CN117141315A

CN117141315A - Method and device for cooling battery in vehicle, processor and vehicle

Info

Publication number: CN117141315A
Application number: CN202311195655.4A
Authority: CN
Inventors: 王燕; 王德平; 刘建康; 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: 2023-09-15
Filing date: 2023-09-15
Publication date: 2023-12-01

Abstract

The invention discloses a cooling method and device for a battery in a vehicle, a processor and the vehicle. The method comprises the following steps: acquiring running condition information of a vehicle and state information of a battery in the vehicle in the running process of the vehicle, wherein the state information is used for representing the working state of the battery in the running process of the vehicle; based on the driving working condition information and the state information, establishing an energy consumption state model of the battery and constraint conditions of the energy consumption state model, wherein the energy consumption state model is used for determining energy consumed by the battery and/or a battery cooling system of the vehicle; determining target cooling strategy data of the battery based on the energy consumption state model and the constraint condition, wherein the target cooling strategy data is used for representing a strategy for executing cooling operation on the battery; and controlling the battery cooling system to perform cooling operation on the battery in response to a control instruction corresponding to the target cooling strategy data. The invention solves the technical problem of poor effect of cooling the battery.

Description

Method and device for cooling battery in vehicle, processor and vehicle

Technical Field

The invention relates to the field of vehicles, in particular to a method and a device for cooling a battery in a vehicle, a processor and the vehicle.

Background

In the related art, if the battery needs to be cooled, the current temperature of the battery needs to be detected, and when the temperature reaches a certain threshold value, the cooling device of the battery needs to be controlled to cool the battery, however, since the battery cannot be cooled according to different working conditions in real time, and since the battery itself electric energy needs to be consumed for cooling, the situation that the vehicle is poor in cruising time exists, and therefore, the technical problem of poor effect of cooling the battery still exists.

Aiming at the technical problem that the effect of cooling the battery is poor in the related art, no effective solution is proposed at present.

Disclosure of Invention

The embodiment of the invention provides a cooling method and device for a battery in a vehicle, a processor and the vehicle, and aims to at least solve the technical problem of poor cooling effect on the battery.

According to one aspect of an embodiment of the present invention, a method of cooling a battery in a vehicle is provided. The method may include: acquiring running condition information of a vehicle and state information of a battery in the vehicle in the running process of the vehicle, wherein the state information is used for representing the working state of the battery in the running process of the vehicle; based on the driving working condition information and the state information, establishing an energy consumption state model of the battery and constraint conditions of the energy consumption state model, wherein the energy consumption state model is used for determining energy consumed by the battery and/or a battery cooling system of the vehicle; determining target cooling strategy data of the battery based on the energy consumption state model and the constraint condition, wherein the target cooling strategy data is used for representing a strategy for executing cooling operation on the battery; and controlling the battery cooling system to perform cooling operation on the battery in response to a control instruction corresponding to the target cooling strategy data.

Optionally, the battery cooling system includes at least one of: fan device, air conditioning equipment and water pump equipment, state information includes at least initial temperature of battery, current temperature of battery and current power of battery, based on driving condition information and state information, establishes the constraint condition of energy consumption state model and energy consumption state model of battery, includes: performing differential processing on the fan power of the fan device, the air conditioning power of the air conditioning device and the water pump power of the water pump device to obtain a first energy consumption state sub-model, wherein the first energy consumption state sub-model is used for determining the energy consumed by the battery cooling system; determining a second energy consumption state sub-model based on a fan load of the fan apparatus, wherein the second energy consumption state sub-model is used to determine a fan power; determining a third energy consumption state sub-model based on an air conditioning compressor load of the air conditioning device, wherein the third energy consumption state sub-model is used for determining air conditioning power; determining a fourth energy consumption state sub-model based on the water pump rotational speed of the water pump device, wherein the fourth energy consumption state sub-model is used for determining the water pump power; determining a fifth energy consumption state sub-model based on the fan load, the air conditioner compressor load, the water pump rotating speed, the initial temperature and the current power, wherein the fifth energy consumption state sub-model is used for determining the current temperature, and the energy consumption state model comprises a first energy consumption state sub-model, a second energy consumption state sub-model, a third energy consumption state sub-model, a fourth energy consumption state sub-model and a fifth energy consumption state sub-model; and establishing constraint conditions of the energy consumption state model.

Optionally, establishing the constraint condition of the energy consumption state model includes: based on fan load, air conditioner compressor load, water pump speed and current temperature in the energy consumption state model, constraint conditions are established.

Optionally, determining target cooling strategy data for the battery based on the energy consumption state model and the constraints includes: and processing the energy consumption state model by adopting a dynamic programming algorithm according to the constraint condition, and determining target cooling strategy data.

Optionally, according to the constraint condition, processing the energy consumption state model by adopting a dynamic programming algorithm, and determining target cooling strategy data, including: initializing dynamic planning information corresponding to a dynamic planning algorithm to obtain an initialization result; discretizing the initialization result to obtain a discretized result; and processing the discrete processing result and the energy consumption state model by adopting a dynamic programming algorithm to obtain target cooling strategy data so that the energy consumption of the battery cooling system reaches a target state.

Optionally, during the running process of the vehicle, acquiring running condition information of the vehicle includes: acquiring navigation information in the running process of the vehicle; and predicting the driving condition information based on the historical driving data and the navigation information of the vehicle.

Optionally, predicting the driving condition information based on the historical driving data and the navigation information of the vehicle includes: discretizing the speed data in the historical driving data and the speed data in the navigation information to obtain a discrete result; determining a state transition probability of the vehicle based on the discrete result, wherein the state transition probability is used for representing the transition probability from the speed state of the vehicle at the current moment to the speed state of the vehicle at the next moment; generating a state transition probability matrix of the vehicle according to the state transition probability; based on the state transition probability matrix, determining an initial speed state vector of the vehicle and a target speed state vector after the occurrence of state transition; generating a predictive model based on the initial speed state vector and the target speed state vector; and predicting the driving condition information based on the prediction model.

According to another aspect of the embodiment of the present invention, there is also provided a cooling device for a battery in a vehicle. The apparatus may include: the system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring running condition information of a vehicle and state information of a battery in the vehicle in the running process of the vehicle, wherein the state information is used for representing the working state of the battery in the running process of the vehicle; the system comprises a building unit, a control unit and a control unit, wherein the building unit is used for building an energy consumption state model of a battery and constraint conditions of the energy consumption state model based on driving working condition information and state information, and the energy consumption state model is used for determining the energy consumed by the battery and/or a battery cooling system of a vehicle; a determining unit configured to determine target cooling strategy data of the battery based on the energy consumption state model and the constraint condition, wherein the target cooling strategy data is used to represent a strategy for performing a cooling operation on the battery; and the control unit is used for responding to the control instruction corresponding to the target cooling strategy data and controlling the battery cooling system to execute cooling operation on the battery.

According to another aspect of an embodiment of the present invention, there is also provided a processor. The processor is used for running a program, wherein the program runs to execute the cooling method of the battery in the vehicle.

According to another aspect of an embodiment of the present invention, there is also provided a computer-readable storage medium. The computer-readable storage medium includes a stored program, wherein the program when executed by a processor controls a device in which the storage medium resides to perform a method of cooling a battery in a vehicle according to an embodiment of the present invention.

