An adaptive battery charging strategy adjustment method and terminal

Abstract

The application provides a self-adaptive battery charging strategy adjustment method and a terminal, which comprises the following steps: obtaining initial battery parameters; detecting the charging state of the battery in real time during the charging process to obtain real-time battery parameters; calculating the predicted full charging time and the predicted full charging capacity under the current charging strategy according to the initial battery parameters and the real-time battery parameters combined with an adaptive algorithm; comparing the predicted full charging time and the predicted full charging capacity with the actual demand, determining the difference and adjusting the current charging strategy; repeating the above steps until the battery reaches the full charging state. The application can adapt to the changes of different battery types and battery states, reduce the invalid charging time, improve the charging efficiency and realize the full charging calibration, and avoid overcharging and overheating of the battery by collecting the state parameters of the battery in real time, combining the adaptive algorithm to calculate the predicted full charging time and the predicted full charging capacity under the current charging strategy, comparing the predicted full charging time and the predicted full charging capacity with the actual demand to determine the difference, and dynamically adjusting the current charging strategy in the reverse direction.

Description

[0001] This case is a divisional application based on the invention patent filed on September 19, 2024, with application number 202411304733.4 and titled "An Adaptive Battery Full Charge Calibration Method and Terminal". Technical Field

[0002] This invention relates to the field of electric vehicle technology, and in particular to an adaptive battery charging strategy adjustment method and terminal. Background Technology

[0003] With the rapid development of electric vehicles and renewable energy technologies, the charging efficiency and full-charge accuracy of batteries, as the power source for devices, have become a key focus for users. Therefore, developing an efficient and accurate adaptive battery full-charge calibration method is of great significance for improving user experience and device performance.

[0004] Currently, most battery full-charge calibration methods on the market use fixed parameter control. This method cannot adapt to changes in different battery types and states, resulting in low charging efficiency and low full-charge accuracy. Furthermore, existing battery full-charge calibration methods often lack dynamic adjustments during the battery charging process, failing to optimize the charging strategy in real time based on the actual battery conditions. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide an adaptive battery charging strategy adjustment method and terminal, which can effectively improve charging efficiency and achieve full charge calibration, thereby increasing battery safety.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: An adaptive battery charging strategy adjustment method includes the following steps: S0. Obtain initial battery parameters, including battery capacity Q_total, battery internal resistance R_int, and battery initial voltage V_init; S1. During the charging process, the charging status of the battery is detected in real time to obtain real-time battery parameters, including the current battery voltage V_current, the current battery current I_current, the current temperature T, and the SOC of each individual cabinet. S2. Based on the initial battery parameters and the real-time battery parameters, combined with an adaptive algorithm, the estimated full charge time and estimated full charge amount under the current charging strategy are calculated, specifically as follows: S21. The formula for calculating the expected full charge time T_full is defined as follows: T_full=f(Q_remaining,I_current,R_int,T)(1); Where Q_remaining represents the current remaining capacity of the battery, and f is a functional relationship determined based on battery characteristics and charging environment factors. The formula for calculating the functional relationship f is as follows: (2); Wherein, α, β and γ are constants determined through experiments and data analysis, used to reflect the influence of battery characteristics and charging environment factors on charging time; S22. The formula for calculating the expected full charge capacity Q_full is defined as follows: Q_full= Q_total-Q_loss(3); Wherein, Q_loss is the energy loss calculated based on the battery internal resistance R_int, the battery current I_current, and the charging time. The formula for calculating energy loss Q_loss is as follows: (4); Where k is the influence coefficient of the battery internal resistance R_int on the power loss Q_loss, and η is the energy conversion efficiency during charging; S3. Compare the estimated full charging time and the estimated full charging amount with the actual demand, determine the difference, and adjust the current charging strategy. S4. Repeat steps S1 to S3 until the battery is fully charged.

