CN113224412B - Temperature control method of power battery, AMPC controller, thermal management system and medium - Google Patents

Temperature control method of power battery, AMPC controller, thermal management system and medium Download PDF

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CN113224412B
CN113224412B CN202110348930.6A CN202110348930A CN113224412B CN 113224412 B CN113224412 B CN 113224412B CN 202110348930 A CN202110348930 A CN 202110348930A CN 113224412 B CN113224412 B CN 113224412B
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battery pack
temperature control
cooling liquid
temperature
battery
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CN113224412A (en
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赵明如
程玉佼
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United Automotive Electronic Systems Co Ltd
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    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/60—Heating or cooling; Temperature control
    • H01M10/63—Control systems
    • H01M10/633—Control systems characterised by algorithms, flow charts, software details or the like
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/24—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries for controlling the temperature of batteries
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/4285—Testing apparatus
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
    • H01M10/486—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte for measuring temperature
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/60—Heating or cooling; Temperature control
    • H01M10/61—Types of temperature control
    • H01M10/613—Cooling or keeping cold
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/60—Heating or cooling; Temperature control
    • H01M10/61—Types of temperature control
    • H01M10/615—Heating or keeping warm
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/60—Heating or cooling; Temperature control
    • H01M10/61—Types of temperature control
    • H01M10/617—Types of temperature control for achieving uniformity or desired distribution of temperature
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/60—Heating or cooling; Temperature control
    • H01M10/62—Heating or cooling; Temperature control specially adapted for specific applications
    • H01M10/625—Vehicles
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/60—Heating or cooling; Temperature control
    • H01M10/63—Control systems
    • H01M10/635—Control systems based on ambient temperature
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
    • H01M2010/4271—Battery management systems including electronic circuits, e.g. control of current or voltage to keep battery in healthy state, cell balancing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

Abstract

The invention provides a temperature control method of a power battery, an AMPC controller, a thermal management system and a medium. The thermal cycle topology of the power battery comprises a battery pack and a temperature control device. The temperature control method comprises the following steps: establishing a temperature control prediction model of the power battery according to a thermal cycle topological structure of the power battery; respectively obtaining the predicted temperature of the battery pack and the energy consumption information of the temperature control device by using a temperature control prediction model and a plurality of groups of preset temperature control parameter information; selecting one group from a plurality of groups of temperature control parameter information according to a preset optimization performance index and a target function as control input of a temperature control device; and correcting the temperature control prediction model in real time according to the acquired state parameters of the battery pack. The temperature control method of the power battery, the AMPC controller, the thermal management system and the medium provided by the invention can control the temperature of the battery pack of the power battery to be in/close to a proper range under the condition of not increasing any hardware cost, and can reduce the energy consumption of an actuator.

Description

Temperature control method of power battery, AMPC controller, thermal management system and medium
Technical Field
The invention relates to the technical field of power batteries, in particular to a battery energy-saving temperature control method, a controller, a thermal management system and a storage medium.
Background
In recent years, due to the pressure of energy cost and the increasing importance of governments and consumers on environmental protection, electric vehicles have been receiving attention as an effective means for alleviating environmental problems and energy crisis, and have been rapidly developed. The power battery is a key component of the electric automobile and determines the power performance of the automobile. During the operation of the automobile, especially under the conditions of acceleration, climbing, etc., the power required by the electric automobile is large, and at the moment, the power battery generates a large amount of heat. This heat must be removed quickly to ensure safe and stable operation of the vehicle. Therefore, the power battery needs to be thermally managed to ensure that the power battery works in a proper temperature and temperature difference range.
The battery pack is the most expensive and therefore the most important thermal management objective. The battery has a severe requirement on the working temperature. According to the data of lithium battery manufacturers, 20-30 ℃ is the most suitable temperature for the lithium battery to work, and the safety, performance and service life of the battery are affected by too high or too low temperature. Among them, the damage caused by the over-high temperature is more serious, and the following is the experimental data of a certain power battery factory.
Table 1: relationship between 80% remaining capacity and calendar life
Temperature/. Degree.C Life/day
23 6238
35 1790
45 670
55 272
As can be seen from the above table, the remaining capacity of the same battery after 6238 days at ambient temperature of 23 ℃ was reduced to 80%, but the remaining capacity of the battery after 272 days in the environment of 55 ℃ was already reduced to 80%. The temperature is increased by 32 ℃, and the calendar life of the battery cell is reduced by more than 95%. Therefore, the influence of temperature on the calendar life is great, and the calendar life deteriorates more seriously as the temperature is higher. The working temperature range of the automobile is generally-25-60 ℃, if the battery is not subjected to effective heat management, the battery can often work at an inappropriate temperature, the service life and the performance are reduced, and in extreme cases, the thermal runaway of the battery can be caused, the ignition and even the explosion can be caused, and the driving safety is damaged.
For cooling of the battery, current mainstream solutions can be divided into active cooling and passive cooling based on the complexity of the battery thermal management system and the difference of the energy consumption level. The system adopting passive cooling is simpler in structure and relatively lower in cost, but the cooling requirements of a high-capacity and high-power battery system in the existing pure electric and plug-in hybrid vehicle type cannot be met. The primary objective of active cooling is therefore to improve the temperature control capability of the battery thermal management system.
However, in a battery thermal management system, there are many difficulties to overcome in temperature control, which are roughly listed as follows:
1. there is a need to deal with multiple input multiple output systems, and in the case of actuators, several numbers of pumps, valves, and fans may be required to work in conjunction.
2. The heat transfer process is a highly non-linear process, with the parameters in the mathematical model not only being time-varying, but also being coupled to each other.
3. A plurality of objectives and constraints need to be satisfied simultaneously, and these objectives and constraints are usually mutually contradictory.
Meanwhile, since the energy consumption level of the whole vehicle is always restricted by the regulations, reducing the energy consumption of the thermal management system is also an important goal. This goal is generally opposed to temperature control capability, which places greater demands on the battery cooling control algorithm.
In the prior art, the most common method of battery cooling control algorithms is rule-based control and PID control, and the algorithm has the advantages of low cost and obvious effect on the control of a simple system after years of engineering experience verification. However, the following disadvantages exist: the method is too dependent on engineering experience, and when the complex thermal management system with large thermal inertia is used, in order to ensure safety preferentially, a large control margin is reserved, so that the energy consumption of an actuator is large, and the optimal working condition point is difficult to follow. In the other way, the temperature of the battery pack is controlled by an air conditioning system (cold air or hot air) in the vehicle body, and on one hand, the air conditioning system is not communicated with a battery management system of the whole vehicle, the temperature control is not timely and inaccurate, and the conditions of poor effect or energy waste exist; on the other hand, it is difficult to ensure effective temperature control of the battery pack in non-air-conditioning seasons and when the air conditioner is not turned on during charging while parking. The other method is that an independent air conditioning system is arranged for the battery pack, and the battery pack is independently cooled or preheated, so that two sets of independent air conditioning systems are arranged on one pure electric bus, on one hand, cost is wasted, and on the other hand, the self weight of the bus body is increased, so that power consumption is increased, and the endurance mileage of the whole bus is influenced.
Therefore, in order to overcome the above-mentioned drawbacks of the prior art, how to provide a temperature control method for a power battery to control the temperature of the power battery within a suitable range and reduce energy consumption is becoming one of the technical problems to be solved by those skilled in the art.
It is noted that the information disclosed in this background of the invention section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The present invention is directed to a method for controlling a temperature of a power battery, an AMPC controller, a thermal management system and a medium thereof, so as to control a temperature of a battery pack of the power battery to be in or close to an appropriate range and reduce energy consumption of an actuator.
