Battery self-heating and state of health collaborative management method, system and vehicle

Abstract

The application provides a battery self-heating and health state cooperative management method and system and a vehicle. The method comprises the following steps: collecting the battery temperature; when the temperature is lower than a preset threshold and the vehicle is parked safely; performing impedance spectrum scanning to select a target frequency point under the conditions of maximum heating efficiency and compliance with polarization tolerance; generating a bidirectional pulse based on the target frequency point matching to self-heat the battery; injecting a small signal at multiple frequency points during the pulse interval, decoupling by DRT, and K-K testing to update the SOC estimation value online; and exiting when any of the temperature, energy consumption, time or risk boundary conditions is reached, and exiting the self-heating mode. The frequency point selection is optimized through spectrum scanning to improve the heat conversion efficiency; EIS detection and DRT decoupling are performed during the pulse interval to realize online SOH estimation and dynamic coupling of the risk threshold, thereby ensuring safety.

Description

Technical Field

[0001] This invention relates to the field of vehicle management technology, and in particular to a method, system, and vehicle for the coordinated management of battery self-heating and health status. Background Technology

[0002] In low-temperature environments (such as below 0°C, especially in -20°C scenarios), LFP lithium iron phosphate power batteries generally face problems such as significant capacity decay, increased internal resistance, decreased rate capability, limited charging, and increased risk of lithium deposition, resulting in a significant decrease in the vehicle's range and available power, while also accelerating aging and bringing safety risks.

[0003] To mitigate the impact of low temperatures, the industry primarily employs thermal management strategies including external heating solutions (PTC electric heating, heating films, liquid cooling circuit preheating) and internal heating solutions (DC / AC / pulse self-heating, etc.). External heating strategies are technologically mature and simple to integrate, but suffer from long heat transfer paths, lower energy efficiency, slow temperature rise, and limited temperature uniformity. Internal heating strategies directly utilize the battery's internal resistance to generate heat, offering higher energy efficiency and temperature uniformity. However, internal DC heating exhibits severe polarization, which can lead to reduced lifespan and safety. It requires complex constraints and calibrations to determine safety boundaries, and the inconsistency between temperature differences within the battery pack and the electrochemical states between individual cells can affect the consistency of temperature rise and subsequent charge-discharge cycles, further exacerbating lifespan dispersion. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the purpose of this invention is to provide a method, system and vehicle for the coordinated management of battery self-heating and health status, which aims to solve the technical problem of poor safety of the endogenous heating scheme in the prior art.

[0005] To achieve the above objectives, in a first aspect, the present invention provides: a method for collaborative management of battery self-heating and health status, comprising the following steps:

[0006] The battery management system collects battery temperature data, and when the temperature is lower than a preset threshold, the vehicle control unit confirms the parking safety status.

[0007] If parking is safe, perform impedance spectrum scanning within the preset frequency range, calculate the heating efficiency index and polarization tolerance corresponding to each frequency point, and select the target frequency point with the condition of maximizing the heating efficiency index and complying with the polarization tolerance.

[0008] Based on the target frequency point matching, a bidirectional pulse is generated to self-heat the battery. The peak pulse current and duty cycle are dynamically adjusted by real-time monitoring of terminal voltage polarization, negative electrode overpotential and temperature rise rate, so as to control the risk score to not exceed the preset safety threshold.

[0009] During the intermittent period of the main pulse, multiple small signals with amplitudes less than or equal to the first preset value are superimposed, characteristic impedance data of several characteristic frequency points are collected, and the battery health status estimate is updated online by decoupling through distributed relaxation time and Kramers-Kronig test.

[0010] The self-heating mode will exit when any one of the following conditions is met: the average temperature of the battery pack is greater than or equal to the second preset value, the energy consumption reaches the preset energy consumption upper limit value, the self-heating time reaches the preset time upper limit value, or the estimated value of the battery health status reaches the safety threshold value.

[0011] According to one aspect of the above technical solution, the method further includes:

[0012] A risk scoring model is established based on terminal voltage polarization, negative electrode overpotential estimation, temperature rise rate, SOC and temperature, and the weighting coefficients of each parameter, so as to output the risk score based on the risk scoring model.