[0007] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows: An adaptive battery charging strategy adjustment terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps: S0. Obtain initial battery parameters, including battery capacity Q_total, battery internal resistance R_int, and battery initial voltage V_init; S1. During the charging process, the charging status of the battery is detected in real time to obtain real-time battery parameters, including the current battery voltage V_current, the current battery current I_current, the current temperature T, and the SOC of each individual cabinet. S2. Based on the initial battery parameters and the real-time battery parameters, combined with an adaptive algorithm, the estimated full charge time and estimated full charge amount under the current charging strategy are calculated, specifically as follows: S21. The formula for calculating the expected full charge time T_full is defined as follows: T_full=f(Q_remaining,I_current,R_int,T)(1); Where Q_remaining represents the current remaining capacity of the battery, and f is a functional relationship determined based on battery characteristics and charging environment factors. The formula for calculating the functional relationship f is as follows: (2); Wherein, α, β and γ are constants determined through experiments and data analysis, used to reflect the influence of battery characteristics and charging environment factors on charging time; S22. The formula for calculating the expected full charge capacity Q_full is defined as follows: Q_full= Q_total-Q_loss(3); Wherein, Q_loss is the energy loss calculated based on the battery internal resistance R_int, the battery current I_current, and the charging time. The formula for calculating energy loss Q_loss is as follows: (4); Where k is the influence coefficient of the battery internal resistance R_int on the power loss Q_loss, and η is the energy conversion efficiency during charging; S3. Compare the estimated full charging time and the estimated full charging amount with the actual demand, determine the difference, and adjust the current charging strategy. S4. Repeat steps S1 to S3 until the battery is fully charged.

[0008] The beneficial effects of this invention are as follows: It provides an adaptive battery charging strategy adjustment method and terminal, which collects the battery's state parameters in real time during the battery charging process, calculates the expected full charge time and expected full charge amount under the current charging strategy by combining an adaptive algorithm, and determines the difference based on the comparison between the expected full charge time and expected full charge amount and the actual demand, so as to adjust the current charging strategy in reverse. That is, it realizes the dynamic adjustment of the charging strategy during the charging process to adapt to the changes of different battery types and battery states, reduces the ineffective charging time, improves charging efficiency and realizes full charge calibration, so as to avoid safety problems such as battery overcharging and overheating. Attached Figure Description

[0009] Figure 1 This is an overall flowchart of an adaptive battery full-charge calibration method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of an adaptive battery full charge calibration terminal according to an embodiment of the present invention.

[0010] Label Explanation: 1. An adaptive battery full charge calibration terminal; 2. A memory; 3. A processor. Detailed Implementation

[0011] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0012] Please refer to Figure 1 An adaptive battery full charge calibration method includes the following steps: S0. Obtain initial battery parameters, including battery capacity Q_total, battery internal resistance R_int, and battery initial voltage V_init; S1. During the charging process, the charging status of the battery is detected in real time to obtain real-time battery parameters, including the current battery voltage V_current, the current battery current I_current, the current temperature T, and the SOC of each individual cabinet. S2. Based on the initial battery parameters and the real-time battery parameters, combined with an adaptive algorithm, the estimated full charge time and estimated full charge amount under the current charging strategy are calculated, specifically as follows: S21. The formula for calculating the expected full charge time T_full is defined as follows: T_full=f(Q_remaining,I_current,R_int,T)(1); Where Q_remaining represents the current remaining capacity of the battery, and f is a functional relationship determined based on battery characteristics and charging environment factors. The formula for calculating the functional relationship f is as follows: (2); Wherein, α, β and γ are constants determined through experiments and data analysis, used to reflect the influence of battery characteristics and charging environment factors on charging time; S22. The formula for calculating the expected full charge capacity Q_full is defined as follows: Q_full= Q_total-Q_loss(3); Wherein, Q_loss is the energy loss calculated based on the battery internal resistance R_int, the battery current I_current, and the charging time. The formula for calculating energy loss Q_loss is as follows: (4); Where k is the influence coefficient of the battery internal resistance R_int on the power loss Q_loss, and η is the energy conversion efficiency during charging; S3. Compare the estimated full charging time and the estimated full charging amount with the actual demand, determine the difference, and adjust the current charging strategy. S4. Repeat steps S1 to S3 until the battery is fully charged.

