Method for monitoring service life of power battery

Abstract

The invention discloses a method for monitoring the service life of a power battery, and the method comprises the steps: collecting the vehicle speed, acceleration, battery discharge current, temperature distribution and other data, combining the vehicle speed fluctuation and a power system load state, precisely dividing urban congestion, suburban medium-speed and high-speed cruise conditions, introducing a dynamic discharge depth factor, and obtaining the service life of the power battery. Factors such as battery charge state variation, a current root-mean-square value and the highest temperature of a battery cell are integrated, different temperature compensation coefficients are set according to a battery chemical system, the discharge depth is scientifically quantified, the proportion of the number of discharge events in each working condition depth is counted, and when the proportion exceeds a corresponding safety threshold value, graded alarm prompts are output. Different early warnings are given out according to the number of working conditions exceeding the threshold value, the peak discharge power of the battery is limited when red early warnings are given out, excessive loss of the battery is avoided, when the dynamic discharge depth factor is increased to exceed a certain amplitude, battery health emergency detection is directly triggered, potential problems are found in time, safe and stable operation of the battery is guaranteed, and the use safety of the vehicle is improved.

Description

Technical Field

[0001] The present application relates to the technical field of power battery service life detection, and in particular to a method for monitoring the service life of a power battery. Background Art

[0002] In today's booming electric vehicle market, the lifespan of power batteries has become a key factor affecting EV performance and user experience. As the electric vehicle market continues to expand, users are increasingly concerned about EV range, charge and discharge efficiency, and battery life. However, monitoring power battery life currently presents numerous challenges that need to be addressed.

[0003] Traditional power battery life monitoring methods are often overly simplistic and fail to accurately reflect the complex operating conditions of batteries during actual use. Most monitoring methods rely on a single parameter, such as battery voltage or charge level, to roughly estimate battery life, ignoring the combined impact of multiple factors on battery life during vehicle operation. During actual driving, operating conditions such as vehicle speed, acceleration, battery discharge current, and temperature distribution are constantly changing. These factors interact and jointly influence battery performance and service life.

[0004] For example, in congested urban traffic conditions, vehicles start and stop frequently, batteries charge and discharge repeatedly, current fluctuates greatly, and due to poor heat dissipation conditions, battery temperature will rise. These complex working conditions will accelerate battery aging; and in high-speed cruising conditions, although the current is relatively stable, the higher vehicle speed will cause the battery to continue to discharge with a large current, which will also have an adverse effect on battery life.

[0005] If the actual status of the battery under these working conditions cannot be accurately monitored, it will be difficult to accurately assess the remaining service life of the battery, which may cause users to face problems such as premature battery failure and sudden shortening of driving range during use, seriously affecting the convenience and reliability of electric vehicles.

[0006] Furthermore, different driving conditions have vastly different impacts on battery life, but existing monitoring technologies fail to fully account for this. Due to a lack of refined classification and targeted monitoring of different driving conditions, it's impossible to accurately determine the battery's health under each condition, making it difficult to develop appropriate battery maintenance strategies and early warning mechanisms. This not only increases user costs and safety risks, but also hinders the further development of electric vehicle technology. Summary of the Invention

[0007] In an exemplary embodiment of the present application, a method for monitoring the service life of a power battery is provided to improve the comprehensiveness and accuracy of monitoring the service life of the power battery.

[0008] The present application provides a method for monitoring the service life of a power battery, which includes the following steps:

[0009] S1: Collect the operating condition data of the vehicle in real time, where the operating condition data includes vehicle speed, acceleration, battery discharge current, and temperature distribution;

[0010] S2: Determine the driving condition based on the vehicle speed fluctuation characteristics and the load state of the power system, where the driving condition includes urban congestion condition, suburban medium-speed condition, and highway cruising condition;

[0011] S3: Determine the dynamic discharge depth factor under the driving condition, where the dynamic discharge depth factor is determined by the change amount of the state of charge in a single cycle, the root mean square value of the current, and the cell temperature value;

[0012] S4: Count the number of deep discharge events corresponding to each driving condition, where the deep discharge event is defined as a discharge cycle in which the dynamic discharge depth factor exceeds a preset threshold;

[0013] S5: Count the proportion of the number of deep discharge events in each driving condition to the total number of cycles in the driving condition to determine whether to output a battery life alarm prompt message.

