Electric drive system oil temperature estimation method based on multi-parameter fusion

Abstract

The invention discloses an electric drive system oil temperature estimation method based on multi-parameter fusion, and aims at solving the problems that a traditional oil temperature sensor is high in cost, poor in reliability and incomplete in monitoring. The method comprises the following steps: acquiring operation parameters of the electric drive system; determining an oil temperature base value based on this; calculating heat production and heat dissipation of the system, and solving an oil temperature theoretical value according to a heat balance equation; intelligent calibration is conducted on the theoretical value through a fuzzy controller with the motor rotating speed, the torque and the winding temperature as input, and the final oil temperature is output; according to the method, a physical sensor is replaced by the algorithm model, the hardware cost and the system complexity are remarkably reduced, the estimation precision and the working condition adaptability are improved by utilizing a fusion architecture of the physical model and fuzzy intelligent correction, and comprehensive, stable and reliable soft measurement of the oil temperature of the electric drive system is realized.

Description

Technical Field

[0001] This invention belongs to the technical field of electric drive systems, specifically relating to a method for estimating oil temperature in electric drive systems based on multi-parameter fusion. Background Technology

[0002] Oil temperature in the electric drive system of a pure electric vehicle is a key parameter reflecting its thermal state, directly affecting the performance limits, operating efficiency, and long-term reliability of core components such as the motor and inverter. Therefore, accurate oil temperature monitoring is crucial for achieving precise thermal management, condition assessment, and fault early warning of the system. Currently, the industry generally relies on installing direct-contact temperature sensors in the oil circuit or oil pan for measurement. While this method is intuitive, it has significant limitations in practical applications: First, cost and integration challenges: adding sensors and their wiring harnesses increases material and assembly costs and is difficult to arrange in increasingly compact integrated electric drive systems; second, reliability challenges: sensors are exposed to harsh environments of high temperature, vibration, and oil corrosion over long periods, potentially becoming weak points in reliability and making maintenance and replacement inconvenient; third, insufficient monitoring capabilities: measurements at single points or a few fixed points cannot fully reflect the complex and uneven temperature field distribution within the system, posing a risk of undetected localized overheating.

[0003] It is evident that traditional physical sensor monitoring methods face challenges in terms of economy, applicability, and monitoring efficiency as electric drive systems evolve towards higher integration, lower costs, and greater intelligence. Therefore, the industry urgently needs to develop an oil temperature estimation technology that can reduce reliance on physical sensors, thereby reducing costs and complexity while achieving more comprehensive, stable, and reliable indirect monitoring of oil temperature, thus improving the overall performance and intelligence level of electric drive systems. Summary of the Invention

[0004] To address the aforementioned shortcomings of existing technologies, the present invention aims to provide a method for estimating oil temperature in electric drive systems based on multi-parameter fusion, thereby solving the technical problems of high cost and poor stability of traditional physical temperature measurement, and achieving the effect of improving the overall performance and intelligence level of electric drive systems.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0006] A method for estimating oil temperature in an electric drive system based on multi-parameter fusion includes the following steps:

[0007] 1) Obtain the operating parameters of the electric drive system;

[0008] 2) Obtain the baseline oil temperature value of the electric drive system based on operating parameters;

[0009] 3) Calculate the heat transferred from the electric drive system to the oil based on the operating parameters;

[0010] 4) Determine the theoretical oil temperature value based on the baseline oil temperature and the stated heat.

[0011] 5) The theoretical oil temperature value is calibrated using the fuzzy control principle to obtain the oil temperature output value.

[0012] Furthermore, in step 1), the operating parameters of the electric drive system include state parameters and temperature parameters; the state parameters include motor speed, motor torque and motor downtime, and the temperature parameters include ambient temperature, motor winding temperature, IGBT temperature and oil pump MOS temperature.

[0013] Further, in step 2), when the ambient temperature is lower than a preset threshold, the maximum value among the ambient temperature, motor winding temperature, IGBT temperature, and oil pump MOS temperature is taken as the maximum initial oil temperature, and the minimum value is taken as the minimum initial oil temperature. The base oil temperature value is then calculated according to the following formula:

[0014]

[0015] in, This is the baseline oil temperature value. This is the maximum initial oil temperature. This is the minimum initial oil temperature. This is the maximum motor downtime. This refers to the downtime of the basic motor.

