A permanent magnet synchronous motor efficiency optimization method based on system efficiency optimization

Abstract

The application provides a permanent magnet synchronous motor efficiency optimization method based on system efficiency optimization, comprising the following steps: S1, analyzing the loss mechanism and dynamic efficiency characteristics of each component of the electric drive system of the electric commercial vehicle, and obtaining the dynamic efficiency model of each component of the electric drive system and the whole electric drive system under different working conditions; S2, formulating a permanent magnet synchronous motor lookup table method field weakening control strategy based on experimental data as the basis of the subsequent permanent magnet synchronous motor control strategy based on system efficiency optimization; S3, introducing the whole electric drive system dynamic efficiency model and the deep deterministic policy gradient algorithm to improve the lookup table method field weakening control, obtaining the permanent magnet synchronous motor field weakening control strategy based on system efficiency optimization, and realizing the efficiency optimization of the permanent magnet synchronous motor. Compared with the traditional motor lookup table method field weakening control strategy, the application can realize the decoupling control of the permanent magnet synchronous motor, can optimize the efficiency of the permanent magnet synchronous motor according to the dynamic efficiency characteristics of the electric drive system, and improves the working efficiency of the vehicle permanent magnet synchronous motor.

Description

TECHNICAL FIELD

[0001] The application relates to a permanent magnet synchronous motor efficiency optimization method, in particular to a permanent magnet synchronous motor efficiency optimization method with optimal system efficiency. BACKGROUND

[0002] The permanent magnet synchronous motor is widely applied in the new energy automobile industry due to small size and high power density, but the permanent magnet synchronous motor has the characteristics of nonlinearity and strong coupling, and how to use an advanced control strategy to reasonably improve the efficiency of the permanent magnet synchronous motor is always difficult to control.

[0003] At present, the permanent magnet synchronous motor efficiency optimization control technology is divided into a minimum loss control strategy (Loss Minimization Control, LMC) and a search control strategy (Search Control, SC). The minimum loss control strategy uses a loss minimum target function to establish a motor loss model by accurate electromagnetic parameters to solve id, iq and other optimal control variables to control the motor. The implementation of the control strategy is more dependent on the accuracy of motor parameter identification, and the change of motor electromagnetic parameters in the control process will cause the motor to be unable to realize decoupling control of the direct-axis current and the quadrature-axis current under some working conditions. The search control strategy calculates the real-time efficiency of the system by detecting the input and output power and other state variables of the motor system, and then adjusts the control parameters, so that the motor continuously works at the highest efficiency point. The optimization strategy for the motor state variables ignores the influence of other key components of the electric drive system, and cannot achieve optimal control of the electric drive system.

[0004] In summary, in order to realize the optimal control of the new energy automobile driving system and maximize the social value of the new energy automobile, based on the limitations of the current minimum loss control strategy and search control strategy of the permanent magnet synchronous motor, the application provides a permanent magnet synchronous motor efficiency optimization method based on optimal system efficiency to solve the problems that the existing technology cannot realize true decoupling control and the efficiency of the electric drive system cannot be optimized. SUMMARY

[0005] The application provides a permanent magnet synchronous motor efficiency optimization method based on optimal system efficiency to solve the problem that the existing control strategy optimization scheme cannot make the overall operation efficiency of the electric drive system optimal.

[0006] The technical scheme adopted by the application is as follows:

[0007] A permanent magnet synchronous motor efficiency optimization method based on optimal system efficiency comprises the following steps:

[0008] Step S1: based on the pure electric commercial vehicle electric drive system structure, the loss mechanism and dynamic efficiency characteristics of the electric drive system are analyzed, and the efficiency model of the key components and the efficiency model of the electric drive system under different working conditions are formulated;

[0009] Step S2: based on the experimental data, the look-up table method of permanent magnet synchronous motor field weakening control strategy is formulated as the basis of the subsequent permanent magnet synchronous motor control strategy based on system efficiency optimization;

[0010] Step S3: the overall dynamic efficiency model of the electric drive system and the deep deterministic policy gradient algorithm are introduced to improve the look-up table method of field weakening control, and the permanent magnet synchronous motor field weakening control strategy based on system efficiency optimization is obtained, and the efficiency optimization of the permanent magnet synchronous motor is realized.