According to another aspect of an embodiment of the present invention, there is also provided a vehicle. The vehicle is used for executing the cooling method of the battery in the vehicle according to the embodiment of the invention.

In the embodiment of the invention, in the running process of the vehicle, the running condition information of the vehicle and the state information of the battery in the vehicle are obtained, wherein the state information is used for representing the working state of the battery in the running process of the vehicle; based on the driving working condition information and the state information, establishing an energy consumption state model of the battery and constraint conditions of the energy consumption state model, wherein the energy consumption state model is used for determining energy consumed by the battery and/or a battery cooling system of the vehicle; determining target cooling strategy data of the battery based on the energy consumption state model and the constraint condition, wherein the target cooling strategy data is used for representing a strategy for executing cooling operation on the battery; and controlling the battery cooling system to perform cooling operation on the battery in response to a control instruction corresponding to the target cooling strategy data. That is, in the running process of the vehicle, the embodiment of the invention can acquire the running condition information of the vehicle and the state information of the battery, and according to the running condition information and the state information, the energy consumption state model of the battery and the constraint condition for constraining the energy consumption state model are established so as to determine the target cooling strategy data of the battery, and when the control instruction corresponding to the target cooling strategy data is acquired, the battery cooling system can be controlled to perform cooling operation on the battery, so that the technical problem of poor effect of cooling the battery is solved, and the technical effect of improving the effect of cooling the battery is realized.

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 method of cooling a battery in a vehicle according to an embodiment of the present application;

fig. 2 is a schematic diagram of a cooling control system of a power battery of a pure electric vehicle of a navigation system according to an embodiment of the present application;

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

fig. 4 is a flowchart of a battery cooling control method according to an embodiment of the present application;

fig. 5 is a schematic view of a cooling apparatus for a battery in a vehicle 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 method of cooling a battery in a vehicle, 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 sequence is shown in the flowchart, in some cases the steps shown or described may be performed in a different order than what is shown or described herein.

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

step S101, in the running process of the vehicle, running condition information of the vehicle and state information of a battery in the vehicle are obtained, wherein the state information is used for representing the working state of the battery in the running process of the vehicle.

In the technical solution provided in the above step S101 of the present invention, the running condition information of the vehicle and the state information of the battery in the vehicle may be obtained. Wherein the battery may be a power battery. The vehicle may be a pure electric vehicle. The driving condition information may be front road condition information collected by the navigation system, and may also be prediction information of driving conditions, for example, distance from the destination, predicted driving time from the destination, average speed of traffic, etc. It should be noted that, the prediction information in the driving condition is only illustrated and not specifically limited herein.

Alternatively, the State information of the battery may be battery information transmitted through a battery management system (Battery Management System, abbreviated as BMS), such as a battery temperature, a State of Charge (SOC), an allowable Charge and discharge power of the battery, and the like. In this case, the method and process for acquiring the state information of the battery are not particularly limited, but are not specifically exemplified herein.

In the embodiment of the invention, the starting position, the interruption position, the average speed, the total distance and the like of the vehicle in the running process can be detected in real time, so that the data are stored through the cloud, and the running condition information of the vehicle can be predicted by the cloud, wherein the cloud can be used for forming the stored data into historical big data. For example, the driving condition of the vehicle during the current driving process can be predicted by the historical big data. At the moment, the technical effect of improving the accuracy of acquiring the running condition information of the vehicle in the running process is achieved by considering the running condition information of the vehicle at different moments.

Step S102, based on the driving condition information and the state information, an energy consumption state model of the battery and constraint conditions of the energy consumption state model are established, wherein the energy consumption state model is used for determining energy consumed by the battery and/or a battery cooling system of the vehicle.

In the technical scheme provided in the step S102, after the running condition information of the vehicle and the state information of the battery in the vehicle are obtained, an energy consumption state model of the battery and a constraint condition for constraining the energy consumption state model can be established, so that the purpose of determining the energy consumed by the battery cooling system by using the energy consumption state model is achieved. For example, the battery cooling system may include a power battery, an electric water pump, an expansion tank, a radiator (including a radiator fan), an air conditioning system, a three-way valve, an air conditioning heat exchanger, and the like, and the battery cooling system is merely exemplified herein without specific limitation to the devices included in the battery cooling system. The energy consumption state model may be an energy consumption state model determined based on a fan load of the fan device, or an energy consumption state model determined based on an air conditioner compressor load of the air conditioner device. The manner of determining the energy consumption state model is only exemplified here, and the manner of determining the energy consumption state model and the process are not specifically limited, and are not specifically exemplified here.

Optionally, the final energy consumption state model is obtained by establishing an energy consumption state equation corresponding to the fan device, an energy consumption state equation corresponding to the air conditioning device, an energy consumption state equation corresponding to the battery and other energy consumption state equations corresponding to other devices, wherein the energy consumption state model can comprise an energy consumption state model of the fan device, an energy consumption state model of the air conditioning device, an energy consumption state model of the water pump device and the like. This is by way of example only.

According to the embodiment of the invention, according to the obtained driving condition information and state information, an energy consumption state model of the fan equipment, an energy consumption state model of the air conditioner equipment, an energy consumption state model of the water pump equipment and the like of the battery and constraint conditions of the energy consumption state model can be established, so that the energy consumed by the battery and/or a battery cooling system of the vehicle is determined, and the technical effect of establishing the accuracy of the energy consumption state model is realized by considering the constraint conditions of hardware equipment such as fans, air conditioners, water pumps and the like.

The energy consumption state equation is only exemplified here, and the design method of the energy consumption state equation is not specifically limited, and is not specifically exemplified here as long as the energy consumption state equation is properly designed within the protection scope of the present invention.

Step S103, determining target cooling strategy data of the battery based on the energy consumption state model and the constraint condition, wherein the target cooling strategy data is used for representing a strategy for performing a cooling operation on the battery.

In the technical solution provided in the above step S103 of the present invention, after the energy consumption state model of the battery and the constraint condition of the energy consumption state model are established, the target cooling policy data of the battery may be determined based on the energy consumption state model and the constraint condition, where the target cooling policy data may be obtained by the constraint condition established according to the fan load, the air conditioner compressor load, the temperature of the battery, and the electric water pump rotation speed, and may include the constraint condition on the fan load, the constraint condition on the air conditioner compressor load, the constraint condition on the temperature of the battery, and the constraint condition on the electric water pump rotation speed. The target cooling strategy data is only exemplified herein, and the manner of acquiring the target cooling data and the acquisition process are not particularly limited.

Optionally, the fan load of the fan device, the air conditioner compressor load of the air conditioner device, the temperature of the battery, and the electric water pump rotational speed are adjusted such that the fan load of the fan device, the air conditioner compressor load of the air conditioner device, the temperature of the battery, and the electric water pump rotational speed are in a certain region range to achieve the target cooling strategy data for determining the battery. For example, the load of the air conditioner compressor can be controlled in the load range of the air conditioner compressor, the rotating speed of the electric water pump needs to be controlled between the rotating speed ranges, and the temperature of the battery is controlled in the temperature range, so that in order to prevent the jump of the fan load, the load of the air conditioner compressor and the rotating speed of the electric water pump from being too large, the change rate of the fan load, the load of the air conditioner compressor and the rotating speed of the electric water pump needs to be ensured not to exceed a certain value when the current moment is compared with the previous moment, and the technical effect of cooling the battery is achieved.