[0014] Further, the S2 specifically includes:

[0015] The urban congestion condition is defined as: the vehicle speed continuously lower than 20 km / h and the interval between two adjacent vehicle starts less than 60 seconds;

[0016] The suburban medium-speed condition is defined as: the vehicle speed in the range of 20 - 60 km / h and the steering wheel angle change rate exceeding 0.5 rad / s;

[0017] The highway cruising condition is defined as: the vehicle speed higher than 80 km / h for more than 10 minutes and the drive motor power volatility lower than 15%.

[0018] Further, the step S3 specifically includes:

[0019] The dynamic discharge depth factor = (ΔSOC × I_rms

[0022] ,

[0021] ,

[0020] ,

[0019] ,

[0023] , , , 2 , , ) / (T_max + temperature compensation coefficient), where:

[0020] The ΔSOC is the maximum change amount of the battery state of charge in a single discharge cycle;

[0021] The I_rms is the root mean square value of the battery current in a single discharge cycle;

[0022] The T_max is the highest temperature value of the cell in a single discharge cycle.

[0023] Further, the temperature compensation coefficient is determined according to the battery chemical system, wherein the temperature compensation coefficient corresponding to the lithium iron phosphate battery is greater than the temperature compensation coefficient corresponding to the ternary lithium battery.

[0024] Further, if the power battery is a lithium iron phosphate battery, the temperature compensation coefficient is 273; if the power battery is a ternary lithium battery, the temperature compensation coefficient is 265.

[0025] Further, the preset threshold is 80%. If the increase in the dynamic discharge depth factor determined three times in a row exceeds 10%, the emergency detection of the battery health state is directly triggered.

[0026] Further, the occurrence times of the deep discharge events in the discharge cycles within the current statistical window are counted for the driving conditions. When the proportion of the deep discharge events of any driving condition exceeds the safety threshold corresponding to that driving condition, wherein the safety threshold corresponding to the urban congestion driving condition is 30%, the safety threshold corresponding to the suburban medium-speed driving condition is 25%, and the safety threshold corresponding to the highway cruise driving condition is 20%.

[0027] Further, when the proportion of the deep discharge events in only one driving condition exceeds the safety threshold, a yellow warning is generated; when the proportion of the deep discharge events in two or more driving conditions exceeds the safety threshold, a red warning is generated and the peak discharge power of the battery is forcibly limited to 80% of the nominal value.

[0028] Further, the method for determining the proportion of the number of deep discharge events in the urban congestion driving condition to the total number of cycles is as follows:

[0029] The proportion of the number of deep discharge events in the urban congestion driving condition to the total number of cycles = (the number of deep discharge events in the urban congestion driving condition × the time weight coefficient of the congestion driving condition) / (the total number of discharge cycles in the current statistical window), where the time weight coefficient of the congestion driving condition is 1.2.

[0030] Further, the method for determining the proportion of the number of deep discharge events in the highway cruise driving condition to the total number of cycles is as follows:

[0031] The proportion of the number of deep discharge events in the highway cruise driving condition to the total number of cycles = (the number of deep discharge events in the highway cruise driving condition × the temperature correction coefficient of the cruise driving condition) / (the total number of discharge cycles in the current statistical window), where the temperature correction coefficient is the square value of the ratio of the average temperature of the power battery to the preset temperature threshold, and the preset temperature threshold is 25°C.

[0032] The present invention provides the following advantageous effects: The present invention's method for monitoring the service life of a power battery accurately categorizes common driving conditions, including urban congestion, suburban medium-speed driving, and high-speed cruising, by collecting data such as vehicle speed, acceleration, battery discharge current, and temperature distribution. By combining speed fluctuation characteristics with the powertrain load, the method provides a basis for analyzing battery usage in different scenarios. A dynamic depth of discharge factor is proposed, comprehensively considering factors such as the change in battery state of charge, the RMS current, and the maximum cell temperature during a single discharge cycle. Different temperature compensation coefficients are set based on the battery chemistry to comprehensively and scientifically quantify the battery's actual depth of discharge. The number of deep discharge events under each driving condition and their proportion to the total number of cycles are counted. When this proportion exceeds a corresponding safety threshold, a graded alarm is issued. Yellow or red alerts are issued based on the number of conditions exceeding the threshold. Red alerts also limit the battery's peak discharge power, providing early detection of battery anomalies, preventing excessive wear and extending battery life. Once the dynamic discharge depth factor determined three times in a row increases by more than a certain amount, it will directly trigger an emergency detection of the battery health status, which can timely discover potential problems, effectively ensure the safe and stable operation of the battery, and improve the safety and reliability of vehicle use. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0034] Figure 1 A flow chart of a method for monitoring the service life of a power battery provided in an embodiment of the present application is exemplarily shown. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0036] To further illustrate the technical solutions provided by the embodiments of the present application, the following is a detailed description of the technical solutions in conjunction with the accompanying drawings and specific implementation methods. Although the embodiments of the present application provide the method operation steps as shown in the following embodiments or drawings, more or fewer operation steps may be included in the method based on routine or no creative work. In steps where there is no necessary causal relationship logically, the execution order of these steps is not limited to the execution order provided in the embodiments of the present application.