[0013] According to one aspect of the above technical solution, the first preset value is 50mA;

[0014] The steps for online updating of battery health state estimates, through distributed relaxation time decoupling and Kramers-Kronig test, specifically include:

[0015] The reliability of the obtained characteristic impedance data is tested based on the Kramers-Kronig test. The tested characteristic impedance data is then input into the distributed relaxation time decoupling model, and the relaxation process distribution spectrum with different time constants is obtained by solving the regularization algorithm.

[0016] Characteristic parameters strongly correlated with battery aging were extracted from the relaxation process distribution maps of different time constants, including the peak resistance corresponding to the charge transfer process and the peak resistance corresponding to the SEI film impedance.

[0017] The extracted feature parameters, real-time temperature, and average SOC are input into a pre-trained battery health state estimation model, which outputs an online updated battery health state estimate.

[0018] The maximum permissible peak current and polarization voltage threshold of the pulse during the self-heating process are adjusted based on the online updated battery health status estimate, and the weight parameters of the risk scoring model are updated in combination with the current temperature and temperature rise rate.

[0019] According to one aspect of the above technical solution, the method further includes:

[0020] When the temperature difference between individual battery cells or modules exceeds the preset temperature difference value, the time-sharing priority heating strategy and active equalization mechanism are activated to forcibly reduce the temperature difference.

[0021] The time-division rotating priority heating strategy includes: identifying the battery area with the lowest temperature as the priority heating target; dividing the heating time into multiple time segments and allocating the time segments to battery modules in different temperature zones according to priority order, so as to dynamically adjust the time segment allocation priority based on real-time temperature feedback;

[0022] The active equalization mechanism includes: extracting energy from high-temperature, high-SOC battery cells, transferring the extracted energy to low-temperature, low-SOC battery cells, monitoring temperature difference changes in real time and adjusting the energy transfer rate, and exiting the equalization mode when the temperature difference drops below a preset temperature difference value.

[0023] According to one aspect of the above technical solution, the calculation expression for the heating efficiency index is as follows:

[0024] M = Re(Z) / |Z| 2 ;

[0025] In the formula, M is the heating efficiency index, Z represents the complex impedance of the battery, Re(Z) is the real part of the complex impedance, representing the heat generation capacity corresponding to the resistance, and |Z| is the modulus of the complex impedance, representing the magnitude of the impedance of the current entering the battery.

[0026] According to one aspect of the above technical solution, in the step of performing a bidirectional pulse to the target frequency for self-heating, the pulse frequency is 200 Hz to 1.5 kHz, the pulse waveform is a symmetrical bidirectional square wave or pulse train, and the duty cycle is limited online according to the SOC and the temperature rise rate.

[0027] According to one aspect of the above technical solution, the method further includes:

[0028] When the risk score exceeds the threshold, pulse derating or frequency band switching is performed;

[0029] When the risk score continues to exceed the limit, self-heating is suspended and the external heating strategy is switched.

[0030] Secondly, this solution also provides a battery self-heating and health status collaborative management system, including:

[0031] The data acquisition module is used to collect battery temperature data through the battery management system. When the temperature is lower than a preset threshold, the vehicle control unit confirms the parking safety status.

[0032] The scanning module is used to perform impedance spectrum scanning within a preset frequency range to ensure parking safety, calculate the heat efficiency index and polarization tolerance corresponding to each frequency point, and select the target frequency point based on maximizing the heat efficiency index and complying with the polarization tolerance.

[0033] The pulse module is used to generate bidirectional pulses based on the target frequency to self-heat the battery. By monitoring the terminal voltage polarization, negative electrode overpotential and temperature rise rate in real time, the peak pulse current and duty cycle are dynamically adjusted to control the risk score to not exceed the preset safety threshold.

[0034] The update module is used to superimpose multi-frequency small signals with amplitudes less than or equal to the first preset value during the main pulse interval period, collect characteristic impedance data of several characteristic frequency points, and update the battery health status estimate online through distributed relaxation time decoupling and Kramers-Kronig test.

[0035] The exit module is used to exit the self-heating mode when any one of the following conditions is met: the average temperature of the battery pack is greater than or equal to the second preset value, the energy consumption reaches the preset energy consumption upper limit value, the self-heating time reaches the preset time upper limit value, or the estimated value of the battery health status reaches the safety threshold.

