A method for estimating internal resistance of a battery

Abstract

The application provides a kind of estimation method for battery internal resistance, including detecting the current change event of battery, judging whether the current change amplitude satisfies the set condition;If it is satisfied, the current and voltage data before and after the current change are recorded, the current change amount and the voltage change amount are calculated, and the battery equivalent internal resistance is obtained based on the ratio of the voltage change amount and the current change amount;The internal resistance result is quality graded, and the operation of updating, aborting updating or discarding data is performed according to the grading result, and finally the valid internal resistance result is output.The beneficial effects of the application are as follows: the internal resistance estimation can be completed during the normal operation of the terminal device, without the need for special test conditions, and the accuracy and reliability of the internal resistance estimation result are improved through quality grading, which is suitable for mobile payment terminals, industrial terminals, Internet of Things terminals and other types of devices that rely on rechargeable batteries for power supply.

Description

Technical Field

[0001] This invention relates to the field of battery health management technology, and in particular to a method for estimating the internal resistance of a battery. Background Technology

[0002] With the widespread application of mobile payment terminals, self-service terminals, industrial PDAs, and portable IoT devices, these terminal devices generally rely on rechargeable batteries as their primary or backup power source, and are characterized by long continuous operating times, diverse operating environments, and long maintenance cycles. During long-term use, batteries inevitably experience capacity decay, increased internal resistance, and changes in thermal characteristics. Among these, battery internal resistance is a core indicator reflecting the battery's health status. An abnormal increase in internal resistance directly leads to decreased battery charging and discharging efficiency, reduced range, and may even cause abnormal temperature rise and safety accidents. SOC, or State of Charge, is used in electric vehicles, mobile phones, and other electronic devices to measure the current battery level, indicating the remaining or charged state of the battery, and is an important indicator for managing battery life and driving range.

[0003] Therefore, accurate and real-time estimation of battery internal resistance is a crucial aspect of battery health management. However, current battery internal resistance estimation methods generally suffer from the following technical problems: battery internal resistance estimation often relies on static or offline test parameters, requiring completion under dedicated test conditions or within a complete charge-discharge cycle, making it impossible to achieve automatic and continuous internal resistance acquisition during normal equipment operation; some online estimation methods do not consider the influence of the sampling environment, resulting in low accuracy and reliability of the estimation results; and there is a lack of quality verification mechanisms for the estimation results, making invalid data prone to misjudging the battery health status.

[0004] The aforementioned issues result in a lack of real-time and effectiveness in monitoring the battery internal resistance of terminal devices, failing to provide reliable parameter support for battery health assessment and risk warning, and hindering the realization of full life cycle management of batteries. Summary of the Invention

[0005] To address the problems in existing technologies, this invention provides a method for estimating battery internal resistance. By detecting current change events in the battery, determining whether the current change amplitude meets set conditions, recording current and voltage data before and after the current change, calculating the current and voltage changes, and obtaining the battery's equivalent internal resistance based on the ratio of the voltage and current changes, classifying the internal resistance results by quality, and performing operations such as updating, stopping updates, or discarding data based on the classification results, ultimately outputting a valid internal resistance result. This method enables internal resistance estimation under all operating conditions of the terminal device, without requiring specialized testing conditions. Furthermore, the accuracy of the estimation results is improved through quality classification and data verification, providing reliable internal resistance parameters for battery health management. This solves the problem in existing technologies where battery internal resistance is difficult to automatically and reliably obtain and estimate during normal device operation.

[0006] The present invention provides a method for estimating the internal resistance of a battery, comprising the following steps: Step 1: Detect the current change event of the battery and determine whether the current change amplitude of the current change event meets the preset conditions. Step 2: When the current change amplitude meets the preset conditions, record the battery current data and voltage data before and after the current change, and calculate the current change ΔI and voltage change ΔV. Step 3: Calculate the battery's equivalent internal resistance R based on the ratio of voltage change ΔV to current change ΔI. Step 4: Perform quality classification on the battery equivalent internal resistance R, and output the internal resistance result after performing the corresponding processing operation based on the classification result.

