CN111605406B - Control method and control device for obtaining temperature of motor rotor and vehicle - Google Patents

Control method and control device for obtaining temperature of motor rotor and vehicle Download PDF

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CN111605406B
CN111605406B CN201910141299.5A CN201910141299A CN111605406B CN 111605406 B CN111605406 B CN 111605406B CN 201910141299 A CN201910141299 A CN 201910141299A CN 111605406 B CN111605406 B CN 111605406B
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rotor
time
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CN111605406A (en
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李玮
刘超
梁海强
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Beijing Electric Vehicle Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/42—Drive Train control parameters related to electric machines
    • B60L2240/421—Speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
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Abstract

The embodiment of the invention provides a control method, a control device and a vehicle for acquiring the temperature of a motor rotor, wherein the control method applied to a motor controller comprises the following steps: when the vehicle meets a preset estimation condition, acquiring and inputting a first stator temperature, a first rotor temperature, a first motor rotating speed and a first motor output torque of each control period of the motor in a first preset time before a current control period to a radial basis function neural network obtained through pre-training to obtain a first temperature deviation; and acquiring and obtaining the second rotor temperature of the current control period according to the second stator temperature of the current control period. Compared with the prior art, the method for obtaining the rotor temperature can estimate the rotor temperature in real time and ensure the accuracy of the rotor temperature when a vehicle runs.

Description

Control method and control device for obtaining temperature of motor rotor and vehicle
Technical Field
The invention relates to the technical field of motor control, in particular to a control method and a control device for acquiring the temperature of a motor rotor and a vehicle.
Background
The driving motor is used as the core of a vehicle driving system, whether the driving motor can be accurately controlled has important influence on the output performance of the driving system, wherein one main reason influencing the control precision of the motor is that the temperature of a motor rotor is difficult to accurately obtain, and the current method for detecting and estimating the temperature of the permanent magnet synchronous motor rotor in real time mainly comprises a numerical calculation method for calculating by establishing a model through finite element analysis and a direct detection method for embedding a temperature sensor in the rotor; the numerical algorithm mostly calculates the temperature of the motor rotor by finite element analysis, needs to establish a finite element temperature field model of the motor body, needs to calculate a large amount of parameters, is not suitable for real-time detection of the temperature of the motor rotor of a pure electric vehicle on line, and has great dependence on modeling parameters, so that the precision of the rotor temperature obtained by the numerical calculation method is low; the direct detection method needs to embed the temperature sensor into the motor rotor, so that the original structure of the motor needs to be damaged, the stability of the motor is affected, the hardware cost is increased, and the method is not suitable for mass production of pure electric vehicles, so that the method suitable for real-time detection or estimation of the rotor temperature of the permanent magnet synchronous motor of the pure electric vehicle is urgently needed.
Disclosure of Invention
The technical purpose to be achieved by the embodiment of the invention is to provide a control method and a control device for obtaining the temperature of a motor rotor and a vehicle, so as to achieve the purpose of accurately detecting or estimating the temperature of the motor rotor on the vehicle in real time.
In order to solve the above technical problem, an embodiment of the present invention provides a control method for obtaining a temperature of a rotor of a motor, which is applied to a motor controller, and includes:
when the vehicle meets a preset estimation condition, acquiring a first stator temperature, a first rotor temperature, a first motor rotating speed and a first motor output torque of the motor in each control period within a first preset time before the current control period, wherein the first rotor temperature is equal to the first stator temperature within a second preset time after the vehicle is powered on;
inputting a first stator temperature, a first rotor temperature, a first motor rotating speed and a first motor output torque of each control period within a first preset time to a Radial Basis Function (RBF) neural network obtained by pre-training to obtain a first temperature deviation;
acquiring a second stator temperature of the current control period;
and calculating to obtain the second rotor temperature of the current control period according to the first temperature deviation and the second stator temperature.
Preferably, the control method for obtaining the rotor temperature of the electric machine as described above further includes, before the step of obtaining the first stator temperature, the first rotor temperature, the first electric machine rotation speed, and the first electric machine output torque of the electric machine in each control cycle within a first preset time before the current control cycle when the vehicle satisfies the preset estimation condition:
acquiring state information of a vehicle, wherein the state information comprises: the status and duration of the drive system and cooling system prior to vehicle power-up;
and determining whether the motor meets a preset estimation condition according to the state information, wherein before the vehicle is powered on, the states of the driving system and the cooling system are both in a closed state, and when the duration time is longer than or equal to a third preset time, the motor is determined to meet the preset estimation condition.
Specifically, the control method for obtaining the rotor temperature of the motor as described above inputs the first stator temperature, the first rotor temperature, the first motor speed, and the first motor output torque of each control cycle in a first preset time to a radial basis function RBF neural network obtained by training in advance, and the step of obtaining the first temperature deviation includes:
obtaining a first stator temperature time area parameter according to a first stator temperature of each control period in a first preset time and a first preset algorithm;
obtaining a first motor rotating speed time area parameter according to a first motor rotating speed of each control period in a first preset time and a second preset algorithm;
obtaining a time area parameter of the first motor output torque according to the first motor output torque of each control period in a first preset time and a third preset algorithm;
obtaining a time area parameter of the temperature of the first motor rotor according to the first rotor temperature of each control period in a first preset time and a fourth preset algorithm;
and inputting the first stator temperature time area parameter, the first motor rotating speed time area parameter, the first motor output torque time area parameter and the first motor rotor temperature time area parameter into a RBF neural network obtained by pre-training to obtain a first temperature deviation.
