A control method for thermal management system of electric vehicle fleet based on traffic information

Abstract

This invention relates to a control method for an electric vehicle fleet thermal management system based on traffic information, belonging to the field of vehicle thermal management technology. The method comprises: S1: establishing a model of the pure electric vehicle's powertrain-motor-battery system; S2: establishing models of the electric vehicle's air conditioning system, battery cooling system, motor cooling system, and passenger compartment based on the lumped parameter method, laying the foundation for the subsequent establishment of the thermal management system control strategy; S3: constructing a traffic light timing scenario model, and establishing a mathematical model for longitudinal speed planning of the fleet based on energy consumption optimization to ensure smooth passage during green lights; S4: designing a model predictive control intelligent thermal management control method combining vehicle speed longitudinal planning. This invention couples vehicle speed control with traffic information, vehicle-to-vehicle information, and energy consumption optimization to achieve temperature control and energy-saving control of the vehicle fleet, exhibiting good temperature control capabilities and energy-saving benefits.

Description

Technical Field

[0001] This invention belongs to the field of vehicle thermal management technology, and relates to a control method for a thermal management system for electric vehicle fleets based on traffic information. Background Technology

[0002] As a complex system composed of components made of various materials and with different structures, electric vehicles have different optimal operating temperatures and temperature tolerance levels for each component. Therefore, a thermal management system is needed to ensure that each component operates within a suitable temperature range through effective heating, heat dissipation, and insulation measures. Numerous experimental studies have shown that battery performance is most significantly affected by temperature. According to Pesaran's research, the optimal operating temperature for batteries is between 15°C and 35°C; temperatures above or below this range will damage the battery's performance. Meanwhile, the powertrain typically requires the motor cooling water temperature to be below 80°C and the motor controller cooling water temperature below 60°C. These two have significantly different optimal operating temperature ranges. Therefore, developing an effective thermal management system is a crucial method for addressing the range and safety issues of pure electric vehicles. Furthermore, in high-temperature environments, the passenger compartment, being a closed environment, easily accumulates heat, causing severe discomfort for passengers and even posing safety hazards. The air conditioning system accounts for 33% of the vehicle's total energy consumption, with the air conditioning compressor contributing the vast majority of energy consumption. This further reduces the limited battery capacity and driving range. Therefore, designing and properly matching a good air conditioning system is crucial. Model Predictive Control (MPC), due to its robustness, dynamic performance, and coordinated multi-objective control characteristics, has received considerable attention and research, and is increasingly being applied to automotive thermal management. It is worth noting that vehicles on the road are not isolated entities; each vehicle is coupled with other vehicles, forming a vehicle platoon system. Within this system, there are pairwise constraints between vehicles, drivers, and the road, creating a complex generalized dynamic system. Managing and controlling a single vehicle has limitations. Related research shows that vehicle platooning can significantly improve traffic efficiency and driving safety, and reduce regional vehicle system energy consumption.

[0003] While most scholars have conducted research on air conditioning modeling, battery modeling, motor modeling, and vehicle thermal management modeling, most studies focus on modeling individual components separately. Research on thermal management control strategies that couple and control the entire vehicle's thermal management components is relatively scarce. Furthermore, vehicle platooning research is largely confined to energy management studies of hybrid electric vehicles, with almost no intelligent control strategies combining vehicle platooning speed planning with overall vehicle thermal management. To address this research deficiency, this invention, based on the problem of vehicle thermal management in high-temperature summer environments, designs an MPC intelligent model control strategy based on energy consumption optimization and longitudinal speed planning. It establishes a vehicle-road cooperative scenario to plan the longitudinal speed of the vehicle platoon during road travel, reducing driving energy consumption. While meeting the thermal management needs of the passenger compartment, battery, and motor in each vehicle, it minimizes the cooling energy consumption of individual vehicles and the entire platoon. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a control method for a thermal management system for electric vehicle fleets based on traffic information.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A control method for a thermal management system for an electric vehicle fleet based on traffic information, the method comprising the following steps:

[0007] S1: Establish a model of the powertrain-motor-battery system for a pure electric vehicle;

[0008] S2: Models of the electric vehicle's air conditioning system, battery cooling system, motor cooling system, and passenger compartment are established based on the lumped parameter method;

[0009] S3: A traffic light timing scenario model was constructed, and a mathematical model for convoy longitudinal speed planning was established based on energy consumption optimization to ensure smooth passage during green light.

[0010] S4: Design a model predictive control (MPC) intelligent thermal management control method that combines vehicle speed longitudinal planning.

[0011] Optionally, S1 specifically includes the following steps:

[0012] S11: The battery electro-thermal coupling model is established using the lumped parameter method. The established model includes the battery electrical model, thermal model, and electro-thermal coupling model.

[0013] S12: Establish a control-oriented longitudinal dynamics model for the vehicle;

[0014] S13: Establish a lumped parameter-based dynamic thermal model of the motor for control purposes.

[0015] Optionally, the battery electro-thermal coupling model used in S11 includes:

[0016] S111: According to Kirchhoff's current law and voltage law, the battery terminal voltage U in the Rint model... L Open circuit voltage U OCV and current I bat The relationship between the resistance and the equivalent internal resistance R is expressed as follows:

[0017] U L =U OCV -I bat R

[0018] Wherein, the equivalent resistance R is the polarization resistance R p The sum of the ohmic resistance R0 and the ampere-hour integral method are used to calculate the state of charge (SOC) of the power battery, as shown in the following expression:

[0019]

[0020] Where SOC0 is the initial value of the SOC of the power battery, η i For Coulomb efficiency, C max I represents the maximum usable capacity of the power battery under the current condition. bat It is the charging and discharging current value of the power battery at time t;

[0021] S112: The battery thermal model is established using the lumped parameter method. According to the law of conservation of energy, the battery thermal model is a balance relationship between the battery's self-generated heat and the heat exchange between the battery and the external environment, as shown below:

[0022]

[0023] Where, m bat For the quality of the battery, c bat For the specific heat capacity of the battery, T bat Q represents the battery temperature. gen Q represents the heat generated during the battery charging and discharging process. dis This refers to the heat exchange between the battery and the external environment, including heat convection Q. conv and thermal conductivity Q cond ;

