New energy automobile BMS temperature monitoring parameter acquisition device

Abstract

The utility model relates to the technical field of new energy automobile battery management systems, in particular to a new energy automobile BMS temperature monitoring parameter acquisition device. The system comprises a composite monitoring unit, a working condition identification unit and a filtering optimization unit. According to the utility model, the composite monitoring unit uses a surface contact type temperature sensor, an internal penetration type temperature sensor and a non-contact type temperature sensor to carry out multi-dimensional acquisition on battery temperature data, and the working condition identification unit uses a support vector machine to establish an optimal classification hyperplane to classify characteristic data of different working conditions. The current operation condition of the battery is judged, the size of a filtering window is dynamically adjusted according to the working condition, the filtering optimization unit carries out multi-stage amplification and filtering processing on collected temperature data through an amplification filtering circuit, the problem that a traditional single sensor cannot comprehensively reflect the thermal state of the battery is solved, and the integrity and accuracy of the temperature data are improved.

Description

Technical Field

[0001] This utility model relates to the field of battery management system technology for new energy vehicles, and more specifically, to a temperature monitoring parameter acquisition device for a new energy vehicle BMS. Background Technology

[0002] Against the backdrop of the rapid development of the new energy vehicle industry, the battery management system (BMS), as a core component ensuring the safe and efficient operation of power batteries, plays a crucial role in temperature monitoring. Battery performance and lifespan are closely related to operating temperature; excessively high temperatures accelerate battery aging and may even trigger safety incidents such as thermal runaway; conversely, excessively low temperatures lead to decreased battery charging and discharging efficiency. Therefore, accurate and reliable temperature monitoring is fundamental to effective battery management.

[0003] Currently, traditional battery management systems mostly use a single thermistor or thermocouple, which cannot simultaneously meet the needs of high-precision temperature measurement of the cell surface, internal temperature monitoring, and acquisition of the overall temperature field distribution. Under conditions such as rapid acceleration and deceleration of the vehicle, vibration and electromagnetic interference can cause temperature data fluctuations. Existing filtering algorithms are difficult to adaptively adjust parameters in real time. In order to amplify the temperature parameters in multiple stages and improve the accuracy of temperature monitoring parameters, we propose a temperature monitoring parameter acquisition device for new energy vehicle BMS. Utility Model Content

[0004] The purpose of this invention is to provide a BMS temperature monitoring parameter acquisition device for new energy vehicles to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, this utility model provides a BMS temperature monitoring parameter acquisition device for new energy vehicles, including a composite monitoring unit, a working condition identification unit, and a filtering optimization unit. The composite monitoring unit is connected to the working condition identification unit, and the working condition identification unit is connected to the filtering optimization unit.

[0006] The composite monitoring unit uses surface contact temperature sensors, internal penetrating temperature sensors, and non-contact temperature sensors to collect battery temperature data from multiple dimensions. The operating condition identification unit acquires vehicle acceleration, motor current, and voltage data. Using a support vector machine, it constructs an optimal classification hyperplane to classify the feature data of different operating conditions, determine the current operating condition of the battery, and dynamically adjusts the size of the filtering window according to the operating condition. The filtering optimization unit performs multi-level amplification and filtering processing on the collected temperature data through an amplification and filtering circuit.

[0007] Compared with the prior art, the beneficial effects of this utility model are as follows:

[0008] 1. This new energy vehicle BMS temperature monitoring parameter acquisition device uses a composite monitoring unit to collect battery temperature data in multiple dimensions using surface contact temperature sensors, internal penetrating temperature sensors and non-contact temperature sensors. It achieves three-dimensional monitoring of the battery temperature field from a single point on the battery surface, deep inside, to the overall temperature field, solving the problem that traditional single sensors cannot fully reflect the battery thermal state, improving the completeness and accuracy of temperature data, and providing more realistic temperature information for the BMS.

[0009] 2. The operating condition identification unit acquires vehicle acceleration, motor current, and voltage data. Using a support vector machine, it constructs an optimal classification hyperplane to classify the feature data of different operating conditions, determining the current operating condition of the battery. The filtering window size is dynamically adjusted based on the operating condition. The filtering optimization unit performs multi-stage amplification and filtering processing on the collected temperature data through an amplification and filtering circuit. Under complex operating conditions such as rapid acceleration and deceleration, this significantly improves the signal-to-noise ratio of the temperature data, reduces data fluctuations, and ensures the accuracy and reliability of the temperature information acquired by the BMS, avoiding decision-making errors caused by data distortion. Simultaneously, by using a multi-stage amplification and filtering circuit, filtering before the next stage of amplification effectively suppresses the influence of high-frequency noise, improving the accuracy of temperature monitoring.

