A low-temperature starting method and system based on sodium-lithium battery mixed battery

Abstract

The application provides a low-temperature starting method and system for sodium-lithium battery mixed batteries, relates to the technical field of low-temperature starting, determines a sodium battery pack and a lithium iron phosphate battery pack, constructs a parallel structure through the sodium battery pack and the lithium iron phosphate battery pack, performs battery pack operation parameter monitoring management on the parallel structure through a BMS, obtains battery pack monitoring management data, analyzes temperature influence data of the battery monitoring management data through the BMS, obtains temperature influence analysis data, analyzes battery pack power supply data according to the temperature influence analysis data, obtains battery pack power supply analysis data, performs power supply comparison distribution adjustment analysis according to the battery pack power supply analysis data, obtains power supply distribution adjustment data, and performs distribution adjustment control until the engine is successfully started. The application can solve the traditional pain points and realize efficient and stable low-temperature starting.

Description

Technical Field

[0001] This invention proposes a low-temperature start-up method and system for batteries based on the use of sodium and lithium batteries, which relates to the field of low-temperature start-up technology, specifically to the field of low-temperature start-up technology for batteries based on the use of sodium and lithium batteries. Background Technology

[0002] Traditional single lithium iron phosphate batteries provide stable power at room temperature, but their adaptability to low temperatures is poor. In low-temperature environments, additional heating is required for startup, increasing energy consumption and leading to startup failures and stalling. While single sodium batteries offer wide-temperature discharge capabilities, making them suitable for extremely cold environments, their high cost fails to meet the energy demands and economic requirements of long-term start-stop operations. Existing dual-battery parallel technology simply achieves power redundancy without considering temperature characteristics for scientific selection and rational allocation. Furthermore, it lacks precise temperature quantification analysis and differentiated power distribution adjustment strategies, resulting in low power efficiency, rapid battery degradation, and low utilization of monitoring data. It fails to achieve the complementary advantages of the two battery types and is ill-suited to the reliable power supply requirements for engine startup in different temperature scenarios. Summary of the Invention

[0003] This invention provides a low-temperature start-up method and system for batteries using a combination of sodium and lithium batteries, to solve the above-mentioned problems: This invention proposes a low-temperature start-up method and system for batteries using a combination of sodium and lithium batteries, the method comprising: S1. Determine the sodium battery pack and the lithium iron phosphate battery pack, construct a parallel structure using the sodium battery pack and the lithium iron phosphate battery pack, and use the BMS to monitor and manage the battery pack operating parameters of the parallel structure to obtain battery pack monitoring and management data. S2. Perform temperature impact data analysis on battery monitoring and management data through BMS to obtain temperature impact analysis data. Based on the temperature impact analysis data, perform battery pack power supply data analysis to obtain battery pack power supply analysis data. S3. Based on the battery pack power supply analysis data, perform power supply comparison, distribution and adjustment analysis to obtain power supply distribution and adjustment data, and perform distribution and adjustment control until the engine starts successfully.

[0004] Further, S1 includes: Acquire battery startup scenario data, and determine the highest and lowest temperature data based on the battery startup scenario data; The lithium iron phosphate battery pack was determined based on the highest temperature data, and the sodium battery pack was determined based on the lowest temperature data. Based on battery startup scenario data, energy demand information is determined, and based on energy demand information, the proportion of the lithium iron phosphate battery pack and the lowest temperature data is determined to determine the proportion of the sodium battery pack. Construct a parallel structure based on battery pack percentage data; Multi-dimensional data acquisition and monitoring were performed on the two types of parallel battery packs using a BMS to obtain multi-dimensional acquisition and monitoring data. Multi-dimensional data monitoring and analysis are conducted based on multi-dimensional collected monitoring data to obtain battery pack monitoring and management data.

[0005] Furthermore, the step of performing multi-dimensional data monitoring and analysis based on multi-dimensional collected monitoring data to obtain battery pack monitoring and management data includes: Preprocess the multi-dimensional collected monitoring data to obtain preprocessed monitoring data; The preprocessed monitoring data is classified and analyzed to obtain classified and analyzed monitoring data. The monitoring classification and analysis data are compared with the corresponding preset data range to obtain monitoring comparison information; Data anomalies are marked based on monitoring and comparison information to obtain battery pack monitoring and management data.

[0006] Further, S2 includes: The monitoring feature data is extracted from the battery monitoring and management data through the BMS to obtain the monitoring feature extraction data. The monitoring feature extraction data is classified into sodium battery packs and lithium iron phosphate battery packs to obtain the first battery pack analysis data and the second battery pack analysis data. A first temperature influence analysis was performed based on the first battery pack classification data to obtain the first temperature influence analysis data. A second temperature influence analysis was performed based on the second battery pack classification data to obtain the second temperature influence analysis data. Based on the first temperature influence analysis data and the second temperature influence analysis data, the battery pack power supply data is analyzed to obtain battery pack power supply analysis data.

[0007] Further, the step of performing a first temperature impact analysis based on the first battery pack classification data to obtain first temperature impact analysis data includes: Temperature correlation parameters are extracted from the analysis data of the first battery pack to obtain the first temperature correlation parameter extraction data. Data is extracted based on the first temperature correlation parameter to obtain multiple data anomaly annotation information corresponding to different temperatures; The first influencing factor is the product of the number of multiple data anomaly labels corresponding to each temperature and the corresponding preset data type label weight. The second influence factor is obtained by multiplying the preset total number of data types by the corresponding preset data type label weights. Calculate the ratio of the first influencing factor to the second influencing factor to obtain data on the influence of temperature characteristics; The first temperature influence analysis data is obtained by combining multiple temperature feature influence analysis data.