[0037] refer to Figure 1As shown in the figure, the present application provides a method for monitoring the service life of a power battery, which includes the following steps:

[0038] S1: Collect the working condition data of the vehicle in real time. The working condition data includes vehicle speed, acceleration, battery discharge current, and temperature distribution.

[0039] The purpose of setting step S1 is to obtain key data closely related to the usage of the power battery during vehicle driving, providing a solid data basis for accurately analyzing the state of the power battery under different driving conditions and monitoring its service life.

[0040] These data include vehicle speed, acceleration, battery discharge current, and temperature distribution, which reflect the operating conditions of the vehicle and the working environment of the battery from multiple dimensions.

[0041] Vehicle speed data is one of the key factors for determining the driving conditions. By real-time monitoring of the vehicle speed and combining with other conditions, it is possible to accurately divide the urban congestion condition (the vehicle speed is continuously lower than 20 km / h and the interval between two consecutive vehicle starts is less than 60 seconds), the suburban medium-speed condition (the vehicle speed is in the range of 20 - 60 km / h and the steering wheel angle change rate exceeds 0.5 rad / s), and the highway cruise condition (the vehicle speed is higher than 80 km / h for more than 10 minutes and the drive motor power volatility is lower than 15%).

[0042] Under different driving conditions, the usage patterns and loads of the power battery are significantly different. Accurately identifying the driving conditions is a prerequisite for in-depth analysis of the battery's usage in different scenarios.

[0043] Although the acceleration data is not directly mentioned in the subsequent description of determining the driving conditions, it reflects the change in the vehicle's power demand from the side. During the vehicle's acceleration or deceleration, the battery's discharge current will change accordingly, affecting the depth of discharge and service life of the battery.

[0044] The battery discharge current is directly related to the battery's discharge process. When calculating the dynamic depth of discharge factor, the root mean square value of the current (I_rms) is one of the important parameters, and its magnitude affects the value of the dynamic depth of discharge factor. The dynamic depth of discharge factor comprehensively considers the change in the state of charge of the battery, the root mean square value of the current, and the cell temperature value during a single discharge cycle, and is used to scientifically quantify the actual depth of discharge of the battery.

[0045] The maximum temperature value (T_max) of the cell is a component of the dynamic depth of discharge factor, and the temperature compensation coefficient is also related to the battery temperature. Under different temperature conditions, the performance and life of the battery will be affected to varying degrees.

[0046] By real-time collecting the temperature distribution data, it can more accurately reflect the actual situation of the battery under different working environments, making the monitoring results more reliable.

[0047] These data collected in step S1 are interrelated and complementary, providing necessary data support for subsequent determination of driving conditions, calculation of dynamic depth of discharge factor, statistics of the number and proportion of deep discharge events, and ultimately realizing battery life alarm prompt and ensuring the safe and stable operation of the battery.

[0048] S2: Determine the driving conditions based on the vehicle speed fluctuation characteristics and the load state of the power system. The driving conditions include urban congestion condition, suburban medium-speed condition, and highway cruise condition.

[0049] The urban congestion condition is defined as: the vehicle speed continuously remains below 20 km / h and the interval between two adjacent vehicle starts is less than 60 seconds.

[0050] The suburban medium-speed condition is defined as: the vehicle speed is in the range of 20 - 60 km / h and the change rate of the steering wheel angle exceeds 0.5 rad / s.

[0051] The highway cruise condition is defined as: the vehicle speed is higher than 80 km / h for more than 10 minutes and the power fluctuation rate of the drive motor is lower than 15%.

[0052] The purpose of setting step S2 is to accurately identify the driving conditions of the vehicle through the vehicle speed fluctuation characteristics and the load state of the power system, and classify them into several common types such as urban congestion condition, suburban medium-speed condition, and highway cruise condition, providing a clear scenario classification basis for subsequent in-depth analysis of the usage status of the power battery under different conditions.