[0024] Based on Bernardi's assumption of uniform heat generation from the heat source inside the battery, and neglecting mixing heat and phase change heat, a simplified battery heat generation power calculation model is proposed, resulting in the battery heat generation model:

[0025]

[0026] Among them, dU ocv / dT is called the temperature entropy coefficient, U ocvRepresents the open-circuit voltage, R0 and R p These are the ohmic internal resistance and the polarization internal resistance, respectively, I bat Indicates battery current;

[0027] A power battery electrothermal coupling model is established. During charging, discharging, or HPPC experiments, individual cells exchange heat with the external environment through convective heat transfer between the battery surface and the external environment. The convective heat transfer is calculated using Newton's law of cooling, as shown below:

[0028] Q conv =h conv A conv (T air -T bat )

[0029] Among them, h conv A is the heat transfer coefficient between the fluid and the power battery; conv T represents the heat exchange area between the fluid and the battery. air T represents ambient air temperature. bat This refers to the battery temperature; heat exchange occurs between the battery and the water-cooled plate via thermal conduction.

[0030] S113: Couple the battery electrothermal model to obtain the battery electro-thermal coupled model, which characterizes the changes of various parameters of lithium-ion power batteries during charging and discharging.

[0031] Optionally, in S12, the control-oriented vehicle longitudinal dynamics model includes:

[0032] The longitudinal kinematic model of the vehicle is established as follows:

[0033] F t =F f +F i +F j +F w

[0034] Among them, F t For vehicle driving force, F f For rolling resistance, F i For slope resistance, F j To increase resistance, F w To account for air resistance, the traction power and wheel torque of the pure electric vehicle during operation are calculated as follows:

[0035] P t =F t v car

[0036]

[0037] Where r is the radius of the wheel.

[0038] Optionally, in S13, the lumped-parameter motor dynamic thermal model oriented towards control includes:

[0039] Assuming all motor losses are in the form of heat, then the motor loss Q motor This manifests as electromagnetic loss Q ele Mechanical loss Q mm and stray loss Q stray The sum of these three, where stray losses are negligible, and electromagnetic losses consist of winding copper losses, core losses, and permanent magnet eddy current losses, expressed as follows:

[0040] Q motorloss =Q ele +Q mm =Q Fe +Q Cu +Q per +Q mm

[0041] A simple model of heat generation from electrical components is established, taking 5% as the average heat loss rate for the motor controller and DC-DC components. The heat generation of the electrical components is Q. E Expressed as follows:

[0042] Q E =k E P m,i

[0043] Where, k E P is the average heat loss rate. m,i Input power to the motor;

[0044] The heat transfer during motor cooling is calculated using the following formula;

[0045] Q motorconv =h m A m (T a -T motor )

[0046] Among them, Q motor,conv For convective heat transfer between the motor and the external fluid, h m Let A be the convective heat transfer coefficient between the fluid and the motor. m T represents the convective heat transfer area between the motor and the fluid. a The ambient temperature, T motor The motor temperature is denoted as . During the motor's heat dissipation process, heat conduction occurs during contact with the cooling water jacket. The heat exchange between the motor and the cooling water jacket is calculated using the following formula.

[0047]

[0048] Among them, Q motor,condLet dis3 and dis4 represent the heat transfer between the motor and the cooling water jacket, respectively, and let λ3 and λ4 represent the distances from the motor and cooling water jacket shafts to the contact surface. Let T be the thermal conductivity of the motor and cooling water jacket, respectively. jacket T represents the temperature of the cooling water jacket. motor For motor temperature, ctr mb The contact thermal resistance between the motor and the cooling water jacket is neglected; the lumped parameter thermal model of the motor is established as follows:

[0049]

[0050] Where, m motor C represents the total mass of the motor. motor The equivalent specific heat capacity of the motor is calculated using the following formula:

[0051]

[0052] Among them, C motor,i and It is divided into the specific heat capacity and mass of each component inside the drive motor.

[0053] Optionally, S2 specifically includes the following steps:

[0054] S21: A model of an electric vehicle air conditioning system is established based on the lumped parameter method. The electric vehicle air conditioning system includes a compressor, a condenser, an evaporator, and an expansion valve. The dynamic model of the evaporator and condenser is established based on the moving boundary method.

[0055] S22: Establish a battery and motor cooling circuit model, wherein the established battery circuit model is coupled with the air conditioning system;

[0056] S23: Establish a control-oriented lumped parameter dynamic thermal model of the crew cabin and model coupling.

[0057] Optionally, step S3 includes the following specific steps:

[0058] S31: The longitudinal dynamics model of the pure electric vehicle is established as follows:

[0059]

[0060]

[0061]

[0062] x i =[s car,i ,v car , i ]

[0063] Among them, s i v represents the position coordinates of the i-th vehicle;i Let u be the speed of the i-th vehicle; i Let be the control variable for the i-th vehicle, representing the traction force or braking force per unit mass at any given time. A positive sign indicates traction force, and a negative sign indicates braking force. Based on energy consumption optimization, vehicle speed planning is performed to minimize the energy consumption per unit distance for each pure electric vehicle by controlling the traction and braking forces, i.e., planning an energy-saving longitudinal speed. The mathematical expression for the control objective is shown below:

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070] v carmin ≤u car,i (t)≤v car,max

[0071] u carmin ≤u car,i (t)≤u car,max

[0072] in, Energy consumption of vehicle i during driving; The power related to the vehicle's traction force; Δt is the time step; t f For the final time; u car,i (t) represents the control variable for vehicle i, and is determined by the control coefficient. and Calculate the control range of the control variables; The total distance traveled; The recovery efficiency obtained by the lower-level controller; v car,min and v car,max Let u represent the minimum and maximum speeds allowed in the established traffic light scenario, respectively. car,min and u car,max These are the minimum and maximum possible accelerations of vehicle i, i.e., the control variable values;

[0073] After determining the target vehicle speed and the position vector information obtained through V2V communication, model predictive control is used for vehicle i to solve the problem of generating a speed curve based on energy consumption optimization, thereby optimizing vehicle traffic efficiency, traffic safety, and driving comfort, as shown in the following equation:

[0074]

[0075] s.tS ij (t)=S0+t hd (v car,i (t)-v car,j (t))+(S i (t)-S j (t))

[0076]

[0077]

[0078] Where vehicle i is the vehicle following vehicle j, S i (t)-S j (t) and v car,i (t)-v car,j (t) represents the relative distance and relative speed difference between the preceding and following vehicles, respectively; T is the model prediction level, and the prediction time domain is T=13; w i To control the weighting factors of the quantities, i = 1, 2, 3, 4; S0 is the initial interval distance between vehicles; t hd The time distance between the front ends of the vehicles; the final planned speed depends on the weighting factor w. i .