[0011] As preferred, the specific process of step S1 is as follows:

[0012] Step S11: based on the electric drive system structure of the electric commercial vehicle, the dynamic efficiency and loss model of the electrical system including battery, converter, inverter, motor and air conditioner and the mechanical system are obtained through theoretical analysis;

[0013] Step S12: for different vehicle driving conditions: 1) driving condition, 2) air conditioning running condition, the dynamic efficiency model of the electric drive system is established respectively.

[0014] As preferred, the specific process of step S2 is as follows:

[0015] Step S21: according to the experiment, the key parameters and dq axis current table of the permanent magnet synchronous motor are obtained;

[0016] Step S22: the look-up table method of permanent magnet synchronous motor field weakening control strategy is established.

[0017] As preferred, the specific process of step S3 is as follows:

[0018] Step S31: the overall dynamic efficiency model of the electric drive system and the deep deterministic policy gradient algorithm are introduced, the state space, action space and reward function are established based on the vehicle state information, and the FW-DDPG control strategy is formed, and the framework is as shown in Figure 5

[0019] Step S32: the FW-DDPG algorithm is trained by using the reconstructed working condition, and the permanent magnet synchronous motor MAP graph under the control of multiple different control strategies is obtained after the algorithm converges.

[0020] The application has the following advantages:

[0021] 1. The permanent magnet synchronous motor efficiency optimization method based on system efficiency optimization realizes the decoupling control of the permanent magnet synchronous motor in a true sense by introducing deep reinforcement learning and electric drive system efficiency model, and improves the working efficiency of the vehicle permanent magnet synchronous motor.​

[0022] 2、Compared with the traditional motor table lookup method field weakening control strategy, the application does not depend on the motor identification parameters, solves the phenomenon that the motor electromagnetic parameters change in the control process, causes the motor to be unable to realize the decoupling control under some working conditions, and realizes the true sense of decoupling control.

[0023] 3、The application breaks the inertia of traditional motor control only considering its own state variables, considers the influence of other key components of the electric drive system, introduces the electric drive system efficiency characteristics under different working conditions as the state variable of motor control into the DDPG algorithm to form the FW-DDPG control strategy, and the strategy can control the permanent magnet synchronous motor according to the actual working condition change of the vehicle to optimize the efficiency of the permanent magnet synchronous motor.

[0024] 4、The permanent magnet synchronous motor efficiency optimization method based on the whole vehicle efficiency optimization of the application, although it is proposed based on a pure electric commercial vehicle, still has certain applicability to the efficiency optimization of the permanent magnet synchronous motor of other vehicle types. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 The permanent magnet synchronous motor efficiency optimization method based on the system efficiency optimization of the application is shown in the flow chart;

[0026] Figure 2 The dynamic efficiency characteristic diagram of the electric drive system is shown in the figure;

[0027] Wherein (a) efficiency MAP (driving working condition), (b) loss MAP (driving working condition), (c) efficiency MAP (air conditioner starting working condition), (d) loss MAP (air conditioner starting working condition);

[0028] Figure 3 (a) and (b) are respectively the d-axis current diagram and the q-axis current diagram obtained by experiment;

[0029] Figure 4 The principle diagram of the table lookup method field weakening control strategy of the application is shown in the figure;

[0030] Figure 5 The principle diagram of the FW-DDPG control strategy of the application is shown in the figure;

[0031] Figure 6 The motor efficiency MAP comparison diagram of the FW-DDPG control strategy and the table lookup method field weakening control strategy of the application is shown in the figure, wherein (a) is the MAP before optimization, and (b) is the MAP after optimization. DETAILED DESCRIPTION

[0032] In order to make the technical concept and advantages of the present application to realize the purpose of the application more clear and explicit, the technical solutions of the present application are described in further detail below in combination with the drawings. It should be understood that the following examples are only used to explain and illustrate the preferred embodiments of the present application, and should not be considered as limiting the scope of the present application.

[0033] Embodiment 1

[0034] Referring to Figure 1 , the present application is based on the efficiency optimization method of the permanent magnet synchronous motor of the whole vehicle efficiency optimization, which comprises the following steps:

[0035] Step S1: analyzing the loss mechanism and dynamic efficiency characteristics of each component of the electric commercial vehicle electric drive system, obtaining the dynamic efficiency model of each component of the electric drive system and the whole electric drive system under different working conditions;

[0036] Step S2: formulating the permanent magnet synchronous motor lookup table method field weakening control strategy based on experimental data as the basis of the subsequent permanent magnet synchronous motor control strategy based on system efficiency optimization;

[0037] Step S3: introducing the whole electric drive system dynamic efficiency model and deep deterministic policy gradient algorithm to improve the lookup table method field weakening control, obtaining the permanent magnet synchronous motor field weakening control strategy based on system efficiency optimization, and realizing the efficiency optimization of the permanent magnet synchronous motor.

