Method and system for predicting thermal runaway in lithium batteries based on capacitive reactance analysis
Capacitive reactance analysis in lithium batteries allows for early detection of thermal runaway, overcoming limitations of external methods by providing timely warnings and reducing sensor reliance.
Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Patents
- Current Assignee / Owner
- YANTAI CHUNGWAY NEW ENERGY TECHNOLOGY CO LTD
- Filing Date
- 2024-06-21
- Publication Date
- 2026-06-09
AI Technical Summary
Existing methods for predicting thermal runaway in lithium batteries are limited by their reliance on external characteristics with short lead times and susceptibility to environmental interference, and internal resistance data is influenced by factors like SOC and temperature, making it unreliable for early detection.
A method and system using capacitive reactance analysis, involving AC injection to measure capacitive reactance curves, determining thermal runaway thresholds, and providing early warnings based on capacitive reactance curves and thermal runaway levels.
Enables effective and immediate prediction of thermal runaway, providing timely intervention and reducing losses by monitoring capacitive reactance changes, ensuring safety with reduced sensor dependence.
Smart Images
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Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of battery management, and more specifically to a method and system for predicting thermal runaway of lithium batteries based on capacitive reactance analysis.
Background Art
[0002] Lithium battery energy storage systems are widely applied in fields such as transportation and power. However, due to the inherent safety risk of thermal runaway in lithium batteries, the problem of thermal runaway in lithium batteries cannot be fundamentally solved. The safety risk of thermal runaway is that an uncontrollable rapid increase in the internal temperature of the lithium battery occurs, ultimately causing a fire accident. Accurate and prior prediction of the thermal runaway of lithium batteries is the basis for avoiding more serious accidents in lithium battery energy storage systems. Currently, the thermal runaway can be eliminated in its incipient state by combining the thermal runaway prediction with an energy storage fire protection system.
[0003] Patent Document 1: Japanese Unexamined Patent Publication No. 2000-123887 Detection of thermal runaway in batteries relies primarily on external characteristics of lithium batteries that are in the early stages of or in the process of thermal runaway, and these external characteristics mainly include temperature signals, mechanical or pressure signals, and gas signals. One of the main manifestations of thermal runaway in a battery is a rise in temperature, and temperature changes in the battery can be measured by placing a temperature sensor on the surface of the battery. However, this method has two problems: it can only measure the surface temperature of the battery, it takes time for the internal temperature of the battery to be transmitted to the surface, and it requires a temperature sensor to be placed on each battery, which is relatively expensive. Mechanical or pressure signals and gas signals are all signs that appear in the early stages of thermal runaway in lithium batteries. Mechanical deformation, which occurs when the internal temperature of the battery is too high, generates gas and causes the battery case to expand and deform. This can be measured by a stress sensor. When the internal pressure reaches a certain level, gas is ejected from the battery's safety valve. At this time, if the battery is in a sealed container, the pressure inside the container increases and can be measured by a pressure sensor. The specific gas ejected can be measured by a corresponding gas sensor, and the ejected gas is accompanied by smoke, which can also be measured by a smoke sensor. Analysis reveals that methods such as mechanical deformation, pressure increase, gas analysis, and smoke sensing can only detect thermal runaway in lithium batteries when clear signs appear. Thermal runaway prediction has a relatively short lead time and is susceptible to interference from the external environment.
[0004] In addition to the methods described above, the equivalent internal resistance of a battery can accurately reflect changes in its internal state, and it is possible to predict the occurrence of thermal runaway in a battery based on its internal resistance. Furthermore, equivalent internal resistance data of a battery or battery module can be obtained through big data analysis in a BMS management system. However, since the internal resistance of a battery is greatly influenced by factors such as SOC and temperature, it is not often considered a necessary condition for determining thermal runaway in a battery. [Overview of the Initiative] [Problems that the invention aims to solve]
[0005] To address the problem of difficulty in detecting the basis for predicting thermal runaway in lithium battery packs and the susceptibility to interference, the present invention provides a method and system for predicting thermal runaway in lithium batteries based on capacitive reactance analysis. [Means for solving the problem]
[0006] A method for predicting thermal runaway of a lithium battery based on capacitive reactance analysis, the method for detecting thermal runaway of a lithium battery includes: step 1 acquiring ambient temperature data, charge state data, and battery material data of a battery module, and determining a measurement frequency and a thermal runaway threshold based on a pre-set expert database; step 2 measuring the capacitive reactance curve of the battery module by AC injection at the measurement frequency; and step 3 determining the thermal runaway level of the battery module based on the capacitive reactance curve and providing an early warning of thermal runaway.