[0079] Optionally, S4 specifically includes the following steps:

[0080] S41: The state vector x, output vector u, control vector y, and disturbance vector v corresponding to the MPC controller are represented as follows:

[0081]

[0082] The cost function J1 and constraints corresponding to the controller are shown in the following equation:

[0083]

[0084] Where, ω i For the weight coefficients, i = 1, 2, 3, 4, the corresponding weight coefficient matrix is ​​represented as follows:

[0085]

[0086]

[0087] The constraints on the control variables are as follows:

[0088]

[0089] S42: Regarding the fan speed N in the intelligent model control strategy adopted in this invention fan and the coolant flow rate m in the motor cooling circuit water All are controlled by corresponding PID controllers;

[0090] S43: A threshold controller is used to monitor the coolant flow rate m in the battery cooling circuit. cooling and the proportion of recirculated air in the mixing damper γ cycle Control is performed, and the control logic expression is as follows:

[0091]

[0092]

[0093] Based on the optimal temperature control value calculated in step S4, and combined with the intelligent model predictive controller based on vehicle speed planning established in steps S1, S2, and S3, a method for achieving temperature control and cooling energy consumption control of the fleet is established.

[0094] The beneficial effects of this invention are as follows:

[0095] (1) This invention establishes a model of the electric vehicle power-motor-battery system and a model of the electric vehicle air conditioning system, battery cooling system, motor cooling system and passenger compartment based on lumped parameters or moving boundary method, which can conveniently, quickly and accurately simulate and calculate the performance of the system under various working conditions.

[0096] (2) Based on traffic and road information obtained from actual roads, this invention establishes an abstract traffic signal timing scenario model. It takes V2V and V2I information without information communication delay as the basis, comprehensively considers the energy consumption, safe following distance, driving comfort, and the ability to prevent vehicles from stopping at red lights to avoid idling. It establishes a mathematical model that can ensure vehicles pass smoothly at green lights using model predictive control as the solution framework, thereby obtaining the longitudinal driving trajectory and speed curve of the queue.

[0097] (3) The vehicle longitudinal planning speed based on traffic information, vehicle information and energy consumption optimization proposed in this invention uses intelligent model control strategy to couple the queue control, realize the temperature control and cooling energy consumption control of each vehicle. It is simple and effective, has short calculation time, significant temperature control effect and obvious energy saving effect.

[0098] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0099] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0100] Figure 1 This is a simplified structural diagram of the overall controller of the present invention;

[0101] Figure 2 This is a schematic diagram of the cooling circuit of the thermal management system.

[0102] Figure 3 This is a diagram showing the location distribution of the water-cooled plate and the battery pack.

[0103] Figure 4 A thermal balance model of the crew cabin environment;

[0104] Figure 5 Diagram of V2I and V2V communication;

[0105] Figure 6 To create a longitudinal speed curve for the convoy;

[0106] Figure 7 Logic diagram of intelligent control strategy for vehicle thermal management;

[0107] Figure 8 This is a schematic diagram of the PID controller design in the intelligent control strategy of this invention. Detailed Implementation

[0108] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0109] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0110] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0111] like Figure 1 As shown, this is a method for controlling the temperature and cooling energy consumption of each vehicle in high-temperature summer environments. It utilizes a smart model control strategy based on traffic information, vehicle-to-vehicle information, and energy consumption optimization to couple the vehicle speed with the queue. The method includes the following steps:

[0112] S1: Establish a model of the powertrain-motor-battery system for a pure electric vehicle;

[0113] S2: Based on the lumped parameter method, models of the electric vehicle air conditioning system, battery cooling system, motor cooling system, and passenger compartment were established, laying the foundation for the establishment of subsequent thermal management system control strategies.

[0114] S3: A traffic light timing scenario model was constructed, and a mathematical model for convoy longitudinal speed planning was established based on energy consumption optimization to ensure smooth passage during green light.

[0115] S4: A model predictive control (MPC) intelligent thermal management control method combining vehicle speed longitudinal planning was designed.

[0116] Furthermore, step S1 specifically includes the following steps:

[0117] Furthermore, step S1 specifically includes the following steps:

[0118] S11: The battery electro-thermal coupling model is established using the lumped parameter method. The established model includes the battery electrical model, thermal model, and electro-thermal coupling model.

[0119] S12: Establish a control-oriented longitudinal dynamics model for vehicles to lay the foundation for subsequent vehicle thermal management and energy consumption calculation of individual vehicles in the fleet;

[0120] S13: Establish a lumped parameter-based dynamic thermal model of the motor for control purposes.

[0121] Furthermore, the battery electro-thermal coupling model used in step S11 includes:

[0122] S111: According to Kirchhoff's current law and voltage law, the battery terminal voltage U in the Rint model... L Open circuit voltage U OCV and current I bat The relationship between the resistance and the equivalent internal resistance R is expressed as follows:

[0123] U L =U OCV -I bat R

[0124] Wherein, the equivalent resistance R is the polarization resistance R p The sum of the ohmic resistance R0 and the battery temperature (SOC) and charge / discharge current gives a relationship between the battery temperature and the charge / discharge current. This relationship can be obtained through standard HPPC experiments at different temperatures and discharge rates, allowing for parameter identification. Accurate SOC ensures the safe and reliable operation of the power battery system. This invention utilizes the ampere-hour integration method (also known as the coulomb counting method) to calculate the power battery SOC, as shown in the following expression:

[0125]

[0126] Where SOC0 is the initial value of the SOC of the power battery, η i For the lithium-ion battery used in this invention, the discharge efficiency is generally taken as 1, and the charging efficiency is generally taken as 0.98 to 1 when it is below 3C. Here, this invention takes a value of 1. max This represents the maximum usable capacity of the power battery under the current condition. Its value will decrease as the battery is used. bat It is the charging and discharging current value of the power battery at time t.