In order to achieve the purpose, the invention is realized by the following technical scheme: a temperature control method of a power battery is characterized in that a thermal cycle topological structure of the power battery comprises a battery pack and a temperature control device; the temperature control method comprises the following steps:
establishing a temperature control prediction model of the power battery according to the thermal cycle topological structure of the power battery;
setting a plurality of groups of temperature control parameter information according to a preset rule, and respectively inputting each group of temperature control parameter information and disturbance parameter information of the power battery into the temperature control prediction model to obtain the corresponding predicted temperature of the battery pack and energy consumption information of the temperature control device;
selecting one group from a plurality of groups of temperature control parameter information as the control input of the temperature control device according to a preset optimized performance index, the predicted temperature, the energy consumption information and a target temperature;
and acquiring state parameters of the battery pack, and correcting the temperature control prediction model in real time by using the state parameters.
Optionally, the temperature control device comprises a coolant pump and a cooler, and the coolant pump drives coolant of the cooler to provide cold energy for the battery pack; the temperature control parameter information comprises the rotating speed of a cooling liquid pump and the refrigerating power of the cooler, and the disturbance parameter information of the power battery comprises the current intensity of the battery pack;
the establishing of the temperature control prediction model of the power battery according to the thermal cycle topological structure of the power battery comprises the following steps:
establishing a cooling liquid pump prediction model by adopting a quasi-static ash box model modeling method;
establishing a battery pack prediction model according to the topological structure of the battery pack;
establishing a cooler prediction model by adopting a lumped parameter method;
the method for setting a plurality of groups of temperature control parameter information according to a preset rule, and respectively inputting each group of temperature control parameter information and disturbance parameter information of the power battery into the temperature control prediction model to obtain the corresponding predicted temperature of the battery pack and energy consumption information of the temperature control device comprises the following steps:
the cooling liquid pump prediction model acquires the mass flow of the cooling liquid and the power consumption of a working medium pump of the cooling liquid pump according to the working condition parameter information of the cooling liquid pump and the rotating speed of the cooling liquid pump;
the battery pack prediction model obtains the predicted temperature of the battery pack according to the current intensity and the mass flow of the cooling liquid;
and the cooler prediction model acquires the predicted temperature at the outlet of the cooler according to the mass flow of the cooling liquid, the predicted temperature of the battery pack and the refrigeration power.
Optionally, the operating condition parameter information of the coolant pump includes: inlet density, volumetric efficiency, working volume, isentropic process outlet specific enthalpy, inlet specific enthalpy and isentropic efficiency;
the method for acquiring the mass flow rate of the cooling liquid and the power consumption of the working medium pump of the cooling liquid pump by the cooling liquid pump prediction model according to the working condition parameter information of the cooling liquid pump and the rotating speed of the cooling liquid pump comprises the following steps:
obtaining the mass flow of the cooling liquid according to the inlet density, the volumetric efficiency, the rotating speed of the cooling liquid pump and the working volume;
and acquiring the power consumption of the working medium pump according to the mass flow of the cooling liquid, the isentropic process outlet specific enthalpy, the inlet specific enthalpy and the isentropic efficiency.
Optionally, the method of obtaining the coolant mass flow rate from the inlet density, the volumetric efficiency, the rotational speed of the coolant pump, and the working volume comprises obtaining the coolant mass flow rate according to:
Figure BDA0003001783740000051
in the formula (I), the compound is shown in the specification,
Figure BDA0003001783740000052
is the cooling liquid mass flow; ρ is the inlet density; eta v For the volumetric efficiency, n is the rotational speed of the coolant pump, V D Is the working volume;
wherein the working medium pump power consumption is obtained by the following formula:
Figure BDA0003001783740000053
in the formula, P pump In order to consume the power of the working medium pump,
Figure BDA0003001783740000054
is the mass flow of the cooling liquid, η s For said isentropic efficiency, h out，s Is the specific enthalpy of the isentropic process outlet, h in Is the inlet specific enthalpy.
Optionally, in the building of the battery pack prediction model according to the topological structure of the battery pack, the battery pack prediction model includes an equivalent circuit model, a battery pack generation model and a battery pack heat dissipation model;
the method for acquiring the predicted temperature of the battery pack by the battery pack prediction model according to the current intensity and the mass flow of the cooling liquid comprises the following steps:
the equivalent circuit model acquires output voltage according to the current intensity of the battery pack, the state parameter of the battery pack at the current moment, the predicted temperature and the charge state of the battery pack;
the battery pack heat generation model acquires the heat generation quantity of the power battery according to the current intensity, the open-circuit voltage and the output voltage of the battery pack;
and the battery pack heat dissipation model acquires the predicted temperature of the battery pack and the predicted temperature of the cooling liquid according to the heat generation quantity, the heat transfer characteristic parameters and the boundary conditions of the power battery.
Optionally, the method for establishing the equivalent circuit model includes: each single battery cell of the battery pack is equivalent to a first-order RC model by adopting an equivalent circuit method;
and/or
The method for establishing the battery pack production thermal model comprises the following steps: establishing a battery pack heat generation model according to the number of the single battery cores of the battery pack and the heat generation principle of each single battery core;
and/or
The method for establishing the heat dissipation model of the battery pack comprises the following steps: and establishing the battery pack heat dissipation model by adopting a lumped parameter method according to energy conservation.
Optionally, the first order RC model comprises: the circuit comprises a power supply element, an inductance element, a resistance element and a capacitance element, wherein the resistance element is connected with the capacitance element in parallel and then connected with the power supply element and the inductance element in series; the state parameter comprises a voltage value at two ends of the resistance element at the current moment;
the method for acquiring the output voltage by the equivalent circuit model according to the current intensity of the battery pack, the state parameter of the battery pack at the current moment, the predicted temperature and the state of charge of the battery pack comprises the following steps:
for each single battery cell, obtaining the output voltage through a continuous state equation of the following formula, and iterating the voltage values at two ends of the resistance element in the whole prediction time domain in real time:
Figure BDA0003001783740000061
V out ＝V OCV -V 1 -I out R int
in the formula, V out Is the output voltage, V OCV Is the open circuit voltage, I out Is the current intensity; r int ，V OCV ，R 1 ，C 1 Are all sig (I) out )、T cell SOC (t) is obtained by looking up a table, wherein sig (I) out ) The current direction is set, the discharging is positive, and the charging is negative; t is cell Obtaining a predicted temperature of the battery pack from the battery pack heat dissipation model; SOC (t) in the prediction time domainThe state of charge of the individual cells;
V 1 the value of the voltage across the resistive element at the present moment,
Figure BDA0003001783740000062
is the voltage value across the resistive element at the next time.
Optionally, obtaining the state of charge of the monomer cell in the prediction time domain by using an ampere-hour integration method according to the following formula:
Figure BDA0003001783740000063
in the formula, SOC 0 For the prediction of the state of charge, I, of the individual cells at the time-domain starting point out Is the current intensity of the cell core, C nom Is the rated capacity, T, of the cell p Is the prediction time domain.
Optionally, the method for obtaining the heat generation amount of the power battery by the battery pack heat generation model according to the current intensity of the battery pack, the open-circuit voltage, the output voltage and the predicted temperature of the battery pack includes:
acquiring joule heat caused by internal resistance and polarization heat caused by mass transfer according to the current intensity of each single battery cell of the battery pack, the open-circuit voltage, the output voltage and the predicted temperature of the battery pack;
and acquiring the heat generation quantity of the power battery according to the topological relation of the single battery cells of the battery pack and the joule heat of each single battery cell.
