Research On The Typical Fault Mechanism And Diagnostic Approach For Power Lithium Iron Phosphate Battery

Power lithium-ion battery has been widely used in applications such as electric vehicle,energy storage,power tool with the rapid development of its industry in recent years.Simultaneously,its safety performance has gradually attracted widespread attention.As an energy-storage device with high power and energy density,once the batteries fail or cause thermal runaway during operation,it will bring huge economic losses and social consequences.Therefore,it is essential to study the fault features of power lithium-ion batteries and develop a fast and reliable diagnostic approach,which will guarantee the safety of the system.Traditional diagnostic systems for lithium-ion batteries usually make diagnostic decisions based on sensor signal or internal resistance.They failed to bridge the connection between external sensor signals and internal eletrochemical mechanisms,which made it difficult to detect the fault features at the early stage or identify the internal fault mechanisms.For these problems,this paper focused on the diagnostic research of power lithium iron phosphate battery.Performance evaluation was carried out to obtain the degradation analysis and parameter extraction within the complete life span of the tested batteries,with which fault feature database and diagnostic approach were established.Finally,a comprehensive layered diagnosis strategy with the solutions of early warning,fault diagnosis and mechanism identification for power lithium-ion battery was proposed.Specific research contents are shown as follows:Traditional diagnostic approaches for lithium-ion battery failed to adequately analyze the internal fault mechanism with the help of external signals.To solve this problem,failure modes,mechanisms and effects analysis was performed for power lithium iron phosphate batteries.Different fault modes were classified as fast-abrupt and slow-variation fault,whose symptoms,mechanisms and causes were discussed respectively.The causal relationship between mechanisms and symptoms of lithium iron phosphate battery was established and would provide theoretical basis for the design of abusive experiment in the following sections.A typical fault mode during accelerating ageing tests: short-term exhaustion was studied.During specific accelerating ageing tests,the performance of tested battery degraded rapidly,and the battery would be exhausted in short term even if the abusive experiment was suspended.Relative degradation rate of resistance with respect to capacity was defined to evaluate the degradation.Short-term exhaustion fault did not present sufficient symptoms at the early stage,but had extremely rapid degradation rate during the subsequent stage,so differential capacity analysis was applied to identify the internal fault mechanism.Then the diagnostic and early-warning approach was established based on IC curve characteristics.Finally,a pre-diagnostic approach based on online OCV estimation was provided with the relationship between IC curve and SOC-OCV curve,which makes online diagnosis available.The research on another typical fault mode during operation: micro-short was also proposed.Two different experiments which might result in micro-short were designed.According to the results and features of micro-short fault,a diagnostic approach based on module redundancy was proposed,which obtained the voltage residuals in serial module and temperature residual in parallel module to evaluate and locate the individual cell suffering micro-short in the module.The differences of the fault eigenvectors from the two different experiments helped to describe the internal electrochemical processes and establish the fault classification approach based on mechanism.Finally,a multi-layered fault diagnostic strategy for lithium iron phosphate battery based on causal relationship was proposed to solve the comprehensive problems in traditional fault diagnostic approach.It accomplished the degradation evaluation,early symptom extraction and mechanism identification by synthesizing the diagnostic approaches of typical fault modes.We established the connection between fault eigenvectors and internal mechanisms and made fault desicions with the help of fuzzy logic deduction.The diagnostic results were validated by post-mortem examination and electrochemical tests to batches of batteries with different capacities,ageing degrees and applications.Fault diagnostic approach proposed in this paper achieves the feature extraction,fault decision-making and mechanism identification for typical faults of lithium iron phosphate battery,which provides a key idea for fault research and online diagnosis of power batteries.The proposed approach can be extended to other types of lithium-ion batteries,which has strong practical significance.