[0053] Under different driving conditions, the operating modes of the vehicle and the working states of the power battery vary greatly. Accurately distinguishing these conditions is the key prerequisite for comprehensively and deeply understanding the performance changes and life losses of the battery during actual use.

[0054] Under the urban congestion condition, the vehicle speed continuously remains below 20 km / h and the interval between two adjacent vehicle starts is less than 60 seconds. In this condition, the vehicle starts and stops frequently, and the battery needs to provide energy frequently to drive the vehicle to start. The discharge current fluctuates greatly. At the same time, more heat is generated during the frequent charge and discharge process of the battery, resulting in temperature rise, which has a greater impact on the service life of the battery. Defining this condition helps to monitor the health status of the battery in scenarios of frequent short-distance driving and high-load starting, and provides a basis for evaluating the durability of the battery in the urban daily commuting environment.

[0055] The suburban medium-speed driving condition is defined as the vehicle speed in the range of 20 - 60 km / h and the steering wheel angle change rate exceeding 0.5 rad / s. Under this driving condition, the vehicle speed is moderate and the vehicle runs relatively smoothly. However, frequent steering operations mean that the battery needs to continuously supply energy to the power system and the power steering system, and the state of charge of the battery is in a relatively stable but continuously consuming process. Accurately identifying the suburban medium-speed driving condition can analyze the performance and discharge law of the battery under such medium-speed and relatively complex road conditions, which is crucial for understanding the battery usage in specific areas such as the suburban fringe.

[0056] The high-speed cruise driving condition is that the vehicle speed is higher than 80 km / h for more than 10 minutes and the drive motor power fluctuation rate is lower than 15%. At this time, the vehicle is in a high-speed stable driving state, the battery outputs power relatively stably, and the current fluctuation is small. However, due to the long-term operation of the motor during high-speed driving, the battery heat dissipation faces challenges, and too high a temperature will accelerate battery aging. By determining the high-speed cruise driving condition, the thermal management and performance degradation of the battery in the scenario of long-term high-speed driving can be monitored, providing data support for ensuring the reliability of the battery during long-distance driving.

[0057] Step S2 enables subsequent operations such as calculating the dynamic depth of discharge factor, counting the number of deep discharge events and their proportions, etc. to be more targeted by clearly dividing different driving conditions. Based on the characteristics of different driving conditions, corresponding safety thresholds for deep discharge events are set, such as 30% for the urban congestion driving condition, 25% for the suburban medium-speed driving condition, and 20% for the high-speed cruise driving condition, so as to achieve more accurate battery life alarm prompts.

[0058] When the proportion of deep discharge events under different driving conditions exceeds the threshold, a hierarchical alarm can be issued in a timely manner, and corresponding measures such as restricting the peak discharge power of the battery can be taken to avoid excessive battery loss, effectively extend the battery service life, improve the safety and reliability of vehicle use, and play a key role in connecting the preceding with the following in the entire power battery service life monitoring system.

[0059] S3: Determine the dynamic depth of discharge factor under this driving condition. The dynamic depth of discharge factor is determined by the change amount of the state of charge in a single cycle, the root mean square value of the current, and the cell temperature value.

[0060] Dynamic depth of discharge factor = (ΔSOC × I_rms 2 ) / (T_max + temperature compensation coefficient), where:

[0061] ΔSOC is the maximum change amount of the battery state of charge in a single discharge cycle.

[0062] I_rms is the root mean square value of the battery current in a single discharge cycle.

[0063] T_max is the highest temperature value of the battery cell in a single discharge cycle.

[0064] The temperature compensation coefficient is determined according to the battery chemistry system. Among them, the temperature compensation coefficient corresponding to the lithium iron phosphate battery for power batteries is greater than that corresponding to the ternary lithium battery for power batteries.

[0065] If the power battery is a lithium iron phosphate battery, the temperature compensation coefficient is 273. If the power battery is a ternary lithium battery, the temperature compensation coefficient is 265.

[0066] From the factors affecting the depth of discharge of the battery, this factor comprehensively considers the change in state of charge (ΔSOC) in a single cycle, the root mean square value of current (I_rms), and the cell temperature value (T_max). At the same time, different temperature compensation coefficients are set according to the battery chemistry system.