Optionally, the battery pack heat dissipation model obtains the predicted temperature of the battery pack and the predicted temperature of the cooling liquid according to the heat generation amount of the power battery, heat transfer characteristic parameters and boundary conditions, where the heat transfer characteristic parameters and the boundary conditions include: the total heat exchange area and heat exchange coefficient of the battery pack and the cooling liquid, the specific heat capacity of the cooling liquid, the inlet temperature and the outlet temperature of the battery pack cooling liquid and the mass flow of the cooling liquid;
the method for obtaining the predicted temperature of the battery pack and the predicted temperature of the cooling liquid comprises the following steps:
acquiring the heat exchange quantity of the battery pack and the cooling liquid according to the total heat exchange area of the battery pack and the cooling liquid, the predicted temperature of the cooling liquid and the heat exchange coefficient of the cooling liquid and the battery pack;
acquiring the thermal mass of the battery pack according to the mass flow of the cooling liquid, the specific heat capacity of the cooling liquid, and the inlet temperature and the outlet temperature of the cooling liquid of the battery pack;
obtaining a predicted temperature of the battery pack according to the heat generation amount, the thermal mass and the heat exchange amount;
and acquiring the predicted temperature of the cooling liquid according to the mass of the cooling liquid retained in the battery pack, the mass flow rate of the cooling liquid, the specific heat capacity of the cooling liquid, the inlet temperature and the outlet temperature of the cooling liquid of the battery pack and the heat exchange amount.
Optionally, the method for obtaining the refrigeration power according to the mass flow of the cooling liquid includes:
obtaining the refrigerating power according to the mass flow of the cooling liquid, the mass of the cooling liquid detained in the cooler, the specific heat capacity of the cooling liquid, the temperature at the inlet of the cooler and the temperature at the outlet of the cooler.
Optionally, the method for selecting one of the plurality of sets of temperature control parameter information as the control input of the temperature control device according to a preset optimized performance index, the predicted temperature, the energy consumption information, and a target temperature includes: selecting the rotating speed of the cooling liquid pump and the refrigerating power of the cooler by adopting an objective function as follows:
Figure BDA0003001783740000081
P TMS ＝P chiller +P pump
wherein T (T) is at time TTemperature, T, of the battery pack r Is a target temperature, P, of the battery pack TMS For energy consumption, R T Is a coefficient matrix of temperature terms, R P A coefficient matrix which is an energy consumption item; p chiller Is the refrigeration power of the cooler, P pump And the power consumption of the working medium pump of the cooling liquid pump is reduced.
Optionally, the method for selecting one of the plurality of sets of temperature control parameter information as the control input of the temperature control device according to a preset optimized performance index, the predicted temperature, the energy consumption information, and a target temperature further includes:
based on a linear quadratic programming method, converting the nonlinear continuous state equation of each working condition point in the prediction time domain into a linear discrete equation by using a Taylor expansion method and a forward Euler method;
and selecting one group as the control input of the temperature control device according to the target function.
Optionally, the method for obtaining the state parameter of the battery pack includes:
and constructing a state observer based on Kalman filtering to acquire the state parameters of the battery pack.
Optionally, the method further comprises:
and acquiring the disturbance parameters in the prediction time domain through the networking information.
In order to achieve the above object, the present invention also provides an AMPC controller of a power battery, the thermal cycle topology of the power battery including a battery pack and a temperature control device, the AMPC controller including:
the temperature control prediction unit is configured to simulate a thermal cycle topological structure of the power battery, set a plurality of groups of temperature control parameter information according to a preset rule, input each group of temperature control parameter information and disturbance parameter information of the power battery into the temperature control prediction model respectively, and obtain corresponding predicted temperature of the battery pack and energy consumption information of the temperature control device;
the rolling optimization unit is configured to select one of the groups of temperature control parameter information as a control input of the temperature control device according to a preset optimization performance index, the predicted temperature, the energy consumption information and a target temperature;
and the feedback correction unit is configured to acquire the state parameters of the battery pack and correct the temperature control prediction model in real time by using the state parameters.
In order to achieve the above object, the present invention further provides a thermal management system for a power battery, including a temperature control device connected to the power battery, a controller, and the AMPC controller as described above;
the controller is configured to acquire the temperature, the state of charge and the output voltage of the battery pack;
the AMPC is configured to determine temperature control parameter information of the temperature control device according to the current intensity, the predicted temperature of the battery pack, the state of charge and the output voltage;
and the temperature control device adjusts the temperature of the battery pack in real time according to the temperature control parameter information.
Optionally, the method further includes an internet connection information obtaining module configured to obtain the disturbance parameter in the prediction time domain.
In order to achieve the above object, the present invention further provides a computer-readable storage medium, on which computer-executable instructions are stored, and when the computer-executable instructions are executed, the steps of the temperature control method for a power battery according to any one of the above mentioned items are implemented.
Compared with the prior art, the temperature control method of the power battery, the AMPC controller, the thermal management system and the medium provided by the invention have the following beneficial effects:
the temperature control method of the power battery provided by the invention comprises the following steps: establishing a temperature control prediction model of the power battery according to the thermal cycle topological structure of the power battery; setting a plurality of groups of temperature control parameter information according to a preset rule, and respectively inputting each group of temperature control parameter information and disturbance parameter information of the power battery into the temperature control prediction model to obtain the corresponding predicted temperature of the battery pack and energy consumption information of the temperature control device; selecting one group from a plurality of groups of temperature control parameter information as the control input of the temperature control device according to a preset optimized performance index, the predicted temperature, the energy consumption information and a target temperature; and acquiring the state parameters of the battery pack, and correcting the temperature control prediction model in real time by using the state parameters. With the configuration, the temperature control method of the power battery, the AMPC controller, the thermal management system and the medium provided by the invention can control the temperature of the battery pack of the power battery to be in or close to an appropriate range without increasing any hardware cost, and simultaneously reduce the energy consumption of the actuator.
Further, the AMPC controller of the power battery and the thermal management system provided by the invention comprise a temperature prediction unit, a rolling optimization unit and a feedback correction unit, and are configured in such a way that the AMPC controller of the power battery can process a nonlinear time-varying system and comprehensively utilize all actuators; multi-objective optimization under constraint conditions can be directly realized; when the controller is used for controlling multiple input and output, the complexity of the controller is not obviously increased relative to the control of single input and output; the predictive information input can obviously improve the control effect and conform to the technical trend of future intelligent network connection.
Drawings
Fig. 1 is a schematic diagram of a thermal cycle topology of a power battery according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a temperature control method for a power battery according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a battery pack model according to an embodiment of the invention;
FIG. 4 is a schematic diagram of a first-order RC model circuit according to an embodiment of the present invention;
FIG. 5 is a block diagram of an AMPC controller according to an embodiment of the present invention;
FIG. 6 is a block diagram of a thermal management system according to an embodiment of the present invention;
FIG. 7 is a block diagram illustrating an architecture of another thermal management system according to an embodiment of the present invention;
wherein the reference numerals are as follows:
100-battery pack, 200-temperature control device, 210-cooler, 220-coolant pump;
310-an equivalent circuit model, 320-a battery pack production thermal model and 330-a battery pack heat dissipation model;
400-AMPC controller, 410-temperature control prediction unit, 420-rolling optimization unit, 430-feedback correction unit;
500-controller, 600-networking information acquisition module.