[0038] Embodiment 2

[0039] The difference between this embodiment and embodiment 1 is that:

[0040] The specific process of step S1 is as follows:

[0041] Step S11: based on the structure of the electric commercial vehicle electric drive system, the dynamic efficiency and loss model of the electrical system including battery, converter, inverter, motor, air conditioner and mechanical system are obtained through theoretical analysis;

[0042] Step S12: for different vehicle driving conditions: 1) driving condition, 2) air conditioning running condition, the dynamic efficiency model of the electric drive system is established respectively.

[0043] The specific process of step S2 is as follows:

[0044] Step S21: obtaining the permanent magnet synchronous motor key parameters and dq axis current table according to the experiment;

[0045] The motor modeling parameters mainly include stator resistance, d-axis inductance, q-axis inductance, permanent magnet flux linkage, etc. The required parameters for modeling come from two sources: 1) provided by the supplier, 2) parameter identification through experiment;

[0046] Step S22: Establishing permanent magnet synchronous motor look-up table method field weakening control strategy;

[0047] The look-up table method field weakening control is based on the evolution of the FOC framework, and the current loop and the speed loop are improved while the SVPWM, Clark, Park and other modules remain unchanged.

[0048] The specific process of the step S3 is as follows:

[0049] Step S31: Introducing the overall dynamic efficiency model of the electric drive system and the deep deterministic policy gradient algorithm, establishing the state space, action space and reward function based on the vehicle state information, and forming the FW-DDPG control strategy, as shown in the framework Figure 5

[0050] The FW-DDPG control strategy is based on the look-up table method field weakening control framework, and the DDPG algorithm is used to replace the FFCDC part, so as to realize the decoupling control of the permanent magnet synchronous motor in a true sense. In addition, the efficiency characteristics of the electric drive system under different working conditions are used as part of the DDPG algorithm, so that the control strategy can control the permanent magnet synchronous motor according to the actual working condition changes of the vehicle;

[0051] Step S32: Training the FW-DDPG algorithm by using the reconstructed working condition, and obtaining the MAP of the permanent magnet synchronous motor under the FW-DDPG control strategy after the algorithm converges.

[0052] In step S32, the CLTC working condition is used as the training working condition of the FW-DDPG control strategy, and the decision method is trained and simulated: in order to complete the training of the FW-DDPG control strategy and shorten the algorithm training time, the motor external characteristic curve is divided into a plurality of specific working condition points (for example: torque 200Nm, speed 3000rpm working condition) for training; after the training is completed, the MAP of the permanent magnet synchronous motor under the FW-DDPG control strategy is obtained. The MAP formed by the look-up table method field weakening control and the FW-DDPG control is shown in Figure 6 .

[0053] Embodiment 3

[0054] Referring to Figure 1 , the permanent magnet synchronous motor efficiency optimization method based on the whole vehicle efficiency optimization, the specific implementation process includes:

[0055] Step S1: Taking a pure electric commercial vehicle as an example, based on the structure of the electric drive system, analyzing the loss mechanism and dynamic efficiency characteristics of the electric drive system, and formulating the efficiency model of the key components and the efficiency model of the electric drive system under different working conditions;

[0056] ​Step S2: Construct a lookup table-based field weakening control strategy for permanent magnet synchronous motors;

[0057] Step S3: Introduce the electric drive system efficiency model and the reinforcement learning DDPG algorithm to form the FW-DDPG control strategy to achieve efficiency optimization of the permanent magnet synchronous motor.

[0058] Specifically, the implementation steps of step S1 are as follows:

[0059] Step S11, the dynamic efficiency characteristics of the electric drive system are analyzed as follows:

[0060] The loss mechanisms and dynamic efficiency characteristics of each component in an electric drive system differ. Battery power loss is mainly caused by charging / discharging current, internal resistance, and voltage drop in the RC circuit; inverter losses are mainly caused by the switching of switching devices (IGBTs and freewheeling diodes); converter losses are generated by the parasitic resistance of switching devices, capacitors, and inductors; permanent magnet synchronous motor losses consist of winding copper losses, stator iron losses, mechanical losses, and stray losses; transmission system losses consist of power losses from gear meshing friction, bearing friction, oil churning friction, and wind resistance; and air conditioning power losses mainly include transmission losses, volumetric losses, and other losses. A mathematical model of the losses and dynamic efficiency characteristics of an electric drive system, including the battery, converter, inverter, motor, air conditioning, and mechanical transmission system, is established. Figure 2 This is a diagram showing the dynamic efficiency characteristics of the electric drive system of the present invention.

