Safety monitoring system for power battery of low-speed electric vehicle

Abstract

The invention belongs to the technical field of power battery management, and discloses a low-speed electric vehicle power battery safety monitoring system which comprises a battery state monitoring unit, a data processing unit, a safety early warning module, a balance control module and an electric room temperature management module. The battery state monitoring unit is used for acquiring single voltage, total voltage, charging and discharging current and temperature data of each battery cell of the electric vehicle battery pack in real time; and the data processing unit is connected with the battery state monitoring unit and is used for converting and preprocessing the acquired data and estimating the electric quantity, the state of charge (SOC), the state of health (SOH) and the power state (SOP) of the battery cell. According to the system, the battery state monitoring module, the data processing module, the safety early warning module, the active and passive electric quantity balancing module, the temperature management module and other functional modules are integrated, so that all-directional monitoring and safety guarantee of the power battery are realized. The battery service efficiency is improved, the battery service life is prolonged, and safe operation of the battery pack is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of power battery management, and more particularly to a power battery safety monitoring system for a low-speed electric vehicle. Background Art

[0002] Low-speed electric vehicles are widely used in short-distance transportation and urban transportation due to their economy and convenience. As low-speed electric vehicles become more popular, the safety and reliability of power batteries are becoming increasingly prominent.

[0003] However, its battery management system (BMS) technology is relatively backward, with issues such as low data acquisition accuracy, poor safety warning performance, slow battery balancing, and inefficient thermal management. These issues lead to low battery efficiency, short lifespan, and even safety accidents.

[0004] In existing technologies, although BMS for high-end passenger vehicles is mature, it is expensive and difficult to directly apply to low-speed electric vehicles. Therefore, there is an urgent need for a low-cost, high-performance low-speed electric vehicle power battery safety monitoring system. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a low-speed electric vehicle power battery safety monitoring system.

[0006] In order to solve the above problems, the present invention adopts the following technical solutions: A low-speed electric vehicle power battery safety monitoring system includes: a battery status monitoring unit, a data processing unit, a safety warning module, a balancing control module and an electrical room temperature management module; The battery status monitoring unit is used to collect the single cell voltage, total voltage, charge and discharge current and each cell temperature data of the electric vehicle battery pack in real time; The data processing unit is connected to the battery status monitoring unit to convert and pre-process the collected data and estimate the battery cell capacity, state of charge (SOC), state of health (SOH) and state of power (SOP); The safety warning module is connected to the main control unit and is used to issue graded warnings based on the state estimation results and the preset safety thresholds; The balancing control module is used to achieve power balancing between battery packs through active and passive balancing strategies; The battery pack temperature management module is used to control the battery pack temperature based on the thermal runaway critical condition (TRC) and the phase change material (PCM) dynamic absorption model.

[0007] As a further solution of the present invention: the battery status monitoring unit includes a temperature acquisition module, a current acquisition module and a voltage acquisition module, the temperature acquisition module includes 12 temperature sensors, the current acquisition module includes 24 current sensors, and the voltage acquisition module includes 72 voltage sensors.

[0008] As a further solution of the present invention: the data processing unit includes: Data conversion module: Receives raw data from the battery status monitoring unit, converts analog signals into digital signals, and performs data calibration and normalization to ensure that the data format is unified and accurate; Data preprocessing module: Filters, removes noise and processes outliers on the converted data to eliminate interference signals and measurement errors, thereby improving data quality and reliability; The state estimation module is used to calculate the state of charge (SOC), state of health (SOH) and state of power (SOP) of the battery pack based on the preprocessed data; Communication control module: responsible for communicating with the vehicle control system or display unit through the CAN bus, uploading battery status data and alarm information, and receiving vehicle control instructions.

[0009] As a further solution of the present invention: the data preprocessing module uses an adaptive unscented Kalman filter algorithm and a least squares algorithm to perform filtering and noise reduction; the adaptive unscented Kalman filter algorithm model is: ,in is the state fitting quantity at time k; is the state transfer amount; is the actual observed value, is the error adjustment amount; is the Kalman gain; To control the input quantity; It is the measured quantity in the current state.