Detailed Description
To make the objects, advantages and features of the present invention more apparent, the temperature control method of the power battery, the AMPC controller, the thermal management system and the medium according to the present invention will be described in further detail with reference to the accompanying drawings. It is to be noted that the drawings are in a very simplified form and are not to precise scale, which is provided for the purpose of facilitating and clearly illustrating embodiments of the present invention. It should be understood that the drawings are not necessarily to scale, showing the particular construction of the invention, and that illustrative features in the drawings, which are used to illustrate certain principles of the invention, may also be somewhat simplified. Specific design features of the invention disclosed herein, including, for example, specific dimensions, orientations, locations, and configurations, will be determined in part by the particular intended application and use environment. In the embodiments described below, the same reference numerals are used in common between different drawings to denote the same portions or portions having the same functions, and a repetitive description thereof will be omitted. In this specification, like reference numerals and letters are used to designate like items, and therefore, once an item is defined in one drawing, further discussion thereof is not required in subsequent drawings.
These terms, as used herein, are interchangeable where appropriate. Similarly, if the method described herein comprises a series of steps, the order in which these steps are presented herein is not necessarily the only order in which these steps may be performed, and some of the described steps may be omitted and/or some other steps not described herein may be added to the method.
The embodiment provides a temperature control method for a power battery. Referring to fig. 1 and fig. 2, in which fig. 1 is a schematic view of a thermal cycle topology of a power battery provided in an embodiment of the present invention, and fig. 2 is a schematic view of a flow chart of a temperature control method of the power battery provided in the embodiment. As can be seen from fig. 1, the thermal cycling topology of the power battery includes a battery pack 100 and a temperature control device 200.
As can be seen from fig. 2, the temperature control method includes the following steps:
s100: and establishing a temperature control prediction model of the power battery according to the thermal cycle topological structure of the power battery.
S200: and setting a plurality of groups of temperature control parameter information according to a preset rule, and respectively inputting each group of temperature control parameter information and the disturbance parameter information of the power battery into the temperature control prediction model to obtain the corresponding predicted temperature of the battery pack and the energy consumption information of the temperature control device.
S300: and selecting one group from a plurality of groups of temperature control parameter information as the control input of the temperature control device according to a preset optimized performance index, the predicted temperature, the energy consumption information and a target temperature.
S400: and acquiring the state parameters of the battery pack, and correcting the temperature control prediction model in real time by using the state parameters.
The invention provides a temperature control method of a power battery, which is a predictive energy-saving temperature control algorithm under a driving working condition. By the configuration, on the premise of meeting the real-time calculation, the temperature control method of the power battery can control the temperature of the battery pack to be in or close to an appropriate range, and meanwhile, the energy consumption of the actuator is reduced.
Specifically, in one exemplary embodiment, the temperature control device 200 includes a coolant pump 220 and a cooler 210, and the coolant pump 220 drives the coolant of the cooler 210 to provide cold energy to the battery pack 100; the temperature control parameter information includes the rotation speed of the coolant pump 220 and the cooling power of the cooler 210, and the disturbance parameter information of the power battery includes the current intensity of the battery pack. For ease of understanding and description, liquid-cooled refrigeration will be used as an example, and those skilled in the art will understand that this is not a limitation of the present invention, and that the above method is also applicable to power batteries using air-cooled or heat pipe technology as a temperature control device.
Specifically, in one preferred embodiment, the establishing a temperature control prediction model of the power battery according to the thermal cycle topology of the power battery includes: establishing a cooling liquid pump prediction model by adopting a quasi-static ash box model modeling method; establishing a battery pack prediction model according to the topological structure of the battery pack 100; and (4) establishing a cooler prediction model by adopting a lumped parameter method. Further, the method for setting a plurality of sets of temperature control parameter information according to the preset rule in step S200, and inputting each set of temperature control parameter information and disturbance parameter information of the power battery into the temperature control prediction model to obtain the corresponding predicted temperature of the battery pack and energy consumption information of the temperature control device includes:
s210: and the cooling liquid pump prediction model acquires the mass flow of the cooling liquid and the power consumption of a working medium pump of the cooling liquid pump according to the working condition parameter information of the cooling liquid pump and the rotating speed of the cooling liquid pump.
S220: and the battery pack prediction model acquires the predicted temperature of the battery pack according to the current intensity and the mass flow of the cooling liquid.
S230: and the cooler prediction model acquires the predicted temperature at the outlet of the cooler according to the mass flow of the cooling liquid, the predicted temperature of the battery pack and the refrigeration power.
As will be understood by those skilled in the art, the present invention performs any limitation on the specific content of "setting a plurality of sets of temperature control parameter information according to the preset rule", that is, the number of sets of temperature control parameter information (the rotation speed of the coolant pump and the cooling power of the chiller) is not limited, and the specific value and the value obtaining method of each set of temperature control parameter information are not limited, and those skilled in the art should reasonably select the temperature control parameter information according to the actual working conditions.
Preferably, in one exemplary embodiment, the coolant pump 220 is a positive displacement pump, and a quasi-static ash tank model modeling method is used, which combines physical principles and empirical equations to obtain the coolant mass flow and the rotational speed of the coolant pump 220. The information on the operating parameters of the coolant pump 220 includes: the operating condition parameter information of the coolant pump 220 includes: inlet density, volumetric efficiency, working volume, isentropic process outlet specific enthalpy, inlet specific enthalpy and isentropic efficiency. Wherein, the volumetric efficiency and the isentropic efficiency can be obtained by calibration or related formulas, which is not limited in any way by the present invention. In step S210, the method for obtaining the mass flow rate of the coolant and the power consumption of the working medium pump of the coolant pump 220 by the coolant pump prediction model according to the working condition parameter information of the coolant pump 220 and the rotation speed of the coolant pump 220 includes the following steps:
s211: and acquiring the mass flow of the cooling liquid according to the inlet density, the volumetric efficiency, the rotating speed of the cooling liquid pump and the working volume.
S212: and acquiring the power consumption of the working medium pump according to the mass flow of the cooling liquid, the isentropic process outlet specific enthalpy, the inlet specific enthalpy and the isentropic efficiency.
Specifically, in one exemplary embodiment, in step S211, the method for obtaining the mass flow rate of the cooling liquid according to the inlet density, the volumetric efficiency, the rotation speed of the cooling liquid pump and the working volume includes obtaining the mass flow rate of the cooling liquid according to the following formula:
Figure BDA0003001783740000131
in the formula (I), the compound is shown in the specification,
Figure BDA0003001783740000132
is the cooling liquid mass flow; ρ is the inlet density; n is the rotation speed of the cooling liquid pump; eta v Is the volumetric efficiency; v D Is the working volume.
Wherein the power consumption of the working medium pump is obtained by the following formula:
Figure BDA0003001783740000133
in the formula, P pump In order to consume the power of the working medium pump,
Figure BDA0003001783740000134
is the mass flow of the cooling liquid, η s For the isentropic efficiency, h out，s Is the specific enthalpy of the isentropic process outlet, h in Is the inlet specific enthalpy.
Preferably, in one exemplary embodiment, referring to fig. 3, fig. 3 is a schematic structural diagram of the battery pack model provided in this embodiment. As can be seen from fig. 3, in the building of the battery pack prediction model according to the topology structure of the battery pack, the battery pack prediction model includes an equivalent circuit model 310, a battery pack generation thermal model 320, and a battery pack heat dissipation model 330. Specifically, in step S220, the method for obtaining the predicted temperature of the battery pack by the battery pack prediction model according to the current intensity and the mass flow of the cooling liquid includes:
s221: the equivalent circuit model 310 obtains an output voltage according to the current intensity of the battery pack 100, the state parameter of the battery pack 100 at the current moment, the predicted temperature and the state of charge of the battery pack 100;
s222: the battery pack heat generation model 320 acquires the heat generation amount of the power battery according to the current intensity, the open-circuit voltage and the output voltage of the battery pack 100;
s223: the battery pack heat dissipation model obtains the predicted temperature of the battery pack 100 and the predicted temperature of the cooling liquid according to the heat generation amount, the heat transfer characteristic parameters and the boundary conditions of the power battery.