[0007] Preferably, measuring the capacitive reactance curve of the battery module by AC injection at the measurement frequency includes detecting the capacitive reactance values of the battery cells in the battery module in real time by AC injection at the measurement frequency, determining the capacitive reactance values of the battery module based on the capacitive reactance values of the battery cells, and defining the capacitive reactance curve of the battery module that changes with temperature.
[0008] Preferably, the capacitive reactance value of the battery module is calculated by the following formula: JPEG0007872313000001.jpg16170 However, jω is a unit in the complex frequency domain, C ESCn This is the capacitive reactance value of the battery cell.
[0009] Preferably, before determining the thermal runaway level of the battery module based on the capacitive reactance curve, the method further includes defining thermal runaway level states based on the thermal runaway threshold, including no thermal runaway, a tendency towards thermal runaway, and an attempt to cause thermal runaway.
[0010] A lithium battery thermal runaway prediction system based on capacitive reactance analysis, comprising an MCU controller, an impedance measuring device, a BMS management system, and a thermal runaway management system, wherein the MCU controller is connected to the BMS management system via communication, the MCU controller collects ambient temperature data and charge state data of the battery module using the BMS management system, and determines a thermal runaway threshold and measurement frequency based on the battery material data of the battery module, the MCU controller is connected to the impedance measuring device via communication, the impedance measuring device measures the capacitive reactance of the battery module at the measurement frequency, and transmits the measurement results to the MCU controller to determine the capacitive reactance curve of the battery module, and the MCU controller is connected to the thermal runaway management system via communication, the MCU controller provides an early warning of thermal runaway based on the capacitive reactance curve using the thermal runaway management system.
[0011] Preferably, the MCU controller includes an expert database module and a three-stage thermal runaway prediction module. The expert database module determines a measurement frequency based on ambient temperature data, charge status data, and battery material data from the BMS management system and transmits it to the impedance measuring device. The expert database module further determines a thermal runaway threshold and transmits it to the three-stage thermal runaway prediction module. The three-stage thermal runaway prediction module determines the capacitive reactance curve of the battery module based on the measurement results of the impedance measuring device and determines a thermal runaway prediction result based on the thermal runaway threshold and transmits it to the thermal runaway management system. [Effects of the Invention]
[0012] Compared to the conventional technology, the beneficial effects of the present invention are as follows: The proposed technology according to the present invention includes acquiring ambient temperature data, charge state data, and battery material data of a battery module, determining a measurement frequency and thermal runaway threshold based on a pre-set expert database, measuring the capacitive reactance curve of the battery module by AC injection at the measurement frequency, determining the thermal runaway level of the battery module based on the capacitive reactance curve, and providing an early warning of thermal runaway. The change in capacitive reactance according to temperature is clear during the temperature rise process of the battery, and the capacitive reactance curve of the battery module is measured by a method for measuring the capacitive reactance of battery cells, thereby enabling effective and immediate prediction of the occurrence of thermal runaway phenomena in the battery, providing more time for firefighting intervention in case of thermal runaway, effectively reducing losses due to thermal runaway of the battery, ensuring the safety of the energy storage system, and the principle of detecting the capacitive reactance of the battery module is simple, enabling real-time monitoring by the BMS system and MCU controller, and having low dependence on detection sensors and devices. [Brief explanation of the drawing]
[0013] [Figure 1] This is an example of a flowchart for a method for predicting thermal runaway in lithium batteries according to one embodiment of the present invention. [Figure 2] This is an example of a flowchart for transmitting detection information in a method for predicting thermal runaway in a lithium battery according to one embodiment of the present invention. [Figure 3] This is an example of a diagram illustrating the effect of the temperature-dependent change in the capacitive reactance of a lithium battery according to one embodiment of the present invention. [Figure 4] This is an example of a schematic diagram of the configuration of a lithium battery thermal runaway prediction system according to one embodiment of the present invention. [Modes for carrying out the invention]
[0014] To better understand the present invention, the content of the present invention will be further described below by referring to the drawings and examples of the specification.
[0015] Example 1 This example provides a method for predicting the thermal runaway of a lithium battery based on capacitive reactance analysis. Before proposing the method, it is discovered through the analysis of a large amount of experimental data that the low-frequency impedance of the lithium battery hardly changes according to the state of charge. Therefore, research is carried out on the method for predicting the thermal runaway of the lithium battery based on capacitive reactance analysis.
