A method for estimating the state of power (SOP) of a lithium-ion power battery for a vehicle

Abstract

This invention discloses a method for estimating the state of operation (SOP) of a lithium-ion power battery for vehicles. By collecting the current power demand of the entire vehicle, it overcomes the limitations of traditional model-based and data-driven methods in terms of real-time updates and estimation accuracy without the need to build complex battery models or conduct extensive data training. By predicting future power change trends, it achieves accurate monitoring and prediction of system power demand, improving the accuracy and reliability of battery energy management. By determining whether the current power demand has changed, and when the current power demand changes, it dynamically adjusts the battery energy supply mode according to the current power demand and future power change trends, enabling timely response to changes in system power demand. This solves the problem of existing technologies that only focus on the static characteristics of the battery, improves the efficiency of battery energy utilization, smooths the switching between peak power and continuous power, and enhances system stability and driving comfort.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, and in particular to a method for estimating the start-up cost (SOP) of a lithium-ion power battery for vehicles. Background Technology

[0002] With the rapid development of new energy vehicles, lithium-ion batteries have become the primary choice for power batteries due to their advantages such as high energy density, low maintenance costs, and long service life. However, the performance of lithium-ion batteries is limited by their state of power (SOP), which is one of the key parameters for evaluating the power distribution capability of a battery system. Accurately estimating the battery's SOP is crucial for ensuring the efficient and safe operation of the system.

[0003] Currently, common SOP estimation methods mainly include model-based methods and data-driven methods. Model-based methods establish battery voltage and current parameters through electrochemical models and form estimation formulas considering thermodynamic characteristics, requiring high accuracy in power battery modeling. Data-driven methods, on the other hand, treat the battery as a complex black-box system, employing advanced algorithms such as neural networks and feature map methods to train and output a large amount of key data, establishing a statistical relationship between the battery's own sampled data input and available power output.

[0004] However, both of these methods have limitations: model-based methods require high-precision battery modeling, while data-driven methods require a large amount of static data for model training, making them unsuitable for real-time dynamic estimation and adjustment. Furthermore, existing SOP estimation methods often only focus on the battery's static characteristics, lacking consideration for dynamic features, and are significantly affected by the accuracy of the State of Charge (SOC). Therefore, there is an urgent need for a method that can dynamically adjust the battery's energy supply mode to optimize battery energy utilization efficiency. Summary of the Invention

[0005] This invention provides a method for estimating the start-up cost (SOP) of lithium-ion power batteries for vehicles, which solves the problems in the prior art where model-based SOP estimation methods require high-precision battery modeling, data-driven SOP estimation methods require a large amount of static data for training, are not suitable for real-time dynamic estimation, and lack consideration of battery dynamic characteristics.

[0006] According to one aspect of the present invention, a method for estimating the start-up cost (SOP) of a lithium-ion power battery for vehicles is provided, comprising:

[0007] S1. Initialize parameters and read the current status information of the battery;

[0008] S2. Collect the current power demand and historical power demand of the vehicle, and predict the future power change trend based on the current power demand and the historical power demand;

[0009] S3. Determine whether the current power demand has changed. If it has not changed, maintain the current power state. If it has changed, proceed to step S4.

[0010] S4. Dynamically adjust the battery energy supply method according to the current power demand and future power change trends, and monitor the adjustment effect;

[0011] S5. Update the SOC-Temp-SOP database to complete this SOP estimation.

[0012] Optionally, S1 includes:

[0013] S101. Set battery operating parameters, including power change rate, maximum battery operating voltage limit, minimum battery operating voltage limit, maximum battery operating current limit, maximum battery operating temperature limit, and minimum battery operating temperature limit.

[0014] S102. Read the current SOC value, current highest cell temperature, and current lowest cell temperature of the battery;

[0015] S103. Based on the current SOC value, the current highest cell temperature, and the current lowest cell temperature, query the SOC-Temp-SOP database to obtain the initial peak power and initial continuous power.

[0016] Optionally, S2 includes:

[0017] S201. Calculate the current energy consumption rate of the system;

[0018] S202. Combining historical power demand and the energy consumption rate, predict the power change trend within a preset time period in the future.

