CN111261973A - Electric automobile whole battery thermal management method based on model predictive control - Google Patents

Electric automobile whole battery thermal management method based on model predictive control Download PDF

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CN111261973A
CN111261973A CN202010062895.7A CN202010062895A CN111261973A CN 111261973 A CN111261973 A CN 111261973A CN 202010062895 A CN202010062895 A CN 202010062895A CN 111261973 A CN111261973 A CN 111261973A
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battery
model
vehicle speed
temperature
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CN111261973B (en
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谢翌
王晨阳
胡晓松
张扬军
刘钊铭
唐小林
冯飞
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Chongqing University
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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
    • H01M10/61—Types of temperature control
    • H01M10/613—Cooling or keeping cold
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/15—Correlation function computation including computation of convolution operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • 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
    • H01M10/633—Control systems characterised by algorithms, flow charts, software details or the like
    • 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
    • H01M10/60—Heating or cooling; Temperature control
    • H01M10/65—Means for temperature control structurally associated with the cells
    • H01M10/655—Solid structures for heat exchange or heat conduction
    • H01M10/6554—Rods or plates
    • 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
    • H01M10/65—Means for temperature control structurally associated with the cells
    • H01M10/656—Means for temperature control structurally associated with the cells characterised by the type of heat-exchange fluid
    • H01M10/6567—Liquids
    • H01M10/6568—Liquids characterised by flow circuits, e.g. loops, located externally to the cells or cell casings
    • 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 relates to a model predictive control-based whole electric automobile battery thermal management method, and belongs to the field of new energy automobiles. The method comprises the following main steps: s1, establishing a system model including an electric-thermal-aging multi-state estimation and a cooling system of the transmission system and the battery pack; s2, designing a state estimator and a cost function of the model predictive controller; s3: coupling a vehicle speed prediction and control system; s4: the ambient temperature is monitored in real time, and an optimal battery temperature reference value related to the ambient temperature is found and coupled to the controller. The algorithm of the invention has low complexity and good feasibility; meanwhile, temperature management and aging management of the battery and energy consumption management of a cooling system are considered in the control system, and a new idea is provided for the heat management system of the whole electric vehicle. The method of the invention can further realize the systematic and high-efficiency battery thermal management strategy.

Description

Electric automobile whole battery thermal management method based on model predictive control
Technical Field
The invention belongs to the field of new energy automobiles, and relates to a model predictive control-based whole electric automobile battery thermal management method.
Background
The power battery is used as an energy storage device of an electric automobile, and many operating characteristics of the power battery are greatly influenced by temperature, so that an efficient battery thermal management system is required to be designed to maintain the temperature of the battery within a reasonable range. The need for battery thermal management technology is an important background that contributes to the present invention.
For several years, there have been many control methods for battery thermal management systems, such as: a control strategy based on-off control, a control strategy based on active control, and a control strategy based on model predictive control. However, these models do not take into account that in the spring and autumn, when the air conditioner is not needed in the passenger compartment, the cooling circuit for the battery does not include a chiller. Furthermore, most of the research on the energy consumption of the cooling system does not take the influence of battery cooling on aging into consideration.
The invention takes a model prediction control algorithm as a main support algorithm, couples the model prediction control algorithm with a vehicle speed prediction model, and controls and manages the whole vehicle battery thermal management system. Meanwhile, the method not only considers the influence of the battery cooling condition on energy consumption, but also finds out the optimal cooling temperature reference value changing along with weather information, and innovatively considers the influence of the battery cooling condition on aging.
Disclosure of Invention
In view of the above, the present invention provides a method for managing thermal of a battery of an electric vehicle based on model predictive control. To reduce vehicle speed disturbances to the state estimator, a vehicle speed prediction model is coupled to a model predictive control algorithm. Meanwhile, the energy consumption of the cooling system and the SOH of the battery are subjected to multi-objective optimization.
In order to achieve the purpose, the invention provides the following technical scheme:
the method for managing the heat of the battery of the whole electric automobile based on model predictive control comprises the following steps:
s1: establishing a system model for thermal management of the battery pack including a transmission system, an electro-thermal-aging multi-state estimation of the battery pack, and a cooling system;
s2: designing a state estimator and a cost function of the model predictive controller;
s3: establishing a vehicle speed prediction model, and coupling the vehicle speed prediction model with a control system;
s4: at different ambient temperatures, an optimum battery temperature reference value is found in relation to the ambient temperature and coupled to the control system.
