A new energy vehicle battery management method and system

Abstract

The application provides a new energy automobile battery management method and system, comprising a battery pack, a distributed acquisition unit, a main control unit, a bidirectional active balancing network and a thermal management execution mechanism, each module cooperates to form a complete battery management closed loop; the main control unit comprises a battery state estimation module, an active balancing control module, a thermal management cooperation module, a safety protection module and a cloud cooperation module, and the modules can support the stable management method based on the management system; the application has high balancing efficiency and high energy utilization rate; the bidirectional active balancing network of'modular level bidirectional DC-DC+public energy storage capacitor' is adopted to realize cross-module energy transfer, compared with the traditional passive balancing, there is no energy waste, the balancing speed is improved by 3-5 times, and the balancing efficiency is greater than or equal to 92%; meanwhile, the energy compensation is preferentially performed on the aging module, the overall aging inconsistency of the battery pack is effectively delayed, and the service life of the battery pack is prolonged by 15%-20%.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, and more specifically to a method and system for managing batteries in new energy vehicles. Background Technology

[0002] Currently, new energy vehicle batteries are divided into two categories: lithium iron phosphate batteries (LFP) and ternary lithium batteries (NCM / NCA). They mainly provide power for new energy vehicles and achieve a recyclable effect through rechargeability. However, when managing new energy vehicle batteries, most systems still use the traditional framework of new energy vehicle battery management systems (BMS), which leads to the following shortcomings in current management methods and systems: Poor equalization control: Existing BMS mostly adopts passive equalization, which achieves voltage equalization of individual cells by consuming energy through resistors. This results in energy waste and slow equalization speed. Even when active equalization is used, it is mostly limited to cell equalization within a single module and cannot achieve energy transfer across modules. It is difficult to solve the problems of aging differences and voltage inconsistencies between different modules. Long-term use will lead to the overall performance degradation of the battery pack, resulting in the "barrel effect". Insufficient accuracy in state estimation: In existing technologies, battery state (SOC, SOH, SOP) estimation is mostly based on static impedance models or two-dimensional impedance characterization, which cannot capture the coupling relationship and aging differences of cells in different spatial locations and at different usage times in real time, resulting in large estimation errors and affecting the accuracy of range prediction and battery protection. Poor coordination among modules: The existing BMS's state estimation, equalization control, and thermal management are mostly independent controls, without deep coordination. For example, the impact of temperature on cell performance is not considered during the equalization process, and the cell aging status adjustment strategy is not combined during the thermal management process, resulting in unstable battery pack operating range and further shortening service life. Insufficient redundancy in safety protection: Some BMS only use a single hardware or software protection mechanism. When a single fault occurs, it cannot effectively cut off the high-voltage circuit, posing safety risks such as high-voltage electric shock and thermal runaway. At the same time, the existing protection strategy has insufficient response speed and is difficult to deal with sudden abnormal situations such as overvoltage, overcurrent, and overheating. To address the aforementioned issues, there is currently no battery management system capable of achieving cross-module active balancing, high-precision state estimation, deep multi-module collaboration, and high-redundancy safety protection. Therefore, a novel battery management system and method for new energy vehicles is needed to overcome the shortcomings of existing technologies. Summary of the Invention

[0003] The technical solution adopted by the present invention to achieve the technical objective is: a new energy vehicle battery management method and system, including: a battery pack, a distributed acquisition unit, a main control unit, a bidirectional active balancing network and a thermal management actuator, wherein each module works in concert to form a complete battery management closed loop; The main control unit includes: a battery state estimation module, an active balancing control module, a thermal management coordination module, a safety protection module, and a cloud coordination module. These modules support the stable operation of the management method. A new energy vehicle battery management method is based on a management system, and the management method consists of the following steps: S1: Data Acquisition; S2: State estimation; S3: Cross-module active balancing; S4: Collaborative thermal management; S5: Graded safety protection; S6: Cloud-based collaborative operation and maintenance.

[0004] As a further improvement of the present invention, the battery pack is composed of multiple battery modules, each battery module containing several individual cells, wherein the individual cells are lithium iron phosphate batteries or ternary lithium batteries, and multiple battery modules are connected in series or in parallel to form a battery pack to provide power output for new energy vehicles.

[0005] As a further improvement of the present invention, the distributed acquisition unit is respectively set in each battery module and electrically connected to the individual cells in each battery module. It is used to collect the voltage and temperature of the individual cells in real time, as well as the total voltage and current data of the corresponding battery module. The acquisition accuracy meets the following requirements: voltage acquisition error ≤ ±10mV, temperature acquisition error ≤ ±1℃, current acquisition error ≤ ±2%, and acquisition frequency is 100Hz-500Hz, ensuring the real-time performance and accuracy of data acquisition.

[0006] As a further improvement of the present invention, the main control unit is connected to each distributed acquisition unit via CAN bus or Ethernet communication. As the core control unit of the entire battery management system, it has a built-in battery state estimation module, active balancing control module, thermal management coordination module and safety protection module, and can also be set to a cloud coordination module. The main control unit adopts a high-performance MCU chip, which has high-speed data processing capability and multi-module linkage control capability. It can receive data uploaded by the distributed acquisition units in real time and execute corresponding control strategies.