[0019] Optionally, S3 includes:

[0020] S301. Calculate the difference between the current power demand and the power at the previous moment;

[0021] S302. If the difference is greater than or equal to the first preset power threshold, it is determined that the current power demand has changed.

[0022] S303. If the difference is less than the first preset power threshold, it is determined that the current power demand has not changed, and the current power output state is maintained.

[0023] Optionally, S4 includes:

[0024] S401 calculates the target output power based on the predicted future power change trend and the current power demand;

[0025] S402. Combine the current state information of the battery to apply multiple constraints to the target output power to ensure that the output power is within the safe operating range.

[0026] S403. According to the preset power change rate, gradually adjust the initial peak power and initial continuous power of the battery to the target output power respectively;

[0027] S404. Monitor the power adjustment effect. If it does not meet expectations, return to step S401 to readjust. If it meets expectations, proceed to step S5.

[0028] Optionally, S401 includes:

[0029] S4011. Calculate the initial target power based on the predicted future power change trend and the initial continuous power.

[0030] S4012. Limit the initial target power within a preset power range to obtain the final target output power;

[0031] S4013. Calculate the power adjustment amount based on the power at the previous moment and the final target output power.

[0032] Optionally, S402 includes:

[0033] S4021. Record the duration of continuous operation of the battery's current output power;

[0034] S4022. Determine the battery's state of charge based on the current SOC value;

[0035] S4023. If the current SOC value is less than or equal to the first preset SOC threshold and the current output power is greater than or equal to the second preset power threshold, then reduce the target output power to avoid over-discharge of the battery.

[0036] S4024. If the current SOC value is greater than the second preset SOC value and the current output power is less than the third preset power threshold, then increase the target output power to avoid battery overcharging.

[0037] Optionally, S403 includes:

[0038] S4031. Adjust the current output power of the battery step by step according to the preset power change rate, with each adjustment being one-tenth of the power adjustment amount;

[0039] S4032. After the output power of the battery to be adjusted stabilizes, proceed with the next adjustment until the target output power is reached.

[0040] Optionally, S404 includes:

[0041] S4041. Calculate the actual energy consumption based on the current output power;

[0042] S4042. Calculate the theoretical energy consumption based on the target output power;

[0043] S4043. Calculate the energy consumption error rate based on the actual energy consumption and the theoretical energy consumption;

[0044] S4044. If the energy consumption error rate is greater than the preset percentage, it is determined that the adjustment has not met expectations and the process needs to return to step S401 to recalculate the target output power; if the energy consumption error rate is less than or equal to the preset percentage, it is determined that the adjustment has met expectations.

[0045] Optionally, S5 includes:

[0046] S501. Store the adjusted power status, battery status and energy consumption data into the SOC-Temp-SOP database and update the SOC-Temp-SOP mapping relationship.

[0047] S502. Initialize the system parameters for the next iteration and prepare for the next round of SOP estimation.

[0048] The technical solution provided by this invention, by collecting the current power demand of the entire vehicle, overcomes the limitations of traditional model-based and data-driven methods in real-time updates and estimation accuracy without the need to build complex battery models or conduct extensive data training, thus improving the accuracy and timeliness of SOP estimation. It predicts future power change trends based on current and historical power demands, achieving precise monitoring and prediction of system power demand, thereby improving the accuracy and reliability of battery energy management. By determining whether the current power demand has changed, and when such changes occur, it dynamically adjusts the battery energy supply method based on the current power demand and future power change trends, enabling timely response to changes in system power demand. This effectively solves the problem of prior art focusing only on static battery characteristics, improves battery energy utilization efficiency, smooths the switching between peak and continuous power, and enhances system stability and driving comfort.