Optionally, the step S1 specifically includes the following steps:
s11: establishing a system model framework and establishing the relationship among all submodels;
s12: establishing a transmission system model which is driven by a battery pack and meets the requirement of vehicle speed dynamic change;
s13: establishing an electro-thermal-aging model of the battery, which changes with the load of the battery pack;
s14: and establishing a cooling system of the battery pack, wherein the cooling system is a single-loop cooling model consisting of a cold plate, a water pump and a radiator.
Optionally, the control objectives of the state estimator and the cost function are: the temperature of the battery pack is maintained at a certain temperature reference value, the aging degree of the battery is minimized, the temperature difference between the battery modules is minimized, and the energy consumption of a water pump of a cooling system is minimized; the state estimator for model predictive control is:
Z＝Λx(k)+ΦU+ΨD#
wherein the content of the first and second substances,
Figure BDA0002375068090000021
the cost function is:
Figure BDA0002375068090000022
optionally, the step S3 includes the following steps:
s31: classifying the vehicle speed database, and establishing a sub-database according to the characteristic values of the vehicle speed, namely average vehicle speed, idle time proportion, speed variance, acceleration mean and speed-plus-acceleration variance; training each sub-database, and establishing a responding neural network model;
s32: selecting a responding neural network model by taking the vehicle speed characteristic value of the historical vehicle speed of the adjacent time period as a judgment basis, and taking the historical vehicle speed and the characteristic value as the input of the network model to obtain initial data of the predicted vehicle speed;
s33: processing abnormal data in the output data of the neural network to finally obtain a prediction data segment at the moment;
s34: the predicted future vehicle speed information is coupled to the control.
Optionally, the step S4 includes the following steps:
s41: under a certain specific environment temperature, making a pareto boundary curve of SOH and cooling energy consumption, and finding out an optimal battery temperature reference value under the environment temperature;
s42: under different environmental temperatures, making pareto boundaries of different SOH and cooling energy consumption, and finding out the relation between the optimal temperature reference value and the environmental temperature;
s43: and inputting a reasonable temperature reference value into the controller according to the real-time monitored environmental temperature.
The invention has the beneficial effects that:
and establishing a system model of the heat management of the battery pack, which comprises a transmission system, an electro-thermal-aging multi-state estimation of the battery pack and a cooling system, and performing model prediction control algorithm on the battery temperature to maintain the battery temperature at about a certain temperature and not exceed the optimal working temperature range. Meanwhile, in order to reduce the disturbance of the vehicle speed to the controller state estimation model, the vehicle speed prediction model is coupled with the controller, future vehicle speed information is provided, the control accuracy is improved, the tracking performance of the controller to the reference temperature is improved, and the energy consumption of a cooling system is reduced. Meanwhile, considering that the maximization of the SOH degree of the battery and the minimization of the energy consumption of the water pump present the pareto characteristic when different battery temperature reference values are set, a reasonable battery reference temperature value which balances the two contradictory targets is selected. Pareto boundaries under different environmental temperatures are made, the relation between the optimal battery temperature and weather temperature information is found out, and on the premise that the SOH of the battery is relatively maximum, the energy consumption of a cooling system is reduced.
The control target is to effectively prolong the service life of the battery and save the energy consumption of the cooling system. Compared with the prior art, the invention has the advantages that:
1) by the model predictive control algorithm, the real-time performance in the calculation process is improved (compared with a dynamic planning algorithm), and the optimization control can be performed without knowing the working condition of the whole process.
2) And predicting the vehicle speed by using the BP neural network, and coupling the vehicle speed with the controller to reduce the disturbance of the vehicle speed to a control system.
3) In battery thermal management, the SOH of the battery is considered for the first time as one of the optimization terms of the cost function.
4) And finding the optimal battery temperature reference value under the real-time environment temperature, increasing the SOH and reducing the energy consumption.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a process flow diagram of the present invention as a whole;
FIG. 2 is a diagram of a system model architecture;
FIG. 3 is a schematic view of a cold plate configuration;
FIG. 4 is a schematic representation of a vehicle speed prediction model;
FIG. 5 is a pareto boundary curve for SOH and cooling power consumption;
fig. 6 is a block diagram of the overall controller of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.