[0008] Further, the step of performing a second temperature impact analysis based on the second battery pack classification data to obtain second temperature impact analysis data includes: Temperature correlation parameters were extracted from the analysis data of the second battery pack to obtain the second temperature correlation parameter extraction data. Data is extracted based on the second temperature correlation parameter to obtain multiple data anomaly annotation information corresponding to different temperatures; The third influencing factor is the product of the number of multiple data anomaly labels corresponding to each temperature and the corresponding preset data type label weight. The fourth influence factor is obtained by multiplying the preset total number of data types by the corresponding preset data type label weights. Calculate the ratio of the third influencing factor to the fourth influencing factor to obtain data on the influence of temperature characteristics; By combining multiple temperature characteristic influence analysis data, a second temperature influence analysis data is obtained.

[0009] Further, the step of analyzing the battery pack power supply data based on the first temperature influence analysis data and the second temperature influence analysis data to obtain battery pack power supply analysis data includes: Obtain the current temperature data, and extract the corresponding temperature feature influence from the first temperature influence analysis data and the second influence analysis data based on the current temperature data to obtain the current corresponding first influence data and the current second influence data; By comparing the current first impact data with the current second impact data, impact comparison data is obtained; The priority power supply battery pack and the backup power supply battery pack were determined based on the impact comparison data. The priority power supply battery pack and the backup power supply battery pack refer to the battery pack power supply analysis data.

[0010] Further, S3 includes: Determine power supply capacity and duration data based on battery pack power supply analysis data; The power supply capacity data is compared with a preset power supply capacity threshold to obtain the first comparison result; The power supply duration data is compared with a preset power supply duration threshold to obtain a second comparison result; Based on the first comparison result and the second comparison result, power distribution analysis and adjustment are performed to obtain power distribution adjustment data; Power supply control is performed based on power distribution adjustment data until the engine starts successfully.

[0011] Furthermore, the step of performing power distribution analysis and adjustment based on the first comparison result and the second comparison result to obtain power distribution adjustment data includes: When the power supply capacity is greater than or equal to the preset threshold and the power supply duration is greater than or equal to the preset threshold, the adjustment data of the current mode is maintained; When the power supply capacity is less than the preset threshold but the power supply duration is greater than or equal to the preset threshold, the power supply ratio of the main power supply unit is increased, and corresponding adjustment data is generated. When the power supply capacity is greater than or equal to the preset threshold but the power supply duration is less than the preset threshold, the power supply ratio of non-main power supply units is reduced, and corresponding adjustment data is generated. If neither of these thresholds is met, the proportion of the main power supply unit will be increased first to generate power distribution adjustment data.

[0012] Furthermore, the system includes: The battery monitoring module is used to identify sodium battery packs and lithium iron phosphate battery packs, construct a parallel structure using sodium battery packs and lithium iron phosphate battery packs, and monitor and manage the operating parameters of the parallel structure using BMS to obtain battery pack monitoring and management data. The power supply analysis module is used to analyze the temperature impact of battery monitoring and management data through the BMS to obtain temperature impact analysis data, and then analyze the battery pack power supply data based on the temperature impact analysis data to obtain battery pack power supply analysis data. The power supply regulation module is used to perform power supply comparison, distribution, and regulation analysis based on the battery pack power supply analysis data, obtain power supply distribution and regulation data, and perform distribution and regulation control until the engine starts successfully.

[0013] The beneficial effects of this invention are as follows: This method solves the technical problems of existing single lithium iron phosphate batteries having weak low-temperature starting performance and requiring additional heating to start normally, as well as the high cost of single sodium batteries as start-stop batteries and their inability to adapt to long-term start-stop scenarios; it achieves the complementary advantages of sodium batteries and lithium iron phosphate batteries, leveraging the wide temperature adaptability of sodium batteries while retaining the energy supply advantages of lithium iron phosphate batteries; it improves the reliability and stability of engine low-temperature starting, avoiding problems such as starting failure and starting jamming in low-temperature environments; it reduces the dependence on additional heating systems for low-temperature starting, reducing system energy consumption and operating costs; at the same time, it improves the overall adaptability of the battery system, broadens the application scenarios of engine start-stop systems, and adapts to starting needs in extremely cold and other harsh environments. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of a low-temperature start-up method for a battery based on a combination of sodium and lithium batteries. Detailed Implementation

[0015] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0016] In one embodiment of the present invention, a low-temperature start-up method and system for a battery based on a combination of sodium and lithium batteries is proposed, the method comprising: S1. Determine the sodium battery pack and the lithium iron phosphate battery pack, construct a parallel structure using the sodium battery pack and the lithium iron phosphate battery pack, and use the BMS to monitor and manage the battery pack operating parameters of the parallel structure to obtain battery pack monitoring and management data. S2. Perform temperature impact data analysis on battery monitoring and management data through BMS to obtain temperature impact analysis data. Based on the temperature impact analysis data, perform battery pack power supply data analysis to obtain battery pack power supply analysis data. S3. Based on the battery pack power supply analysis data, perform power supply comparison, distribution, and adjustment analysis to obtain power supply distribution and adjustment data, and perform distribution and adjustment control until the engine starts successfully. Figure 1 As shown.

[0017] The working principle and technical effects of the above solution are as follows: This method involves selecting dual battery packs, constructing a parallel structure, and monitoring operating parameters; using a Battery Management System (BMS) to analyze the temperature impact and power supply capacity of the monitoring data, clarifying the power supply characteristics and synergistic potential of the two types of batteries at different temperatures; comparing the matching degree between power supply capacity and starting requirements, dynamically adjusting the power supply distribution ratio of the dual battery packs, and continuously optimizing the power supply status until the engine starts successfully. The entire process uses the BMS as the core control unit and temperature characteristics as the driving basis to achieve the synergistic linkage of the dual battery packs, overcoming the performance limitations of a single battery.

