Multi-sensor fusion battery state monitoring system for low-speed electric vehicle

Abstract

The invention discloses a multi-sensor fusion battery state monitoring system for a low-speed electric vehicle, which comprises a lithium ion battery pack, a sensor group mounted in the lithium ion battery pack, an analog-to-digital converter, a microprocessor, a memory and an early warning module, and is characterized in that the memory stores processing schemes for different states of the lithium ion battery pack; the sensor group comprises a temperature sensor, a voltage sensor and a current sensor, the temperature sensor is used for detecting internal temperature data of the lithium ion battery pack, and the voltage sensor and the current sensor are respectively used for monitoring transmission voltage and current of the lithium ion battery pack; and the analog-to-digital converter is electrically connected with the sensor group. According to the method, the state of charge (SOC), the state of health (SOH) and the danger degree of the battery are estimated more accurately, the use safety and the service life of the battery are improved, and the safety of the battery when the low-speed electric vehicle is used is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of battery status monitoring, and more particularly to a multi-sensor fusion battery status monitoring system for low-speed electric vehicles. Background Art

[0002] Dashboard and on-board display: Most low-speed electric vehicles will display key information such as battery charge and range on the dashboard, which can be used to intuitively understand the basic status of the battery. In addition, some models are also equipped with an on-board display that can provide more detailed battery information, such as battery temperature and number of charge cycles. However, traditional low-speed electric vehicle battery status monitoring systems usually use limited sensors and a single estimation algorithm, which also leads to large errors between the monitoring values ​​and the actual battery status data of low-speed electric vehicles, seriously affecting the battery status assessment results. In addition, the single estimation algorithm will affect the poor assessment ability, the warning time will be seriously delayed, and the battery fault information cannot be understood and resolved in time. Summary of the Invention

[0003] In view of the problems existing in the prior art, the object of the present invention is to provide a multi-sensor fusion battery status monitoring system for low-speed electric vehicles.

[0004] In order to solve the above problems, the present invention adopts the following technical solutions: A multi-sensor fusion battery status monitoring system for low-speed electric vehicles includes a lithium-ion battery pack, a sensor group installed in the lithium-ion battery pack, an analog-to-digital converter, a microprocessor, a memory, and an early warning module. The memory stores processing solutions for different states of the lithium-ion battery pack; The sensor group consists of a temperature sensor, a voltage sensor and a current sensor. The temperature sensor is used to detect the internal temperature data of the lithium-ion battery pack, and the voltage sensor and current sensor are used to monitor the transmission voltage and current of the lithium-ion battery pack respectively. The analog-to-digital converter is electrically connected to the sensor group, and is used to receive monitoring data from the temperature sensor, voltage sensor, current sensor, and pressure sensor, convert the analog signal into a digital signal, and upload the converted data signal to the microprocessor; The microprocessor includes a communication control unit, a data processing unit and a logic control unit. The communication control unit is used for transmitting and communicating data signals. The data processing unit is used to receive the monitoring data received by the communication control unit and perform SOH state estimation, SOC state estimation and temperature field distribution processing on the detection data. The logic control unit sends warning instructions and lithium-ion battery pack voltage and current adjustment instructions based on the processing results of the data processing unit. The early warning module sends corresponding early warning information according to the early warning instruction of the logic control unit.

[0005] As a further description of the above technical solution: The data processing unit uses an adaptive unscented Kalman filter algorithm to process the monitoring data and incorporates a hazard index analysis algorithm. The formula of the hazard index analysis algorithm is as follows: Combine the FedAvg formula with data volume weighting ; in is the global simulation weight, D is the node of all data volumes; The inverse of the health theory index is integrated to predict the crisis index Prisk phase; ; in Function, maps data to the 0-1 interval; 、 and is the weight parameter; Calculate the dynamic trend of thermal runaway; Converts a poor battery health status into a high-risk status indicator.

[0006] As a further description of the above technical solution: The adaptive unscented Kalman filter algorithm is combined with the least squares method to filter and reduce noise. The noise of the sensor group and the accuracy of real-time data are improved as follows: ; Where K is the state vector of the system at the moment; A is the state transition evolution matrix, the amount of data from one state to another; Input data for external data at time k; for Process noise, data jitter of the detection power; is the noisy detection data at time k.

[0007] As a further description of the above technical solution: The temperature sensor is also used to detect the working environment temperature of the lithium-ion battery pack.

[0008] As a further description of the above technical solution: The sensor group further includes a pressure sensor, which is used to detect the expansion state of the lithium-ion battery pack.

[0009] As a further description of the above technical solution: The early warning instructions of the logic control unit are divided into a three-level response mechanism: a first-level early warning, reducing the output power; a second-level early warning, starting the high-efficiency heat dissipation system; a third-level early warning, executing an emergency power outage.

[0010] As a further description of the above technical solution: A plurality of folders are provided in the memory, and each folder stores different status data of the lithium-ion battery pack and processing instructions corresponding to the status data.