Referring to fig. 1, the steps of constructing the model predictive control-based overall electric vehicle battery thermal management strategy are as follows:
s1: establishing a system model for thermal management of the battery pack including a transmission system, an electro-thermal-aging multi-state estimation of the battery pack, and a cooling system;
s2: designing a state estimator and a cost function of the model predictive controller;
s3: establishing a vehicle speed prediction model, and coupling the vehicle speed prediction model with a control system;
s4: the ambient temperature is measured in real time, and an optimal battery temperature reference value associated with the ambient temperature is found and coupled to the controller.
Step S1 is to construct the system model shown in fig. 2, and the specific steps are as follows:
s11: and establishing a transmission model of the electric automobile, wherein a transmission system of the whole automobile is provided with required traction power by a battery pack so as to meet the required vehicle speed. The mathematical model is as follows:
Figure BDA0002375068090000041
Figure BDA0002375068090000042
Figure BDA0002375068090000051
wherein, PtracIs the traction power, P, of the vehicletracIs the electric power supplied by the battery pack, IpacIs the current, v, of the battery packvehIs the running speed, δ mvehavehIs the acceleration resistance, fm, required for the vehiclevehg is the rolling resistance of the road to which the vehicle is subjected, an
Figure BDA0002375068090000056
Is the air resistance experienced by the vehicle and η is the mechanical efficiency between the battery pack and the drive train.
S12: an electro-thermal-aging model of the battery pack is established. The battery pack is composed of 24 modules, each module is composed of 12 single bodies 3 in parallel and 4 in series. Ignoring the differences between the cells, from ampere's law:
Figure BDA0002375068090000052
Figure BDA0002375068090000053
the terminal voltage of the monomer is calculated by a first-order R-C model, and the electric model of the monomer is shown as the formula-6:
Ucell＝Uocv(SOC，C)+Icell·Ro(SOC，T，C)-Up#(6)
the thermal model of the battery module ignores the heat generation difference between the battery monomers, and the mathematical model is shown as the formula-7:
Figure BDA0002375068090000054
wherein, the specific heat capacity c of the battery modulepIs obtained by a centralized parameter method according to the mass specific heat of each layer of the battery material,
Figure BDA0002375068090000057
the temperature coefficient of the battery is determined by experiments, hAmod(Td-T) represents the heat exchange between the battery module and the cold plate.
After the battery capacity is lost by 20% in the state of health of the battery, the SOH is zero, and the mathematical model is shown as the formula-8:
Figure BDA0002375068090000055
where B is a function of discharge rate, and k, Rh, and z are correlation coefficients, respectively.
S13: and establishing a cooling system model. The cooling structure of the system is a single cooling loop consisting of a cold plate, a water pump and a radiator.
The structure between the cold plates is shown in figure 3, the structure comprises 2 main cooling branches which are connected in parallel, each branch is connected in series by three parts, the first part is connected in parallel by four cold plates, and two modules are arranged on each cold plate in an imitated way; the second part is formed by connecting three cold plates in parallel, and a module is arranged on each cold plate; the third part has a cold plate, and a module is placed on each cold plate. The control object of this embodiment is the module set on the third portion. Assuming that there is no difference between the cold plates in parallel connection, namely the inlet and outlet temperatures are consistent; the outlet temperature of the former cold plate is the inlet temperature of the latter cold plate in series. The heat exchange process of the cold plate has no heat loss and no phase change, and the heat exchange formula is shown as the formula-9:
Figure BDA0002375068090000061
wherein, water is used as coolant, and the flow rate is controlled by a water pump.
The power of the water pump is determined by water flow and flow acceleration, and a polynomial response surface model of the water pump power obtained by fitting experimental data is shown as the formula-10:
Figure BDA0002375068090000062
the radiator model is established by adopting an NTU (thermal transfer unit) method, and assuming that no phase change process and no heat loss exist in the radiator, the mathematical model is shown as the formula-11:
Figure BDA0002375068090000063
s2: the battery thermal management system adopts a control method of model predictive control. The control system aims at maintaining the battery temperature at a specific temperature reference, optimizing the state of health (SOH) of the battery aging, minimizing the temperature difference between modules, controlling the maximum temperature difference within 5 ℃, and minimizing the energy consumption of the cooling system. Taking a state space equation obtained after the model linearization as a state estimator of the controller, as shown in the formula-12:
Z＝ΛX(k)+ΦU+ΨD#(12)
wherein
Figure BDA0002375068090000064
The cost function is:
Figure BDA0002375068090000065
wherein N ispIs the prediction time domain; n is a radical ofUIs the control time domain; z (k + i | k) is the state output quantity of the system at the k moment and the k + i predicted moment, and comprises the temperature of the battery, the SOH and the temperature difference between modules; u is the control quantity of the system, namely the flow of the water pump, and is controlled within 24L/min; d is the disturbance quantity of the system model, namely the vehicle speed; Λ, Φ, and Ψ are the coefficient matrices for the state space after linearization of the system model, ωb、ωsohAnd ωdifIs the weight of different terms
Figure BDA0002375068090000066
Is the battery reference temperature value of the controller.