[0018] This method addresses the technical problems of existing single lithium iron phosphate batteries, which have weak low-temperature starting performance and require additional heating to start normally, as well as the high cost of single sodium batteries as start-stop batteries and their inability to adapt to long-term start-stop scenarios. It achieves complementary advantages between sodium batteries and lithium iron phosphate batteries, leveraging the wide temperature adaptability of sodium batteries while retaining the energy supply advantages of lithium iron phosphate batteries. It improves the reliability and stability of engine low-temperature starting, avoiding start-up failures and start-up stuttering in low-temperature environments. It reduces the dependence on additional heating systems for low-temperature starting, reducing system energy consumption and operating costs. At the same time, it improves the overall adaptability of the battery system, broadens the application scenarios of engine start-stop systems, and adapts to starting requirements in extremely cold and other harsh environments.

[0019] In one embodiment of the present invention, S1 includes: Acquire battery startup scenario data, and determine the highest and lowest temperature data based on the battery startup scenario data; The lithium iron phosphate battery pack was determined based on the highest temperature data, and the sodium battery pack was determined based on the lowest temperature data. Based on battery startup scenario data, energy demand information is determined, and based on energy demand information, the proportion of the lithium iron phosphate battery pack and the lowest temperature data is determined to determine the proportion of the sodium battery pack. For example: Sodium battery pack (selected based on a minimum temperature of -35℃ to ensure low-temperature start-up): Considering its wide temperature range advantage, 30% is allocated to provide the main power supply for low-temperature start-up, corresponding to an energy of 30kWh; Lithium iron phosphate battery pack (selected based on a maximum temperature of 25℃ to ensure stable power supply at normal temperature): Relying on its high energy supply advantage, it is allocated 70% of the battery pack to provide the main power supply at normal temperature and auxiliary power supply at low temperature, corresponding to an energy of 70kWh; The final battery pack ratio of lithium iron phosphate battery pack to sodium battery pack is determined to be 7:3, which not only meets the total energy requirements for the start-stop of the engineering vehicle, but also adapts to the temperature range of -35℃ to 25℃.

[0020] Construct a parallel structure based on battery pack percentage data; Multi-dimensional data acquisition and monitoring were performed on the two types of parallel battery packs using a BMS to obtain multi-dimensional acquisition and monitoring data. Multi-dimensional data monitoring and analysis are conducted based on multi-dimensional collected monitoring data to obtain battery pack monitoring and management data.

[0021] The working principle and technical effects of the above-mentioned technical solution are as follows: This method focuses on the rational selection, scientific construction, and precise monitoring of dual battery packs. By acquiring battery startup scenario data, the temperature range (maximum and minimum temperatures) and energy requirements of the startup environment are clarified, ensuring that the battery selection is highly matched with the actual application scenario. Based on the maximum temperature data, the lithium iron phosphate battery pack is determined, making full use of its stable power supply characteristics at room temperature and medium-high temperatures. Based on the minimum temperature data, the sodium battery pack is determined, relying on its wide-temperature discharge advantage to ensure basic power supply in low-temperature scenarios. Combining the scenario's energy requirements, the proportion data of the dual battery packs is determined. Under the premise of balancing energy supply and cost control, a parallel structure is constructed to avoid circulating current risks and ensure that the dual battery packs can supply power collaboratively. Through BMS, multi-dimensional data collection is carried out on the two types of battery packs, covering the core parameters of battery operation. Then, through multi-dimensional monitoring and analysis, the collected data is sorted and verified to form complete and accurate battery pack monitoring and management data, providing data support for temperature impact analysis and power supply analysis, and ensuring the reliability of subsequent analysis results.

[0022] This method addresses the technical problems in existing dual-battery parallel systems, such as mismatched battery selection with the application scenario and unreasonable battery ratio design, leading to low power supply efficiency, rapid battery wear, and incomplete and inaccurate monitoring data that affect subsequent control and regulation. It achieves precise battery selection and scientific ratio, improving the rationality and stability of the dual-battery parallel structure and reducing circulating current risks and battery wear. It also enhances the completeness and accuracy of monitoring data, avoiding subsequent analysis biases and adjustment errors caused by missing or abnormal data. Furthermore, it reduces the design and maintenance costs of the battery system, ensuring that the battery pack performance is highly compatible with the application scenario. Simultaneously, it improves the compatibility of the parallel structure, ensuring stable and coordinated operation of the two battery packs and extending the battery system's lifespan.

[0023] In one embodiment of the present invention, the step of performing multi-dimensional data monitoring and analysis based on multi-dimensional collected monitoring data to obtain battery pack monitoring and management data includes: Preprocess the multi-dimensional collected monitoring data to obtain preprocessed monitoring data; The preprocessed monitoring data is classified and analyzed to obtain classified and analyzed monitoring data. The monitoring classification and analysis data are compared with the corresponding preset data range to obtain monitoring comparison information; Data anomalies are marked based on monitoring and comparison information to obtain battery pack monitoring and management data.