Next, the equivalent circuit model 310 of the battery pack model, the battery pack generation model 320, and the battery pack heat dissipation model 330 will be described in detail.
Specifically, in one preferred embodiment, the method for establishing the equivalent circuit model includes: each single battery cell of the battery pack 100 is equivalent to a first-order RC model by using an equivalent circuit method. Referring to fig. 4, fig. 4 is a schematic diagram of a first-order RC model circuit structure provided in one embodiment. As can be seen from fig. 4, the first order RC model includes: the circuit comprises a power supply element, an inductance element, a resistance element and a capacitance element, wherein the resistance element and the capacitance element are connected in parallel and then are connected in series with the power supply element and the inductance element. The state parameter includes a voltage value across the resistive element at a present time. Specifically, the method for obtaining the output voltage by the equivalent circuit model according to the current intensity of the battery pack, the state parameter of the battery pack at the current moment, the predicted temperature and the state of charge of the battery pack includes: for each single battery cell, obtaining the output voltage through a continuous state equation of the following formula, and iterating the voltage values at the two ends of the resistance element in real time in the whole prediction time domain:
Figure BDA0003001783740000141
V out ＝V OCV -V 1 -I out R int
in the formula, V out Is the output voltage, V OCV Is the open circuit voltage, I out Is the current intensity (current output of the cell); r is int ，V OCV ，R 1 ，C 1 Are all formed by sig (I) out )、T cell SOC (t) lookup table (provided by BMS developer); wherein sig (I) out ) The current direction is, discharge is positive, charge is negative; t is cell Obtaining a predicted temperature of the battery pack from the battery pack heat dissipation model; SOC (t) is the state of charge of the single battery cell in a prediction time domain; v 1 The voltage value of the two ends of the resistance element at the current moment is a state parameter which can not be directly measured;
Figure BDA0003001783740000142
is the voltage value across the resistive element at the next time. The output voltage V out Can be used to build an observer to reconstruct said state parameter V 1 。
Further, in one exemplary embodiment, the method includes obtaining the state of charge of the monomer cell in the prediction time domain by using an ampere-hour integration method according to the following formula:
Figure BDA0003001783740000151
wherein SOC (t) is the state of charge, SOC, of the monomer battery cell in the prediction time domain 0 For the prediction of the state of charge, I, of the individual cells at the time-domain starting point out Is the current intensity of the cell core, C nom Is the rated capacity, T, of the single cell p Is the prediction time domain.
Preferably, in one of the exemplary embodiments, the method of establishing the battery pack thermal model includes: and establishing a battery pack heat generation model according to the number of the single battery cores of the battery pack and the heat generation principle of each single battery core.
Specifically, in step S222, the battery pack heat generation model 320 obtains the heat generation amount of the power battery according to the current intensity, the open-circuit voltage and the output voltage of the battery pack 100, and includes the following steps:
s222-1: acquiring joule heat caused by internal resistance and polarization heat caused by mass transfer according to the current intensity of each single battery cell of the battery pack, the open-circuit voltage, the output voltage and the predicted temperature of the battery pack;
s222-2: and acquiring the heat generation quantity of the power battery according to the topological relation of the single battery cores of the battery pack and the joule heat of each single battery core. In one embodiment, if the battery pack includes N battery cells, the heat generation amount of the power battery is the sum of the heat generation amounts of the N battery cells.
Taking a lithium battery as an example, the heat generated by a single cell includes joule heat caused by internal resistance of the lithium ion battery and polarization heat caused by mass transfer loss, and the heat of reaction (entropy change heat) of the positive and negative electrodes of the lithium ion battery. Under the condition that the power battery works normally, the joule and the polarization heat play a leading role, and preferably, the battery pack production thermal model can be simplified, reaction heat is ignored, and calculation force is saved.
Preferably, in one exemplary embodiment, the method for establishing the heat dissipation model of the battery pack includes: and establishing the battery pack heat dissipation model by adopting a lumped parameter method according to energy conservation.
Specifically, in step S223, the battery pack heat dissipation model obtains the predicted temperature of the battery pack and the predicted temperature of the cooling liquid according to the heat generation amount of the power battery, the heat transfer characteristic parameters, and the boundary conditions, where the heat transfer characteristic parameters and the boundary conditions include: the total heat exchange area between the battery pack and the cooling liquid, the heat exchange coefficient, the specific heat capacity of the cooling liquid, the inlet temperature and the outlet temperature of the cooling liquid of the battery pack and the mass flow of the cooling liquid. Specifically, the method for acquiring the temperature of the battery pack comprises the following steps:
s223-1: and acquiring the heat exchange quantity of the battery pack and the cooling liquid according to the total heat exchange area of the battery pack and the cooling liquid, the predicted temperature of the cooling liquid and the heat exchange coefficient of the cooling liquid and the battery pack.
Specifically, in one embodiment, the heat exchange amount may be obtained according to the reason of uniform wall surface heat exchange, and as can be understood by those skilled in the art, the heat exchange coefficient may be calibrated according to experiments, which is not limited by the present invention.
S223-2: and acquiring the thermal mass of the battery pack according to the mass flow of the cooling liquid, the specific heat capacity of the cooling liquid, and the inlet temperature and the outlet temperature of the cooling liquid of the battery pack.
As will be understood by those skilled in the art, the temperature of the coolant in the battery pack can be modeled by a lumped parameter method because the temperature of the coolant does not change during the whole heat exchange process and the temperature difference between the inlet and the outlet is relatively small. Before dynamic modeling of the coolant in the battery pack, the following assumptions can be made: based on a lumped parameter method, the state parameters of the cooling liquid are kept consistent in all places except an inlet; the state of the coolant at the outlet is identical to the state in the battery pack; neglecting the pressure loss and the kinetic energy loss of the cooling liquid in the battery pack; neglecting the heat dissipation loss to the outside.
S223-3: and acquiring the predicted temperature of the battery pack according to the heat generation quantity, the thermal mass and the heat exchange quantity. Continuing to take the aforementioned liquid cooling as an example, since the power battery is cooled by liquid cooling, the heat dissipation medium of the battery pack is only the cooling liquid. Thus, the predicted temperature of the battery pack can be predicted according to the energy conservation equation.
S223-4: and acquiring the predicted temperature of the cooling liquid according to the mass of the cooling liquid retained in the battery pack, the mass flow rate of the cooling liquid, the specific heat capacity of the cooling liquid, the inlet temperature and the outlet temperature of the cooling liquid of the battery pack and the heat exchange amount.
It can be understood that the battery cooling in the present invention uses liquid cooling, so the heat dissipation medium of the battery pack only uses the cooling liquid, and the energy equation of the predicted temperature (average temperature) of the battery pack can be obtained according to the energy conservation equation.
By means of the configuration, the temperature control method of the power battery provided by the invention can not only express the dynamic characteristics of the battery accurately enough, but also realize smaller occupation of computing resources by establishing the dynamic model of the battery pack.
Further, in one embodiment, the method for obtaining the refrigeration power according to the cooling liquid mass flow rate includes:
obtaining the refrigerating power according to the mass flow of the cooling liquid, the mass of the cooling liquid detained in the cooler, the specific heat capacity of the cooling liquid, the temperature at the inlet of the cooler and the temperature at the outlet of the cooler. It is understood that the heat exchange process of the cooling fluid in the Chiller can be physically modeled according to the refrigeration power. Because the cooling liquid does not have phase change in the whole heat exchange process and the temperature difference between the inlet and the outlet is relatively small, the modeling can be carried out by adopting a lumped parameter method. The quality of the coolant retained in the cooler can be calibrated by experiment, assuming that the state of the coolant at the outlet is consistent with the state of the coolant in the cooler, and the invention is not limited thereto.
