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CN-122017570-A - Charging remaining time estimation method and system based on self-identification and segmentation prediction

CN122017570ACN 122017570 ACN122017570 ACN 122017570ACN-122017570-A

Abstract

The invention discloses a charging remaining time estimation method and a charging remaining time estimation system based on self-identification and segmentation prediction, which are used for acquiring battery parameters in real time; the method comprises the steps of determining a current charging mode based on battery parameters, identifying whether to start thermal management according to the battery parameters, and settling the real-time charging residual time in stages based on the battery parameters, the charging mode and the identification result of the thermal management. According to the invention, through collecting the battery parameters in real time, determining the charging mode, identifying the thermal management state and settling the charging remaining time in stages, the accuracy and stability of charging remaining time estimation can be improved, and particularly, when the charging mode is switched and the thermal management strategy is adjusted, the actual charging process can be better reflected, and the user experience and the system reliability are improved.

Inventors

  • WANG BO

Assignees

  • 孝感楚能新能源创新科技有限公司

Dates

Publication Date
20260512
Application Date
20260327

Claims (10)

  1. 1. A charging remaining time estimation method based on self-identification and segmentation prediction is characterized by comprising the following steps of: collecting battery parameters in real time; Determining a current charging mode based on the battery parameters; Identifying whether to start thermal management according to the battery parameters; And settling the real-time charging residual time in stages based on the battery parameters, the charging mode and the recognition result of thermal management.
  2. 2. The method for estimating charge remaining time based on self-identification and segment prediction as set forth in claim 1, wherein determining the current charge mode based on the battery parameters comprises determining a charge mode based on any one of a battery charge current, a charge voltage, and a charge power, wherein the charge mode is a fast charge mode or a slow charge mode.
  3. 3. The method for estimating remaining charge time based on self-identification and segment prediction as set forth in claim 1, wherein said identifying whether to initiate thermal management based on battery parameters comprises: Setting an ultralow temperature zone, a low temperature zone, a normal temperature zone, a high temperature zone and an ultrahigh temperature zone; if the temperature of the single battery cell is in the normal temperature range, not starting heat management, otherwise starting heat management, and determining a heat management mode; When the thermal management is started, if the minimum temperature of the single battery cell is in an ultralow temperature range, determining that the thermal management mode is a heating-only mode; If the minimum temperature of the single battery cell is in a low temperature range, determining that the thermal management mode is a charging and heating mode; if the maximum temperature of the single battery core is in a high-temperature interval, determining that the thermal management mode is a charging and cooling mode; And if the maximum temperature of the single battery cell is in the ultra-high temperature range, determining that the thermal management mode is a cooling-only mode.
  4. 4. The method for estimating charge remaining time based on self-identification and segment prediction as recited in claim 1, wherein when the charge mode is a fast charge mode, the charge remaining time is determined by the following formula: ; Wherein T RDCT is the charge remaining time in the quick charge mode, T TMM is the expected duration of the current thermal management mode, T TMMPre is the expected duration of the next thermal management mode, T SOCTemp is the charge time of the current SOC stage, and T Q－SOCTemp is the charge time of the remaining SOC at normal temperature after the current SOC stage is removed.
  5. 5. The method for estimating remaining charge time based on self-identification and segment prediction as set forth in claim 4, wherein the determining of the charging time period T SOCTemp of the current SOC stage is: Determining a preset SOC stage in which the current SOC is located through table lookup; Calculating a difference value between the upper limit value of the preset SOC stage and the real SOC value at the previous moment as the residual electric quantity of the current SOC stage; Determining a required charging capacity Q SOCTemp corresponding to the residual electric quantity of the current SOC stage; The required charging duration of the current SOC stage is the Q SOCTemp divided by the steady charging current I real ; The stable charging current I real is obtained by carrying out moving average filtering on the battery charging current in a set time window before the current moment.
  6. 6. The method of estimating charge remaining time based on self-identification and segmentation prediction as set forth in claim 4, wherein the T Q－SOCTemp is determined by the following equation: Wherein N is the number of SOC stages contained in the residual SOC at normal temperature after eliminating the current SOC stage, I is the number of the SOC stages contained in the residual SOC at normal temperature, delta SOC is the preset interval length of the SOC stages, C is the rated capacity of the battery, and I map_i is the charging rate corresponding to the ith SOC stage.
  7. 7. The method for estimating charge remaining time based on self-identification and segmentation prediction as set forth in claim 1, wherein when the charge mode is a slow charge mode, the charge remaining time is determined by the following formula: ; Wherein T RACT is the charge remaining time in the slow charge mode, T TMM is the expected duration of the current thermal management mode, T TMMPre is the expected duration of the next-stage thermal management mode; charging the SOC from the terminal threshold to 100% of the charge-required time period; Is the charge-required period from the current SOC to the end threshold SOC chrgEnd .
  8. 8. The method for estimating charge remaining time based on self-identification and segment prediction as recited in claim 7 wherein said SOC is charged from an end threshold to 100% of a charge-required time period The determination process of (1) is as follows: Acquiring a plurality of latest historical terminal charging time lengths when the SOC is charged to 100% from the terminal threshold; taking the average value of a plurality of historical terminal charging time periods as the 。
  9. 9. The method for estimating charge remaining time based on self-identification and segment prediction as recited in claim 7 wherein the charge-required period from the current SOC to the terminal threshold is determined by the following equation : ; The battery pack energy is the battery pack energy when full power is supplied at normal temperature, the SOC chrgEnd is the end threshold value, the SOC real is the real SOC value at the current moment, and the P charger is the rated power of the charger.
  10. 10. A charging remaining time estimation system based on self-identification and segmentation prediction is characterized by comprising The data acquisition module is used for acquiring battery parameters in real time; the mode judging module is used for determining a current charging mode based on the battery parameters; a thermal management module for identifying whether thermal management is to be initiated based on the battery parameters; And the time estimation module is used for settling the real-time charging residual time in stages based on the battery parameters, the charging mode and the recognition result of thermal management.