[0010] As a further improvement to this technical solution, the composite monitoring unit collects data by attaching a surface contact temperature sensor to the battery surface, uses an internal penetrating temperature sensor to obtain the internal temperature of the battery, and utilizes a non-contact temperature sensor to obtain the overall temperature distribution of the battery pack.

[0011] The beneficial effects of the above-mentioned further improvements are that by comprehensively utilizing surface contact, internal penetration, and non-contact temperature sensors, multi-dimensional and three-dimensional accurate monitoring can be achieved from single points on the battery surface, deep internal layers, to the overall temperature field. This comprehensively captures temperature information from different parts of the battery and the battery as a whole, effectively overcoming the shortcomings of single-sensor monitoring. It provides more complete, accurate, and systematic temperature data for the BMS of new energy vehicles, helping to achieve efficient and reliable battery thermal management.

[0012] As a further improvement to this technical solution, the working condition identification unit divides the acquired data samples into a training set and a test set, uses the training set data to train the support vector machine model, and continuously optimizes the model performance by adjusting the model parameters.

[0013] As a further improvement to this technical solution, the working condition identification unit adopts a polynomial kernel function to map the nonlinear data in the original low-dimensional space to a high-dimensional space, making the data linearly separable in the high-dimensional space.

[0014] The beneficial effects of the above-mentioned further improvements are that by combining multi-source data with support vector machines to achieve accurate working condition classification, and by dynamically adjusting the size of the filtering window according to the working condition, the filtering algorithm can adaptively match the interference characteristics of different vehicle operating states, effectively suppress the interference of vibration, electromagnetic and other noises on temperature data in complex working conditions, significantly improve the real-time performance and accuracy of temperature data acquisition, and provide a reliable decision-making basis for BMS.

[0015] As a further improvement to this technical solution, the filtering optimization unit includes an amplification and filtering circuit, wherein the amplification and filtering circuit consists of a first-stage amplification circuit, a filtering circuit and a second-stage amplification circuit, and the first-stage amplification circuit includes a transistor Q1.

[0016] The base of transistor Q1 is connected to one end of resistor R1 and in parallel with one end of resistor R4. The other end of resistor R1 is connected to the positive terminal of input voltage UI. The emitter of transistor Q1 is connected to the negative terminal of input voltage UI. The collector of transistor Q1 is connected to one end of resistor R3 and in parallel with one end of resistor R4.

[0017] As a further improvement to this technical solution, the filtering optimization unit includes a filtering circuit, wherein the filtering circuit includes an operational amplifier A;

[0018] Pin 2 of the operational amplifier A is connected in parallel with one end of resistor R6 and one end of capacitor C1. Pin 3 of the operational amplifier A is connected to one end of resistor R5, and the other end of resistor R5 is grounded. Pin 1 of the operational amplifier A is connected to one end of resistor R6 and the other end of capacitor C1.

[0019] As a further improvement to this technical solution, the filtering optimization unit includes a diode amplifier circuit, wherein the diode amplifier circuit includes a transistor Q2;

[0020] The collector of transistor Q2 is connected to one end of resistor R7 and in parallel to the positive terminal of output voltage UO. Resistor R7 is connected in parallel to resistors R2 and R3 and connected to power supply VCC. The emitter of transistor Q2 is connected to the negative terminal of output voltage UO.

[0021] The beneficial effects of the above-mentioned further improvements are that the multi-stage amplification and filtering circuit can enhance weak temperature signals step by step, while selectively filtering out interference in different frequency bands, significantly improving signal amplitude and purity, ensuring accurate and stable temperature data during transmission and processing, providing reliable temperature parameter data for new energy vehicle BMS, and helping to achieve efficient and accurate battery thermal management. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the overall process of this utility model;

[0023] Figure 2This is the amplification and filtering circuit diagram of this utility model.

[0024] The meanings of the labels in the diagram are as follows:

[0025] 100. Composite monitoring unit; 200. Operating condition identification unit; 300. Filtering optimization unit. Detailed Implementation

[0026] The technical solutions of the embodiments of this utility model will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this utility model, and not all embodiments. Based on the embodiments of this utility model, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this utility model.