[0016] When the ambient temperature is equal to or higher than the preset threshold, the motor winding temperature is used as the base value for oil temperature.

[0017] Further, in step 3), the heat transferred from the electric drive system to the oil is calculated based on the following heat balance equation:

[0018]

[0019] in, To transfer heat from the electric drive system to the oil. The heat generated by the motor windings. The heat generated by the loss of the motor core. This refers to the heat transferred by convection between the oil and the solid wall surface. This refers to the heat transferred through the oil.

[0020] Further, in step 4), the theoretical oil temperature value is determined using the following formula:

[0021]

[0022] in, This is the theoretical oil temperature. The heat capacity of the oil. The quality of the oil used in heat exchange.

[0023] Further, step 5) includes the following sub-steps:

[0024] 51) The motor speed, motor torque, and motor winding temperature are used as input quantities and then fuzzified.

[0025] 52) Reasoning is performed based on a preset fuzzy control rule base to obtain the reasoning result;

[0026] 53) The inference results are defuzzified to obtain the oil temperature verification factor;

[0027] 54) Use the oil temperature calibration factor to calibrate the theoretical oil temperature value to obtain the oil temperature output value.

[0028] Further, in step 51), at least one fuzzy set is defined for each input quantity, and a membership function is configured for each fuzzy set to convert the actual value of the input quantity into a fuzzy quantity based on the membership function.

[0029] Furthermore, in step 52), the fuzzy control rule base consists of multiple fuzzy rules in the form of "If-Then". The antecedent of each rule is defined based on the fuzzy set of the input quantity, and the consequent is defined based on the fuzzy set of the oil temperature check factor.

[0030] Further, in step 53), the centroid method is used for defuzzification to obtain the oil temperature verification factor.

[0031] Further, in step 54), the theoretical oil temperature value is converted into the output oil temperature value using the following formula:

[0032]

[0033] in, This is the oil temperature output value. This is the theoretical oil temperature. Oil temperature calibration factor This is the reference value for oil temperature compensation.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. The oil temperature estimation method for electric drive systems based on multi-parameter fusion described in this invention fundamentally eliminates the reliance on physical sensors, solving the core pain points of cost, reliability, and comprehensive monitoring. By constructing a multi-parameter fusion estimation framework and reusing existing operating parameters of the electric drive system, this invention replaces physical sensors with algorithmic models, directly eliminating the need for additional hardware. This not only significantly reduces system material and manufacturing costs and avoids sensors themselves becoming points of failure, but also indirectly obtains a more representative overall thermal state of the system through model calculation, achieving more economical, stable, and comprehensive oil temperature monitoring.

[0036] 2. The oil temperature estimation method for electric drive systems based on multi-parameter fusion described in this invention achieves complementary advantages between the mechanistic model and the intelligent algorithm, improving estimation accuracy and adaptability. This invention combines a physical model based on thermodynamic laws (used to calculate theoretical oil temperature) with an intelligent correction model based on fuzzy logic (used for dynamic calibration). The physical model ensures that the estimation results have a solid theoretical basis and good extrapolation, while the fuzzy controller can embed practical experience to compensate for model errors and unmodeled dynamic factors in real time. This hybrid architecture enables the system to maintain the interpretability of the mechanism while possessing self-learning and adaptive capabilities to cope with complex and changing actual working conditions, thereby obtaining high-precision oil temperature estimation values ​​under various operating conditions.

[0037] 3. The oil temperature estimation method for electric drive systems based on multi-parameter fusion described in this invention supports continuous and reliable estimation from cold start to all operating conditions, providing key support for the intelligent management and control of electric drive systems. This invention addresses key scenarios such as low-temperature cold start after prolonged vehicle idling and high-load dynamic operation. Through specific initialization strategies and continuous thermal balance calculations, it ensures the effectiveness and continuity of oil temperature estimation throughout the vehicle's entire lifecycle and all operating conditions. It provides all-weather, high-confidence core data input for real-time thermal management, power protection, efficiency optimization, and status assessment of electric drive systems, improving the overall energy efficiency, safety, and intelligent management level of the system. Attached Figure Description