[0067] The change in state of charge reflects the actual degree of power consumption of the battery in a single discharge cycle, which is directly related to the usage of the battery; the root mean square value of current reflects the fluctuation and magnitude of the current during the discharge process. The greater the current fluctuation and the higher the value, the greater the loss of the battery usually is; the cell temperature value has an important impact on the battery performance and life. High temperature will accelerate the chemical reactions inside the battery, leading to more serious battery aging.

[0068] The temperature compensation coefficient further considers the differences in temperature characteristics of different battery chemistry systems. For example, lithium iron phosphate batteries and ternary lithium batteries, each corresponding to different temperature compensation coefficients (273 for lithium iron phosphate batteries and 265 for ternary lithium batteries), enabling the dynamic depth of discharge factor to better fit the actual situation of different types of batteries.

[0069] Lithium iron phosphate batteries have good thermal stability and small internal resistance affected by temperature. Setting a higher temperature compensation coefficient of 273 results in a relatively gentle adjustment of its depth of discharge during calculation, which is in line with its stable characteristics. Ternary lithium batteries have poor thermal stability and large internal resistance changes with temperature. Setting a lower temperature compensation coefficient of 265 can more sensitively reflect the impact of temperature changes on the depth of discharge during calculation, thus more scientifically quantifying the actual depth of discharge of different types of batteries at different temperatures and providing more accurate data support for evaluating the battery health status and life loss.

[0070] S4: Count the number of deep discharge events corresponding to each driving condition, where a deep discharge event is defined as a discharge cycle in which the dynamic depth of discharge factor exceeds a preset threshold.

[0071] The preset threshold is 80%. If the growth of the dynamically determined depth of discharge factor exceeds 10% for three consecutive times, the emergency detection of the battery health status is directly triggered.

[0072] A deep discharge event is defined as a discharge cycle in which the dynamic depth of discharge factor exceeds a preset threshold, and the preset threshold is 80%. This provides a clear quantitative criterion for determining whether the battery is in a deep discharge state. By counting the number of deep discharge events under various driving conditions, it is possible to intuitively understand the frequency of deep discharges of the battery in different scenarios. If deep discharge events occur frequently, it means that the battery is used under relatively harsh conditions in this driving condition and its life is consumed relatively quickly.

[0073] S5: Calculate the proportion of the number of deep discharge events in each driving condition to the total number of cycles in that driving condition to determine whether to output a battery life alarm prompt message.

[0074] For each driving condition, count the number of deep discharge events occurring in the discharge cycles within the current statistical window. When the proportion of deep discharge events in any driving condition exceeds the safety threshold, output a graded alarm prompt.

[0075] The safety threshold corresponding to the urban congestion driving condition is 30%, the safety threshold corresponding to the suburban medium-speed driving condition is 25%, and the safety threshold corresponding to the highway cruising driving condition is 20%.

[0076] In the urban congestion driving condition, the vehicle starts and stops frequently. Frequent starts require the battery to provide a large current in a short time, resulting in an intensification of the internal chemical reactions of the battery, an increase in temperature, and an accelerated aging rate of the battery. However, in this driving condition, the vehicle speed is low, and the change in the state of charge (SOC) of the battery in a single discharge cycle is relatively small. Setting a safety threshold of 30% takes into account that although the battery works intensively, the depth of each discharge is limited. A higher threshold can avoid frequent false alarms while ensuring the normal use of the battery. If the threshold is set too low, it may cause too frequent alarms due to the frequent starts and stops during daily urban commuting, affecting the normal user experience; while setting it too high, the battery abnormality cannot be detected in time, and the battery cannot be effectively protected.

[0077] In the suburban medium-speed driving condition, the vehicle speed is between 20 - 60 km / h, and the driving is relatively stable, but frequent steering causes the battery to continuously supply power to multiple systems. In this driving condition, the battery discharges relatively stably. However, due to continuous long-term discharge at a moderate speed, the battery temperature will also gradually increase, and the impact on the battery life is more serious than that in the urban congestion driving condition. Setting the safety threshold at 25%, which is lower than that in the urban congestion driving condition, can more timely monitor the health status of the battery in this continuous medium-intensity discharge driving condition. Once the proportion of deep discharge events approaches or exceeds this threshold, it indicates that the battery has withstood greater pressure in this driving condition, and it is necessary to pay timely attention and adjust the usage method to prevent excessive battery loss.