[0049] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A flowchart of a method for estimating the start-up cost (SOP) of a lithium-ion power battery for vehicles, provided in an embodiment of the present invention;

[0052] Figure 2 A flowchart of another method for estimating the start-up cost (SOP) of a vehicle lithium-ion power battery provided in an embodiment of the present invention;

[0053] Figure 3 A flowchart of another method for estimating the start-up cost (SOP) of a lithium-ion power battery for vehicles provided in this embodiment of the invention;

[0054] Figure 4 A flowchart illustrating another method for estimating the start-up cost (SOP) of a lithium-ion power battery for vehicles, provided as an embodiment of the present invention;

[0055] Figure 5 A flowchart illustrating another method for estimating the start-up cost (SOP) of a lithium-ion power battery for vehicles, provided as an embodiment of the present invention;

[0056] Figure 6 This is a schematic diagram of a SOP estimation device for automotive lithium-ion power batteries provided in an embodiment of the present invention;

[0057] Figure 7 This is a schematic diagram of an electronic device for estimating the state-of-the-art (SOP) of a lithium-ion power battery for vehicles, as provided in an embodiment of the present invention. Detailed Implementation

[0058] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0059] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0060] Figure 1 This is a flowchart illustrating a method for estimating the state of operation (SOP) of a lithium-ion power battery for vehicles, provided by an embodiment of the present invention. This embodiment is applicable to existing SOP estimation methods that require high-precision battery modeling and a large amount of static data for model training. These methods are not suitable for real-time dynamic estimation or for situations that only focus on the static characteristics of the battery and lack consideration for its dynamic features. This method can be executed by a lithium-ion power battery SOP estimation device for vehicles. This device can be implemented in hardware and / or software and can be configured in any electronic device with communication capabilities. See also... Figure 1 The method includes:

[0061] S1. Initialize parameters and read the current status information of the battery.

[0062] Specifically, S1 includes:

[0063] S101. Set battery operating parameters, including power change rate, maximum battery operating voltage limit, minimum battery operating voltage limit, maximum battery operating current limit, maximum battery operating temperature limit, and minimum battery operating temperature limit.

[0064] Among these parameters, the power change rate limits the maximum speed of power adjustment to avoid sudden power jumps that could cause a jolt in the driving experience; the minimum / maximum operating voltage limit of the battery is the absolute safe voltage range of the cell, and under no circumstances should the output power cause the cell voltage to exceed this range, preventing overcharging / over-discharging; the maximum operating current limit of the battery is the maximum allowable charging and discharging current of the cell, preventing overcurrent damage to the cell or thermal runaway; and the minimum / maximum operating temperature limit of the battery is the safe operating temperature range of the cell, at which power output will be forcibly limited. These parameters are pre-stored in the Battery Management System (BMS) by the cell manufacturer based on the electrochemical characteristics of the cell, after extensive laboratory testing and calibration.

[0065] S102. Read the current SOC value, current highest cell temperature, and current lowest cell temperature of the battery.

[0066] The current highest cell temperature differs from the maximum operating temperature limit of the battery mentioned above. The current highest cell temperature refers to the maximum value among all individual cell temperature data, while the current lowest cell temperature refers to the minimum value among all individual cell temperature data. Specifically, the current SOC value, current highest cell temperature, and current lowest cell temperature are read by the BMS's hardware acquisition module. For example, the SOC value is estimated by the BMS using an algorithm of "ampere-hour integration + open-circuit voltage correction"; the temperature is collected in real time by a thermistor (Negative Temperature Coefficient, NTC) temperature sensor attached to the surface of each cell.

[0067] S103. Based on the current SOC value, the current highest cell temperature, and the current lowest cell temperature, query the SOC-Temp-SOP database to obtain the initial peak power and initial continuous power.

[0068] The SOC-Temp-SOP database is pre-stored in the BMS. This database is a static mapping table established by the manufacturer after conducting power tests on the same type of battery cells under all operating conditions and temperature ranges.

[0069] S2. Collect the current power demand and historical power demand of the vehicle, and predict the future power change trend based on the current power demand and historical power demand.

[0070] Specifically, the BMS receives total power request signals from the Vehicle Control Unit (VCU) in real time. These signals are generated by the VCU after aggregating the power demands of all onboard loads. The current power demand is the total demand at the vehicle level, not the demand of a single cell or module. Based on this total demand and the battery's own state, the BMS calculates the maximum power that the battery can safely output. Historical power demands are samples of all power demands stored continuously in the BMS's local cache over a fixed period of time, which can be directly read. Vehicle power demands are not random and abrupt but exhibit clear continuity, and historical power demands serve as the basis for capturing this continuous pattern.