[0061] Mathematical model of battery loss and efficiency:

[0062]

[0063] P RC =NI1(∫((I1-V) p1 / R p1 ) / C p1 )+∫((I1-V p2 / R p2 ) / C p2 ));

[0064] η bat =P out / (P out +P R +P RC (1)

[0065] In the formula, P R P RC These represent the battery internal resistance and polarization circuit power loss, respectively, in kW and V. p1 V p2 These are the first-order polarization voltage and the second-order polarization voltage, respectively, in V and P. p1 P p1These are the first-order polarization resistance and the second-order polarization resistance, respectively, in Ω; C p1 C p2 These represent the first-order polarization capacitor and the second-order polarization capacitor, respectively, in F; N is the number of individual cells; and I1 is the main circuit current of the battery, in A.

[0066] Mathematical model of inverter losses and efficiency

[0067] P cond_IGBT =I c (V ce0 +I c (R2+K RT ΔT J_IGBT ));

[0068] P cond_Diode =I f (V f0 +I f (R3+K RT' ΔT J_Diode ));

[0069] P sw_IGBT =f(E sw_on +E sw_off );

[0070] P sw_Diode =fE sw_rec ;

[0071] η inverter =P out / (P out +P cond_IGBT +P cond_Diode +P sw_IGBT +P sw_Diode (2)

[0072] In the formula, ΔT j_IGBT ΔT j_Diode The junction temperature difference between IGBTs and diodes, in °C and K. RT K RT' The on-resistance temperature coefficient of IGBTs and diodes is 3.0E-6; f is the switching frequency in Hz; E sw_on E sw_off E sw_rec Energy consumption for IGBT turn-on, IGBT turn-off, and Diode reverse recovery, in mJ; c I f For IGBT collector-emitter current and diode on-state current, respectively, in A and V. ceo V fo R1 represents the threshold voltage of the IGBT and Diode, in V; R2 and R3 represent the on-resistance of the IGBT and Diode, in Ω.

[0073] Converter loss and efficiency model

[0074]

[0075] η dc =P out / (P out +P IGBT +P LC_R ); (3)

[0076] In formula (3), P LC_R , P IGBT are inductance parasitic resistance loss power, kW; I2, I3 are capacitance parasitic resistance loss power, kW.

[0077] Motor loss and efficiency model:

[0078]

[0079] P M =B(ω m ) 2 ;

[0080] η pmsm =P out / (P out +P Cu +P Fe +P M ); (4)

[0081] In formula (4), P Cu , P Fe , P M are motor iron loss, copper loss, mechanical loss, respectively, kW.

[0082] Air conditioner loss and efficiency model

[0083]

[0084] η Hvac =P out / Tn; (5)

[0085] In formula (5), F d , F c , F di , F D are roof, body, bottom, power cabin heat transfer area, respectively, m2; K d , K c , K di , K D are roof, body, bottom, power cabin heat transfer coefficient, respectively; T o , T iTin, Tin are the outside and inside temperature of the vehicle cabin, K; K is the solar radiation penetration coefficient through the glass, 0.85; I is the solar radiation intensity on the outside surface of the vehicle window, KW / m2; F I is the effective area in the direct sunlight direction of the vehicle window, m2; p s is the absorption coefficient of the solar radiation on the outside surface of the vehicle enclosure, 0.9; v is the vehicle driving speed, m / s; n is the number of vehicle passengers; V is the fresh air volume required by each person per hour, m3 / (h*person); p is the air density, km / m3; h o , h i is the specific enthalpy of the outside and inside of the vehicle cabin.