[0024] The working principle and technical effects of the above-mentioned technical solution are as follows: This method focuses on the optimized processing of monitoring data to ensure that the monitoring data can be directly used for subsequent analysis, thereby improving the efficiency and accuracy of the analysis. The multi-dimensional monitoring data collected by the BMS is preprocessed to remove invalid data and abnormal fluctuation data, and to complete missing data, avoiding interference from abnormal data in subsequent analysis. The preprocessed monitoring data is then classified and analyzed, splitting it according to sodium battery packs, lithium iron phosphate battery packs, and parameter types to clarify the operating status of different battery packs. The classified and analyzed data is compared with a preset data range to determine whether each parameter is within the normal operating range and to identify data anomalies. Based on the comparison results, data anomalies are marked, clarifying the abnormal parameters, the degree of anomaly, and the corresponding battery pack, forming complete battery pack monitoring and management data. This clearly presents the real-time operating status of the dual battery packs, providing precise guidance for subsequent temperature impact analysis and anomaly troubleshooting.

[0025] This method addresses the technical problem of impurities, chaotic classification, and unclear anomalies in raw monitoring data collected by the BMS, which prevents direct use in subsequent temperature analysis and power supply regulation, leading to low analysis efficiency and large errors. It purifies, classifies, and anomaly-labels the monitoring data, improving its usability and accuracy. It reduces the difficulty and workload of subsequent data analysis, avoiding analytical biases and adjustment errors caused by data issues. It enhances the monitorability of the battery system's operating status, enabling timely detection of abnormal conditions during battery operation, facilitating early fault diagnosis, and reducing the risk of battery damage. Furthermore, it facilitates the correlation analysis between abnormal parameters and temperature in subsequent temperature impact analysis.

[0026] In one embodiment of the present invention, S2 includes: The monitoring feature data is extracted from the battery monitoring and management data through the BMS to obtain the monitoring feature extraction data. The monitoring feature extraction data is classified into sodium battery packs and lithium iron phosphate battery packs to obtain the first battery pack analysis data and the second battery pack analysis data. A first temperature influence analysis was performed based on the first battery pack classification data to obtain the first temperature influence analysis data. A second temperature influence analysis was performed based on the second battery pack classification data to obtain the second temperature influence analysis data. Based on the first temperature influence analysis data and the second temperature influence analysis data, the battery pack power supply data is analyzed to obtain battery pack power supply analysis data.

[0027] The working principle and technical effects of the above-mentioned technical solution are as follows: This method, as the core analysis step, focuses on the impact of temperature on battery performance and the evaluation of the power supply capacity of dual battery packs. Monitoring feature data is extracted from battery pack monitoring and management data through the BMS, and core parameters related to temperature and power supply are screened out, redundant information is removed, and analysis efficiency is improved. The feature extraction data is then classified into sodium battery packs and lithium iron phosphate battery packs, clarifying the independent operating parameters of the two types of batteries, and performing temperature impact analysis separately to obtain performance data of the two types of batteries at different temperatures. Combining the temperature impact analysis results of the two types of batteries, the collaborative power supply capacity of the dual battery packs is comprehensively evaluated, identifying the dominant and auxiliary power supply battery packs at the current temperature, forming complete battery pack power supply analysis data, and ensuring that power supply regulation is aligned with the current temperature scenario and battery performance status.

[0028] This method addresses the technical problems in existing dual-battery parallel systems, which fail to consider the impact of temperature on battery performance and lack targeted power supply analysis, leading to unreasonable power supply allocation and low low-temperature start-up efficiency. It achieves precise analysis of the impact of temperature on the performance of dual battery packs, clearly identifying the performance advantages and disadvantages of the two types of batteries at different temperatures, providing a scientific basis for power supply allocation adjustment. It improves the targeting and accuracy of power supply analysis, avoiding excessive power loss and start-up failures caused by blind adjustments. It reduces the impact of temperature changes on power supply stability, ensuring optimal power supply configuration under different temperature scenarios. Simultaneously, it improves the collaborative power supply efficiency of the battery system, fully leveraging the advantages of dual battery packs, providing reliable power supply for engine low-temperature start-up, and further expanding the system's temperature adaptability range.

[0029] In one embodiment of the present invention, the step of performing a first temperature influence analysis based on the first battery pack classification data to obtain first temperature influence analysis data includes: Temperature-related parameters were extracted from the analysis data of the first battery pack to obtain the first temperature-related parameter extraction data, including the internal temperature data of the sodium battery pack, the corresponding ambient temperature data, and the associated voltage, current, SOC value, internal resistance, and discharge efficiency data. Data is extracted based on the first temperature correlation parameter to obtain multiple data anomaly annotation information corresponding to different temperatures; The first influencing factor is the product of the number of multiple data anomaly labels corresponding to each temperature and the corresponding preset data type label weight. The second influence factor is obtained by multiplying the preset total number of data types by the corresponding preset data type label weights. Calculate the ratio of the first influencing factor to the second influencing factor to obtain data on the influence of temperature characteristics; The first temperature influence analysis data is obtained by combining multiple temperature feature influence analysis data.

[0030] The working principle and technical effects of the above-mentioned technical solution are as follows: This method focuses on the temperature impact analysis of sodium battery packs. Through quantitative analysis, it accurately assesses the impact of different temperatures on the operating performance of sodium battery packs. Temperature-related parameters are extracted from the analysis data of sodium battery packs, covering the internal temperature of the sodium battery pack, the ambient temperature, and related core power supply parameters, comprehensively capturing the correlation between temperature and battery performance. Then, based on these parameters, abnormal parameter annotation information of the sodium battery pack at different temperatures is obtained to identify the nodes of temperature impact on the operation of the sodium battery pack. By calculating the ratio of the first impact factor (the product of the number of abnormal annotations and their weights at each temperature) and the second impact factor (the product of the preset total number of data types and their weights), the impact of temperature on the sodium battery pack is quantified into temperature characteristic impact analysis data, intuitively presenting the performance stability of the sodium battery pack at different temperatures. The quantitative analysis data from multiple temperatures are combined to form complete first temperature impact analysis data, clearly presenting the performance change law of the sodium battery pack across the entire temperature range, highlighting its performance advantages at low temperatures.