[0071] The future power change trend refers to the direction and approximate rate of change in the vehicle's power demand over a preset period, predicted by the battery management system based on historical power demand over a fixed period and current power demand, using a simplified algorithm such as a sliding window linear fitting algorithm. The direction of change indicates whether the power demand will increase, decrease, or remain stable; the rate of change refers to how quickly the power demand increases / decreases. The preset period can be set in advance according to testing requirements.

[0072] S3. Determine whether the current power demand has changed. If it has not changed, maintain the current power state. If it has changed, proceed to step S4.

[0073] Specifically, determining whether the current power demand has changed involves comparing the difference between the current power demand and the power at the previous moment with a preset power threshold. If the difference is greater than or equal to the preset power threshold, the current power demand is considered to have changed; if the difference is less than the first preset power threshold, the current power demand is considered not to have changed, and the current power output state is maintained. The current power output state refers to the complete set of parameters that the battery is continuously outputting after the BMS has completed and stabilized the previous power adjustment. It is not a single "current output power value," but includes the actual stable power output value of the battery, the duration of continuous stable output, whether it is currently in continuous power mode or peak power mode, and the corresponding real-time battery status at that power output: current SOC, current highest / lowest cell temperature, and the power change rate used during the last adjustment, among other parameters.

[0074] S4. Dynamically adjust the battery energy supply method according to the current power demand and future power change trends, and monitor the adjustment effect.

[0075] Specifically, the target output power is calculated based on the current power demand and future power change trends; multiple constraints are applied to the target output power in conjunction with the current state information of the battery to ensure that the output power is within the safe operating range; the initial peak power and initial continuous power of the battery are gradually adjusted to the target output power according to the preset power change rate; the power adjustment effect is monitored, and if the expected result is not achieved, the target output power is calculated.

[0076] S5. Update the SOC-Temp-SOP database to complete this SOP estimation.

[0077] The technical solution provided by this invention, by collecting the current power demand of the entire vehicle, overcomes the limitations of traditional model-based and data-driven methods in real-time updates and estimation accuracy without the need to build complex battery models or conduct extensive data training, thus improving the accuracy and timeliness of SOP estimation. It predicts future power change trends based on current and historical power demands, achieving precise monitoring and prediction of system power demand, thereby improving the accuracy and reliability of battery energy management. By determining whether the current power demand has changed, and when such changes occur, it dynamically adjusts the battery energy supply method based on the current power demand and future power change trends, enabling timely response to changes in system power demand. This effectively solves the problem of prior art focusing only on static battery characteristics, improves battery energy utilization efficiency, smooths the switching between peak and continuous power, and enhances system stability and driving comfort.

[0078] Figure 2 This is a flowchart illustrating another method for estimating the state of operation (SOP) of a lithium-ion power battery for vehicles, provided by an embodiment of the present invention. This embodiment further refines the aforementioned embodiments. See also... Figure 2 Optionally, S2 specifically includes:

[0079] S201, Calculate the current energy consumption rate of the system.

[0080] Specifically, the current energy consumption rate of the system is obtained by calculating the ratio of the current power demand to the rated power of the battery.

[0081] S202. Based on historical power demand and energy consumption rate, predict the power change trend within a preset time period.

[0082] The preset duration can be set in advance according to needs.

[0083] Specifically, a trend line is first fitted using the least squares method based on historical power demand to obtain an initial slope. Then, the initial slope is weighted according to the current energy consumption rate to determine a correction coefficient. Finally, the final slope is determined by multiplying the initial slope by the correction coefficient. This slope is compared with a preset threshold. If it is greater than or equal to the preset threshold, it is determined to be a power growth trend; if it is less than the preset threshold, it is determined to be a power decline trend.

[0084] The technical solution provided by this invention quantifies the current actual load intensity of the battery by energy consumption rate, and then uses it as a "trend credibility correction factor" to be superimposed on the mathematical fitting results of historical power, so as to obtain a short-cycle power change prediction that is more in line with engineering practice.