[0038] Figure 1 This is a flowchart of the oil temperature estimation method for the electric drive system described in the embodiment. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0040] Example:

[0041] Please see Figure 1 A method for estimating oil temperature in an electric drive system based on multi-parameter fusion includes the following steps:

[0042] 1) Obtain the operating parameters of the electric drive system;

[0043] 2) Obtain the baseline oil temperature value of the electric drive system based on operating parameters;

[0044] 3) Calculate the heat transferred from the electric drive system to the oil based on the operating parameters;

[0045] 4) Determine the theoretical oil temperature value based on the baseline oil temperature and the stated heat.

[0046] 5) The theoretical oil temperature value is calibrated using the fuzzy control principle to obtain the oil temperature output value.

[0047] The oil temperature estimation method for electric drive systems based on multi-parameter fusion described in this invention constructs a complete technical closed loop of "multi-parameter acquisition → baseline value determination → heat calculation → theoretical value solution → calibration". It fundamentally eliminates the reliance on direct temperature sensors in the oil circuit, replacing "direct measurement" with "estimation" based on software algorithms. This transforms oil temperature monitoring from a function dependent on specific hardware into an intelligent service that can be optimized with software upgrades, significantly improving the integration, maintainability, and long-term evolution capability of the electric drive system. This invention effectively solves the problems of high cost and poor stability of traditional physical temperature measurement, contributing to improved overall performance and intelligence level of the electric drive system.

[0048] In step 1), during the operation of the electric drive system, operating parameters are collected in real time using the motor control unit and other relevant sensors. These operating parameters include at least state parameters and temperature parameters. State parameters include at least motor speed, motor torque, and motor downtime. Temperature parameters include at least ambient temperature, motor winding temperature, IGBT temperature, and oil pump MOS temperature. The motor downtime is divided into maximum motor downtime and basic motor downtime. Maximum motor downtime refers to the difference between the current and previous power-on times of the electric drive system. Basic motor downtime refers to the time the electrodes remain stationary after the current power-on state. IGBT refers to the inverter (Insulated Gate Bipolar Transistor) in the electric drive system. IGBT temperature... The temperature refers to the heat generated by the IGBT module of the electric drive system due to switching and conduction losses during operation. The oil pump MOS refers to the MOSFET (Metal-Oxide-Semiconductor Field-Effect Transistor) used to drive the oil pump in the electric drive system. The oil pump MOS temperature refers to the operating temperature of the MOSFET power device that drives the oil pump motor. In addition, during the operation of the electric drive system, the signals of the state parameters and temperature parameters often contain high-frequency noise or instantaneous fluctuations. Direct use will interfere with the stability of the model calculation and the judgment of fuzzy control. Therefore, in this embodiment, the operating parameters are also subjected to low-pass filtering. The filtering can effectively remove abnormal disturbances and provide smoother data that better reflects the macroscopic thermal state of the system, which is beneficial to improving the stability and anti-interference ability of all subsequent calculation links.

[0049] In step 2), when the ambient temperature is lower than a preset threshold (in this embodiment, the preset threshold is 0℃), the maximum value among the ambient temperature, motor winding temperature, IGBT temperature, and oil pump MOS temperature is taken as the maximum initial oil temperature, and the minimum value is taken as the minimum initial oil temperature. The base oil temperature value is then calculated according to the following formula:

[0050]

[0051] in, This is the baseline oil temperature value. This is the maximum initial oil temperature. This is the minimum initial oil temperature. This is the maximum motor downtime. This refers to the downtime of the basic motor.

[0052] When the ambient temperature is equal to or higher than the preset threshold, the motor winding temperature is used as the base value of the oil temperature.

[0053] In this way, by comprehensively comparing the ambient temperature and the temperatures of multiple key components, and combining the motor shutdown time to model the oil temperature decay (as shown in the formula), the present invention can quickly and reasonably calculate the initial temperature of the oil at the moment of cold start when the oil temperature cannot be directly measured. This solves the problem of thermal model initialization, ensures the real-time effectiveness and accuracy of the entire estimation process throughout the vehicle's entire life cycle (including after long periods of inactivity), and avoids continuous deviations in subsequent estimations caused by improper initial value settings.