S3: the vehicle prediction model shown in fig. 4 is established and coupled to the controller, and the specific steps are as follows:
s31: determining characteristic values of historical vehicle speed (60s) database samples, i.e. average vehicle speed, idle time ratio, speed variance, acceleration mean and speed-plus-accelerationThe variance. And according to the average vehicle speed vaveAnd idle time ratio IpAnd dividing 5 sub-database with the dividing limit as follows: 1): v. ofave<5m/s；2)：5m/s≤vave<15m/s，Ip>20％；3)：5m/s≤vave<15m/s，Ip≤20％；4)：15m/s≤vave<25m/s；5)：25m/s≤vave。
S32: and training the sub-neural network by taking the historical vehicle speed (previous 20s) and the vehicle speed characteristic value in the sub-database as an input layer and taking the future vehicle speed (future 60s) as an output layer to obtain 5 neural network models.
S33: and judging and selecting a corresponding neural network model according to the characteristic value, taking the historical vehicle speed and the vehicle speed characteristic value as input, obtaining the output of the neural network model according to the formula-14, and processing the output to ensure that the acceleration is in the maximum acceleration range, thus obtaining the predicted future vehicle speed information.
[Y]30×1＝[W2]30×80×([Wl]80×25×[X]25×1+[B1]80×1)+[B2]30×1#(14)
Wherein [ Y ]]30×1And [ X ]]25×1Are the output and input matrices, [ W ] respectively2]30×80And [ W ]1]80×25Weights for the output layer and the hidden layer, [ B ] respectively2]30×1And [ B1]80×1Respectively, the bias values of the output layer and the hidden layer.
S34: the future vehicle speed information is coupled to the controller. The disturbance input of the state estimator is converted into a future vehicle speed sequence instead of the current vehicle speed constant, namely, the disturbance in the formula-12 is changed into the following sequence:
D＝[v(k+1|k)，v(k+2|k)，...，v(k+Np|k)]T
s4: finding the relation between the optimal battery temperature reference value and the environment temperature, and coupling the relation with a control system, wherein the specific steps are as follows:
s41: when the environmental temperature is 20 ℃ and the temperature reference value is 20 ℃, the running process of the vehicle is simulated, and the SOC of the battery pack is consumed from 90% to 10%. And calculating to obtain the SOH of the battery and the energy consumption of the water pump. The temperature reference value is changed to obtain a pareto boundary curve of SOH and cooling power consumption as shown in fig. 5, and it is reasonable to select the optimal temperature reference value of 21 ℃.
S42: and changing the ambient temperature to obtain a pareto boundary curve of SOH and cooling energy consumption at different ambient temperatures, and obtaining the relation between the optimal temperature reference value and the ambient temperature.
S43: the ambient temperature is measured in real time, and the temperature reference value of the controller is changed in real time, namely the cost function of the formula-13 is changed into the formula-15:
Figure BDA0002375068090000071
according to the steps, the whole electric vehicle battery thermal management strategy based on the model prediction control shown in FIG. 6 can be established.
Effects and effects of the embodiments
The method for managing the heat of the battery of the electric vehicle based on the model predictive control has the advantages that: 1) the service life of the battery is effectively prolonged, and the energy consumption of a cooling system is saved; 2) by the model predictive control algorithm, the real-time performance in the calculation process is improved (compared with a dynamic planning algorithm, for example), and the optimization control can be carried out without knowing the working condition of the whole process; 3) predicting the vehicle speed by using a BP neural network, and coupling the vehicle speed with a controller to reduce the disturbance of the vehicle speed to a control system; 4) in the battery heat management, the SOH of the battery is considered as one of the optimization terms of the cost function for the first time; 5) and finding the optimal battery temperature reference value under the real-time environment temperature, increasing the SOH and reducing the energy consumption.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.