[0011] Compared with the prior art, the advantages of the present invention are: (1) This solution can more accurately estimate the battery's state of charge (SOC), state of health (SOH), and degree of danger, improve battery safety and lifespan, and ensure battery safety when used in low-speed electric vehicles.

[0012] (2) This solution ensures the timeliness and accuracy of battery status data acquisition, improves the timeliness of early warning, and ensures the safety of lithium-ion battery packs and low-speed electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 Schematic diagram of the principle of the battery status monitoring system of the present invention; Figure 2 Schematic diagram of the working principle of the microprocessor of the present invention; Description of the numbers in the figure: 1. Lithium-ion battery pack; 2. Sensor group; 21. Temperature sensor; 22. Voltage sensor; 23. Current sensor; 24. Pressure sensor; 3. Analog-to-digital converter; 4. Microprocessor; 41. Communication control unit; 42. Data processing unit; 43. Logic control unit; 5. Memory; 6. Early warning module. DETAILED DESCRIPTION

[0014] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention; See also Figure 1 and Figure 2 , the present invention provides the following embodiments: A multi-sensor fusion battery status monitoring system for low-speed electric vehicles includes a lithium-ion battery pack 1, a sensor group 2 installed in the lithium-ion battery pack 1, an analog-to-digital converter 3, a microprocessor 4, a memory 5 and an early warning module 6. The memory 5 stores processing solutions for different states of the lithium-ion battery pack 1; the sensor group 2 consists of a temperature sensor 21, a voltage sensor 22 and a current sensor 23. The temperature sensor 21 is used to detect the internal temperature data of the lithium-ion battery pack 1, and the voltage sensor 22 and the current sensor 23 are used to monitor the transmission voltage and current of the lithium-ion battery pack 1 respectively; the analog-to-digital converter 3 is electrically connected to the sensor group 2 and is used to receive the temperature sensor 21, the voltage sensor 22 and the current sensor 23. The microprocessor 4 receives the monitoring data of the pressure sensor 23 and the pressure sensor 24, converts the analog signal into a data signal, and uploads the converted data signal to the microprocessor 4; the microprocessor 4 includes a communication control unit 41, a data processing unit 42 and a logic control unit 43, the communication control unit 41 is used for transmitting and communicating the data signal; the data processing unit 42 is used to receive the monitoring data received by the communication control unit 41, and perform SOH state estimation, SOC state estimation and temperature field distribution processing on the detection data; the logic control unit 43 sends an early warning instruction and a voltage and current adjustment instruction of the lithium-ion battery pack 1 according to the processing result of the data processing unit 42; the early warning module 6 sends corresponding early warning information according to the early warning instruction of the logic control unit 43.

[0015] The multi-sensor fusion battery status monitoring system comprehensively utilizes multiple sensor data and intelligent algorithms to achieve comprehensive and accurate monitoring of the battery pack status. By integrating multiple sensors such as voltage, current, and temperature, it can more accurately estimate the battery's state of charge (SOC), state of health (SOH), and degree of danger, thereby improving battery safety and lifespan, and ensuring battery safety when used in low-speed electric vehicles.

[0016] The data processing unit 42 uses an adaptive unscented Kalman filter algorithm to process the monitoring data and incorporates a hazard index analysis algorithm. The formula of the hazard index analysis algorithm is as follows: Combine the FedAvg formula with data volume weighting ; in is the global simulation weight, D is the node of all data volumes; The inverse of the health theory index is integrated to predict the crisis index Prisk phase; ; in Function, maps data to the 0-1 interval; 、 and is the weight parameter; Calculate the dynamic trend of thermal runaway; Converts a poor battery health status into a high-risk status indicator.

[0017] By adding data volume weighting, the fairness of model and data measurement can be improved, and the warning threshold can be increased to improve security warning performance and solve the problem of difficulty in extracting features from multiple parameters.

[0018] The adaptive unscented Kalman filter algorithm is combined with the least squares method to perform filtering and noise reduction. The noise and real-time data accuracy of sensor group 2 are improved as follows: ; Where K is the state vector of the system at the moment; A is the state transition evolution matrix, the amount of data from one state to another; Input data for external data at time k; for Process noise, data jitter of the detection power; is the noisy detection data at time k.

[0019] Adaptive unscented Kalman filtering and least squares filtering and noise reduction are used to filter sensor noise and improve real-time data accuracy, thereby ensuring the timeliness and accuracy of battery status data acquisition and facilitating subsequent battery judgment and implementation of response measures.

[0020] The temperature sensor 21 is also used to detect the working environment temperature of the lithium-ion battery pack 1 .

[0021] By acquiring the ambient temperature, the influence of the ambient temperature on the lithium-ion battery pack 1 can be understood, and whether the abnormal battery temperature is affected by the environment can be determined based on the ambient temperature.

[0022] The sensor group 2 further includes a pressure sensor 24 , which is used to detect the expansion state of the lithium-ion battery pack 1 .

[0023] The pressure sensor 24 can be used to understand the deformation state of the lithium-ion battery pack 1 and timely obtain information on excessive battery deformation to ensure normal driving of the low-speed battery vehicle.

