature change curve and the battery temperature.

In an alternative embodiment, the temperature change difference of the battery is determined from the temperature change curve for a period of time in the future. And determining a cooling strategy of the battery according to the battery temperature and the temperature variation difference, so as to control the cooling of the battery of the vehicle according to the cooling strategy. According to the cooling strategy which is more suitable for the current road condition and can be matched with the battery temperature and the temperature variation difference, the cooling of the vehicle battery is controlled according to the cooling strategy, the temperature of the vehicle battery can be stabilized in a proper range, the problem that the battery is overheated in a future period of time is avoided, the situation that the battery is overheated is prevented, and the safety of the battery is improved.

Through the steps, the historical big data of the vehicle running on the current path can be obtained, and the battery temperature and the battery attribute of the vehicle can be obtained; based on the historical big data, predicting to obtain a predicted speed value of the vehicle running on the current path; determining a temperature change curve of the battery based on the battery attribute and the predicted speed value; vehicle battery cooling is controlled based on the temperature profile and the battery temperature. It should be noted that, because the running speed of the vehicle affects the heat dissipation of the battery, the predicted speed value of the running of the vehicle on the current path is predicted by using the historical big data, and the temperature change curve of the battery is determined, so that the battery can be cooled in advance, the purpose of improving the safety of the battery is achieved, the technical effect of cooling the battery in advance is achieved, and the technical problem that the safety of the battery is poor due to overheat of the battery is solved.

It should be noted that fig. 2 is a schematic diagram of an alternative battery cooling execution system according to an embodiment of the present invention, as shown in fig. 2, in the battery cooling execution system, 21 is a power battery, 22 is an expansion tank, 23 is an electric water pump, 24 is a three-way valve, 25 is a radiator, 26 is a radiator fan, 27 is an air conditioning system, and 28 is an air conditioning heat exchanger, wherein 1 of the three-way valve is a first port of the three-way valve, 2 of the three-way valve is a second port of the three-way valve, and 3 is a third port of the three-way valve, when the first port and the second port of the three-way valve are connected, and the first port and the third port are closed, the radiator is the power battery for cooling, and when the first port and the second port of the three-way valve are closed, and the first port and the third port are connected, the air conditioning system is the power battery for cooling.

The technical scheme of the application can be realized by a battery cooling control system consisting of a vehicle navigation system, a vehicle control unit (Vehicle Control Unit, VCU for short), a cooling fan, an air conditioner controller, a battery management system (Battery Management System, BMS for short), an electric water pump and a cloud server. Fig. 3 is a schematic diagram of an optional battery cooling control System according to an embodiment of the present application, as shown in fig. 3, a vehicle navigation System sends front road condition information to a VCU (for example, a predicted driving time from a starting point to a destination, a driving distance, and a front vehicle flow average speed) in real time, an air conditioner controller executes an air conditioner compressor load control command sent by the VCU and feeds back an air conditioner compressor actual load and an air conditioner on/off state to the VCU, a cooling fan executes a fan load command sent by the VCU and feeds back a fan actual load state to the VCU, a battery management System feeds back a battery pack temperature, a battery System on Chip SOC (simply referred to as SOC), and a battery pack available charge/discharge power to the VCU, a cloud server sends vehicle driving history big data to the VCU, an electric water pump executes an electric water pump rotation speed command sent by the VCU and feeds back an electric water pump actual rotation speed to the VCU, and the VCU are comprehensively determined, calculated and analyzed to obtain a fan control command, an air conditioner control command, an electric water pump control command, and an electric water pump control command, etc., and then corresponding components are sent.

The plurality of vehicles may be vehicles traveling on a current route at a current time.

The road condition data may be a road condition of a current path at a current time. The travel data may be data generated during the current travel of the plurality of vehicles.

The state transition prediction model may be a model for predicting a state of the vehicle at a next time, and may predict whether a state transition occurs at the next time, and predict and simulate a state transition of the vehicle from a current state to other states.

In an alternative embodiment, the historical driving speed in the historical big data is scattered into a plurality of limited speed values, different speed values are numbered, the different speed values are ordered according to the numbers, the occurrence times of the different speed values are determined, so that the state transition probability among the different speed values is calculated, the state transition probability is discharged according to the numbers, and a state transition probability matrix is formed. Thereby constructing a state transition prediction model by using the historical big data and the state transition matrix. And simultaneously acquiring preset big data related to the current path, and substituting the preset big data into a state transition prediction model to obtain a predicted speed value.