[0027] Traditional battery management systems often use a single thermistor or thermocouple, which cannot simultaneously meet the needs of high-precision temperature measurement of the cell surface, internal temperature monitoring, and acquisition of the overall temperature field distribution. Under conditions such as rapid acceleration and deceleration of the vehicle, vibration and electromagnetic interference can cause temperature data fluctuations. Existing filtering algorithms are difficult to adaptively adjust parameters in real time. In order to amplify the temperature parameters in multiple stages and improve the accuracy of temperature monitoring parameters, the following measures are needed.

[0028] like Figure 1 As shown, this utility model provides a BMS temperature monitoring parameter acquisition device for new energy vehicles, including a composite monitoring unit 100, a working condition identification unit 200 and a filtering optimization unit 300. The composite monitoring unit 100 is connected to the working condition identification unit 200, and the working condition identification unit 200 is connected to the filtering optimization unit 300.

[0029] The composite monitoring unit 100 uses surface contact temperature sensors, internal penetrating temperature sensors and non-contact temperature sensors to collect battery temperature data from multiple dimensions. The operating condition identification unit 200 acquires vehicle acceleration, motor current and voltage data, and uses support vector machines to classify the feature data of different operating conditions by constructing an optimal classification hyperplane to determine the current operating condition of the battery. The size of the filtering window is dynamically adjusted according to the operating condition. The filtering optimization unit 300 performs multi-level amplification and filtering processing on the collected temperature data through an amplification and filtering circuit.

[0030] In order to better collect multi-dimensional data, the composite monitoring unit 100 collects data by attaching a surface contact temperature sensor to the battery surface, uses an internal penetrating temperature sensor to obtain the internal temperature of the battery, and uses a non-contact temperature sensor to obtain the overall temperature distribution of the battery pack.

[0031] Surface contact temperature sensor: Utilizing high-precision NTC thermistors, which offer high sensitivity, good linearity, and stability, enabling precise sensing of changes in battery surface temperature. For example, thin-film NTC thermistors provide rapid response and are small in size, facilitating close contact with the battery surface.

[0032] Internal penetrating temperature sensor: Employing fiber optic grating temperature sensors and utilizing fiber optic sensing technology, this type of sensor enables distributed measurement of the battery's internal temperature, unaffected by electromagnetic interference, and can penetrate deep into the battery to obtain the temperature of critical components. For example, the fiber optic grating sensor can be embedded inside the battery module, arranged along key locations such as the electrodes and electrolyte, to monitor the internal temperature in real time.

[0033] Non-contact temperature sensor: Selecting an infrared thermal imaging sensor can quickly acquire an image of the temperature distribution on the surface of the battery pack, and intuitively present the overall temperature status of the battery pack through thermal imaging technology. It is suitable for large-area and rapid temperature monitoring.

[0034] At the center of each battery cell surface, an NTC thermistor is tightly attached using thermally conductive silicone or tape to ensure good heat conduction and reduce measurement errors. During battery module assembly, fiber optic grating sensors are arranged along the designed path and fixed in appropriate positions inside the battery to ensure close contact between the sensors and the internal components of the battery, accurately measuring the internal temperature. Infrared thermal imaging sensors are installed at appropriate positions on the top or side of the battery pack to ensure that their field of view can cover the entire surface of the battery pack and obtain complete temperature distribution information.

[0035] In order to better train the support vector machine model, the working condition recognition unit 200 divides the acquired data samples into training set and test set, uses the training set data to train the support vector machine model, and continuously optimizes the model performance by adjusting the model parameters.

[0036] By utilizing multi-source parameters such as vehicle acceleration, motor current, and voltage, and scientifically dividing the acquired data samples into training and testing sets, the support vector machine model is systematically trained using the training set data. During training, model parameters are flexibly adjusted to continuously optimize model performance. Based on machine learning algorithms, accurate classification of various operating conditions, such as normal driving, rapid acceleration, and rapid deceleration, is achieved, providing a reliable basis for dynamic adjustment of filtering parameters and enabling the filtering algorithm to better adapt to temperature monitoring needs under different operating conditions. This process effectively improves the model's generalization ability, ensuring that in practical applications, the operating condition identification unit 200 can quickly and accurately determine the battery's operating condition. Compared to models that have not undergone scientific training and optimization, the identification accuracy is significantly improved, and the probability of false positives is effectively reduced.

[0037] In order to better process nonlinear data, the working condition identification unit 200 uses a polynomial kernel function to map the nonlinear data in the original low-dimensional space to a high-dimensional space, making the data linearly separable in the high-dimensional space.