[0085] Figure 3This is a flowchart illustrating another method for estimating the state of operation (SOP) of a lithium-ion power battery for vehicles, provided by an embodiment of the present invention. This embodiment further refines the aforementioned embodiments. See also... Figure 3 Optionally, S3 specifically includes:

[0086] S301. Calculate the difference between the current power demand and the power at the previous moment.

[0087] S302. If the difference is greater than or equal to the first preset power threshold, it is determined that the current power demand has changed.

[0088] S303. If the difference is less than the first preset power threshold, it is determined that the current power demand has not changed, and the current power output state is maintained.

[0089] The first preset power threshold can be preset according to the battery performance.

[0090] Figure 4 This is a flowchart illustrating another method for estimating the state of operation (SOP) of a lithium-ion power battery for vehicles, provided by an embodiment of the present invention. This embodiment further refines the aforementioned embodiments. See also... Figure 4 Optionally, S4 specifically includes:

[0091] S401. Calculate the target output power based on the predicted future power change trend and current power demand.

[0092] Specifically, based on the predicted future power change trend, the initial target power is calculated in combination with the initial continuous power; the initial target power is limited to a preset power range to obtain the final target output power; and the power adjustment amount is calculated based on the power at the previous moment and the final target output power.

[0093] The preset power range can be pre-set based on battery performance and vehicle motor power. The power adjustment amount can be obtained by calculating the difference between the final target output power and the power at the previous moment.

[0094] S402. Combine the current state information of the battery to apply multiple constraints to the target output power to ensure that the output power is within the safe operating range.

[0095] Specifically, the continuous working time of the battery's current output power is recorded; the battery's state of charge is determined based on the current SOC value; if the current SOC value is less than or equal to the first preset SOC threshold and the current output power is greater than or equal to the second preset power threshold, the target output power is reduced to avoid over-discharge of the battery; if the current SOC value is greater than the second preset SOC threshold and the current output power is less than the third preset power threshold, the target output power is increased to avoid overcharging of the battery.

[0096] The current state information of the battery is its current SOC value. A first preset SOC threshold is less than a second preset SOC threshold. The first preset SOC threshold can be set based on experience; in this embodiment, it is set to 0.3. The second preset SOC threshold can also be set based on experience; in this embodiment, it is set to 0.7. A second preset power threshold is greater than a third preset power threshold. The second preset power threshold is 0.5 times the initial peak power, and the third preset power threshold is 0.3 times the initial peak power. Both the second and third preset power thresholds are preset based on experience.

[0097] For example, if the current SOC value is ≤0.3 and the current output power is ≥0.5 times the initial peak power, the target output power is reduced to avoid over-discharging of the battery; if the current SOC value is >0.7 and the current output power is <0.3 times the initial peak power, the target output power is increased to avoid overcharging of the battery.

[0098] S403. According to the preset power change rate, gradually adjust the initial peak power and initial continuous power of the battery to the target output power.

[0099] Specifically, the current output power of the battery is gradually adjusted according to the preset power change rate, with each adjustment being one-tenth of the power adjustment amount; after the battery output power is stabilized, the next adjustment is performed until the target output power is reached.

[0100] The preset power change rate can be pre-set based on empirical values.

[0101] S404. Monitor the power adjustment effect. If it does not meet expectations, return to step S401 to readjust. If it meets expectations, proceed to step S5.

[0102] Specifically, the actual energy consumption is calculated based on the current output power; the theoretical energy consumption is calculated based on the target output power; the energy consumption error rate is calculated based on the actual energy consumption and the theoretical energy consumption; if the energy consumption error rate is greater than a preset percentage, it is determined that the adjustment has not met expectations and the process needs to return to step S401 to recalculate the target output power; if the energy consumption error rate is less than or equal to a preset percentage, it is determined that the adjustment has met expectations.

[0103] The energy consumption error rate can be calculated using the following formula:

[0104] In the formula, Energy consumption error rate, This represents actual energy consumption. This represents theoretical energy consumption.

[0105] The preset percentage can be pre-set based on empirical values; in this embodiment of the invention, the preset percentage is set to 5%. When the energy consumption error rate... If the error rate is greater than 5%, it is determined that the adjustment has not achieved the expected result, and it is necessary to return to step S401 to recalculate the target output power; when the energy consumption error rate is... If the result is ≤5%, the adjustment is considered to have met expectations, and subsequent steps can be performed.