Specifically, in the control method for acquiring the temperature of the motor rotor as described above, the first preset algorithm is:
Figure BDA0001978606060000031
wherein, TSIs a first stator temperature time area parameter;
Tstator(k) the temperature of a first motor stator in a kth control period is k, the value of k is 1 to n, and n is the number of control periods in a first preset time;
tsthe time of a single control cycle.
Specifically, in the control method for acquiring the temperature of the motor rotor as described above, the second preset algorithm is:
Figure BDA0001978606060000032
wherein S ismThe time area parameter is the first motor rotating speed;
(k) the first motor rotating speed of the kth control period, wherein the value of k is 1 to n, and n is the number of the control periods in the first preset time;
tsthe time of a single control cycle.
Specifically, in the control method for acquiring the temperature of the motor rotor as described above, the third preset algorithm is:
Figure BDA0001978606060000033
wherein, TcmdOutputting a torque time area parameter for the first motor;
Tq(k) outputting torque of a first motor in a kth control period, wherein the value of k is 1-n, and n is the number of control periods in a first preset time;
tsthe time of a single control cycle.
Specifically, in the control method for acquiring the temperature of the motor rotor as described above, the fourth preset algorithm is:
Figure BDA0001978606060000034
wherein, TRThe temperature, time and area parameters of the first motor rotor are obtained;
Trotator(k) the temperature of a first motor rotor in a kth control period is k, the value of k is 1 to n, and n is the number of control periods in a first preset time;
tsthe time of a single control cycle.
Preferably, the control method for obtaining the rotor temperature of the electric motor as described above further includes, before the step of obtaining the first stator temperature, the first rotor temperature, the first motor speed, and the first motor output torque when the vehicle satisfies the preset estimation condition:
acquiring training data of the RBF neural network;
and training the RBF neural network through the training data to obtain the trained RBF neural network.
Specifically, in the control method for acquiring the temperature of the motor rotor, the step of acquiring the training data of the RBF neural network includes:
when the test motor meets preset test conditions, acquiring a third stator temperature, a third rotor temperature, a second motor rotating speed and a second motor output torque of each control period within a first preset time before a preset control period, wherein after the test motor is powered off and stands still for a fourth preset time, when a difference value between a fourth rotor temperature acquired by a temperature sensor arranged on a rotor of the test motor and a fourth stator temperature of a stator is smaller than a preset value, the test motor is determined to meet the preset test conditions;
respectively obtaining a second stator temperature time area parameter, a second motor rotation speed time area parameter, a second motor output torque time area parameter and a second motor rotor temperature time area parameter according to a third stator temperature, a third rotor temperature, a second motor rotation speed and a second motor output torque of each control period within a first preset time;
acquiring a fifth stator temperature and a fifth rotor temperature of a preset control period, and acquiring a second temperature deviation;
and determining a second stator temperature time area parameter, a second motor rotating speed time area parameter, a second motor output torque time area parameter, a second motor rotor temperature time area parameter and a second temperature deviation as a group of training data.
Another preferred embodiment of the present invention also provides a control apparatus for acquiring a temperature of a rotor of an electric motor, including:
the first obtaining module is used for obtaining a first stator temperature, a first rotor temperature, a first motor rotating speed and a first motor output torque of the motor in each control period within a first preset time before a current control period when the vehicle meets a preset estimation condition, wherein the first rotor temperature is equal to the first stator temperature within a second preset time after the vehicle is powered on;
the first processing module is used for inputting the first stator temperature, the first rotor temperature, the first motor rotating speed and the first motor output torque of each control period in a first preset time into a Radial Basis Function (RBF) neural network obtained through pre-training to obtain a first temperature deviation;
the second acquisition module is used for acquiring the second stator temperature of the current control period;
and the second processing module is used for calculating the second rotor temperature of the current control period according to the first temperature deviation and the second stator temperature.
Preferably, the control device for acquiring the temperature of the rotor of the motor as described above further includes:
the third acquisition module is used for acquiring the state information of the vehicle, and the state information comprises: the status and duration of the drive system and cooling system prior to vehicle power-up;
and the judging module is used for determining whether the motor meets the preset estimation condition or not according to the state information, wherein before the vehicle is powered on, the states of the driving system and the cooling system are both in a closed state, and when the duration time is longer than or equal to a third preset time, the motor is determined to meet the preset estimation condition.
Specifically, as the control device for acquiring the temperature of the rotor of the motor, the first processing module includes:
the first processing unit is used for obtaining a first stator temperature time area parameter according to a first stator temperature of each control period in a first preset time and a first preset algorithm;
the second processing unit is used for obtaining a time area parameter of the first motor rotating speed according to the first motor rotating speed of each control period in the first preset time and a second preset algorithm;
the third processing unit is used for obtaining a time area parameter of the first motor output torque according to the first motor output torque of each control period in the first preset time and a third preset algorithm;
the fourth processing unit is used for obtaining a time area parameter of the temperature of the first motor rotor according to the first rotor temperature of each control period in the first preset time and a fourth preset algorithm;
and the fifth processing unit is used for inputting the first stator temperature time area parameter, the first motor rotating speed time area parameter, the first motor output torque time area parameter and the first motor rotor temperature time area parameter to a RBF neural network obtained by pre-training to obtain a first temperature deviation.
Further, the control device for obtaining the temperature of the rotor of the motor as described above further includes:
the fourth acquisition module is used for acquiring training data of the RBF neural network;
and the third processing module is used for training the RBF neural network through the training data to obtain the trained RBF neural network.