[0010] As a further solution of the present invention: the state estimation module uses an adaptive unscented Kalman filter (AUKF) algorithm based on federated learning to estimate SOC and SOH, specifically including: 1. Aggregate the local model parameters of distributed nodes through federated learning and update the global AUKF model weights. The formula is: ; in, Local AUKF parameters of the i-th node; is the size of the local dataset; 2. Adaptive crisis index calculation, integrating multiple source indicators of battery risk, the formula is: ; in, is the Sigmoid function, which normalizes the crisis index to the interval [0,1]; is the failure probability; is the coupling coefficient between the temperature change rate and the internal resistance change; is a weight parameter that controls the contribution of each indicator to the risk and determines the failure tendency of the data; Score the battery health, the lower the risk index.

[0011] As a further solution of the present invention: the safety warning module includes a three-level response mechanism: Level 1 warning: When the health level is lower than 80%, the output power is reduced; Level 2 warning: When the health level is lower than 70% or the risk value exceeds 80%, the cooling system is activated; Level 3 warning: When the health level is lower than 60% or the risk value exceeds 90%, an emergency power outage is executed.

[0012] As a further solution of the present invention: the balancing control module adopts a six-phase staggered matrix and a model predictive control algorithm to achieve efficient power balancing between battery packs.

[0013] As a further solution of the present invention: the electrical room temperature management module includes: 1. Thermal runaway critical regulation blocking strategy, the formula is: , in, is the temperature change rate, is the temperature linear term, l is the thermal conductivity of the material, T is the real-time temperature; Contributes to the discrete heat source, For the i Heat generation rate of each cell; 2. PCM dynamic absorption model: Introducing the latent heat absorption effect, the formula is: ; in, is the PCM heat absorption ratio, 0 means inactive, and 1 means complete phase change; is the PCM phase transition temperature, It is the phase change buffer zone.

[0014] As a further solution of the present invention: it also includes a remote monitoring module for uploading battery status data to a mobile device or a cloud platform via a wireless communication network.

[0015] Compared with the prior art, the advantages of the present invention are: This solution provides a low-speed electric vehicle power battery safety monitoring system. By integrating functional modules such as battery status monitoring, data processing, safety warning, active and passive battery balancing, and temperature management, the system achieves comprehensive monitoring and safety assurance of the power battery. This improves battery efficiency, extends battery life, and ensures the safe operation of the battery pack. The details are as follows: 1. High-precision data acquisition and processing: Real-time battery data is collected through multiple sensors, and filtering and noise reduction are performed using an adaptive unscented Kalman filter algorithm and a least squares algorithm, significantly improving the accuracy and reliability of the data.

[0016] 2. Intelligent state estimation: Adopting the adaptive unscented Kalman filter (AUKF) algorithm based on federated learning, it can achieve accurate estimation of battery state of charge, health state and power state, and dynamically assess battery risks through multi-source indicators.

[0017] 3. Hierarchical safety warning: Set up a three-level response mechanism (such as power reduction, cooling startup, and emergency power off), dynamically adjust the warning level according to health and risk value, and effectively prevent battery failure and thermal runaway.

[0018] 4. Efficient balancing and temperature management: Achieve efficient power balancing between battery packs through a six-phase staggered matrix and model predictive control; regulate temperature based on thermal runaway critical conditions and a dynamic absorption model of phase change materials, significantly improving the safety and life of the battery pack.

[0019] 5. Remote monitoring support: Upload battery status data to mobile devices or cloud platforms via wireless communication networks, facilitating real-time monitoring and remote management. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is one of the system block diagrams of the present invention; Figure 2 This is the second system block diagram of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention; it is obvious that the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0022] See also Figure 1-2 A low-speed electric vehicle power battery safety monitoring system includes: a battery status monitoring unit, a data processing unit, a safety warning module, a balancing control module, an electrical room temperature management module and a remote monitoring module.

[0023] Among them, the battery status monitoring unit is used to collect the single cell voltage, total voltage, charge and discharge current and temperature data of each battery cell of the electric vehicle battery pack in real time.

[0024] The data processing unit is connected to the battery status monitoring unit to convert and pre-process the collected data and estimate the battery cell capacity, state of charge (SOC), state of health (SOH) and state of power (SOP).

[0025] The safety warning module is connected to the main control unit and is used to issue graded warnings based on state estimation results and preset safety thresholds. Appropriate measures are taken according to the warning level to ensure the safe operation of the battery pack. This includes a three-level response mechanism: a level one warning (power reduction) is triggered when the system detects that the battery health is below 80%; a level two warning (cooling activation) is triggered when the health is below 70% or the risk value exceeds 80%; and a level three warning (emergency power outage) is triggered when the health is below 60% or the risk value exceeds 90%.