[0036] According to one aspect of the above technical solution, the system further includes:

[0037] The risk module is used to establish a risk scoring model based on terminal voltage polarization, negative electrode overpotential estimation, temperature rise rate, SOC and temperature, and the weighting coefficients corresponding to each parameter, so as to output the risk score based on the risk scoring model.

[0038] According to one aspect of the above technical solution, the first preset value is 50mA; the update module is specifically used for:

[0039] The reliability of the obtained characteristic impedance data is tested based on the Kramers-Kronig test. The tested characteristic impedance data is then input into the distributed relaxation time decoupling model, and the relaxation process distribution spectrum with different time constants is obtained by solving the regularization algorithm.

[0040] Characteristic parameters strongly correlated with battery aging were extracted from the relaxation process distribution maps of different time constants, including the peak resistance corresponding to the charge transfer process and the peak resistance corresponding to the SEI film impedance.

[0041] The extracted feature parameters, real-time temperature, and average SOC are input into a pre-trained battery health state estimation model, which outputs an online updated battery health state estimate.

[0042] The maximum permissible peak current and polarization voltage threshold of the pulse during the self-heating process are adjusted based on the online updated battery health status estimate, and the weight parameters of the risk scoring model are updated in combination with the current temperature and temperature rise rate.

[0043] According to one aspect of the above technical solution, the system further includes:

[0044] The temperature difference adjustment module is used to activate the time-sharing priority heating strategy and active equalization mechanism to forcibly reduce the temperature difference when the temperature difference between a single battery cell or between modules exceeds the preset temperature difference value.

[0045] The time-division rotating priority heating strategy includes: identifying the battery area with the lowest temperature as the priority heating target; dividing the heating time into multiple time segments and allocating the time segments to battery modules in different temperature zones according to priority order, so as to dynamically adjust the time segment allocation priority based on real-time temperature feedback;

[0046] The active equalization mechanism includes: extracting energy from high-temperature, high-SOC battery cells, transferring the extracted energy to low-temperature, low-SOC battery cells, monitoring temperature difference changes in real time and adjusting the energy transfer rate, and exiting the equalization mode when the temperature difference drops below a preset temperature difference value.

[0047] According to one aspect of the above technical solution, the system further includes:

[0048] The risk control module is used to perform pulse derating or frequency band switching when the risk score exceeds the threshold.

[0049] When the risk score continues to exceed the limit, self-heating is suspended and the external heating strategy is switched.

[0050] Thirdly, the present invention also provides a vehicle including the battery self-heating and health status collaborative management system described in the above technical solution.

[0051] Compared with existing technologies, the beneficial effects of this invention are as follows: By finding the maximum heating frequency and matching bidirectional pulses, with adaptive duty cycle and amplitude, the package temperature can be raised from -20℃ to 5℃ within 6 minutes. Based on a 90 kWh package and a typical light truck parking preheating scenario, the SOC cost is approximately 5%, significantly shortening the preheating time and reducing energy consumption compared to external heating. Through hard constraints on polarization voltage and negative electrode overpotential estimation, bidirectional pulse cancellation, and SOH / impedance characteristic linkage, the risk of low-temperature lithium deposition and thermal runaway is significantly reduced. Once the risk score exceeds the limit, automatic derating or mode switching occurs. Layered and zoned rotating heating + active equalization ensures that the temperature difference between individual cells converges to ≤3℃, laying the foundation for subsequent charging / discharging consistency and available power release, reducing the "weakest link" effect. Multi-point impedance + DRT in low-temperature dynamic... It can still provide highly relevant features in various scenarios. In the example, the SOH estimation error is reduced from ±8% to about ±3%, and the correction can be completed during the heating process, providing a reliable input for subsequent charging strategy optimization. Under the premise of suppressing polarization and lithium precipitation, the cycle stress is reduced, and the lifespan is estimated to be extended by about 18%~22% in the example. The reuse of inverter / motor generated pulses reduces the investment in new hardware, and the cost of the whole vehicle system is controllable. The combination of spectrum adaptation and multi-point small signal injection balances real-time performance and diagnostic value. It can be seamlessly integrated with existing thermal management / charging strategies, which is convenient for mass application on light truck platforms. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating the battery self-heating and health status collaborative management method in the first embodiment of the present invention;