The preset speed discrete interval value may be a speed discrete interval value set in advance according to a specific situation, and the historical running vehicle speed is valued according to the preset speed discrete interval value, so that the historical running vehicle speed is dispersed into a plurality of limited vehicle speed values, and the running vehicle speed is divided into a plurality of possible types, which may be but not limited to: 5km/h, 4km/h.

The plurality of limited vehicle speed values described above may be a limited plurality of vehicle speed values including, but not limited to: a first vehicle speed value, a second vehicle speed value, and other vehicle speed values.

In an alternative embodiment, the historical driving speed is discretized into a plurality of finite values according to the preset speed discrete interval values by using a neighbor method, and the plurality of finite values can be expressed as: v (V) _X ∈{V ₁ ，V ₂ ，…，V _n }. And the different vehicle speed values are numbered u=1, 2, …, i, …, j, …, n, wherein i represents the ith vehicle speed state and j represents the jth vehicle speed state. Calculating a plurality of finite values from any one of the vehicle speed values V _i Shift to another vehicle speed value V _j Probability P of (2) _i,j The calculation formula is expressed as: p (P) _i,j ＝P(v(k+1)＝V _j |v(k)＝V _i ). Thereby constructing a state transition probability matrix. Wherein P is _i,j Representing the ith row and jth column elements. Thus, a state transition prediction model is constructed according to the state transition probability matrix and the historical driving speed.

The first vehicle speed value may be any one of a plurality of limited vehicle speed values.

The second vehicle speed value may be any one of a plurality of limited vehicle speed values different from the first vehicle speed value.

The other vehicle speed value may be a vehicle speed value different from the first vehicle speed value and the second vehicle speed value among the plurality of limited vehicle speed values.

The number of vehicle speed transitions described above may be the number of transitions from the first vehicle speed value to the second vehicle speed value among a plurality of limited vehicle speed values.

The total number of transfers may be a total number of transfers of a first vehicle speed value to other vehicle speed values of the plurality of limited vehicle speed values.

The above-described state transition probability may be a probability that the vehicle transitions from the first vehicle speed value to the second vehicle speed value.

In an alternative embodiment, a first vehicle speed value V is calculated _i Transfer to a second vehicle speed value V _j Number of vehicle speed transitions F _i,j And a first vehicle speed value V _i Total transfer times to other vehicle speed values, and the vehicle speed transfer times F _i,j Adding the total transfer times to obtain a first addition number F _i . Calculating the number F of the first vehicle speed value and the vehicle speed transfer _i,j And a first addition number F _i To obtain the state transition probability P _i,j Thereby constructing a state transition probability matrix P.

The initial vehicle speed state vector may be a plurality of mutually independent vehicle speed state vectors determined according to the historical traveling vehicle speed.

The preset step number may be a state transition step number set in advance according to specific requirements, and may be, but is not limited to: 5. 6, 7.

The target state vector may be a vehicle speed state vector after the initial vehicle speed state is transferred according to a preset step state.

In an alternative embodiment, the historical driving speed is vectorized to obtain a plurality of independent initial vehicle speed state vectors S (0), which can be expressed specifically as:

S(0)＝[S ₁ (0)，S ₂ (0)，…，S _m (0)，…，S _n (0)]

Wherein S is _m (0) An initial vehicle speed vector denoted as m-state. Performing state transition on the initial vehicle speed state vector according to the preset step number to obtain a target state vector S (k), which can be specifically expressed as:

S(k)＝[S ₁ (k)，S ₂ (k)，…，S _m (k)，…，S _n (k)]

wherein S is _m (k) A state vector denoted as the m-state at time k. Using the target state vector S (k) and the initial state vector S (0), the state transition probability matrix P constructs a state transition prediction model, and the specific state transition prediction model is expressed as:

simplifying the state transition prediction model to obtain

V(k)＝[(U _k -1)+r _k ]d

Wherein V (k) is expressed as the running speed of the vehicle at time k, U _k The predicted speed value is expressed as k time, d is expressed as a preset speed discrete interval value, and r _k The random numbers are uniformly distributed for the k time.

In an alternative embodiment, the product of the heat generation rate q of the battery and the battery volume V is calculated to obtain a first product, and the specific heat capacity c of the battery and the battery mass m are calculated _batt Obtaining a second product value, and further calculating the ratio of the first product value to the second product value to obtain a heat generation model, wherein the heat generation model is specifically as follows:

wherein q is the heat generation rate of the battery, V is the battery volume, c is the specific heat capacity of the battery, m _batt Is the battery quality.