[0013] As can be seen from the above description, the beneficial effects of the present invention are as follows: It provides an adaptive battery full-charge calibration method, which collects the battery's state parameters in real time during the battery charging process, combines an adaptive algorithm to calculate the expected full-charge time and expected full-charge amount under the current charging strategy, and determines the difference based on the comparison between the expected full-charge time and expected full-charge amount and the actual demand, so as to adjust the current charging strategy in reverse. That is, it realizes the dynamic adjustment of the charging strategy during the charging process to adapt to the changes in different battery types and battery states, reduces ineffective charging time, improves charging efficiency, and achieves full-charge calibration, so as to avoid safety problems such as battery overcharging and overheating. The specific implementation of the calculation formula of the adaptive algorithm can be based on the comprehensive consideration of the battery's chemical characteristics, physical characteristics, and charging environment. That is, the battery's chemical characteristics include the state of charge (SOC) of each individual battery, the physical characteristics include the battery's internal resistance, and the charging environment includes parameters such as charging current, charging voltage, and temperature. By real-time monitoring and calculation of these parameters, a more accurate expected full-charge time and expected full-charge amount can be obtained, thereby achieving precise battery full-charge calibration.

[0014] Further, step S3 specifically includes: S31. Compare the estimated full charge time with the user's required time. If the estimated full charge time is less than or equal to the user's required time, then the current charging strategy meets the user's needs and no adjustment is required. Otherwise, increase the current battery current I_current or the current battery charging voltage V_set in the current charging strategy; S32. Compare the expected full charge capacity with the user's required charge capacity. If the expected full charge capacity is greater than or equal to the user's required charge capacity, then the current charging strategy meets the user's needs and no adjustment is required. Otherwise, extend the charging time of the current charging strategy or reduce the power loss Q_loss during the charging process; S33. Taking into account both the expected full charge time and the expected full charge amount, if the expected full charge time and the expected full charge amount simultaneously meet the user's needs, then the current charging strategy meets the user's needs and no adjustment is required. Otherwise, adjustments should be made based on the priority of user needs.

[0015] As described above, the charging strategy is dynamically adjusted based on the difference between the expected full charge time and expected full charge amount calculated in real time by the adaptive algorithm and the actual demand. This can adapt to changes in different battery types and states, improve charging efficiency and full charge accuracy, and is applicable to different types of batteries and different charging environments, thus having wide applicability.

[0016] Please refer to Figure 2An adaptive battery full charge calibration terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps: S0. Obtain initial battery parameters, including battery capacity Q_total, battery internal resistance R_int, and battery initial voltage V_init; S1. During the charging process, the charging status of the battery is detected in real time to obtain real-time battery parameters, including the current battery voltage V_current, the current battery current I_current, the current temperature T, and the SOC of each individual cabinet. S2. Based on the initial battery parameters and the real-time battery parameters, combined with an adaptive algorithm, the estimated full charge time and estimated full charge amount under the current charging strategy are calculated, specifically as follows: S21. The formula for calculating the expected full charge time T_full is defined as follows: T_full=f(Q_remaining,I_current,R_int,T)(1); Where Q_remaining represents the current remaining capacity of the battery, and f is a functional relationship determined based on battery characteristics and charging environment factors. The formula for calculating the functional relationship f is as follows: (2); Wherein, α, β and γ are constants determined through experiments and data analysis, used to reflect the influence of battery characteristics and charging environment factors on charging time; S22. The formula for calculating the expected full charge capacity Q_full is defined as follows: Q_full= Q_total-Q_loss(3); Wherein, Q_loss is the energy loss calculated based on the battery internal resistance R_int, the battery current I_current, and the charging time. The formula for calculating energy loss Q_loss is as follows: (4); Where k is the influence coefficient of the battery internal resistance R_int on the power loss Q_loss, and η is the energy conversion efficiency during charging; S3. Compare the estimated full charging time and the estimated full charging amount with the actual demand, determine the difference, and adjust the current charging strategy. S4. Repeat steps S1 to S3 until the battery is fully charged.

[0017] As described above, the beneficial effects of this invention are as follows: Based on the same technical concept, and in conjunction with the aforementioned adaptive battery full-charge calibration method, an adaptive battery full-charge calibration terminal is provided. This terminal collects battery state parameters in real time during battery charging, combines an adaptive algorithm to calculate the expected full-charge time and expected full-charge amount under the current charging strategy, and determines the difference between the expected full-charge time and expected full-charge amount and the actual demand to adjust the current charging strategy accordingly. This achieves dynamic adjustment of the charging strategy during charging to adapt to changes in different battery types and states, reducing ineffective charging time, improving charging efficiency, and achieving full-charge calibration, thereby avoiding safety issues such as battery overcharging and overheating. The specific implementation of the adaptive algorithm's calculation formula can be based on a comprehensive consideration of the battery's chemical characteristics, physical characteristics, and charging environment. Specifically, the battery's chemical characteristics include the state of charge (SOC) of each individual battery cell, and its physical characteristics include the battery's internal resistance. The charging environment includes parameters such as charging current, charging voltage, and temperature. By real-time monitoring and calculation of these parameters, a more accurate expected full-charge time and expected full-charge amount can be obtained, thus achieving precise battery full-charge calibration.

