Key Technologies Research On The Fault Diagnosis And Echelon Use Of Decommissioning Lithium-ion Power Battery Of Electric Vehicles

Focusing on electric vehicle Lithium-ion power battery system,this paper firstly analyzed the causes and effects of monomer consistency among the group utilization of power batteries,illustrated the importance of research on the consistency of decommissioned batteries when re-grouping,and provided relevant consistency evaluation methods.Secondly,the various power battery models were analyzed and Thevenin equivalent model of battery was selected according to the application characteristics of power battery echelon utilization,and the corresponding parameters were obtained through Hybrid Pulse Power Characterization(HPPC)test.Based on the analysis of failure mode and effect of power batteries,the on-line and off-line diagnostic technology of echelon utilization of decommissioned batteries was proposed,and the hardware system of battery fault diagnosis was built for echelon utilization batteries.Based on the analysis of performance parameters attenuation of decommissioned power batteries,the evaluation methods of State of Charge(SOC),State of Health(SOH),State of Function(SOF)and State of Safety(SOS)of power batteries for echelon utilization were proposed and verified through experiments.Specific research contents include:First,the causes of consistency problem of power battery in electric vehicle was systematically analyzed.According to the influence degree on the overall performance of battery system,the consistency problem of lithium-ion power batteries in EV was classified and analyzed.The evolution law of the consistency of decommissioned batteries was studied to provide the basis for fault diagnosis and state assessment of decommissioned power batteries.Second,based on the Improved Unscented Kalman Filter(IUKF)and BP-EKF(Back Propagation-Extended Kalman Filter)algorithm,the SOC estimation strategy of decommissioned lithium-ion power battery was studied,and the estimation accuracy was verified.In order to improve the SOC estimation accuracy of EKF algorithm,this paper proposed a method to compensate and optimize the EKF algorithm through BP neural network algorithm.The BP neural network was trained at first,and then the training results were verified to obtain the relative error between the predicted value and the actual value of the neural network,and to verify the improvement of SOC estimation accuracy,convergence speed and robustness of the algorithm.Third,the effects of capacity method,open circuit voltage method and internal resistance method on evaluating the health status of retired lithium-ion power batteries were analyzed,and the minimum internal resistance health factor with better advantage in evaluating the aging status of power batteries for echelon utilization was also put forward.Based on the fact that the charging and discharging capacity of lithium-ion batteries is related to the reversible insertion of lithium ions into the anode and cathode,the ability to insert lithium ions gradually decreases with the aging of lithium-ion batteries.The change of lithium-ion inserting capacity reflects the consideration of the change of SOH.A SOH evaluation method combining charging and discharging health conditions was proposed in this paper.The method can not only reflect the health state of discharge,but also reflect the health condition of charging.The Experiment results show that the SOH evaluation method indirectly reflected the changes of internal resistance and lithium intercalation capacity of batteries caused by the increase of cycle times,and then comprehensively evaluated the aging degree of the battery.Fourth,Aiming at the problem that the uncertainties of parameters affecting SOF of decommissioned batteries for cascade utilization lead to uncertainties in the target application scenario,a method of SOF estimation based on fuzzy logic was proposed in this paper.Full Operational Capability(FOC),Internal Resistance(R)and Charge Retention Rate(CRate)were used as input variables to evaluate SOF of lithium-ion power batteries.The membership function of fuzzy logic algorithm was established,the fuzzy logic rules were set up,and the general fuzzy control diagram was drawn.The SOF estimation of the decommissioned power batteries was validated.The results show that the SOF estimation method can quickly determine the use situation of cascade utilization batteries.The proportion of non-matchable batteries was significantly reduced when the batteries were reconfigured,and the reuse efficiency of decommissioned batteries was improved.Fifth,Based on the analysis of failure modes and effects of power batteries,an on-line and off-line fault diagnosis technology was proposed to diagnose electric vehicles lithium-ion power batteries,to judge the decommissioning conditions of power batteries system,and to give the results of consistency analysis and state evaluation of decommissioned batteries,so as to improve the efficiency of separation and evaluation and value of cascade utilization of decommissioning power batteries.The on-line fault diagnosis system was designed based on open source electronic prototype platform and LabVIEW virtual instrument.The communication interface of controller LAN bus(CAN bus)for battery fault diagnosis was added to build the online fault diagnosis system based on the hardware structure of battery management system.On-line test of battery parameters was carried out.Through the analysis of CAN bus data,the on-line running faults of power battery were obtained.The data stream of battery unit was read by battery management system.Then it was judged intuitively and rapidly whether the battery was in normal working state.Through consistency analysis,it was verified whether the power battery was in conformity with the condition of retirement.At the same time,it provided the basis for the later formulation of cascade utilization plan and the selection of cascade utilization occasions.The off-line fault diagnosis system was developed based on the hardware such as industrial computer,power battery data acquisition unit and battery charge-discharge test unit,and virtual instrument software.It was independent of the original power battery management system.It was composed of a data acquisition unit with higher test precision and a data processing unit with stronger computing power by working independently.By judging the performance indicators of the batteries for cascade utilization and using the powerful data processing function of the computer,the complex off-line analysis of the parameters of batteries were carried out,and the state of health,state of function,state of safety and state of charge of batteries for cascade utilization were estimated,so as to judge the reuse occasions of battery for cascade utilization and to formulate the reconstitution schemes of batteries for cascade utilization.