[0053] Figure 2 This is a structural block diagram of the battery self-heating and health status collaborative management system in the fourth embodiment of the present invention;

[0054] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0055] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0056] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0058] Example 1

[0059] Please see Figure 1 The figure shows a flowchart of the battery self-heating and health status collaborative management method in the first embodiment of the present invention. As shown in the figure, the method includes the following steps:

[0060] Step S100: Battery temperature data is collected through the battery management system. When the temperature is lower than a preset threshold, the parking safety status is confirmed based on the vehicle control unit. Specifically, in this embodiment, taking a light truck equipped with lithium iron phosphate power as an example, the battery pack uses 90 kWh, nominal voltage 600 V, LFP square cells, modular layout, module layer temperature sensors (≥2 / module), and cell sampling temperature points; sampling is performed through a 10 kHz equivalent bandwidth current sensor and voltage acquisition to meet kHz-level pulse measurement. The preset threshold is -10℃. Preheating is triggered when the ambient temperature or the average battery pack temperature is lower than -10℃, and a fast module is used when the temperature is lower than -20℃.

[0061] Step S200: If parking is safe, perform an impedance spectrum scan within a preset frequency range to calculate the heating efficiency index and polarization tolerance corresponding to each frequency point. Select a target frequency point based on maximizing the heating efficiency index and ensuring compliance with polarization tolerance. Specifically, the preset frequency range is 100 Hz to 2 kHz, in steps of 50 to 100 Hz. The calculation expression for the heating efficiency index is as follows:

[0062] M = Re(Z) / |Z| 2 ;

[0063] In the formula, M is the heating efficiency index, Z represents the complex impedance of the battery, Re(Z) is the real part of the complex impedance, representing the heat generation capacity corresponding to the resistance, and |Z| is the modulus of the complex impedance, representing the magnitude of the impedance of the current entering the battery.

[0064] Step S300: Based on the target frequency point matching, a bidirectional pulse is generated to self-heat the battery. The peak pulse current and duty cycle are dynamically adjusted by real-time monitoring of terminal voltage polarization, negative electrode overpotential and temperature rise rate to control the risk score from not exceeding the preset safety threshold.

[0065] Specifically, in the step of performing bidirectional pulses for self-heating at the target frequency, the pulse frequency is 200Hz~1.5kHz, the pulse waveform is a symmetrical bidirectional square wave or pulse train, and the duty cycle is adjustable from 20% to 60%. The duty cycle is limited online according to the SOC and temperature rise rate. Preferably, the bidirectional pulses are generated by the vehicle's three-phase inverter and the motor stator windings. No wheels rotate when the vehicle is parked, and when necessary, bidirectional DC / DC converters achieve cross-zone pulse interaction and thermal homogenization, avoiding the need for additional high-power heating hardware.

[0066] Preferably, in this embodiment, the method further includes:

[0067] A risk scoring model is established based on terminal voltage polarization, negative electrode overpotential estimation, temperature rise rate, state of charge (SOC), and temperature, along with the corresponding weighting coefficients for each parameter, to output the risk score. Further, the risk scoring model is a weighted scoring model, achieving a quantitative assessment of safety risks through multi-parameter fusion. Preferably, the weighting coefficient for terminal voltage polarization is 0.3, the weighting coefficient for negative electrode overpotential estimation is 0.25, the weighting coefficient for temperature rise rate is 0.2, the weighting coefficient for SOC is 0.15, and the weighting coefficient for temperature is 0.1.

[0068] By real-time monitoring of terminal voltage polarization (controlled <100mV) and negative electrode overpotential estimation based on electrochemical models (controlled >0V), the pulse current is dynamically adjusted within the 1C range to ensure that the risk score is always below the preset safety threshold of 0.8.

[0069] Preferably, the above method further includes:

[0070] When the risk score exceeds the threshold, pulse derating or frequency band switching is performed;

[0071] When the risk score continues to exceed the limit, self-heating is suspended and the system switches to an external heating strategy. The external heating strategy involves transferring external heat into the battery pack through methods such as PTC / electrothermal film / liquid circuit heat exchange.