[0007] The present invention is further improved in that, in step 1, the current change event is the current fluctuation generated by the battery during normal operation, charging or standby of the terminal device, and the preset condition is that the current change amplitude is greater than a preset threshold. The preset threshold is adaptively adjusted according to the battery model and the usage scenario of the terminal device.

[0008] The present invention is further improved in that, in step 2, when recording the battery current data and voltage data before and after the current change, the data stability condition must be met. The stability condition is that the fluctuation range of the current and voltage data within 3-5 consecutive sampling periods does not exceed ±2%.

[0009] The present invention is further improved in that, in step 3, the formula for calculating the equivalent internal resistance R of the battery is R=ΔV / ΔI, where ΔV is the voltage change in V, ΔI is the current change in A, and the unit of the equivalent internal resistance R of the battery is Ω.

[0010] The present invention is further improved in that, in step 4, the quality grading is based on the sampling environment conditions, which include battery temperature, terminal device load status, and SOC value. The quality grading is divided into three levels: excellent, medium, and poor.

[0011] The present invention is further improved in that, in step 4, the corresponding processing operation is performed according to the grading result as follows: if the grading result is excellent, the battery equivalent internal resistance R is updated to the internal resistance parameter library of the battery health management system and the battery equivalent internal resistance R is output; if the grading result is medium, the current internal resistance parameter update is stopped and only the battery equivalent internal resistance R is output as a reference; if the grading result is poor, the current battery equivalent internal resistance R data is directly discarded and no internal resistance result is output.

[0012] The present invention is further improved in that, in step 4, the criteria for determining the quality grade as excellent are: the battery temperature is within the optimal operating range of 25±5℃, the load fluctuation of the terminal device is ≤5%, and the SOC value is within the range of 30%-80%; the criteria for determining the quality grade as medium are: the battery temperature is within the range of 0-45℃, the load fluctuation of the terminal device is 5%-15%, and the SOC value is within the range of 10%-30% or 80%-95%; the criteria for determining the quality grade as poor are: the battery temperature exceeds the range of 0-45℃, the load fluctuation of the terminal device is >15%, and the SOC value is <10% or >95%.

[0013] The present invention is further improved in that, in step 1, if the current change amplitude does not meet the preset conditions, the detection of the current change event of the battery is resumed until a current change event that meets the set conditions is detected.

[0014] The present invention is further improved in that, in step 2, the sampling period is 100ms-500ms, which can be adaptively adjusted according to the operating status of the terminal device.

[0015] The beneficial effects of this invention are as follows: This invention provides a method for estimating battery internal resistance. By detecting current change events in the battery, determining whether the current change amplitude meets set conditions, recording current and voltage data before and after the current change, calculating the current and voltage changes, and obtaining the battery's equivalent internal resistance based on the ratio of the voltage and current changes, the method performs quality grading on the internal resistance results, and executes operations such as updating, stopping updates, or discarding data based on the grading results, ultimately outputting a valid internal resistance result. This method can achieve internal resistance estimation under all operating conditions of the terminal device, without requiring special testing conditions. Furthermore, the accuracy of the estimation results is improved through quality grading and data verification, providing reliable internal resistance parameters for battery health management. By setting current change amplitude and data stability conditions, the sampled data is pre-verified, effectively avoiding estimation errors caused by invalid fluctuation data and improving the accuracy of internal resistance estimation. Based on battery temperature, device load status, and SOC... The system performs quality grading of internal resistance results based on three sampling environment conditions and executes different processing operations according to the grading results. This achieves post-processing quality control of the estimated results, avoids interference from erroneous data on battery health management, and provides reliable internal resistance parameters for battery health assessment. The preset thresholds for sampling period and current variation amplitude can be adaptively adjusted according to battery model and device usage scenario, exhibiting good versatility and adaptability. It can be applied to various terminal devices that rely on rechargeable batteries for power. Running in the background of the terminal device in software mode, it requires no hardware modification to existing devices, offering flexible deployment, low modification costs, and easy integration with existing battery health management systems. This solves the problem in existing technologies where battery internal resistance is difficult to automatically and reliably obtain and estimate during normal device operation. Attached Figure Description

[0016] Figure 1 This is a flowchart of a method for estimating the internal resistance of a battery according to the present invention. Detailed Implementation

[0017] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0018] Please see Figure 1 The present invention provides a method for estimating the internal resistance of a battery, comprising the following steps: Step 1: Detect battery current change events and determine whether the current change amplitude of the current change event meets the preset conditions. The current change event is the current fluctuation generated by the battery during normal operation, charging or standby of the terminal device. The preset condition is that the current change amplitude is greater than a preset threshold. The preset threshold is adaptively adjusted according to the battery model and the usage scenario of the terminal device. If the current change amplitude does not meet the preset conditions, return to continue detecting battery current change events until a current change event that meets the set conditions is detected.