[0031] This method addresses the technical problem in existing technologies where the temperature impact analysis of sodium battery packs is mostly qualitative and lacks quantitative evidence, leading to an inability to accurately determine their power supply capacity at different temperatures and affecting the rationality of power allocation in dual-battery pack collaboration. It achieves quantitative analysis of the temperature impact of sodium battery packs, improving the accuracy of temperature impact analysis; it clarifies the performance of sodium battery packs at different temperatures, especially their stable operating characteristics in low-temperature environments, providing a reliable basis for prioritizing sodium battery packs as the main power supply unit in low-temperature scenarios; it reduces power supply adjustment errors caused by inaccurate judgment of sodium battery pack temperature performance, improving the rationality of dual-battery pack collaborative power supply; and it further improves the stability and efficiency of low-temperature start-up.

[0032] In one embodiment of the present invention, the step of performing a second temperature influence analysis based on second battery pack classification data to obtain second temperature influence analysis data includes: Temperature-related parameters were extracted from the analysis data of the second battery pack to obtain the second temperature-related parameter extraction data, including the internal temperature data of the lithium iron phosphate battery pack, the corresponding ambient temperature data, and the associated voltage, current, SOC value, internal resistance, and discharge efficiency data. Data is extracted based on the second temperature correlation parameter to obtain multiple data anomaly annotation information corresponding to different temperatures; The third influencing factor is the product of the number of multiple data anomaly labels corresponding to each temperature and the corresponding preset data type label weight. The fourth influence factor is obtained by multiplying the preset total number of data types by the corresponding preset data type label weights. Calculate the ratio of the third influencing factor to the fourth influencing factor to obtain data on the influence of temperature characteristics; By combining multiple temperature characteristic influence analysis data, a second temperature influence analysis data is obtained.

[0033] The working principle and technical effects of the above-mentioned technical solution are as follows: This method corresponds to the temperature influence analysis of sodium battery packs, focusing on the quantitative analysis of the temperature influence of lithium iron phosphate battery packs, accurately capturing their temperature performance shortcomings, and providing complementary data support for the analysis of dual-battery pack collaborative power supply. Temperature-related parameters are extracted from the analysis data of lithium iron phosphate battery packs, covering the internal temperature of the battery, the ambient temperature, and related core power supply parameters, comprehensively covering the correlation dimensions between temperature and battery performance. Then, based on these parameters, abnormal parameter annotation information of lithium iron phosphate battery packs at different temperatures is obtained, clarifying their performance weaknesses at different temperatures, especially the abnormal parameter situations in low-temperature environments. By calculating the ratio of the third influence factor (the product of the number of abnormal annotations and their weights at each temperature) and the fourth influence factor (the product of the preset total number of data types and their weights), the influence of temperature on lithium iron phosphate battery packs is quantified into temperature characteristic influence analysis data, intuitively presenting its performance stability at different temperatures. The quantitative analysis data from multiple temperatures are combined to form complete second temperature influence analysis data, clearly presenting the performance change law of lithium iron phosphate battery packs across the entire temperature range, with a focus on clarifying its performance limitations at low temperatures.

[0034] This method addresses the technical problems in existing technologies where the temperature impact analysis of lithium iron phosphate (LFP) battery packs is not precise enough, lacks quantitative evidence, and cannot clearly identify their low-temperature performance shortcomings, leading to unreasonable power distribution in the collaborative power supply of dual battery packs and insufficient power supply during low-temperature startup. It achieves quantitative analysis of the temperature impact of LFP battery packs, improving the scientific rigor and relevance of temperature impact analysis; it clarifies the performance characteristics of LFP battery packs at different temperatures, especially their performance limitations in low-temperature environments, providing a reliable basis for avoiding over-reliance on LFP battery packs in low-temperature scenarios; it reduces problems such as startup failure and excessive battery degradation caused by misjudgment of the temperature performance of LFP battery packs; and it complements the temperature impact analysis data of sodium battery packs, further improving the rationality and reliability of the collaborative power supply of dual battery packs.

[0035] In one embodiment of the present invention, the step of analyzing battery pack power supply data based on first temperature influence analysis data and second temperature influence analysis data to obtain battery pack power supply analysis data includes: Obtain the current temperature data, and extract the corresponding temperature feature influence from the first temperature influence analysis data and the second influence analysis data based on the current temperature data to obtain the current corresponding first influence data and the current second influence data; By comparing the current first impact data with the current second impact data, impact comparison data is obtained; Based on the impact comparison data, the priority power supply battery pack and the backup power supply battery pack were determined; based on the impact comparison data, the battery pack with less temperature impact and stronger power supply stability was determined as the priority power supply battery pack, and the other pack was the backup power supply battery pack. The priority power supply battery pack and the backup power supply battery pack refer to the battery pack power supply analysis data.

[0036] The working principle and technical effect of the above technical solution are as follows: This method focuses on assessing the power supply capacity of dual battery packs at the current temperature and clarifies the division of labor in collaborative power supply. It acquires the actual temperature data of the current startup environment to ensure the analysis aligns with the real-time scenario; based on the current temperature data, it extracts quantitative impact data at the corresponding temperature from the first and second temperature impact analysis data to accurately capture the performance status of the sodium battery pack and lithium iron phosphate battery pack at the current temperature; it compares the two sets of quantitative impact data to analyze the performance advantages and disadvantages, parameter anomalies, and power supply stability of the two types of battery packs at the current temperature; based on the comparison results, it determines the battery pack with less temperature impact and stronger power supply stability as the priority power supply battery pack, undertaking the main power supply task, while the other set serves as the backup power supply battery pack to supplement power supply, clarifying the power supply division of the dual battery packs; finally, it uses the information of the priority power supply battery pack and the backup power supply battery pack as battery pack power supply analysis data to ensure that the power supply allocation aligns with the battery performance status at the current temperature.