[0072] Step S400 involves superimposing multi-frequency small signals with amplitudes less than or equal to a first preset value during the main pulse interval, collecting characteristic impedance data from several characteristic frequency points, and updating the battery health status estimate online through distributed relaxation time decoupling and Kramers-Kronig test. Specifically, the first preset value is preferably 50mA. The steps for updating the battery health status estimate online through distributed relaxation time decoupling and Kramers-Kronig test include:

[0073] The reliability of the obtained characteristic impedance data is tested based on the Kramers-Kronig test. The tested characteristic impedance data is then input into the distributed relaxation time decoupling model, and the relaxation process distribution spectrum with different time constants is obtained by solving the regularization algorithm.

[0074] Characteristic parameters strongly correlated with battery aging were extracted from the relaxation process distribution maps of different time constants, including the peak resistance corresponding to the charge transfer process and the peak resistance corresponding to the SEI film impedance.

[0075] The extracted feature parameters, real-time temperature, and average SOC are input into a pre-trained battery health state estimation model, which outputs an online updated battery health state estimate.

[0076] The maximum permissible peak current and polarization voltage threshold of the pulse during the self-heating process are adjusted based on the online updated battery health status estimate, and the weight parameters of the risk scoring model are updated in combination with the current temperature and temperature rise rate.

[0077] In some application scenarios of this embodiment, a multi-frequency small signal with an amplitude of 20mA (selecting three characteristic frequencies: 1kHz / 500Hz / 200Hz) is injected during the 5ms pulse interval. Impedance data is collected, passes the KK test, and is input into the DRT model. Characteristic parameters are obtained through Tikhonov regularization decoupling. Combined with an ambient temperature of 25℃, the pre-trained gradient boosting tree model is used to update the SOH estimate online to 95.2%. Based on this, the peak current safety threshold is lowered from 1.5C to 1.3C, while the temperature weight coefficient in the risk scoring model is increased from 0.1 to 0.15. By utilizing the small-signal EIS and DRT, the microscopic state inside the battery is accurately diagnosed to collaboratively optimize subsequent pulse parameters and ensure safety.

[0078] Step S500: When any one of the following conditions is met: the average battery pack temperature is greater than or equal to a second preset value, the energy consumption reaches a preset energy consumption upper limit, the self-heating time reaches a preset time upper limit, or the estimated battery health status reaches a safety threshold, the self-heating mode is exited. The aforementioned second preset value can be set according to the scenario, preferably 5℃. After exiting the self-heating mode, the system enters temperature control maintenance or switches to a regular charging / driving strategy.

[0079] Preferably, in this embodiment, the method further includes:

[0080] When the temperature difference between individual battery cells or modules exceeds the preset temperature difference value, the time-sharing priority heating strategy and active equalization mechanism are activated to forcibly reduce the temperature difference.

[0081] The time-division rotating priority heating strategy includes: identifying the battery area with the lowest temperature as the priority heating target; dividing the heating time into multiple time segments and allocating the time segments to battery modules in different temperature zones according to priority order, so as to dynamically adjust the time segment allocation priority based on real-time temperature feedback;

[0082] The active equalization mechanism includes: extracting energy from high-temperature, high-SOC battery cells, transferring the extracted energy to low-temperature, low-SOC battery cells, monitoring temperature difference changes in real time and adjusting the energy transfer rate, and exiting the equalization mode when the temperature difference drops below a preset temperature difference value.

[0083] Specifically, when a temperature difference of 4°C is detected in module 3, the system divides the 10-second heating cycle into 10 one-second time slices. Six time slices are allocated to prioritize heating the lower-temperature cell 3-2. Simultaneously, a Buck-Boost circuit draws 2A of current from the higher-temperature cell 3-5 and transfers it to cell 3-2. The time slice allocation ratio is dynamically adjusted based on real-time temperature measurement data. When the temperature difference drops to 2°C, balanced heating is restored. This rotating strategy improves the convergence speed of temperature difference by allocating time resources preferentially. Combined with active equalization, it reduces the duration of the maximum abnormal temperature difference during the battery pack's lifespan, significantly delaying the degradation of battery pack consistency caused by temperature differences.