[0127] S112: The battery thermal model is established using the lumped parameter method. According to the law of conservation of energy, the battery thermal model is a balance relationship between the battery's self-generated heat and the heat exchange between the battery and the external environment, as shown below:

[0128]

[0129] Where, m bat For the quality of the battery, c bat For the specific heat capacity of the battery, T batQ represents the battery temperature. gen Q represents the heat generated during the battery charging and discharging process. dis This refers to the heat exchange between the battery and the external environment, including heat convection Q. conv and thermal conductivity Q cond .

[0130] Based on Bernardi's assumption of uniform heat generation from the heat source inside the battery, and neglecting mixing heat and phase change heat, a simplified battery heat generation power calculation model is proposed, which yields the battery heat generation model:

[0131]

[0132] Among them, dU ocv / dT is called the temperature entropy coefficient, U ocv Represents the open-circuit voltage, R0 and R p These are the ohmic internal resistance and the polarization internal resistance, respectively, I bat This indicates the battery current.

[0133] A power battery electrothermal coupling model is established. During charging, discharging, or HPPC experiments, individual cells exchange heat with the external environment, primarily through convective heat transfer between the battery surface and the surroundings. This invention uses Newton's law of cooling for convective heat transfer calculations, as shown below:

[0134] Q conv =h conv A conv (T air -T bat )

[0135] Among them, h conv A is the heat transfer coefficient between the fluid and the power battery; conv T represents the heat exchange area between the fluid and the battery. air T represents ambient air temperature. bat This refers to the battery temperature. Heat exchange between the battery and the water-cooled plate primarily occurs through thermal conduction.

[0136] S113: Couple the battery electrothermal model. First, based on the real-time current measurement value I and the initial SOC0 or the SOC value at the previous moment, calculate the real-time SOC at the current moment using the ampere-hour integration method. Second, based on the battery temperature obtained from the battery thermal model, the previously obtained real-time SOC value, and the real-time current measurement value I, use them as the input values ​​of the battery electrical model to obtain the output value, the open-circuit voltage U. ocv ohmic internal resistance R o and polarization internal resistance R p Therefore, the terminal voltage U can be calculated. L Based on the real-time SOC obtained by the ampere-hour integration method and the SOC and terminal voltage U under the current operating conditions corrected by the battery electrical model feedback,L Open circuit voltage U ocv Other parameters, such as thermal resistance, external heat transfer medium temperature, and heat transfer coefficient, are used as inputs to the battery thermal model. The current real-time temperature is obtained and fed back to the battery electrical model. It can be seen that the calculated resistance value will in turn affect the battery temperature, thus obtaining a complete battery electro-thermal coupling model, which can better characterize the changes of various parameters of lithium-ion power batteries during charging and discharging.

[0137] Furthermore, the control-oriented vehicle longitudinal dynamics model described in step S12 includes:

[0138] The longitudinal kinematic model of the vehicle is established as follows:

[0139] F t =F f +F i +F f +F w

[0140] Among them, F t For vehicle driving force, F f For rolling resistance, F i For slope resistance, F j To increase resistance, F w Given air resistance, the traction power and wheel torque of a pure electric vehicle during operation can be calculated, where r is the wheel radius.

[0141] P t =F t v car

[0142]

[0143] Furthermore, the lumped-parameter motor dynamic thermal model for control described in step S12 includes:

[0144] S131: Assuming all motor losses are in the form of heat, then the motor loss Q motor This can be manifested as electromagnetic loss Q ele Mechanical loss Q mm and stray loss W stray The sum of these three factors is negligible due to the relatively small impact of stray losses compared to the others. Electromagnetic losses consist of winding copper losses, core losses, and permanent magnet eddy current losses, expressed as follows:

[0145] Q motorloss =Q el e+Q mm =Q Fe +Q Cu +Q per +Qmm

[0146] S132: Core losses have a significant impact on the temperature rise of the drive motor. The core loss in the classical loss model is an extension of the Steinmetz equation with added losses, resulting in the Bertotti model as shown below. That is, the stator and rotor cores of the motor, which are made of silicon steel sheets, are in a complex and variable magnetic field, thus generating hysteresis loss Q. hys Eddy current loss Q cur and additional losses Q add .

[0147]

[0148] Among them, k h , k c and k a These are the hysteresis loss coefficient, eddy current loss coefficient, and additional loss coefficient, respectively, which can be obtained through experimental fitting. f is the alternating current frequency, and B... n The magnetic flux density amplitude can be represented by α, where α is the Steinmetz coefficient (α = 1.5–2.0).

[0149] S133: Combining Joule's law, the following formula can be obtained for analyzing winding copper losses without considering current harmonics and skin effect:

[0150]

[0151] Where m is the number of winding phases, and its value is 3, I eff For sinusoidal equivalent current, I max For peak current, R ph The phase resistance can be solved using the following formula.

[0152]

[0153] Where, ρ cu L is the resistivity of the copper wire inside the winding. av N is the length of a half-turn coil. Z and N t These represent the number of series turns and the number of parallel windings per phase, respectively; 'a' is the number of parallel branches; and 'd' is the diameter of the copper wire in the winding. To simplify calculations, the motor designed in this invention is an ideal motor, free from stator slot currents and irregular harmonic currents, thus eddy current losses in the permanent magnet are ignored.

[0154] S134: Mechanical losses can be divided into bearing losses and rotor friction losses. Friction losses are mainly related to rotor speed and increase with increasing speed. The friction loss Q can be calculated by the following formula. wind :

[0155]

[0156] Where, k s ρ represents the rotor surface roughness, which is 1 when the surface is smooth. air For air density, ω rotor r is the angular velocity of the motor rotor. rotor l is the radius of the motor rotor. R C is the axial length of the motor rotor. f Let be the air friction coefficient. Since air friction loss is related to the motor's structural parameters and the fluid state, for simplicity, it is usually considered to be proportional to the cube of the motor speed, and can be calculated using the following formula:

[0157] Q mm =k m nm otor +k w n motor 3

[0158] Where, k m and k w These are the coefficient of friction and the drag coefficient, respectively, n motor This represents the motor speed.