[0086] Transmission system loss and efficiency model

[0087]

[0088]

[0089] η mt = (P mesh + P bearing + P churning + P wind ) / T in ω in ; (6)

[0090] In formula (6), P mesh , P bearing , P churning , P wind are respectively the meshing friction power loss, bearing friction power loss, oil stirring friction power loss, and wind resistance power loss, kW; T in is the torque input of the transmission system, Nm; ω in is the rotational speed input of the transmission system, r / min; f s , f1, f2, f3, f g are respectively the sliding friction coefficient of the tooth surface, the bearing type, the coefficient related to the immersion depth of the bearing in the lubricating oil, the left bearing sealing element coefficient, and the right bearing sealing element coefficient; F nj is the j-part contact surface method load, N; ω 1j , ω 2j are the rotational angular velocities of the two gears in the j-part, r / min; r 1j , r 2j are the comprehensive curvature radii of the two gears in the j-part, mm; h R is the elastic fluid dynamic lubricating oil film thickness, mm; β is the gear helix angle, °; B is the tooth width, mm; F d is the pressure load borne by the bearing, N; d mis the average radius of the bearing, mm; v is the kinematic viscosity of the lubricating oil at ambient temperature, m2 / s; n is the working speed of the gear, r / min; D is the diameter of the gear, mm; L is the length of the gear shaft, mm; A g is a configuration constant; R f , m t are the roughness coefficient and the gear face modulus, respectively, R f = 7.93-4.648 / m t ; R is the gear pitch circle radius, mm; p eq is the oil-gas mixture density in the transmission, kg / m3; m eq is the oil-gas mixture viscosity in the transmission, m2 / s;

[0091] Step S12, mathematical model of electric drive system loss and efficiency under different working conditions is established. The established dynamic efficiency characteristics of the electric drive system under different working conditions are as shown in FIG. 7, and the establishment process is as follows: Figure 2

[0092] Mathematical model of electric drive system loss and efficiency under driving working condition

[0093] ΔP sys = ΔP bat + ΔP dc + ΔP inver + ΔP pmsm + ΔP mt ;

[0094] η sys = η bat · η dc · η inver · η pmsm · η mt ; (7)

[0095] In formula (7), ΔP sys , ΔP bat , ΔP dc , ΔP inver , ΔP pmsm , ΔP mt are the electric drive system power loss, the battery power loss, the converter power loss, the inverter power loss, the permanent magnet synchronous motor power loss, and the transmission system power loss, respectively, kW.

[0096] Mathematical model of electric drive system loss and efficiency under air conditioning starting working condition

[0097]

[0098] In formula (7), ΔP hvac is the battery loss containing the air conditioning system loss, kW; ΔP ref is the air conditioning system loss, kW; ​

[0099] The implementation process of step S2 is as follows:

[0100] Step S21: Obtain the key parameters of the permanent magnet synchronous motor and the dq-axis current table according to experiments;

[0101] Step S22: Establish a look-up table method for the permanent magnet synchronous motor field weakening control strategy.

[0102] In step S22, the look-up table method for the field weakening control strategy is established based on experimental data:

[0103] As the basis of the control strategy of the present patent, the look-up table method for field weakening control forms a data table of the relationship between the flux linkage and torque, current by measuring data. When the motor is running, the d and q-axis current reference values are obtained by look-up table to perform field weakening control. Since the look-up table method for field weakening control does not depend on motor parameters, it effectively avoids the influence of motor parameter changes on control, and is widely used in practical engineering.

[0104] Modeling idea:

[0105] Main parameter acquisition: The d-axis current table and the q-axis current table obtained by experiments, the stator resistance Rs required by FFCDC, the D-axis inductance, the Q-axis inductance, and the permanent magnet flux linkage f. The d-axis current graph and the q-axis current obtained by experiments are as shown in Figure 3 .

[0106] Framework building: The look-up table method for field weakening control is based on the evolution of the FOC framework, and in the case of retaining SVPWM, Clark and Park, the current loop and the speed loop are improved. Specifically: the reference torque is obtained by using the speed loop, and then the d and q-axis reference currents are obtained by querying the current table through the speed limit as part of the input of the current loop. The modeling principle of the look-up table method for field weakening control strategy of the present invention is as shown in Figure 4 .

[0107] The implementation process of step S3 is as follows:

[0108] Step S31: Introduce the overall dynamic efficiency model of the electric drive system and the deep deterministic policy gradient algorithm, establish the state space, action space and reward function based on the vehicle state information, and form the FW-DDPG control strategy, the framework of which is as shown in Figure 5 .