[0007] As a further improvement of the present invention, the bidirectional active balancing network includes multiple bidirectional DC-DC conversion units and a common energy storage capacitor. Each bidirectional DC-DC conversion unit corresponds to a battery module, with one end electrically connected to the corresponding battery module and the other end electrically connected to the common energy storage capacitor. Under the control of the main control unit, it is used to transfer excess energy from the high-voltage module to the low-voltage module or the common energy storage capacitor, realizing cross-module energy transfer and solving the problems of voltage inconsistency and aging differences between different modules. The bidirectional DC-DC conversion unit adopts a full-bridge topology, with a balancing current of up to 2–5A and a balancing efficiency of ≥92%, balancing both balancing speed and energy utilization.

[0008] As a further improvement of the present invention, the thermal management actuator includes a liquid cooling circuit, a PTC heater, and a flow regulating valve, which is electrically connected to the main control unit and used to regulate the operating temperature of the battery pack. The liquid cooling circuit is located inside the battery pack and is in contact with each battery module, and heat dissipation is achieved through coolant circulation. The PTC heater is used to preheat the battery pack in a low-temperature environment. The flow regulating valve is used to regulate the flow rate of the coolant to achieve precise temperature control and ensure that the battery pack operates within the optimal temperature range.

[0009] As a further improvement of the present invention, the battery state estimation module of the main control unit: based on the voltage, temperature and current data collected by the distributed acquisition unit, establishes a dynamic impedance matrix and updates it in real time, and estimates SOC (remaining capacity), SOH (health status) and SOP (power capability) by combining the extended Kalman filter algorithm; the dynamic impedance matrix is ​​a four-dimensional matrix, which includes the three-dimensional spatial position of the battery cell and the time dimension, and is used to characterize the coupling relationship and aging difference between the battery cells in real time, so that the SOC estimation error is ≤2%, the SOH estimation error is ≤3%, and the SOP estimation accuracy meets the power requirements of the whole vehicle.

[0010] As a further improvement of the present invention, the active balancing control module of the main control unit dynamically generates a cross-module active balancing strategy based on the voltage difference of individual cells, the temperature difference between modules, and the dynamic impedance distribution. It prioritizes the bidirectional energy transfer of modules with voltage differences exceeding the threshold (≥50mV) and stops balancing when the voltage difference between modules is ≤20mV. At the same time, it prioritizes energy compensation for modules with lower SOH to delay the overall aging inconsistency of the battery pack.

[0011] As a further improvement of the present invention, the thermal management coordination module of the main control unit: synchronously links with the thermal management actuator, adjusts the coolant flow rate of the liquid cooling circuit and the working power of the PTC heater according to the real-time temperature data of the battery pack, controls the battery working range at 25–40℃, and ensures that the temperature difference between modules does not exceed 3℃, so as to avoid the battery performance and lifespan being affected by excessively high or low temperatures.

[0012] As a further improvement of the present invention, the safety protection module of the main control unit adopts a three-level redundant protection mechanism: the first level is hardware overcurrent cutoff, the second level is software logic protection, and the third level is high voltage interlock (HVIL) linkage; it monitors the overvoltage, undervoltage, overcurrent, overheating, insulation abnormality and other states of the battery pack in real time. When an abnormality is detected, the high voltage circuit is cut off within 100ms and a graded alarm is triggered to prevent a single fault from causing high voltage risk or thermal runaway.

[0013] As a further improvement of the present invention, the cloud collaboration module of the main control unit is used to communicate with the cloud server through the vehicle network, receive the aging prediction model and environmental adaptive parameters sent by the cloud, and realize the dynamic update of the local control strategy; at the same time, it uploads the real-time operating data of the battery pack (voltage, temperature, current, SOC, SOH, etc.) to the cloud, so as to facilitate data analysis, fault warning and life prediction in the cloud, and realize full life cycle operation and maintenance.

[0014] As a further improvement of the present invention, the data acquisition in S1 is as follows: the distributed acquisition unit acquires the voltage and temperature of each individual cell in real time, as well as the total voltage and current data of the corresponding battery module, and uploads the acquired data to the main control unit in real time through the communication bus; during the acquisition process, abnormal data is filtered to avoid interference data from affecting the accuracy of the control strategy.

[0015] As a further improvement of the present invention, the state estimation of S2 is as follows: the battery state estimation module of the main control unit constructs and updates a four-dimensional dynamic impedance matrix in real time based on the collected voltage, temperature and current data, and combines the extended Kalman filter algorithm to accurately estimate the three core state parameters of SOC, SOH and SOP, wherein the SOC estimation error is ≤2% and the SOH estimation error is ≤3%, and the estimation results are fed back to the active equalization control module, the thermal management coordination module and the safety protection module.

