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CN-122025875-A - Secondary battery life optimization system and method based on dynamic frequency adaptation

CN122025875ACN 122025875 ACN122025875 ACN 122025875ACN-122025875-A

Abstract

The invention discloses a secondary battery life optimization system and method based on dynamic frequency adaptation, and belongs to the technical field of battery management. The system comprises a state monitoring unit (200), an electrochemical impedance spectrum analysis module (300), a dynamic frequency adaptation controller (400), a programmable pulse generation unit (500) and a bidirectional power conversion unit (600), wherein the electrochemical impedance spectrum analysis module is used for extracting multidimensional electrochemical characteristic parameters such as interface reaction impedance, solid-phase diffusion impedance, ohmic internal resistance and the like by adopting a relaxation time distribution method, the dynamic frequency adaptation controller is used for continuously adjusting optimal intervention frequency according to dynamic change trend of the parameters, the programmable pulse generation unit (500) and the bidirectional power conversion unit (600) are used for generating pulse waveforms and superposing the pulse waveforms on a battery working process, and the effect verification module (700) comprises a capacity attenuation tracking unit (710) and a self-learning optimization unit (720) and self-optimizes a decision model based on historical intervention data by a reinforcement learning algorithm. The invention realizes the technological span from diagnosis to treatment for the first time, forms a complete closed loop of monitoring, analysis, intervention and optimization, improves the cycle life by more than 179.7 percent, and can be widely applied to the fields of electric automobiles, energy storage power stations, consumer electronics and the like.

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Dates

Publication Date
20260512
Application Date
20260319

Claims (10)

  1. 1. A secondary battery life optimization system based on dynamic frequency adaptation, comprising: the state monitoring unit (200) is used for collecting voltage, current and temperature data of the battery in real time; the electrochemical impedance spectrum analysis module (300) is electrically connected with the state monitoring unit (200) and is used for calculating the alternating current impedance characteristic of the battery on line based on the acquired data and extracting electrochemical characteristic parameters of at least three dimensions by adopting a relaxation time distribution method, wherein the electrochemical characteristic parameters comprise interface reaction impedance, solid-phase diffusion impedance and ohmic internal resistance; The dynamic frequency adaptation controller (400) is electrically connected with the electrochemical impedance spectrum analysis module (300) and the state monitoring unit (200) and is used for continuously calculating and adjusting optimal intervention frequency and waveform parameters according to the dynamic change trend of the electrochemical characteristic parameters and with the aim of actively delaying the aging of electrochemical reaction in the battery; A programmable pulse generating unit (500) electrically connected with the dynamic frequency adaptation controller (400) and used for generating a corresponding pulse current or voltage waveform according to the optimal intervention frequency and waveform parameters; the bidirectional power conversion unit (600) is electrically connected with the programmable pulse generation unit (500) and the battery module (100) and is used for superposing the pulse waveform on the normal working process of the battery; the effect verification module (700) is electrically connected with the state monitoring unit (200) and the dynamic frequency adaptation controller (400), comprises a capacity fading tracking unit (710) and a self-learning optimization unit (720), and is used for establishing a long-term aging database and automatically updating a frequency decision model through a reinforcement learning algorithm based on historical intervention effects.
  2. 2. The system of claim 1, wherein the dynamic frequency adaptation controller (400) comprises a frequency decision module (410), the frequency decision module (410) having built-in a "frequency-battery state" optimization model whose input parameters include interface reaction impedance, solid phase diffusion impedance, ohmic internal resistance, SOC, temperature, and SOH.
  3. 3. The system of claim 2, wherein the dynamic frequency adaptation controller (400) further comprises a self-learning module (420), the self-learning module (420) being electrically connected to the effect verification module (700) for iteratively optimizing a frequency decision strategy through historical intervention data using a reinforcement learning algorithm with a capacity fade rate minimized as a reward function.
  4. 4. The system of claim 1, wherein the electrochemical impedance spectroscopy module (300) is further configured to decouple intermediate frequency region EIS data using a relaxation time distribution method to separate characteristic peaks corresponding to SEI film resistance and charge transfer resistance.
  5. 5. The system of claim 1, wherein the pulse waveform amplitude generated by the programmable pulse generating unit (500) is controlled to be in the range of 0.01c to 0.1 c.
  6. 6. The service life optimization method of the secondary battery based on dynamic frequency adaptation is characterized by comprising the following steps of: Step S1, collecting voltage, current and temperature data of a battery in real time; S2, on-line calculating alternating current impedance characteristics of the battery based on acquired data, and extracting electrochemical characteristic parameters of at least three dimensions by adopting a relaxation time distribution method; step S3, continuously calculating and adjusting optimal intervention frequency and waveform parameters with the aim of actively delaying the aging of the electrochemical reaction in the battery according to the dynamic change trend of the electrochemical characteristic parameters; S4, generating a pulse waveform according to the optimal intervention frequency and waveform parameters; S5, superposing the pulse waveform on the normal working process of the battery; and S6, establishing a long-term aging database, and automatically updating a frequency decision model through a reinforcement learning algorithm based on the historical intervention effect.
  7. 7. The method according to claim 6, wherein the calculating of the optimal intervention frequency in step S3 comprises in particular the following decision rules: when the interface reaction impedance is detected to be increased, selecting a pulse with the intermediate frequency of 10 Hz-100 Hz; when the increase of the solid phase diffusion impedance is detected, selecting low-frequency 0.01 Hz-1 Hz pulses; when the temperature is lower than a preset threshold value, a high-frequency band 100 Hz-1 kHz pulse is selected for preheating; when abnormal impedance change of the characteristic frequency point is detected, a safety early warning mode is triggered.
  8. 8. The method according to claim 6, wherein the online calculation of the ac impedance characteristic of the battery in step S2 specifically comprises: and applying a multi-frequency composite small-amplitude current signal to the battery, collecting a voltage response signal, calculating the real part and the imaginary part of impedance of the battery at different frequencies through fast Fourier transformation, and generating a real-time electrochemical impedance spectrum.
  9. 9. A battery management system comprising the dynamic frequency adaptation-based secondary battery life optimization system of any one of claims 1-5.
  10. 10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method of any of claims 6-8.

