WO2022021210A1 - 一种温度的预测方法以及装置 - Google Patents

一种温度的预测方法以及装置 Download PDF

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Publication number
WO2022021210A1
WO2022021210A1 PCT/CN2020/105791 CN2020105791W WO2022021210A1 WO 2022021210 A1 WO2022021210 A1 WO 2022021210A1 CN 2020105791 W CN2020105791 W CN 2020105791W WO 2022021210 A1 WO2022021210 A1 WO 2022021210A1
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WIPO (PCT)
Prior art keywords
motor
loss
temperature
time
current
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PCT/CN2020/105791
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English (en)
French (fr)
Inventor
王健刚
周飞
董腾辉
朱翀
张希
李泉明
陈君
Original Assignee
华为技术有限公司
上海交通大学
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Application filed by 华为技术有限公司, 上海交通大学 filed Critical 华为技术有限公司
Priority to CN202080006687.3A priority Critical patent/CN114365413A/zh
Priority to PCT/CN2020/105791 priority patent/WO2022021210A1/zh
Priority to EP20947691.0A priority patent/EP4184785A4/en
Publication of WO2022021210A1 publication Critical patent/WO2022021210A1/zh
Priority to US18/160,830 priority patent/US20230179131A1/en

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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P29/00—Arrangements for regulating or controlling electric motors, appropriate for both AC and DC motors
    • H02P29/60—Controlling or determining the temperature of the motor or of the drive
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P23/00—Arrangements or methods for the control of AC motors characterised by a control method other than vector control
    • H02P23/14—Estimation or adaptation of motor parameters, e.g. rotor time constant, flux, speed, current or voltage
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/08—Thermal analysis or thermal optimisation

Definitions

  • the present application relates to the field of electrical machines, and more particularly, to a temperature prediction method and device.
  • the present application provides a temperature prediction method and device, which can improve the accuracy of motor temperature prediction.
  • a method for predicting temperature comprising: determining a loss of a motor according to information of a motor controller, where the loss of the motor includes a first loss and a second loss, and the first loss is the loss of the motor Loss caused by the fundamental component of the current; the temperature of the motor is determined according to the loss of the motor and a temperature prediction model.
  • the determined motor loss includes other losses (for example, the second loss) in addition to the loss generated by the current fundamental wave component of the motor, so that the calculation accuracy of the motor loss can be improved, thereby increasing the temperature of the motor prediction accuracy.
  • the second loss is a loss generated by a current harmonic component of the motor.
  • the loss of the motor will also increase significantly due to the harmonic components of the current. Therefore, the fundamental wave and harmonics of the current can be included in the loss calculation of the motor, and the fundamental wave loss and harmonic loss of the motor can be calculated by calculating the loss of the motor. To obtain the total loss of the motor, to improve the calculation accuracy of the motor loss, so as to improve the prediction accuracy of the motor temperature.
  • the first loss is determined according to the fundamental wave component of the current; the loss of the motor is obtained according to the first loss and a first coefficient.
  • the loss of the motor can be determined directly based on the first loss generated by the fundamental wave component of the current, which is relatively simple to implement.
  • the first loss is determined according to the fundamental wave component of the current; the second loss is determined according to the harmonic component of the current; the first loss and the first loss are determined according to the harmonic component of the current.
  • the second loss results in the loss of the motor.
  • the information of the motor controller is the voltage vector in the dq rotating coordinate system
  • the method further includes: acquiring a voltage vector in the dq rotating coordinate system from the motor controller; rotating the coordinate system according to the dq
  • the harmonic component of the voltage is obtained from the voltage vector under , and the harmonic component of the current is obtained according to the harmonic component of the voltage.
  • the temperature of the motor is the temperature of the motor at time t
  • the method further includes: correcting the loss of the motor according to the temperature of the motor at time t, to obtain a correction According to the corrected motor loss, the temperature of the motor at time t and the temperature prediction model determine the temperature of the motor at time t+1, and the time t is the time t+1 the moment before the moment.
  • the loss of the motor at time t+1 can be corrected based on the temperature of the motor at time t, and the temperature of the motor at time t+1 can be determined based on the corrected loss at time t+1, thereby further improving the performance of the motor.
  • the accuracy of the motor temperature prediction can be used to determine the corrected loss at time t+1.
  • the loss of the motor includes one or more of the following: coil loss of the motor, stator and rotor loss of the motor, and magnet steel loss of the motor.
  • the temperature of the motor at time t includes one or more of the following: the coil temperature of the motor at time t, the temperature of the stator and rotor of the motor at time t, and the temperature of the motor at time t.
  • the magnet temperature of the motor at time t includes one or more of the following: the coil temperature of the motor at time t, the temperature of the stator and rotor of the motor at time t, and the temperature of the motor at time t.
  • the temperature prediction model is any one of the following: an equivalent thermal resistance network model, a neural network model, a linear least squares model, and a nonlinear least squares model.
  • a temperature prediction device comprising:
  • a loss calculation module configured to determine the loss of the motor according to the information of the motor controller, the loss of the motor includes a first loss and a second loss, and the first loss is the loss generated by the current fundamental wave component of the motor;
  • a temperature prediction module configured to determine the temperature of the motor according to the loss of the motor and a temperature prediction model.
  • the second loss is a loss generated by a current harmonic component of the motor.
  • the loss calculation module is specifically configured to: determine the first loss according to the fundamental wave component of the current; obtain the loss of the motor according to the first loss and the first coefficient .
  • the loss calculation module is specifically configured to: determine the first loss according to the fundamental wave component of the current; determine the second loss according to the harmonic component of the current; The first loss and the second loss result in the loss of the motor.
  • the information of the motor controller is the voltage vector in the dq rotating coordinate system
  • the prediction device further includes:
  • an acquisition module for acquiring the voltage vector in the dq rotating coordinate system from the motor controller
  • a voltage harmonic analysis module used for obtaining the harmonic components of the voltage according to the voltage vector in the dq rotating coordinate system
  • the current harmonic analysis module obtains the harmonic components of the current according to the harmonic components of the voltage.
  • the temperature of the motor is the temperature of the motor at time t
  • the loss calculation module is further configured to correct the loss of the motor according to the temperature of the motor at time t , to obtain the corrected motor loss
  • the temperature prediction module is further configured to determine, according to the corrected motor loss, the temperature of the motor at time t and the temperature prediction model, the temperature of the motor at time t+1 temperature, the time t is the time immediately preceding the time t+1.
  • the loss of the motor includes one or more of the following: coil loss of the motor, stator and rotor loss of the motor, and magnet steel loss of the motor.
  • the temperature of the motor at time t includes one or more of the following: the coil temperature of the motor at time t, the temperature of the stator and rotor of the motor at time t, and the temperature of the motor at time t.
  • the magnet temperature of the motor at time t includes one or more of the following: the coil temperature of the motor at time t, the temperature of the stator and rotor of the motor at time t, and the temperature of the motor at time t.
  • the temperature prediction model is any one of the following: an equivalent thermal resistance network model, a neural network model, a linear least squares model, and a nonlinear least squares model.
  • a device comprising: a memory for storing a program; a processor for executing the program stored in the memory, and when the program stored in the memory is executed, the processor is used for executing: according to the motor
  • the information of the controller determines the loss of the motor, the loss of the motor includes a first loss and a second loss, and the first loss is the loss generated by the current fundamental wave component of the motor; predicted according to the loss of the motor and the temperature
  • the model determines the temperature of the motor.
  • the second loss is a loss generated by a current harmonic component of the motor.
  • the first loss is determined according to the fundamental wave component of the current; the loss of the motor is obtained according to the first loss and a first coefficient.
  • the first loss is determined according to the fundamental wave component of the current; the second loss is determined according to the harmonic component of the current; the first loss and the first loss are determined according to the harmonic component of the current.
  • the second loss results in the loss of the motor.
  • the information of the motor controller is the voltage vector in the dq rotating coordinate system
  • the method further includes: acquiring a voltage vector in the dq rotating coordinate system from the motor controller; rotating the coordinate system according to the dq
  • the harmonic component of the voltage is obtained from the voltage vector under , and the harmonic component of the current is obtained according to the harmonic component of the voltage.
  • the temperature of the motor is the temperature of the motor at time t
  • the method further includes: correcting the loss of the motor according to the temperature of the motor at time t, to obtain a correction According to the corrected motor loss, the temperature of the motor at time t and the temperature prediction model determine the temperature of the motor at time t+1, and the time t is the time t+1 the moment before the moment.
  • the loss of the motor includes one or more of the following: coil loss of the motor, stator and rotor loss of the motor, and magnet steel loss of the motor.
  • the temperature of the motor at time t includes one or more of the following: the coil temperature of the motor at time t, the temperature of the stator and rotor of the motor at time t, and the temperature of the motor at time t.
  • the magnet temperature of the motor at time t includes one or more of the following: the coil temperature of the motor at time t, the temperature of the stator and rotor of the motor at time t, and the temperature of the motor at time t.
  • the temperature prediction model is any one of the following: an equivalent thermal resistance network model, a neural network model, a linear least squares model, and a nonlinear least squares model.
  • a computer storage medium stores program code, the program code including instructions for performing the steps in the method in the first aspect and any one of the implementations of the first aspect.
  • the above-mentioned storage medium may specifically be a non-volatile storage medium.
  • ROM read-only memory
  • PROM programmable ROM
  • EPROM erasable PROM
  • Flash memory electrical EPROM (electrically EPROM, EEPROM) and hard drive (hard drive).