Calculating the battery temperature T (i) at the ith moment and the ambient temperature T _envir And obtaining a first difference. Calculating the liquid cooling convective heat transfer coefficient h and the liquid cooling heat transfer area A _batt And obtaining a third product value. And calculating the product value of the third product value and the first difference value to obtain a fourth product value. Calculating the ratio of the fourth product value to the second product value to obtain a heat dissipation model, wherein the heat dissipation model is specifically as follows:

wherein T (i) is the battery temperature at the ith moment, T _envir At ambient temperature, A _batt The heat exchange area is the liquid cooling heat dissipation convection heat exchange area, and h is the liquid cooling heat convection heat exchange coefficient.

According to the heat generation model and the heat radiation model, calculating the battery temperature T (i+1) at the i+1 time, wherein the specific calculation formula is as follows:

and drawing a graph by taking time as an abscissa and temperature values as an ordinate to obtain a temperature change curve.

The temperature change difference may be a temperature difference of a vehicle running on a current path for a period of time in the future, wherein a positive number indicates a temperature rise and a negative number indicates a temperature drop.

The cooling strategy described above may be a strategy for cooling the battery in advance, including but not limited to: the first cooling strategy, the second cooling strategy, the third cooling strategy and the fourth cooling strategy.

In an alternative embodiment, the temperature change difference of the battery is determined from the temperature change curve for a period of time in the future. If the battery temperature is less than the first temperature threshold and the temperature variation difference is less than the first temperature difference, determining the cooling strategy of the battery as the first cooling strategy, wherein the first temperature threshold may be a smaller temperature value set in advance according to specific conditions for determining the cooling strategy of the battery, and may be but is not limited to: 35 ℃. The first temperature difference is a larger temperature variation difference previously set for the battery cooling strategy according to circumstances, and may be, but is not limited to: 5 ℃. If the battery temperature is less than the first temperature threshold and the temperature variation difference is greater than the first temperature difference, determining the cooling strategy of the battery as a second cooling strategy. If the battery temperature is greater than or equal to the first temperature threshold and less than the second temperature threshold, and the temperature variation difference is less than the second temperature difference, determining the cooling strategy of the battery as the first cooling strategy, wherein the second temperature threshold can be a larger temperature value preset according to specific conditions for determining the cooling strategy of the battery, and the second temperature threshold is greater than the first temperature threshold. The second temperature difference may be a smaller temperature variation difference value previously set for determining the battery cooling strategy according to circumstances, the second temperature difference being smaller than the first temperature difference. If the temperature of the battery is greater than or equal to the first temperature threshold and less than the second temperature threshold, and the temperature variation difference is greater than or equal to the second temperature difference and less than the first temperature difference, determining the cooling strategy of the battery as a second cooling strategy. If the temperature of the battery is greater than or equal to the first temperature threshold and less than the second temperature threshold, and the temperature variation difference is greater than the first temperature difference, determining the cooling strategy of the battery as a third cooling strategy; if the temperature of the battery is greater than the second temperature threshold and the temperature variation difference is smaller than the second temperature threshold, determining the cooling strategy of the battery as a second cooling strategy; if the temperature of the battery is greater than the second temperature threshold and the temperature variation difference is greater than or equal to the second temperature difference and less than the first temperature difference, determining the cooling strategy of the battery as a third cooling strategy; if the battery temperature is greater than the second temperature threshold and the temperature variation difference is greater than the first temperature difference, determining the cooling strategy of the battery as a fourth cooling strategy. Thereby controlling vehicle battery cooling in accordance with the battery cooling strategy.

As shown in fig. 2, tbatt is a battery temperature, T1 is a first temperature threshold, T2 is a second temperature threshold, Δt is a temperature change difference, Δt2 is a first temperature difference, and Δt1 is a second temperature difference, table 2 is a battery cooling strategy determination condition table.