[0016] As a further improvement of the present invention, the cross-module active balancing in S3 is as follows: the active balancing control module determines whether the pressure difference and temperature difference between modules exceed the set threshold (pressure difference > 50mV or temperature difference > 3℃) based on the state estimation result; if the threshold is exceeded, cross-module bidirectional active balancing is triggered, and the excess energy of the high-voltage module is transferred to the low-voltage module or the common energy storage capacitor through the bidirectional DC-DC conversion unit. During the balancing process, the module with lower SOH is given priority for energy compensation until the pressure difference between modules is ≤ 20mV and the temperature difference is ≤ 3℃, at which point the balancing stops.

[0017] As a further improvement of the present invention, the S4 collaborative thermal management is as follows: the thermal management collaborative module adjusts the temperature in conjunction with the thermal management actuator based on the real-time temperature data of the battery pack; when the battery temperature is below 25°C, it controls the PTC heater to start and preheat the battery pack; when the battery temperature is above 40°C, it controls the liquid cooling circuit to start and adjusts the flow regulating valve to increase the coolant flow rate to achieve heat dissipation; the battery temperature is kept stable at 25–40°C throughout the process, and the temperature difference between modules is ≤3°C.

[0018] As a further improvement of the present invention, the graded safety protection of S5 is as follows: the safety protection module monitors the operating status of the battery pack in real time, such as high voltage insulation, overcurrent, overtemperature, overvoltage, and undervoltage; if an abnormality is detected, the three-level redundant protection mechanism is immediately activated, the high voltage circuit is cut off within 100ms, and graded alarms are triggered according to the level of abnormality (minor abnormalities only trigger local alarms, while severe abnormalities trigger alarms to the cloud and the vehicle controller simultaneously), until the abnormality is resolved and normal operation is restored.

[0019] As a further improvement of the present invention, the cloud-based collaborative operation and maintenance of S6 is as follows: the main control unit uploads the real-time operating data of the battery pack to the cloud server through the cloud collaboration module. The cloud server analyzes the battery aging trend based on the aging prediction model and issues environmental adaptive parameters. The main control unit dynamically updates the local control strategy according to the issued parameters to realize the full life cycle optimization operation and maintenance of the battery pack.

[0020] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention features high balancing efficiency and high energy utilization: It adopts a bidirectional active balancing network of "module-level bidirectional DC-DC + common energy storage capacitor" to realize cross-module energy transfer. Compared with traditional passive balancing, there is no energy waste, the balancing speed is increased by 3-5 times, and the balancing efficiency is ≥92%. At the same time, it prioritizes energy compensation for aging modules, effectively delaying the overall aging inconsistency of the battery pack and extending the battery pack service life by 15%-20%.

[0021] 2. This invention features high state estimation accuracy: It innovatively adopts a four-dimensional dynamic impedance matrix (space + time) combined with an extended Kalman filter algorithm to accurately capture the coupling relationship and aging differences between battery cells. The SOC estimation error is ≤2% and the SOH estimation error is ≤3%. Compared with existing technologies (error 5%-8%), the estimation accuracy is significantly improved, which can more accurately predict the driving range and provide a reliable basis for battery protection.

[0022] 3. Deep multi-module collaboration of the present invention: It realizes deep linkage between state estimation, active balancing and thermal management. During the balancing process, it combines temperature adjustment strategy and during the thermal management process, it combines aging state optimization control to stabilize the battery pack operating range within the optimal range, thereby further improving battery performance and life and reducing energy consumption.

[0023] 4. This invention boasts high safety and reliability: it employs a three-level redundant safety protection mechanism with a fast response speed (cutting off high voltage within 100ms), effectively handling various abnormal situations and avoiding safety risks such as high-voltage electric shock and thermal runaway, thus improving the overall vehicle operational safety. Simultaneously, the optional cloud-based collaborative module enables full lifecycle operation and maintenance, providing early warnings of faults and reducing maintenance costs. Attached Figure Description Figure 1 This is a schematic diagram of a type of battery management system for new energy vehicles.

[0024] Figure 2 This is a flowchart illustrating a method for managing batteries in new energy vehicles.

[0025] Figure 3 This is a schematic diagram of a main control unit that includes modules. Detailed Implementation

[0026] The present invention will be further described below with reference to the accompanying drawings: Example: Figures 1 to 3 As shown: This invention provides a method and system for managing batteries in new energy vehicles. It includes: battery pack, distributed acquisition unit, main control unit, bidirectional active balancing network and thermal management actuator. The modules work together to form a complete battery management closed loop. The main control unit includes: a battery state estimation module, an active balancing control module, a thermal management coordination module, a safety protection module, and a cloud coordination module. These modules support the stable operation of the management method. A new energy vehicle battery management method is based on a management system, and the management method consists of the following steps: S1: Data Acquisition; S2: State estimation; S3: Cross-module active balancing; S4: Collaborative thermal management; S5: Graded safety protection; S6: Cloud-based collaborative operation and maintenance.

[0027] The battery pack consists of multiple battery modules, each containing several individual battery cells, which are either lithium iron phosphate batteries or ternary lithium batteries. Multiple battery modules are connected in series or in parallel to form a battery pack, providing power output for new energy vehicles.