Specifically, as described above for the motor controller, the fourth obtaining module includes:
the first obtaining unit is used for obtaining a third stator temperature, a third rotor temperature, a second motor rotating speed and a second motor output torque of each control period within a first preset time before a preset control period when the test motor meets a preset test condition, wherein after the test motor is powered off and stands still for a fourth preset time, a difference value between a fourth rotor temperature obtained by a temperature sensor arranged on a rotor of the test motor and a fourth stator temperature of a stator is smaller than a preset value, and the test motor is determined to meet the preset test condition;
the sixth processing unit is used for obtaining a second stator temperature time area parameter, a second motor rotation speed time area parameter, a second motor output torque time area parameter and a second motor rotor temperature time area parameter according to a third stator temperature, a third rotor temperature, a second motor rotation speed and a second motor output torque of each control period in a first preset time;
the seventh processing unit acquires a fifth stator temperature and a fifth rotor temperature of a preset control period and acquires a second temperature deviation;
and the eighth processing unit is used for determining the second stator temperature time area parameter, the second motor rotating speed time area parameter, the second motor output torque time area parameter, the second motor rotor temperature time area parameter and the second temperature deviation as a group of training data.
Still another preferred embodiment of the present invention also provides a vehicle including: a motor controller having the control device as described above.
Compared with the prior art, the control method, the control device and the vehicle for obtaining the temperature of the motor rotor provided by the embodiment of the invention at least have the following beneficial effects:
in the embodiment of the invention, on the premise that the vehicle meets the preset estimation condition, the first stator temperature, the first rotor temperature, the first motor rotating speed and the first motor output torque of each control cycle in the first preset time before the current control cycle are acquired and input into a radial basis function RBF neural network obtained by training in advance to acquire the first temperature deviation, and then the second electronic temperature of the current control cycle and the first temperature deviation are acquired and summed to acquire the second rotor temperature of the current control cycle, so that the influence of a sensor embedded in a rotor of a vehicle motor on the original structure and the stability of the motor is avoided, the safety of the motor and the vehicle is ensured on the basis of accurately acquiring the second rotor temperature of the current control cycle, and meanwhile, when the second rotor temperature of the current control cycle is acquired, the basic characteristic of nonlinearity of the RBF neural network and the natural advantage of solving the nonlinearity problem are utilized The temperature deviation between the rotor and the stator is obtained by relying on the close relation between the temperature of the stator and the temperature of the rotor, and the temperature of the second rotor is further obtained according to the temperature of the second stator in the current control period, so that the defects that the result precision is low and the method is not suitable for real-time estimation due to the fact that the existing numerical calculation method is influenced by modeling parameters and large calculation amount are overcome, and the embodiment of the invention can estimate the temperature of the rotor in real time and ensure the precision of the temperature of the rotor when a vehicle runs.
Drawings
FIG. 1 is a schematic flow chart of a control method for obtaining a temperature of a rotor of an electric machine according to the present invention;
FIG. 2 is a second flowchart of the control method for obtaining the temperature of the rotor of the motor according to the present invention;
FIG. 3 is a third schematic flow chart of the control method for obtaining the temperature of the rotor of the motor according to the present invention;
FIG. 4 is a fourth flowchart illustrating a control method for obtaining a temperature of a rotor of a motor according to the present invention;
FIG. 5 is a fifth flowchart illustrating a control method for obtaining a rotor temperature of a motor according to the present invention;
fig. 6 is a schematic structural diagram of a control device for acquiring the temperature of a motor rotor according to the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments. In the following description, specific details such as specific configurations and components are provided only to help the full understanding of the embodiments of the present invention. Thus, it will be apparent to those skilled in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the invention. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
In various embodiments of the present invention, it should be understood that the sequence numbers of the following processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
It should be understood that the term "and/or" herein is merely one type of association relationship that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
In the embodiments provided herein, it should be understood that "B corresponding to a" means that B is associated with a from which B can be determined. It should also be understood that determining B from a does not mean determining B from a alone, but may be determined from a and/or other information.
Referring to fig. 1, a preferred embodiment of the present invention provides a control method for obtaining a temperature of a rotor of an electric motor, applied to a motor controller, including:
step S101, when a vehicle meets a preset estimation condition, acquiring a first stator temperature, a first rotor temperature, a first motor rotating speed and a first motor output torque of a motor in each control period within a first preset time before a current control period, wherein the first rotor temperature is equal to the first stator temperature within a second preset time after the vehicle is powered on;
step S102, inputting a first stator temperature, a first rotor temperature, a first motor rotating speed and a first motor output torque of each control period in a first preset time to a Radial Basis Function (RBF) neural network obtained through pre-training to obtain a first temperature deviation;
step S103, acquiring a second stator temperature of the current control period;
and step S104, calculating to obtain a second rotor temperature of the current control period according to the first temperature deviation and the second stator temperature.