In the embodiment of the application, the target cooling strategy data of the battery is determined based on the obtained energy consumption state model and the constraint conditions established for the fan load, the air conditioner compressor load and the electric water pump rotating speed, so that the purpose of executing cooling operation on the battery is achieved.

Step S104, the battery cooling system is controlled to execute cooling operation on the battery in response to the control instruction corresponding to the target cooling strategy data.

In the technical solution provided in the above step S104 of the present application, after the target cooling policy data is obtained, the battery cooling system may be controlled to perform a cooling operation on the battery according to a control instruction corresponding to the target cooling policy data, where the control instruction may be a target pre-control instruction.

Optionally, a dynamic programming algorithm can be utilized to obtain a target pre-control instruction corresponding to the fan load, the air conditioner compressor load and the rotation speed of the water pump, so as to control the battery cooling system to perform cooling operation on the battery, and achieve the purpose of cooling the battery.

In the process of running the vehicle, the steps S101 to S104 of the present application acquire running condition information of the vehicle and state information of a battery in the vehicle, where the state information is used to characterize the working state of the battery in the running process of the vehicle; based on the driving working condition information and the state information, establishing an energy consumption state model of the battery and constraint conditions of the energy consumption state model, wherein the energy consumption state model is used for determining energy consumed by the battery and/or a battery cooling system of the vehicle; determining target cooling strategy data of the battery based on the energy consumption state model and the constraint condition, wherein the target cooling strategy data is used for representing a strategy for executing cooling operation on the battery; and controlling the battery cooling system to perform cooling operation on the battery in response to a control instruction corresponding to the target cooling strategy data. That is, the embodiment of the application can acquire the running condition information of the vehicle and the state information of the battery in the running process of the vehicle, and according to the running condition information and the state information, the energy consumption state model of the battery and the constraint condition for constraining the energy consumption state model are established so as to determine the target cooling strategy data of the battery, and when a control instruction corresponding to the target cooling strategy data is acquired, the battery cooling system can be controlled to perform cooling operation on the battery, so that the technical problem of poor effect of cooling the battery is solved, and the technical effect of improving the effect of cooling the battery is realized.

The above-described method of this embodiment is further described below.

As an alternative embodiment, step S102, the battery cooling system includes at least one of: fan device, air conditioning equipment and water pump equipment, state information includes at least initial temperature of battery, current temperature of battery and current power of battery, based on driving condition information and state information, establishes the constraint condition of energy consumption state model and energy consumption state model of battery, includes: performing differential processing on the fan power of the fan device, the air conditioning power of the air conditioning device and the water pump power of the water pump device to obtain a first energy consumption state sub-model, wherein the first energy consumption state sub-model is used for determining the energy consumed by the battery cooling system; determining a second energy consumption state sub-model based on a fan load of the fan apparatus, wherein the second energy consumption state sub-model is used to determine a fan power; determining a third energy consumption state sub-model based on an air conditioning compressor load of the air conditioning device, wherein the third energy consumption state sub-model is used for determining air conditioning power; determining a fourth energy consumption state sub-model based on the water pump rotational speed of the water pump device, wherein the fourth energy consumption state sub-model is used for determining the water pump power; determining a fifth energy consumption state sub-model based on the fan load, the air conditioner compressor load, the water pump rotating speed, the initial temperature and the current power, wherein the fifth energy consumption state sub-model is used for determining the current temperature, and the energy consumption state model comprises a first energy consumption state sub-model, a second energy consumption state sub-model, a third energy consumption state sub-model, a fourth energy consumption state sub-model and a fifth energy consumption state sub-model; and establishing constraint conditions of the energy consumption state model.

In this embodiment, a control system of a battery cooling system may be pre-deployed, wherein the battery cooling system may include: fan apparatus, air conditioning apparatus, and water pump apparatus, wherein the fan apparatus may be a radiator fan apparatus or a cooling fan apparatus. The air conditioning equipment can be an air conditioning controller, an air conditioning compressor or an air conditioning heat exchanger. The water pump device may be an electric water pump. Only the fan apparatus, the air conditioner apparatus, and the water pump apparatus are illustrated herein, and reference apparatuses of the fan apparatus, the air conditioner apparatus, and the water pump apparatus are not particularly limited. The state information of the battery may include an initial temperature of the battery, a current temperature of the battery, and a current power of the battery.

In this embodiment, the fan power of the fan device, the air-conditioning power of the air-conditioning device, and the water pump power of the water pump device are subjected to differential processing, so that a first energy consumption state sub-model is obtained, with which the energy consumed by the battery cooling system can be determined. From the fan load of the fan device, a second energy consumption state submodel may be determined, which in turn determines the fan power. Based on the air conditioning compressor load of the air conditioning apparatus, a third energy consumption state sub-model may be determined to determine the air conditioning power. According to the water pump rotation speed of the water pump device, a fourth energy consumption state submodel can be determined, and the water pump power can be determined. The fifth energy consumption state sub-model is determined based on the fan load, the air conditioner compressor load, the water pump speed, the initial temperature, and the current power, so that the battery temperature can be determined. And finally, establishing constraint conditions of the energy consumption state model according to the obtained first energy consumption state sub-model, second energy consumption state sub-model, third energy consumption state sub-model, fourth energy consumption state sub-model and fifth energy consumption state sub-model.

Alternatively, the energy consumption state model may include a first energy consumption state sub-model, a second energy consumption state sub-model, a third energy consumption state sub-model, a fourth energy consumption state sub-model, and a fifth energy consumption state sub-model, and the energy consumption state model may be constrained by the first energy consumption state equation, the second energy consumption state equation, the third energy consumption state equation, the fourth energy consumption state equation, and the fifth energy consumption state equation, and the energy consumption state equation is as follows:

E _auxi ＝∫(P _fan +P _AC +P _pump )dt

P _fan ＝f ₁ (Load _fan )

P _AC ＝f ₂ (Load _AC )

P _pump ＝f ₃ (n _pump )

wherein P is _fan Representing fan power, P _AC Represents the air conditioning power, P _pump Represents the power of an electric water pump, E _auxi Representing the electrical energy consumed by the accessory for the entire cycle, where Load _fan Representing fan Load, load _AC Represents the load of the air conditioner compressor, n _pump The rotation speed of the electric water pump, temp _batt The real-time temperature of the battery is shown,represents the initial temperature of the battery, P _batt The battery real-time power is shown. The initial temperature of the battery can be determined byThe representation is performed. The current temperature of the battery may be the real-time temperature of the battery, and may be Temp _batt The representation is performed. The current power of the battery can be the real-time power of the battery and can be P _batt The representation is performed.

Alternatively, the first energy consumption state submodel may be constrained by a first energy consumption state equation, and the first energy consumption state equation may be E _auxi ＝∫(P _fan +P _AC +P _pump )dt。

Alternatively, the second energy consumption state submodel may be constrained by a second energy consumption state equation, and the second energy consumption state equation may be P _fan ＝f ₁ (Load _fan )。

Alternatively, the third energy consumption state submodel may be constrained by a third energy consumption state equation, and the third energy consumption state equation may be P _AC ＝f ₂ (Load _AC )。

Alternatively, the fourth energy consumption state submodel may be constrained by a fourth energy consumption state equation, and the fourth energy consumption state equation may be P _pump ＝f ₃ (n _pump )。

Alternatively, the fifth energy consumption state submodel may be constrained by a fifth energy consumption state equation, and the fifth energy consumption state equation may be

As an optional embodiment, step S102, establishing constraint conditions of the energy consumption state model includes: based on fan load, air conditioner compressor load, water pump speed and current temperature in the energy consumption state model, constraint conditions are established.