[0109] Step S32: Train the FW-DDPG algorithm using the reconstructed working conditions, and obtain the MAP graph of the permanent magnet synchronous motor under the FW-DDPG control strategy after the algorithm converges.

[0110] In step S31, the FW-DDPG control strategy is formulated as follows:

[0111] The FW-DDPG control strategy is based on a look-up table field weakening control framework, and uses a DDPG algorithm to replace the FFCDC part, so as to realize decoupling control of the permanent magnet synchronous motor in a true sense. In addition, the efficiency characteristics of the electric drive system under different working conditions are taken as part of the DDPG algorithm, so that the control strategy can control the permanent magnet synchronous motor according to the actual working condition changes of the vehicle.

[0112] Based on the look-up table field weakening control strategy framework that has been constructed, the FW-DDPG control strategy is formulated in three parts, including state space, action space and reward function formulation, which are defined as follows:

[0113] State space:

[0114]

[0115] In formula (9), n ref , n are the motor reference speed and actual speed, rpm; i d_err , i q_err are the motor d-axis reference current, the difference between the d-axis reference current and the actual current, the q-axis reference current, and the difference between the d-axis reference current and the actual current, A; e ff is the current efficiency of the electric drive system, 0-1.

[0116] Action space:

[0117] A = [Δu d , Δu d ] (10)

[0118] In formula (10), Δu d , Δu d are the motor d-axis and q-axis voltage correction values, -40-40.

[0119] The reward function is:

[0120]

[0121] In formula (11), α, β, γ, χ, ζ are weights; i d_err , i q_err are the motor d-axis reference current and the difference between the d-axis reference current and the actual current, A; e a , e b are the motor front and rear electric drive system efficiency values, 0-1; Δu d , Δu d are the motor d-axis and q-axis voltage correction values, -40-40.

[0122] In step S32, the decision method is trained and simulated:

[0123] The CLTC working condition is used as the training working condition of the FW-DDPG control strategy. In order to completely train the FW-DDPG control strategy and shorten the algorithm training time, the motor external characteristic curve is divided into a plurality of specific working condition points (for example, torque 200Nm, speed 3000rpm working condition) within the motor external characteristic curve for training. After training, the permanent magnet synchronous motor MAP graph under the FW-DDPG control strategy is obtained. The MAP graphs formed under the table lookup method field weakening control and the FW-DDPG control are compared as shown in FIG. 2. Figure 6

[0124] It is found through comparison that the efficient area of the FW-DDPG control strategy is greater than that of the table lookup method field weakening control, which plays a role in decoupling control and efficiency optimization.

[0125] The present application is based on the permanent magnet synchronous motor efficiency optimization method based on system efficiency optimization, and aims to realize decoupling control of the permanent magnet synchronous motor and improve the working efficiency of the vehicle permanent magnet synchronous motor. Compared with the traditional motor table lookup method field weakening control strategy, the present application can realize decoupling control of the permanent magnet synchronous motor, and can optimize the efficiency of the permanent magnet synchronous motor according to the overall dynamic efficiency change of the electric drive system.

[0126] The above only describes the preferred embodiments of the present application, and does not constitute a limitation on the present application. Those skilled in the art can modify the implementation of the present application without creative labor under the guidance of the prior art, and any modification or simple replacement or equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.​

Claims

1. A method for optimizing the efficiency of a permanent magnet synchronous motor based on optimal system efficiency, characterized in that: Includes the following steps: Step S1: Analyze the loss mechanism and dynamic efficiency characteristics of each component of the electric drive system of electric commercial vehicles to obtain the dynamic efficiency models of each component and the overall electric drive system under different operating conditions. Step S2: Based on experimental data, formulate a lookup table method field weakening control strategy for permanent magnet synchronous motors as the basis for subsequent permanent magnet synchronous motor control strategies based on optimal system efficiency; Step S3: Introduce the overall dynamic efficiency model of the electric drive system and the deep deterministic strategy gradient algorithm to improve the lookup table method field weakening control, and obtain the permanent magnet synchronous motor field weakening control strategy based on the optimal system efficiency, thereby realizing the efficiency optimization of the permanent magnet synchronous motor. The specific process for step S3 is as follows: Step S31: Introduce the overall dynamic efficiency model of the electric drive system and the deep deterministic strategy gradient algorithm, establish the state space, action space and reward function based on vehicle state information, and form the FW-DDPG control strategy; The process for developing the FW-DDPG control strategy is as follows: Based on the established lookup table-based field weakening control strategy framework, the FW-DDPG control strategy formulation includes three parts: state space, action space, and reward function formulation, which are defined as follows: State space: In equation (9), n ref and n represent the motor's reference speed and actual speed, respectively; i d_err , i q_err These are the motor d-axis reference current, the difference between the d-axis reference current and the actual current, the q-axis reference current, and the difference between the q-axis reference current and the actual current, respectively. e ff This represents the current efficiency of the electric drive system. Action space: A=[Δu d ,D q ] (10) In equation (10), Δu d ,Δu q These are the correction values ​​for the d-axis and q-axis voltages of the motor. The reward function is: In equation (11), α, β, Y, X, and ζ are weights; e a e b The efficiency values ​​of the front and rear electric drive systems of the motor; Step S32: Train the FW-DDPG algorithm. After the algorithm converges, obtain the MAP diagram of the permanent magnet synchronous motor under the FW-DDPG control strategy.