[0019] Step 2: When the current change amplitude meets the preset conditions, record the battery current data and voltage data before and after the current change, and calculate the current change ΔI and voltage change ΔV. When recording the battery current data and voltage data before and after the current change, the data stability condition must be met. The stability condition is that the fluctuation amplitude of the current and voltage data within 3-5 consecutive sampling periods does not exceed ±2%. The sampling period is 100ms-500ms, which can be adaptively adjusted according to the operating status of the terminal device.

[0020] Step 3: Calculate the battery's equivalent internal resistance R based on the ratio of voltage change ΔV to current change ΔI. The formula for calculating the battery's equivalent internal resistance R is R = ΔV / ΔI, where ΔV is the voltage change in V, ΔI is the current change in A, and the unit of the battery's equivalent internal resistance R is Ω.

[0021] Step 4: The battery equivalent internal resistance R is graded according to its quality. Based on the grading results, corresponding processing operations are performed, and the internal resistance result is output. The quality grading is based on the sampling environment conditions, including battery temperature, terminal device load status, and SOC value. The quality grading is divided into three levels: excellent, medium, and poor. Specifically, if the grading result is excellent, the battery equivalent internal resistance R is updated to the internal resistance parameter library of the battery health management system, and the battery equivalent internal resistance R is output. If the grading result is medium, the current internal resistance parameter update is stopped, and only the battery equivalent internal resistance R is output as a reference. If the grading result is poor, the current battery equivalent internal resistance R data is discarded, and no data is output. The results of the internal resistance test are as follows: The criteria for determining the quality grade as "excellent" are: battery temperature within the optimal operating range of 25±5℃, terminal equipment load fluctuation range ≤5%, and SOC value within the range of 30%-80%; the criteria for determining the quality grade as "medium" are: battery temperature within the range of 0-45℃, terminal equipment load fluctuation range 5%-15%, and SOC value within the range of 10%-30% or 80%-95%; the criteria for determining the quality grade as "poor" are: battery temperature exceeding the range of 0-45℃, terminal equipment load fluctuation range >15%, and SOC value <10% or SOC value >95%.

[0022] Please see Figure 1As an embodiment of the present invention, this embodiment is applied to the estimation of the internal resistance of a lithium battery in a handheld POS machine. The lithium battery model of the POS machine is 18650, with a rated capacity of 2000mAh, an optimal operating temperature of 25±5℃, a preset threshold for current change amplitude of 0.1A, and a sampling period of 200ms. In this embodiment, the battery internal resistance estimation method includes the following steps: Step 1, during the normal operation of the POS machine, the background continuously detects current change events of the lithium battery. A current change event of rising from 0.2A to 0.4A is detected, with a current change amplitude of 0.2A, which is greater than the preset threshold of 0.1A, thus meeting the set conditions; Step 2, the current data (0.2A, 0.4A) and voltage data (3.7V, 3.6V) before and after the current change are recorded. The fluctuation amplitude of the current and voltage data within four consecutive sampling periods (800ms) is ±1.5%, which meets the stability requirements. Under the condition (≤±2%), the current change ΔI = 0.2A and the voltage change ΔV = -0.1V are calculated. Step 3: According to the formula R = ΔV / ΔI, the equivalent internal resistance of the battery is calculated as R = |-0.1V| / 0.2A = 0.5Ω. Step 4: The sampling environment conditions at this time are collected: battery temperature 26℃, POS machine load fluctuation amplitude 3%, and SOC value 65%, all of which meet the judgment conditions for excellent quality classification. The equivalent internal resistance of the battery of 0.5Ω is updated to the internal resistance parameter library of the POS machine's battery health management system, and the internal resistance result is output as a valid parameter for battery health assessment.