In this embodiment, constraints of the energy consumption state model may be established based on fan load, air conditioner compressor load, water pump speed, and current temperature of the battery in the energy consumption state model. The air conditioner compressor load can be simply called as the compressor load, the water pump rotating speed can be the electric water pump rotating speed, and the current temperature of the battery can be the real-time temperature of the battery.

Alternatively, the constraint condition for building the energy consumption state model may be that the load of the fan needs to be controlled between the maximum load and the minimum load, the load of the compressor needs to be controlled between the maximum load and the minimum load, the rotating speed of the electric water pump needs to be controlled between the maximum rotating speed and the minimum rotating speed, and the temperature of the battery is controlled within a certain interval. Therefore, in order to prevent the jump of the fan load, the air conditioner compressor load and the rotation speed of the electric water pump from being too large, the change rate of the fan load, the air conditioner compressor load and the rotation speed of the electric water pump needs to be ensured not to exceed a certain value compared with the current moment and the last moment. The fixed value may be a change rate threshold corresponding to a fan load, an air conditioner compressor load, and a change rate of the electric water pump rotation speed, and the change rate threshold may be a preset threshold.

From the above, the constraint condition for building the energy consumption state model can be expressed by the following formula, namely:

wherein,expressed as maximum value of fan load,/>Expressed as a minimum value of the fan load,the fan load at the previous moment relative to the current moment is indicated +.>A rate of change threshold, expressed as fan load, +.>Expressed as minimum value of air conditioner compressor load,/- >Expressed as maximum value of air conditioner compressor load, +.>The air-conditioning compressor load of the last moment, relative to the current moment, is indicated +.>Representing the rate of change threshold value of the air conditioner compressor load,/->Represents the minimum value of the rotational speed of the electric water pump, < >>Represents the maximum value of the rotational speed of the electric water pump, +.>The rotating speed of the electric water pump at the last moment relative to the current moment is shown,the change rate threshold value of the rotation speed of the electric water pump is shown, < >>Representing the most real-time temperature of the batteryThe small value of the sum,the maximum value of the real-time temperature of the battery is shown.

As an alternative embodiment, step S103, determining target cooling strategy data of the battery based on the energy consumption state model and the constraint condition, includes: and processing the energy consumption state model by adopting a dynamic programming algorithm according to the constraint condition, and determining target cooling strategy data.

In this embodiment, the energy consumption state model is processed using a dynamic programming algorithm based on the established energy consumption state model and constraints, so that the target cooling strategy data can be determined. Wherein a dynamic programming algorithm can be used to solve a problem with certain optimal properties.

Optionally, based on the principle of lowest energy consumption, a target pre-control instruction of fan load, air conditioner compressor load and water pump rotating speed is obtained after the solution is carried out by utilizing a dynamic programming algorithm, so that target cooling strategy data can be determined, and the battery can be cooled.

Alternatively, the dynamic programming algorithm may initialize the dynamic programming information first, and then discretize the variables after the initialization of the dynamic programming information is completed, so as to obtain the optimal control amount at each moment, so as to determine the target cooling strategy data. It should be noted that, here, only a preferred embodiment of determining the target cooling policy data is not specifically limited to the process and manner of determining the target cooling policy data, so long as the method and process for obtaining the target cooling policy data are within the scope of the present invention, which is not specifically recited herein.

As an optional embodiment, step S103, processing the energy consumption state model by using a dynamic programming algorithm according to the constraint condition, to determine target cooling strategy data, includes: initializing dynamic planning information corresponding to a dynamic planning algorithm to obtain an initialization result; discretizing the initialization result to obtain a discretized result; and processing the discrete processing result and the energy consumption state model by adopting a dynamic programming algorithm to obtain target cooling strategy data so that the energy consumption of the battery cooling system reaches a target state.

In this embodiment, when the dynamic programming algorithm is adopted to process the energy consumption state model, the dynamic programming information corresponding to the dynamic programming algorithm may be initialized to obtain an initialization result, then the initialization result is discretized to obtain a discrete processing result, and finally the discrete processing result and the energy consumption state model may be processed by adopting the dynamic programming algorithm to obtain target cooling strategy data, so that the energy consumption of the battery cooling system may reach the target state.

Optionally, based on the principle of lowest energy consumption, solving by using a dynamic programming algorithm to obtain target pre-control instructions of fan load, air conditioner compressor load and water pump rotating speed. The implementation manner of adopting the dynamic programming algorithm can be as follows: the dynamic programming information is initialized firstly, and the dynamic programming information comprises battery temperature (namely initial temperature reported by the BMS), fan load (initial 0), air conditioner compressor load (initial 0) and electric water pump rotating speed (initial 0).

Optionally, after the dynamic programming information is initialized, the variables are discretized, including known conditions (e.g., predicted conditions) and controlled variables (e.g., fan load, compressor load, electric water pump speed). The objective of the optimal decision is to find the optimal control amount at each moment so that the performance index is minimized. And after the optimal control sequence of the whole process is obtained, substituting the optimal control sequence into a state equation to obtain the optimal state track of each component. After traversing all state points at the k moment, pushing to the previous moment, for the kth step i discrete state point, calculating the transfer cost of the kth step i discrete state point between the kth step i discrete state point and the next state under the action of each possible discrete control quantity so as to obtain the corresponding optimal cost vector from the current moment to the endpoint moment, and storing the optimal cost vector: this process is repeated until a second time, and the backward calculation of the dynamic programming can be completed. Finally, starting from the initial state, repeating the steps, calculating the optimal control quantity of the first step, then calculating forward according to a state equation, sequentially obtaining state points at the next moment, and interpolating to solve the optimal control sequence, wherein the discrete state points can be discrete time points for obtaining variable values in various running processes of the vehicle, for example, variable values such as fan load and electric water pump rotating speed can be obtained at a certain time point, only the discrete state points are illustrated, and the types of the obtained index values in the discrete state points, the number of the discrete state points and the like are not specifically limited.

As an optional embodiment, step S101, during the running process of the vehicle, obtains running condition information of the vehicle, including: acquiring navigation information in the running process of the vehicle; and predicting the driving condition information based on the historical driving data and the navigation information of the vehicle.

In this embodiment, navigation information of the vehicle in the running process may be acquired, and running condition information of the vehicle may be predicted according to historical running data and navigation data of the vehicle. The navigation information may be road traffic information transmitted through a navigation system, such as an average speed of a traffic flow, a distance from a start point to an end point of a vehicle, or a time when the vehicle is expected to reach the end point. The driving condition information may be simply referred to as condition information. The historical driving data of the vehicle can be stored through a cloud server of the vehicle, and also can be historical using data of the vehicle, and can be simply called as historical big data.

Alternatively, the vehicle driving condition information may be predicted by using a vehicle controller (Vehicle Control Unit, abbreviated as VCU) of the vehicle based on the navigation information and the historical big data stored in the cloud server.