[0159] Heat generation from motor controllers and DC-DC components is another important source of heat in motor thermal management. This invention uses 5% as the average heat loss rate to establish a simple model of heat generation from electrical components, where the heat generated by the electrical components is Q. E It can be expressed as follows:

[0160] Q E =k E P m,i

[0161] Where, k E P is the average heat loss rate. m,i This is the input power to the motor.

[0162] S135: The heat transfer during the motor cooling process can be calculated using the following formula.

[0163] Q motorconv =h m A m (T a -T motor )

[0164] Among them, Q motor,conv For convective heat transfer between the motor and the external fluid, h m Let A be the convective heat transfer coefficient between the fluid and the motor. m T represents the convective heat transfer area between the motor and the fluid. aThe ambient temperature, T motor Let represent the motor temperature. During the motor's heat dissipation process, heat transfer occurs through contact with the cooling water jacket. The amount of heat exchanged between the motor and the cooling water jacket can be calculated using the following formula.

[0165]

[0166] Among them, Q notor,cond Let dis3 and dis4 represent the heat transfer between the motor and the cooling water jacket, respectively, and let λ3 and λ4 represent the distances from the motor and cooling water jacket shafts to the contact surface. Let T be the thermal conductivity of the motor and cooling water jacket, respectively. jacket T represents the temperature of the cooling water jacket. motor For motor temperature, ctr mb The contact thermal resistance between the motor and the cooling water jacket is relatively small and can be disregarded. Therefore, the lumped-parameter thermal model of the motor is established as follows:

[0167]

[0168] Where, m motor C represents the total mass of the motor. motor The equivalent specific heat capacity of the motor can be calculated using the following formula:

[0169]

[0170] Among them, C motor,i and It is divided into the specific heat capacity and mass of each component inside the drive motor.

[0171] Furthermore, step S2 specifically includes the following steps:

[0172] S21: A model of an electric vehicle air conditioning system is established based on the lumped parameter method. The electric vehicle air conditioning system includes a compressor, a condenser, an evaporator, and an expansion valve. The dynamic model of the evaporator and condenser is established based on the moving boundary method.

[0173] S22: Establish a battery and motor cooling circuit model, wherein the established battery circuit model is coupled with the air conditioning system;

[0174] S23: Establish a control-oriented lumped parameter dynamic thermal model of the crew cabin and model coupling.

[0175] Furthermore, the electric vehicle air conditioning system model mentioned in step S11 includes:

[0176] S211: For the compressor, static relational modeling is used to describe its working process. The model mainly focuses on two parameters: the calculation of mass flow rate and the calculation of outlet specific enthalpy, as shown in the following formula:

[0177]

[0178]

[0179] in, η is the refrigerant mass flow rate through the compressor. vol For volumetric efficiency; ρ comp N is the density of the refrigerant at the compressor inlet. comp V is the compressor speed; d For compressor displacement; h comp,i and h comp,o The specific enthalpy, h, are the compressor inlet and outlet, respectively. is,o η is the outlet specific enthalpy under isentropic conditions. is It is isentropic efficiency.

[0180] S212: For electronic expansion valves, assuming the refrigerant flows through the valve in an isenthalpic pressure-reducing process, i.e., the outlet specific enthalpy equals the inlet specific enthalpy, the formula for calculating its mass flow rate is as follows:

[0181]

[0182] in,, C is the mass flow rate of refrigerant R134a when it flows through the electronic expansion valve. exv A is the mass flow coefficient of the electronic expansion valve. eev ρ is the throttling area at the current opening of the expansion valve. exv P represents the density of the refrigerant at the inlet of the electronic expansion valve. c and P e The pressure of the refrigerant at the inlet and outlet of the expansion valve.

[0183] S213: For the outdoor heat exchanger, in cooling mode, the outdoor heat exchanger acts as a condenser. The moving boundary method is used to model the condenser as a dynamic model with lumped parameters, assuming it has two fluid regions: a two-phase region of gas-liquid mixing and a superheated gas phase region. Based on the principle of energy conservation, the dynamic relationship between this model and the refrigerant-side heat transfer and the air-side heat transfer is as follows:

[0184]

[0185]

[0186] According to the principle of mass conservation, the dynamic relationship of the evaporator wall temperature is expressed as follows:

[0187]

[0188] Where; ρ lc h is the density of the refrigerant in its current liquid phase.lgc h represents the latent enthalpy released during the two-phase change of the refrigerant. cond,s h is the enthalpy of a saturated liquid at the current pressure. cond,i A represents the enthalpy of the refrigerant at the condenser inlet. c This represents the cross-sectional area of ​​the flat tube on the condenser side; The volume fraction of refrigerant gas in the gas-liquid two-phase region; l c α is the length of the gas-liquid two-phase region during condensation; ic It is the convective heat transfer coefficient between the refrigerant and the pipe wall in the two-phase region; D ic It is the hydraulic diameter of the flat tube inside the condenser; T wc It is the temperature of the condenser tube wall; T rc It is the saturation temperature of the refrigerant; L c ρ is the total length of the condenser flat tube; gc P is the density of the refrigerant in the current gas phase. c n is the pressure of the refrigerant in the condenser; n is the number of refrigerant channels; m c C is the total mass of the condenser wall and fins. c dT is the specific heat capacity of the condenser wall. wc / dt represents the time derivative of the condenser tube wall temperature; a ish,c The convective heat transfer coefficient between the refrigerant in the superheated zone and the condenser tube wall; a oc A is the convective heat transfer coefficient between the air side and the condenser tube wall; oc T represents the air-side heat exchange area of ​​the condenser. ic T represents the refrigerant temperature at the condenser inlet. ac Let T be the air temperature on the condenser air side. According to the research of BPRASmussen et al., T can be calculated. ac As shown in the following formula:

[0189]

[0190] Among them, C air,c T represents the specific heat capacity of the air on the air side of the condenser. air,e The ambient temperature in front of the condenser; Let be the mass flow rate on the air side, whose value is related to the vehicle speed and can be obtained by the following formula:

[0191]

[0192] Where, ρ air V is the density of air. car For vehicle speed

[0193] S214: For indoor heat exchangers, using the same lumped parameter method and moving boundary method does not affect the establishment of the dynamic model. Since it functions as an evaporator, the modeling process is similar to that of a condenser. The relevant differential equations are shown below:

[0194]

[0195]

[0196]

[0197] The symbol is the same as that used for the condenser, T ae Let be the air temperature on the air side of the evaporator; it can be solved using the following formula:

[0198]

[0199] in, T is the mass flow rate on the air side of the evaporator. air,e The air temperature after passing through the mixing damper before the evaporator; C air,e The specific heat capacity of the air after passing through the mixing damper on the air side; and T air,e A functional relationship exists; this value can be solved using the following formula:

[0200]

[0201] Among them, C air,cab C represents the specific heat capacity of the old air from the crew compartment loop. air,ext Fresh air is generated through natural circulation from the external environment; and These represent the mass flow rates of the old air and the fresh air, respectively.