[0038] By using kernel function mapping, nonlinear features such as acceleration change rate and current fluctuation patterns are transformed into linearly separable patterns in a high-dimensional space, significantly improving the accuracy of work condition recognition. By adjusting the order and coefficients of the polynomial kernel function, the feature mapping dimension is optimized, avoiding overfitting while ensuring classification accuracy. This allows the model to maintain good performance even when facing complex work conditions that have not been trained. The kernel function technique avoids the computational explosion problem caused by explicit high-dimensional mapping, keeping the real-time work condition recognition latency within 10ms, meeting the fast decision-making requirements of BMS.

[0039] like Figure 2 As shown, the filter optimization unit 300 includes an amplification and filtering circuit, which consists of a first-stage amplifier circuit, a filter circuit, and a second-stage amplifier circuit. The first-stage amplifier circuit includes a transistor Q1.

[0040] The base of transistor Q1 is connected to one end of resistor R1 and in parallel with one end of resistor R4. The other end of resistor R1 is connected to the positive terminal of the input voltage UI. The emitter of transistor Q1 is connected to the negative terminal of the input voltage UI. The collector of transistor Q1 is connected to one end of resistor R3 and in parallel with one end of resistor R4.

[0041] In this circuit, transistor Q1 and resistors R1, R2, R3, and R4 constitute a first-stage amplifier circuit. When the input voltage UI changes slightly, it will cause a change in the base current of transistor Q1. According to the current amplification effect of transistor Q1, the collector current of transistor Q1 will be amplified. Due to the increase in the collector current of transistor Q1, the voltage across resistor R4 will increase, thereby achieving a first-stage amplification of the input voltage UI.

[0042] To prevent noise signals from being amplified, the filtering optimization unit 300 includes a filtering circuit, which includes an operational amplifier A.

[0043] Pin 2 of operational amplifier A is connected in parallel with one end of resistor R6 and one end of capacitor C1. Pin 3 of operational amplifier A is connected to one end of resistor R5, and the other end of resistor R5 is grounded. Pin 1 of operational amplifier A is connected to one end of resistor R6 and the other end of capacitor C1.

[0044] In this circuit, the voltage amplified by the first-stage amplifier circuit is connected to a filter circuit consisting of operational amplifier A, resistors R5 and R6, and capacitor C1. Based on the virtual short and virtual open characteristics of operational amplifier A, the voltages at pin 2 of the non-inverting input and pin 1 of the inverting input are approximately equal (virtual short), and the current flowing into pin 1 of the inverting input is approximately zero (virtual open). The input voltage is then filtered by the low-pass filter network consisting of resistor R6 and capacitor C1.

[0045] In order to better amplify the collected temperature data in two stages, the filter optimization unit 300 includes a diode amplifier circuit, which includes a transistor Q2.

[0046] The collector of transistor Q2 is connected to one end of resistor R7 and in parallel to the positive terminal of output voltage UO. Resistor R7 is connected in parallel to resistors R2 and R3 and connected to power supply VCC. The emitter of transistor Q2 is connected to the negative terminal of output voltage UO.

[0047] In this circuit, the voltage after low-pass filtering by the filter circuit is amplified a second time by the diode amplifier circuit composed of transistor Q2 and resistor R7, which is the same as the first-stage amplifier circuit, to obtain the output voltage UO.

[0048] The input voltage UI enters the first-stage amplifier circuit for amplification, resulting in an amplified voltage signal. This signal then enters the low-pass filter circuit. Since the capacitive reactance of a capacitor decreases as the frequency increases, high-frequency noise signals are more easily bypassed to ground through the capacitor, thus filtering out high-frequency noise and allowing only low-frequency useful signals to pass. The filtered signal then enters the second-stage amplifier circuit for further amplification to an appropriate amplitude for subsequent processing. By filtering before proceeding to the next stage of amplification, the influence of high-frequency noise can be effectively suppressed, improving the quality of the entire circuit's output signal and ensuring the accuracy and reliability of temperature acquisition signal detection.

[0049] In summary, the working principle of this solution is as follows:

[0050] This new energy vehicle BMS temperature monitoring parameter acquisition device uses a composite monitoring unit 100 to collect battery temperature data in multiple dimensions using surface contact temperature sensors, internal penetrating temperature sensors and non-contact temperature sensors. It achieves three-dimensional monitoring of the battery temperature field from a single point on the battery surface, deep inside, to the overall temperature field, solving the problem that traditional single sensors cannot fully reflect the battery thermal state, improving the integrity and accuracy of temperature data, and providing more realistic temperature information for the BMS.