With the configuration, the cooler prediction model established by the temperature control method for the power battery is the dynamic model of the cooler, so that the dynamic characteristics of the cooler can be expressed accurately enough, and the small occupation of computing resources can be realized.
In step S300, the method for selecting one of the plurality of sets of temperature control parameter information as the control input of the temperature control device according to a preset optimized performance index, the predicted temperature, the energy consumption information, and the target temperature includes: selecting the rotating speed of the cooling liquid pump and the refrigerating power of the cooler by adopting an objective function as follows:
Figure BDA0003001783740000171
P TMS ＝P chiller +P pump
wherein T (T) is the temperature of the battery pack at time T, T r Is a target temperature, P, of the battery pack TMS For energy consumption, R T Is a coefficient matrix of temperature terms, R P A coefficient matrix which is an energy consumption item; p chiller For the refrigerating capacity, P, of the cooler pump The power consumption of the working medium pump of the cooling liquid pump is reduced. The weight of temperature optimization and energy consumption optimization in the objective function can be adjusted by adjusting the two coefficient matrixes. The configuration not only has simple form,the method can be used for solving a linear optimization algorithm with low calculation power consumption, and can also optimize the temperature and the energy consumption at the same time.
Preferably, in step S300, the method for selecting one of the plurality of sets of temperature control parameter information as the control input of the temperature control device according to a preset optimized performance index, the predicted temperature, the energy consumption information, and the target temperature further includes the following steps:
s310: based on a linear quadratic programming method, converting the nonlinear continuous state equation of each working condition point in the prediction time domain into a linear discrete equation by utilizing a Taylor expansion method and a forward Euler method;
s320: and selecting one group as the control input of the temperature control device according to the target function.
Because the invention is based on the method of Linear Quadratic Programming (LQP), the analytic solution of the optimal control sequence in the prediction time domain can be obtained under extremely small calculation force.
Specifically, before solving the LQP problem, the nonlinear continuous state equations for each operating point (t (k), x (k)) in the prediction time domain need to be converted into linear discrete equations by using the taylor expansion method and the Forward-Euler method, wherein
Figure BDA0003001783740000181
Represents the new state transition matrix:
Figure BDA0003001783740000182
Figure BDA0003001783740000183
with the configuration, the temperature control method of the power battery provided by the invention takes the time-varying property of the system into consideration in the process of linear discretization.
Figure BDA0003001783740000184
Time varying ofThe probability may be related to x (k), or to u (k), or to time T (k). To obtain
Figure BDA0003001783740000185
Figure BDA0003001783740000186
The necessary information is to predict x (k) and u (k) at each time point/(k) in the time domain.
Furthermore, the temperature control method of the power battery provided by the invention adopts a method of borrowing self parameters, namely calling the optimal control sequence delta U obtained in the last prediction time domain to provide each step of acquisition in the current prediction time domain
Figure BDA0003001783740000187
The required information.
Further, when there is a time-varying disturbance parameter (I) in the system out (k) In the prediction time domain, the future prediction information can be input into a linear discrete equation in the prediction time domain, so that the model prediction algorithm can respond to future changes in advance and achieve better pre-control. After a linear discrete state equation under each working condition point (t (k), x (k)) in the prediction time domain is obtained, the LQP problem with constraints can be solved by combining the objective function.
In step S400, the method for obtaining the state parameter of the battery pack includes:
and constructing a state observer based on Kalman filtering to acquire the state parameters of the battery pack.
Since there must be an error between the prediction model and the object, the actual output state of the system needs to be fed back to the model before each roll optimization. When the prediction model state space equation contains the state quantity which can not be directly measured, a state observer needs to be built, the purpose is to reconstruct the state quantity in the nonlinear model according to the output quantity which can be directly measured, and the purpose of avoiding accumulative errors is also achieved.
The temperature control method of the power battery provided by the invention is implemented by constructing a Kalman filtering-based methodA state observer, and using the output voltage V of the battery pack out To reconstruct the state quantity V which can not be directly measured 1 And comprehensively feeding back an optimal estimation value according to the model prediction value and the actual measurement value. By the configuration, the state quantity in the nonlinear model can be reconstructed according to the directly measured output quantity, and the purpose of avoiding accumulative errors is achieved.
Preferably, in one exemplary embodiment, the temperature control method further includes: and acquiring disturbance parameters in the prediction time domain through networking information, wherein the disturbance parameters comprise the current intensity. Through the configuration, the battery thermal management system is pre-controlled according to the model predictive control algorithm with the assistance of the intelligent network connection function, so that the aims of temperature control and energy saving are better fulfilled.
Understandably, when there is no intelligent networking information, a perturbation parameter (I) can be assumed out (k) The current value is kept unchanged in the prediction time domain.
Based on the same inventive concept as the temperature Control method for the power battery, a further embodiment of the present invention further provides an AMPC (Adaptive Model Predictive Control, AMPC) controller for the power battery, referring to fig. 5, where fig. 5 is a schematic structural diagram of the AMPC controller provided in this embodiment. As can be seen from fig. 5 in conjunction with fig. 1, the thermal cycling topology of the power battery includes a battery pack and a temperature control device, and the AMPC controller 400 includes: a temperature controlled prediction unit 410, a roll optimization unit 420, and a feedback correction unit 430.
Specifically, the temperature control prediction unit 410 is configured to simulate a thermal cycle topology of the power battery, and is configured to set a plurality of sets of temperature control parameter information according to a preset rule, and input each set of the temperature control parameter information and disturbance parameter information of the power battery into the temperature control prediction model, so as to obtain a corresponding predicted temperature of the battery pack and energy consumption information of the temperature control device. For AMPC, the model is able to predict future outputs to the system based on historical information and future inputs to the system. In the invention, a nonlinear time-varying state space model which can embody the nonlinear characteristic of the system is selected as a prediction model, and the model is linearized in real time, so that the configuration can meet the calculation force requirement of an exponential reduction control algorithm, ensure higher model precision and fully embody the Adaptive (Adaptive) place of the algorithm.
The rolling optimization unit 420 is configured to select one of the plurality of sets of temperature control parameter information as a control input of the temperature control device according to a preset optimization performance index, the predicted temperature, the energy consumption information, and a target temperature. On-line roll optimization is the most prominent feature of AMPC. In AMPC, the performance index of an optimization algorithm is not a system objective function under a certain static time point, but the integral of the system objective function in a prediction time domain, and the optimal control sequence of the system in the prediction time domain is determined by optimizing the performance index. And the rolling optimization is embodied in that the performance index reaches the optimal effect within the limited time from the moment to the future at each sampling moment by obtaining the state parameters of the battery pack, but only the first item of the obtained optimal control sequence is output to the controlled object as the current control instruction, and at the next sampling moment, the performance index reaches the optimal effect within the same time period from the current moment to the future again, and so on, and the control effect of the rolling optimization is finally realized by model predictive control.
The feedback correction unit 430 is configured to obtain the state parameters of the battery pack, and correct the temperature control prediction model in real time using the state parameters. By the configuration, the defect that the prediction model adopted by the AMPC can only roughly describe the dynamic characteristics of an object generally, and meanwhile, the control effect is usually deviated from the ideal due to environmental interference and errors of a measuring element and an actuator, so that a certain deviation exists between the prediction output of the prediction model and the actual output of a system. And detecting the state parameters of the battery pack of the system at each sampling moment through the AMPC, correcting the model according to the deviation of the state parameters and the predicted output, and performing rolling optimization on the system again on the basis. Therefore, the feedback information can be used for correcting the model, and closed-loop optimization control is realized.