Description

Charging remaining time estimation method and system based on self-identification and segmentation prediction Technical Field The invention belongs to the technical field of battery charging management, and particularly relates to a charging remaining time estimation method and system based on self-identification and segmentation prediction. Background In new energy automobile applications, the accuracy requirements of users on charging time predictions continue to increase. Traditional charge remaining time estimation methods mainly rely on linear extrapolation or fixed empirical formulas at constant current/constant voltage charge stages, and such methods fail to effectively cope with dynamic response differences of batteries in different thermal management modes. For example, when the ambient temperature changes, the system may trigger a heating or cooling mechanism, resulting in significant fluctuation in the battery temperature rise or fall rate, whereas conventional methods lack the ability to identify the thermal management mode in real time, and cannot accommodate such changes, such that the estimation results have a large deviation under ultra-fast charge or high-low temperature conditions. Meanwhile, the charging process has obvious stepwise characteristics, and charging current, voltage and power change rules corresponding to different charge state intervals are different, but the whole charging process is often simplified into single-stage processing in the prior art, so that transition characteristics among stages are ignored, dynamic correction cannot be performed according to a real-time running state, an actual charging process is difficult to accurately reflect, user experience and system reliability are seriously influenced, and performance exertion of an electric automobile and an energy storage system in practical application is restricted. In view of the above, there is a need in the art for improvements. Disclosure of Invention The invention aims to solve the defects of the background technology, and provides a charging remaining time estimation method and a charging remaining time estimation system based on self-identification and segmentation prediction, which can improve the accuracy and the stability of charging remaining time estimation, better reflect the actual charging process and improve the user experience and the system reliability. The invention adopts the technical scheme that the charging remaining time estimation method based on self-identification and segmentation prediction comprises the following steps: collecting battery parameters in real time; Determining a current charging mode based on the battery parameters; Identifying whether to start thermal management according to the battery parameters; And settling the real-time charging residual time in stages based on the battery parameters, the charging mode and the recognition result of thermal management. A charging remaining time estimation system based on self-identification and segmentation prediction comprises The data acquisition module is used for acquiring battery parameters in real time; the mode judging module is used for determining a current charging mode based on the battery parameters; a thermal management module for identifying whether thermal management is to be initiated based on the battery parameters; And the time estimation module is used for settling the real-time charging residual time in stages based on the battery parameters, the charging mode and the recognition result of thermal management. The beneficial effects of the invention are as follows: According to the invention, through collecting the battery parameters in real time, determining the charging mode, identifying the thermal management state and settling the charging remaining time in stages, the accuracy and stability of charging remaining time estimation can be improved, and particularly, when the charging mode is switched and the thermal management strategy is adjusted, the actual charging process can be better reflected, and the user experience and the system reliability are improved. Drawings Fig. 1 is a flowchart of a charging remaining time estimation method according to the present invention. Detailed Description The invention will now be described in further detail with reference to the drawings and specific examples, which are given for clarity of understanding and are not to be construed as limiting the invention. It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Furthermore, references to "one embodiment" or "some embodiments" or the like described in this specification mean that a particular feature, s