CN-122017641-A - Power battery state monitoring system and method based on identification theory and topology measurement

CN122017641ACN 122017641 ACN122017641 ACN 122017641ACN-122017641-A

Abstract

The present disclosure provides a power battery state monitoring system and method based on an identification theory and topology measurement, and relates to the technical field of lithium ion battery state monitoring, comprising the steps of obtaining original characteristic data in a battery charging process, and preprocessing the original characteristic data; the method comprises the steps of regarding constant-current charging response of a battery as step response of a dynamic system, adopting a low-order process model as an equivalent dynamic feature extractor, taking preprocessed original feature data as input fitting voltage response tracks of the low-order process model, outputting to obtain identified model parameters, constructing a dynamic transfer function, substituting the identified model parameters into the dynamic transfer function to obtain a current dynamic transfer function model, calculating topology gap measurement of the current model and a reference model on a frequency domain, constructing a regression mapping model, inputting the calculated topology gap measurement into the regression mapping model, and mapping and outputting to obtain an SOH estimated value. The present disclosure enables SOH estimation with high robustness and high accuracy.

Inventors

DUAN BIN
PENG LU
ZHANG YING
Du Wanyin
KANG YONGZHE
Gu Pingwei

Assignees

山东大学

Dates

Publication Date: 20260512
Application Date: 20260123

Claims (10)

1. The power battery state monitoring method based on the identification theory and the topology measurement is characterized by comprising the following steps of: acquiring original characteristic data in the battery charging process, and preprocessing the original characteristic data; Regarding the constant-current charging response of the battery as a step response of a dynamic system, adopting a low-order process model as an equivalent dynamic feature extractor, taking the preprocessed original feature data as an input fitting voltage response track of the low-order process model, and outputting to obtain identified model parameters; constructing a dynamic transfer function, substituting the identified model parameters into the dynamic transfer function to obtain a current dynamic transfer function model, and calculating topology gap measurement of the current model and the reference model on a frequency domain; and constructing a regression mapping model, inputting the calculated topological gap measurement into the regression mapping model, and mapping and outputting to obtain an SOH estimated value.
2. The method for monitoring the state of a power battery based on recognition theory and topology measurement according to claim 1, wherein the acquiring the raw characteristic data in the battery charging process comprises: Collecting voltage and current data in the battery charging process; judging whether the current charging process is a constant-current charging segment or not, wherein the current I is more than 0.05C, the current fluctuation variance is less than or equal to 0.01C2, and C is the rated capacity of the battery; when the constant current charging segment is detected and the voltage is in a preset electrochemical sensitive interval, the segment is intercepted as original characteristic data.
3. The method for monitoring the state of a power battery based on recognition theory and topology metrics of claim 1, wherein preprocessing raw feature data comprises: Resampling and depolarizing the original characteristic data, wherein the depolarizing is to subtract the value of the starting moment from the intercepted voltage sequence and current sequence to change from zero; the resampling operation comprises setting a global uniform sampling interval, calculating the total duration of the fragments, generating a standard uniform time axis, and mapping the original non-uniform or non-fixed-length voltage and current values onto the uniform time axis by using a linear interpolation method to obtain a standardized input sequence and an output sequence with fixed lengths.
4. The method for monitoring the state of the power battery based on the identification theory and the topology measurement according to claim 1, wherein the constant current charging process is regarded as a step excitation applied to the battery system, a first-order process model is adopted as an equivalent dynamics feature extractor, the preprocessed original feature data is used as the input of a low-order process model to fit the voltage response track in the segment, the identified optimal model parameters are output, and the optimal model parameters are used as a group of generalized waveform structure parameters to represent the topology shape of the voltage response track in the current aging state.
5. The power battery state monitoring method based on the recognition theory and the topology metric according to claim 1, wherein the optimal model parameters at the current moment are recognized with the aim of minimizing the error between the predicted output and the actual output sequence of the model by utilizing a least square method or an iterative optimization algorithm, and the optimal model parameters obtained through recognition are substituted into a dynamic transfer function structure to obtain a dynamic transfer function model of the current battery state.
6. The method for monitoring the state of a power battery based on recognition theory and topology measurement according to claim 1, wherein calculating the topology gap measurement of the current model and the reference model in the frequency domain comprises: the method comprises the steps of (1) identifying and obtaining a reference model in the 1 st cycle of the full life cycle of the battery; for any moment in the running process, calculating topology gap measurement between the dynamic transfer function model and the reference model; the definition of the topological gap metric is based on graph metric theory, and the mathematical essence is to measure the maximum distance of two linear dynamic systems under closed loop feedback.
7. The method for monitoring the state of the power battery based on the recognition theory and the topology metric according to claim 6, wherein the topology gap metric directly reflects the 'drift distance' of the current dynamic transfer function model of the battery relative to the new battery reference model in the frequency domain topology space, comprehensively reflects the overall difference of steady-state gain change and time constant change caused by aging, gradually and monotonically rises from 0 as the cycle number increases, and the curve is very smooth.
8. The power battery state monitoring method based on the identification theory and the topology measurement according to claim 1, wherein a mapping relation between the topology gap measurement and the real SOH is established based on offline experimental data, a significant nonlinear monotonic relation is displayed between the topology gap measurement and the SOH, and a quadratic polynomial regression model is adopted for fitting based on the mapping relation to obtain a regression mapping model.
9. The method for monitoring the state of a power battery based on an identification theory and a topology metric of claim 8, wherein the polynomial regression coefficients are obtained by a mean square error training that minimizes historical data.
10. Power battery state monitoring system based on discernment theory and topology measurement, characterized by comprising: the constant current segment identification and preprocessing module is used for acquiring original characteristic data in the battery charging process and preprocessing the original characteristic data; The characteristic calculation module is used for regarding the constant current charging response of the battery as the step response of the dynamic system, adopting a low-order process model as an equivalent dynamic characteristic extractor, taking the preprocessed original characteristic data as the input fitting voltage response track of the low-order process model, and outputting the obtained identified model parameters; the state estimation module is used for constructing a regression mapping model, inputting the calculated topological gap measurement into the regression mapping model, and mapping and outputting to obtain an SOH estimated value.