[0054] In step 3), the heat transferred from the electric drive system to the oil is calculated based on the following heat balance equation. ;

[0055]

[0056] in, The heat generated by the motor windings. The heat generated by the loss of the motor core. This refers to the heat transferred by convection between the oil and the solid wall surface, where the solid wall surface refers to the inner wall surface of the oil passage through which the oil flows. The heat transferred by the oil.

[0057] In this way, the present invention abstracts the complex heat exchange process of the electric drive system into a balance between two major stages: heat generation and heat dissipation (convection and conduction). It links electrical parameters such as motor current and frequency with the final oil temperature change, so that the estimated value has a solid physical interpretation. This significantly improves the extrapolation ability and reliability of the algorithm under new unverified operating conditions, and provides a theoretical foundation for accurate estimation.

[0058] In the above heat balance equation, the heat generated by the motor windings... The following formula is used for calculation:

[0059]

[0060] in, The current in the motor windings, The resistance of the motor windings;

[0061] In the above heat balance equation, the heat generated by the loss of the motor core... The following formula is used for calculation:

[0062]

[0063] in, It refers to the frequency of the alternating magnetic field, specifically the frequency of the magnetic field generated when alternating current is applied to the motor windings. Magnetic flux density refers specifically to the strength of the magnetic field generated by the permanent magnets and stator current of the motor, and distributed within the air gap, iron core, and permanent magnet body. , , It is a coefficient related to the core material, specifically, The hysteresis loss coefficient depends on the core material. This is the eddy current loss coefficient, which is related to the resistivity of the material. The Steinmetz index is typically 1.6-2.0 for soft magnetic materials, determined by relevant experiments.

[0064] In the above heat balance equation, the heat transferred by convection between the oil and the solid wall is... The following formula is used for calculation:

[0065]

[0066] in, The coefficient for convective heat transfer. The wall temperature, The oil temperature in the previous calculation cycle; the coefficient of convective heat transfer. This is obtained through experimental testing, which typically requires simulating the actual operating conditions of the motor (such as oil flow rate, oil temperature range, and motor load) by heating the solid wall surface to a stable temperature. Measure the oil temperature and the heating power of the solid wall surface. Typically between 50-500 W / (m²) 2 Within the range of K (the higher the oil flow rate, the larger h is);

[0067] In the above heat balance equation, the heat transferred by the oil is... The following formula is used for calculation:

[0068]

[0069] in, The thermal conductivity of the oil is given by [value]. This represents the oil temperature gradient, i.e., the rate of change of oil temperature.

[0070] In step 4), the theoretical oil temperature value is determined according to the following formula:

[0071]

[0072] in, This is the theoretical oil temperature. The heat capacity of the oil. For the quality of the oil involved in heat exchange;

[0073] In this way, the calculation results based on the thermal balance relationship are transformed into specific temperature values ​​through the heat capacity formula, completing the key transformation from the energy domain to the temperature domain. This makes the output of the abstract physical model a temperature signal that can be directly used for thermal management control. This step, combined with the oil temperature basis, constitutes a complete open-loop oil temperature estimator based on first principles. Its output does not depend on historical oil temperature data and has stronger anti-interference and initial independence.

[0074] In step 5), the theoretical oil temperature value is calibrated using the fuzzy control principle, including the following sub-steps:

[0075] 51) The motor speed, motor torque, and motor winding temperature are used as input quantities and then fuzzified.

[0076] 52) Reasoning is performed based on a preset fuzzy control rule base to obtain the reasoning result;

[0077] 53) The inference results are defuzzified to obtain the oil temperature verification factor;

[0078] 54) The theoretical oil temperature value is calibrated using an oil temperature calibration factor to obtain the oil temperature output value;

[0079] In this way, by introducing fuzzy control as an intelligent correction step for the estimated value of the physical model, the knowledge and experience of model error in experimental data are transformed into intelligent calibration rules that can be executed automatically. Through the process of fuzzification-inference-defuzzification, the system can handle complex problems such as model uncertainty, unmodeled dynamics and time-varying parameters. Thus, on the basis of the theoretical correctness of the physical model, an optimization of empirical accuracy is superimposed, which significantly improves the practical accuracy of the final output value.