[0037] This method addresses the technical problems in existing dual-battery parallel systems, such as unclear power supply division of labor and lack of targeted power distribution, leading to low power supply efficiency, rapid battery wear, and especially the inability to fully utilize the advantages of sodium battery packs in low-temperature scenarios. It achieves precise determination of the power supply division of the dual battery packs at the current temperature, ensuring that the performance of the main power supply unit is highly matched to the current temperature scenario. It improves power supply efficiency and stability, avoids waste of power supply resources, and reduces battery operating losses. Furthermore, it strengthens the complementary advantages of the dual battery packs, fully utilizing the wide-temperature advantage of the sodium battery pack in low-temperature scenarios and relying on the stable power supply characteristics of the lithium iron phosphate battery pack in normal-temperature scenarios. It reduces the difficulty of power distribution adjustment, ensures precise adjustment direction, provides reliable power supply division support for rapid and stable engine starting, and improves the efficiency of low-temperature starting.

[0038] In one embodiment of the present invention, S3 includes: Determine power supply capacity and duration data based on battery pack power supply analysis data; The power supply capacity data is compared with a preset power supply capacity threshold to obtain the first comparison result; The power supply duration data is compared with a preset power supply duration threshold to obtain a second comparison result; The preset power supply capacity threshold is the power supply capacity standard corresponding to the minimum power and minimum current required for engine start-up, and the preset power supply duration threshold is the power supply duration standard corresponding to the longest allowable time for engine start-up. Based on the first comparison result and the second comparison result, power distribution analysis and adjustment are performed to obtain power distribution adjustment data; Power supply control is performed based on power distribution adjustment data until the engine starts successfully.

[0039] The working principle and technical effect of the above technical solution are as follows: This method, as the core execution link of the entire starting method, focuses on matching power supply capacity with starting requirements, and achieves smooth engine starting through dynamic adjustment. Based on battery pack power supply analysis data, the power supply capacity and sustainable power supply duration of the current priority / backup power supply battery pack are determined, quantifying the current power supply status. The power supply capacity data is compared with a preset power supply capacity threshold (the minimum power supply standard required for engine starting) to determine whether the current power supply capacity meets the starting requirements. The power supply duration data is compared with a preset power supply duration threshold (the maximum allowable time for engine starting) to determine whether the current power supply duration can support the completion of the start-up. The specific standards for the two thresholds are clarified to ensure the rationality and relevance of the comparison results. Based on the two comparison results, the deficiencies of the current power supply status are comprehensively analyzed, and a targeted power supply allocation adjustment scheme is formulated to obtain power supply allocation adjustment data. The power supply allocation of the dual battery packs is controlled according to the adjustment data, dynamically optimizing the power supply status and continuously adapting to the engine starting requirements until the engine starts successfully.

[0040] This method addresses the technical problems in existing dual-battery parallel starting systems, such as the lack of targeted power supply regulation and the failure to precisely match power supply to starting requirements, leading to low starting efficiency and start-up failures at low temperatures, as well as the blind adjustment of power supply and excessive battery wear. It achieves precise matching of power supply capacity with engine starting needs, improving the targeting and rationality of power supply regulation; ensures the stability and sufficiency of power supply during engine starting, avoiding start-up failures and start-up stutters caused by insufficient or interrupted power supply; reduces battery system operating losses and extends battery life; improves the efficiency and reliability of engine starting at low temperatures and shortens start-up time; and reduces the need for human intervention, achieving automation and intelligence in power supply regulation, thus enhancing the practicality and convenience of the entire start-stop system.

[0041] In one embodiment of the present invention, the step of performing power distribution analysis and adjustment based on a first comparison result and a second comparison result to obtain power distribution adjustment data includes: When the power supply capacity is greater than or equal to the preset threshold and the power supply duration is greater than or equal to the preset threshold, the adjustment data of the current mode is maintained; When the power supply capacity is less than the preset threshold but the power supply duration is greater than or equal to the preset threshold, the power supply ratio of the main power supply unit (sodium battery pack at low temperature and lithium iron phosphate battery pack at normal temperature) is increased, and corresponding adjustment data is generated. When the power supply capacity is greater than or equal to the preset threshold but the power supply duration is less than the preset threshold, the power supply ratio of non-main power supply units is reduced, and corresponding adjustment data is generated. If neither of these thresholds is met, the proportion of the main power supply unit will be increased first to generate power distribution adjustment data.

[0042] The working principle and technical effects of the above-mentioned technical solution are as follows: This method focuses on precise adjustment under different power supply scenarios. For different matching situations of power supply capacity and power supply duration, it formulates differentiated power supply allocation adjustment schemes to ensure that the adjustment effect meets the starting requirements. Four different comparison result scenarios are identified, covering all possible power supply states. For each scenario, a targeted adjustment strategy is formulated: When both power supply capacity and power supply duration meet the threshold requirements, the current power supply mode is maintained to avoid unnecessary adjustments that could lead to battery loss; when power supply capacity is insufficient but power supply duration is sufficient, the focus is on improving power supply capacity, increasing the power supply ratio of the main power supply unit, and leveraging the performance advantages of the main power supply unit to quickly improve the overall power supply capacity and meet the starting requirements; when power supply capacity is sufficient but power supply duration is insufficient, the focus is on extending the power supply duration, reducing the power supply ratio of non-main power supply units, reducing power loss, and ensuring that the power supply duration can support the completion of the start-up; when neither meets the threshold requirements, priority is given to improving power supply capacity, increasing the proportion of the main power supply unit, while also considering power supply duration optimization, and a comprehensive adjustment strategy is formulated; through differentiated adjustment, corresponding power supply allocation adjustment data is generated to ensure that the adjusted power supply capacity and power supply duration meet the engine starting requirements.