[0024] The warning instructions of the logic control unit 43 are divided into a three-level response mechanism: a first-level warning, reducing the output power; a second-level warning, starting the high-efficiency heat dissipation system; a third-level warning, executing an emergency power off.

[0025] By setting up a three-level response mechanism and presetting the triggering conditions of the early warning mechanism, such as when the health value is lower than 70% or the risk value exceeds 90%, timely early warning and control of the voltage and current output of the lithium-ion battery pack 1 can be achieved to prevent further deterioration of battery abnormalities.

[0026] A plurality of folders are provided in the memory 5 , and each folder stores different status data of the lithium-ion battery pack 1 and processing instructions corresponding to the status data.

[0027] The early warning processing instructions are preset in advance. When the lithium-ion battery pack 1 has an abnormal problem, the processing instructions matching the status data in the memory 5 can be directly called for execution, thereby improving the timeliness of the early warning and ensuring the safety of the lithium-ion battery pack 1 and the low-speed electric vehicle.

[0028] The above description is merely a preferred embodiment of the present invention; however, the scope of protection of the present invention is not limited thereto. Any person skilled in the art who, within the technical scope disclosed by the present invention, makes equivalent substitutions or modifications based on the technical solutions and improved concepts of the present invention shall be covered by the scope of protection of the present invention.

Claims

1. A multi-sensor fusion battery status monitoring system for low-speed electric vehicles, characterized by: The invention comprises a lithium-ion battery pack (1), a sensor group (2) installed in the lithium-ion battery pack (1), an analog-to-digital converter (3), a microprocessor (4), a memory (5) and an early warning module (6), wherein the memory (5) stores processing solutions for different states of the lithium-ion battery pack (1); The sensor group (2) comprises a temperature sensor (21), a voltage sensor (22) and a current sensor (23); the temperature sensor (21) is used to detect internal temperature data of the lithium-ion battery pack (1); and the voltage sensor (22) and the current sensor (23) are used to monitor the transmission voltage and current of the lithium-ion battery pack (1), respectively. The analog-to-digital converter (3) is electrically connected to the sensor group (2) and is used to receive monitoring data from the temperature sensor (21), the voltage sensor (22), the current sensor (23), and the pressure sensor (24), convert the analog signal into a digital signal, and upload the converted data signal to the microprocessor (4); The microprocessor (4) includes a communication control unit (41), a data processing unit (42) and a logic control unit (43). The communication control unit (41) is used for data signal transmission communication; the data processing unit (42) is used to receive the monitoring data received by the communication control unit (41), and perform SOH state estimation, SOC state estimation and temperature field distribution processing on the detection data; the logic control unit (43) sends an early warning instruction and a voltage and current adjustment instruction of the lithium-ion battery pack (1) according to the processing result of the data processing unit (42); The early warning module (6) sends corresponding early warning information according to the early warning instruction of the logic control unit (43).

2. The multi-sensor fusion battery status monitoring system for low-speed electric vehicles according to claim 1, characterized in that: The data processing unit (42) processes the monitoring data using an adaptive unscented Kalman filter algorithm and incorporates a hazard index analysis algorithm. The hazard index analysis algorithm formula is as follows: Combine the FedAvg formula with data volume weighting ; in is the global simulation weight, D is the node of all data volumes; Integrate the inverse of the health theory index to predict the crisis index Prisk phase 3. Among them Function, maps data to the 0-1 interval; 、 and is the weight parameter; Calculate the dynamic trend of thermal runaway; Converts a poor battery health status into a high-risk status indicator.

4. The multi-sensor fusion battery status monitoring system for low-speed electric vehicles according to claim 2, characterized in that: The adaptive unscented Kalman filter algorithm is combined with the least squares method to perform filtering and noise reduction. The noise of the sensor group (2) and the accuracy of real-time data are improved as follows: ； Where K is the state vector of the system at the moment; A is the state transition evolution matrix, the amount of data from one state to another; Input data for external data at time k; for Process noise, data jitter of the detection power; is the noisy detection data at time k.

5. The multi-sensor fusion battery status monitoring system for low-speed electric vehicles according to claim 1, characterized in that: The temperature sensor (21) is also used to detect the working environment temperature of the lithium-ion battery pack (1).

6. The multi-sensor fusion battery status monitoring system for low-speed electric vehicles according to claim 1, characterized in that: The sensor group (2) further includes a pressure sensor (24), and the pressure sensor (24) is used to detect the expansion state of the lithium-ion battery pack (1).

7. The multi-sensor fusion battery status monitoring system for low-speed electric vehicles according to claim 1, characterized in that: The warning instructions of the logic control unit (43) are divided into a three-level response mechanism: a first-level warning, reducing the output power; Level 2 warning, start the high-efficiency cooling system; Level 3 warning, emergency power outage.

8. The multi-sensor fusion battery status monitoring system for low-speed electric vehicles according to claim 1, characterized in that: A plurality of folders are provided in the memory (5), and each folder stores different status data of the lithium-ion battery pack (1) and processing instructions corresponding to the status data.

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