Alternatively, the navigation controller of the vehicle may record and store the start position and the end position of the vehicle corresponding to each driving cycle, and the VCU records and stores the average speed, the total distance travelled, the average acceleration, and the like of the vehicle for each driving cycle, and the air conditioner controller stores the external average ambient temperature data for each driving cycle. After each driving cycle, the navigation and VCU uploads the information to the cloud server for storage.

As an optional embodiment, step S101, predicting driving condition information based on historical driving data and navigation information of the vehicle, includes: discretizing the speed data in the historical driving data and the speed data in the navigation information to obtain a discrete result; determining a state transition probability of the vehicle based on the discrete result, wherein the state transition probability is used for representing the transition probability from the speed state of the vehicle at the current moment to the speed state of the vehicle at the next moment; generating a state transition probability matrix of the vehicle according to the state transition probability; based on the state transition probability matrix, determining an initial speed state vector of the vehicle and a target speed state vector after the occurrence of state transition; generating a predictive model based on the initial speed state vector and the target speed state vector; and predicting the driving condition information based on the prediction model.

In this embodiment, when predicting the driving condition information according to the historical driving data and the navigation information of the vehicle, the speed data in the historical driving data and the speed data in the navigation information may be discretized to obtain a discretized result, so that the state transition probability of the vehicle may be determined, the state transition probability matrix of the vehicle may be generated according to the obtained state transition probability of the vehicle, and further, the initial speed state vector and the target speed state vector after the occurrence of the state transition of the vehicle may be determined, and a prediction model may be generated based on the obtained initial speed state vector and the target speed state vector, so as to achieve the purpose of predicting the driving condition information of the vehicle. Wherein the speed data in the history running data and the speed data in the navigation information may be collectively referred to as a running vehicle speed.

Alternatively, the velocity data in the history travel data and the velocity data in the navigation information may be discretized by a neighbor method, so that the vehicle speed may be discretized into a finite value. Wherein, the vehicle speed is discretized into a finite value, which can be expressed as v _s ∈{v ₁ ,v ₂ ,…,v _N }, where v ₁ 、v ₂ 、v _N And v _s Which may be expressed as the speed of the vehicle at different moments in time. Here, only the result of the discretization process is subjected to For example, the result of the discretization processing is not particularly limited.

Alternatively, the vehicle speed during running may be first divided into different states according to the discrete result, and the state transition probability of the vehicle may be determined based on the different states. For example, the speed of the vehicle during running is divided into 100 possible states, the speed discrete interval takes a value of 5 kilometers per hour (km/h), the running speed state numbers u=1, 2, …,25, and the speed of the vehicle running is changed from the current speed state U _i Vehicle speed state U to the next moment _j The probability of (1) is the state transition probability P _i,j . The running speed at the current moment is v _i At the next moment, the running speed is v _j The probability of (2) is:

P _i,j ＝P(v(k+1)＝v _j ∣v(k)＝v _i )

wherein P is _i,j Represented are the ith row and jth column elements of the state transition probability matrix.

Alternatively, P _i,j The value of (2) can be obtained by a maximum likelihood estimation method, that is, by the following formula:wherein F is _i,j Refers to the running speed of the vehicle from v _i Transfer to v _j Number of times F _i Refers to the running speed of the vehicle from v _i Total number of transfers, and i, j=0, 1, …, N.

Alternatively, the state transition probability matrix may be a markov transition probability matrix, which may be represented by P. Wherein, the transition probability and the times from the current running speed to the next running speed can be calculated, each state probability value is combined to generate a Markov transition probability matrix P, and the matrix P is as follows:

Alternatively, the initial speed state vector may be an initial vehicle speed state vector. For example, hypothesis systemN mutually independent running vehicle speed states are unified, and the initial vehicle speed state vector of the system is as follows: s (0) = [ S ] ₁ (0),S ₂ (0),…,S _m (0),…,S _n (0)]Wherein S is _m (0) Refers to the initial probability when the vehicle speed state is m.

Alternatively, the target speed state vector may be a state vector after state transition. For example, through a k-step state transition, the probability of the system in the vehicle speed state m is S _m (k) The state vector after state transition is: s (k) = [ S ] ₁ (k),S ₂ (k),…,S _m (k),…,S _n (k)]Wherein S is _m (k) Refers to the probability that the system is in state m at time k.

Alternatively, the predictive model may be a Markov predictive model, and the Markov predictive model may be expressed asAnd solving a prediction model and a state transition matrix based on a Markov chain to obtain a prediction speed value as follows:

v(k)＝[(U _k -1)+r _k ]d

wherein v (k) represents the running speed of the vehicle at time k, U _k The running speed state at the time k is represented, d represents the dividing length of the speed state, and the value can be 5,r, which represents random numbers uniformly distributed at the time k.

In the embodiment of the invention, the running condition information of the vehicle and the state information of the battery can be acquired in the running process of the vehicle, the energy consumption state model of the battery and the constraint condition for constraining the energy consumption state model are established according to the running condition information and the state information so as to determine the target cooling strategy data of the battery, and when a control instruction corresponding to the target cooling strategy data is acquired, the battery cooling system can be controlled to perform cooling operation on the battery, so that the technical problem of poor effect of cooling the battery is solved, and the technical effect of improving the effect of cooling the battery is realized.

Example 2

The technical solution of the embodiment of the present invention will be illustrated in the following with reference to a preferred embodiment.

At present, when a pure electric vehicle runs in a high-temperature environment, the output of the charge and discharge power of the battery can be influenced when the temperature of a power battery reaches a certain upper limit, and the service life of the battery can be influenced. In the prior art, different cooling modes are mainly adopted according to the actual temperature of the battery, when the temperature of the battery is not too high, a radiator is used for cooling, and when the temperature exceeds a certain value, an air conditioning system is used for cooling the battery. However, the prior art is easy to cause battery cooling lag, thereby influencing battery life, cooling is not intelligent enough, front road conditions are not considered, cooling energy consumption is not comprehensively considered, and further, the method has the technical problem of poor effect of cooling the battery.

In one possible implementation, a cooling system for a power cell is provided that may include an internal coolant passage disposed within the power cell and having an inlet and an outlet for circulating a coolant; the external cooling unit is arranged outside the power battery and comprises an external cooling liquid channel which is communicated with an inlet and an outlet of the internal cooling liquid channel; and a reversing unit for periodically changing a flow direction of the cooling liquid such that the cooling liquid of the external cooling liquid channel can flow in from the inlet, flow out from the outlet via the internal cooling liquid channel, or flow in from the outlet, and flow out from the inlet via the internal cooling liquid channel. The cooling system can effectively balance the temperature of each battery core in the power battery, thereby improving the charge and discharge efficiency of the power battery and prolonging the service life of the power battery. However, the above method still has a technical problem in that the effect of cooling the battery is poor.

In another possible implementation manner, a cooling method for a vehicle and a power battery thereof is provided, and the method may include the following steps: acquiring a running condition of a vehicle running currently, a driving mode adopted currently and a target state of a current environment; determining an estimated temperature coefficient of a power battery in the vehicle according to the driving condition, the driving mode and the target state; acquiring the current temperature of the power battery, and acquiring the estimated temperature of the power battery according to the current temperature and the estimated temperature coefficient; and cooling and controlling the power battery according to the estimated temperature. The cooling method can be used for estimating the power battery by combining the running working condition, the driving mode, the current temperature and the temperature of the power battery of the vehicle, and avoids cooling control of the battery only according to the collected temperature, thereby reducing service life attenuation and energy consumption of the battery, ensuring that the discharge capacity of the battery is kept optimal, and preventing the problems of untimely cooling and thermal runaway of the battery caused by temperature collection lag. However, the above method still has a technical problem in that the effect of cooling the battery is poor.