[0106] In practical applications, an uneven transition between peak power and continuous power can cause a jolt for drivers and passengers. Meanwhile, for batteries with low SOC and low State of Health (SOH), the accuracy of SOP estimation is particularly important. This is because, under these conditions, the battery's Direct Current Internal Resistance (DCR) increases exponentially. Discharging with a large current at the same SOC can lead to a sudden voltage drop, easily causing over-discharge.

[0107] Therefore, the technical solution provided by the embodiments of the present invention, by combining the current state information of the battery to impose multiple constraints on the target output power, ensures that the output power is within the safe operating range. According to the preset power change rate, the initial peak power and initial continuous power of the battery are gradually adjusted to the target output power. It is particularly suitable for batteries in low SOC and low SOH states, and effectively avoids the risk of voltage drop and battery over-discharge / overcharge caused by high current discharge.

[0108] Figure 5 This is a flowchart illustrating another method for estimating the state of operation (SOP) of a lithium-ion power battery for vehicles, provided by an embodiment of the present invention. This embodiment further refines the aforementioned embodiments. See also... Figure 5 Optionally, S5 specifically includes:

[0109] S501. Store the adjusted power status, battery status and energy consumption data into the SOC-Temp-SOP database and update the SOC-Temp-SOP mapping relationship.

[0110] S502. Initialize the system parameters for the next iteration and prepare for the next round of SOP estimation.

[0111] The following is a specific embodiment to illustrate the SOP estimation method for automotive lithium-ion power batteries provided in this application:

[0112] Step 1: Initialize system parameters and read the current status information of the battery.

[0113] Step 101: Set system operating parameters, including power change rate. =15kW / s, minimum / maximum battery operating voltage limit =2.8V, =4.25V, maximum battery operating current limit =300A ​​and maximum battery operating temperature limit =55℃.

[0114] Step 102: Read the current SOC value of the battery. =45%, Current highest cell temperature =38℃ and the current lowest cell temperature =32℃.

[0115] Step 103: Based on the current SOC value Current highest cell temperature and the current lowest cell temperature Query the SOC-Temp-SOP database to obtain the initial peak power. =150kW and initial continuous power =120kW.

[0116] Step 2: Monitor system power requirements in real time.

[0117] Step 201: Collect real-time power demand =100kW;

[0118] Step 202: Calculate the current energy consumption rate of the system to be 0.8C;

[0119] Step 203: Based on historical power data and current energy consumption rate, predict future power trends as an increasing trend.

[0120] Step 3: Determine if the system power demand has changed.

[0121] Step 301: Calculate real-time power Compared with the previous power The difference is 10kW;

[0122] Step 302: When the difference is greater than or equal to the preset threshold of 15kW, it is determined to be a power change;

[0123] Step 303: When the difference is less than the preset threshold of 15kW, maintain the current power state.

[0124] Step 4: Dynamically adjust the battery energy supply method according to real-time power demand.

[0125] Step 401: Calculate the target output power based on the predicted power trend and the current power state. =110kW;

[0126] Step 4011: Predict future power demand as a continuously growing trend, target output power. Set as ;

[0127] Step 4012: Considering system stability and ride comfort, set the target output power... Limited to the range of [80kW, 160kW];

[0128] Step 4013: Calculate the power adjustment amount =10kW.

[0129] Step 402: Target output power Limit restrictions to ensure they remain within safe limits.

[0130] Step 4021: Record the current power The duration is 30 minutes;

[0131] Step 4022: Based on the current SOC value The battery's operating state is determined to be a medium SOC state.

[0132] Step 4023, due to the current SOC value =0.45>0.3, and the current power Therefore, no power adjustment is required.

[0133] Step 403: According to the preset power change rate Adjust the initial peak power and initial continuous power to the target output power respectively. .

[0134] Step 4031: According to the preset power change rate Adjust the power gradually, with each adjustment being [amount]. ;

[0135] Step 4032: Monitor whether the adjusted power is stable. If it is stable, proceed to step 404.