[0028] The distributed acquisition unit is located in each battery module and electrically connected to the individual cells in each battery module. It is used to collect the voltage and temperature of the individual cells in real time, as well as the total voltage and current data of the corresponding battery module. The acquisition accuracy meets the following requirements: voltage acquisition error ≤ ±10mV, temperature acquisition error ≤ ±1℃, current acquisition error ≤ ±2%, and acquisition frequency 100Hz-500Hz, to ensure the real-time performance and accuracy of data acquisition.

[0029] The main control unit is connected to each distributed acquisition unit via CAN bus or Ethernet. As the core control unit of the entire battery management system, it has a built-in battery state estimation module, active balancing control module, thermal management coordination module and safety protection module, and can also be set to a cloud coordination module. The main control unit adopts a high-performance MCU chip, which has high-speed data processing capability and multi-module linkage control capability. It can receive data uploaded by the distributed acquisition units in real time and execute corresponding control strategies.

[0030] The bidirectional active balancing network comprises multiple bidirectional DC-DC converters and a common energy storage capacitor. Each bidirectional DC-DC converter corresponds to a battery module, with one end electrically connected to the corresponding battery module and the other end electrically connected to the common energy storage capacitor. Under the control of the main control unit, it transfers excess energy from the high-voltage module to the low-voltage module or the common energy storage capacitor, realizing cross-module energy transfer and solving the problems of voltage inconsistency and aging differences between different modules. The bidirectional DC-DC converter adopts a full-bridge topology, with a balancing current of 2–5A and a balancing efficiency of ≥92%, balancing both balancing speed and energy utilization.

[0031] The thermal management actuator includes a liquid cooling circuit, a PTC heater, and a flow regulating valve, which is electrically connected to the main control unit and used to regulate the operating temperature of the battery pack. The liquid cooling circuit is located inside the battery pack and is in contact with each battery module, achieving heat dissipation through coolant circulation. The PTC heater is used to preheat the battery pack in low-temperature environments. The flow regulating valve is used to regulate the flow rate of the coolant to achieve precise temperature control and ensure that the battery pack operates within the optimal temperature range.

[0032] The battery state estimation module of the main control unit establishes a dynamic impedance matrix and updates it in real time based on voltage, temperature and current data collected by the distributed acquisition unit. It then uses an extended Kalman filter algorithm to estimate SOC (remaining charge), SOH (health status) and SOP (power capability). The dynamic impedance matrix is ​​a four-dimensional matrix that includes the three-dimensional spatial position and time dimension of the battery cells. It is used to characterize the coupling relationship and aging differences between the battery cells in real time, so that the SOC estimation error is ≤2%, the SOH estimation error is ≤3%, and the SOP estimation accuracy meets the power requirements of the whole vehicle.

[0033] The active balancing control module of the main control unit dynamically generates a cross-module active balancing strategy based on the voltage difference of individual cells, the temperature difference between modules, and the dynamic impedance distribution. It prioritizes bidirectional energy transfer for modules with voltage differences exceeding a threshold (≥50mV) and stops balancing when the voltage difference between modules is ≤20mV. At the same time, it prioritizes energy compensation for modules with lower SOH to delay the overall aging inconsistency of the battery pack.

[0034] The thermal management coordination module of the main control unit synchronously links with the thermal management actuator to adjust the coolant flow rate of the liquid cooling circuit and the working power of the PTC heater according to the real-time temperature data of the battery pack, so as to control the battery working range between 25-40℃ and ensure that the temperature difference between modules does not exceed 3℃, so as to avoid the battery performance and lifespan being affected by excessively high or low temperatures.

[0035] The main control unit's safety protection module employs a three-level redundant protection mechanism: the first level is hardware overcurrent cutoff, the second level is software logic protection, and the third level is high-voltage interlock (HVIL) linkage. It monitors the battery pack's overvoltage, undervoltage, overcurrent, overheating, and insulation abnormalities in real time. When an abnormality is detected, it cuts off the high-voltage circuit within 100ms and triggers a graded alarm to prevent a single fault from causing high-voltage risks or thermal runaway.

[0036] The cloud-based collaborative module of the main control unit is used to communicate with the cloud server through the vehicle network, receive aging prediction models and environmental adaptive parameters sent from the cloud, and realize dynamic updates of local control strategies. At the same time, it uploads the real-time operating data of the battery pack (voltage, temperature, current, SOC, SOH, etc.) to the cloud, which facilitates data analysis, fault warning and life prediction in the cloud, and realizes full life cycle operation and maintenance.

[0037] Specifically, the data acquisition in S1 involves a distributed acquisition unit that collects the voltage and temperature of each individual battery cell, as well as the total voltage and current data of the corresponding battery module in real time. The collected data is then uploaded to the main control unit in real time via a communication bus. During the acquisition process, abnormal data is filtered to prevent interference from affecting the accuracy of the control strategy.