In the embodiment of the invention, on the premise that the vehicle meets the preset estimation condition, the first stator temperature, the first rotor temperature, the first motor rotating speed and the first motor output torque of each control cycle in the first preset time before the current control cycle are acquired and input into a radial basis function RBF neural network obtained by training in advance to acquire the first temperature deviation, and then the second electronic temperature of the current control cycle and the first temperature deviation are acquired and summed to acquire the second rotor temperature of the current control cycle, so that the influence of a sensor embedded in a rotor of a vehicle motor on the original structure and the stability of the motor is avoided, the safety of the motor and the vehicle is ensured on the basis of accurately acquiring the second rotor temperature of the current control cycle, and meanwhile, when the second rotor temperature of the current control cycle is acquired, the basic characteristic of nonlinearity of the RBF neural network and the natural advantage of solving the nonlinearity problem are utilized The temperature deviation between the rotor and the stator is obtained by relying on the close relation between the temperature of the stator and the temperature of the rotor, and the temperature of the second rotor is further obtained according to the temperature of the second stator in the current control period, so that the defects that the result precision is low and the method is not suitable for real-time estimation due to the fact that the existing numerical calculation method is influenced by modeling parameters and large calculation amount are overcome, and the embodiment of the invention can estimate the temperature of the rotor in real time and ensure the precision of the temperature of the rotor when a vehicle runs.
Optionally, the rotor temperature of each control period, the stator temperature, the motor speed, and the motor output torque of the same control period are stored in at least one storage device after being obtained, so as to facilitate subsequent direct retrieval, where the stator temperature, the motor speed, and the motor output torque are detected by preset sensors, the rotor temperature is the same as the stator temperature at a second preset time after the vehicle is powered on, and there is no inevitable connection between the second preset time and the first preset time, and those skilled in the art can set the temperatures according to actual requirements.
Referring to fig. 2, preferably, the control method for obtaining the rotor temperature of the electric machine as described above further includes, before the step of obtaining the first stator temperature, the first rotor temperature, the first motor speed and the first motor output torque of the electric machine in each control cycle within a first preset time before the current control cycle when the vehicle satisfies the preset estimation condition:
step S201, acquiring the state information of the vehicle, wherein the state information comprises: the status and duration of the drive system and cooling system prior to vehicle power-up;
and step S202, determining whether the motor meets a preset estimation condition or not according to the state information, wherein before the vehicle is powered on, the states of the driving system and the cooling system are both in a closed state, and when the duration time is longer than or equal to a third preset time, the motor is determined to meet the preset estimation condition.
In the embodiment of the present invention, before determining that the vehicle meets the preset estimation condition and performing the steps in the method, it is required to obtain state information of the vehicle, and determine whether the motor meets the preset estimation condition according to the state information, wherein when the driving system and the cooling system are both in the off state and the duration is greater than or equal to a third preset time before determining that the power-on period of the vehicle back is determined according to the state information of the vehicle, and the temperature state of the motor is stable after the vehicle is powered on, that is, the temperatures of the stator and the rotor are consistent, it is determined that the motor meets the preset estimation condition, which is beneficial to avoiding the influence on the final estimation result caused by the accumulated deviation due to the inconsistency of the temperatures of the stator and the rotor, and further beneficial to ensuring the accuracy of the obtained second rotor temperature of the current control period.
Referring to fig. 3, in the control method for obtaining a rotor temperature of a motor as described above, the step of inputting the first stator temperature, the first rotor temperature, the first motor speed, and the first motor output torque of each control cycle in a first preset time to a radial basis function RBF neural network obtained by training in advance to obtain a first temperature deviation includes:
step S301, obtaining a first stator temperature time area parameter according to a first stator temperature of each control period in a first preset time and a first preset algorithm;
step S302, obtaining a first motor rotating speed time area parameter according to a first motor rotating speed of each control period in a first preset time and a second preset algorithm;
step S303, obtaining a time area parameter of the first motor output torque according to the first motor output torque of each control period in a first preset time and a third preset algorithm;
step S304, obtaining a time area parameter of the temperature of the first motor rotor according to the first rotor temperature of each control period in a first preset time and a fourth preset algorithm;
step S305, inputting the first stator temperature time area parameter, the first motor rotating speed time area parameter, the first motor output torque time area parameter and the first motor rotor temperature time area parameter to a RBF neural network obtained through pre-training to obtain a first temperature deviation.
In the embodiment of the invention, a discrete expression of the motor stator temperature and the area enclosed by a time axis in a first preset time is represented by using the first stator temperature of each control period in the first preset time and a first stator temperature time area parameter obtained by a first preset algorithm, and the discrete expression is used for reflecting the accumulated change condition of the motor stator temperature in the first preset time; reflecting the accumulated change condition of the motor rotating speed within the first preset time by utilizing the first motor rotating speed of each control period within the first preset time and the first motor rotating speed time area parameter obtained by a second preset algorithm; reflecting the accumulated change condition of the output torque of the motor in the first preset time by utilizing the output torque of the first motor in each control period in the first preset time and the time area parameter of the output torque of the first motor obtained by a third preset algorithm; reflecting the accumulated change condition of the rotor temperature in the first preset time by using the first rotor temperature in each control period in the preset time and the time area parameter of the rotor temperature of the first motor obtained by using a fourth preset algorithm; the four parameters are used as input, a first temperature deviation is directly obtained by utilizing a pre-trained RBF neural network, in the process, the first stator temperature, the first rotor temperature, the first motor rotating speed and the first motor output torque of each control period within a first preset time are respectively processed to obtain an expression of a corresponding parameter, the first temperature deviation is conveniently and subsequently input to the RBF neural network to obtain the first temperature deviation, meanwhile, the calculated amount is favorably reduced, the first temperature deviation and the subsequent real-time property of the obtained second rotor temperature are ensured, and therefore the control method is suitable for the operation process of mass production of vehicles, and the safety and the energy saving performance of the vehicles are improved by improving the accuracy of the control of the vehicles to the motors. Optionally, the step of obtaining the first rotor temperature time area parameter, the first stator temperature time area parameter, the first motor rotation speed time area parameter, and the first motor output torque time area parameter may be performed sequentially or simultaneously according to an actual situation.