2. The method for optimizing the efficiency of a permanent magnet synchronous motor based on optimal system efficiency as described in claim 1, characterized in that: The specific process of step S1 is as follows: Step S11: Based on the electric drive system structure of electric commercial vehicles, obtain dynamic efficiency and loss models of electrical and mechanical systems, including batteries, converters, inverters, motors, and air conditioners, through theoretical analysis. Step S12: Establish dynamic efficiency models for electric drive systems for different vehicle driving conditions: 1) driving conditions and 2) air conditioning operating conditions.

3. The method for optimizing the efficiency of a permanent magnet synchronous motor based on optimal system efficiency as described in claim 2, characterized in that: Step S12, the process of formulating mathematical models for the losses and efficiency of the electric drive system under different operating conditions is as follows: Mathematical Model of Electric Drive System Losses and Efficiency under Driving Conditions ΔP sys =ΔP bat +ΔP dc +ΔP inver +ΔP pmsm +ΔP mt ; or sys =the bat .or dc .or inver .or pmsm .or mt (7) In equation (7), ΔP sys ΔP bat ΔP dc ΔP inver ΔP pmsm ΔP mt These are, respectively, the power losses of the electric drive system, the battery power loss, the converter power loss, the inverter power loss, the permanent magnet synchronous motor power loss, and the transmission system power loss; η sys η bat η dc η inver η pmsm η mt These are, respectively, the efficiency of the electric drive system, the efficiency of the battery, the efficiency of the converter, the efficiency of the inverter, the efficiency of the permanent magnet synchronous motor, and the efficiency of the transmission system; Mathematical model of losses and efficiency of electric drive system under air conditioning start-up conditions: In equation (8), Battery losses include those from the air conditioning system; ΔP hvac This is for losses in the air conditioning system.

4. The method for optimizing the efficiency of a permanent magnet synchronous motor based on optimal system efficiency as described in claim 1, characterized in that: The specific process of step S2 is as follows: Step S21: Obtain the parameters of the permanent magnet synchronous motor and the d-axis and q-axis ammeters based on the experiment; Step S22: Establish a lookup table method for field weakening control of permanent magnet synchronous motor.

5. The method for optimizing the efficiency of a permanent magnet synchronous motor based on optimal system efficiency as described in claim 4, characterized in that: In step S22, a lookup table method for field weakening control is established based on experimental data: A data table showing the relationship between flux linkage, torque, and current is generated by measuring data, which serves as the basis for the lookup table method field weakening control strategy. During motor operation, the reference values ​​of d-axis and q-axis currents are obtained by looking up a table for field weakening control. Its modeling process includes: (1) Parameter acquisition: The d-axis ammeter, q-axis ammeter, stator resistance Rs, D-axis inductance, Q-axis inductance, and permanent magnet flux f were obtained through experiments. (2) Framework construction: Based on the traditional FOC dual closed-loop control logic, the reference torque is obtained by using the speed loop in the speed loop, and then the reference current of the d and q axes is obtained by querying the ammeter through the speed limit as the input of the current loop.

6. The method for optimizing the efficiency of a permanent magnet synchronous motor based on optimal system efficiency as described in claim 1, characterized in that: Step S32, train and simulate the decision-making method: In order to train the FW-DDPG control strategy completely and shorten the algorithm training time, the external characteristic curve of the motor is divided into several operating points for training. After training, a MAP diagram of the permanent magnet synchronous motor under the FW-DDPG control strategy is obtained.

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