[0038] The state estimation of S2 is as follows: The battery state estimation module of the main control unit constructs and updates a four-dimensional dynamic impedance matrix in real time based on the collected voltage, temperature and current data. Combined with the extended Kalman filter algorithm, it accurately estimates the three core state parameters of SOC, SOH and SOP. The SOC estimation error is ≤2% and the SOH estimation error is ≤3%. The estimation results are fed back to the active equalization control module, the thermal management coordination module and the safety protection module.

[0039] Specifically, the cross-module active balancing in S3 involves the active balancing control module determining, based on the state estimation results, whether the pressure difference and temperature difference between modules exceed the set thresholds (pressure difference > 50mV or temperature difference > 3℃). If the thresholds are exceeded, cross-module bidirectional active balancing is triggered. The excess energy of the high-voltage module is transferred to the low-voltage module or the common energy storage capacitor through the bidirectional DC-DC converter unit. During the balancing process, energy compensation is preferentially performed on the module with the lower SOH until the pressure difference between modules is ≤ 20mV and the temperature difference is ≤ 3℃, at which point the balancing stops.

[0040] Among them, the S4 collaborative thermal management: the thermal management collaborative module, based on the real-time temperature data of the battery pack, links with the thermal management actuator to perform temperature adjustment; when the battery temperature is below 25℃, it controls the PTC heater to start to preheat the battery pack; when the battery temperature is above 40℃, it controls the liquid cooling circuit to start, adjusts the flow regulating valve to increase the coolant flow, and achieves heat dissipation; throughout the process, the battery temperature is stabilized at 25–40℃, and the temperature difference between modules is ≤3℃.

[0041] The S5 features graded safety protection: the safety protection module monitors the battery pack's high-voltage insulation, overcurrent, overtemperature, overvoltage, and undervoltage operating status in real time; if an abnormality is detected, a three-level redundant protection mechanism is immediately activated, cutting off the high-voltage circuit within 100ms, and triggering graded alarms according to the level of abnormality (minor abnormalities trigger only local alarms, while severe abnormalities trigger alarms simultaneously to the cloud and the vehicle controller), until the abnormality is resolved and normal operation is restored.

[0042] Specifically, the cloud-based collaborative operation and maintenance of S6 involves the main control unit uploading real-time operating data of the battery pack to the cloud server through the cloud collaboration module. The cloud server analyzes the battery aging trend based on the aging prediction model and issues environmental adaptive parameters. The main control unit dynamically updates its local control strategy according to the issued parameters to achieve optimized operation and maintenance of the battery pack throughout its entire life cycle.

[0043] The specific functions and operation procedures of this embodiment are as follows: In this invention, the new energy vehicle battery management system includes: a battery pack, a distributed acquisition unit, a main control unit, a bidirectional active balancing network, and a thermal management actuator, with the following specific configuration: Battery pack: It consists of 10 battery modules connected in series. Each battery module contains 16 ternary lithium battery cells. The rated voltage of each cell is 3.7V and the rated capacity is 200Ah. The total rated voltage of the battery pack is 37V and the total rated capacity is 200Ah, providing power output for new energy pure electric vehicles.

[0044] Distributed acquisition unit: It adopts 10 acquisition modules, each corresponding to 10 battery modules. Each acquisition module is electrically connected to 16 individual cells in the corresponding module. It uses a high-precision voltage acquisition chip (model: ADS1256) and a temperature sensor (model: NTC 10K) to acquire the voltage (acquisition error ±8mV) and temperature (acquisition error ±0.8℃) of the individual cells in real time, as well as the total voltage and current of the corresponding battery module (acquisition using a shunt, error ±1.5%). The acquisition frequency is 300Hz, and the acquired data is uploaded to the main control unit via CAN bus.

[0045] Main control unit: It adopts STM32H743VIT6 MCU chip, which integrates battery status estimation module, active balancing control module, thermal management coordination module, safety protection module and cloud coordination module. The main control unit communicates with distributed acquisition unit, bidirectional active balancing network, thermal management actuator and vehicle controller through CAN bus, and communicates with cloud server through 4G module, and has high-speed data processing and multi-module linkage control capabilities.

[0046] Bidirectional active balancing network: It includes 10 bidirectional DC-DC converter units (model: TPS63070) and 1 common energy storage capacitor (capacity 1000μF, withstand voltage 50V). Each bidirectional DC-DC converter unit corresponds to one battery module. One end is electrically connected to the positive and negative terminals of the corresponding module, and the other end is electrically connected to the common energy storage capacitor. The bidirectional DC-DC converter unit adopts a full-bridge topology structure. The balancing current can be adjusted within the range of 2-5A, and the balancing efficiency is ≥93%, which can realize bidirectional energy transfer across modules.

[0047] Thermal management actuator: includes liquid cooling circuit, PTC heater (2000W power) and flow regulating valve (model: EMV-60); the liquid cooling circuit uses aluminum water-cooled plate, which is attached to the surface of each battery module, and the coolant is a 50% ethylene glycol aqueous solution; the PTC heater is connected in series in the liquid cooling circuit for low-temperature preheating; the flow regulating valve is controlled by the main control unit, which adjusts the coolant flow rate in the range of 5-20L / min to achieve precise temperature control; Meanwhile, the specific workflows of each module carried by the main control unit are as follows: Battery State Estimation Module: Receives voltage, temperature, and current data uploaded by the distributed acquisition unit, constructs a four-dimensional dynamic impedance matrix (X / Y / Z three-dimensional spatial position + time dimension), and updates the coupling relationship and aging differences between cells in real time; combined with the extended Kalman filter algorithm, it estimates SOC, SOH, and SOP, where the SOC estimation error is ≤1.8% and the SOH estimation error is ≤2.8%, and feeds back the estimation results to other modules in real time.