[0136] Step 404: Monitor the adjustment effect.

[0137] Step 4041: Calculate the actual energy consumption based on the current output power. =30kWh;

[0138] Step 4042: Calculate the theoretical energy consumption based on the target output power. =32kWh;

[0139] Step 4043: Calculate the energy consumption error rate =5%;

[0140] Step 4044, due to =5%, therefore the expected requirement has been met, proceed to step 5.

[0141] Step 5: Update the SOC-Temp-SOP database.

[0142] Step 501: Store the adjusted power status, battery status, and energy consumption data into the SOC-Temp-SOP database and update the SOC-Temp-SOP mapping relationship;

[0143] Step 502: Initialize the system parameters for the next iteration to prepare for the next round of SOP estimation.

[0144] The technical solution provided by this invention, by estimating future power demand and combining it with historical power demand data analysis, achieves accurate monitoring and prediction of system power demand, thereby improving the accuracy and reliability of battery energy management. By adopting a dynamic adjustment method for battery energy supply, it can respond promptly when system power demand changes, effectively smoothing the switching between peak power and continuous power, and improving system stability and driving comfort.

[0145] Figure 6 This is a schematic diagram of a SOP (Start of Production) estimation device for automotive lithium-ion power batteries provided in an embodiment of the present invention. See also... Figure 6 The device includes an initialization module 610, an acquisition and prediction module 620, a judgment module 630, an adjustment module 640, and an update module 650.

[0146] The initialization module 610 is used to initialize parameters and read the current status information of the battery;

[0147] The data acquisition and prediction module 620 is used to collect the current power demand and historical power demand of the vehicle, and predict the future power change trend based on the current power demand and the historical power demand;

[0148] The judgment module 630 is used to determine whether the current power demand has changed. If it has not changed, the current power state is maintained. If it has changed, step S4 is executed.

[0149] The adjustment module 640 is used to dynamically adjust the battery energy supply mode according to the current power demand and future power change trends, and monitor the adjustment effect;

[0150] The update module 650 is used to update the SOC-Temp-SOP database to complete this SOP estimation.

[0151] The SOP estimation device for automotive lithium-ion power batteries provided in this embodiment of the invention can execute the SOP estimation method for automotive lithium-ion power batteries provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0152] Figure 7 This is a schematic diagram of an electronic device for estimating the state of operation (SOP) of a lithium-ion power battery for vehicles, provided as an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0153] like Figure 7 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 and a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, the ROM 12, and the RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0154] Multiple components in electronic device 10 are connected to input / output I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of monitors, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0155] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a method for estimating the state of operation (SOP) of a lithium-ion battery for automotive applications.

[0156] In some embodiments, a method for estimating the start-up cost (SOP) of a lithium-ion battery for vehicles can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded into and / or installed on electronic device 10 via read-only memory (ROM) 12 and / or communication unit 19. When the computer program is loaded into random access memory (RAM) 13 and executed by processor 11, one or more steps of the method for estimating the SOP of a lithium-ion battery for vehicles described above can be performed. Alternatively, in other embodiments, processor 11 can be configured in any other suitable manner to perform a method for estimating the SOP of a lithium-ion battery for vehicles.

[0157] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0158] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0159] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0160] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device for displaying information to a user; and a keyboard and pointing device through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with a user; for example, feedback provided to the user can be any form of sensory feedback; and input from the user can be received in any form.

[0161] The systems and technologies described herein can be implemented in computing systems that include backend components, middleware components, or frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium. Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0162] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0163] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0164] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for estimating the state of power (SOP) of a lithium-ion power battery for vehicles, characterized in that, include: S1. Initialize parameters and read the current status information of the battery; S2. Collect the current power demand and historical power demand of the vehicle, and predict the future power change trend based on the current power demand and the historical power demand; S3. Determine whether the current power demand has changed. If it has not changed, maintain the current power state. If it has changed, proceed to step S4. S4. Dynamically adjust the battery energy supply method according to the current power demand and future power change trends, and monitor the adjustment effect; S5. Update the SOC-Temp-SOP database to complete this SOP estimation.