In conclusion, the AMPC controller of the power battery provided by the invention has the following beneficial advantages: the nonlinear time-varying system can be processed, and all actuators are comprehensively utilized; multi-objective optimization under constraint conditions can be directly realized; when the controller is used for the control of multiple input and output, the complexity of the controller is not obviously increased relative to the control of single input and output; the predictive information input can obviously improve the control effect and conform to the technical trend of future intelligent network connection.
Still another embodiment of the present invention further provides a thermal management system for a power battery, referring to fig. 6, fig. 6 is a schematic diagram of an architecture of the thermal management system according to one embodiment of the present invention, and as can be seen from fig. 6, the thermal management system includes a temperature control device 200 connected to a battery pack 100 of the power battery, a controller 500, and an AMPC controller 400 according to any one of the above embodiments; the controller 500 is configured to obtain a predicted temperature, state of charge, and output voltage of the battery pack 100; the AMPC controller 400 is configured to determine temperature control parameter information of the temperature control device 200 according to a current intensity, a temperature prediction degree of the battery pack 100, the state of charge, and the output voltage; the temperature control device 200 adjusts the temperature of the battery pack 100 in real time according to the temperature control parameter information. The controller 500 includes, but is not limited to, a BMS, a VCU, and the like.
Further, referring to fig. 7, the thermal management system further includes a networking information obtaining module 600, where the networking information obtaining module 600 is configured to obtain a disturbance parameter in the prediction time domain, where the disturbance parameter includes the current intensity. Through the configuration, the battery thermal management system is pre-controlled according to the model predictive control algorithm with the assistance of the intelligent network connection function, so that the aims of temperature control and energy saving are better fulfilled.
Because the thermal management system of the power battery provided by the invention and the AMPC controller provided by the above embodiments belong to the same inventive concept, at least the same beneficial effects are achieved, and the detailed description is omitted.
It should be noted that the methods and apparatuses disclosed in the embodiments herein can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments herein. In this regard, each block in the flowchart or block diagrams may represent a module, a program, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments herein may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Moreover, various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without being mutually inconsistent.
Yet another embodiment of the present invention further provides a computer-readable storage medium, on which computer-executable instructions are stored, and when the computer-executable instructions are executed, the steps of the temperature control method for a power battery according to any one of the above embodiments are implemented. Because the computer-readable storage medium provided by the invention belongs to the same inventive concept as the temperature control method of the power battery provided by the above embodiments, the computer-readable storage medium at least has the same beneficial effects as the above methods, and thus, the description is omitted.
The readable storage medium of this embodiment may be any combination of one or more computer-readable media. The readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this context, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
Therefore, the temperature control method of the power battery, the AMPC controller, the thermal management system and the medium provided by the invention can control the temperature of the battery pack of the power battery to be in or close to an appropriate range on the premise of not increasing any hardware cost, and simultaneously reduce the energy consumption of an actuator.
In summary, the above embodiments have been described in detail on different configurations of the temperature control method of the power battery, the AMPC controller, the thermal management system and the medium, and it is understood that the above description is only a description of the preferred embodiments of the present invention and does not limit the scope of the present invention in any way.

Claims (17)

1. The temperature control method of the power battery is characterized in that the thermal cycle topological structure of the power battery comprises a battery pack and a temperature control device; the temperature control device comprises a cooling liquid pump and a cooler, and the temperature control method comprises the following steps:
establishing a temperature control prediction model of the power battery according to the thermal cycle topological structure of the power battery; the temperature control prediction model comprises a cooling liquid pump prediction model, a battery pack prediction model and a cooler prediction model;
setting a plurality of groups of temperature control parameter information according to a preset rule, and respectively inputting each group of temperature control parameter information and disturbance parameter information of the power battery into the temperature control prediction model to obtain the corresponding predicted temperature of the battery pack and energy consumption information of the temperature control device; the temperature control parameter information comprises the rotating speed of a cooling liquid pump and the refrigerating power of the cooler, and the disturbance parameter information of the power battery comprises the current intensity of the battery pack;
selecting one group from a plurality of groups of temperature control parameter information as the control input of the temperature control device according to a preset optimized performance index, the predicted temperature, the energy consumption information and a target temperature;
acquiring state parameters of the battery pack, and correcting the temperature control prediction model in real time by using the state parameters;
wherein, according to the preset rule, setting a plurality of groups of temperature control parameter information, and respectively inputting each group of temperature control parameter information and disturbance parameter information of the power battery into the temperature control prediction model, and acquiring the corresponding predicted temperature of the battery pack and energy consumption information of the temperature control device, comprises:
the coolant pump prediction model acquires the mass flow of the coolant and the power consumption of a working medium pump of the coolant pump according to the working condition parameter information of the coolant pump and the rotating speed of the coolant pump;
the battery pack prediction model obtains the predicted temperature of the battery pack according to the current intensity and the mass flow of the cooling liquid;
and the cooler prediction model acquires the predicted temperature at the outlet of the cooler according to the mass flow of the cooling liquid, the predicted temperature of the battery pack and the refrigeration power.
2. The temperature control method for the power battery according to claim 1, wherein the coolant pump drives the coolant of the cooler to provide cold energy for the battery pack;
the establishing of the temperature control prediction model of the power battery according to the thermal cycle topological structure of the power battery comprises the following steps:
establishing a cooling liquid pump prediction model by adopting a quasi-static ash box model modeling method;
establishing a battery pack prediction model according to the topological structure of the battery pack;
and (4) establishing a cooler prediction model by adopting a lumped parameter method.
3. The temperature control method for the power battery according to claim 2, wherein the operating condition parameter information of the cooling liquid pump comprises: inlet density, volumetric efficiency, working volume, isentropic process outlet specific enthalpy, inlet specific enthalpy and isentropic efficiency;
the method for acquiring the mass flow rate of the cooling liquid and the power consumption of the working medium pump of the cooling liquid pump by the cooling liquid pump prediction model according to the working condition parameter information of the cooling liquid pump and the rotating speed of the cooling liquid pump comprises the following steps:
obtaining the mass flow of the cooling liquid according to the inlet density, the volumetric efficiency, the rotating speed of the cooling liquid pump and the working volume;
and acquiring the power consumption of the working medium pump according to the mass flow of the cooling liquid, the specific enthalpy of the outlet of the isentropic process, the specific enthalpy of the inlet and the isentropic efficiency.
4. The method for controlling temperature of a power battery according to claim 3, wherein the method for obtaining the mass flow rate of the cooling liquid according to the inlet density, the volumetric efficiency, the rotation speed of the cooling liquid pump and the working volume comprises obtaining the mass flow rate of the cooling liquid according to the following formula:
Figure FDA0003860897930000024
in the formula (I), the compound is shown in the specification,
Figure FDA0003860897930000021
is the cooling liquid mass flow; ρ is the inlet density; eta v For the volumetric efficiency, n is the rotational speed of the coolant pump, V D Is the working volume;
wherein the power consumption of the working medium pump is obtained by the following formula:
Figure FDA0003860897930000022
in the formula, P pump In order to consume power by the working medium pump,
Figure FDA0003860897930000023
is the mass flow of the cooling liquid, η s For said isentropic efficiency, h out,s Is the specific enthalpy of the isentropic process outlet, h in Is the inlet specific enthalpy.
