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CN-122017607-A - Capacity measurement compensation method under influence of output ripple in formation and composition process

CN122017607ACN 122017607 ACN122017607 ACN 122017607ACN-122017607-A

Abstract

The invention discloses a capacity measurement compensation method under the influence of output ripple in a formation composition process, which comprises the steps of obtaining an output current sequence of a target channel in a capacity statistics window, identifying a ripple period boundary corresponding to the output current sequence, dividing the capacity statistics window into a front-end incomplete period, a middle complete period section and a rear-end incomplete period according to the ripple period boundary, integrating the output current sequence in the middle complete period section to obtain a main capacity value, constructing a front-end partial ripple template and a rear-end partial ripple template, integrating the front-end partial ripple template corresponding to the front-end incomplete period to obtain a front-end compensation quantity, integrating the rear-end partial ripple template corresponding to the rear-end incomplete period to obtain a rear-end compensation quantity, and adding the main capacity value, the front-end compensation quantity and the rear-end compensation quantity to obtain a final compensation capacity value. The invention improves the capacity measurement precision under the condition of output ripple in the process of forming the components.

Inventors

  • CHEN JINGJUN
  • LIU HAIBIN

Assignees

  • 深圳市智佳能自动化有限公司

Dates

Publication Date
20260512
Application Date
20260413

Claims (10)

  1. 1. The capacity measurement compensation method under the influence of output ripple waves in the process of forming the capacity, is characterized by comprising the following steps: acquiring an output current sequence of a target channel in a capacity statistical window; Identifying a ripple cycle boundary corresponding to the output current sequence, and dividing the capacity statistical window into a front-end incomplete cycle, a middle complete cycle section and a rear-end incomplete cycle according to the ripple cycle boundary; Integrating the output current sequence in the middle complete period section to obtain a main body capacity value; Respectively constructing a front-end partial ripple template and a rear-end partial ripple template according to a complete ripple period set adjacent to the front-end incomplete period and a complete ripple period set adjacent to the rear-end incomplete period, integrating the front-end partial ripple template corresponding to the front-end incomplete period to obtain a front-end compensation amount, and integrating the rear-end partial ripple template corresponding to the rear-end incomplete period to obtain a rear-end compensation amount; and adding the main body capacity value, the front-end compensation quantity and the back-end compensation quantity to obtain a final compensation capacity value.
  2. 2. The capacity measurement compensation method of claim 1 wherein the output current sequence comprises current sample values collected at a uniform sampling period and sampling time stamps corresponding one-to-one to each of the current sample values, wherein the capacity statistics window is defined by a capacity statistics window start time and a capacity statistics window end time, and wherein integrating the output current sequence within the middle full period section comprises performing an integration calculation on each of the current sample values within the middle full period section at the sampling time stamps.
  3. 3. The capacity measurement compensation method according to claim 1 or 2, wherein the identifying the ripple period boundary to which the output current sequence corresponds includes: inputting the output current sequence into a ripple cycle identification model; outputting boundary labels corresponding to the sampling time stamps by the ripple cycle identification model; and determining the ripple period boundary, the front end incomplete period, the middle complete period section and the rear end incomplete period according to the boundary label.
  4. 4. The capacity measurement compensation method of claim 3 wherein the ripple period recognition model is a time-series split neural network comprising an input layer, a time-series convolution layer, a bidirectional circulation layer, and a sequence labeling layer; The training process of the ripple cycle identification model comprises the steps of obtaining an output current sequence with ripple cycle boundary marks as a training sample set, inputting the output current sequence in the training sample set into the time sequence segmentation neural network, using the corresponding ripple cycle boundary marks as supervision labels, and performing parameter training on the time sequence segmentation neural network according to the supervision labels to obtain the ripple cycle identification model.
  5. 5. The capacity measurement compensation method of claim 1 further comprising, prior to constructing the front-end local ripple template and the back-end local ripple template: acquiring an output voltage sequence and a work step instruction sequence corresponding to the target channel; Inputting the output current sequence, the output voltage sequence and the step command sequence into a stable segment identification model; Outputting an unstable zone by the stable zone identification model; And eliminating the complete ripple period positioned in the unstable interval from the complete ripple period set adjacent to the front-end incomplete period and the complete ripple period set adjacent to the rear-end incomplete period.
  6. 6. The capacity measurement compensation method of claim 5 wherein the stable segment recognition model is a time-series classification neural network comprising a feature encoding layer, a time-series fusion layer, and an interval output layer; The training process of the stable segment identification model comprises the steps of obtaining an output current sequence, an output voltage sequence and a process step command sequence with stable segment labels and unstable segment labels as training sample sets, inputting the output current sequence, the output voltage sequence and the process step command sequence in the training sample sets into the time sequence classification neural network, using the corresponding stable segment labels and unstable segment labels as supervision labels, and performing parameter training on the time sequence classification neural network according to the supervision labels to obtain the stable segment identification model.
  7. 7. The capacity measurement compensation method of claim 1 or 5 or 6 wherein the step of constructing the front-end partial ripple template and the back-end partial ripple template comprises: Selecting the complete ripple periods of the quantity indicated by the template quantity configuration values in the template configuration table according to the sequence from small to large of the time distance from the starting moment of the capacity statistical window to form a front-end template period set; performing phase alignment on each complete ripple cycle in the front-end template cycle set, and performing average processing on current values at the same phase position to obtain the front-end local ripple template; selecting the complete ripple periods of which the number is indicated by the template number configuration values in the template configuration table according to the sequence from small to large of the time distance from the termination time of the capacity statistical window to form a back-end template period set; And performing phase alignment on each complete ripple cycle in the back-end template cycle set, and performing average processing on current values at the same phase position to obtain the back-end local ripple template.
  8. 8. The capacity measurement compensation method of claim 7 wherein when the number of complete ripple periods is less than a template number configuration value in the template configuration table, further comprising: Acquiring an output current sequence of a reference channel set, wherein the reference channel set belongs to the same power supply unit channel group as the target channel, and the process step identifier is consistent with the process step identifier corresponding to the capacity statistics window; extracting synchronous ripple segments from the output current sequence of the reference channel set; Constructing a public ripple auxiliary template according to the synchronous ripple plate segment; And utilizing the common ripple auxiliary template to participate in determining the front-end compensation amount and the back-end compensation amount.
  9. 9. The capacity measurement compensation method of claim 8 wherein the same power supply unit channel group is a channel set composed of the target channel and the reference channel set supplied with power by the same power supply unit; the constructing a common ripple auxiliary template according to the synchronous ripple plate segment comprises the following steps: performing time alignment on each of the synchronized waveplate segments according to the sampling time stamps; performing normalization processing on each synchronous ripple plate segment according to the average current of the complete ripple period of each synchronous ripple plate segment; And performing aggregation processing on each normalized synchronous ripple plate segment according to the channel weight in the auxiliary template configuration table to obtain the common ripple auxiliary template.
  10. 10. The capacity measurement compensation method of claim 8 or 9 wherein the utilizing the common ripple assist template to participate in determining the front-end compensation amount and the back-end compensation amount comprises: Inquiring a channel mapping template library to obtain a mapping record corresponding to a channel identifier of the target channel and a step identifier corresponding to the capacity statistics window; performing amplitude mapping on the public ripple auxiliary template according to the mapping record to obtain a target auxiliary template; When the number of complete ripple periods used for constructing the front-end local ripple template is smaller than the template number configuration value in the template configuration table, integrating the target auxiliary template corresponding to the front-end incomplete period to obtain a front-end compensation quantity; And when the number of complete ripple periods used for constructing the back-end partial ripple template is smaller than the template number configuration value in the template configuration table, integrating the target auxiliary template corresponding to the back-end incomplete period to obtain a back-end compensation quantity.