Claims (5)

1. The electric vehicle battery heat management method based on model predictive control is characterized in that: the method comprises the following steps:
s1: establishing a system model for thermal management of the battery pack including a transmission system, an electro-thermal-aging multi-state estimation of the battery pack, and a cooling system;
s2: designing a state estimator and a cost function of the model predictive controller;
s3: establishing a vehicle speed prediction model, and coupling the vehicle speed prediction model with a control system;
s4: at different ambient temperatures, an optimum battery temperature reference value is found in relation to the ambient temperature and coupled to the control system.
2. The model predictive control-based electric vehicle battery thermal management method according to claim 1, characterized in that: the step S1 specifically includes the following steps:
s11: establishing a system model framework and establishing the relationship among all submodels;
s12: establishing a transmission system model which is driven by a battery pack and meets the requirement of vehicle speed dynamic change;
s13: establishing an electro-thermal-aging model of the battery, which changes with the load of the battery pack;
s14: and establishing a cooling system of the battery pack, wherein the cooling system is a single-loop cooling model consisting of a cold plate, a water pump and a radiator.
3. The model predictive control-based electric vehicle battery thermal management method according to claim 1, characterized in that: the control objectives of the state estimator and the cost function are: the temperature of the battery pack is maintained at a certain temperature reference value, the aging degree of the battery is minimized, the temperature difference between the battery modules is minimized, and the energy consumption of a water pump of a cooling system is minimized; the state estimator for model predictive control is:
Z＝Λx(k)+ΦU+ΨD#
wherein the content of the first and second substances,
Figure FDA0002375068080000011
the cost function is:
Figure FDA0002375068080000012
4. the model predictive control-based overall battery thermal management method for the electric vehicle according to claim 3, characterized in that: the step S3 includes the following steps:
s31: classifying the vehicle speed database, and establishing a sub-database according to the characteristic values of the vehicle speed, namely average vehicle speed, idle time proportion, speed variance, acceleration mean and speed-plus-acceleration variance; training each sub-database, and establishing a responding neural network model;
s32: selecting a responding neural network model by taking the vehicle speed characteristic value of the historical vehicle speed of the adjacent time period as a judgment basis, and taking the historical vehicle speed and the characteristic value as the input of the network model to obtain initial data of the predicted vehicle speed;
s33: processing abnormal data in the output data of the neural network to finally obtain a prediction data segment at the moment;
s34: the predicted future vehicle speed information is coupled to the control.
5. The model predictive control-based electric vehicle battery thermal management method according to claim 4, characterized in that: the step S4 includes the following steps:
s41: under a certain specific environment temperature, making a pareto boundary curve of SOH and cooling energy consumption, and finding out an optimal battery temperature reference value under the environment temperature;
s42: under different environmental temperatures, making pareto boundaries of different SOH and cooling energy consumption, and finding out the relation between the optimal temperature reference value and the environmental temperature;
s43: and inputting a reasonable temperature reference value into the controller according to the real-time monitored environmental temperature.