  • a fifth aspect provides a chip, the chip includes a processor and a data interface, the processor reads an instruction stored in a memory through the data interface, and executes the first aspect and any one of the implementations of the first aspect. method.
  • the chip can be a central processing unit (CPU), a microcontroller (MCU), a microprocessor (microprocessing unit, MPU), a digital signal processor (digital signal processor) processing, DSP), system on chip (system on chip, SoC), application-specific integrated circuit (application-specific integrated circuit, ASIC), field programmable gate array (field programmable gate array, FPGA) or programmable logic device (programmable logic device) , PLD).
  • CPU central processing unit
  • MCU microcontroller
  • MPU microprocessor
  • DSP digital signal processor
  • SoC system on chip
  • ASIC application-specific integrated circuit
  • FPGA field programmable gate array
  • PLD programmable logic device
  • the chip may further include a memory, the memory stores an instruction, the processor is used to execute the instruction stored on the memory, and when the instruction is executed, the processor is used to execute the first.
  • a powertrain including a temperature prediction device in any possible implementation manner of the second aspect and the first aspect.
  • an automobile including a temperature prediction device as in the second aspect and any possible implementation manner of the second aspect.
  • FIG. 1 is a schematic structural diagram of an automobile provided by an embodiment of the present application.
  • FIG. 2 is a schematic flowchart of a temperature prediction method provided by an embodiment of the present application.
  • FIG. 3 is a schematic block diagram of a temperature prediction apparatus provided by an embodiment of the present application.
  • FIG. 4 is a schematic flowchart of another temperature prediction method provided by an embodiment of the present application.
  • FIG. 5 is a schematic diagram of a hardware structure of a temperature prediction apparatus 800 provided by an embodiment of the present application.
  • the network architecture and service scenarios described in the embodiments of the present application are for the purpose of illustrating the technical solutions of the embodiments of the present application more clearly, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application.
  • the evolution of the architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.
  • references in this specification to "one embodiment” or “some embodiments” and the like mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application.
  • appearances of the phrases “in one embodiment,” “in some embodiments,” “in other embodiments,” “in other embodiments,” etc. in various places in this specification are not necessarily All refer to the same embodiment, but mean “one or more but not all embodiments” unless specifically emphasized otherwise.
  • the terms “including”, “including”, “having” and their variants mean “including but not limited to” unless specifically emphasized otherwise.
  • At least one means one or more, and “plurality” means two or more.
  • “And/or”, which describes the relationship of the associated objects, means that there can be three relationships, for example, A and/or B, which can mean: including the existence of A alone, the existence of A and B at the same time, and the existence of B alone, where A and B can be singular or plural.
  • the character “/” generally indicates that the associated objects are an “or” relationship.
  • “At least one item(s) below” or similar expressions thereof refer to any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one item (a) of a, b, or c may represent: a, b, c, ab, ac, bc, or abc, where a, b, and c may be single or multiple .
  • FIG. 1 is a schematic structural diagram of an automobile provided by an embodiment of the present application. As shown in Figure 1, the vehicle may include, but is not limited to, one or more powertrains, battery packs, and wheels.
  • Powertrains may include, but are not limited to: motors, motor controllers, inverters.
  • the motor is a device used to realize the mutual conversion of electrical energy and mechanical energy, and is composed of two parts: a stator and a rotor.
  • the stator of the motor is the stationary part of the motor, which consists of three parts: the stator core, the stator winding and the frame. Its main function is to generate a rotating magnetic field.
  • the motor rotor is the rotating part of the motor, and its main function is to be cut by magnetic lines of force in the rotating magnetic field to generate (output) current.
  • the whole system is first controlled by the motor controller to convert the direct current (DC) current into DC
  • motor controller and the inverter may be integrated into one device, or may be two independent devices, which are not specifically limited in this application.
  • the motor controller controls the inverter to convert the DC current into an AC current
  • the fundamental wave current the corresponding frequency
  • signals of other frequencies will inevitably be generated (for example, higher harmonic currents of other frequencies, whose frequencies are related to the fundamental frequency and the PWM carrier frequency, called harmonic frequencies). These signals will also cause the loss of the motor to increase significantly. Therefore, the above losses include not only the losses caused by the fundamental current, but also the losses caused by signals of other frequencies.
  • the loss of the motor (which may also be referred to as the fundamental wave loss) is only calculated based on the fundamental wave current, and the temperature of the motor is predicted in real time based on the fundamental wave loss. Since signals of other frequencies (for example, harmonic currents) can also cause motor losses, the motor losses calculated only based on the fundamental wave current will make the calculation accuracy of the motor losses lower, and the reduced calculation accuracy of the motor losses will reduce the motor losses. Prediction accuracy of temperature.
  • the embodiment of the present application provides a temperature prediction method, which incorporates the fundamental wave of the current and signals of other frequencies into the loss calculation of the motor, and obtains the motor by calculating the fundamental wave loss of the motor and the loss of other frequencies.
  • the prediction accuracy of motor temperature can be improved.
  • a temperature prediction method provided by an embodiment of the present application will be described in detail below with reference to FIG. 2 .
  • the method may include steps 210-220, and the steps 210-220 will be described in detail below respectively.
  • Step 210 Determine the loss of the motor according to the information of the motor controller, where the loss of the motor includes a first loss and a second loss, and the first loss is the loss generated by the fundamental component of the current of the motor.
  • the second loss in the embodiment of the present application may be a loss generated by other signals of the motor, for example, the other signals of the motor may be current harmonic components of the motor. As an example, the second losses may be losses due to harmonic components of the electrical machine's current.
  • the information of the motor controller may also be referred to as the output information of the motor controller, and the output information of the motor controller may be various, which is not specifically limited in this application.
  • the output information of the motor controller is the components ud and u q of the voltage vector in the dq rotating coordinate system.
  • the output information of the motor controller is the components id and i q of the current vector in the dq rotating coordinate system.
  • Step 220 Determine the temperature of the motor according to the loss of the motor and a temperature prediction model.
  • the temperature prediction model is not specifically limited in the embodiment of the present application, as long as the corresponding motor temperature can be output according to the input loss of the motor. Several possible temperature prediction models are described in detail below.
  • the temperature prediction model is an equivalent thermal resistance network model.
  • the equivalent thermal resistance network model is a thermal circuit model equivalent to a circuit model.
  • the motor can be subdivided into unit nodes, with heat transfer between the nodes, and thermal resistance connections between the nodes. And increase the heat capacity at the node to form the thermal resistance network of the motor.
  • Each node is regarded as a thermal unit with lumped parameters, and the equivalent thermal resistance network model can use Kirchhoff's current law (KCL), Kirchhoff's voltage law (Kirchhoff's voltage) for the connection of each unit law, KVL) to establish a heat balance equation, and the temperature of the motor node in the thermal resistance network can be calculated through the motor node loss + heat balance equation.
  • KCL Kirchhoff's current law
  • Kirchhoff's voltage Kirchhoff's voltage
  • the temperature prediction model is a neural network model.
  • the neural network model may also be called an artificial neural network model, which is a neural network that simulates the human brain. From the perspective of information processing, it mathematically abstracts the neural network of the human brain, establishes a mathematical model, and forms different networks according to different connection methods, so as to realize the machine learning model of artificial intelligence.
  • the artificial neural network model can use the motor loss value as input to calculate and output the temperature value to be measured by the motor.
  • the temperature prediction model is a least squares model.
  • the least squares model may be linear or non-linear, which is not specifically limited in this application.
  • the least squares model is a method of using the least squares algorithm to establish a fitting relationship between the input motor loss and the motor temperature. This fitting relationship is divided into linear and nonlinear. After the least squares model is established to fit the relationship, the model can be used to calculate the motor temperature value to be measured by the motor.
  • the determined motor loss includes other losses (for example, the second loss generated by other signals) in addition to the loss generated by the current fundamental component of the motor, so that the calculation accuracy of the motor loss can be improved, Thereby, the prediction accuracy of the motor temperature is improved.
  • the temperature prediction apparatus 300 may include: a motor controller 310 , a loss calculation module 320 , and a temperature prediction module 330 .
  • the temperature prediction apparatus 300 may further include: a voltage harmonic analysis module 340 and a current harmonic analysis module 350 .
  • the temperature predicting apparatus 300 may further include: a temperature sensor 360 .
  • the motor controller 310 is used for outputting a plurality of signals, for example, S1, S2...Sn shown in FIG. 3 .
  • the plurality of signals may correspond to the information of the motor controller above.
  • control signals output by the motor controller 310 may include, but are not limited to, the components ud and u q of the voltage vector in the dq rotating coordinate system, the components id and i q of the current vector in the dq rotating coordinate system, and DC bus voltage U dc , carrier frequency, etc.
  • the voltage harmonic analysis module 340 is configured to perform Fourier decomposition on the control signal output by the motor controller 310 (for example, the components ud and u q of the voltage vector in the dq rotating coordinate system) according to the carrier frequency to obtain the Sum of amplitudes of harmonic voltages in abc stationary coordinate system and will Converted to the calculated value of the harmonic voltage component in the dq rotating coordinate system and
  • the current harmonic analysis module 350 is used for the calculated value of the harmonic voltage component in the dq rotating coordinate system output by the voltage harmonic analysis module 340 and Determining the magnitude of the harmonic current at the kth harmonic frequency
  • the loss calculation module 320 is used to determine the loss of the motor.
  • the loss of the motor may include any one or a combination of the following: coil loss of the motor, stator and rotor loss of the motor, magnetic steel loss of the motor, and the like.
  • the loss calculation module 320 may determine the loss of the motor according to the control signal output by the motor controller 310 .