[0018] Further, step S3 specifically includes: S31. Compare the estimated full charge time with the user's required time. If the estimated full charge time is less than or equal to the user's required time, then the current charging strategy meets the user's needs and no adjustment is required. Otherwise, increase the current battery current I_current or the current battery charging voltage V_set in the current charging strategy; S32. Compare the expected full charge capacity with the user's required charge capacity. If the expected full charge capacity is greater than or equal to the user's required charge capacity, then the current charging strategy meets the user's needs and no adjustment is required. Otherwise, extend the charging time of the current charging strategy or reduce the power loss Q_loss during the charging process; S33. Taking into account both the expected full charge time and the expected full charge amount, if the expected full charge time and the expected full charge amount simultaneously meet the user's needs, then the current charging strategy meets the user's needs and no adjustment is required. Otherwise, adjustments should be made based on the priority of user needs.

[0019] As described above, the charging strategy is dynamically adjusted based on the difference between the expected full charge time and expected full charge amount calculated in real time by the adaptive algorithm and the actual demand. This can adapt to changes in different battery types and states, improve charging efficiency and full charge accuracy, and is applicable to different types of batteries and different charging environments, thus having wide applicability.

[0020] The present invention provides an adaptive battery full charge calibration method and terminal, which is mainly applied to the battery charging scenario of electric vehicles. The following is a detailed description with reference to specific embodiments.

[0021] Please refer to Figure 1 Embodiment 1 of the present invention is as follows: An adaptive battery full charge calibration method, such as Figure 1 As shown, the steps include: S0. Obtain the initial battery parameters, which include the battery capacity Q_total, the battery internal resistance R_int, and the battery initial voltage V_init. By combining the initial battery parameters, the expected full charge time and expected full charge amount can be accurately calculated under the adaptive algorithm.

[0022] S1. During the charging process, the charging status of the battery is detected in real time to obtain real-time battery parameters, including the current battery voltage V_current, the current battery current I_current, the current temperature T, and the SOC of each individual cabinet.

[0023] S2. Based on the initial battery parameters and real-time battery parameters, combined with an adaptive algorithm, the estimated full charge time and estimated full charge amount under the current charging strategy are calculated as follows: S21. The formula for calculating the expected full charge time T_full is defined as follows: T_full=f(Q_remaining,I_current,R_int,T)(1); Where Q_remaining represents the current remaining capacity of the battery, and f is a function relationship determined based on battery characteristics and charging environment factors.

[0024] The formula for calculating the functional relationship f is as follows: (2); α, β, and γ are constants determined through experiments and data analysis, used to reflect the influence of battery characteristics and charging environment factors on charging time.

[0025] S22. The formula for calculating the expected full charge capacity Q_full is defined as follows: Q_full= Q_total-Q_loss(3); Wherein, Q_loss is the power loss calculated based on the battery internal resistance R_int, the battery current I_current, and the charging time.

[0026] The formula for calculating the power loss Q_loss is as follows: (4); Where k is the influence coefficient of the battery internal resistance R_int on the power loss Q_loss, and η is the energy conversion efficiency during charging. In this embodiment, the values ​​of k and η need to be determined through experiments and data analysis.

[0027] S3. Compare the expected full charge time and expected full charge amount with the actual demand, identify the difference, and adjust the current charging strategy.

[0028] S4. Repeat steps S1 to S3 until the battery is fully charged.