[0084] In summary, the battery self-heating and health status collaborative management method in the above embodiments of the present invention, by finding the maximum heating frequency and matching bidirectional pulses, with adaptive duty cycle and amplitude, can raise the battery pack temperature from -20°C to 5°C within 6 minutes. Based on a 90 kWh battery pack and a typical light truck parking preheating scenario, the SOC cost is approximately 5%, significantly shortening the preheating time and reducing energy consumption compared to external heating. Through hard constraints on polarization voltage and negative electrode overpotential estimation, bidirectional pulse cancellation, and SOH / impedance characteristic linkage, it significantly reduces the risk of low-temperature lithium deposition and thermal runaway. Once the risk score exceeds the limit, it automatically reduces the rate or switches modes. Layered and zoned rotating heating + active equalization ensures that the temperature difference between individual cells converges to ≤3°C, laying the foundation for subsequent charging / discharging consistency and available power release, reducing the "weakest link" effect. Multi-point impedance + DRT in low-temperature dynamic... It can still provide highly relevant features in various scenarios. In the example, the SOH estimation error is reduced from ±8% to about ±3%, and the correction can be completed during the heating process, providing a reliable input for subsequent charging strategy optimization. Under the premise of suppressing polarization and lithium precipitation, the cycle stress is reduced, and the lifespan is estimated to be extended by about 18%~22% in the example. The reuse of inverter / motor generated pulses reduces the investment in new hardware, and the cost of the whole vehicle system is controllable. The combination of spectrum adaptation and multi-point small signal injection balances real-time performance and diagnostic value. It can be seamlessly integrated with existing thermal management / charging strategies, which is convenient for mass application on light truck platforms.

[0085] Example 2

[0086] A second embodiment of this application also provides a battery self-heating and health status collaborative management system, which is used to implement the embodiments and preferred embodiments described above, and will not be repeated hereafter. As used below, the terms "module," "unit," "subunit," etc., can refer to a combination of software and / or hardware that performs a predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0087] like Figure 2 As shown, the system includes: acquisition module 100, scanning module 200, pulse module 300, update module 400 and exit module 500.

[0088] The data acquisition module 100 is used to collect battery temperature data through the battery management system. When the temperature is lower than a preset threshold, the vehicle control unit confirms the parking safety status.

[0089] The scanning module 200 is used to perform impedance spectrum scanning within a preset frequency range to ensure parking safety, calculate the heating efficiency index and polarization tolerance corresponding to each frequency point, and select the target frequency point based on maximizing the heating efficiency index and complying with the polarization tolerance.

[0090] The pulse module 300 is used to generate bidirectional pulses based on the target frequency point to self-heat the battery. By monitoring the terminal voltage polarization, negative electrode overpotential and temperature rise rate in real time, the peak pulse current and duty cycle are dynamically adjusted to control the risk score to not exceed the preset safety threshold.

[0091] The update module 400 is used to superimpose multi-frequency small signals with amplitudes less than or equal to the first preset value during the main pulse interval period, collect characteristic impedance data of several characteristic frequency points, and update the battery health status estimate online through distributed relaxation time decoupling and Kramers-Kronig test.

[0092] The exit module 500 is used to exit the self-heating mode when any one of the following conditions is met: the average temperature of the battery pack is greater than or equal to the second preset value, the energy consumption reaches the preset energy consumption upper limit value, the self-heating time reaches the preset time upper limit value, or the estimated value of the battery health status reaches the safety threshold.

[0093] Preferably, in this embodiment, the system further includes:

[0094] The risk module is used to establish a risk scoring model based on terminal voltage polarization, negative electrode overpotential estimation, temperature rise rate, SOC and temperature, and the weighting coefficients corresponding to each parameter, so as to output the risk score based on the risk scoring model.

[0095] Preferably, in this embodiment, the first preset value is 50mA; the update module 400 is specifically used for:

[0096] The reliability of the obtained characteristic impedance data is tested based on the Kramers-Kronig test. The tested characteristic impedance data is then input into the distributed relaxation time decoupling model, and the relaxation process distribution spectrum with different time constants is obtained by solving the regularization algorithm.