In order to solve the above problems, an embodiment of the present invention provides a method for cooling a battery in a vehicle, where the method may obtain driving condition information of the vehicle and state information of the battery during running of the vehicle, and according to the driving condition information and the state information, establish an energy consumption state model of the battery and constraint conditions for constraining the energy consumption state model so as to determine target cooling policy data of the battery, and when a control instruction corresponding to the target cooling policy data is obtained, may control a battery cooling system to perform a cooling operation on the battery, so as to solve a technical problem of poor effect of cooling the battery, and achieve a technical effect of improving the effect of cooling the battery.

Embodiments of the present invention are further described below.

Fig. 2 is a schematic diagram of a cooling control system for a power battery of a pure electric vehicle of a navigation system according to an embodiment of the present invention, and as shown in fig. 2, the control system may include: a car navigation system 201, a Vehicle Control Unit (VCU) 202, a cooling fan 203, an air conditioning controller 204, a Battery Management System (BMS) 205, an electric water pump 206, and a cloud server 207.

The car navigation system 201 transmits the front traffic information to the VCU in real time, for example, a distance from a start point to an end point of the vehicle, a predicted travel time from the start point to the end point, an average speed of the traffic, and the like.

The cooling fan 203 executes a fan load command sent from the VCU and feeds back an actual load state to the VCU.

The air conditioner controller 204 executes the air conditioner compressor load control command sent by the VCU, and feeds back the air conditioner compressor load state to the VCU.

The battery management system 205 may feed back the battery pack temperature, SOC, and battery pack available charge-discharge power to the VCU.

The cloud server 207 sends the vehicle history big data to the VCU, the electric water pump 206 executes the electric water pump rotation speed command sent by the VCU, and feeds back the actual rotation speed of the water pump to the VCU, and after the VCU comprehensively judges the received information, the fan control command, the air conditioner compressor control command, the electric water pump control command and the like can be obtained and then sent to the corresponding components.

Fig. 3 is a schematic view of a battery cooling system according to an embodiment of the present invention, as shown in fig. 3, the battery cooling system may include: the three-way valve comprises a power battery 301, an electric water pump 302, an expansion tank 303, a radiator (comprising a radiator fan) 304, an air conditioning system 305, a three-way valve 306, an air conditioning heat exchanger 307, a three-way valve port 308, a three-way valve port 309 and a three-way valve port 310, wherein the three-way valve 306 comprises the three-way valve port 308, the three-way valve port 309 and the three-way valve port 310.

In this embodiment, in the battery cooling system, when the three-way valve port 308 and the three-way valve port 309 are communicated and the three-way valve port 310 is closed, the radiator 304 cools the power battery, and when the three-way valve port 309 is closed and the three-way valve port 308 and the three-way valve port 310 are communicated, the air conditioning system cools the power battery.

Fig. 4 is a flowchart of a battery cooling control method according to an embodiment of the present invention, which may include the steps of:

in step S401, the navigation system collects the front road condition information and sends the front road condition information to the VCU.

In the technical scheme provided in the step S401, the navigation system collects the front road condition information and sends the front road condition information to the VCU, and the specific road condition information includes the distance from the destination, the estimated driving time from the destination, the average speed of the traffic flow, and the like.

In step S402, the VCU predicts the working condition according to the navigation information and the history big data.

In the technical scheme provided in the step S402, the VCU predicts the working condition according to the navigation information and the historical big data stored in the cloud server.

Optionally, the navigation controller records and stores the starting point position and the end point position of the vehicle corresponding to each driving cycle, the VCU records and stores the average speed, the total distance travelled and the average acceleration of the vehicle in each driving cycle, and the air conditioner controller stores the external average environment temperature data in each driving cycle. Table 1 is history use data of the vehicle, and the stored history use data of the vehicle obtained as described above is shown in table 1. Then, after each driving cycle, the navigation and VCU uploads the information to the cloud server for storage.

Optionally, the working condition prediction is performed according to the markov theory, and the specific implementation and operation method are as follows: firstly, calculating a state transition probability matrix, and dispersing the running speed into a limited numerical value by utilizing a neighbor method: v _s ∈{v ₁ ,v ₂ ,…,v _N }, where v ₁ 、v ₂ 、v _N And v _s Which may be expressed as the speed of the vehicle at different moments in time.

Optionally, dividing the speed of the driving process into 100 possible states, wherein the speed discrete interval takes a value of 5km/h, the driving speed state numbers u=1, 2, …,25, and the speed of the automobile driving is represented by the current speed state U _i Vehicle speed state U to the next moment _j The probability of (1) is the state transition probability P _i,j . The running speed at the current moment is v _i At the next moment, the running speed is v _j The probability of (2) is:

P _i,j ＝P(v(k+1)＝v _j ∣v(k)＝v _i )

wherein P is _i,j Represented are the ith row and jth column elements of the state transition probability matrix. P (P) _i,j The value of (2) can be obtained by a maximum likelihood estimation method, that is, by the following formula:wherein F is _i,j Refers to the running speed of the vehicle from v _i Transfer to v _j Number of times F _i Refers to the running speed of the vehicle from v _i Total number of transfers, and i, j=0, 1, …, N.

Table 1 historical usage data of vehicles

Alternatively, the markov transition probability matrix P may be generated by calculating the transition probability and the number of times from the current running vehicle speed to the next running vehicle speed, and combining each state probability value, and the matrix P is as follows:assuming that the system has n running vehicle speed states independent of each other, the initial vehicle speed state vector of the system is: s (0) = [ S ] ₁ (0),S ₂ (0),…,S _m (0),…,S _n (0)]Wherein S is _m (0) Refers to the initial probability when the vehicle speed state is m. Through the state transition of the k steps, the probability of the system in the vehicle speed state m is S _m (k) The state vector after state transition is: s (k) = [ S ] ₁ (k),S ₂ (k),…,S _m (k),…,S _n (k)]Wherein S is _m (k) Refers to the probability that the system is in state m at time k. The Markov prediction model may be expressed as +. >And solving a prediction model and a state transition matrix based on a Markov chain to obtain a prediction speed value as follows:

v(k)＝[(U _k -1)+r _k ]d

Step S403, an energy consumption state equation is established according to the predicted working condition.

In this embodiment, an energy consumption state equation may be established based on the predicted operating conditions. Because the energy consumed by the driving motor is closely related to the working condition after the working condition is predicted, and only the condition at high temperature is concerned at this time, whether the battery discharging and power recovering capacity can meet the requirements or not is judged, after the working condition is judged, the energy consumed by the driving is also judged, the energy consumed by the battery cooling accessory is mainly optimized, the accessory energy consumption mainly comprises the energy consumed by a fan, an air conditioner and an electric water pump, and an energy consumption state equation is established as follows:

E _auxi ＝∫(P _fan +P _AC +P _pump )dt

P _fan ＝f ₁ (Load _fan )

P _AC ＝f ₂ (Load _AC )

P _pump ＝f ₃ (n _pump )

wherein P is _fan Representing fan power, P _AC Represents the air conditioning power, P _pump Represents the power of an electric water pump, E _auxi Representing the power consumed by the accessories for the whole cycle, wherein Load _fan Representing fan Load, load _AC Represents the load of the air conditioner compressor, n _pump The rotation speed of the electric water pump, temp _batt The real-time temperature of the battery is shown,represents the initial temperature of the battery, P _batt The battery real-time power is shown. The initial temperature of the battery can be determined byThe representation is performed. The current temperature of the battery may be the real-time temperature of the battery, and may be Temp _batt The representation is performed. The current power of the battery canTo be the real-time power of the battery, can pass through P _batt The representation is performed.