[0051] The operating condition identification unit 200 acquires vehicle acceleration, motor current, and voltage data. Using a support vector machine, it constructs an optimal classification hyperplane to classify the feature data of different operating conditions, determine the current operating condition of the battery, and dynamically adjusts the size of the filtering window according to the operating condition. The filtering optimization unit 300 performs multi-stage amplification and filtering processing on the collected temperature data through an amplification and filtering circuit. Under complex operating conditions such as rapid acceleration and deceleration, it can significantly improve the signal-to-noise ratio of the temperature data, reduce data fluctuations, ensure the authenticity and reliability of the temperature information acquired by the BMS, and avoid decision-making errors caused by data distortion. At the same time, by using a multi-stage amplification and filtering circuit, filtering before the next stage of amplification can effectively suppress the influence of high-frequency noise and improve the accuracy of temperature monitoring.

[0052] The foregoing has shown and described the basic principles, main features, and advantages of this utility model. Those skilled in the art should understand that this utility model is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the utility model. Various changes and modifications can be made to this utility model without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed utility model. The scope of protection of this utility model is defined by the appended claims and their equivalents.

Claims

1. A device for acquiring temperature monitoring parameters of a BMS (Battery Management System) for new energy vehicles, characterized in that: It includes a composite monitoring unit (100), a working condition identification unit (200), and a filtering optimization unit (300). The composite monitoring unit (100) is connected to the working condition identification unit (200), and the working condition identification unit (200) is connected to the filtering optimization unit (300). The composite monitoring unit (100) uses surface contact temperature sensors, internal penetration temperature sensors and non-contact temperature sensors to collect battery temperature data in multiple dimensions. The operating condition identification unit (200) acquires vehicle acceleration, motor current and voltage data. Using support vector machines, it constructs the optimal classification hyperplane to classify the feature data of different operating conditions, determines the current operating condition of the battery, and dynamically adjusts the size of the filtering window according to the operating condition. The filtering optimization unit (300) performs multi-level amplification and filtering processing on the collected temperature data through the amplification and filtering circuit.

2. The new energy vehicle BMS temperature monitoring parameter acquisition device according to claim 1, characterized in that: The composite monitoring unit (100) collects data by attaching a surface contact temperature sensor to the battery surface, uses an internal penetrating temperature sensor to obtain the internal temperature of the battery, and uses a non-contact temperature sensor to obtain the overall temperature distribution of the battery pack.

3. The new energy vehicle BMS temperature monitoring parameter acquisition device according to claim 1, characterized in that: The working condition identification unit (200) divides the acquired data samples into a training set and a test set, uses the training set data to train the support vector machine model, and continuously optimizes the model performance by adjusting the model parameters.

4. The new energy vehicle BMS temperature monitoring parameter acquisition device according to claim 1, characterized in that: The operating condition identification unit (200) uses a polynomial kernel function to map the nonlinear data in the original low-dimensional space to a high-dimensional space, making the data linearly separable in the high-dimensional space.

5. The new energy vehicle BMS temperature monitoring parameter acquisition device according to claim 1, characterized in that: The filtering optimization unit (300) includes an amplification and filtering circuit, wherein the amplification and filtering circuit consists of a first-stage amplification circuit, a filtering circuit and a second-stage amplification circuit, and the first-stage amplification circuit includes a transistor Q1. The base of transistor Q1 is connected to one end of resistor R1 and in parallel with one end of resistor R4. The other end of resistor R1 is connected to the positive terminal of input voltage UI. The emitter of transistor Q1 is connected to the negative terminal of input voltage UI. The collector of transistor Q1 is connected to one end of resistor R3 and in parallel with one end of resistor R4.

6. The new energy vehicle BMS temperature monitoring parameter acquisition device according to claim 1, characterized in that: The filtering optimization unit (300) includes a filtering circuit, wherein the filtering circuit includes an operational amplifier A; Pin 2 of the operational amplifier A is connected in parallel with one end of resistor R6 and one end of capacitor C1. Pin 3 of the operational amplifier A is connected to one end of resistor R5, and the other end of resistor R5 is grounded. Pin 1 of the operational amplifier A is connected to one end of resistor R6 and the other end of capacitor C1.

7. The new energy vehicle BMS temperature monitoring parameter acquisition device according to claim 1, characterized in that: The filtering optimization unit (300) includes a diode amplifier circuit, wherein the diode amplifier circuit includes a transistor Q2; The collector of transistor Q2 is connected to one end of resistor R7 and in parallel to the positive terminal of output voltage UO. Resistor R7 is connected in parallel to resistors R2 and R3 and connected to power supply VCC. The emitter of transistor Q2 is connected to the negative terminal of output voltage UO.

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