Table 2 battery cooling strategy based on battery temperature and temperature rise

	△T＜△T1	△T1≤△T＜△T2	△T2≤△T
				Tbatt＜T1	First cooling strategy	First cooling strategy	Second cooling strategy
T1≤Tbatt＜T2	First cooling strategy	Second cooling strategy	Third Cooling strategy
				T2≤Tbatt	Second cooling strategy	Third Cooling strategy	Fourth cooling strategy

Table 3 is a battery cooling strategy table, and as shown in fig. 3, the first cooling strategy is that the first interface and the second interface of the three-way valve are turned on, the first interface and the third interface are turned off, and the highest rotation speed of the electric water pump is adjusted to 20%, the load of the cooling fan is 0%, and the load of the air conditioner compressor is 0%. The second cooling strategy is that the first interface and the second interface of the three-way valve are conducted, the first interface and the third interface are closed, the highest rotating speed of the electric water pump is adjusted to be 50%, the load of the cooling fan is 80%, and the load of the air conditioner compressor is 0%. The third cooling strategy is that the first interface and the second interface of the three-way valve are closed, the first interface and the third interface are conducted, the highest rotating speed of the electric water pump is adjusted to 80%, the load of the cooling fan is 0%, and the load of the air conditioner compressor is 50%. The fourth cooling strategy is that the first interface and the second interface of the three-way valve are closed, the first interface and the third interface are conducted, the highest rotating speed of the electric water pump is adjusted to be 100%, the load of the cooling fan is 0%, and the load of the air conditioner compressor is 100%.

Table 3 battery cooling strategy table

The preset temperature interval may be a temperature interval set in advance according to specific conditions, including but not limited to a first temperature interval, a second temperature interval, a third temperature interval, and a fourth temperature interval, where the first temperature interval may be, but not limited to: below 30 ℃, the second temperature interval may be, but is not limited to: [30-35 ℃) the third temperature interval may be, but is not limited to: [35-40 ℃) the fourth temperature interval may be, but is not limited to: greater than 40 ℃.

The above-mentioned time period may be a time period during which the battery is in a preset temperature interval.

The preset time period may be a time period set in advance according to a specific situation for judging whether the battery is in a high temperature state for a long time, including but not limited to: the method comprises the steps of a first preset time period, a second preset time period, a third preset time period and a fourth preset time period. The first preset duration may be, but is not limited to: 12 minutes and 15 minutes. The second preset duration may be, but is not limited to: 10 minutes and 8 minutes. The third preset duration may be, but is not limited to: 5 minutes, 4 minutes. The fourth preset duration may be, but is not limited to: 1 minute, 30 seconds.

In an alternative embodiment, the battery temperature and the duration of the time when the battery temperature is in the preset temperature interval are obtained in real time, wherein table 4 is a table of temperature rise decision cooling strategies, and if the battery temperature is in the first temperature interval and the duration exceeds the first preset duration, the cooling strategy of the battery is determined to be the first cooling strategy as shown in table 4. And if the battery temperature is in the second temperature interval and the duration exceeds a second preset duration, determining the cooling strategy of the battery to be a second cooling strategy. And if the temperature of the battery is in the third temperature interval and the duration exceeds a third preset duration, determining the cooling strategy of the battery as a third cooling strategy. And if the temperature of the battery is in the fourth temperature interval and the duration exceeds the fourth preset duration, determining the cooling strategy of the battery as a fourth cooling strategy.

Table 4 is a temperature rise determining cooling strategy table

Tbatt	＜30℃	[30,35℃)	[35,40℃)	＞40℃
					△t	15min	10min	5min	1min
Cooling strategy	First cooling strategy	Second cooling strategy	Third Cooling strategy	Fourth cooling strategy

Fig. 4 is a flowchart of an alternative battery cooling control method according to an embodiment of the present invention, as shown in fig. 2, which is as follows:

in step S401, the vehicle navigation system collects preset big data and sends the data to the battery cooling control system.

Step S402, the battery cooling control system predicts the road condition of the current path according to the navigation information and the historical big data, and obtains the predicted speed value of the vehicle.

Step S403, determining a battery temperature change curve according to the predicted speed value and the battery attribute.

Step S404, determining a battery cooling strategy according to the battery temperature and the battery temperature change curve.

Step S405, the battery is subjected to cooling treatment according to the battery cooling strategy.

Example 2

According to the embodiment of the present invention, there is further provided a cooling control device for a vehicle battery, where the device may execute the cooling control method for a vehicle battery in the foregoing embodiment, and the specific implementation manner and the preferred application scenario are the same as those in the foregoing embodiment, and are not described herein in detail.

Fig. 5 is a schematic view of a cooling control device for a vehicle battery according to an embodiment of the present invention, as shown in fig. 5, including the following parts: the acquisition module 50, the prediction module 52, the determination module 54, and the control module 56.