And step S404, determining constraint conditions according to the energy consumption state equation.

In this embodiment, the constraint may be determined according to an energy consumption state equation. Wherein the constraint condition may be expressed as follows:wherein (1)>Expressed as maximum value of fan load,/>Expressed as minimum of fan load, +.>The fan load at the previous time with respect to the current time is shown,a rate of change threshold, expressed as fan load, +.>Expressed as minimum value of air conditioner compressor load,/->Expressed as maximum value of air conditioner compressor load, +.>The air-conditioning compressor load of the last moment, relative to the current moment, is indicated +.>Representing the rate of change threshold value of the air conditioner compressor load,/- >Represents the minimum value of the rotational speed of the electric water pump, < >>Represents the maximum value of the rotational speed of the electric water pump, +.>The speed of the electric water pump at the previous moment relative to the current moment is shown by +.>The change rate threshold value of the rotation speed of the electric water pump is shown, < >>Representing the minimum value of the real-time temperature of the battery, < >>The maximum value of the real-time temperature of the battery is shown.

Optionally, the load of the fan needs to be controlled between the maximum load and the minimum load, the load of the compressor needs to be controlled between the maximum load and the minimum load, the rotation speed of the electric water pump needs to be controlled between the maximum rotation speed and the minimum rotation speed, the temperature of the battery is controlled within a certain interval, and then in order to prevent the jump of the fan load, the load of the air conditioner compressor and the rotation speed of the electric water pump from being too large, the change rate of the fan load, the load of the air conditioner compressor and the rotation speed of the electric water pump needs to be ensured not to exceed a certain value compared with the current moment and the last moment.

And step S405, obtaining target pre-control instructions of fan load, air conditioner compressor load and water pump rotating speed after solving by using a dynamic programming algorithm based on the energy consumption minimum principle.

In this embodiment, based on step S403 and step S404, a pre-control instruction of the fan load, the air conditioner compressor load and the electric water pump rotation speed in real time is obtained by solving through a dynamic programming algorithm, and is sent to a corresponding accessory for execution.

Optionally, the dynamic programming information may be initialized, including a battery temperature (i.e., an initial temperature reported by the BMS), a fan load (initial 0), an air conditioner compressor load (initial 0), and an electric water pump rotation speed (initial 0). Then, after the initialization of the dynamic programming information is completed, the variables are discretized, including known conditions (i.e., predicted conditions) and control variables (e.g., fan load, compressor load, electric water pump speed). The objective of the optimal decision is to find the optimal control amount at each moment so that the performance index is minimized. After the whole process optimal control sequence is obtained, substituting the optimal control sequence into a state equation to obtain the optimal state track of each component. After traversing all state points at the k moment, pushing to the previous moment, for the kth step i discrete state point, calculating the transfer cost between the kth step i discrete state point and the next state under the action of each possible discrete control quantity, obtaining and storing the corresponding optimal cost vector from the current moment to the terminal: and repeating the process until the time 2, and completing the backward calculation of the dynamic programming. Finally, starting from the initial state, repeating the steps, calculating the optimal control quantity of the first step, then calculating forward according to a state equation, sequentially obtaining the state point of the next moment, and interpolating to solve the optimal control sequence.

Example 3

According to an embodiment of the invention, there is also provided a cooling device for a battery in a vehicle. The cooling device for a battery in a vehicle may be used to perform the cooling method for a battery in a vehicle in embodiment 1.

Fig. 5 is a schematic view of a cooling apparatus of a battery in a vehicle according to an embodiment of the present invention, and as shown in fig. 5, the cooling apparatus 500 of a battery in a vehicle may include: an acquisition unit 501, a setup unit 502, a determination unit 503, and a control unit 504.

The acquiring unit 501 is configured to acquire driving condition information of a vehicle and state information of a battery in the vehicle during operation of the vehicle, where the state information is used to characterize an operating state of the battery during operation of the vehicle.

The establishing unit 502 is configured to establish an energy consumption state model of the battery and constraints of the energy consumption state model based on the driving condition information and the state information, where the energy consumption state model is used to determine an amount of energy consumed by the battery and/or a battery cooling system of the vehicle.

A determining unit 503 for determining target cooling policy data of the battery based on the energy consumption state model and the constraint condition, wherein the target cooling policy data is used for representing a policy for performing a cooling operation on the battery.

And a control unit 504 for controlling the battery cooling system to perform a cooling operation on the battery in response to a control instruction corresponding to the target cooling strategy data.

Optionally, the establishing unit 502 may include: the first acquisition module is used for carrying out differential processing on the fan power of the fan equipment, the air conditioning power of the air conditioning equipment and the water pump power of the water pump equipment to obtain a first energy consumption state sub-model, wherein the first energy consumption state sub-model is used for determining the energy consumed by the battery cooling system; a first determining module configured to determine a second energy consumption state sub-model based on a fan load of the fan apparatus, wherein the second energy consumption state sub-model is configured to determine a fan power; the second determining module is used for determining a third energy consumption state sub-model based on the air conditioner compressor load of the air conditioning equipment, wherein the third energy consumption state sub-model is used for determining air conditioner power; the third determining module is used for determining a fourth energy consumption state sub-model based on the water pump rotating speed of the water pump equipment, wherein the fourth energy consumption state sub-model is used for determining the water pump power; a fourth determining module, configured to determine a fifth energy consumption state sub-model based on the fan load, the air conditioner compressor load, the water pump rotational speed, the initial temperature, and the current power, where the fifth energy consumption state sub-model is configured to determine the current temperature, and the energy consumption state model includes a first energy consumption state sub-model, a second energy consumption state sub-model, a third energy consumption state sub-model, a fourth energy consumption state sub-model, and a fifth energy consumption state sub-model; the building module is used for building constraint conditions of the energy consumption state model.

Optionally, the establishing module may include: and the building sub-module is used for building constraint conditions based on the fan load, the air conditioner compressor load, the water pump rotating speed and the current temperature in the energy consumption state model.

Alternatively, the determining unit 503 may include: and the fifth determining module is used for processing the energy consumption state model by adopting a dynamic programming algorithm according to the constraint conditions and determining target cooling strategy data.

Optionally, the fifth determining module may include: the first acquisition sub-module is used for initializing dynamic planning information corresponding to a dynamic planning algorithm to obtain an initialization result; the second acquisition sub-module is used for carrying out discretization on the initialization result to obtain a discretization result; and the third acquisition sub-module is used for processing the discrete processing result and the energy consumption state model by adopting a dynamic programming algorithm to obtain target cooling strategy data so that the energy consumption of the battery cooling system reaches a target state.

Alternatively, the acquisition unit 501 may include: the second acquisition module is used for acquiring navigation information in the running process of the vehicle; and the prediction module is used for predicting the driving condition information based on the historical driving data and the navigation information of the vehicle.