[0080] In step 51), at least one fuzzy set is defined for each input quantity, and a membership function is configured for each fuzzy set to convert the actual value of the input quantity into a fuzzy quantity based on the membership function;

[0081] Specifically, in this embodiment, the motor speed (absolute value), motor torque (absolute value), and motor winding temperature are used as inputs for fuzzy control, and the oil temperature verification factor is used as the output of fuzzy control. The motor speed is standardized to the [0, 1] interval through a calibrated mapping relationship, with the mapping relationship being [0, 20000] rpm corresponding to [0, 1]. The motor torque is standardized to the [0, 1] interval through a calibrated mapping relationship, with the mapping relationship being [0, 400] N·m corresponding to [0, 1]. The motor winding temperature is standardized to the [0, 1] interval through a calibrated mapping relationship, with the mapping relationship being [-50, 200] ℃ corresponding to [0, 1]. The oil temperature verification factor is used as the output of fuzzy control, with the output range set to [0, 1].

[0082] The standardized motor speed is divided into three fuzzy sets {L, M, H}, ranging from [0, 1], where L represents low motor speed, M represents medium motor speed, and H represents high motor speed. The standardized motor torque is divided into three fuzzy sets {S, M, B}, ranging from [0, 1], where S represents low motor torque, M represents medium motor torque, and B represents high motor torque. The standardized motor winding temperature is divided into four fuzzy sets {VS, S, M, B}, ranging from [0, 1], where VS represents extremely low motor winding temperature, S represents low motor winding temperature, M represents medium motor winding temperature, and B represents high motor winding temperature. The oil temperature calibration factor is divided into four fuzzy sets {S, M, B, VB}, ranging from [0, 1], where S represents small fuzziness of the oil temperature calibration factor, M represents moderate fuzziness of the oil temperature calibration factor, B represents large fuzziness of the oil temperature calibration factor, and VB represents extremely large fuzziness of the oil temperature calibration factor.

[0083] The membership functions of the three fuzzy sets that configure the motor speed are all S-shaped. The parameters of the membership function of fuzzy set L are [0, 0.10, 0.16, 0.20, 0.30], the parameters of the membership function of fuzzy set M are [0.40, 0.48, 0.54, 0.62, 0.70], and the parameters of the membership function of fuzzy set H are [0.60, 0.68, 0.84, 0.88, 1.00].

[0084] The membership functions of the three fuzzy sets configuring the motor torque are all S-shaped. The parameters of the membership function of fuzzy set S are [0, 0.12, 0.24, 0.30, 0.40], the parameters of the membership function of fuzzy set M are [0.30, 0.40, 0.50, 0.60, 0.70], and the parameters of the membership function of fuzzy set B are [0.60, 0.65, 0.70, 0.80, 1.00].

[0085] The membership functions of the four fuzzy sets used to configure the motor winding temperature are all S-shaped. The parameters of the membership function of fuzzy set VS are [0, 0.04, 0.12, 0.14, 0.20], the parameters of the membership function of fuzzy set S are [0.10, 0.16, 0.28, 0.32, 0.40], the parameters of the membership function of fuzzy set M are [0.30, 0.36, 0.48, 0.62, 0.70], and the parameters of the membership function of fuzzy set B are [0.60, 0.74, 0.80, 0.92, 1.00].

[0086] The membership function of the oil temperature verification factor is configured as S-type, with the parameters of fuzzy set S being [0.01, 0.12, 0.28, 0.32, 0.40], the parameters of the membership function of fuzzy set M being [0.30, 0.32, 0.46, 0.52, 0.60], the parameters of the membership function of fuzzy set B being [0.50, 0.54, 0.68, 0.74, 0.90], and the parameters of fuzzy set VB being [0.80, 0.82, 0.88, 0.92, 1.00].

[0087] This defines the specific implementation method of fuzzification (defining fuzzy sets and membership functions), and assigns semantically meaningful fuzzy language descriptions (such as "high" and "medium") to continuous and precise input signals (such as rotation speed and temperature). This is not only the foundation of fuzzy control, but more importantly, it achieves smooth processing of boundary conditions through membership functions, making the system insensitive to small fluctuations in input signals, enhancing the robustness of the algorithm, and creating conditions for subsequent reasoning based on language rules.