[0202] In an ideal scenario, without considering leakage, the refrigerant mass in an air conditioning system is a constant value, meaning the refrigerant mass flow rate exiting the condenser is equal to the refrigerant mass flow rate entering the evaporator, as shown in the following expression:

[0203]

[0204] In addition, a certain amount of superheat T is retained to ensure that the refrigerant completely vaporizes when it enters the compressor. sh The solution can be obtained using the empirical formula as follows:

[0205]

[0206] Among them, a ioe It is the equivalent heat transfer coefficient between the refrigerant and the air side in the evaporator.

[0207] Furthermore, the electric vehicle cooling system model described in step S22 includes:

[0208] S221: As Figure 2 The diagram illustrates a battery-cabin coupled thermal management system. Therefore, the refrigerant circulation loop of the air conditioning system consists of two parallel branches: one for cooling the passenger compartment and the other for cooling the battery cells. The mathematical expression for the electronic water pump is established as follows:

[0209] P pump =U pump I pump

[0210] Among them, P pump For the power loss of the electronic water pump, U pump and I pump These are the rated voltage of the electronic water pump and the current flowing through it at the current moment, respectively, which are related to the duty cycle under the current operation.

[0211] S222: The mathematical model of the battery cooler is established using the moving boundary method.

[0212]

[0213]

[0214]

[0215] The meanings of the symbols are the same as those for heat exchangers mentioned earlier, T cooling The temperature of the coolant in a plate heat exchanger can be calculated using the following formula:

[0216]

[0217] Where, m cooling and C cooling These represent the total mass and specific heat capacity of the coolant in the plate heat exchanger, respectively.

[0218] The mass of the water-cooled plate is divided into 24 equal parts corresponding to the battery pack, forming 24 heat exchange units, such as... Figure 3 As shown. Based on the law of conservation of energy, the dynamic equation for the temperature of the water-cooled plate is obtained:

[0219]

[0220] Where, m plate C represents the total mass of the water-cooled plate. palte d represents the specific heat capacity of the water-cooled plate. plate and d batλ1 and λ2 are the distances from the center of mass of the water-cooled plate and the center of mass of the battery to their contact surface, respectively; λ1 and λ2 are the thermal conductivity of the water-cooled plate and the battery, respectively; ctr is the contact thermal resistance between the water-cooled plate and the battery; αi wat Let A be the convective heat transfer coefficient between the coolant and the water-cooled plate. plate The convective heat transfer area between the water-cooled plate and the coolant is determined by the structural parameters of the water-cooled plate, T. bat With T plate The temperatures of the battery and the water-cooled plate are respectively, T wat Let be the temperature of the coolant at the current moment. Combining the above equation with the electrothermal model of the battery established earlier, we can obtain the dynamic expression for the battery temperature:

[0221]

[0222] Where, m bat C represents the total mass of the battery. bat Let be the specific heat capacity of the battery. The temperature of the coolant in the cooling plate can be calculated using the following formula:

[0223]

[0224] S223: Assuming there is no heat loss between the motor radiator and the coolant and air, that is, the heat carried away by the air side of the motor radiator is equal to the heat exchanged by the coolant side, the mathematical model of the motor radiator is established as follows.

[0225]

[0226] Where, m rad For the wall quality of the motor radiator, C rad a is the specific heat capacity of the motor radiator wall. wrad and a orad These are the convective heat transfer coefficients between the coolant and the motor radiator wall, and the air-side convective heat transfer coefficient of the motor radiator, respectively. arad and A wrad T represents the cross-sectional area of ​​the motor radiator on the air side and the cross-sectional area on the coolant side, respectively. wrad It is the temperature of the motor radiator wall, T water,rad It is the temperature of the coolant in the motor heat exchanger at the current moment, T. arad The ambient temperature on the air side can be calculated using the following formula:

[0227]

[0228] Where, m water and C water These represent the mass and specific heat capacity of the coolant in the motor radiator, respectively.

[0229] Based on Newton's law of cooling and the law of conservation of energy, the dynamic temperature expression of the cooling water jacket is obtained.

[0230]

[0231] Where, m jacket C represents the total mass of the cooling water jacket. jacket α represents the specific heat capacity of the cooling water jacket. wat,m Let A be the convective heat transfer coefficient between the coolant and the cooling water jacket. jactket The convective heat transfer area between the water-cooled plate and the coolant is determined by the structural parameters of the water-cooled plate, T. motor With T jacket The temperatures of the battery and the water-cooled plate are respectively, T wat The temperature of the coolant in the cooling water jacket at the current moment can be calculated using the following formula:

[0232]

[0233] Based on the previously established thermal model of the motor, the dynamic expression for the motor temperature can be obtained:

[0234]

[0235] Furthermore, the lumped-parameter dynamic thermal model of the crew cabin and model coupling described in step S22 include:

[0236] like Figure 4 As shown, the heat load Q on the crew compartment total It can be expressed as follows:

[0237] Q total =Q solar +Q conv +Q hum +Q mech +Q flow

[0238] Among them, Q solar Solar radiation load, Q conv Q represents the convective heat transfer load between the vehicle interior and the external environment. mech Q represents the heat load on in-vehicle instruments and other electronic equipment. flow Q represents the heat load generated by ventilation and airtightness leakage within the vehicle. hum The heat load generated by the activities of the driver and passengers inside the vehicle.

[0239] According to the principle of energy conservation, the lumped parameter model of the temperature change in the crew cabin over time is expressed as follows:

[0240]

[0241] Among them, m cabFor the air quality inside the crew cabin, C cab Q is the specific heat capacity of the air inside the crew cabin. hp This is for the heat exchange between the indoor evaporator and the passenger compartment.