[0016] Battery impedance includes real-axis impedance and imaginary-axis impedance. In the low-frequency range, the real-axis impedance is mainly represented as resistance, and the imaginary-axis impedance is mainly represented as capacitance. Therefore, the value of the imaginary-axis part of the low-frequency impedance may be shown as capacitive reactance. The real-axis value of the low-frequency impedance of the battery module is shown in the following formula: JPEG0007872313000002.jpg11170Where Z RE is the real-axis value of the low-frequency impedance of the battery module, and R ESRn is the equivalent series internal resistance of the battery cell.
[0017] The real-axis value of the low-frequency impedance of the battery module is composed of the sum of the resistances of each battery cell, and the change in the internal resistance of each cell accumulates to the change in the equivalent internal resistance of the battery module. Therefore, it is impossible to determine which battery cell's thermal runaway causes the change based on the equivalent series internal resistance of the battery pack.
[0018] The imaginary-axis value of the low-frequency impedance of the battery module, that is, the capacitive reactance, is shown in the following formula: JPEG0007872313000003.jpg16170However, jω is the unit of the complex frequency domain, and C ESCn is the capacitive reactance value of the battery cell.
[0019] The imaginary axis value of the low-frequency impedance of the battery module is formed by connecting the equivalent capacitances of each battery cell in series. Therefore, the capacitive reactance value of the battery module is determined by the minimum cell equivalent capacitance value. That is, a decrease in the equivalent capacitance value of any one cell can be expressed by the capacitive reactance of the battery pack. Since the equivalent capacitances are connected in series and the total capacitance value of the series connection is not determined by the sum of each series capacitance value but mainly by the minimum capacitance value, if the capacitance value of the battery cells connected in series in the thermal runaway state decreases, it can be clearly reflected by the capacitive reactance value of the module.
[0020] Based on this idea, a thermal runaway simulation experiment of the battery pack was carried out. The battery pack is composed of five 30Ah lithium iron phosphate battery cells. The effect diagram of the change of the capacitive reactance measured by heating one cell and two cells respectively with respect to temperature is shown in Figure 3. During the heating process, the constant temperature bath was set at 85°C, the temperature was slowly increased, and finally stabilized at 85°C. As can be seen from the above, during the heating process of the battery cell, the imaginary axis value of the low-frequency impedance of the battery pack clearly increases with the increase of the battery temperature, and such an increasing trend does not change clearly with the increase of the number of heated battery cells. Therefore, the imaginary axis value of the low-frequency impedance of the battery pack can be used for predicting the thermal runaway of the battery, and the increase in the temperature of any one battery will be detected by being reflected in the imaginary axis value of the low-frequency impedance.
[0021] Based on the above analysis, this embodiment provides a method for predicting the thermal runaway of a lithium battery based on capacitive reactance analysis. The flowchart of the method is shown in Figure 1. The method includes: step 1 acquiring ambient temperature data, charge status data, and battery material data of a battery module, and determining a measurement frequency and thermal runaway threshold based on a pre-configured expert database; step 2 measuring the capacitive reactance curve of the battery module by AC injection at the measurement frequency; and step 3 determining the thermal runaway level of the battery module based on the capacitive reactance curve and providing an early warning for thermal runaway.
[0022] Measuring the capacitive reactance curve of the battery module by AC injection at the measurement frequency includes detecting the capacitive reactance values of the battery cells in the battery module in real time by AC injection at the measurement frequency, determining the capacitive reactance values of the battery module based on the capacitive reactance values of the battery cells, and defining the capacitive reactance curve of the battery module that changes with temperature.
[0023] The capacitive reactance value of the aforementioned battery module is calculated by the following formula: JPEG0007872313000004.jpg17170 However, jω is a unit in the complex frequency domain, C ESCn This is the capacitive reactance value of the battery cell.
[0024] Before determining the thermal runaway level of the battery module based on the capacitive reactance curve, the system further includes defining thermal runaway level states based on the thermal runaway threshold, including no thermal runaway, a tendency towards thermal runaway, and an attempt to cause thermal runaway.
[0025] In the above method, the flowchart for transmitting detection information in the lithium battery thermal runaway prediction method is shown in Figure 2. First, experimental studies are conducted on critical thermal runaway under different types of batteries, different ambient temperatures, and different charge states. Expert database information is obtained based on the experimental data. Next, information such as the measured battery material and voltage range, as well as information such as the ambient temperature and charge state of the battery pack measured in real time, are input into the expert database. The frequency points for impedance measurement and thermal runaway threshold information are output from the expert database. Subsequently, a real-time impedance measurement device tests the impedance of the battery pack online in real time based on the measurement frequencies input into the expert database and outputs the capacitive reactance value. Finally, the process enters a real-time thermal runaway level prediction stage, where the thermal runaway level is predicted based on the capacitive reactance value and thermal runaway threshold, and the prediction result is output to the fire prevention and thermal runaway management system of the energy storage device.