Specifically, an RBF neural network provided by an embodiment of the present invention includes: the device comprises an input layer, a hidden layer and an output layer, wherein the input quantity is the first stator temperature time area parameter, the first rotor temperature time area parameter, the first motor rotating speed time area parameter and the first motor output torque time area parameter, the output quantity is a first temperature deviation between the rotor temperature and the stator temperature, and the specific expression is as follows:
Figure BDA0001978606060000111
where x is the input vector, x ═ Ts Sm Tcmd TR]T，TsIs the first stator temperature time area parameter, SmIs the first motor speed time area parameter, TcmdOutputting a torque time area parameter, T, for the first motorRThe temperature, time and area parameters of the first motor rotor are obtained;
Ciis a central vector;
||x-ci| | is the distance from the input vector to the central vector;
l is the number of hidden layer neurons preset in the RBF neural network;
wiis a weight;
Figure BDA0001978606060000114
is a radial basis function, preferably a gaussian radial basis function;
wherein the weight wiParameters in radial basis function and center vector ciThe RBF neural network is obtained by training according to the training data of the RBF neural network.
Specifically, in the control method for acquiring the temperature of the motor rotor as described above, the first preset algorithm is:
Figure BDA0001978606060000112
wherein, TSIs a first stator temperature time area parameter;
Tstator(k) the temperature of a first motor stator in a kth control period is k, the value of k is 1 to n, and n is the number of control periods in a first preset time;
tsthe time of a single control cycle.
Specifically, in the control method for acquiring the temperature of the motor rotor as described above, the second preset algorithm is:
Figure BDA0001978606060000113
wherein S ismThe time area parameter is the first motor rotating speed;
(k) the first motor rotating speed of the kth control period, wherein the value of k is 1 to n, and n is the number of the control periods in the first preset time;
tsis controlled individuallyThe time of the cycle.
The first motor rotating speed S (k) in the kth control period in the second preset algorithm is calculated after the absolute value is obtained, because the influence of the motor on the temperature of the motor rotor is the same no matter whether the motor rotates forwards or backwards, the calculation resources occupied by judging and calculating the forward rotation and the backward rotation of the motor respectively are reduced, and the calculation efficiency is improved on the basis of ensuring the accuracy of the result.
Specifically, in the control method for acquiring the temperature of the motor rotor as described above, the third preset algorithm is:
Figure BDA0001978606060000121
wherein, TcmdOutputting a torque time area parameter for the first motor;
Tq(k) outputting torque of a first motor in a kth control period, wherein the value of k is 1-n, and n is the number of control periods in a first preset time;
tsthe time of a single control cycle.
First motor output torque T of kth control cycle in second preset algorithmq(k) The absolute value is calculated, because the influence of the output torque of the motor on the temperature of the motor rotor is the same whether the output torque of the motor is positive or negative, the calculation resources occupied by judging the output torque of the motor and calculating the output torque of the motor respectively are reduced, and the calculation efficiency is improved on the basis of ensuring the accuracy of the result.
Specifically, in the control method for acquiring the temperature of the motor rotor as described above, the fourth preset algorithm is:
Figure BDA0001978606060000122
wherein, TRThe temperature, time and area parameters of the first motor rotor are obtained;
Trotator(k) for the kth control periodThe temperature of a first motor rotor, wherein the value of k is 1 to n, and n is the number of control cycles in a first preset time;
tsthe time of a single control cycle.
The specific expressions of the first preset algorithm to the fourth preset algorithm are a preprocessing method provided by the invention for a subsequent RBF neural network, and a person skilled in the art obtains the same technical effects of reducing the calculated amount and ensuring the real-time performance and the accuracy of the calculated result by setting different preprocessing methods and/or different design modes of the RBF neural network according to actual conditions, and the specific expressions belong to the protection scope of the invention.
Referring to fig. 4, preferably, the control method for obtaining the rotor temperature of the electric machine as described above further includes, before the step of obtaining the first stator temperature, the first rotor temperature, the first motor speed and the first motor output torque when the vehicle satisfies the preset estimation condition:
step S401, acquiring training data of the RBF neural network;
and S402, training the RBF neural network through the training data to obtain the trained RBF neural network.
Referring to fig. 5, in particular, in the control method for acquiring the temperature of the rotor of the motor as described above, the step of acquiring the training data of the RBF neural network includes:
step S501, when a test motor meets a preset test condition, acquiring a third stator temperature, a third rotor temperature, a second motor rotating speed and a second motor output torque of each control period within a first preset time before a preset control period, wherein after the test motor is powered off and stands still for a fourth preset time, when a difference value between a fourth rotor temperature acquired by a temperature sensor arranged on a rotor of the test motor and a fourth stator temperature of the stator is smaller than a preset value, the test motor is determined to meet the preset test condition;
step S502, obtaining a second stator temperature time area parameter, a second motor rotation speed time area parameter, a second motor output torque time area parameter and a second motor rotor temperature time area parameter according to a third stator temperature, a third rotor temperature, a second motor rotation speed and a second motor output torque of each control period in a first preset time;
step S503, acquiring a fifth stator temperature and a fifth rotor temperature of a preset control period, and acquiring a second temperature deviation;
step S504, determining a second stator temperature time area parameter, a second motor rotation speed time area parameter, a second motor output torque time area parameter, a second motor rotor temperature time area parameter and a second temperature deviation as a set of training data.