[0242] Furthermore, step S3 specifically includes the following steps:

[0243] S31: To minimize energy consumption and reduce the number of stops at traffic lights in a pure electric vehicle, it is necessary to control traction and braking forces. Based on the vehicle dynamics described above, the longitudinal dynamics model of the pure electric vehicle is established as follows:

[0244]

[0245]

[0246]

[0247] x i =[s car,i ,v car,i ]

[0248] Among them, s i Let v be the position coordinates of the i-th vehicle; i Let u be the speed of the i-th vehicle; i Let be the control variable for the i-th vehicle, representing the traction force or braking force per unit mass at any given time. A positive sign indicates traction force, and a negative sign indicates braking force. This invention plans vehicle speed based on energy consumption optimization. By controlling traction and braking forces, it minimizes the energy consumption per unit distance for each pure electric vehicle, i.e., it plans an energy-saving longitudinal speed. The mathematical expression for the control objective is shown below:

[0249]

[0250]

[0251]

[0252]

[0253]

[0254]

[0255] v carmin ≤u car,i (t)≤v car , max

[0256] u carmin ≤u car,i(t)≤u car,max

[0257] in, Energy consumption of vehicle i during driving; The power related to the vehicle's traction force; Δt is the time step; t f For the final time; u car,i (t) represents the control variable for vehicle i, and is determined by the control coefficient. and Calculate the control range of the control variables; The total distance traveled; The recovery efficiency obtained by the lower-level controller; v car,min and v car,max Let u represent the minimum and maximum speeds allowed in the established traffic light scenario, respectively. car,min and u car,max Let be the minimum and maximum possible accelerations of vehicle i, i.e., the control variable values. The expressions for the lower and upper limits of the target speed range are as follows:

[0258]

[0259]

[0260]

[0261] constraint:

[0262] in, and These are the lower and upper limits of the target speed range for the vehicle, which are the minimum and maximum speeds for the vehicle to pass through the traffic lights. d represents the target speed of the vehicle. ia (k) represents the distance between vehicle i and the traffic light it is about to pass; K w Let k be the number of cycles of the traffic light, when k = K w t cycle The value increases by 1 and is taken as an integer; t cycle t is the cycle time of the traffic lights. cycle =t g +t r k represents the vehicle's travel time; t represents the time it takes for the vehicle to travel. g and t r These represent the durations of the green and red lights, respectively. If the duration of the green light when a vehicle is passing through the intersection is shorter than the initial duration of the green light, then... The initial moment when the vehicle is driving in the established scenario will definitely encounter a green light and no red light during the time interval. The target speed of vehicle i can be calculated using the following formula:

[0263]

[0264] constraint:

[0265]

[0266] Here, mod() is a modulo function that generates k / t. cycle The remainder. From equation 4.24, we know that when the traffic light is green and d... ia (k) / (K w t cycle -t g -k)≤v car,max The maximum speed allowed at any given time is the target vehicle speed. Under the condition that the speed constraint is met, and that the vehicle can pass through the intersection during the green light period as a necessary condition, the input limits of the control variables can be calculated, thereby preventing vehicles from stopping before the traffic light. With a time step Δt = 1s, the input range of the control variables can be expressed by the following formula:

[0267]

[0268]

[0269]

[0270]

[0271] The same

[0272] Therefore, there is

[0273] in

[0274] From the above formula, we can see that the control variable u car,i (t) When the desired range is met, the target vehicle speed satisfies the constraint, thus enabling vehicles to pass through intersections on a green light and avoid stopping on a red light. For example... Figure 5 As shown, after determining the target vehicle speed and the position vector information obtained through V2V communication, model predictive control can be used for vehicle i to solve the problem of generating a speed curve based on energy consumption optimization, thereby optimizing vehicle traffic efficiency, traffic safety, and driving comfort, as shown in the following equation:

[0275]

[0276] s.tS ij (t)=S0+t hd (vcar,i (t)-v car,j (t))+(S i (t)-S j (t))

[0277]

[0278]

[0279] Where vehicle i is the vehicle following vehicle j, S i (t)-S j (t) and v car,i (t)-v car,j (t) represents the relative distance and relative speed difference between the preceding and following vehicles, respectively; T is the model prediction level, and the prediction time domain of this invention is T = 13; w i (i = 1, 2, 3, 4) are the weighting factors for the control pair; S0 is the initial interval distance between vehicles; t hd Let be the headway between vehicles. In the formula, the first term represents the minimum energy consumption per unit distance within the predicted time domain T, which can be calculated using Equation 4.10 above; the second term represents the minimum distance deviation between the preceding and following vehicles in the convoy and the deviation from the ideal relative distance, ensuring a safe distance between vehicles; the third term represents the actual speed of the vehicles in the convoy being as close as possible to the target speed to ensure vehicles can smoothly pass through the intersection during the green light duration; the fourth term represents the minimum control value to ensure vehicles avoid sudden acceleration or braking during driving. It can be seen that the optimal energy efficiency speed of the vehicle is different from the target speed, and to a certain extent, the optimal speed profile values ​​of both are considered, with the target speed range... This helps in selecting weight values ​​and also ensures that the deviation between the optimal energy efficiency speed and the target vehicle speed is within a certain limit. At this point, the final planned speed depends on the weight factor w. i (i = 1, 2, 3, 4). The weighting factor values ​​are shown in the following formula:

[0280] w1 = 10 + 100e (0.05Δv)

[0281]

[0282] w3 = 10 + 50e (-0.07Δv)

[0283] w4 = 10 + 100e (-0.1Δv)

[0284] in This represents the difference between the two. Furthermore, we can obtain... Figure 6 The diagram shows the longitudinal speed planning curve for the convoy.