  • the input of the loss calculation module 320 is the amplitude of the harmonic current at the k-th harmonic frequency output by the current harmonic analysis module 350
  • the loss calculation module 320 may be based on the output of the current harmonic analysis module 350. Determine the losses of the motor.
  • the loss of the above-mentioned motor in the embodiment of the present application may include both the loss caused by the fundamental wave and the loss caused by the harmonic wave.
  • the temperature prediction module 330 is configured to determine the temperature of each part of the motor according to the loss of the motor output by the loss calculation module 320 .
  • the temperature of each part of the motor may include any one or a combination of the following: coil temperature of the motor, stator rotor temperature of the motor, magnetic steel temperature of the motor, and the like.
  • FIG. 4 is a schematic flowchart of another temperature prediction method provided by an embodiment of the present application.
  • the method may include steps 410-430, and the steps 410-430 will be described in detail below, respectively.
  • Step 410 The loss calculation module 320 determines the motor loss Q at time t+1 generated by the fundamental wave and the harmonic.
  • the loss calculation module 320 may determine the loss Q of the motor at time t+1 generated by the fundamental wave and the harmonic wave according to the formula (1) shown below.
  • Q represents the total loss of the motor caused by the fundamental and harmonics
  • Q 0 represents the loss of the motor generated by the fundamental wave
  • f a coefficient
  • the loss Q 0 of the motor generated by the fundamental wave may include any one or a combination of the following: Coil loss of the motor generated by the fundamental wave Motor stator losses due to fundamental waves Motor rotor losses due to fundamental waves Wait.
  • equation (2) enumerates the stator losses of a motor generated by the fundamental wave calculation formula.
  • a1 , b 1 , c 1 represent coefficients
  • ⁇ f rotor flux linkage
  • equation (3) enumerates the rotor loss of a motor generated by the fundamental wave calculation formula.
  • formula (4) lists a formula for calculating the coil loss Q coil-1 of the motor generated by the fundamental wave.
  • R s represents the phase resistance
  • the loss calculation module 320 may also calculate the loss Q 1 of the motor generated by the fundamental wave and the loss Q k of the motor generated by the harmonic wave respectively, and determine them according to the following formula (5) The loss Q of the motor at time t+1 caused by the fundamental and harmonics.
  • Q k represents the loss of the motor caused by the k-th harmonic.
  • the loss Q k of the motor generated by the harmonics may include any one or a combination of any of the following: Coil losses of the motor generated by the harmonics Motor stator losses due to harmonics Motor rotor losses due to harmonics Wait.
  • equations (6)-(8) respectively show a possible way of calculating .
  • a k , b k , c k is the coefficient
  • the voltage harmonic analysis module 340 determines the sum of the voltage amplitudes at the k-th harmonic frequency according to the control signal output by the motor control module 310
  • the voltage harmonic analysis module 340 may determine according to equation (10) shown below
  • n the sideband harmonic coefficient
  • s represents the baseband harmonic coefficient
  • j means plural;
  • ⁇ c represents the angular frequency of the carrier
  • ⁇ m represents the angular frequency of the modulating wave
  • ⁇ n and A sn can be determined by equations (11)-(12).
  • the voltage harmonic analysis module 340 will Converted to the calculated value of the harmonic voltage component in the dq rotating coordinate system and
  • the current harmonic analysis module 350 may determine by Equation (13) and
  • L d represents the d-axis inductance
  • ⁇ represents the speed of the motor
  • L q represents the q-axis inductance
  • Step 420 The loss calculation module 320 corrects the loss Q of the motor at time t+1 according to the temperature of the motor at time t.
  • the temperature of the motor at time t may include any one or a combination of the following: coil temperature of the motor, stator and rotor temperature of the motor, magnet steel temperature of the motor, and the like.
  • the loss calculation module 320 may obtain a temperature feedback signal (that is, the temperature calculated by the temperature prediction module 330 ) from the temperature prediction module 330 through the motor controller 310 , and the temperature feedback signal includes the temperature of the motor at time t. temperature.
  • the loss calculation module 320 may also directly acquire the temperature of the motor at time t from the temperature sensor 360 .
  • the loss calculation module 320 may correct the loss Q of the motor at time t+1 determined in step 410 according to the temperature of the motor at time t.
  • the loss Q of the motor at time t+1 please refer to the description in step 410, which will not be repeated here.
  • the loss calculation module 320 can correct the stator and rotor losses of the motor at time t+1 according to the temperature of the stator and rotor of the motor at time t; or, it can also calculate the motor coil loss at time t+1 according to the coil temperature of the motor at time t. Correction.
  • the loss calculation module 320 may calculate the coefficient according to the coil temperature of the motor at time t Make corrections, according to the corrected coefficients and according to the corrected coefficient Correct the coil loss of the motor at time t+1, and obtain the corrected coil loss of the motor at time t+1.
  • the loss calculation module 320 may calculate the coefficient of the stator temperature of the motor according to the time t corrected, and according to the corrected coefficient Correct the stator loss of the motor at time t+1, and obtain the stator loss of the motor at time t+1 after the correction.
  • the loss calculation module 320 may calculate the coefficient of the rotor temperature of the motor according to the time t corrected, and according to the corrected coefficient Correct the rotor loss of the motor at time t+1, and obtain the rotor loss of the motor at time t+1 after correction.
  • coefficients can be Any one of them can be modified, or any combination of any of them can be modified, which is not specifically limited in this application.
  • Step 430 The temperature prediction module 330 determines the motor temperature at time t+1 according to the motor temperature at time t, the corrected loss of the motor at time t+1, and the temperature prediction model.
  • the temperature prediction module 330 may determine the coil temperature of the motor at time t+1 according to the corrected motor coil loss at time t+1, the motor coil temperature at time t, and the temperature prediction model. As another example, the temperature prediction module 330 may determine the stator temperature of the motor at time t+1 according to the corrected motor stator loss at time t+1, the motor stator temperature at time t, and the temperature prediction model. As another example, the temperature prediction module 330 may determine the rotor temperature of the motor at time t+1 according to the corrected motor rotor loss at time t+1, the motor rotor temperature at time t, and the temperature prediction model.
  • FIG. 5 is a schematic diagram of a hardware structure of a temperature prediction apparatus 800 provided by an embodiment of the present application.
  • the temperature prediction apparatus 800 shown in FIG. 5 may include a memory 801 , a processor 802 , a communication interface 803 and a bus 804 .
  • the memory 801 , the processor 802 , and the communication interface 803 are connected to each other through the bus 804 for communication.
  • the memory 801 may be a read-only memory (ROM), a static storage device, and a random access memory (RAM).
  • the memory 801 may store a program, and when the program stored in the memory 801 is executed by the processor 802, the processor 802 and the communication interface 803 are used to execute each step of the temperature prediction method of the embodiment of the present application. For example, FIG. 2 or The individual steps of the temperature prediction method are shown in FIG. 4 .
  • the processor 802 may use a general-purpose CPU, a microprocessor, an ASIC, a GPU, or one or more integrated circuits, and is used to execute a related program to implement all the units in the temperature prediction apparatus shown in FIG. 3 in the embodiment of the present application.
  • the function to be executed, or the temperature prediction method of the method embodiment of the present application is executed.
  • the processor 802 may also be an integrated circuit chip with signal processing capability.
  • each step of the temperature prediction method in the embodiment of the present application may be completed by an integrated logic circuit of hardware in the processor 802 or instructions in the form of software.
  • the above-mentioned processor 802 may also be a general-purpose processor, DSP, ASIC, FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
  • the methods, steps, and logic block diagrams disclosed in the embodiments of this application can be implemented or executed.
  • a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
  • the steps of the method disclosed in conjunction with the embodiments of the present application may be directly embodied as executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor.
  • the software modules may be located in random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers and other storage media mature in the art.
  • the storage medium is located in the memory 801, and the processor 802 reads the information in the memory 801 and, in combination with its hardware, completes the functions required to be performed by the units included in the temperature prediction apparatus of the embodiment of the present application, or executes the temperature of the method embodiment of the present application. forecasting method.
  • the communication interface 803 uses a transceiver such as, but not limited to, a transceiver to implement communication between the temperature prediction device 800 and other devices or a communication network.
  • a transceiver such as, but not limited to, a transceiver to implement communication between the temperature prediction device 800 and other devices or a communication network.
  • the bus 804 may include a pathway for communicating information between the various components of the prediction apparatus 800 for temperature (eg, the memory 801, the processor 802, the communication interface 803).
  • the above-mentioned temperature prediction apparatus 800 only shows a memory, a processor, and a communication interface, in the specific implementation process, those skilled in the art should understand that the temperature prediction apparatus 800 may also include necessary components for normal operation. of other devices. Meanwhile, according to specific needs, those skilled in the art should understand that the above-mentioned temperature prediction apparatus 800 may further include hardware devices that implement other additional functions. In addition, those skilled in the art should understand that the above-mentioned apparatus 800 for resource allocation may also only include the necessary components for implementing the embodiments of the present application, rather than all the components shown in FIG. 5 .
  • Embodiments of the present application further provide a chip, where the chip includes a transceiver unit and a processing unit.
  • the transceiver unit may be an input/output circuit or a communication interface;
  • the processing unit may be a processor, a microprocessor or an integrated circuit integrated on the chip; and the chip may execute the methods in the above method embodiments.
  • the chip can be a central processing unit (CPU), a microcontroller (MCU), a microprocessor (microprocessing unit, MPU), a digital signal processor (digital signal processor) processing, DSP), system on chip (system on chip, SoC), application-specific integrated circuit (application-specific integrated circuit, ASIC), field programmable gate array (field programmable gate array, FPGA) or programmable logic device (programmable logic device) , PLD).