[0023] Please see Figure 1As a second embodiment of the present invention, this embodiment is applied to the estimation of the internal resistance of lithium batteries in industrial PDAs. The rated capacity of the lithium battery in the industrial PDA is 3000mAh, the preset threshold for current change amplitude is set to 0.15A, and the sampling period is 300ms. In this embodiment, the battery internal resistance estimation method includes the following steps: Step 1, during the logistics scanning operation of the industrial PDA, the background continuously detects the current change events of the lithium battery. A current change event is detected in which the current rises from 0.3A to 0.5A, and the current change amplitude is 0.2A, which is greater than the preset threshold of 0.15A, thus meeting the set conditions; Step 2, the current data (0.3A, 0.5A) and voltage data (3.8V, 3.72V) before and after the current change are recorded, and the current and voltage data fluctuations within 3 consecutive sampling periods (900ms) are recorded. The fluctuation range is ±2%, which meets the stability condition. The current change ΔI = 0.2A and the voltage change ΔV = -0.08V are calculated. Step 3: According to the formula R = ΔV / ΔI, the equivalent internal resistance of the battery is calculated as R = |-0.08V| / 0.2A = 0.4Ω. Step 4: The sampling environment conditions at this time are collected. The battery temperature is 40℃, the industrial PDA load fluctuation range is 10%, and the SOC value is 25%. All of these meet the judgment conditions of the quality classification. The update operation of the internal resistance parameter library is stopped, and only 0.4Ω is output as the reference internal resistance result.

[0024] Please see Figure 1 As a third embodiment of the present invention, this embodiment is applied to the estimation of the internal resistance of a lithium battery in an IoT sensor. The rated capacity of the lithium battery in the IoT sensor is 500mAh, the preset threshold for current change amplitude is set to 0.05A, and the sampling period is 500ms. In this embodiment, the battery internal resistance estimation method includes the following steps: Step 1, during the data transmission of the IoT sensor, the background continuously detects current change events of the lithium battery. A current change event is detected where the current rises from 0.08A to 0.14A, with a current change amplitude of 0.06A, which is greater than the preset threshold of 0.05A, thus meeting the set condition; Step 2, the current data (0.08A, 0.14A) and voltage data (3.6V, 3.55V) before and after the current change are recorded, and the fluctuation of the current and voltage data within 5 consecutive sampling periods (2500ms) is recorded. The amplitude is ±1.8%, which meets the stability condition. The calculated current change ΔI = 0.06A and voltage change ΔV = -0.05V are obtained. Step 3: According to the formula R = ΔV / ΔI, the equivalent internal resistance of the battery is calculated as R = |-0.05V| / 0.06A ≈ 0.83Ω. Step 4: The sampling environment conditions at this time are collected: battery temperature 50℃, sensor load fluctuation amplitude 20%, and SOC value 8%. All of these meet the judgment conditions of poor quality classification. The 0.83Ω internal resistance data is directly discarded, no results are output, and the system returns to continue detecting current change events.

[0025] As can be seen from the above, the beneficial effects of the present invention are as follows: The present invention provides a method for estimating the internal resistance of a battery. By detecting current change events in the battery, determining whether the current change amplitude meets the set conditions, recording the current and voltage data before and after the current change, calculating the current change and voltage change, obtaining the equivalent internal resistance of the battery based on the ratio of the voltage change to the current change, classifying the internal resistance results by quality, and performing operations such as updating, stopping the update, or discarding data according to the classification results, finally outputting a valid internal resistance result. This method can achieve internal resistance estimation under all operating conditions of the terminal device, without the need for special testing conditions, and improves the accuracy of the estimation results through quality classification and data verification, providing reliable internal resistance parameters for battery health management. By setting current change amplitude setting conditions and data stability conditions, the sampled data is pre-verified, effectively avoiding invalid fluctuations. This approach improves the accuracy of internal resistance estimation by reducing estimation errors. It categorizes internal resistance results based on three sampling environmental conditions: battery temperature, device load state, and SOC value. Different processing operations are performed according to the categorization results, enabling post-processing quality control of the estimation results. This avoids interference from error data with battery health management and provides reliable internal resistance parameters for battery health assessment. The preset thresholds for sampling period and current variation amplitude can be adaptively adjusted according to battery model and device usage scenario, exhibiting good versatility and adaptability. It can be applied to various terminal devices powered by rechargeable batteries. Running in the background of the terminal device via software, it requires no hardware modification to existing devices, offering flexible deployment, low modification costs, and easy integration with existing battery health management systems. This solves the problem of difficulty in automatically and reliably acquiring and estimating battery internal resistance during normal device operation in existing technologies.