[0285] Step S4 specifically includes the following steps:

[0286] Furthermore, step S4 specifically includes the following steps:

[0287] S41: As Figure 7 The diagram shows the intelligent control strategy designed in this invention. The state vector x, output vector u, control vector y, and disturbance vector v of the MPC controller designed in this invention are represented as follows:

[0288]

[0289] The cost function J1 and constraints corresponding to the controller are shown in the following equation:

[0290]

[0291] Where, ω i (i = 1, 2, 3, 4) are the weight coefficients, and the corresponding weight coefficient matrix can be represented as follows:

[0292]

[0293]

[0294] The constraints on the control variables are as follows:

[0295]

[0296] S42: As Figure 8 The diagram shows a schematic of the PID controller design in the intelligent control strategy of this invention. For the fan speed N in the intelligent model control strategy adopted in this invention... fan and the coolant flow rate m in the motor cooling circuit water All are controlled by corresponding PID controllers. The heat exchanger's heat exchange capacity is adjusted by changing the fan speed. When heat dissipation is insufficient, forced airflow is provided to cool the external heat exchanger; when heat dissipation is sufficient, the fan speed is reduced to decrease overall vehicle energy consumption. The target temperature T in the passenger compartment is used as the benchmark. cab,target As a target reference value, the current real-time temperature T of the crew cabin obtained by the temperature sensor cab As the feedback control input, the fan speed N is calculated based on the deviation between the two. fan The control parameters ultimately control the temperature of the passenger compartment. The coolant flow rate in the motor cooling system is supplied by an electric water pump; the higher the pump speed, the greater the coolant flow rate, directly indicating the heat exchange capacity of the cooling system. The target motor temperature T is used as an example. motor,targetAs a target reference value, when the electric vehicle starts driving, the water pump in the motor cooling circuit first runs at a constant speed, and the real-time motor temperature T is detected by a sensor. motor Feedback control of the water pump speed to control the coolant flow rate (m) in the motor cooling circuit water .

[0297] S43: This invention uses a threshold controller to control the coolant flow rate m in the battery cooling circuit. cooling and the proportion of recirculated air in the mixing damper γ cycle Control is performed, and the control logic expression is shown below.

[0298]

[0299]

[0300] Based on the optimal temperature control value obtained from step S4, and combined with the intelligent model predictive controller based on vehicle speed planning established in steps S1, S2, and S3, a method for achieving temperature control and cooling energy consumption control of the fleet can be finally established.

[0301] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A control method for a thermal management system for an electric vehicle fleet based on traffic information, characterized in that: The method includes the following steps: S1: Establish a model of the powertrain-motor-battery system for a pure electric vehicle; S2: Models of the electric vehicle's air conditioning system, battery cooling system, motor cooling system, and passenger compartment are established based on the lumped parameter method; S3: A traffic light timing scenario model was constructed, and a mathematical model for convoy longitudinal speed planning was established based on energy consumption optimization to ensure smooth passage during green light. S4: Design a model predictive control (MPC) intelligent thermal management control method that combines vehicle speed longitudinal planning; S1 specifically includes the following steps: S11: The battery electro-thermal coupling model is established using the lumped parameter method. The established model includes the battery electrical model, thermal model, and electro-thermal coupling model. S12: Establish a control-oriented longitudinal dynamics model for the vehicle; S13: Establish a lumped parameter-based dynamic thermal model of the motor for control purposes; In S11, the battery electro-thermal coupling model used includes: S111: According to Kirchhoff's current law and voltage law, the battery terminal voltage U in the Rint model... L Open circuit voltage U OCV and current I bat The relationship between the resistance and the equivalent internal resistance R is expressed as follows: U L =U OCV -I bat R Wherein, the equivalent resistance R is the polarization resistance R p The sum of the ohmic resistance R0 and the ampere-hour integral method are used to calculate the state of charge (SOC) of the power battery, as shown in the following expression: Where SOC0 is the initial value of the SOC of the power battery, η i For Coulomb efficiency, C max I represents the maximum usable capacity of the power battery under the current condition. bat It is the charging and discharging current value of the power battery at time t; S112: The battery thermal model is established using the lumped parameter method. According to the law of conservation of energy, the battery thermal model is a balance relationship between the battery's self-generated heat and the heat exchange between the battery and the external environment, as shown below: Where, m bat For the quality of the battery, c bat For the specific heat capacity of the battery, T bat Q represents the battery temperature. conv Q represents heat convection. cond Indicates heat conduction; Based on Bernardi's assumption of uniform heat generation from the heat source inside the battery, and neglecting mixing heat and phase change heat, a simplified battery heat generation power calculation model is proposed, resulting in the battery heat generation model: Among them, dU ocv / dT is called the temperature entropy coefficient, U ocv Represents the open-circuit voltage, R0 and R p These are the ohmic internal resistance and the polarization internal resistance, respectively, I bat Indicates battery current; A power battery electrothermal coupling model is established. During charge-discharge experiments or HPPC experiments, individual cells exchange heat with the external environment through convective heat transfer between the battery surface and the external environment. The convective heat transfer is calculated using Newton's law of cooling, as shown below: Q conv =h conv A conv (T air -T bat ) Among them, h conv A is the heat transfer coefficient between the fluid and the power battery; conv T represents the heat exchange area between the fluid and the battery. air The ambient air temperature; T bat This refers to the battery temperature; heat exchange occurs between the battery and the water-cooled plate via thermal conduction. S113: Couple the battery electrothermal model to obtain the battery electro-thermal coupled model, which characterizes the changes of various parameters of lithium-ion power batteries during charging and discharging.

2. The control method for an electric vehicle fleet thermal management system based on traffic information according to claim 1, characterized in that: In S12, the control-oriented longitudinal dynamics model of the vehicle includes: The longitudinal kinematic model of the vehicle is established as follows: F t =F f +F i +F j +F w Among them, F t For vehicle driving force, F f For rolling resistance, F i For slope resistance, F j To increase resistance, F w To account for air resistance, the traction power and wheel torque of the pure electric vehicle during operation are calculated as follows: P t =F t v car Where r is the radius of the wheel.

3. The control method for a thermal management system for an electric vehicle fleet based on traffic information according to claim 1, characterized in that: S2 specifically includes the following steps: S21: A model of an electric vehicle air conditioning system is established based on the lumped parameter method. The electric vehicle air conditioning system includes a compressor, a condenser, an evaporator, and an expansion valve. The dynamic model of the evaporator and condenser is established based on the moving boundary method. S22: Establish a battery and motor cooling circuit model, wherein the established battery circuit model is coupled with the air conditioning system; S23: Establish a control-oriented lumped parameter dynamic thermal model of the crew cabin and model coupling.

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