[0078] When cruising at high speed, the vehicle speed is higher than 80 km / h and lasts for a long time. The power fluctuation rate of the drive motor is low, and the battery outputs power relatively stably. However, when driving at high speed, the motor runs for a long time, increasing the difficulty of battery heat dissipation. High temperature will accelerate the aging of the internal materials of the battery and the attenuation of its performance. At the same time, during high-speed driving, the change in SOC in a single discharge cycle may be relatively large. A lower 20% safety threshold can more sensitively capture abnormal conditions of the battery when driving at high speed. Because the high-speed driving scenario has high requirements for vehicle performance and battery reliability, early warning can avoid potential safety hazards caused by battery problems during long-distance high-speed driving and ensure driving safety.

[0079] When the proportion of deep discharge events in only one driving condition exceeds the safety threshold, a yellow warning is generated. When the proportion of deep discharge events in two or more driving conditions exceeds the safety threshold, a red warning is generated and the peak discharge power of the battery is forcibly limited to 80% of the nominal value.

[0080] The method for determining the proportion of the number of deep discharge events in the urban congestion condition to the total number of cycles is as follows:

[0081] The proportion of the number of deep discharge events in the urban congestion condition to the total number of cycles = the time weight coefficient of the congestion condition × the number of deep discharge events in the urban congestion condition / (the total number of discharge cycles in the current statistical window), where the time weight coefficient of the congestion condition is 1.2.

[0082] When calculating the proportion of the number of deep discharge events in the urban congestion condition to the total number of cycles, the time weight coefficient of 1.2 for the congestion condition is used because in the urban congestion condition, the vehicle starts and stops frequently, the battery charge and discharge cycles are more frequent, the discharge current fluctuates greatly, and the discharge depth changes significantly, resulting in much greater battery loss than other conditions. This coefficient can reflect the particularity of this condition, comprehensively consider complex loss factors, make the calculated proportion more in line with the actual situation, thereby improving the accuracy of the assessment of the battery health status. When the proportion approaches or exceeds the 30% safety threshold, it can issue a warning more timely and accurately, so as to take measures in advance to extend the battery life and ensure the safe and stable operation of the vehicle.

[0083] The method for determining the proportion of the number of deep discharge events in the high-speed cruise condition to the total number of cycles is as follows:

[0084] The proportion of the number of deep discharge events in the high-speed cruise condition to the total number of cycles = the number of deep discharge events in the high-speed cruise condition × the cruise condition temperature correction coefficient / (the total number of discharge cycles in the current statistical window), where the temperature correction coefficient is the square value of the ratio of the average temperature of the power battery to the preset temperature threshold, and the preset temperature threshold is 25°C.

[0085] Different usage environments and driving conditions can lead to differences in the temperature of the power battery during high-speed cruising. For example, in the hot summer, the battery temperature of a vehicle traveling at high speed may reach 40°C or even higher; while in the cold winter, the battery temperature may be lower than 10°C. If the temperature factor is not considered and the proportion of deep discharge events is evaluated only according to a fixed calculation method, the impact of temperature on battery life will be ignored, resulting in inaccurate evaluation results.

[0086] By introducing a temperature correction coefficient and performing correction calculations based on the relationship between the actual average temperature of the power battery and the preset temperature threshold (25°C), the evaluation requirements for the battery health status in various temperature environments can be met, making the evaluation results more scientific and reliable.

[0087] Under the high-speed cruising condition, the power battery discharges continuously for a long time, and the battery temperature will change. When the temperature deviates from the appropriate operating range, the internal chemical reaction rate, electrolyte conductivity, and battery internal resistance of the battery will all be affected. For example, too high a temperature will accelerate the internal chemical reaction of the battery, resulting in a faster decline in battery capacity; too low a temperature will increase the battery internal resistance and reduce the charge and discharge efficiency.

[0088] By introducing a temperature correction coefficient, the impact of the average temperature of the power battery on battery performance can be incorporated into the calculation of the proportion of deep discharge events, so as to more accurately reflect the actual loss of the battery under different temperature conditions.

[0089] By counting the proportion of the number of deep discharge events in each driving condition to the total number of cycles and comparing it with the safety thresholds corresponding to the driving conditions (30% for urban congestion conditions, 25% for suburban medium-speed conditions, and 20% for high-speed cruising conditions), it is determined whether to output a battery life alarm prompt message. The dynamic discharge depth factor provides accurate data support for this comparison, making the alarm prompt more scientific and reasonable.