[0029] In this embodiment, by collecting battery state parameters in real time during battery charging, and combining this with an adaptive algorithm to calculate the estimated full charge time and estimated full charge amount under the current charging strategy, the difference between the estimated full charge time and estimated full charge amount and the actual demand is determined to adjust the current charging strategy accordingly. This achieves dynamic adjustment of the charging strategy during the charging process to adapt to changes in different battery types and battery states, reducing ineffective charging time, improving charging efficiency, and achieving full charge calibration to avoid safety issues such as battery overcharging and overheating. The specific implementation of the adaptive algorithm's calculation formula can be based on a comprehensive consideration of the battery's chemical characteristics, physical characteristics, and charging environment. The battery's chemical characteristics include the state of charge (SOC) of each individual battery cell, and its physical characteristics include the battery's internal resistance. The charging environment includes parameters such as charging current, charging voltage, and temperature. By monitoring and calculating these parameters in real time, a more accurate estimated full charge time and estimated full charge amount can be obtained, thereby achieving precise battery full charge calibration.

[0030] Embodiment 2 of the present invention is as follows: An adaptive battery full charge calibration method, based on the above embodiment one, specifically includes step S3 as follows: S31. Compare the estimated full charge time with the user's required time. If the estimated full charge time is less than or equal to the user's required time, it means that the current charging strategy can meet the user's needs and no adjustment is needed. Otherwise, it means that the current charging strategy is inefficient and needs to be adjusted to shorten the charging time. This can be achieved by increasing the battery current I_current or the battery current charging voltage V_set in the current charging strategy.

[0031] S32. Compare the expected full charge capacity with the user's required capacity. If the expected full charge capacity is greater than or equal to the user's required capacity, it means that the current charging strategy can meet the user's needs and no adjustment is required. Otherwise, it means that the battery may not be able to reach the user's expected capacity and the charging strategy needs to be adjusted to increase the full charge capacity. This can be achieved by extending the charging time of the current charging strategy or reducing the power loss Q_loss during the charging process (such as by reducing the charging current to reduce internal resistance heating).

[0032] S33. Taking into account both the expected full charge time and the expected full charge amount, if the expected full charge time and the expected full charge amount meet the user's needs, it means that the current charging strategy can meet the user's needs and no adjustment is needed; if there is a conflict between the expected full charge time and the expected full charge amount (such as increasing the charging current can shorten the charging time but may increase the power loss), then it is necessary to weigh and adjust according to the priority of the user's needs.

[0033] This means that the charging strategy is dynamically adjusted based on the difference between the expected full charge time and expected full charge amount calculated in real time by the adaptive algorithm and the actual demand. It can adapt to changes in different battery types and states, improve charging efficiency and achieve full charge calibration. It is suitable for different types of batteries and different charging environments, and has wide applicability.

[0034] Please refer to Figure 2 Embodiment 3 of the present invention is as follows: An adaptive battery full charge calibration terminal 1 includes a memory 2, a processor 3, and a computer program stored on the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, it completes the steps in the adaptive battery full charge calibration method described in Embodiment 1 or Embodiment 2 above.

[0035] In summary, the adaptive battery full-charge calibration method and terminal provided by the present invention have the following beneficial effects: 1. Improve charging efficiency: By monitoring the battery's charging status in real time and adjusting the charging strategy, ineffective charging time is reduced, thereby improving charging efficiency.

[0036] 2. Improve full charge accuracy: Based on the real-time status of the battery and historical charging data, combined with an adaptive algorithm, high-precision battery full charge calibration is achieved.

[0037] 3. Enhanced safety: By monitoring battery temperature changes in real time and adjusting charging strategies, safety issues such as overcharging and overheating of the battery can be avoided.