  • CPU central processing unit
  • MCU microcontroller
  • MPU microprocessor
  • DSP digital signal processor
  • SoC system on chip
  • ASIC application-specific integrated circuit
  • FPGA field programmable gate array
  • PLD programmable logic device
  • the embodiments of the present application further provide a computer-readable storage medium, on which instructions are stored, and when the instructions are executed, the methods in the foregoing method embodiments are performed.
  • the computer-readable medium stores program code that, when executed on a computer, causes the computer to perform the methods of the above-described aspects.
  • These computer-readable storages include, but are not limited to, one or more of the following: read-only memory (ROM), programmable ROM (PROM), erasable PROM (erasable PROM, EPROM), Flash memory, electrical EPROM (electrically EPROM, EEPROM) and hard drive (hard drive).
  • the embodiments of the present application further provide a computer program product including an instruction, when the instruction is executed, the method in the foregoing method embodiment is performed.
  • the processor in the embodiment of the present application may be a central processing unit (central processing unit, CPU), and the processor may also be other general-purpose processors, digital signal processors (digital signal processors, DSP), application-specific integrated circuits (application specific integrated circuit, ASIC), off-the-shelf programmable gate array (field programmable gate array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
  • the memory in the embodiments of the present application may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.
  • the non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically programmable Erase programmable read-only memory (electrically EPROM, EEPROM) or flash memory.
  • Volatile memory may be random access memory (RAM), which acts as an external cache.
  • RAM random access memory
  • SRAM static random access memory
  • DRAM dynamic random access memory
  • DRAM synchronous dynamic random access memory
  • SDRAM synchronous dynamic random access memory
  • DDR SDRAM double data rate synchronous dynamic random access memory
  • enhanced SDRAM enhanced synchronous dynamic random access memory
  • SLDRAM synchronous connection dynamic random access memory Fetch memory
  • direct memory bus random access memory direct rambus RAM, DR RAM
  • the above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination.
  • the above-described embodiments may be implemented in whole or in part in the form of a computer program product.
  • the computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated.
  • the computer may be a general purpose computer, special purpose computer, computer network, or other programmable device.
  • the computer instructions may be stored in or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be downloaded from a website site, computer, server or data center Transmission to another website site, computer, server or data center by wire (eg, infrared, wireless, microwave, etc.).
  • the computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, a data center, or the like that contains one or more sets of available media.
  • the usable media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, DVDs), or semiconductor media.
  • the semiconductor medium may be a solid state drive.
  • the size of the sequence numbers of the above-mentioned processes does not mean the sequence of execution, and the execution sequence of each process should be determined by its functions and internal logic, and should not be dealt with in the embodiments of the present application. implementation constitutes any limitation.
  • the disclosed system, apparatus and method may be implemented in other manners.
  • the apparatus embodiments described above are only illustrative.
  • the division of the units is only a logical function division. In actual implementation, there may be other division methods.
  • multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented.
  • the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and components shown as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
  • the functions, if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable storage medium.
  • the technical solution of the present application can be embodied in the form of a software product in essence, or the part that contributes to the prior art or the part of the technical solution.
  • the computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application.
  • the aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk and other media that can store program codes .

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Abstract

一种温度的预测方法、装置，该方法包括：根据电机控制器的信息确定电机的损耗，所述电机的损耗包括第一损耗和第二损耗，所述第一损耗为所述电机的电流基波分量产生的损耗（S210）；根据所述电机的损耗以及温度预测模型确定所述电机的温度（S220）。该方法可以提高电机温度预测的精度。

Description