Description

Capacity measurement compensation method under influence of output ripple in formation and composition process Technical Field The invention relates to the field of battery formation component capacity test, in particular to a capacity measurement compensation method under the influence of output ripple waves in a formation component process. Background After the lithium ion battery cell is manufactured, the lithium ion battery cell is generally required to undergo formation and capacity separation procedures. The formation is used for enabling the battery cells to gradually form a stable working state in a controlled charging and discharging process, and the capacity division is used for testing and screening the capacity and consistency of the battery cells. In the chemical composition device, the capacity measurement is usually based on the integration of the output current with time, so that the sampling accuracy, the sampling timing and the determination mode of the integration interval of the output current directly affect the capacity measurement result. In the prior art, chinese patent with publication number CN102645636B, entitled "a battery capacity detection method", discloses a scheme for performing capacity detection based on parameters such as open circuit voltage, state of charge curve, and internal dc resistance, and the like, and is focused on shortening capacity test time and reducing test energy consumption by using a battery parameter model. The scheme mainly focuses on capacity detection mechanism and detection efficiency. In the prior art, chinese patent publication No. CN107450024A, entitled "ripple absorption device and method of battery charge and discharge tester" discloses a ripple absorption scheme for DC side of charge and discharge testing equipment, which reduces DC ripple content at output end of testing equipment by setting ripple absorption circuit composed of passive devices so as to reduce influence of ripple on accuracy of testing parameters. This type of scheme mainly suppresses output ripple from the hardware side. However, in the actual test of the formation of the capacitance, the capacitance measurement is not simply to integrate the current in the whole measurement interval once. If there are ripples at the output end, if the middle part of the capacity statistics interval contains more complete ripple periods, the current fluctuation inside the complete ripple periods usually spreads around a certain average output level, and from the view point of capacity integration, the influence of the high-low fluctuation in the middle section on the result is relatively dispersed in a plurality of complete periods, so that the middle complete ripple period is not usually a main source of capacity measurement deviation. In contrast, the start and end times of the capacity statistics interval are typically triggered by a step control event and are not specifically aligned with the ripple period boundaries, which makes it easier for the head end and tail end to form boundary segments that intercept only a portion of the ripple waveform. On the one hand, the boundary section only contains incomplete waveforms, and on the other hand, the factors of asynchronous sampling phases, limited number of sampling points, transient switching of the process steps and the like are easily overlapped, so that the integral result of the boundary section is easier to fluctuate, and the amplification effect on the final capacity measurement result is further generated. In other words, in the prior art, one type of scheme mainly researches how to complete capacity detection according to battery parameters, and another type of scheme mainly researches how to reduce output ripple from a hardware side, but in the process of forming capacity, the influence of a middle complete ripple period is relatively minor, and the end-to-end boundary section is more likely to be a main error source of capacity measurement due to ripple interception, sampling asynchronization and step switching, and a special processing scheme is still lacking. Therefore, how to perform more targeted capacity measurement processing for the head-tail boundary section of the capacity statistics section becomes a technical problem to be solved. Disclosure of Invention The invention aims to provide a capacity measurement compensation method under the influence of output ripple waves in a composition process, so as to solve the problems in the background art. In order to achieve the above purpose, the invention adopts the following technical scheme: A capacity measurement compensation method under the influence of output ripple waves in a composition process comprises the following steps: acquiring an output current sequence of a target channel in a capacity statistical window; Identifying a ripple cycle boundary corresponding to the output current sequence, and dividing the capacity statistical window into a front-end incomplete cycl