Research On Fast Evaluation Method Of Health State Of Retired Batteries Based On Feature Extraction

Aiming at the sustainable development goal of "peak carbon dioxide emission and carbon neutrality" proposed by China,the second-use application of retired batteries form electric vehicles(EVs)as an important technical support to promote the vigorous development of the new energy vehicle industry has attracted more and more attention from scholars around the world.Although researchers have conducted relevant studies in terms of technical feasibility and efficiency,the second-use application is still under the stage of theoretical research.The main bottleneck problems that restrict the industrialization development of second-use application are the time-consuming test and the poor performance consistency of the re-grouped retired batteries.The poor performance consistency is caused by the evaluation basis of the single health state,which makes the maximum discharge capacity is hardly achieved.These issues expand the cost of second-use application in the later period.Aiming at the above problems,this study provides the evaluation basis of the health state under the condition of decreasing the test time of battery performance,including the residual capacity,fault state,and inner health state of battery,such as the changes in the performance of battery active material and electrolyte.This study lays a theoretical foundation for the further research and practical application of the second-use application of retired batteries from EVs.First,due to the complicated aging paths of the retired batteries' performance and the current residual capacity test is generally carried out in the stable working condition,the fast and accurate estimation for residual capacity is very hard.Thus,the fast estimation method of residual capacity of retired batteries is detailed studied in the paper.The features that represent the active lithium loss and material loss are established on the basis of the mechanism analysis in the capacity loss of retired batteries.Combining the identified features with the SVR model,and at the same time,based on the observability analysis results of the capacity loss features under constant current charging,the fast estimation method of the residual capacity of retired batteries from EVs is proposed based on the capacity loss mechanism.Next,considering the second-use application stage,the performance of the retired battery pack can be degraded if the retired battery with early accelerated degradation fault is not diagnosed in advance,even causing safety hazards such as the thermal runaway.This study focuses on the early accelerated degradation fault diagnosis technology of retired batteries from EVs,and the incremental capacity analysis technology of lithium battery is utilized to analyze the mechanism of the accelerated degradation fault.The unique correlation between the abnormal voltage increase and the accelerated degradation fault mechanism is established.Also,the fault feature is founded to diagnose the accelerated degradation fault,and the early accelerated degradation fault diagnosis strategy is established based on the abnormal performance of the voltage increase.Besides,considering that the internal health states of retired batteries with similar residual capacity may be different,which is caused by the differences in aging paths.This study focuses on the diagnosis method of internal health states(including negative active material and electrolyte)of retired batteries,based on the simplified P2 D model.Since the high-order,nonlinear and parameter coupling of P2 D model,the effective online identification of model parameters is challenging.Based on the analysis of the liquid-phase diffusion process in the P2 D model and the conservation theory of lithium-ion concentration in the electrolyte,the mathematical expressions that reflect the dynamic characteristics of electrolyte concentration are studied.The Páde approximation principle is utilized to simplify the liquid-phase diffusion process and solid-phase diffusion process of P2 D model.The related mechanism behaviors of the targeted parameters are mathematically decoupled and simplified by using the mathematical operation,and then the diagnosis method of the changes in battery negative electrode material and electrolyte characteristics is proposed under the constant-current charging condition.Last,the experimental platform is built to test the performance of a large sample of retired batteries from EVs.The operating data of different aging stages is measured by simulating the working status of the retired batteries under different typical operating conditions.The rationality and correctness of the various feature identification results and diagnostic methods are verified,which provides the data information and practical experience for further adjustment,optimization and practical engineering application of retired batteries from EVs in the later stage of application.