[0090] When the proportion of deep discharge events exceeds the safety threshold, yellow or red warnings are issued respectively according to the number of conditions exceeding the threshold. When a red warning is issued, the peak discharge power of the battery is also limited to 80% of the nominal value.

[0091] Based on the accurate quantification of the actual discharge depth of the battery by the dynamic discharge depth factor, the battery anomaly can be detected in advance, excessive loss can be avoided, and the battery service life can be extended. Once the growth of the dynamically determined discharge depth factor exceeds 10% for three consecutive times, the emergency detection of the battery health status is directly triggered. This function also depends on the accurate reflection of the battery discharge state by the dynamic discharge depth factor, which can timely detect potential problems, effectively ensure the safe and stable operation of the battery, and improve the safety and reliability of vehicle use.

[0092] During the actual use of power batteries, different driving conditions result in different degrees of battery loss. For example, in urban congestion conditions, frequent starts and stops cause the battery to charge and discharge repeatedly. In suburban medium-speed conditions, the vehicle speed is moderate, but steering operations can affect the battery load. In high-speed cruising conditions, the battery continuously outputs stable power.

[0093] If only the number of deep discharge events is simply counted, it cannot accurately reflect the actual battery loss under different conditions. By calculating the proportion of the number of deep discharge events to the total number of cycles, the health status of the battery in different scenarios can be accurately evaluated in combination with the characteristics of each condition.

[0094] For example, assume that a vehicle has completed 200 discharge cycles in total within the statistical window. The system calculates the following results based on the division and correction of driving conditions:

[0095] Urban congestion condition: 36 deep discharge events are detected. After correction with a time weight coefficient of 1.2, the actual proportion is 36×1.2 / 200 = 21.6%, which is lower than the safety threshold of 30%, so it is judged to be normal.

[0096] Suburban medium-speed condition: 65 deep discharge events are detected. Without enabling the correction coefficient, the proportion is 65 / 200 = 32.5%, which exceeds the safety threshold of 25%, triggering an abnormal determination.

[0097] High-speed cruising condition: 25 deep discharge events are detected. Considering the average temperature of the battery cell is 30°C (preset threshold 25°C), the temperature correction coefficient is 1.44. After correction, the proportion is 25×1.44 / 200 = 18.0%, which is lower than the safety threshold of 20%, so it is judged to be normal.

[0098] Only the suburban medium-speed condition exceeds the threshold. The system generates a yellow warning to prompt the user to pay attention to the battery usage status under this condition.

[0099] When it is continuously detected that the growth of the dynamic discharge depth factor exceeds the limit three times (for example: the first value is 88, the second time it grows by 10.2% to 97, and the third time it grows by 10.3% to 107), the system will skip the cycle calculation of the regular statistical window and directly trigger an emergency detection of the battery health status, giving priority to performing in-depth diagnoses such as the internal resistance and capacity attenuation rate of the battery cells to ensure a quick response to potential failures.

[0100] By collecting data such as vehicle speed, acceleration, battery discharge current, and temperature distribution, and combining the vehicle speed fluctuation characteristics and the load status of the power system, common driving conditions such as urban congestion, suburban medium-speed, and high-speed cruising can be accurately divided, providing a basis for analyzing the battery usage in different scenarios.

[0101] A dynamic depth of discharge factor is proposed, comprehensively considering factors such as the change in the state of charge of the battery during a single discharge cycle, the root mean square value of the current, and the maximum temperature of the battery cell, and setting different temperature compensation coefficients according to the battery chemistry system to comprehensively and scientifically quantify the actual depth of discharge of the battery. The number of deep discharge events under each driving condition and the proportion of them in the total number of cycles are statistically counted. When this proportion exceeds the corresponding safety threshold, a graded alarm prompt will be output.

[0102] According to the number of conditions exceeding the threshold, yellow or red warnings are issued respectively. When a red warning is issued, the peak discharge power of the battery is also limited, detecting battery abnormalities in advance, avoiding excessive loss, and extending the service life of the battery. Once the growth of the determined dynamic depth of discharge factor exceeds a certain amplitude three times in a row, an emergency detection of the battery health state is directly triggered, which can timely detect potential problems, effectively ensure the safe and stable operation of the battery, and improve the safety and reliability of vehicle use.

[0103] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0104] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0105] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0106] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the functions specified in one process or a plurality of processes and / or blocks Figure 1 one process or a plurality of processes and / or blocks Figure 1 in one block or a plurality of blocks.