一种温度的预测方法以及装置 技术领域
本申请涉及电机领域，并且更具体地，涉及一种温度的预测方法以及装置。
背景技术
近年来随着汽车领域对动力总成的高速化&小型化的不断追求，这会导致电机热损耗密度大幅度增加。一方面，电机在高转速区域反复加速时，由于温升积累，电机的磁钢有超温风险。另一方面，电机在峰值功率运行时，电机的线圈有超温风险。过高的电机温度会导致绕组烧毁，转子磁钢退磁等问题。
因此，需要对电机的温度进行实时预测，并进行相应的降温处理，否则电机将存在超温风险和热设计冗余过大等问题。
所以，如何对电机的上述节点的温度进行精确预测成为亟需要解决的问题。
发明内容
本申请提供一种温度的预测方法、装置，可以提高电机温度预测的精度。
第一方面，提供了一种温度的预测方法，包括：根据电机控制器的信息确定电机的损耗，所述电机的损耗包括第一损耗和第二损耗，所述第一损耗为所述电机的电流基波分量产生的损耗；根据所述电机的损耗以及温度预测模型确定所述电机的温度。
上述技术方案中，确定的电机损耗除了包括电机的电流基波分量产生的损耗之外，还包括其他的损耗(例如，第二损耗)，这样，可以提高电机损耗的计算精度，从而提高电机温度的预测精度。
在一种可能的实现方式中，所述第二损耗为所述电机的电流谐波分量产生的损耗。
上述技术方案中，由于电流的谐波成分也会造成电机的损耗明显增加，因此，可以将电流的基波和谐波都纳入电机的损耗计算中，通过计算电机的基波损耗以及谐波损耗来得到电机的总损耗，以提高电机损耗的计算精度，从而可以提高电机温度的预测精度。
在另一种可能的实现方式中，根据所述电流的基波分量确定所述第一损耗；根据所述第一损耗和第一系数得到所述电机的损耗。
上述技术方案中，可以直接基于电流的基波分量产生的第一损耗确定所述电机的损耗，实现较为简单。
在另一种可能的实现方式中，根据所述电流的基波分量确定所述第一损耗；根据所述电流的谐波分量确定所述第二损耗；根据所述第一损耗和所述第二损耗得到所述电机的损耗。
在另一种可能的实现方式中，电机控制器的信息为dq旋转坐标系下的电压矢量，
在所述根据所述电流的谐波分量确定所述第一损耗之前，所述方法还包括：从所述电机控制器获取所述dq旋转坐标系下的电压矢量；根据所述dq旋转坐标系下的电压矢量得 到电压的谐波分量；根据电压的谐波分量得到所述电流的谐波分量。
在另一种可能的实现方式中，所述电机的温度为所述电机在t时刻的温度，所述方法还包括：根据所述电机在t时刻的温度对所述电机的损耗修正，得到修正后的电机损耗；根据所述修正后的电机损耗，所述电机在t时刻的温度以及所述温度预测模型确定所述电机在t+1时刻的温度，所述t时刻为所述t+1时刻的前一个时刻。
上述技术方案中，可以基于电机在t时刻的温度，对电机在t+1时刻的损耗进行修正，并基于修正后t+1时刻的损耗确定该电机在t+1时刻的温度，从而进一步提高电机温度预测的精度。
在另一种可能的实现方式中，所述电机的损耗包括以下中的一种或多种：所述电机的线圈损耗、所述电机的定转子损耗、所述电机的磁钢损耗。
在另一种可能的实现方式中，所述电机在t时刻的温度包括以下中的一种或多种：所述电机在t时刻的线圈温度、所述电机在t时刻的定转子温度、所述电机在t时刻的磁钢温度。
在另一种可能的实现方式中，所述温度预测模型为以下中的任意一种：等效热阻网络模型、神经网络模型、线性最小二乘模型、非线性最小二乘模型。
第二方面，提供了一种温度的预测装置，包括：
损耗计算模块，用于根据电机控制器的信息确定电机的损耗，所述电机的损耗包括第一损耗和第二损耗，所述第一损耗为所述电机的电流基波分量产生的损耗；
温度预测模块，用于根据所述电机的损耗以及温度预测模型确定所述电机的温度。
在一种可能的实现方式中，所述第二损耗为所述电机的电流谐波分量产生的损耗。
在另一种可能的实现方式中，所述损耗计算模块具体用于：根据所述电流的基波分量确定所述第一损耗；根据所述第一损耗和第一系数得到所述电机的损耗。
在另一种可能的实现方式中，所述损耗计算模块具体用于：根据所述电流的基波分量确定所述第一损耗；根据所述电流的谐波分量确定所述第二损耗；根据所述第一损耗和所述第二损耗得到所述电机的损耗。
在另一种可能的实现方式中，电机控制器的信息为dq旋转坐标系下的电压矢量，所述预测装置还包括：
获取模块，用于从所述电机控制器获取所述dq旋转坐标系下的电压矢量；
电压谐波分析模块，用于根据所述dq旋转坐标系下的电压矢量得到电压的谐波分量；
电流谐波分析模块，根据电压的谐波分量得到所述电流的谐波分量。
在另一种可能的实现方式中，所述电机的温度为所述电机在t时刻的温度，所述损耗计算模块，还用于根据所述电机在t时刻的温度对所述电机的损耗修正，得到修正后的电机损耗；所述温度预测模块，还用于根据所述修正后的电机损耗，所述电机在t时刻的温度以及所述温度预测模型确定所述电机在t+1时刻的温度，所述t时刻为所述t+1时刻的前一个时刻。
在另一种可能的实现方式中，所述电机的损耗包括以下中的一种或多种：所述电机的线圈损耗、所述电机的定转子损耗、所述电机的磁钢损耗。
在另一种可能的实现方式中，所述电机在t时刻的温度包括以下中的一种或多种：所述电机在t时刻的线圈温度、所述电机在t时刻的定转子温度、所述电机在t时刻的磁钢 温度。
在另一种可能的实现方式中，所述温度预测模型为以下中的任意一种：等效热阻网络模型、神经网络模型、线性最小二乘模型、非线性最小二乘模型。
应理解，在上述第一方面中对相关内容的扩展、限定、解释和说明也适用于第二方面中相同的内容。
第三方面，提供了一种的装置，包括：存储器，用于存储程序；处理器，用于执行该存储器存储的程序，当该存储器存储的程序被执行时，该处理器用于执行：根据电机控制器的信息确定电机的损耗，所述电机的损耗包括第一损耗和第二损耗，所述第一损耗为所述电机的电流基波分量产生的损耗；根据所述电机的损耗以及温度预测模型确定所述电机的温度。
在一种可能的实现方式中，所述第二损耗为所述电机的电流谐波分量产生的损耗。
在另一种可能的实现方式中，根据所述电流的基波分量确定所述第一损耗；根据所述第一损耗和第一系数得到所述电机的损耗。
在另一种可能的实现方式中，根据所述电流的基波分量确定所述第一损耗；根据所述电流的谐波分量确定所述第二损耗；根据所述第一损耗和所述第二损耗得到所述电机的损耗。
在另一种可能的实现方式中，电机控制器的信息为dq旋转坐标系下的电压矢量，
在所述根据所述电流的谐波分量确定所述第一损耗之前，所述方法还包括：从所述电机控制器获取所述dq旋转坐标系下的电压矢量；根据所述dq旋转坐标系下的电压矢量得到电压的谐波分量；根据电压的谐波分量得到所述电流的谐波分量。
在另一种可能的实现方式中，所述电机的温度为所述电机在t时刻的温度，所述方法还包括：根据所述电机在t时刻的温度对所述电机的损耗修正，得到修正后的电机损耗；根据所述修正后的电机损耗，所述电机在t时刻的温度以及所述温度预测模型确定所述电机在t+1时刻的温度，所述t时刻为所述t+1时刻的前一个时刻。
在另一种可能的实现方式中，所述电机的损耗包括以下中的一种或多种：所述电机的线圈损耗、所述电机的定转子损耗、所述电机的磁钢损耗。
在另一种可能的实现方式中，所述电机在t时刻的温度包括以下中的一种或多种：所述电机在t时刻的线圈温度、所述电机在t时刻的定转子温度、所述电机在t时刻的磁钢温度。
在另一种可能的实现方式中，所述温度预测模型为以下中的任意一种：等效热阻网络模型、神经网络模型、线性最小二乘模型、非线性最小二乘模型。
第四方面，提供一种计算机存储介质，该计算机存储介质存储有程序代码，该程序代码包括用于执行第一方面以及第一方面中的任意一种实现方式中的方法中的步骤的指令。
上述存储介质具体可以是非易失性存储介质。
这些计算机可读存储包括但不限于如下的一个或者多个：只读存储器(read-only memory，ROM)、可编程ROM(programmable ROM，PROM)、可擦除的PROM(erasable PROM，EPROM)、Flash存储器、电EPROM(electrically EPROM，EEPROM)以及硬盘驱动器(hard drive)。
第五方面，提供一种芯片，该芯片包括处理器与数据接口，该处理器通过该数据接口 读取存储器上存储的指令，执行上述第一方面以及第一方面的任意一种实现方式中的方法。
在具体实现过程中，该芯片可以以中央处理器(central processing unit，CPU)、微控制器(micro controller unit，MCU)、微处理器(micro processing unit，MPU)、数字信号处理器(digital signal processing，DSP)、片上系统(system on chip，SoC)、专用集成电路(application-specific integrated circuit，ASIC)、现场可编程门阵列(field programmable gate array，FPGA)或可编辑逻辑器件(programmable logic device，PLD)的形式实现。
可选地，作为一种实现方式，该芯片还可以包括存储器，该存储器中存储有指令，该处理器用于执行该存储器上存储的指令，当该指令被执行时，该处理器用于执行第一方面以及第一方面中的任意一种实现方式中的方法。
第六方面，提供了一种动力总成，包括如第二方面及第一方面的任意一种可能的实现方式中的温度的预测装置。
第七方面，提供了一种汽车，包括如第二方面及第二方面的任意一种可能的实现方式中的温度的预测装置。
附图说明
图1是本申请实施例提供的一种汽车的示意性结构图。
图2是本申请实施例提供的一种温度的预测方法的示意性流程图。
图3是本申请实施例提供的一种温度的预测装置的示意性框图。
图4是本申请实施例提供的另一种温度的预测方法的示意性流程图。
图5是本申请实施例提供的温度的预测装置800的硬件结构示意图。
具体实施方式
下面将结合附图，对本申请中的技术方案进行描述。
本申请将围绕包括多个设备、组件、模块等的系统来呈现各个方面、实施例或特征。应当理解和明白的是，各个系统可以包括另外的设备、组件、模块等，并且/或者可以并不包括结合附图讨论的所有设备、组件、模块等。此外，还可以使用这些方案的组合。
另外，在本申请实施例中，“示例的”、“例如”等词用于表示作例子、例证或说明。本申请中被描述为“示例”的任何实施例或设计方案不应被解释为比其它实施例或设计方案更优选或更具优势。确切而言，使用示例的一词旨在以具体方式呈现概念。
本申请实施例中，“相应的(corresponding，relevant)”和“对应的(corresponding)”有时可以混用，应当指出的是，在不强调其区别时，其所要表达的含义是一致的。
本申请实施例描述的网络架构以及业务场景是为了更加清楚的说明本申请实施例的技术方案，并不构成对于本申请实施例提供的技术方案的限定，本领域普通技术人员可知，随着网络架构的演变和新业务场景的出现，本申请实施例提供的技术方案对于类似的技术问题，同样适用。
在本说明书中描述的参考“一个实施例”或“一些实施例”等意味着在本申请的一个或多个实施例中包括结合该实施例描述的特定特征、结构或特点。由此，在本说明书中的不同之处出现的语句“在一个实施例中”、“在一些实施例中”、“在其他一些实施例中”、 “在另外一些实施例中”等不是必然都参考相同的实施例，而是意味着“一个或多个但不是所有的实施例”，除非是以其他方式另外特别强调。术语“包括”、“包含”、“具有”及它们的变形都意味着“包括但不限于”，除非是以其他方式另外特别强调。
