The cycling performance fade of LFP-based Li-ion cylindrical batteries is evaluated under maximum cycling voltage amplitude. Diagnostic evaluation of the ageing mechanisms included in-situ electrochemical measurements and ex-situ destructive physico-chemical and electrochemical analyses of cell components. SEM, EDS, XRD and electrochemical measurements of harvested electrodes confirmed that the primary cell performance degradation modes are loss of active lithium inventory (LLI) and loss of active material (LAM) related to graphite electrode. Ageing phenomena were associated with the progressive decomposition of the electrolyte. Cell capacity loss was concluded to be dominated by SEI layer growth, which also led to a sharp power loss together with localised lithium plating on the negative electrode surface upon prolonged cycling. Graphite surface was polymerised and inactivated in localised central parts of the jelly-roll, leading to large cavities as a result of metallic lithium and electrolyte reactions. No degradation of the structure or performance of the LFP positive electrode was detected. In this paper, ageing processes are examined in the overall context of cell performance fade during accelerated cycling operation.

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h i g h l i g h t sMethodology for cycle ageing semi-empirical model dynamic validation. Ageing tests acceleration evaluation. Detailed analysis of the influence of DOD, C-rate and Ah-throughput on cell ageing. Keywords: Li-ion battery ageing LiFePO 4 (LFP)/graphite Cycle-life performance model Dynamic storage profiles Lithium inventory loss (LLI) Loss of active material (LAM) a b s t r a c tThe present investigation work compares cycling ageing behaviour upon constant operation conditions (static tests) with degradation under non-constant schemes (dynamic tests), through a novel methodology for ageing model dynamic validation. A LFP-based Li-ion cell performance loss is analysed using a DOD and C-rate stress factors Ah-throughput-domain characterisation method, and represented using a semi-empirical model. The proposed stepwise validation approach enables assessing the static ageing tests acceleration factor and systematically improving the ageing model. The result is a simple but comprehensive model. The predictions root-mean-square error, under dynamic cycling operation conditions, is just 1.75%.

Battery lifetime prognosis is a key requirement for successful market introduction of rechargeable EnergyStorage Systems (ESS) based on lithium-ion (Li-ion) technology. In order to make decisions at the system design stage, a procedure for making efficient predictions of battery performance over time is necessary to be developed.In this paper, a general methodology for the evaluation of lifetime prediction is presented, covering the semi-empirical aging model precision and validity. Both calendar-life and cycle-life performance were investigated. Moreover, standing time and working operation were examined jointly using realistic operating profiles. The aim was the predictive model to be suitable for any application, including electric vehicle (EV), within the considered operating range. The efforts were especially focused on model ratification procedures and predictions goodness evaluation. The validation processes not only dealt with static impact factors evaluation but also with dynamic operation schemes. Besides, integration of ageing monitoring algorithm into Battery Management System (BMS) was evaluated. Battery pack design and operation strategies definition criteria were also discussed based on the stress factors influence on cell performance. The presented results correspond to a lithium iron phosphate (LFP) cathode 26650-size Li-ion cell.

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