[0026] The balancing control module combines a six-phase staggered matrix with a model predictive control (MPC) algorithm, incorporating active and passive balancing strategies to efficiently and accurately manage power and temperature between battery packs. The six-phase matrix disperses and balances current, reducing localized heating; MPC dynamically optimizes power distribution and temperature control, predicting future conditions and adjusting strategies. Active balancing prioritizes energy transfer to low-temperature battery packs, while passive balancing serves as a backup for safety. This combination significantly improves balancing efficiency (reducing energy loss by 30%), controls temperature differences within ±2°C, extends battery life, and adapts to complex operating conditions such as fast charging and low temperatures, achieving coordinated optimization of power and temperature.

[0027] The battery pack temperature management module is used to control the battery pack temperature based on the thermal runaway critical condition (TRC) and the phase change material (PCM) dynamic absorption model.

[0028] The remote monitoring module is used to upload battery status data to mobile devices or cloud platforms via wireless communication networks, supporting remote fault diagnosis and OTA upgrades. The remote monitoring module uploads battery status data to the cloud platform via 4G / 5G networks, allowing users to view battery status and warning information in real time through a mobile app.

[0029] Through the collaborative operation of the above modules, this system achieves comprehensive safety monitoring of the power battery of low-speed electric vehicles, significantly improving the reliability, safety, and service life of the batteries. This invention is applicable to low-speed four-wheeled vehicles and low-speed two- and three-wheeled vehicles, and has broad application prospects.

[0030] Furthermore, the battery status monitoring unit includes a temperature acquisition module, a current acquisition module, and a voltage acquisition module. The temperature acquisition module includes 12 temperature sensors, the current acquisition module includes 24 current sensors, and the voltage acquisition module includes 72 voltage sensors. Deploying 12 temperature sensors, 24 current sensors, and 72 voltage sensors in the low-speed electric vehicle battery pack collects real-time data on single cell voltage, total voltage, charge and discharge current, and cell temperature, ensuring data accuracy of 15 bits and a response time of 5ms.

[0031] The data processing unit includes a data conversion module, a data preprocessing module, a state estimation module and a communication control module.

[0032] The data conversion module receives the raw data from the battery status monitoring unit, converts the analog signal into a digital signal, and performs data calibration and normalization to ensure that the data format is unified and accurate.

[0033] The data preprocessing module filters, denoises, and processes outliers on the converted data, eliminating interference signals and measurement errors to improve data quality and reliability. The data preprocessing module uses an adaptive unscented Kalman filter algorithm and a least squares algorithm for filtering and denoising. The unscented Kalman filter (UKF) directly approximates the state distribution of a nonlinear system through an unscented transformation, avoiding linearization errors and making it particularly suitable for nonlinear systems. The adaptive unscented Kalman filter algorithm model is: ,in is the state fitting quantity at time k; is the state transfer amount; is the actual observed value, is the error adjustment amount; is the Kalman gain; To control the input quantity; It is the measured quantity in the current state.

[0034] Adaptive mechanism dynamically adjusts Kalman gain and noise statistical characteristics, further reducing the impact of model uncertainty and sudden noise, and improving the fitting quality The introduction of the least squares algorithm can assist in processing redundant observation data and suppress accidental errors. When combined with the AUKF, the system's tolerance to outliers or noise is significantly enhanced. The UKF avoids the Jacobian matrix calculation required by the extended Kalman filter (EKF), reducing computational complexity. The complementary use of the least squares method can reduce the number of iterations, balancing accuracy and real-time requirements, especially when dealing with large data volumes.

[0035] Through algorithm fusion and adaptive optimization, this design achieves a good balance between accuracy, robustness and efficiency, solving the problem of low real-time data accuracy in current battery management systems.

[0036] The state estimation module is used to calculate the battery pack's state of charge (SOC), state of health (SOH), and state of power (SOP) based on preprocessed data. The module uses the federated learning-based adaptive unscented Kalman filter (AUKF) algorithm to estimate SOC and SOH, specifically including: 1. Aggregate the local model parameters of distributed nodes through federated learning and update the global AUKF model weights. The formula is: ; in, Local AUKF parameters of the i-th node; is the size of the local dataset; 2. Adaptive crisis index calculation, integrating multiple source indicators of battery risk, the formula is: ; in, is the Sigmoid function, which normalizes the crisis index to the interval [0,1]; is the failure probability; is the coupling coefficient between the temperature change rate and the internal resistance change; is a weight parameter that controls the contribution of each indicator to the risk and determines the failure tendency of the data; Score the battery health, the lower the risk index.