[0048] Active balancing control module: Real-time monitoring of inter-module pressure and temperature differences. When an inter-module pressure difference > 50mV or a temperature difference > 3℃ is detected, active balancing across modules is triggered. For example, when the voltage of module 1 is 38.5V, the voltage of module 2 is 37.9V, and the pressure difference is 600mV, the bidirectional DC-DC converter unit is controlled to transfer excess energy from module 1 to module 2. At the same time, energy compensation is prioritized for modules with SOH below 85% until the inter-module pressure difference is ≤ 20mV and the temperature difference is ≤ 3℃, at which point balancing stops.

[0049] Thermal management coordination module: Receives real-time temperature data of the battery pack. When the battery temperature is below 25℃, it controls the PTC heater to start, heating the coolant and preheating the battery pack through the liquid cooling circuit. When the battery temperature is above 40℃, it controls the flow regulating valve to increase the coolant flow rate to 15-20L / min to accelerate heat dissipation. Throughout the process, it stabilizes the battery temperature at 28-38℃, with a temperature difference between modules ≤2.5℃.

[0050] Safety protection module: Real-time monitoring of the battery pack's total voltage (overvoltage threshold 42V, undervoltage threshold 32V), current (overcurrent threshold 300A), temperature (overheating threshold 60℃, overcooling threshold -20℃), and insulation resistance (insulation threshold ≥100MΩ); when the battery total voltage reaches 42.5V (overvoltage), the high-voltage circuit is cut off within 80ms, triggering the first-level hardware overcurrent cutoff, the second-level software logic protection, and the third-level high-voltage interlock linkage, while sending alarm signals to the vehicle controller and cloud server until the fault is cleared.

[0051] Cloud-based collaborative module: Real-time operating data of the battery pack (voltage, temperature, current, SOC, SOH, etc.) is uploaded to the cloud server via the 4G module. The cloud server analyzes the battery aging trend based on the aging prediction model and issues environmental adaptive parameters (such as low temperature equalization threshold in winter and high temperature thermal management parameters in summer). The main control unit dynamically updates the local control strategy according to the issued parameters to achieve optimized operation and maintenance throughout the entire life cycle. Based on the detailed workflow of the above system and the accompanying management methods, according to Figure 2 As shown, the specific workflow is as follows: S1. Data Acquisition: Ten distributed acquisition units collect the voltage and temperature of the 16 individual cells in the corresponding battery module, as well as the total voltage and current data of the corresponding module in real time. The acquisition frequency is 300Hz. Abnormal data (such as voltage surges and temperature anomalies) are filtered and processed. The valid data is uploaded to the main control unit 3 in real time via the CAN bus.

[0052] S2. State Estimation: The battery state estimation module of the main control unit constructs and updates a four-dimensional dynamic impedance matrix (X / Y / Z three-dimensional spatial position + time dimension) based on the collected effective data. Combined with the extended Kalman filter algorithm, it accurately estimates the three core state parameters: SOC, SOH, and SOP. The SOC estimation error is ≤1.8%, and the SOH estimation error is ≤2.8%. The estimation results are fed back to the active equalization control module, the thermal management coordination module, and the safety protection module.

[0053] S3. Cross-module active balancing: The active balancing control module determines whether the pressure difference and temperature difference between modules exceed the set thresholds (pressure difference > 50mV or temperature difference > 3℃) based on the state estimation results. If the thresholds are exceeded, cross-module bidirectional active balancing is triggered, controlling the corresponding bidirectional DC-DC converter to transfer excess energy from the high-voltage module to the low-voltage module or the common energy storage capacitor. During the balancing process, energy compensation is prioritized for modules with a SOH of less than 85% until the pressure difference between modules is ≤ 20mV and the temperature difference is ≤ 3℃, at which point balancing stops. If the thresholds are not exceeded, the current state is maintained and balancing is not initiated.

[0054] S4. Collaborative Thermal Management: The thermal management collaboration module, based on real-time temperature data of the battery pack, coordinates with the thermal management actuator to adjust the temperature. When the battery temperature is below 25℃, it controls the PTC heater to start, adjusting the power to 1500-2000W to heat the coolant and preheat the battery pack through the liquid cooling circuit. When the battery temperature is between 25-40℃, it maintains the coolant flow rate at 10-15L / min to keep the temperature stable. When the battery temperature is above 40℃, it controls the flow regulating valve to increase the coolant flow rate to 15-20L / min to accelerate heat dissipation. Throughout the process, the battery temperature is stabilized at 28-38℃, with a temperature difference between modules ≤2.5℃.