本申请中，“至少一个”是指一个或者多个，“多个”是指两个或两个以上。“和/或”，描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：包括单独存在A，同时存在A和B，以及单独存在B的情况，其中A，B可以是单数或者复数。字符“/”一般表示前后关联对象是一种“或”的关系。“以下至少一项(个)”或其类似表达，是指的这些项中的任意组合，包括单项(个)或复数项(个)的任意组合。例如，a，b，或c中的至少一项(个)，可以表示：a，b，c，a-b，a-c，b-c，或a-b-c，其中a，b，c可以是单个，也可以是多个。
为了便于描述，下面先结合图1，对汽车的结构进行描述。
图1是本申请实施例提供的一种汽车的示意性结构图。如图1所示，该汽车可以包括但不限于：一个或多个动力总成，电池包以及车轮。
动力总成可以包括但不限于：电机，电机控制器，逆变器。其中，电机是一种用来实现电能和机械能相互转换的装置，由定子和转子两部分组成。电机定子是电动机静止不动的部分，由定子铁芯、定子绕组和机座三部分组成，其主要作用是产生旋转磁场。电机转子为电机中旋转的部分，其主要作用是在旋转磁场中被磁力线切割进而产生(输出)电流。
整个系统先由电机控制器控制逆变器，将直流(direct current，DC)电流转换成
(alternating current，AC)电流。这器件由于转换效率的原因会有一定的能量损失，这部分损失会转换成热量。同时AC电流会进入电机，由电磁感应转换成电机转动的机械能，这个过程由于转换效率的原因也会产生热能。最后电机的高转速会由减速器将转速调低，这部分转换依旧会产生损耗。以上三部分由于能量转换产生的热量均需通过散热将热量及时排出动力总成。
应理解，电机控制器和逆变器可以集成在一个设备中，或者也可以独立的两个设备，本申请对此不做具体限定。
在上述电机控制器控制逆变器将DC电流转换成AC电流的过程中，除了会产生与控制器指令中参考电压频率相同的正弦波分量(即基波电流，对应频率称为基波频率)，不可避免也会产生其他频率的信号(例如，其他频率的高次谐波电流，其频率与基波频率和PWM载波频率相关，称为谐波频率)。这些信号也会造成电机的损耗明显增加，因此，上述损耗中既包括基波电流产生的损耗，又包括其他频率的信号产生的损耗。
近年来随着汽车领域对动力总成的高速化&小型化的不断追求，这会导致电机热损耗密度大幅度增加。一方面，电机在高转速区域反复加速时，由于温升积累，电机的磁钢有超温风险。另一方面，电机在峰值功率运行时，电机的线圈有超温风险。过高的电机温度会导致绕组烧毁，转子磁钢退磁等问题。
因此，在上述工况下，需要对电机的温度进行实时预测，并进行相应的降温处理，否则电机将存在超温风险和热设计冗余过大等问题。
相关的技术方案中，仅基于基波电流计算电机的损耗(也可以称为基波损耗)，并基于基波损耗对电机的温度进行实时预测。由于其他频率的信号(例如，谐波电流)也会造成电机的损耗，因此，仅基于基波电流计算的电机损耗，会使得电机损耗的计算精度较低， 电机损耗的计算精度降低会降低电机温度的预测精度。
有鉴于此，本申请实施例提供了一种温度的预测方法，将电流的基波和其他频率的信号都纳入电机的损耗计算中，通过计算电机的基波损耗以及其他频率的损耗来得到电机的总损耗，以提高电机损耗的计算精度，从而可以提高电机温度的预测精度。
下面结合图2，对本申请实施例提供的一种温度的预测方法进行详细描述。如图2所示，该方法可以包括步骤210-220，下面分别对步骤210-220进行详细描述。
步骤210：根据电机控制器的信息确定电机的损耗，所述电机的损耗包括第一损耗和第二损耗，所述第一损耗为所述电机的电流基波分量产生的损耗。
本申请实施例中的第二损耗可以是电机的其他信号产生的损耗，该电机的其他信号例如可以是电机的电流谐波分量。作为示例，该第二损耗可以是电机的电流谐波分量产生的损耗。
电机控制器的信息也可以称为电机控制器的输出信息，该电机控制器的输出信息可以有多种，本申请对此不做具体限定。例如，该电机控制器的输出信息为电压矢量在dq旋转坐标系下的分量u d和u q。又如，该电机控制器的输出信息为电流矢量在dq旋转坐标系下的分量i d和i q。
步骤220：根据所述电机的损耗以及温度预测模型确定所述电机的温度。
本申请实施例对温度预测模型不做具体限定，只要可以实现根据输入的电机的损耗可以输出对应的电机温度即可。下面分别对几种可能的温度预测模型进行详细描述。
一种可能的实现方式中，温度预测模型为等效热阻网络模型。其中，该等效热阻网络模型是一种将热路模型等效成电路模型。作为示例，可以将电机细分成单元节点，各节点之间有热量传递，节点之间都用热阻连接。并在节点增加热容，形成电机的热阻网络。各个节点看成是具有集总参数的热单元，等效热阻网络模型可以对每个单元的连接利用基尔霍夫电流定律(Kirchhoff’s current law，KCL)、基尔霍夫电压定律(Kirchhoff’s voltage law，KVL)建立热平衡方程，可通过电机节点损耗+热平衡方程计算得到热阻网络中电机节点的温度。
另一种可能的实现方式中，温度预测模型为神经网络模型。其中，该神经网络模型也可以称为人工神经网络模型，是一种模拟人脑的神经网络。它从信息处理角度对人脑神经元网络进行数学抽象，建立数学模型，按不同的连接方式组成不同的网络，从而能够实现类人工智能的机器学习模型。人工神经网络模型可以利用电机损耗值作为输入，计算并输出电机需测量的温度值。
另一种可能的实现方式中，温度预测模型为最小二乘模型。其中，该最小二乘模型可以是线性的，或者也可以是非线性的，本申请对此不做具体限定。最小二乘模型是一种利用最小二乘算法，将输入的电机损耗与电机温度建立拟合关系，这种拟合关系分为线性和非线性两种。最小二乘模型建立拟合关系之后，可利用该模型计算电机需测量的电机温度值。
上述技术方案中，确定的电机损耗除了包括电机的电流基波分量产生的损耗之外，还包括其他的损耗(例如，其他信号产生的第二损耗)，这样，可以提高电机损耗的计算精度，从而提高电机温度的预测精度。
下面结合图3，以第二损耗为电机的电流基波分量产生的损耗为例，对电机的温度预 测装置进行详细说明。应理解，图3的例子仅仅是为了帮助本领域技术人员理解本申请实施例，而非要将申请实施例限制于图3的具体数值或具体场景。本领域技术人员根据所给出的例子，显然可以进行各种等价的修改或变化，这样的修改和变化也落入本申请实施例的范围内。
如图3所示，温度的预测装置300中可以包括：电机控制器310、损耗计算模块320、温度预测模块330。
可选地，该温度的预测装置300中还可以包括：电压谐波分析模块340、电流谐波分析模块350。
可选地，该温度的预测装置300中还可以包括：温度传感器360。
下面分别对温度的预测装置300中的各个模块的功能进行详细描述。
电机控制器310，用于输出多个信号，例如，图3所示的S1、S2···Sn。该多个信号可以对应于上文中电机控制器的信息。
作为示例，电机控制器310输出的控制信号可以包括但不限于：电压矢量在dq旋转坐标系下的分量u d和u q，电流矢量在dq旋转坐标系下的分量i d和i q，以及直流母线电压U dc，载波频率等。
应理解，如果将d轴定位于转子磁链，则q轴与转矩方向重合。
电压谐波分析模块340，用于根据载波频率对电机控制器310输出的控制信号(例如电压矢量在dq旋转坐标系下的分量u d和u q)进行傅里叶分解得到每个频率下的谐波电压在abc静止坐标系下的幅值之和
Figure PCTCN2020105791-appb-000001
并将
Figure PCTCN2020105791-appb-000002
转换成dq旋转坐标系下谐波电压分量的计算值
Figure PCTCN2020105791-appb-000003
和
Figure PCTCN2020105791-appb-000004
电流谐波分析模块350，用于电压谐波分析模块340输出的dq旋转坐标系下谐波电压分量的计算值
Figure PCTCN2020105791-appb-000005
和
Figure PCTCN2020105791-appb-000006
确定k次谐波频率下谐波电流的幅值
Figure PCTCN2020105791-appb-000007
损耗计算模块320，用于确定电机的损耗。
本申请实施例中，电机的损耗可以包括以下中的任意一种或多种的组合：电机的线圈损耗、电机的定子转子损耗、电机的磁钢损耗等。
作为一个示例，如果损耗计算模块320的输入为电机控制器310输出的控制信号，损耗计算模块320可以根据电机控制器310输出的控制信号确定电机的损耗。
作为另一个示例，如果损耗计算模块320的输入为电流谐波分析模块350输出的k次谐波频率下谐波电流的幅值
Figure PCTCN2020105791-appb-000008
损耗计算模块320可以根据电流谐波分析模块350输出的
Figure PCTCN2020105791-appb-000009
确定电机的损耗。
应理解，本申请实施例中上述电机的损耗既可以包括由基波所产生的损耗，又可以包括由谐波产生的损耗。
温度预测模块330，用于根据损耗计算模块320输出的电机的损耗确定该电机各个部分的温度。
本申请实施例中，电机各个部分的温度可以包括以下中的任意一种或多种的组合：电机的线圈温度、电机的定子转子温度、电机的磁钢温度等。
下面以图3所示的电机控制器300为例，结合图4中具体的例子，对本申请实施例提供的温度的预测方法的具体实现过程进行详细说明。
应理解，图4的例子仅仅是为了帮助本领域技术人员理解本申请实施例，而非要将申请实施例限制于图4的具体数值或具体场景。本领域技术人员根据所给出的例子，显然可以进行各种等价的修改或变化，这样的修改和变化也落入本申请实施例的范围内。
图4是本申请实施例提供的另一种温度的预测方法的示意性流程图。参见图4，该方法可以包括步骤410-430，下面分别对步骤410-430进行详细描述。
步骤410：损耗计算模块320确定由基波和谐波所产生的t+1时刻的电机损耗Q。
一种可能的实现方式中，损耗计算模块320可以根据如下所示的公式(1)确定由基波和谐波所产生的电机在t+1时刻的损耗Q。
Q＝Q 0×f       (1)
其中，Q表示由基波和谐波所产生的电机的总损耗；
Q 0表示由基波所产生的电机的损耗；
f表示系数。
应理解，由基波所产生的电机的损耗Q 0可以包括以下中的任意一种或多种的组合：由基波所产生的电机的线圈损耗
Figure PCTCN2020105791-appb-000010
由基波所产生的电机的定子损耗
Figure PCTCN2020105791-appb-000011
由基波所产生的电机的转子损耗
Figure PCTCN2020105791-appb-000012
等。
作为示例，公式(2)列举了一种由基波所产生的电机的定子损耗
Figure PCTCN2020105791-appb-000013
的计算公式。
Figure PCTCN2020105791-appb-000014
其中，
Figure PCTCN2020105791-appb-000015
表示由基波所产生的电机的定子损耗；
a 1、
Figure PCTCN2020105791-appb-000016
b 1、c 1表示系数；
ψ f表示转子磁链；
Figure PCTCN2020105791-appb-000017
表示基波频率下电流的幅值。
作为示例，公式(3)列举了一种由基波所产生的电机的转子损耗
Figure PCTCN2020105791-appb-000018
的计算公式。
Figure PCTCN2020105791-appb-000019
作为示例，公式(4)列举了一种由基波所产生的电机的线圈损耗Q coil-1的计算公式。
Figure PCTCN2020105791-appb-000020