[0038] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An adaptive battery charging strategy adjustment method, characterized in that, Including the following steps: S0, Obtain initial battery parameters; S1. Real-time monitoring of the battery's charging status during the charging process to obtain real-time battery parameters; S2. Based on the initial battery parameters and the real-time battery parameters, combined with an adaptive algorithm, calculate the expected full charge time and expected full charge amount under the current charging strategy; S3. Compare the estimated full charging time and estimated full charge capacity with the actual demand, determine the difference, and adjust the current charging strategy, specifically including: Compare the estimated full charge time with the user's required time. If the estimated full charge time is greater than the user's required time, then increase the current battery current or the current battery charging voltage in the current charging strategy. Compare the expected full charge capacity with the user's required power capacity. If the expected full charge capacity is less than the user's required power capacity, then extend the charging time or reduce the power loss during the charging process in the current charging strategy. Repeat steps S1 to S3 until the battery is fully charged. The initial battery parameters include battery capacity Q_total, battery internal resistance R_int, and battery initial voltage V_init. The real-time battery parameters include current battery voltage V_current, current battery current I_current, current temperature T, and SOC of each individual battery. The adaptive algorithm is applicable to different types of batteries and different charging environments. Step S2 specifically includes: Based on the initial battery parameters and the real-time battery parameters, combined with an adaptive algorithm, the estimated full charge time T_full under the current charging strategy is calculated as follows: T_full=f(Q_remaining,I_current,R_int,T)(1); Where Q_remaining represents the current remaining capacity of the battery, and f is a functional relationship determined based on battery characteristics and charging environment factors. The formula for calculating the functional relationship f is as follows: （2）； Wherein, α, β and γ are constants determined through experiments and data analysis, used to reflect the influence of battery characteristics and charging environment factors on charging time; Based on the initial battery parameters and the real-time battery parameters, combined with an adaptive algorithm, the expected full charge capacity Q_full under the current charging strategy is calculated: Q_full= Q_total-Q_loss(3); Wherein, Q_loss is the energy loss calculated based on the battery internal resistance R_int, the battery current I_current, and the charging time. The formula for calculating energy loss Q_loss is as follows: （4）； Where k is the influence coefficient of the battery internal resistance R_int on the power loss Q_loss, and η is the energy conversion efficiency during charging.

2. The adaptive battery charging strategy adjustment method according to claim 1, characterized in that, Step S3 further includes: If the expected full charge time and the expected full charge amount both meet the user's needs, then the current charging strategy meets the user's needs and no adjustment is required; otherwise, adjustments are made based on the priority of the user's needs.

3. An adaptive battery charging strategy adjustment terminal, characterized in that, Includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, performs the following steps: S0, Obtain initial battery parameters; S1. Real-time monitoring of the battery's charging status during the charging process to obtain real-time battery parameters; S2. Based on the initial battery parameters and the real-time battery parameters, combined with an adaptive algorithm, calculate the expected full charge time and expected full charge amount under the current charging strategy; S3. Compare the estimated full charging time and estimated full charge capacity with the actual demand, determine the difference, and adjust the current charging strategy, specifically including: Compare the estimated full charge time with the user's required time. If the estimated full charge time is greater than the user's required time, then increase the current battery current or the current battery charging voltage in the current charging strategy. Compare the expected full charge capacity with the user's required power capacity. If the expected full charge capacity is less than the user's required power capacity, then extend the charging time or reduce the power loss during the charging process in the current charging strategy. Repeat steps S1 to S3 until the battery is fully charged. The initial battery parameters include battery capacity Q_total, battery internal resistance R_int, and battery initial voltage V_init. The real-time battery parameters include current battery voltage V_current, current battery current I_current, current temperature T, and SOC of each individual battery. The adaptive algorithm is applicable to different types of batteries and different charging environments. Step S2 specifically includes: Based on the initial battery parameters and the real-time battery parameters, combined with an adaptive algorithm, the estimated full charge time T_full under the current charging strategy is calculated as follows: T_full=f(Q_remaining,I_current,R_int,T)(1); Where Q_remaining represents the current remaining capacity of the battery, and f is a functional relationship determined based on battery characteristics and charging environment factors. The formula for calculating the functional relationship f is as follows: （2）； Wherein, α, β and γ are constants determined through experiments and data analysis, used to reflect the influence of battery characteristics and charging environment factors on charging time; Based on the initial battery parameters and the real-time battery parameters, combined with an adaptive algorithm, the expected full charge capacity Q_full under the current charging strategy is calculated: Q_full= Q_total-Q_loss(3); Wherein, Q_loss is the energy loss calculated based on the battery internal resistance R_int, the battery current I_current, and the charging time. The formula for calculating energy loss Q_loss is as follows: （4）； Where k is the influence coefficient of the battery internal resistance R_int on the power loss Q_loss, and η is the energy conversion efficiency during charging.

4. The adaptive battery charging strategy adjustment terminal according to claim 3, characterized in that, Step S3 further includes: If the expected full charge time and the expected full charge amount both meet the user's needs, then the current charging strategy meets the user's needs and no adjustment is required; otherwise, adjustments are made based on the priority of the user's needs.

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