2. The method for estimating the state of operation (SOP) of a lithium-ion power battery for vehicles according to claim 1, characterized in that, S1 includes: S101. Set battery operating parameters, including power change rate, maximum battery operating voltage limit, minimum battery operating voltage limit, maximum battery operating current limit, maximum battery operating temperature limit, and minimum battery operating temperature limit. S102. Read the current SOC value, current highest cell temperature, and current lowest cell temperature of the battery; S103. Based on the current SOC value, the current highest cell temperature, and the current lowest cell temperature, query the SOC-Temp-SOP database to obtain the initial peak power and initial continuous power.

3. The method for estimating the SOP of a lithium-ion power battery for vehicles according to claim 1, characterized in that, S2 include: S201. Calculate the current energy consumption rate of the system; S202. Combining historical power demand and the energy consumption rate, predict the power change trend within a preset time period in the future.

4. The method for estimating the SOP of a lithium-ion power battery for vehicles according to claim 1, characterized in that, S3 include: S301. Calculate the difference between the current power demand and the power at the previous moment; S302. If the difference is greater than or equal to the first preset power threshold, it is determined that the current power demand has changed. S303. If the difference is less than the first preset power threshold, it is determined that the current power demand has not changed, and the current power output state is maintained.

5. The method for estimating the start-up cost (SOP) of a lithium-ion power battery for vehicles according to claim 1, characterized in that, S4 include: S401. Calculate the target output power based on the predicted future power change trend and current power demand; S402. Combine the current state information of the battery to apply multiple constraints to the target output power to ensure that the output power is within the safe operating range. S403. According to the preset power change rate, gradually adjust the initial peak power and initial continuous power of the battery to the target output power respectively; S404. Monitor the power adjustment effect. If it does not meet expectations, return to step S401 to readjust. If it meets expectations, proceed to step S5.

6. The method for estimating the start-up cost (SOP) of a lithium-ion power battery for vehicles according to claim 5, characterized in that, S401 includes: S4011. Calculate the initial target power based on the predicted future power change trend and the initial continuous power. S4012. Limit the initial target power within a preset power range to obtain the final target output power; S4013. Calculate the power adjustment amount based on the power at the previous moment and the final target output power.

7. The method for estimating the start-up cost (SOP) of a lithium-ion power battery for vehicles according to claim 5, characterized in that, S402 includes: S4021. Record the duration of continuous operation of the battery's current output power; S4022. Determine the battery's state of charge based on the current SOC value; S4023. If the current SOC value is less than or equal to the first preset SOC threshold and the current output power is greater than or equal to the second preset power threshold, then reduce the target output power to avoid over-discharge of the battery. S4024. If the current SOC value is greater than the second preset SOC value and the current output power is less than the third preset power threshold, then increase the target output power to avoid battery overcharging.

8. The method for estimating the start-up cost (SOP) of a lithium-ion power battery for vehicles according to claim 5, characterized in that, S403 includes: S4031. Adjust the current output power of the battery step by step according to the preset power change rate, with each adjustment being one-tenth of the power adjustment amount; S4032. After the output power of the battery to be adjusted stabilizes, proceed with the next adjustment until the target output power is reached.

9. The method for estimating the start-up cost (SOP) of a lithium-ion power battery for vehicles according to claim 5, characterized in that, S404 includes: S4041. Calculate the actual energy consumption based on the current output power; S4042. Calculate the theoretical energy consumption based on the target output power; S4043. Calculate the energy consumption error rate based on the actual energy consumption and the theoretical energy consumption; S4044. If the energy consumption error rate is greater than the preset percentage, it is determined that the adjustment has not met expectations and the process needs to return to step S401 to recalculate the target output power; if the energy consumption error rate is less than or equal to the preset percentage, it is determined that the adjustment has met expectations.

10. The method for estimating the SOP of a lithium-ion power battery for vehicles according to claim 1, characterized in that, S5 include: S501. Store the adjusted power status, battery status and energy consumption data into the SOC-Temp-SOP database and update the SOC-Temp-SOP mapping relationship. S502. Initialize the system parameters for the next iteration and prepare for the next round of SOP estimation.

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