Alternatively, the prediction module may include: a fourth obtaining sub-module, configured to perform discretization processing on the speed data in the historical driving data and the speed data in the navigation information, so as to obtain a discrete result; a first determining sub-module for determining a state transition probability of the vehicle based on the discrete result, wherein the state transition probability is used for representing a transition probability from a speed state at a current moment of the vehicle to a speed state at a next moment of the vehicle; the first generation submodule is used for generating a state transition probability matrix of the vehicle according to the state transition probability; the second determining submodule is used for determining an initial speed state vector of the vehicle and a target speed state vector after the state transition based on the state transition probability matrix; the second generation sub-module is used for generating a prediction model based on the initial speed state vector and the target speed state vector; and the prediction sub-module is used for predicting the driving condition information based on the prediction model.

In the embodiment of the invention, the running condition information of the vehicle and the state information of the battery in the vehicle are acquired by the acquisition unit in the running process of the vehicle, wherein the state information is used for representing the working state of the battery in the running process of the vehicle; the system comprises a building unit, a control unit and a control unit, wherein the building unit is used for building an energy consumption state model of a battery and constraint conditions of the energy consumption state model based on driving working condition information and state information, and the energy consumption state model is used for determining the energy consumed by the battery and/or a battery cooling system of a vehicle; a determining unit configured to determine target cooling strategy data of the battery based on the energy consumption state model and the constraint condition, wherein the target cooling strategy data is used to represent a strategy for performing a cooling operation on the battery; and the control unit is used for responding to the control instruction corresponding to the target cooling strategy data and controlling the battery cooling system to perform cooling operation on the battery, so that the technical problem of poor effect of cooling the battery is solved, and the technical effect of improving the effect of cooling the battery is realized.

Example 4

According to an embodiment of the present application, there is also provided a computer-readable storage medium including a stored program, wherein the method of cooling a battery in a vehicle in embodiment 1 is performed when the program is executed by a processor.

Example 5

According to an embodiment of the present application, there is also provided a processor for running a program, wherein the program, when run, performs the method of cooling the battery in the vehicle of embodiment 1.

Example 6

According to an embodiment of the present application, there is also provided a vehicle for performing the cooling method of the battery in the vehicle in embodiment 1 of the present application.

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 method of cooling a battery in a vehicle, comprising:

acquiring running condition information of a vehicle and state information of a battery in the vehicle in the running process of the vehicle, wherein the state information is used for representing the working state of the battery in the running process of the vehicle;

establishing an energy consumption state model of the battery and constraint conditions of the energy consumption state model based on the driving working condition information and the state information, wherein the energy consumption state model is used for determining energy consumed by the battery and/or a battery cooling system of the vehicle;

determining target cooling strategy data of the battery based on the energy consumption state model and the constraint condition, wherein the target cooling strategy data is used for representing a strategy for performing a cooling operation on the battery;

and responding to a control instruction corresponding to the target cooling strategy data, and controlling the battery cooling system to execute the cooling operation on the battery.

2. The method of claim 1, wherein the battery cooling system comprises at least one of:

fan equipment, air conditioning equipment and water pump equipment, state information includes at least initial temperature of battery, current temperature of battery and current power of battery, based on driving operating mode information and state information, establish the energy consumption state model of battery and the constraint condition of energy consumption state model, include:

performing differential processing on the fan power of the fan device, the air conditioning power of the air conditioning device and the water pump power of the water pump device to obtain a first energy consumption state sub-model, wherein the first energy consumption state sub-model is used for determining the energy consumed by the battery cooling system;

determining a second energy consumption state sub-model based on a fan load of the fan apparatus, wherein the second energy consumption state sub-model is used to determine the fan power;

determining a third energy consumption state sub-model based on an air conditioning compressor load of the air conditioning equipment, wherein the third energy consumption state sub-model is used for determining the air conditioning power;

determining a fourth energy consumption state sub-model based on the water pump rotational speed of the water pump device, wherein the fourth energy consumption state sub-model is used for determining the water pump power;

Determining a fifth energy consumption state sub-model based on the fan load, the air conditioner compressor load, the water pump rotational speed, the initial temperature, and the current power, wherein the fifth energy consumption state sub-model is used to determine the current temperature, and the energy consumption state model includes the first energy consumption state sub-model, the second energy consumption state sub-model, the third energy consumption state sub-model, the fourth energy consumption state sub-model, and the fifth energy consumption state sub-model;

and establishing the constraint condition of the energy consumption state model.

3. The method of claim 2, wherein establishing the constraints of the energy consumption state model comprises:

and establishing the constraint condition based on the fan load, the air conditioner compressor load, the water pump rotating speed and the current temperature in the energy consumption state model.

4. The method of claim 1, wherein determining target cooling strategy data for the battery based on the energy consumption state model and the constraints comprises:

and processing the energy consumption state model by adopting a dynamic programming algorithm according to the constraint condition, and determining the target cooling strategy data.

5. The method of claim 4, wherein processing the energy consumption state model with a dynamic programming algorithm based on the constraints, determining the target cooling strategy data, comprises:

initializing the dynamic programming information corresponding to the dynamic programming algorithm to obtain an initialization result;

discretizing the initialization result to obtain a discretized result;

and processing the discrete processing result and the energy consumption state model by adopting the dynamic programming algorithm to obtain the target cooling strategy data so that the energy consumption of the battery cooling system reaches a target state.

6. The method of claim 1, wherein obtaining driving condition information of the vehicle during operation of the vehicle comprises:

acquiring navigation information in the running process of the vehicle;

and predicting the driving condition information based on the historical driving data of the vehicle and the navigation information.

7. The method of claim 6, wherein predicting the driving condition information based on historical driving data of the vehicle and the navigation information comprises:

Discretizing the speed data in the historical driving data and the speed data in the navigation information to obtain a discrete result;

determining a state transition probability of the vehicle based on the discrete result, wherein the state transition probability is used for representing the transition probability from the speed state of the vehicle at the current moment to the speed state of the vehicle at the next moment;

generating a state transition probability matrix of the vehicle according to the state transition probability;

determining an initial speed state vector of the vehicle and a target speed state vector after the occurrence of state transition based on the state transition probability matrix;

generating the predictive model based on the initial speed state vector and the target speed state vector;

and predicting the driving condition information based on the prediction model.

8. A cooling device for a battery in a vehicle, the device comprising:

the system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring running condition information of a vehicle and state information of a battery in the vehicle in the running process of the vehicle, and the state information is used for representing the working state of the battery in the running process of the vehicle;

The building unit is used for building an energy consumption state model of the battery and constraint conditions of the energy consumption state model based on the driving working condition information and the state information, wherein the energy consumption state model is used for determining the energy consumed by the battery and/or a battery cooling system of the vehicle;

a determining unit configured to determine target cooling policy data of the battery based on the energy consumption state model and the constraint condition, wherein the target cooling policy data is used to represent a policy to perform a cooling operation on the battery;

and the control unit is used for responding to the control instruction corresponding to the target cooling strategy data and controlling the battery cooling system to execute the cooling operation on the battery.

9. A processor for running a program, wherein the program when run by the processor performs the method of any one of claims 1 to 7.

10. A vehicle, characterized by being adapted to perform the method of any one of claims 1 to 7.