[0088] In step 52), the fuzzy control rule base consists of multiple fuzzy rules in the form of "If-Then". The antecedent of each rule is defined based on the fuzzy set of the input quantity, and the consequent is defined based on the fuzzy set of the oil temperature check factor.

[0089] Specifically, in this embodiment, the fuzzy control rule base established based on the fuzzy sets of input and output quantities is shown in the following table:

[0090]

[0091] In this way, a highly interpretable and maintainable rule base is built. Engineers can directly incorporate experience using natural logic such as "if the speed is high, the torque is large, and the winding temperature is medium, then the verification factor should take a large value" without having to deal with complex mathematical functions. This makes the calibration logic of the algorithm transparent, which makes it easy to intuitively adjust and optimize the calibration strategy based on actual test data. This greatly reduces the threshold and cost of algorithm iteration and adaptation to new platforms.

[0092] In step 53), the centroid method is used for defuzzification to obtain the oil temperature verification factor;

[0093] Specifically, in this embodiment, based on the currently input standardized value, membership function, and fuzzy control rule base, the centroid method is used to defuzzify the output fuzzy quantity to obtain the value K of the oil temperature verification factor. The defuzzification calculation formula is as follows:

[0094]

[0095] in, For the first The original value of the oil temperature verification factor output by a fuzzy control rule; For the first The numerator coefficient of a fuzzy control rule For the first The denominator coefficients of a fuzzy control rule This indicates the number of fuzzy control rules activated under the current operating condition;

[0096] In this way, the centroid method can smoothly and continuously integrate the influence of all activated rules by calculating the centroid of the output fuzzy set, avoiding the output jumps that may occur in other methods (such as the maximum membership method), ensuring the continuity and smoothness of the final verification factor K value change, thereby making the oil temperature calibration process stable and shock-free, and improving the control quality of the thermal management system.

[0097] In step 54), the theoretical oil temperature value is converted into the output oil temperature value using the following formula:

[0098]

[0099] in, This is the oil temperature output value. This is the theoretical oil temperature. Oil temperature calibration factor This is the reference value for oil temperature compensation;

[0100] In this way, the intelligent correction of fuzzy control and the physical model are decoupled and fused in the form of "baseline value + compensation value", and the theoretical oil temperature value is obtained. As a reliable foundation, the verification factor K output by the fuzzy controller serves as a dynamically adjusted "compensation coefficient." This structure clearly separates the main body from the correction quantity, making the algorithm structure modular. It retains the interpretability of the physical model's mechanism while incorporating the adaptive capability of the intelligent algorithm.

[0101] To better understand the oil temperature estimation method for electric drive systems based on multi-parameter fusion described in this invention, an example of centroid method defuzzification is given below:

[0102] 1. Examples with background information;

[0103] Before providing examples, the following core assumptions must be clarified:

[0104] Membership function processing: The membership function parameters of all input and output quantities are analyzed according to the trapezoidal membership function. The above S-shape is a simplified description. The five parameters are defined in the following order: left inflection point 1 (membership degree = 0), left inflection point 2 (membership degree = 1), flat top left boundary, flat top right boundary and right inflection point (membership degree = 0).

[0105] Rule trigger strength calculation: The smaller value method commonly used in fuzzy control is adopted, i.e., the first... The trigger strength of the rule (corresponding formula) = min(membership degree of input 1, membership degree of input 2, membership degree of input 3);

[0106] Original value Sure: Take the midpoint value of the flat top of the corresponding fuzzy set (the core representative value of the trapezoidal membership function);

[0107] Denominator coefficient Definition: Combining the essence of the center of gravity method, we assume... =1, then the defuzzification formula can be simplified to:

[0108]

[0109] in, This represents the number of currently active fuzzy control rules.

[0110] 2. Specific examples and calculations;

[0111] Example 1: Single rule activation scenario:

[0112] S1 Setting Operating Conditions and Standardizing Inputs: Assume the current operating conditions of the electric drive system are as follows. First, standardize the input quantities to the [0,1] range.