其中，R s表示相电阻。
另一种可能的实现方式中，损耗计算模块320还可以分别计算由基波所产生的电机的损耗Q 1以及由谐波所产生的电机的损耗Q k，并根据如下的公式(5)确定由基波和谐波所产生的电机在t+1时刻的损耗Q。
Q＝Q 1+Q k       (5)
其中，Q k表示k次谐波所产生的电机的损耗。
应理解，由谐波所产生的电机的损耗Q k可以包括以下中的任意一种或多种的组合：由谐波所产生的电机的线圈损耗
Figure PCTCN2020105791-appb-000021
由谐波所产生的电机的定子损耗
Figure PCTCN2020105791-appb-000022
由谐波所产生的电机的转子损耗
Figure PCTCN2020105791-appb-000023
等。
作为示例，公式(6)-(8)分别示出了
Figure PCTCN2020105791-appb-000024
的一种可能的计算方式。
Figure PCTCN2020105791-appb-000025
其中，
Figure PCTCN2020105791-appb-000026
表示k次谐波频率下谐波电流的幅值；
Figure PCTCN2020105791-appb-000027
为系数；
Figure PCTCN2020105791-appb-000028
表示k次谐波所产生的电机的线圈总损耗。
Figure PCTCN2020105791-appb-000029
其中，a k、b k、c k、
Figure PCTCN2020105791-appb-000030
为系数；
Figure PCTCN2020105791-appb-000031
表示k次谐波所产生的电机的定子总损耗。
Figure PCTCN2020105791-appb-000032
其中，
Figure PCTCN2020105791-appb-000033
表示k次谐波所产生的电机的转子总损耗；
Figure PCTCN2020105791-appb-000034
为系数。
在上述公式中，k次谐波频率下谐波电流的幅值
Figure PCTCN2020105791-appb-000035
是根据如下的公式(9)确定的。
Figure PCTCN2020105791-appb-000036
其中，
Figure PCTCN2020105791-appb-000037
表示dq旋转坐标系中k次谐波电流在d方向上的分量；
Figure PCTCN2020105791-appb-000038
表示dq旋转坐标系中k次谐波电流在q方向上的分量。
具体的，下面对确定
Figure PCTCN2020105791-appb-000039
和
Figure PCTCN2020105791-appb-000040
的实现过程进行详细描述。
1、电压谐波分析模块340根据电机控制模块310输出的控制信号确定k次谐波频率下电压的幅值之和
Figure PCTCN2020105791-appb-000041
作为示例，电压谐波分析模块340可以根据如下所示的公式(10)确定
Figure PCTCN2020105791-appb-000042
Figure PCTCN2020105791-appb-000043
其中，n表示边带谐波系数；
s表示基带谐波系数；
j表示复数；
ω c表示载波的角频率；
ω m表示调制波的角频率；
λ n以及A sn可以通过公式(11)-(12)确定。
λ n＝1-e -j2nπ/3          (11)
Figure PCTCN2020105791-appb-000044
其中，U dc表示直流母线电压。
2、电压谐波分析模块340将
Figure PCTCN2020105791-appb-000045
转换成dq旋转坐标系下谐波电压分量的计算值
Figure PCTCN2020105791-appb-000046
和
Figure PCTCN2020105791-appb-000047
3、电流谐波分析模块350根据
Figure PCTCN2020105791-appb-000048
和
Figure PCTCN2020105791-appb-000049
坐标确定k次谐波频率下谐波电流的幅值
Figure PCTCN2020105791-appb-000050
作为示例，电流谐波分析模块350可以通过公式(13)确定
Figure PCTCN2020105791-appb-000051
和
Figure PCTCN2020105791-appb-000052
Figure PCTCN2020105791-appb-000053
其中，L d表示d轴电感；
ω表示电机的转速；
L q表示q轴电感。
步骤420：损耗计算模块320根据电机在t时刻的温度修正电机在t+1时刻的损耗Q。
应理解，电机在t时刻的温度可以包括以下中的任意一种或多种的组合：电机的线圈温度、电机的定子转子温度、电机的磁钢温度等。
损耗计算模块320获取电机在t时刻的温度的具体实现方式有多种，本申请实施例对此不做具体限定。一种可能的实现方式中，损耗计算模块320可以通过电机控制器310从温度预测模块330获取温度反馈信号(即通过温度预测模块330计算得到的温度)，该温度反馈信号包括电机在t时刻的温度。另一种可能的实现方式中，损耗计算模块320还可以从温度传感器360直接获取电机在t时刻的温度。
本申请实施例中，损耗计算模块320可以根据电机在t时刻的温度对步骤410中确定的电机在t+1时刻的损耗Q进行修正。具体的有关电机在t+1时刻的损耗Q请参考步骤410中的描述，此处不再赘述。
下面会对电机在t+1时刻的损耗Q进行修正的具体实现过程进行详细描述。
具体的，损耗计算模块320可以根据t时刻电机的定转子温度对t+1时刻的电机定转子损耗进行修正；或者，还可以根据t时刻电机的线圈温度对t+1时刻的电机线圈损耗进行修正。
在一种可能的实现方式中，以谐波产生的电机损耗为例，对上述修正过程的具体实现方式进行详细描述。
例如，损耗计算模块320可以根据t时刻电机的线圈温度对系数
Figure PCTCN2020105791-appb-000054
进行修正，根据修正后的系数
Figure PCTCN2020105791-appb-000055
并根据修正后的系数
Figure PCTCN2020105791-appb-000056
修正电机在t+1时刻的线圈损耗，得到修正后t+1时刻的电机线圈损耗。
又如，损耗计算模块320可以根据t时刻电机的定子温度对系数
Figure PCTCN2020105791-appb-000057
进行修正，并根据修正后的系数
Figure PCTCN2020105791-appb-000058
修正电机在t+1时刻的定子损耗，得到修正后t+1时刻的电机定子损耗。
又如，损耗计算模块320可以根据t时刻电机的转子温度对系数
Figure PCTCN2020105791-appb-000059
进行修正，并根据修正后的系数
Figure PCTCN2020105791-appb-000060
修正电机在t+1时刻的转子损耗，得到修正后t+1时刻的电机转子损耗。
应理解，可以对系数
Figure PCTCN2020105791-appb-000061
中的任意一个进行修正，或也可以任意多个的 组合进行修正，本申请对此不做具体限定。
步骤430：温度预测模块330根据t时刻的电机温度，修正后的t+1时刻电机的损耗，以及温度预测模型确定t+1时刻的电机温度。
在一种可能的实现方式中，以谐波产生的电机损耗为例，对上述确定t+1时刻的电机温度的具体实现方式进行详细描述。
作为一个示例，温度预测模块330可以根据修正后t+1时刻的电机线圈损耗，t时刻的电机线圈温度，以及温度预测模型确定t+1时刻电机的线圈温度。作为另一个示例，温度预测模块330可以根据修正后t+1时刻的电机定子损耗，t时刻的电机定子温度，以及温度预测模型确定t+1时刻电机的定子温度。作为另一个示例，温度预测模块330可以根据修正后t+1时刻的电机转子损耗，t时刻的电机转子温度，以及温度预测模型确定t+1时刻电机的转子温度。
上文结合图1至图4，详细描述了本申请实施例中的方法，下面将结合图5，详细描述本申请的装置实施例。应理解，本申请实施例中的资源分配的装置可以执行前述本申请实施例的各种方法，即以下各种产品的具体工作过程，可以参考前述方法实施例中的对应过程。
图5是本申请实施例提供的温度的预测装置800的硬件结构示意图。
图5所示的温度的预测装置800可以包括存储器801、处理器802、通信接口803以及总线804。其中，存储器801、处理器802、通信接口803通过总线804实现彼此之间的通信连接。
存储器801可以是只读存储器(read-only memory，ROM)，静态存储设备和随机存取存储器(random access memory，RAM)。存储器801可以存储程序，当存储器801中存储的程序被处理器802执行时，处理器802和通信接口803用于执行本申请实施例的温度的预测方法的各个步骤，例如，可以执行图2或图4所示的温度的预测方法的各个步骤。
处理器802可以采用通用的CPU、微处理器、ASIC、GPU或者一个或多个集成电路，用于执行相关程序，以实现本申请实施例的图3所示的温度的预测装置中的单元所需执行的功能，或者执行本申请方法实施例的温度的预测方法。
处理器802还可以是一种集成电路芯片，具有信号的处理能力。在实现过程中，本申请实施例的温度的预测方法的各个步骤可以通过处理器802中的硬件的集成逻辑电路或者软件形式的指令完成。
上述处理器802还可以是通用处理器、DSP、ASIC、FPGA或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。可以实现或者执行本申请实施例中的公开的各方法、步骤及逻辑框图。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。结合本申请实施例所公开的方法的步骤可以直接体现为硬件译码处理器执行完成，或者用译码处理器中的硬件及软件模块组合执行完成。软件模块可以位于随机存储器，闪存、只读存储器，可编程只读存储器或者电可擦写可编程存储器、寄存器等本领域成熟的存储介质中。该存储介质位于存储器801，处理器802读取存储器801中的信息，结合其硬件完成本申请实施例的温度的预测装置中包括的单元所需执行的功能，或者执行本申请方法实施例的温度的预测方法。
通信接口803使用例如但不限于收发器一类的收发装置，来实现温度的预测装置800与其他设备或通信网络之间的通信。
总线804可包括在温度的预测装置800各个部件(例如，存储器801、处理器802、通信接口803)之间传送信息的通路。
应注意，尽管上述温度的预测装置800仅仅示出了存储器、处理器、通信接口，但是在具体实现过程中，本领域的技术人员应当理解，温度的预测装置800还可以包括实现正常运行所必须的其他器件。同时，根据具体需要，本领域的技术人员应当理解，上述温度的预测装置800还可包括实现其他附加功能的硬件器件。此外，本领域的技术人员应当理解，上述资源分配的装置800也可仅仅包括实现本申请实施例所必须的器件，而不必包括图5中所示的全部器件。
本申请实施例还提供一种芯片，该芯片包括收发单元和处理单元。其中，收发单元可以是输入输出电路、通信接口；处理单元为该芯片上集成的处理器或者微处理器或者集成电路；该芯片可以执行上述方法实施例中的方法。