In the embodiment of the invention, the step of acquiring the training data of the RBF neural network is a step of acquiring the training data in a test stage, in the process, a test environment is firstly set up, namely a temperature sensor is arranged on a motor rotor and used for acquiring the temperature of the rotor, and a feedback signal of the temperature sensor is acquired through a slip ring device arranged on the periphery of the motor, preferably the temperature sensor at the moment is a platinum resistance temperature sensor arranged on the outer surface of a rotor permanent magnet; and the working state of the test motor controlled by the upper computer comprises the following steps: the motor speed and the motor output torque are detected, and the temperatures of the stator and the rotor are detected simultaneously. And then, carrying out standing treatment on the driving motor and the motor controller in a time environment for ensuring that the temperature of the stator and the rotor is consistent with the ambient temperature, wherein the fourth preset time of standing comprises but is not limited to 8 hours. After standing, if the difference value of the fourth rotor temperature and the fourth stator temperature obtained at the moment is less than a preset value including but not limited to 0.6 ℃, determining that the test motor meets preset test conditions, then controlling the test motor to work under the conditions of preset motor rotation speed and motor output torque by using an upper computer, obtaining the third stator temperature, the third rotor temperature, the second motor rotation speed and the second motor output torque of each control period within a first preset time before a preset control period by using a motor controller at the moment, respectively obtaining corresponding second stator temperature time area parameter, second motor rotation speed time area parameter, second motor output torque time area parameter and second motor rotor temperature time area parameter according to the third stator temperature, the third rotor temperature, the second motor rotation speed time area parameter, the second motor output torque time area parameter and the second motor rotor temperature time area parameter, and obtaining a fifth stator temperature and a fifth rotor temperature of the preset control period, thereby obtaining a group of training data, the upper computer continuously changes the rotating speed of the motor and the output torque of the motor to obtain corresponding training data, and then the step of training the RBF neural network according to the training data can be carried out.
Alternatively, the above steps are only for those skilled in the art to understand how to obtain the training data, and in practical applications, the training data obtained by the test can be stored in an internal or external storage device, and can be directly retrieved from the storage device when the RBF neural network training needs to be performed on a new motor controller of the same type. The storage device described herein may be a different storage device than the storage device described above for storing the first rotor temperature, the first stator temperature, the first motor speed, and the first motor output torque.
Referring to fig. 6, another preferred embodiment of the present invention also provides a control apparatus for acquiring a rotor temperature of an electric motor, including:
the first obtaining module 601 is configured to obtain a first stator temperature, a first rotor temperature, a first motor rotation speed, and a first motor output torque of the motor in each control period within a first preset time before a current control period when the vehicle meets a preset estimation condition, where the first rotor temperature is equal to the first stator temperature within a second preset time after the vehicle is powered on;
the first processing module 602 is configured to input a first stator temperature, a first rotor temperature, a first motor rotation speed, and a first motor output torque of each control cycle within a first preset time to a radial basis function RBF neural network obtained through pre-training, so as to obtain a first temperature deviation;
a second obtaining module 603, configured to obtain a second stator temperature of the current control period;
and a second processing module 604, configured to calculate a second rotor temperature of the current control period according to the first temperature deviation and the second stator temperature.
Preferably, the control device for acquiring the temperature of the rotor of the motor as described above further includes:
a third obtaining module 605, configured to obtain status information of the vehicle, where the status information includes: the status and duration of the drive system and cooling system prior to vehicle power-up;
and a determining module 606, configured to determine whether the motor meets a preset estimation condition according to the state information, where before the vehicle is powered on, the states of the driving system and the cooling system are both in an off state, and when the duration is greater than or equal to a third preset time, it is determined that the motor meets the preset estimation condition.
Specifically, as described above for the control device for obtaining the temperature of the rotor of the electric machine, the first processing module 602 includes:
the first processing unit 6021 is configured to obtain a first stator temperature time area parameter according to a first stator temperature of each control period within a first preset time and a first preset algorithm;
the second processing unit 6022 is configured to obtain a time area parameter of the first motor rotation speed according to the first motor rotation speed of each control period within the first preset time and a second preset algorithm;
the third processing unit 6023 is configured to obtain a time area parameter of the first motor output torque according to the first motor output torque of each control cycle within the first preset time and a third preset algorithm;
a fourth processing unit 6024, configured to obtain a time-area parameter of the first motor rotor temperature according to the first rotor temperature of each control period within the first preset time and a fourth preset algorithm;
a fifth processing unit 6025, configured to input the first stator temperature time area parameter, the first motor rotation speed time area parameter, the first motor output torque time area parameter, and the first motor rotor temperature time area parameter to the pre-trained RBF neural network, so as to obtain the first temperature deviation.
If the step of obtaining the first rotor temperature time area parameter, the first stator temperature time area parameter, the first motor rotation speed time area parameter, and the first motor output torque time area parameter is changed according to actual needs by a person skilled in the art, the position of the corresponding module and/or unit is also changed accordingly.
Further, the motor controller as described above further includes:
the fourth acquisition module is used for acquiring training data of the RBF neural network;
and the third processing module is used for training the RBF neural network through the training data to obtain the trained RBF neural network.