Description

Secondary battery life optimization system and method based on dynamic frequency adaptation Technical Field The invention belongs to the technical field of battery management, and particularly relates to a system and a method for prolonging the cycle life of a battery by electrochemical impedance spectroscopy on-line analysis and dynamic frequency adaptation technology, which are suitable for various secondary batteries such as a lithium ion battery, a lead-acid battery, a sodium ion battery and the like. Background State of the art The lithium ion battery is widely applied to the fields of new energy automobiles, energy storage power stations and consumer electronics. However, the battery inevitably ages during use, and is mainly characterized by capacity fade, increased internal resistance and reduced power capacity. The core mechanisms responsible for battery aging include continuous thickening of the negative electrode surface solid electrolyte interface film (SEI film), lithium precipitation (lithium dendrite growth), active material structural degradation, and the like. Currently, battery Management Systems (BMS) mainly take charge of state monitoring (voltage, current, temperature), state of charge (SOC) estimation, equalization management and protection functions. The traditional BMS has limited intervention means on the service life of the battery and mainly depends on passive equalization, charge-discharge cut-off voltage limitation and thermal management. Deficiency of the prior art In recent years, academia and industry have begun focusing on the application of "dynamic adjustment" strategies in battery management. For example, patent CN121643153a proposes a distributed battery management system optimization method that evaluates the battery aging phase by taking the core parameters and dynamically adjusts the equalization threshold and equalization rate according to the aging phase. The technology embodies the thought of 'dynamic adjustment according to the battery state', but the core is balanced management, namely the consistency is improved by adjusting the balanced strategy among the battery cells, and the active intervention on the electrochemical process inside the battery is not involved. Limitations of diagnostic and repair techniques Recently disclosed patent CN121299509a (new sea-state energy) proposes a battery health monitoring method, which obtains electrochemical impedance spectrum by suspending charging and injecting excitation signals with specific frequency into the battery during charging, and estimates the health state of the battery by fusing charging data and EIS data. Although the technology realizes EIS on-line measurement, the application of the technology is limited to health state estimation (diagnosis level), and active intervention and closed loop optimization on battery aging are not involved. Meanwhile, U.S. patent application US2025/0330033A1 (REON Technology) discloses a technique for in-situ repair by pulse trains, which applies repair current to aged cells according to parameters such as frequency, duty cycle, etc. The technology focuses on passive repair of aged batteries, and lacks a dynamic adaptation mechanism based on real-time state feedback and active maintenance capability of full life cycle. The technology represents two technical routes of diagnosis and repair respectively, and a closed loop active maintenance system based on real-time electrochemical state feedback is not formed. In the aspect of academic research, the 'Life extension of lithium-ion batteries using bidirectional pulse current' research published in journal of Energy in 10 of 2025 shows that by adopting a bidirectional pulse current strategy, the equivalent cycle life of a battery can be improved by 179.7%, and the calendar life can be improved by 19.1%. This study validated the significant impact of frequency intervention on battery life, but again did not provide a closed loop dynamic adaptation system based on real-time state feedback. Technical problem to be solved by the invention The invention aims to solve the technical problem that the prior art cannot actively optimize the internal electrochemical process according to the real-time state of a battery, provides a dynamic frequency adaptation system based on multi-dimensional electrochemical parameter real-time feedback, and forms a complete closed loop of monitoring, analyzing, intervening and optimizing. Disclosure of Invention 1. Summary of the technical solution The invention provides a secondary battery life optimization system and method based on dynamic frequency adaptation, which are characterized in that real-time impedance characteristics of a battery are obtained through online EIS monitoring, electrochemical characteristic parameters of at least three dimensions are extracted by adopting a relaxation time distribution method, intervention frequency is continuously adjusted based on dynamic change trend of the intrin