在具体实现过程中，该芯片可以以中央处理器(central processing unit，CPU)、微控制器(micro controller unit，MCU)、微处理器(micro processing unit，MPU)、数字信号处理器(digital signal processing，DSP)、片上系统(system on chip，SoC)、专用集成电路(application-specific integrated circuit，ASIC)、现场可编程门阵列(field programmable gate array，FPGA)或可编辑逻辑器件(programmable logic device，PLD)的形式实现。
本申请实施例还提供一种计算机可读存储介质，其上存储有指令，该指令被执行时执行上述方法实施例中的方法。
该计算机可读介质存储有程序代码，当该计算机程序代码在计算机上运行时，使得计算机执行上述各方面中的方法。这些计算机可读存储包括但不限于如下的一个或者多个：只读存储器(read-only memory，ROM)、可编程ROM(programmable ROM，PROM)、可擦除的PROM(erasable PROM，EPROM)、Flash存储器、电EPROM(electrically EPROM，EEPROM)以及硬盘驱动器(hard drive)。
本申请实施例还提供一种包含指令的计算机程序产品，该指令被执行时执行上述方法实施例中的方法。
应理解，本申请实施例中的处理器可以为中央处理单元(central processing unit，CPU)，该处理器还可以是其他通用处理器、数字信号处理器(digital signal processor，DSP)、专用集成电路(application specific integrated circuit，ASIC)、现成可编程门阵列(field programmable gate array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
还应理解，本申请实施例中的存储器可以是易失性存储器或非易失性存储器，或可包括易失性和非易失性存储器两者。其中，非易失性存储器可以是只读存储器(read-only memory，ROM)、可编程只读存储器(programmable ROM，PROM)、可擦除可编程只读存储器(erasable PROM，EPROM)、电可擦除可编程只读存储器(electrically EPROM，EEPROM)或闪存。易失性存储器可以是随机存取存储器(random access memory，RAM)，其用作外部高速缓存。通过示例性但不是限制性说明，许多形式的随机存取存储器(random access memory，RAM)可用，例如静态随机存取存储器(static RAM，SRAM)、动态随机存取存储器(DRAM)、同步动态随机存取存储器(synchronous DRAM，SDRAM)、 双倍数据速率同步动态随机存取存储器(double data rate SDRAM，DDR SDRAM)、增强型同步动态随机存取存储器(enhanced SDRAM，ESDRAM)、同步连接动态随机存取存储器(synchlink DRAM，SLDRAM)和直接内存总线随机存取存储器(direct rambus RAM，DR RAM)。
上述实施例，可以全部或部分地通过软件、硬件、固件或其他任意组合来实现。当使用软件实现时，上述实施例可以全部或部分地以计算机程序产品的形式实现。所述计算机程序产品包括一个或多个计算机指令或计算机程序。在计算机上加载或执行所述计算机指令或计算机程序时，全部或部分地产生按照本申请实施例所述的流程或功能。所述计算机可以为通用计算机、专用计算机、计算机网络、或者其他可编程装置。所述计算机指令可以存储在计算机可读存储介质中，或者从一个计算机可读存储介质向另一个计算机可读存储介质传输，例如，所述计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。所述计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包含一个或多个可用介质集合的服务器、数据中心等数据存储设备。所述可用介质可以是磁性介质(例如，软盘、硬盘、磁带)、光介质(例如，DVD)、或者半导体介质。半导体介质可以是固态硬盘。
应理解，本文中术语“和/或”，仅仅是一种描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况，其中A,B可以是单数或者复数。另外，本文中字符“/”，一般表示前后关联对象是一种“或”的关系，但也可能表示的是一种“和/或”的关系，具体可参考前后文进行理解。
应理解，在本申请的各种实施例中，上述各过程的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本申请实施例的实施过程构成任何限定。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统、装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在本申请所提供的几个实施例中，应该理解到，所揭露的系统、装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络 单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。
所述功能如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(read-only memory，ROM)、随机存取存储器(random access memory，RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以所述权利要求的保护范围为准。

Claims (23)

  1. 一种温度的预测方法，其特征在于，包括：
    根据电机控制器的信息确定电机的损耗，所述电机的损耗包括第一损耗和第二损耗，所述第一损耗为所述电机的电流基波分量产生的损耗；
    根据所述电机的损耗以及温度预测模型确定所述电机的温度。
  2. 根据权利要求1所述的方法，其特征在于，所述第二损耗为所述电机的电流谐波分量产生的损耗。
  3. 根据权利要求1或2所述的方法，其特征在于，所述根据电机控制器的信息确定电机的损耗，包括：
    根据所述电流的基波分量确定所述第一损耗；
    根据所述第一损耗和第一系数得到所述电机的损耗。
  4. 根据权利要求2所述的方法，其特征在于，所述根据电机控制器的信息确定电机的损耗，包括：
    根据所述电流的基波分量确定所述第一损耗；
    根据所述电流的谐波分量确定所述第二损耗；
    根据所述第一损耗和所述第二损耗得到所述电机的损耗。
  5. 根据权利要求4所述的方法，其特征在于，电机控制器的信息为dq旋转坐标系下的电压矢量，
    在所述根据所述电流的谐波分量确定所述第一损耗之前，所述方法还包括：
    从所述电机控制器获取所述dq旋转坐标系下的电压矢量；
    根据所述dq旋转坐标系下的电压矢量得到电压的谐波分量；
    根据电压的谐波分量得到所述电流的谐波分量。
  6. 根据权利要求1至5中任一项所述的方法，其特征在于，所述电机的温度为所述电机在t时刻的温度，
    所述方法还包括：
    根据所述电机在t时刻的温度对所述电机的损耗修正，得到修正后的电机损耗；
    根据所述修正后的电机损耗，所述电机在t时刻的温度以及所述温度预测模型确定所述电机在t+1时刻的温度，所述t时刻为所述t+1时刻的前一个时刻。
  7. 根据权利要求1至6中任一项所述的方法，其特征在于，所述电机的损耗包括以下中的一种或多种：所述电机的线圈损耗、所述电机的定转子损耗、所述电机的磁钢损耗。
  8. 根据权利要求1至7中任一项所述的方法，其特征在于，所述电机在t时刻的温度包括以下中的一种或多种：所述电机在t时刻的线圈温度、所述电机在t时刻的定转子温度、所述电机在t时刻的磁钢温度。
  9. 根据权利要求1至8中任一项所述的方法，其特征在于，所述温度预测模型为以下中的任意一种：等效热阻网络模型、神经网络模型、线性最小二乘模型、非线性最小二乘模型。
  10. 一种温度的预测装置，其特征在于，包括：
    损耗计算模块，用于根据电机控制器的信息确定电机的损耗，所述电机的损耗包括第一损耗和第二损耗，所述第一损耗为所述电机的电流基波分量产生的损耗；
    温度预测模块，用于根据所述电机的损耗以及温度预测模型确定所述电机的温度。
  11. 根据权利要求10所述的预测装置，其特征在于，所述第二损耗为所述电机的电流谐波分量产生的损耗。
  12. 根据权利要求10或11所述的预测装置，其特征在于，所述损耗计算模块具体用于：
    根据所述电流的基波分量确定所述第一损耗；
    根据所述第一损耗和第一系数得到所述电机的损耗。
  13. 根据权利要求11所述的预测装置，其特征在于，所述损耗计算模块具体用于：
    根据所述电流的基波分量确定所述第一损耗；
    根据所述电流的谐波分量确定所述第二损耗；
    根据所述第一损耗和所述第二损耗得到所述电机的损耗。
  14. 根据权利要求13所述的预测装置，其特征在于，电机控制器的信息为dq旋转坐标系下的电压矢量，
    所述预测装置还包括：
    获取模块，用于从所述电机控制器获取所述dq旋转坐标系下的电压矢量；
    电压谐波分析模块，用于根据所述dq旋转坐标系下的电压矢量得到电压的谐波分量；
    电流谐波分析模块，根据电压的谐波分量得到所述电流的谐波分量。
  15. 根据权利要求10至14中任一项所述的预测装置，其特征在于，所述电机的温度为所述电机在t时刻的温度，
    所述损耗计算模块，还用于根据所述电机在t时刻的温度对所述电机的损耗修正，得到修正后的电机损耗；
    所述温度预测模块，还用于根据所述修正后的电机损耗，所述电机在t时刻的温度以及所述温度预测模型确定所述电机在t+1时刻的温度，所述t时刻为所述t+1时刻的前一个时刻。
  16. 根据权利要求10至15中任一项所述的预测装置，其特征在于，所述电机的损耗包括以下中的一种或多种：所述电机的线圈损耗、所述电机的定转子损耗、所述电机的磁钢损耗。
  17. 根据权利要求10至16中任一项所述的预测装置，其特征在于，所述电机在t时刻的温度包括以下中的一种或多种：所述电机在t时刻的线圈温度、所述电机在t时刻的定转子温度、所述电机在t时刻的磁钢温度。
  18. 根据权利要求10至17中任一项所述的预测装置，其特征在于，所述温度预测模型为以下中的任意一种：等效热阻网络模型、神经网络模型、线性最小二乘模型、非线性最小二乘模型。
  19. 一种温度的预测装置，其特征在于，包括处理器和存储器，所述存储器中用于存储计算机执行指令，当所述预测装置运行时，所述处理器运行所述存储器中的计算机执行指令以执行如权利要求1至9中任一项所述的温度的预测方法。
  20. 一种电机控制器，其特征在于，包括权利要求10至18中任一项所述的温度的预 测装置或权利要求19所述的温度的预测装置。
  21. 一种动力总成，其特征在于，包括：电机以及权利要求20所述的电机控制器。
  22. 一种汽车，其特征在于，包括权利要求21所述的动力总成。
  23. 一种计算机可读存储介质，其特征在于，包括计算机程序，当所述计算机程序在计算机上运行时，使得所述计算机执行如权利要求1至9中任一项所述的温度的预测方法。
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