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Cited By (20)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111562741A (en) * 2020-05-09 2020-08-21 上海交通大学 Method for prolonging service life of battery of electric automobile
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CN112467243A (en) * 2020-11-12 2021-03-09 浙江合众新能源汽车有限公司 Battery pack cooling control method and device
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CN112498282A (en) * 2020-11-12 2021-03-16 东风汽车集团有限公司 Automatic alarm device for electric automobile fire
CN112606692A (en) * 2020-12-28 2021-04-06 重庆大学 Electric automobile safety protection device and method
CN112820976A (en) * 2021-01-06 2021-05-18 张展浩 Battery heat exchange fan system of electric vehicle and control method thereof
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CN115195530A (en) * 2022-08-11 2022-10-18 深圳职业技术学院 Battery cooling management control system of electric automobile
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WO2022258079A1 (en) * 2021-06-09 2022-12-15 山东大学 Deep learning-based thermal management control method and system for electric drive bulldozer battery
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CN116826245A (en) * 2023-08-31 2023-09-29 山东鑫泰莱光电股份有限公司 Energy storage battery thermal management method and system

Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100090650A1 (en) * 2005-08-03 2010-04-15 Rachid Yazami Battery State of Health Assessment System
CN104733801A (en) * 2013-12-20 2015-06-24 北汽福田汽车股份有限公司 Power cell heat management device and method
TWM509444U (en) * 2015-04-24 2015-09-21 Acer Inc Battery temperature control system
CN204791125U (en) * 2015-07-21 2015-11-18 桂林电子科技大学 Prediction of electric automobile power battery temperature and heat abstractor
CN105319509A (en) * 2014-07-01 2016-02-10 福特全球技术公司 System and method for battery open circuit voltage estimation
US20160246266A1 (en) * 2015-02-25 2016-08-25 Mitsubishi Electric Research Laboratories, Inc. System and Method for Controlling System
JP2017152302A (en) * 2016-02-26 2017-08-31 三菱自動車工業株式会社 Method for predicting temperature tendency of battery module, predictor, and prediction program
CN107134604A (en) * 2017-03-29 2017-09-05 南京航空航天大学 A kind of power battery thermal management method based on working characteristicses
JP2017212764A (en) * 2016-05-23 2017-11-30 本田技研工業株式会社 Charge/discharge device, transport equipment and control method
US20180145379A1 (en) * 2016-11-21 2018-05-24 Samsung Electronics Co., Ltd. Method and apparatus to control temperature of battery
CN108365986A (en) * 2018-02-07 2018-08-03 重庆大学 Hybrid power fleet based on Model Predictive Control cooperates with energy management method
US10355327B1 (en) * 2015-10-30 2019-07-16 Loon Llc Dynamic battery pack thermal management
CN110161423A (en) * 2019-06-26 2019-08-23 重庆大学 A kind of dynamic lithium battery state joint estimation method based on various dimensions coupling model
CN110532600A (en) * 2019-07-19 2019-12-03 北京航空航天大学 A kind of power battery thermal management system and method
CN110696680A (en) * 2019-09-17 2020-01-17 中国矿业大学 Power battery pack temperature pre-regulation and control system and method and thermal management system control method

Patent Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100090650A1 (en) * 2005-08-03 2010-04-15 Rachid Yazami Battery State of Health Assessment System
CN104733801A (en) * 2013-12-20 2015-06-24 北汽福田汽车股份有限公司 Power cell heat management device and method
CN105319509A (en) * 2014-07-01 2016-02-10 福特全球技术公司 System and method for battery open circuit voltage estimation
US20160246266A1 (en) * 2015-02-25 2016-08-25 Mitsubishi Electric Research Laboratories, Inc. System and Method for Controlling System
TWM509444U (en) * 2015-04-24 2015-09-21 Acer Inc Battery temperature control system
CN204791125U (en) * 2015-07-21 2015-11-18 桂林电子科技大学 Prediction of electric automobile power battery temperature and heat abstractor
US10355327B1 (en) * 2015-10-30 2019-07-16 Loon Llc Dynamic battery pack thermal management
JP2017152302A (en) * 2016-02-26 2017-08-31 三菱自動車工業株式会社 Method for predicting temperature tendency of battery module, predictor, and prediction program
JP2017212764A (en) * 2016-05-23 2017-11-30 本田技研工業株式会社 Charge/discharge device, transport equipment and control method
US20180145379A1 (en) * 2016-11-21 2018-05-24 Samsung Electronics Co., Ltd. Method and apparatus to control temperature of battery
CN107134604A (en) * 2017-03-29 2017-09-05 南京航空航天大学 A kind of power battery thermal management method based on working characteristicses
CN108365986A (en) * 2018-02-07 2018-08-03 重庆大学 Hybrid power fleet based on Model Predictive Control cooperates with energy management method
CN110161423A (en) * 2019-06-26 2019-08-23 重庆大学 A kind of dynamic lithium battery state joint estimation method based on various dimensions coupling model
CN110532600A (en) * 2019-07-19 2019-12-03 北京航空航天大学 A kind of power battery thermal management system and method
CN110696680A (en) * 2019-09-17 2020-01-17 中国矿业大学 Power battery pack temperature pre-regulation and control system and method and thermal management system control method

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Publication number Priority date Publication date Assignee Title
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CN114441192B (en) * 2022-01-26 2023-07-04 重庆长安汽车股份有限公司 Test and evaluation method for heat accumulation and power attenuation of passenger car at normal temperature
CN115195530B (en) * 2022-08-11 2023-03-10 深圳职业技术学院 Battery cooling management control system of electric automobile
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