[0113] motor speed rpm → Standardized value (Corresponding to the fuzzy set L);

[0114] Motor torque N·m → Standardized value (Corresponding to the fuzzy set S);

[0115] motor winding temperature →Standardized value (Corresponding to the fuzzy set S);

[0116] S2 calculates the membership degree of the input:

[0117] (Fuzzy set L), the parameters of L are [0, 0.10, 0.16, 0.20, 0.30] (trapezoidal), 0.25 is in the interval "0.20, 0.30", membership degree ;

[0118] (Fuzzy set S): The parameters of S are [0, 0.12, 0.24, 0.30, 0.40]. 0.25 is in the interval "0.24, 0.30", and the membership degree is... ;

[0119] (Fuzzy set S): The parameters of S are [0.10, 0.16, 0.28, 0.32, 0.40]. 0.3 is in the interval "0.28, 0.32", and the membership degree is... ;

[0120] S3 determines the activation rules and Activation rules Nmot=L, Tmot=S, Twireless=S (check rule base) → fuzzy set of K = S (small), rule trigger strength , Calculate the parameters of the fuzzy set S [0.01, 0.12, 0.28, 0.32, 0.40], and the midpoint of the flat top = (0.28 + 0.32) / 2 = 0.3 → ;

[0121] S4 is used to calculate K using the centroid method formula: the number of currently active rules. :

[0122]

[0123]

[0124] .

[0125] Example 2: Scenario where multiple rules are activated:

[0126] S1 sets operating conditions and standardized inputs;

[0127] Assuming the current operating conditions: rpm → Standardized value (Simultaneously activate fuzzy sets M and H);

[0128] Nm → Standardized value (Activate fuzzy set B);

[0129] →Standardized value (Activate fuzzy set B);

[0130] S2: Calculate the membership degree of the input quantity;

[0131] (Fuzzy sets M and H), fuzzy set M parameters [0.40, 0.48, 0.54, 0.62, 0.70], 0.7 is in the interval "0.62, 0.70", (Boundary values), fuzzy set H parameters [0.60, 0.68, 0.84, 0.88, 1.00], 0.7 is in the interval "0.68, 0.84". (Flat-topped area);

[0132] (Fuzzy set B), parameters [0.60, 0.65, 0.70, 0.80, 1.00], where 0.75 falls within the interval "0.70, 0.80". (Flat-topped area);

[0133] (Fuzzy set B), parameters [0.60, 0.74, 0.80, 0.92, 1.00], 0.8 is in the interval "0.80, 0.92". (Flat-topped area);

[0134] S3 determines the activation rules and ;

[0135] Activation rule 1: Nmot=H, Tmot=B, Twireless=B → Check rule base → K=VB, trigger strength The fuzzy set VB parameters are [0.80, 0.82, 0.88, 0.92, 1.00]. The midpoint of the flat top is (0.88 + 0.92) / 2 = 0.9. ;

[0136] Activation rule 2: Nmot=M, Tmot=B, Twireless=B → Check rule base → K=VB, trigger strength (because (The rule is invalid and excluded).

[0137] Supplement: If (If M and H are activated simultaneously), then , Activation rule 1 (H, B, B): , Activate rule 2 (M, B, B): , (M, B, B correspond to K=VB);

[0138] S4 is substituted into the formula to calculate K (effective activation rule n=2);

[0139]

[0140]

[0141]

[0142] 3. Examples and Results: As seen in the examples above, the core of the centroid method for defuzzification is to activate the corresponding fuzzy rules based on the input conditions, using the rule triggering strength as the weight, and then applying this weight to the output of each rule. The weighted average of the fuzzy set representative values ​​is used to obtain the accurate oil temperature calibration factor K.