[0055] S5. Tiered Safety Protection: The safety protection module monitors the battery pack's high-voltage insulation, overcurrent, overtemperature, overvoltage, and undervoltage operating status in real time. If a minor abnormality is detected (such as insulation resistance of 80-100MΩ), only a local alarm is triggered to remind the driver. If a serious abnormality is detected (such as overvoltage of 42.5V, overcurrent of 350A, or overheating of 65℃), a three-level redundant protection mechanism is immediately activated. The high-voltage circuit is cut off within 80ms, and an alarm signal is sent to the vehicle controller and cloud server. The vehicle controller stops the vehicle from driving until the abnormality is resolved and normal operation resumes.

[0056] S6. Cloud-based collaborative operation and maintenance: The main control unit uploads real-time operating data of the battery pack to the cloud server through the cloud collaboration module. The cloud server analyzes the battery aging trend based on the aging prediction model and issues environmental adaptive parameters (such as adjusting the low-temperature equalization threshold in winter to start equalization when the differential pressure is >40mV, and adjusting the high-temperature thermal management threshold in summer to start heat dissipation at 38℃). The main control unit dynamically updates the local control strategy according to the issued parameters to achieve optimized operation and maintenance of the battery pack throughout its entire life cycle, provide early warning of faults, and reduce maintenance costs.

[0057] Any technical solution that achieves the above-mentioned technical effects by utilizing the technical solutions described in this invention, or by designing similar technical solutions by those skilled in the art under the inspiration of the technical solutions described in this invention, falls within the protection scope of this invention.

Claims

1. A battery management system for new energy vehicles, comprising: The battery pack, distributed acquisition unit, main control unit, bidirectional active balancing network, and thermal management actuator work together to form a complete battery management closed loop. The main control unit includes: a battery state estimation module, an active balancing control module, a thermal management coordination module, a safety protection module, and a cloud coordination module. These modules support the stable operation of the management method. A new energy vehicle battery management method is based on a management system, and the management method consists of the following steps: S1: Data Acquisition; S2: State estimation; S3: Cross-module active balancing; S4: Collaborative thermal management; S5: Graded safety protection; S6: Cloud-based collaborative operation and maintenance.

2. The new energy vehicle battery management system according to claim 1, characterized in that: The battery pack consists of multiple battery modules, each battery module containing several individual cells, which are lithium iron phosphate batteries or ternary lithium batteries. Multiple battery modules are connected in series or in parallel to form a battery pack to provide power output for new energy vehicles. The distributed acquisition unit is installed in each battery module and electrically connected to the individual cells in each battery module. It is used to collect the voltage and temperature of the individual cells in real time, as well as the total voltage and current data of the corresponding battery module. The acquisition accuracy meets the following requirements: voltage acquisition error ≤ ±10mV, temperature acquisition error ≤ ±1℃, current acquisition error ≤ ±2%, and acquisition frequency is 100Hz-500Hz to ensure the real-time performance and accuracy of data acquisition. The main control unit is connected to each distributed acquisition unit via CAN bus or Ethernet. As the core control unit of the entire battery management system, it has a built-in battery state estimation module, active balancing control module, thermal management coordination module and safety protection module, and can also be set to a cloud coordination module. The main control unit adopts a high-performance MCU chip, which has high-speed data processing capability and multi-module linkage control capability. It can receive data uploaded by the distributed acquisition units in real time and execute corresponding control strategies. The bidirectional active balancing network comprises multiple bidirectional DC-DC converter units and a common energy storage capacitor. Each bidirectional DC-DC converter unit corresponds to a battery module, with one end electrically connected to the corresponding battery module and the other end electrically connected to the common energy storage capacitor. Under the control of the main control unit, it is used to transfer excess energy from high-voltage modules to low-voltage modules or the common energy storage capacitor, realizing cross-module energy transfer and solving the problems of voltage inconsistency and aging differences between different modules. The bidirectional DC-DC converter unit adopts a full-bridge topology, with a balancing current of 2–5A and a balancing efficiency of ≥92%, balancing balancing speed and energy utilization. The thermal management actuator includes a liquid cooling circuit, a PTC heater, and a flow regulating valve, and is electrically connected to the main control unit to regulate the operating temperature of the battery pack. The liquid cooling circuit is located inside the battery pack and is in contact with each battery module, achieving heat dissipation through coolant circulation. The PTC heater is used to preheat the battery pack in low-temperature environments. The flow regulating valve is used to regulate the flow rate of the coolant to achieve precise temperature control and ensure that the battery pack operates within the optimal temperature range.