[0097] Characteristic parameters strongly correlated with battery aging were extracted from the relaxation process distribution maps of different time constants, including the peak resistance corresponding to the charge transfer process and the peak resistance corresponding to the SEI film impedance.

[0098] The extracted feature parameters, real-time temperature, and average SOC are input into a pre-trained battery health state estimation model, which outputs an online updated battery health state estimate.

[0099] The maximum permissible peak current and polarization voltage threshold of the pulse during the self-heating process are adjusted based on the online updated battery health status estimate, and the weight parameters of the risk scoring model are updated in combination with the current temperature and temperature rise rate.

[0100] Preferably, in this embodiment, the system further includes:

[0101] The temperature difference adjustment module is used to activate the time-sharing priority heating strategy and active equalization mechanism to forcibly reduce the temperature difference when the temperature difference between a single battery cell or between modules exceeds the preset temperature difference value.

[0102] The time-division rotating priority heating strategy includes: identifying the battery area with the lowest temperature as the priority heating target; dividing the heating time into multiple time segments and allocating the time segments to battery modules in different temperature zones according to priority order, so as to dynamically adjust the time segment allocation priority based on real-time temperature feedback;

[0103] The active equalization mechanism includes: extracting energy from high-temperature, high-SOC battery cells, transferring the extracted energy to low-temperature, low-SOC battery cells, monitoring temperature difference changes in real time and adjusting the energy transfer rate, and exiting the equalization mode when the temperature difference drops below a preset temperature difference value.

[0104] Preferably, in this embodiment, the system further includes:

[0105] The risk control module is used to perform pulse derating or frequency band switching when the risk score exceeds the threshold.

[0106] When the risk score continues to exceed the limit, self-heating is suspended and the external heating strategy is switched.

[0107] It should be noted that the modules can be functional modules or program modules, and can be implemented in software or hardware. For modules implemented in hardware, the modules can reside in the same processor; or the modules can be located in different processors in any combination.

[0108] The third embodiment of this application provides a vehicle including the battery self-heating and health status collaborative management system described in the above embodiments.

[0109] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for collaborative management of battery self-heating and health status, characterized in that, Includes the following steps: The battery management system collects battery temperature data, and when the temperature is lower than a preset threshold, the vehicle control unit confirms the parking safety status. If parking is safe, perform impedance spectrum scanning within the preset frequency range, calculate the heating efficiency index and polarization tolerance corresponding to each frequency point, and select the target frequency point with the condition of maximizing the heating efficiency index and complying with the polarization tolerance. Based on the target frequency point matching, a bidirectional pulse is generated to self-heat the battery. The peak pulse current and duty cycle are dynamically adjusted by real-time monitoring of terminal voltage polarization, negative electrode overpotential and temperature rise rate, so as to control the risk score to not exceed the preset safety threshold. During the intermittent period of the main pulse, multiple small signals with amplitudes less than or equal to the first preset value are superimposed, characteristic impedance data of several characteristic frequency points are collected, and the battery health status estimate is updated online by decoupling through distributed relaxation time and Kramers-Kronig test. The self-heating mode will exit when any one of the following conditions is met: the average temperature of the battery pack is greater than or equal to the second preset value, the energy consumption reaches the preset energy consumption limit, the self-heating time reaches the preset time limit, or the estimated battery health status reaches the safety threshold.

2. The method for coordinated management of battery self-heating and health status according to claim 1, characterized in that, The method further includes: A risk scoring model is established based on terminal voltage polarization, negative electrode overpotential estimation, temperature rise rate, SOC and temperature, and the weighting coefficients of each parameter, so as to output the risk score based on the risk scoring model.

3. The method for coordinated management of battery self-heating and health status according to claim 2, characterized in that, The first preset value is 50mA; The steps for online updating of battery health state estimates, through distributed relaxation time decoupling and Kramers-Kronig test, specifically include: The reliability of the obtained characteristic impedance data is tested based on the Kramers-Kronig test. The tested characteristic impedance data is then input into the distributed relaxation time decoupling model, and the relaxation process distribution spectrum with different time constants is obtained by solving the regularization algorithm. Characteristic parameters strongly correlated with battery aging were extracted from the relaxation process distribution maps of different time constants, including the peak resistance corresponding to the charge transfer process and the peak resistance corresponding to the SEI film impedance. The extracted feature parameters, real-time temperature, and average SOC are input into a pre-trained battery health state estimation model, which outputs an online updated battery health state estimate. The maximum permissible peak current and polarization voltage threshold of the pulse during the self-heating process are adjusted based on the online updated battery health status estimate, and the weight parameters of the risk scoring model are updated in combination with the current temperature and temperature rise rate.