[0143] In summary, this invention provides a method for estimating oil temperature in an electric drive system based on a thermophysical model and intelligent correction. This method combines thermophysical principles with intelligent algorithms to construct a high-precision oil temperature estimation model, eliminating the reliance on numerous physical sensors, thereby significantly reducing system hardware costs and complexity, and improving the reliability and stability of the electric drive system. This method can dynamically adjust according to real-time operating parameters to achieve accurate and comprehensive oil temperature estimation, which not only greatly improves the accuracy and adaptability of the estimation results, but also provides reliable data support for system performance optimization, fault diagnosis, and scientific decision-making, thus providing a strong guarantee for the stable and efficient operation of the electric drive system under various complex operating conditions.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit the technical solutions. Those skilled in the art should understand that any modifications or equivalent substitutions to the technical solutions of the present invention without departing from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A method for estimating oil temperature in an electric drive system based on multi-parameter fusion, characterized in that: Includes the following steps: 1) Obtain the operating parameters of the electric drive system; 2) Obtain the baseline oil temperature value of the electric drive system based on operating parameters; 3) Calculate the heat transferred from the electric drive system to the oil based on the operating parameters; 4) Determine the theoretical oil temperature value based on the baseline oil temperature and the stated heat. 5) The theoretical oil temperature value is calibrated using the fuzzy control principle to obtain the oil temperature output value.

2. The method for estimating oil temperature in an electric drive system based on multi-parameter fusion as described in claim 1, characterized in that: In step 1), the operating parameters of the electric drive system include state parameters and temperature parameters; the state parameters include motor speed, motor torque and motor downtime, and the temperature parameters include ambient temperature, motor winding temperature, IGBT temperature and oil pump MOS temperature.

3. The method for estimating oil temperature in an electric drive system based on multi-parameter fusion as described in claim 2, characterized in that: In step 2), when the ambient temperature is lower than the preset threshold, the maximum value among the ambient temperature, motor winding temperature, IGBT temperature, and oil pump MOS temperature is taken as the maximum initial oil temperature, and the minimum value is taken as the minimum initial oil temperature. The base oil temperature value is then calculated according to the following formula: in, This is the baseline oil temperature value. This is the maximum initial oil temperature. This is the minimum initial oil temperature. This is the maximum motor downtime. This refers to the downtime of the basic motor. When the ambient temperature is equal to or higher than the preset threshold, the motor winding temperature is used as the base value for oil temperature.

4. The method for estimating oil temperature in an electric drive system based on multi-parameter fusion according to claim 3, characterized in that: Step 3) Calculate the heat transferred from the electric drive system to the oil, based on the following heat balance equation: in, To transfer heat from the electric drive system to the oil. The heat generated by the motor windings. The heat generated by the loss of the motor core. This refers to the heat transferred by convection between the oil and the solid wall surface. This refers to the heat transferred through the oil.

5. The method for estimating oil temperature in an electric drive system based on multi-parameter fusion according to claim 4, characterized in that: Step 4) Determine the theoretical oil temperature value using the following formula: in, This is the theoretical oil temperature. The heat capacity of the oil. The quality of the oil used in heat exchange.

6. The method for estimating oil temperature in an electric drive system based on multi-parameter fusion according to claim 2, characterized in that: Step 5) includes the following sub-steps: 51) The motor speed, motor torque, and motor winding temperature are used as input quantities and then fuzzified. 52) Reasoning is performed based on a preset fuzzy control rule base to obtain the reasoning result; 53) The inference results are defuzzified to obtain the oil temperature verification factor; 54) Use the oil temperature calibration factor to calibrate the theoretical oil temperature value to obtain the oil temperature output value.

7. The method for estimating oil temperature in an electric drive system based on multi-parameter fusion as described in claim 6, characterized in that: In step 51), at least one fuzzy set is defined for each input quantity, and a membership function is configured for each fuzzy set to convert the actual value of the input quantity into a fuzzy quantity based on the membership function.

8. The method for estimating oil temperature in an electric drive system based on multi-parameter fusion according to claim 7, characterized in that: In step 52), the fuzzy control rule base consists of multiple fuzzy rules in the form of "If-Then". The antecedent of each rule is defined based on the fuzzy set of the input quantity, and the consequent is defined based on the fuzzy set of the oil temperature check factor.

9. The method for estimating oil temperature in an electric drive system based on multi-parameter fusion as described in claim 6, characterized in that: In step 53), the centroid method is used for defuzzification to obtain the oil temperature verification factor.

10. The method for estimating oil temperature in an electric drive system based on multi-parameter fusion according to claim 6, characterized in that: In step 54), the theoretical oil temperature value is converted into the output oil temperature value using the following formula: in, This is the oil temperature output value. This is the theoretical oil temperature. Oil temperature calibration factor This is the reference value for oil temperature compensation.

Images