3. The new energy vehicle battery management system according to claim 1, characterized in that: The battery state estimation module of the main control unit: Based on the voltage, temperature and current data collected by the distributed acquisition unit, it establishes a dynamic impedance matrix and updates it in real time. It then uses an extended Kalman filter algorithm to estimate SOC (remaining charge), SOH (sound health), and SOP (power capability). The dynamic impedance matrix is ​​a four-dimensional matrix that includes the three-dimensional spatial position of the battery cells and the time dimension. It is used to characterize the coupling relationship and aging differences between the battery cells in real time, so that the SOC estimation error is ≤2%, the SOH estimation error is ≤3%, and the SOP estimation accuracy meets the power requirements of the whole vehicle. The active balancing control module of the main control unit dynamically generates a cross-module active balancing strategy based on the voltage difference of individual cells, the temperature difference between modules, and the dynamic impedance distribution. It prioritizes bidirectional energy transfer for modules with voltage differences exceeding a threshold (≥50mV) and stops balancing when the voltage difference between modules is ≤20mV. At the same time, it prioritizes energy compensation for modules with lower SOH to delay the overall aging inconsistency of the battery pack. The thermal management coordination module of the main control unit synchronously links with the thermal management actuator to adjust the coolant flow rate of the liquid cooling circuit and the working power of the PTC heater according to the real-time temperature data of the battery pack, so as to control the battery working range between 25-40℃ and ensure that the temperature difference between modules does not exceed 3℃, so as to avoid the battery performance and life due to excessively high or low temperatures. The main control unit's safety protection module adopts a three-level redundant protection mechanism: the first level is hardware overcurrent cutoff, the second level is software logic protection, and the third level is high voltage interlock (HVIL) linkage; it monitors the battery pack's overvoltage, undervoltage, overcurrent, overheating, insulation abnormality and other states in real time. When an abnormality is detected, it cuts off the high voltage circuit within 100ms and triggers a graded alarm to prevent a single fault from causing high voltage risk or thermal runaway. The cloud-based collaborative module of the main control unit is used to communicate with the cloud server through the vehicle network, receive aging prediction models and environmental adaptive parameters sent from the cloud, and realize dynamic updates of local control strategies. At the same time, it uploads the real-time operating data of the battery pack (voltage, temperature, current, SOC, SOH, etc.) to the cloud, which facilitates data analysis, fault warning and life prediction in the cloud, and realizes full life cycle operation and maintenance.

4. The battery management method for new energy vehicles according to claim 1, characterized in that: The data acquisition in S1: The distributed acquisition unit collects the voltage and temperature of each individual cell in real time, as well as the total voltage and current data of the corresponding battery module, and uploads the collected data to the main control unit in real time through the communication bus; during the acquisition process, abnormal data is filtered to avoid interference data from affecting the accuracy of the control strategy.

5. A method for managing a new energy vehicle battery according to claim 1, characterized in that: The state estimation of S2: Based on the collected voltage, temperature and current data, the battery state estimation module of the main control unit constructs and updates the four-dimensional dynamic impedance matrix in real time. Combined with the extended Kalman filter algorithm, it accurately estimates the three core state parameters of SOC, SOH and SOP. The SOC estimation error is ≤2% and the SOH estimation error is ≤3%. The estimation results are fed back to the active equalization control module, the thermal management coordination module and the safety protection module.

6. The battery management method for new energy vehicles according to claim 1, characterized in that: The S3 cross-module active balancing: The active balancing control module determines whether the pressure difference and temperature difference between modules exceed the set threshold (pressure difference > 50mV or temperature difference > 3℃) based on the state estimation result. If the threshold is exceeded, cross-module bidirectional active balancing is triggered. The excess energy of the high-voltage module is transferred to the low-voltage module or the common energy storage capacitor through the bidirectional DC-DC conversion unit. During the balancing process, the module with lower SOH is given priority for energy compensation until the pressure difference between modules is ≤ 20mV and the temperature difference is ≤ 3℃, at which point the balancing stops.

7. A method for managing a new energy vehicle battery according to claim 1, characterized in that: The S4-based collaborative thermal management module adjusts the temperature based on the real-time temperature data of the battery pack, in conjunction with the thermal management actuator. When the battery temperature is below 25°C, it controls the PTC heater to start and preheat the battery pack. When the battery temperature is above 40°C, it controls the liquid cooling circuit to start and adjusts the flow regulating valve to increase the coolant flow rate for heat dissipation. Throughout the process, the battery temperature is kept stable between 25°C and 40°C, with a temperature difference between modules ≤3°C.

8. A method for managing a new energy vehicle battery according to claim 1, characterized in that: The S5 provides graded safety protection: the safety protection module monitors the battery pack's high-voltage insulation, overcurrent, overtemperature, overvoltage, and undervoltage operating status in real time; if an abnormality is detected, a three-level redundant protection mechanism is immediately activated, cutting off the high-voltage circuit within 100ms, and triggering graded alarms according to the level of abnormality (minor abnormalities only trigger local alarms, while severe abnormalities simultaneously trigger alarms to the cloud and the vehicle controller), until the abnormality is resolved and normal operation is restored.

9. A method for managing a new energy vehicle battery according to claim 1, characterized in that: The cloud-based collaborative operation and maintenance of the S6: The main control unit uploads the real-time operating data of the battery pack to the cloud server through the cloud collaboration module. The cloud server analyzes the battery aging trend based on the aging prediction model and issues environmental adaptive parameters. The main control unit dynamically updates the local control strategy according to the issued parameters to realize the full life cycle optimization operation and maintenance of the battery pack.

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