4. The method for coordinated management of battery self-heating and health status according to claim 1, characterized in that, The method further includes: When the temperature difference between individual battery cells or modules exceeds the preset temperature difference value, the time-sharing priority heating strategy and active equalization mechanism are activated to forcibly reduce the temperature difference. The time-division rotating priority heating strategy includes: identifying the battery area with the lowest temperature as the priority heating target; dividing the heating time into multiple time segments and allocating the time segments to battery modules in different temperature zones according to priority order, so as to dynamically adjust the time segment allocation priority based on real-time temperature feedback; The active equalization mechanism includes: extracting energy from high-temperature, high-SOC battery cells, transferring the extracted energy to low-temperature, low-SOC battery cells, monitoring temperature difference changes in real time and adjusting the energy transfer rate, and exiting the equalization mode when the temperature difference drops below a preset temperature difference value.

5. The method for coordinated management of battery self-heating and health status according to claim 1, characterized in that, The formula for calculating the heating efficiency index is as follows: M=Re(Z) / |Z| 2 ; In the formula, M is the heating efficiency index, Z represents the complex impedance of the battery, Re(Z) is the real part of the complex impedance, representing the heat generation capacity corresponding to the resistance, and |Z| is the modulus of the complex impedance, representing the magnitude of the impedance of the current entering the battery.

6. The method for coordinated management of battery self-heating and health status according to claim 1, characterized in that, In the step of performing bidirectional pulses to the target frequency for self-heating, the pulse frequency is 200 Hz to 1.5 kHz, the pulse waveform is a symmetrical bidirectional square wave or pulse train, and the duty cycle is limited online according to the SOC and the temperature rise rate.

7. The method for coordinated management of battery self-heating and health status according to claim 1, characterized in that, The method further includes: When the risk score exceeds the threshold, pulse derating or frequency band switching is performed; When the risk score continues to exceed the limit, the self-heating is suspended and the external heating strategy is switched.

8. A battery self-heating and health status collaborative management system, characterized in that, include: The data acquisition module is used to collect battery temperature data through the battery management system. When the temperature is lower than a preset threshold, the vehicle control unit confirms the parking safety status. The scanning module is used to perform impedance spectrum scanning within a preset frequency range to ensure parking safety, calculate the heat efficiency index and polarization tolerance corresponding to each frequency point, and select the target frequency point based on maximizing the heat efficiency index and complying with the polarization tolerance. The pulse module is used to generate bidirectional pulses based on the target frequency to self-heat the battery. By monitoring the terminal voltage polarization, negative electrode overpotential and temperature rise rate in real time, the peak pulse current and duty cycle are dynamically adjusted to control the risk score to not exceed the preset safety threshold. The update module is used to superimpose multi-frequency small signals with amplitudes less than or equal to the first preset value during the main pulse interval period, collect characteristic impedance data of several characteristic frequency points, and update the battery health status estimate online through distributed relaxation time decoupling and Kramers-Kronig test. The exit module is used to exit the self-heating mode when any one of the following conditions is met: the average temperature of the battery pack is greater than or equal to the second preset value, the energy consumption reaches the preset energy consumption upper limit value, the self-heating time reaches the preset time upper limit value, or the estimated value of the battery health status reaches the safety threshold.

9. The battery self-heating and health status collaborative management system according to claim 8, characterized in that, The system also includes: The risk module is used to establish a risk scoring model based on terminal voltage polarization, negative electrode overpotential estimation, temperature rise rate, SOC and temperature, and the weighting coefficients corresponding to each parameter, so as to output the risk score based on the risk scoring model.

10. A vehicle, characterized in that, The battery self-heating and health status collaborative management system as described in any one of claims 8-9.

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