Description

Power battery state monitoring system and method based on identification theory and topology measurement Technical Field The disclosure relates to the technical field of lithium ion battery state monitoring, in particular to a power battery state monitoring system and method based on an identification theory and topology measurement. Background The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. Along with the rapid development of new energy automobiles and energy storage power stations, the lithium ion battery is used as a core energy storage component, and the accurate assessment of the health state of the lithium ion battery is important for guaranteeing the safety of a system, prolonging the service life and optimizing the charge and discharge strategy. SOH is generally defined as the ratio of the current maximum available capacity to the factory rated capacity. However, in practical engineering applications, the existing SOH estimation techniques still have the following limitations: (1) Data acquisition is difficult-traditional ampere-hour integration methods rely on complete charge-discharge cycles of the battery from 0% to 100% to calibrate capacity. However, in the actual use situation of the user (such as daily commute of the electric automobile), the battery is often in a random "fragmentation" use state, and it is difficult to obtain complete capacity data, so that SOH estimation cannot be calibrated in a closed loop, and accumulated errors are large. (2) The characteristic extraction has poor anti-interference capability, and the collected voltage sequence is directly subjected to differential or differential processing by the existing voltage characteristic-based method (such as a direct voltage difference method and an incremental capacity analysis method ICA). This approach amplifies current fluctuations and sensor sampling noise, resulting in the extracted characteristic curves (e.g., IC curves or internal resistance curves) being filled with burrs and oscillations that are difficult to use for high-accuracy state estimation. (3) The model parameter identification is unstable, namely an Equivalent Circuit Model (ECM) -based method tries to identify microscopic parameters such as ohmic internal resistance, polarization capacitance and the like on line. However, the constant current charging condition approximates a simple step excitation, which contains limited frequency domain information, so that the parameter identification process of the higher-order model is very easy to scatter (not converge), or the identified parameters jump severely between adjacent cycles, and the physical consistency is lacking. Disclosure of Invention In order to solve the problems, the present disclosure provides a power battery state monitoring system and method based on an identification theory and topology measurement, which do not rely on complete circulation, only utilize short segment data in a constant current charging process, filter noise through a low-order process model and perform feature extraction, and utilize dynamic characteristic offset of a topology gap measurement quantization system, thereby realizing SOH estimation with high robustness and high precision. According to some embodiments, the present disclosure employs the following technical solutions: the power battery state monitoring method based on the identification theory and the topology measurement comprises the following steps: acquiring original characteristic data in the battery charging process, and preprocessing the original characteristic data; Regarding the constant-current charging response of the battery as a step response of a dynamic system, adopting a low-order process model as an equivalent dynamic feature extractor, taking the preprocessed original feature data as an input fitting voltage response track of the low-order process model, and outputting to obtain identified model parameters; constructing a dynamic transfer function, substituting the identified model parameters into the dynamic transfer function to obtain a current dynamic transfer function model, and calculating topology gap measurement of the current model and the reference model on a frequency domain; and constructing a regression mapping model, inputting the calculated topological gap measurement into the regression mapping model, and mapping and outputting to obtain an SOH estimated value. According to some embodiments, the present disclosure employs the following technical solutions: the constant current segment identification and preprocessing module is used for acquiring original characteristic data in the battery charging process and preprocessing the original characteristic data; The characteristic calculation module is used for regarding the constant current charging response of the battery as the step response of the dynamic system, adopting a low-order