Specifically, as described above for the motor controller, the fourth obtaining module includes:
the first obtaining unit is used for obtaining a third stator temperature, a third rotor temperature, a second motor rotating speed and a second motor output torque of each control period within a first preset time before a preset control period when the test motor meets a preset test condition, wherein after the test motor is powered off and stands still for a fourth preset time, a difference value between a fourth rotor temperature obtained by a temperature sensor arranged on a rotor of the test motor and a fourth stator temperature of a stator is smaller than a preset value, and the test motor is determined to meet the preset test condition;
the sixth processing unit is used for obtaining a second stator temperature time area parameter, a second motor rotation speed time area parameter, a second motor output torque time area parameter and a second motor rotor temperature time area parameter according to a third stator temperature, a third rotor temperature, a second motor rotation speed and a second motor output torque of each control period in a first preset time;
the seventh processing unit acquires a fifth stator temperature and a fifth rotor temperature of a preset control period and acquires a second temperature deviation;
and the eighth processing unit is used for determining the second stator temperature time area parameter, the second motor rotating speed time area parameter, the second motor output torque time area parameter, the second motor rotor temperature time area parameter and the second temperature deviation as a group of training data.
The embodiment of the control device of the invention is the control device corresponding to the embodiment of the control method, and all implementation means in the embodiment of the control method are applicable to the embodiment of the control device, so that the same technical effects can be achieved.
Still another preferred embodiment of the present invention also provides a vehicle including: a motor controller having the control device as described above.
In the embodiment of the invention, the vehicle comprises the motor controller with the control device, so that the vehicle can accurately estimate the rotor temperature of the motor in real time, and further the motor can be accurately controlled, and the stability and the safety of the vehicle are ensured.
Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed.
It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (15)

1. A control method for obtaining the temperature of a motor rotor is applied to a motor controller and is characterized by comprising the following steps:
when the vehicle meets a preset estimation condition, acquiring a first stator temperature, a first rotor temperature, a first motor rotating speed and a first motor output torque of the motor in each control period within a first preset time before the current control period, wherein the first rotor temperature is equal to the first stator temperature within a second preset time after the vehicle is powered on;
inputting the first stator temperature, the first rotor temperature, the first motor rotating speed and the first motor output torque of each control period in the first preset time to a Radial Basis Function (RBF) neural network obtained through pre-training to obtain a first temperature deviation;
acquiring a second stator temperature of the current control period;
and calculating to obtain a second rotor temperature of the current control period according to the first temperature deviation and the second stator temperature.
2. The control method for obtaining the rotor temperature of the electric motor according to claim 1, wherein before the step of obtaining the first stator temperature, the first rotor temperature, the first motor speed and the first motor output torque of the electric motor in each control cycle within a first preset time before the current control cycle when the vehicle meets the preset estimation condition, the method further comprises:
acquiring state information of a vehicle, wherein the state information comprises: the status and duration of the drive system and cooling system prior to vehicle power-up;
and determining whether the motor meets the preset estimation condition or not according to the state information, wherein before the vehicle is powered on, the states of the driving system and the cooling system are both in a closed state, and when the duration is longer than or equal to a third preset time, the motor is determined to meet the preset estimation condition.
3. The control method of claim 1, wherein the step of inputting the first stator temperature, the first rotor temperature, the first motor speed, and the first motor output torque of each control cycle in the first preset time to a pre-trained Radial Basis Function (RBF) neural network to obtain a first temperature deviation comprises:
obtaining a first stator temperature time area parameter according to the first stator temperature of each control period in the first preset time and a first preset algorithm;
obtaining a first motor rotating speed time area parameter according to the first motor rotating speed of each control period in the first preset time and a second preset algorithm;
obtaining a time area parameter of the first motor output torque according to the first motor output torque of each control period in the first preset time and a third preset algorithm;
obtaining a time area parameter of the temperature of the first motor rotor according to the first rotor temperature of each control period in the first preset time and a fourth preset algorithm;
and inputting the first stator temperature time area parameter, the first motor rotating speed time area parameter, the first motor output torque time area parameter and the first motor rotor temperature time area parameter to the RBF neural network obtained by pre-training to obtain the first temperature deviation.
4. The control method for obtaining the rotor temperature of an electric machine according to claim 3, wherein the first preset algorithm is:
Figure FDA0001978606050000021
wherein, TSIs the first stator temperature time area parameter;
Tstator(k) the temperature of the first motor stator in the kth control period is k, the value of k is 1 to n, and n is the number of control periods in the first preset time;
tsthe time of a single control cycle.
5. The control method for obtaining the rotor temperature of an electric machine according to claim 3, wherein the second preset algorithm is:
Figure FDA0001978606050000022
wherein S ismThe time area parameter is the first motor rotating speed;
(k) the first motor rotating speed of the kth control period, the value of k is 1 to n, and n is the number of control periods in the first preset time;
tsthe time of a single control cycle.
6. The control method for obtaining the rotor temperature of an electric machine according to claim 3, wherein the third preset algorithm is:
Figure FDA0001978606050000023
wherein, TcmdOutputting a torque time area parameter for the first motor;
Tq(k) the first motor output torque is in the kth control period, the value of k is 1 to n, and n is the number of control periods in the first preset time;
tsthe time of a single control cycle.
7. The control method for obtaining the rotor temperature of the electric machine according to claim 3, wherein the fourth preset algorithm is:
Figure FDA0001978606050000031
wherein, TRThe temperature, time and area parameters of the first motor rotor are obtained;
Trotator(k) the temperature of the first motor rotor in the kth control period is k, the value of k is 1 to n, and n is the number of control periods in the first preset time;
tsfor a single control cycleThe time of the session.