[0026] Example 2 This embodiment provides a lithium battery thermal runaway prediction system based on capacitive reactance analysis. The configuration of the lithium battery thermal runaway prediction system is shown in Figure 4 and comprises an MCU controller, an impedance measuring device, a BMS management system, and a thermal runaway management system. The MCU controller is connected to the BMS management system via communication, and the MCU controller collects ambient temperature data and charge status data of the battery module using the BMS management system, and determines a thermal runaway threshold and measurement frequency based on the battery material data of the battery module. The MCU controller is also connected to the impedance measuring device via communication, and the impedance measuring device measures the capacitive reactance of the battery module at the measurement frequency and transmits the measurement results to the MCU controller to determine the capacitive reactance curve of the battery module. The MCU controller is also connected to the thermal runaway management system via communication, and the MCU controller provides an early warning of thermal runaway based on the capacitive reactance curve using the thermal runaway management system.
[0027] The MCU controller includes an expert database module and a three-stage thermal runaway prediction module. The expert database module determines a measurement frequency based on ambient temperature data, charge status data, and battery material data from the BMS management system and transmits it to the impedance measuring device. The expert database module further determines a thermal runaway threshold and transmits it to the three-stage thermal runaway prediction module. The three-stage thermal runaway prediction module determines the capacitive reactance curve of the battery module based on the measurement results of the impedance measuring device and determines a thermal runaway prediction result based on the thermal runaway threshold and transmits it to the thermal runaway management system.
[0028] In the above system, both the expert database formed from experimental data and the thermal runaway prediction are implemented in the MCU controller. The ambient temperature and SOC information of the battery pack, entered into the expert database, are acquired from the BMS management system via communication, and the battery material information is manually entered externally. Impedance measurement is performed using an impedance measuring device based on the AC injection method. The current frequency to be injected into the battery pack is output from the expert database, and the impedance measuring device outputs the capacitive reactance value to the MCU. Based on the thermal runaway threshold and the capacitive reactance value, the thermal runaway level is divided into three levels: no thermal runaway, tendency towards thermal runaway, and attempting to occur. Finally, the thermal runaway result is output to the fire prevention and thermal runaway management systems of the energy storage device. Levels where no thermal runaway occurs do not require operation, levels with a tendency towards thermal runaway activate the thermal management system to cool the device, and levels where thermal runaway is attempting activate the fire prevention system.
[0029] Clearly, the embodiments described are only a part of the embodiments of the present invention, not all of them. Any other embodiments that a person skilled in the art can obtain based on the embodiments of the present invention without requiring inventive work are all within the scope of the protection of the present invention.
Claims
1. A method for detecting thermal runaway in lithium batteries based on capacitive reactance analysis, Step 1 involves determining a low-frequency measurement frequency for a battery module in which multiple battery cells are connected in series, Step 2 for pre-determining the capacitive reactance curve of the battery module by an AC injection method based on a low-frequency current frequency corresponding to the measurement frequency, comprising: detecting the capacitive reactance value of the battery cells in the battery module by an AC injection method based on a low-frequency current frequency corresponding to the measurement frequency; determining the capacitive reactance value of the battery module based on the capacitive reactance value of the battery cells; and determining the capacitive reactance curve of the battery module that changes according to the temperature of the battery module. A method for detecting thermal runaway in a lithium battery based on capacitive reactance analysis, characterized by including step 3, which involves detecting in real time a change in the capacitive reactance value of a battery module of the same type as the aforementioned battery module, determining the thermal runaway level of the battery module of the same type as the aforementioned battery module based on the capacitive reactance curve defined in step 2, and providing an early warning of thermal runaway.
2. A lithium battery thermal runaway prediction system based on capacitive reactance analysis, using the lithium battery thermal runaway detection method based on capacitive reactance analysis described in claim 1, Equipped with an MCU controller, impedance measuring device, BMS management system, and thermal runaway management system, The MCU controller is connected to the BMS management system via communication, and the MCU controller determines a low-frequency measurement frequency for a battery module in which multiple battery cells are connected in series by the BMS management system. The MCU controller is connected to the impedance measuring device via communication, the impedance measuring device measures the capacitive reactance of the battery module at the measurement frequency, and transmits the measurement result to the MCU controller to determine the capacitive reactance curve of the battery module. A lithium battery thermal runaway prediction system based on capacitive reactance analysis, characterized in that the MCU controller is connected to the thermal runaway management system via communication, and the MCU controller provides an early warning of thermal runaway by the thermal runaway management system based on the capacitive reactance curve.