The acquiring module 50 is configured to acquire historical big data of a current path of the vehicle, a battery temperature of the vehicle, and a battery attribute, where the historical big data is historical road condition data and historical driving data of the vehicle driving to the current path, and the battery temperature is a current temperature of the battery;

A prediction module 52, configured to predict, based on the historical big data, a predicted speed at which the vehicle travels on the current path;

a determining module 54 for determining a temperature change curve of the battery based on the battery attribute and the predicted speed value;

a control module 56 for controlling vehicle battery cooling based on the temperature profile and the battery temperature.

Optionally, the prediction module includes: the first acquisition unit is used for acquiring preset big data of a plurality of vehicles running on a current path, wherein the preset big data are road condition data and running data of the plurality of vehicles running on the current path; a first construction unit for constructing a state transition prediction model based on the history big data, wherein the state transition prediction model is used for determining the state transition probability of the vehicle at the next moment; and the prediction unit is used for obtaining a predicted speed value by using a state transition prediction model based on preset big data.

Optionally, the first building unit comprises: the discrete processing subunit is used for performing discrete processing on the historical running speed in the historical big data according to the preset speed discrete interval value to obtain a plurality of limited speed values; a first construction subunit configured to construct a state transition probability matrix based on a plurality of finite vehicle speed values, where the state transition probability matrix is a matrix formed by probabilities that vehicle speeds of vehicles transition from any one of the plurality of finite vehicle speed values to another vehicle speed value, and the other vehicle speed value is a vehicle speed value other than any one of the plurality of finite vehicle speed values; and the second construction subunit is used for constructing a state transition prediction model based on the state transition probability matrix and the historical driving speed.

Optionally, the first construction subunit is further configured to determine, based on the plurality of limited vehicle speed values, a number of times of vehicle speed transition of the vehicle from the first vehicle speed value to the second vehicle speed value, and a total number of times of vehicle transition from the first vehicle speed value to other vehicle speed values, where the first vehicle speed value is a running vehicle speed of the vehicle at a first moment, the second vehicle speed value is a running vehicle speed of the vehicle at a next moment of the first moment, and the other vehicle speed values are vehicle speed values other than the first vehicle speed value and the second vehicle speed value among the plurality of limited vehicle speed values; determining a state transition probability of the transition from the first vehicle speed value to the second vehicle speed value based on the number of vehicle speed transitions and the total number of transitions; based on the state transition probabilities, a state transition probability matrix of the vehicle is constructed.

Optionally, the second construction subunit is further configured to perform vectorization processing on the historical driving vehicle speed to obtain an initial vehicle speed state vector, where the initial vehicle speed state vector is an initial vehicle speed vector without state transition; performing state transition on the initial vehicle speed state vector according to a preset step number to obtain a target state vector, wherein the target state vector is a vehicle speed vector subjected to state transition; and constructing a state transition prediction model of the vehicle based on the state transition probability matrix and the target state vector.

Optionally, the determining module includes: a second acquisition unit configured to acquire a historical ambient temperature of the battery; a second construction unit for constructing a heat generation model of the battery based on the battery attribute and the predicted speed value; the third construction unit is used for constructing a heat dissipation model of the battery based on the battery attribute and the historical environment temperature; and a first determination unit for determining a temperature change curve of the battery based on the heat generation model and the heat radiation model.

Optionally, the control module includes: a second determination unit configured to determine a temperature variation difference of the vehicle based on the temperature variation curve and the battery temperature; a third determination unit for determining a cooling strategy of the battery based on the temperature variation difference and the battery temperature; and the control unit is used for controlling the cooling of the vehicle battery according to the cooling strategy.

Optionally, the obtaining module is further configured to obtain a duration of the battery temperature in the preset temperature interval based on the battery temperature; the determining module is further configured to determine a cooling strategy of the battery based on the battery temperature in response to the duration exceeding a preset duration; the control module is also configured to control cooling of the vehicle battery in accordance with a cooling strategy.

Example 3

Example 4

According to another aspect of an embodiment of the present application, there is also provided a vehicle including: one or more processors; a storage means for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors are caused to perform the cooling control method of the vehicle battery of any one of the above embodiments.

The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.

In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.

In the several embodiments provided in the present application, it should be understood that the disclosed technology may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.

Claims

1. A cooling control method of a vehicle battery, characterized by comprising:

acquiring historical big data of the vehicle running on a current path, wherein the historical big data are historical road condition data and historical running data of the vehicle running on the current path, and the battery temperature is the current temperature of the battery;

based on the historical big data, predicting a predicted speed value of the vehicle running on the current path;

determining a temperature change curve of the battery based on the battery attribute and the predicted speed value;

and controlling the vehicle battery to cool based on the temperature change curve and the battery temperature.