[0037] Federated learning AUIKF aggregates local parameters of distributed nodes (such as SOC / SOH data) and uses global information to optimize model weights, significantly improving the accuracy of state estimation while reducing the impact of single node data deviation. The adaptive crisis index monitors multi-source risk indicators (failure probability, temperature change rate, health score) in real time and dynamically adjusts weights ( l 1, l 2, l 3) Enhance the system’s robustness to abnormal operating conditions (such as overheating and internal resistance mutation). Federated learning allows each node (such as different battery cells or vehicles) to share model parameters instead of raw data, which not only protects data privacy but also improves the system’s robustness to abnormal operating conditions (such as overheating and internal resistance mutation). Integrate multi-node knowledge to solve the problem of insufficient data volume in a single node. The output is normalized by the Sigmoid function to intuitively quantify the battery risk level (0-1). Combined with the real-time state estimation of AUIKF, maintenance or power limit strategies (SOP adjustment) can be triggered in advance to avoid failures such as thermal runaway. The "adaptive" characteristics of AUIKF are integrated with the multi-indicator of the crisis index ( 、 ) work together to optimize computing efficiency and ensure the long-term adaptability of the model as batteries age and the environment changes.

[0038] The communication control module is responsible for communicating with the vehicle control system or display unit through the CAN bus, uploading battery status data and alarm information, and receiving vehicle control instructions (such as charge and discharge management requests).

[0039] The electrical room temperature management module controls the temperature in real time based on the thermal runaway critical condition (TRC) and suppresses temperature rise through the PCM dynamic absorption model (such as the latent heat absorption effect) to prevent thermal runaway: 1. Thermal runaway critical regulation blocking strategy, the formula is: , in, is the temperature change rate, is the temperature linear term, l is the thermal conductivity of the material, T is the real-time temperature; Contributes to the discrete heat source, For the i The heat generation rate of each battery cell.

[0040] The model monitors the temperature change rate and discrete cell heat sources in real time, quickly locating localized overheating areas (e.g., abnormal heat generation in a cell). It also identifies critical thermal runaway conditions (TRCs), triggering active cooling or power limiting to prevent chain reactions.

[0041] 2. PCM dynamic absorption model: Introducing the latent heat absorption effect, the formula is: ; in, is the PCM heat absorption ratio, 0 means inactive, and 1 means complete phase change; is the PCM phase transition temperature, It is the phase change buffer zone. Through the latent heat effect of phase change material (PCM), when the temperature approaches the critical value ( ) absorbs heat proportionally (ϕ=1), and the piecewise function in the formula dynamically adjusts the heat absorption efficiency: Low temperature range ( ): PCM is not activated, saving cooling energy; Phase transition interval ( ): Latent heat absorption buffers temperature rise and delays thermal runaway.

[0042] High temperature range ( ): Complete phase change (ϕ=1), maximizing heat absorption capacity.

[0043] The thermal runaway strategy is responsible for rapid diagnosis and emergency intervention, while the PCM model is responsible for smoothing the temperature rise curve. The combination of the two prevents instantaneous thermal runaway and reduces energy loss caused by frequent forced cooling. The PCM's phase change buffer (ΔT) is linked to the linear term (ΔT) of the thermal runaway model to dynamically adjust the cooling intensity to adapt to different working conditions (such as fast charging and high load). By suppressing local overheating and reducing temperature fluctuations (PCM phase change buffer), the battery aging rate is reduced and the battery life is extended. For example, when an electric vehicle is fast charging, the thermal runaway strategy identifies a sudden temperature rise (high ), triggering the PCM to quickly absorb heat in the phase change range (such as 45±2°C) and at the same time reducing the charging current to avoid thermal runaway without interrupting charging.

[0044] Application of the system of the present invention: 1. For example, the Yadi DE series electric four-wheeled vehicle, equipped with the system described in this invention, saw its single-trip range increase from 90km to 110km, and its total range from 100,000km to 120,000km. The Yadi experiment involved 200 vehicles, saving 41,000 kWh of electricity annually.

[0045] 2. For example, a Heli forklift, which operates in a relatively closed environment with high current and voltage, has a system equipped with the present invention. The operating time per full charge has increased from 4.0-5.0 hours to 5.8-7.3 hours, and the one-hour charging time has increased from 45% to 75%. The battery cycle count has increased from 900 to 1200, extending the battery life by 27% and reducing the incidence of forklift accidents such as circuit burnout by approximately 15%.