[0026] The specific embodiments described above are preferred embodiments of the present invention and are not intended to limit the specific scope of the present invention. The scope of the present invention includes, but is not limited to, these specific embodiments. All equivalent changes made in accordance with the present invention are within the protection scope of the present invention.

Claims

1. A method for estimating the internal resistance of a battery, characterized in that, Includes the following steps: Step 1: Detect the current change event of the battery and determine whether the current change amplitude of the current change event meets the preset conditions. Step 2: When the current change amplitude meets the preset conditions, record the battery current data and voltage data before and after the current change, and calculate the current change ΔI and voltage change ΔV. Step 3: Calculate the battery's equivalent internal resistance R based on the ratio of voltage change ΔV to current change ΔI. Step 4: Perform quality classification on the battery equivalent internal resistance R, and output the internal resistance result after performing the corresponding processing operation based on the classification result.

2. The method for estimating battery internal resistance as described in claim 1, characterized in that: In step 1, the current change event is the current fluctuation generated by the battery during normal operation, charging or standby of the terminal device. The preset condition is that the current change amplitude is greater than a preset threshold. The preset threshold is adaptively adjusted according to the battery model and the usage scenario of the terminal device.

3. The method for estimating battery internal resistance as described in claim 2, characterized in that: In step 2, when recording battery current and voltage data before and after the current change, the data stability condition must be met. The stability condition is that the fluctuation range of current and voltage data within 3-5 consecutive sampling periods does not exceed ±2%.

4. The method for estimating battery internal resistance as described in claim 3, characterized in that: In step 3, the formula for calculating the battery's equivalent internal resistance R is R=ΔV / ΔI, where ΔV is the voltage change in V, ΔI is the current change in A, and the unit of the battery's equivalent internal resistance R is Ω.

5. The method for estimating battery internal resistance as described in claim 4, characterized in that: In step 4, the quality grading is based on the sampling environment conditions, which include battery temperature, terminal device load status, and SOC value. The quality grading is divided into three levels: excellent, medium, and poor.

6. The method for estimating battery internal resistance as described in claim 5, characterized in that: In step 4, the corresponding processing operation based on the grading result is as follows: if the grading result is excellent, the battery equivalent internal resistance R is updated to the internal resistance parameter library of the battery health management system, and the battery equivalent internal resistance R is output; if the grading result is medium, the current internal resistance parameter update is stopped, and only the battery equivalent internal resistance R is output as a reference; if the grading result is poor, the current battery equivalent internal resistance R data is discarded directly, and no internal resistance result is output.

7. The method for estimating battery internal resistance as described in claim 6, characterized in that: In step 4, the criteria for determining a quality grade of "excellent" are: battery temperature within the optimal operating range of 25±5℃, terminal device load fluctuation ≤5%, and SOC value within the range of 30%-80%; the criteria for determining a quality grade of "medium" are: battery temperature within the range of 0-45℃, terminal device load fluctuation 5%-15%, and SOC value within the range of 10%-30% or 80%-95%. The criteria for determining a poor quality rating are: battery temperature exceeding the 0-45℃ range, terminal device load fluctuation >15%, and SOC value <10% or SOC value >95%.

8. The method for estimating battery internal resistance as described in claim 7, characterized in that: In step 1, if the current change amplitude does not meet the preset conditions, the process returns to continue detecting the current change event of the battery until a current change event that meets the set conditions is detected.

9. The method for estimating battery internal resistance as described in claim 8, characterized in that: In step 2, the sampling period is 100ms-500ms, which can be adaptively adjusted according to the operating status of the terminal device.

Images