[0043] This method addresses the technical problems of existing power distribution and adjustment schemes, which are often simplistic, lack differentiated strategies for different power supply scenarios, resulting in poor adjustment effects, inability to quickly adapt to starting requirements, and failure to meet starting requirements even after adjustment. It achieves differentiated and precise adjustment of power distribution, improving adjustment effectiveness and efficiency; ensures rapid optimization of power supply status under different power supply scenarios to meet engine starting requirements, further enhancing the reliability and stability of engine low-temperature starting; reduces power supply loss in the battery system, avoiding unnecessary energy waste and extending battery life; improves the flexibility and adaptability of power supply adjustment, addressing power supply needs under different temperatures and battery conditions; and simplifies the adjustment logic, reducing adjustment difficulty and ensuring rapid implementation of the adjustment scheme.

[0044] In one embodiment of the present invention, the system includes: The battery monitoring module is used to identify sodium battery packs and lithium iron phosphate battery packs, construct a parallel structure using sodium battery packs and lithium iron phosphate battery packs, and monitor and manage the operating parameters of the parallel structure using BMS to obtain battery pack monitoring and management data. The power supply analysis module is used to analyze the temperature impact of battery monitoring and management data through the BMS to obtain temperature impact analysis data, and then analyze the battery pack power supply data based on the temperature impact analysis data to obtain battery pack power supply analysis data. The power supply regulation module is used to perform power supply comparison, distribution, and regulation analysis based on the battery pack power supply analysis data, obtain power supply distribution and regulation data, and perform distribution and regulation control until the engine starts successfully.

[0045] The working principle and technical effects of the above solution are as follows: This method involves selecting dual battery packs, constructing a parallel structure, and monitoring operating parameters; using a Battery Management System (BMS) to analyze the temperature impact and power supply capacity of the monitoring data, clarifying the power supply characteristics and synergistic potential of the two types of batteries at different temperatures; comparing the matching degree between power supply capacity and starting requirements, dynamically adjusting the power supply distribution ratio of the dual battery packs, and continuously optimizing the power supply status until the engine starts successfully. The entire process uses the BMS as the core control unit and temperature characteristics as the driving basis to achieve the synergistic linkage of the dual battery packs, overcoming the performance limitations of a single battery.

[0046] This method addresses the technical problems of existing single lithium iron phosphate batteries, which have weak low-temperature starting performance and require additional heating to start normally, as well as the high cost of single sodium batteries as start-stop batteries and their inability to adapt to long-term start-stop scenarios. It achieves complementary advantages between sodium batteries and lithium iron phosphate batteries, leveraging the wide temperature adaptability of sodium batteries while retaining the energy supply advantages of lithium iron phosphate batteries. It improves the reliability and stability of engine low-temperature starting, avoiding start-up failures and start-up stuttering in low-temperature environments. It reduces the dependence on additional heating systems for low-temperature starting, reducing system energy consumption and operating costs. At the same time, it improves the overall adaptability of the battery system, broadens the application scenarios of engine start-stop systems, and adapts to starting requirements in extremely cold and other harsh environments.

[0047] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A low-temperature start-up method for a battery based on a combination of sodium and lithium batteries, characterized in that, The method includes: S1. Determine the sodium battery pack and the lithium iron phosphate battery pack, construct a parallel structure using the sodium battery pack and the lithium iron phosphate battery pack, and use the BMS to monitor and manage the battery pack operating parameters of the parallel structure to obtain battery pack monitoring and management data. S2. Perform temperature impact data analysis on battery monitoring and management data through BMS to obtain temperature impact analysis data. Based on the temperature impact analysis data, perform battery pack power supply data analysis to obtain battery pack power supply analysis data. S3. Based on the battery pack power supply analysis data, perform power supply comparison, distribution and adjustment analysis to obtain power supply distribution and adjustment data, and perform distribution and adjustment control until the engine starts successfully.

2. The low-temperature start-up method for a battery based on a combination of sodium and lithium batteries according to claim 1, characterized in that, S1 includes: Acquire battery startup scenario data, and determine the highest and lowest temperature data based on the battery startup scenario data; The lithium iron phosphate battery pack was determined based on the highest temperature data, and the sodium battery pack was determined based on the lowest temperature data. Based on battery startup scenario data, energy demand information is determined, and based on energy demand information, the proportion of the lithium iron phosphate battery pack and the lowest temperature data is determined to determine the proportion of the sodium battery pack. Construct a parallel structure based on battery pack percentage data; Multi-dimensional data acquisition and monitoring were performed on the two types of parallel battery packs using a BMS to obtain multi-dimensional acquisition and monitoring data. Multi-dimensional data monitoring and analysis are conducted based on multi-dimensional collected monitoring data to obtain battery pack monitoring and management data.

3. The low-temperature start-up method for a battery based on a combination of sodium and lithium batteries according to claim 2, characterized in that, The process of performing multi-dimensional data monitoring and analysis based on multi-dimensional collected monitoring data to obtain battery pack monitoring and management data includes: Preprocess the multi-dimensional collected monitoring data to obtain preprocessed monitoring data; The preprocessed monitoring data is classified and analyzed to obtain classified and analyzed monitoring data. The monitoring classification and analysis data are compared with the corresponding preset data range to obtain monitoring comparison information; Data anomalies are marked based on monitoring and comparison information to obtain battery pack monitoring and management data.