[0107] Obviously, those skilled in the art can make various changes and modifications to this application without departing from the scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalent technologies, this application is also intended to include these modifications and variations.

Claims

1. A monitoring method for the service life of a power battery, characterized in that, It includes the following steps: S1: Collect the working condition data of the vehicle in real time, where the working condition data includes vehicle speed, acceleration, battery discharge current and temperature distribution; S2: Determine the driving condition based on the vehicle speed fluctuation characteristics and the load state of the power system, and the driving condition includes urban congestion condition, suburban medium-speed condition and high-speed cruise condition; S3: Determine the dynamic depth of discharge factor under the driving condition, and the dynamic depth of discharge factor is determined by the change in state of charge of a single cycle, the root mean square value of the current and the cell temperature value; S4: Count the number of deep discharge events corresponding to each driving condition, where the deep discharge event is defined as a discharge cycle in which the dynamic depth of discharge factor exceeds a preset threshold; S5: Count the proportion of the number of deep discharge events of each driving condition in the total number of cycles of the driving condition to determine whether to output a battery life alarm prompt message.

2. The method according to claim 1, wherein The specific content of S2 includes: The urban congestion condition is defined as: the vehicle speed is continuously lower than 20 km / h and the interval between two adjacent vehicle starts is less than 60 seconds; The suburban medium-speed condition is defined as: the vehicle speed is in the range of 20 - 60 km / h and the change rate of the steering wheel angle exceeds 0.5 rad / s; The high-speed cruise condition is defined as: the vehicle speed is higher than 80 km / h for more than 10 minutes and the power fluctuation rate of the drive motor is lower than 15%; 3. The method according to claim 1, characterized in that The specific content of S3 includes: The dynamic depth of discharge factor = (ΔSOC × I_rms 2 ) / (T_max + temperature compensation coefficient), where: The ΔSOC is the maximum change in the state of charge of the battery during a single discharge cycle; The I_rms is the root mean square value of the battery current during a single discharge cycle; The T_max is the highest temperature value of the cell during a single discharge cycle.

4. The method according to claim 3, characterized in that, The temperature compensation coefficient is determined according to the battery chemical system, where the temperature compensation coefficient corresponding to the lithium iron phosphate battery is greater than the temperature compensation coefficient corresponding to the ternary lithium battery.

5. The method according to claim 4, wherein If the power battery is a lithium iron phosphate battery, the temperature compensation coefficient is 273; if the power battery is a ternary lithium battery, the temperature compensation coefficient is 265.

6. The method according to claim 1, wherein The preset threshold is 80%. If the increase in the dynamic depth of discharge factor determined continuously three times exceeds 10%, the emergency detection of the battery health state is directly triggered.

7. The method according to claim 1, wherein Count the number of occurrences of the deep discharge event in the discharge cycle within the current statistical window for the driving condition. When the proportion of the deep discharge event of any driving condition exceeds the safety threshold corresponding to the driving condition, a graded alarm prompt is output, where the safety threshold corresponding to the urban congestion condition is 30%, the safety threshold corresponding to the suburban medium-speed condition is 25%, and the safety threshold corresponding to the high-speed cruise condition is 20%.

8. The method according to claim 7, characterized in that, When only the proportion of the deep discharge event in one driving condition exceeds the safety threshold, a yellow warning is generated; when the proportion of the deep discharge event in two or more driving conditions exceeds the safety threshold, a red warning is generated and the peak discharge power of the battery is forcibly limited to 80% of the nominal value.

9. The method according to claim 7, wherein The method for determining the proportion of the number of deep discharge events of the urban congestion condition in the total number of cycles is: The proportion of the number of deep discharge events in the urban congestion condition to the total number of cycles = (the number of deep discharge events in the urban congestion condition × the time weight coefficient for the congestion condition) / (the total number of discharge cycles in the current statistical window), where the time weight coefficient for the congestion condition is 1.

2.

10. The method according to claim 9, wherein The method for determining the proportion of the number of deep discharge events in the high-speed cruising condition to the total number of cycles is as follows: The proportion of the number of deep discharge events in the high-speed cruising condition to the total number of cycles = (the number of deep discharge events in the high-speed cruising condition × the temperature correction coefficient for the cruising condition) / (the total number of discharge cycles in the current statistical window), where the temperature correction coefficient is the square value of the ratio of the average temperature of the power battery to the preset temperature threshold, and the preset temperature threshold is 25°C.

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