8. The control method for obtaining a rotor temperature of an electric machine according to claim 1, wherein before the step of obtaining the first stator temperature, the first rotor temperature, the first electric machine rotation speed, and the first electric machine output torque when the vehicle satisfies a preset estimation condition, further comprising:
acquiring training data of the RBF neural network;
and training the RBF neural network through the training data to obtain the trained RBF neural network.
9. The method of claim 8, wherein the step of obtaining training data for the RBF neural network comprises:
when a test motor meets preset test conditions, acquiring a third stator temperature, a third rotor temperature, a second motor rotating speed and a second motor output torque of each control period within the first preset time before a preset control period, wherein after the test motor is powered off and stands still for a fourth preset time, when a difference value between a fourth rotor temperature acquired by a temperature sensor arranged on a rotor of the test motor and a fourth stator temperature of a stator is smaller than a preset value, the test motor is determined to meet the preset test conditions;
respectively obtaining a second stator temperature time area parameter, a second motor rotation speed time area parameter, a second motor output torque time area parameter and a second motor rotor temperature time area parameter according to the third stator temperature, the third rotor temperature, the second motor rotation speed and the second motor output torque of each control period within the first preset time;
acquiring a fifth stator temperature and a fifth rotor temperature of a preset control period, and acquiring a second temperature deviation;
and determining the second stator temperature time area parameter, the second motor rotating speed time area parameter, the second motor output torque time area parameter, the second motor rotor temperature time area parameter and the second temperature deviation as a group of training data.
10. A control device for obtaining a temperature of a rotor of an electric machine, comprising:
the first obtaining module is used for obtaining a first stator temperature, a first rotor temperature, a first motor rotating speed and a first motor output torque of the motor in each control period within a first preset time before a current control period when the vehicle meets a preset estimation condition, wherein the first rotor temperature is equal to the first stator temperature within a second preset time after the vehicle is powered on;
the first processing module is used for inputting the first stator temperature, the first rotor temperature, the first motor rotating speed and the first motor output torque of each control cycle in the first preset time to a Radial Basis Function (RBF) neural network obtained through pre-training to obtain a first temperature deviation;
the second acquisition module is used for acquiring the second stator temperature of the current control period;
and the second processing module is used for calculating a second rotor temperature of the current control period according to the first temperature deviation and the second stator temperature.
11. The control device for obtaining the rotor temperature of the motor according to claim 10, further comprising:
a third obtaining module, configured to obtain status information of a vehicle, where the status information includes: the status and duration of the drive system and cooling system prior to vehicle power-up;
and the judging module is used for determining whether the motor meets the preset estimation condition or not according to the state information, wherein before the vehicle is powered on, the states of the driving system and the cooling system are both in a closed state, and when the duration time is longer than or equal to a third preset time, the motor is determined to meet the preset estimation condition.
12. The control device for obtaining the rotor temperature of the motor according to claim 10, wherein the first processing module comprises:
the first processing unit is used for obtaining a first stator temperature time area parameter according to the first stator temperature of each control period in the first preset time and a first preset algorithm;
the second processing unit is used for obtaining a first motor rotating speed time area parameter according to the first motor rotating speed of each control period in the first preset time and a second preset algorithm;
the third processing unit is used for obtaining a time area parameter of the first motor output torque according to the first motor output torque of each control period in the first preset time and a third preset algorithm;
the fourth processing unit is used for obtaining a time area parameter of the temperature of the first motor rotor according to the first rotor temperature of each control period in the first preset time and a fourth preset algorithm;
and the fifth processing unit is used for inputting the first stator temperature time area parameter, the first motor rotating speed time area parameter, the first motor output torque time area parameter and the first motor rotor temperature time area parameter to the RBF neural network obtained through pre-training to obtain the first temperature deviation.
13. The control device for obtaining the rotor temperature of the motor according to claim 10, further comprising:
the fourth acquisition module is used for acquiring the training data of the RBF neural network;
and the third processing module is used for training the RBF neural network through the training data to obtain the trained RBF neural network.
14. The control device for acquiring the rotor temperature of the motor according to claim 13, wherein the fourth acquisition module comprises:
the device comprises a first obtaining unit, a second obtaining unit and a control unit, wherein the first obtaining unit is used for obtaining a third stator temperature, a third rotor temperature, a second motor rotating speed and a second motor output torque of each control period within a first preset time before a preset control period when a test motor meets a preset test condition, and when the test motor is powered off and stands still for a fourth preset time, a difference value between a fourth rotor temperature obtained by a temperature sensor arranged on a rotor of the test motor and a fourth stator temperature of a stator is smaller than a preset value, the test motor is determined to meet the preset test condition;
a sixth processing unit, configured to obtain a second stator temperature time area parameter, a second motor rotation speed time area parameter, a second motor output torque time area parameter, and a second motor rotor temperature time area parameter according to the third stator temperature, the third rotor temperature, the second motor rotation speed, and the second motor output torque in each control period within the first preset time, respectively;
the seventh processing unit acquires a fifth stator temperature and a fifth rotor temperature of a preset control period and acquires a second temperature deviation;
and the eighth processing unit is used for determining the second stator temperature time area parameter, the second motor rotating speed time area parameter, the second motor output torque time area parameter, the second motor rotor temperature time area parameter and the second temperature deviation as a group of training data.
15. A vehicle, characterized by comprising: a motor controller having a control device as claimed in any one of claims 10 to 14.
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