4. The low-temperature start-up method for a battery based on a combination of sodium and lithium batteries according to claim 1, characterized in that, S2 includes: The monitoring feature data is extracted from the battery monitoring and management data through the BMS to obtain the monitoring feature extraction data. The monitoring feature extraction data is classified into sodium battery packs and lithium iron phosphate battery packs to obtain the first battery pack analysis data and the second battery pack analysis data. A first temperature influence analysis was performed based on the first battery pack classification data to obtain the first temperature influence analysis data. A second temperature influence analysis was performed based on the second battery pack classification data to obtain the second temperature influence analysis data. Based on the first temperature influence analysis data and the second temperature influence analysis data, the battery pack power supply data is analyzed to obtain battery pack power supply analysis data.

5. The low-temperature start-up method for a battery based on a combination of sodium and lithium batteries according to claim 4, characterized in that, The step of performing a first temperature impact analysis based on the first battery pack classification data to obtain first temperature impact analysis data includes: Temperature correlation parameters are extracted from the analysis data of the first battery pack to obtain the first temperature correlation parameter extraction data. Data is extracted based on the first temperature correlation parameter to obtain multiple data anomaly annotation information corresponding to different temperatures; The first influencing factor is the product of the number of multiple data anomaly labels corresponding to each temperature and the corresponding preset data type label weight. The second influence factor is obtained by multiplying the preset total number of data types by the corresponding preset data type label weights. Calculate the ratio of the first influencing factor to the second influencing factor to obtain data on the influence of temperature characteristics; The first temperature influence analysis data is obtained by combining multiple temperature feature influence analysis data.

6. The low-temperature start-up method for a battery based on a combination of sodium and lithium batteries according to claim 4, characterized in that, The step of performing a second temperature impact analysis based on the second battery pack classification data to obtain second temperature impact analysis data includes: Temperature correlation parameters were extracted from the analysis data of the second battery pack to obtain the second temperature correlation parameter extraction data. Data is extracted based on the second temperature correlation parameter to obtain multiple data anomaly annotation information corresponding to different temperatures; The third influencing factor is the product of the number of multiple data anomaly labels corresponding to each temperature and the corresponding preset data type label weight. The fourth influence factor is obtained by multiplying the preset total number of data types by the corresponding preset data type label weights. Calculate the ratio of the third influencing factor to the fourth influencing factor to obtain data on the influence of temperature characteristics; By combining multiple temperature characteristic influence analysis data, a second temperature influence analysis data is obtained.

7. The low-temperature start-up method for a battery based on a combination of sodium and lithium batteries according to claim 4, characterized in that, The step of analyzing battery pack power supply data based on the first temperature influence analysis data and the second temperature influence analysis data to obtain battery pack power supply analysis data includes: Obtain the current temperature data, and extract the corresponding temperature feature influence from the first temperature influence analysis data and the second influence analysis data based on the current temperature data to obtain the current corresponding first influence data and the current second influence data; By comparing the current first impact data with the current second impact data, impact comparison data is obtained; The priority power supply battery pack and the backup power supply battery pack were determined based on the impact comparison data. The priority power supply battery pack and the backup power supply battery pack refer to the battery pack power supply analysis data.

8. The low-temperature start-up method for a battery based on a combination of sodium and lithium batteries according to claim 1, characterized in that, S3 includes: Determine power supply capacity and duration data based on battery pack power supply analysis data; The power supply capacity data is compared with a preset power supply capacity threshold to obtain the first comparison result; The power supply duration data is compared with a preset power supply duration threshold to obtain a second comparison result; Based on the first comparison result and the second comparison result, power distribution analysis and adjustment are performed to obtain power distribution adjustment data; Power supply control is performed based on power distribution adjustment data until the engine starts successfully.

9. A low-temperature start-up method for a battery based on a combination of sodium and lithium batteries according to claim 8, characterized in that, The step of performing power distribution analysis and adjustment based on the first comparison result and the second comparison result to obtain power distribution adjustment data includes: When the power supply capacity is greater than or equal to the preset threshold and the power supply duration is greater than or equal to the preset threshold, the adjustment data of the current mode is maintained; When the power supply capacity is less than the preset threshold but the power supply duration is greater than or equal to the preset threshold, the power supply ratio of the main power supply unit is increased, and corresponding adjustment data is generated. When the power supply capacity is greater than or equal to the preset threshold but the power supply duration is less than the preset threshold, the power supply ratio of non-main power supply units is reduced, and corresponding adjustment data is generated. If neither of these thresholds is met, the proportion of the main power supply unit will be increased first to generate power distribution adjustment data.

10. A low-temperature start-up system for batteries based on a combination of sodium and lithium batteries, characterized in that, The system includes: The battery monitoring module is used to identify sodium battery packs and lithium iron phosphate battery packs, construct a parallel structure using sodium battery packs and lithium iron phosphate battery packs, and monitor and manage the operating parameters of the parallel structure using BMS to obtain battery pack monitoring and management data. The power supply analysis module is used to analyze the temperature impact of battery monitoring and management data through the BMS to obtain temperature impact analysis data, and then analyze the battery pack power supply data based on the temperature impact analysis data to obtain battery pack power supply analysis data. The power supply regulation module is used to perform power supply comparison, distribution, and regulation analysis based on the battery pack power supply analysis data, obtain power supply distribution and regulation data, and perform distribution and regulation control until the engine starts successfully.

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