5. The temperature control method for the power battery according to claim 2, wherein in the building of the battery pack prediction model according to the topological structure of the battery pack, the battery pack prediction model comprises an equivalent circuit model, a battery pack generation model and a battery pack heat dissipation model;
the method for obtaining the predicted temperature of the battery pack by the battery pack prediction model according to the current intensity and the mass flow of the cooling liquid comprises the following steps:
the equivalent circuit model acquires output voltage according to the current intensity of the battery pack, the state parameter of the battery pack at the current moment, the predicted temperature and the charge state of the battery pack;
the battery pack heat generation model acquires the heat generation quantity of the power battery according to the current intensity, the open-circuit voltage and the output voltage of the battery pack;
and the battery pack heat dissipation model acquires the predicted temperature of the battery pack and the predicted temperature of the cooling liquid according to the heat generation quantity, the heat transfer characteristic parameters and the boundary conditions of the power battery.
6. The temperature control method for the power battery according to claim 5, wherein the method for establishing the equivalent circuit model comprises the following steps: each single battery cell of the battery pack is equivalent to a first-order RC model by adopting an equivalent circuit method;
and/or
The method for establishing the battery pack production thermal model comprises the following steps: establishing a battery pack heat generation model according to the number of the single battery cells of the battery pack and the heat generation principle of each single battery cell;
and/or
The method for establishing the heat dissipation model of the battery pack comprises the following steps: and establishing the battery pack heat dissipation model by adopting a lumped parameter method according to energy conservation.
7. The temperature control method for the power battery according to claim 5, wherein the method for obtaining the heat generation amount of the power battery by the battery pack heat generation model according to the current intensity of the battery pack, the open-circuit voltage, the output voltage and the predicted temperature of the battery pack comprises:
acquiring joule heat caused by internal resistance and polarization heat caused by mass transfer according to the current intensity of each single battery cell of the battery pack, the open-circuit voltage, the output voltage and the predicted temperature of the battery pack;
and acquiring the heat generation quantity of the power battery according to the topological relation of the single battery cells of the battery pack and the joule heat of each single battery cell.
8. The temperature control method for the power battery according to claim 5, wherein the battery pack heat dissipation model obtains the predicted temperature of the battery pack and the predicted temperature of the cooling liquid according to the heat generation amount of the power battery, heat transfer characteristic parameters and boundary conditions, wherein the heat transfer characteristic parameters and the boundary conditions include: the total heat exchange area, the heat exchange coefficient, the specific heat capacity, the inlet temperature and the outlet temperature of the cooling liquid in the battery pack and the mass flow of the cooling liquid of the battery pack are determined;
the method for obtaining the predicted temperature of the battery pack and the predicted temperature of the cooling liquid comprises the following steps:
acquiring the heat exchange quantity of the battery pack and the cooling liquid according to the total heat exchange area of the battery pack and the cooling liquid, the predicted temperature of the cooling liquid and the heat exchange coefficient of the cooling liquid and the battery pack;
acquiring the thermal mass of the battery pack according to the mass flow of the cooling liquid, the specific heat capacity of the cooling liquid, and the inlet temperature and the outlet temperature of the cooling liquid of the battery pack;
obtaining a predicted temperature of the battery pack according to the heat production amount, the thermal mass and the heat exchange amount;
and acquiring the predicted temperature of the cooling liquid according to the mass of the cooling liquid retained in the battery pack, the mass flow rate of the cooling liquid, the specific heat capacity of the cooling liquid, the inlet temperature and the outlet temperature of the cooling liquid of the battery pack and the heat exchange amount.
9. The method for controlling the temperature of the power battery according to claim 5, wherein the method for obtaining the refrigeration power comprises:
the cooling power is obtained according to the mass flow of the cooling liquid, the mass of the cooling liquid retained in the cooler, the specific heat capacity of the cooling liquid, the temperature at the inlet of the cooler, and the temperature at the outlet of the cooler.
10. The method for controlling the temperature of a power battery according to claim 2, wherein the method for selecting one of the plurality of sets of temperature control parameter information as the control input of the temperature control device according to a preset optimized performance index, the predicted temperature, the energy consumption information and a target temperature comprises: selecting the rotating speed of the cooling liquid pump and the refrigerating power of the cooler by adopting an objective function as follows:
Figure FDA0003860897930000041
P TMS ＝P chiller +P pump
wherein T (T) is the temperature of the battery pack at time T, T r Is a target temperature, P, of the battery pack TMS For energy consumption, R T Is a coefficient matrix of temperature terms, R P A coefficient matrix which is an energy consumption item; p chiller For the refrigerating capacity, P, of the cooler pump The power consumption of the working medium pump of the cooling liquid pump is reduced.
11. The method according to claim 10, wherein the method for selecting one of the plurality of sets of temperature control parameter information as the control input of the temperature control device according to a preset optimized performance index, the predicted temperature, the energy consumption information and a target temperature further comprises:
based on a linear quadratic programming method, converting a nonlinear continuous state equation of each working condition point in a prediction time domain into a linear discrete equation by using a Taylor expansion method and a forward Euler method;
and selecting one group as the control input of the temperature control device according to the target function.
12. The temperature control method for the power battery according to claim 1, wherein the method for obtaining the state parameter of the battery pack comprises:
and constructing a state observer based on Kalman filtering to obtain the state parameters of the battery pack.
13. The temperature control method for the power battery according to claim 1, further comprising:
and acquiring disturbance parameters in a prediction time domain through the internet connection information.
14. An AMPC controller for a power cell, the thermal cycling topology for the power cell comprising a battery pack and a temperature control device, the temperature control device comprising a coolant pump and a cooler, the AMPC controller comprising:
the temperature control prediction unit is configured to simulate a thermal cycle topological structure of the power battery, set a plurality of groups of temperature control parameter information according to a preset rule, input each group of temperature control parameter information and disturbance parameter information of the power battery into a temperature control prediction model respectively, and obtain corresponding predicted temperature of the battery pack and energy consumption information of the temperature control device; the temperature control parameter information comprises the rotating speed of a cooling liquid pump and the refrigerating power of the cooler, and the disturbance parameter information of the power battery comprises the current intensity of the battery pack; the temperature control prediction model comprises a cooling liquid pump prediction model, a battery pack prediction model and a cooler prediction model;
the rolling optimization unit is configured to select one of the plurality of sets of temperature control parameter information as a control input of the temperature control device according to a preset optimization performance index, the predicted temperature, the energy consumption information and a target temperature;
the feedback correction unit is configured to acquire state parameters of the battery pack and correct the temperature control prediction model in real time by using the state parameters;
wherein, according to the preset rule, setting a plurality of groups of temperature control parameter information, respectively inputting each group of temperature control parameter information and disturbance parameter information of the power battery into a temperature control prediction model, and acquiring corresponding predicted temperature of the battery pack and energy consumption information of the temperature control device, comprises:
the cooling liquid pump prediction model acquires the mass flow of the cooling liquid and the power consumption of a working medium pump of the cooling liquid pump according to the working condition parameter information of the cooling liquid pump and the rotating speed of the cooling liquid pump;
the battery pack prediction model obtains the predicted temperature of the battery pack according to the current intensity and the mass flow of the cooling liquid;
and the cooler prediction model acquires the predicted temperature at the outlet of the cooler according to the mass flow of the cooling liquid, the predicted temperature of the battery pack and the refrigeration power.
15. A thermal management system for a power cell comprising a temperature control device connected to the power cell, a controller and an AMPC controller according to claim 14;
the controller is configured to obtain a temperature, a state of charge, and an output voltage of the battery pack;
the AMPC is configured to determine temperature control parameter information of the temperature control device according to the current intensity of the battery pack, the predicted temperature, the state of charge and the output voltage of the battery pack;
and the temperature control device adjusts the temperature of the battery pack in real time according to the temperature control parameter information.
16. The thermal management system of the power battery according to claim 15, further comprising an internet information acquisition module configured to acquire the disturbance parameter in the prediction time domain.
17. A computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed, the computer-readable storage medium implements the steps of the temperature control method for power battery according to any one of claims 1 to 13.
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