2. The cooling control method of the vehicle battery according to claim 1, characterized in that predicting a predicted speed value at which the vehicle runs on the current path based on the history big data includes:

Acquiring preset big data of a plurality of vehicles running on the current path, wherein the preset big data are road condition data and running data of the plurality of vehicles running on the current path;

based on the historical big data, constructing a state transition prediction model, wherein the state transition prediction model is used for determining the state transition probability of the vehicle at the next moment;

and based on the preset big data, predicting the predicted speed value by using the state transition prediction model.

3. The cooling control method of the vehicle battery according to claim 2, characterized by constructing a state transition prediction model based on the history big data, comprising:

performing discrete processing on the historical driving vehicle speed in the historical big data according to a preset speed discrete interval value to obtain a plurality of limited vehicle speed values;

constructing a state transition probability matrix based on the plurality of limited vehicle speed values, wherein the state transition probability matrix is a matrix formed by the probability that the vehicle speed of the vehicle is transited from any one of the plurality of limited vehicle speed values to another vehicle speed value, and the another vehicle speed value is a vehicle speed value except any one of the plurality of limited vehicle speed values;

And constructing a state transition prediction model based on the state transition probability matrix and the historical driving speed.

4. The cooling control method of the vehicle battery according to claim 3, characterized by constructing a state transition probability matrix based on the plurality of limited vehicle speed values, comprising:

determining a number of vehicle speed transitions of the vehicle from a first vehicle speed value, which is a running vehicle speed of the vehicle at a first time, to a second vehicle speed value, which is a running vehicle speed of the vehicle at a time next to the first time, and a total number of transitions of the vehicle from the first vehicle speed value to other vehicle speed values, which are vehicle speed values other than the first vehicle speed value and the second vehicle speed value, of the plurality of limited vehicle speed values, based on the plurality of limited vehicle speed values;

determining a state transition probability of the first vehicle speed value transitioning to the second vehicle speed value based on the number of vehicle speed transitions and the total number of transitions;

and constructing a state transition probability matrix of the vehicle based on the state transition probability.

5. The cooling control method of the vehicle battery according to claim 3, characterized in that constructing a state transition prediction model based on the state transition probability matrix and the historic running vehicle speed includes:

vectorizing the historical driving vehicle speed to obtain an initial vehicle speed state vector, wherein the initial vehicle speed state vector is an initial vehicle speed vector without state transition;

performing state transition on the initial vehicle speed state vector according to a preset step number to obtain a target state vector, wherein the target state vector is a vehicle speed vector subjected to state transition;

and constructing a state transition prediction model of the vehicle based on the state transition probability matrix and the target state vector.

6. The cooling control method of the vehicle battery according to claim 1, characterized in that determining a temperature change curve of the battery based on the battery property and the predicted speed value includes:

acquiring a historical ambient temperature of the battery;

constructing a heat generation model of the battery based on the battery attribute and the predicted speed value;

constructing a heat dissipation model of the battery based on the battery attributes and the historical ambient temperature;

And determining a temperature change curve of the battery based on the heat generation model and the heat dissipation model.

7. The cooling control method of the vehicle battery according to claim 1, characterized in that controlling the vehicle battery to cool based on the temperature change curve and the battery temperature, comprises:

determining a temperature variation difference of the vehicle based on the temperature variation curve and the battery temperature;

determining a cooling strategy of the battery based on the temperature variation difference and the battery temperature;

and controlling the cooling of the vehicle battery according to the cooling strategy.

8. The cooling control method of a vehicle battery according to claim 1, characterized in that the method further comprises:

acquiring the duration of the battery temperature in a preset temperature interval based on the battery temperature;

determining a cooling strategy of the battery based on the battery temperature in response to the duration exceeding a preset duration;

9. A cooling control device for a vehicle battery, characterized by comprising:

the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring historical big data of a current path of the vehicle, battery temperature and battery attribute of the vehicle, wherein the historical big data are historical road condition data and historical driving data of the vehicle driving to the current path, and the battery temperature is the current temperature of the battery;

The prediction module is used for predicting the predicted speed of the vehicle running on the current path based on the historical big data;

a determining module configured to determine a temperature change curve of the battery based on the battery attribute and the predicted speed value;

and the control module is used for controlling the cooling of the vehicle battery based on the temperature change curve and the battery temperature.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium includes a stored program, wherein the cooling control method of the vehicle battery according to any one of claims 1 to 7 is executed in a processor of a device where the program is controlled to run.