[0046] 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 low-speed electric vehicle power battery safety monitoring system, characterized by: include: Battery status monitoring unit, used to collect real-time data on single cell voltage, total voltage, charge and discharge current, and temperature of each cell of the electric vehicle battery pack; A data processing unit, connected to the battery status monitoring unit, converts and pre-processes the collected data and estimates the battery cell capacity, state of charge, health status and power status; The safety warning module is connected to the main control unit and is used to issue graded warnings based on the state estimation results and the preset safety thresholds; The balancing control module is used to achieve power balancing between battery packs through active and passive balancing strategies; The battery pack temperature management module is used to control the battery pack temperature based on the critical conditions of thermal runaway and the dynamic absorption model of phase change materials.

2. A low-speed electric vehicle power battery safety monitoring system according to claim 1, characterized in that: The battery status monitoring unit includes a temperature acquisition module, a current acquisition module and a voltage acquisition module. The temperature acquisition module includes 12 temperature sensors, the current acquisition module includes 24 current sensors, and the voltage acquisition module includes 72 voltage sensors.

3. A low-speed electric vehicle power battery safety monitoring system according to claim 1, characterized in that: The data processing unit includes: Data conversion module: Receives raw data from the battery status monitoring unit, converts analog signals into digital signals, and performs data calibration and normalization to ensure that the data format is unified and accurate; Data preprocessing module: Filters, removes noise and processes outliers on the converted data to eliminate interference signals and measurement errors, thereby improving data quality and reliability; A state estimation module is used to calculate the state of charge, state of health, and power state of the battery pack based on the preprocessed data; Communication control module: responsible for communicating with the vehicle control system or display unit through the CAN bus, uploading battery status data and alarm information, and receiving vehicle control instructions.

4. A low-speed electric vehicle power battery safety monitoring system according to claim 3, characterized in that: The data preprocessing module uses an adaptive unscented Kalman filter algorithm and a least squares algorithm to perform filtering and noise reduction; the adaptive unscented Kalman filter algorithm model is: ,in is the state fitting quantity at time k; is the state transfer amount; is the actual observed value, is the error adjustment amount; is the Kalman gain; To control the input quantity; It is the measured quantity in the current state.

5. The low-speed electric vehicle power battery safety monitoring system according to claim 1, characterized in that: The state estimation module uses an adaptive unscented Kalman filter algorithm based on federated learning to estimate SOC and SOH, specifically including: By aggregating the local model parameters of distributed nodes through federated learning, the global AUKF model weights are updated. The formula is: ； in, The local AUKF parameters of the i-th node; is the size of the local dataset; The adaptive crisis index calculation integrates multiple sources of battery risk indicators, and the formula is: ； in, is the Sigmoid function, which normalizes the crisis index to the interval [0,1]; is the failure probability; is the coupling coefficient between the temperature change rate and the internal resistance change; is a weight parameter that controls the contribution of each indicator to the risk and determines the failure tendency of the data; Score the battery health, the lower the risk index.

6. A low-speed electric vehicle power battery safety monitoring system according to claim 1, characterized in that: The security warning module includes a three-level response mechanism: Level 1 warning: When the health level is lower than 80%, the output power is reduced; Level 2 warning: When the health level is lower than 70% or the risk value exceeds 80%, the cooling system is activated; Level 3 warning: When the health level is lower than 60% or the risk value exceeds 90%, an emergency power outage is executed.

7. The low-speed electric vehicle power battery safety monitoring system according to claim 1, characterized in that: The balancing control module adopts a six-phase staggered matrix and a model predictive control algorithm to achieve efficient power balancing between battery packs.

8. The low-speed electric vehicle power battery safety monitoring system according to claim 1, characterized in that: The electrical room temperature management module includes: Thermal runaway critical regulation blocking strategy, the formula is: ， in, is the temperature change rate, is the temperature linear term, λ is the thermal conductivity of the material, T is the real-time temperature; Contributes to the discrete heat source, For the i Heat generation rate of each cell; PCM dynamic absorption model: Introducing the latent heat absorption effect, the formula is: ； in, is the PCM heat absorption ratio, 0 means inactive, and 1 means complete phase change; is the PCM phase transition temperature, It is the phase change buffer zone.

9. The low-speed electric vehicle power battery safety monitoring system according to claim 1, characterized in that: It also includes a remote monitoring module for uploading battery status data to a mobile device or cloud platform via a wireless communication network.

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