Among lithium-ion battery diagnostic tests, electrochemical impedance spectroscopy, being highly informative on the physics of battery operation within limited testing times, deserves a prominent role in the identification of model parameters and the interpretation of battery state. Nevertheless, a reliable physical simulation and interpretation of battery impedance spectra is still to be addressed, due to its intrinsic complexity. An improved methodology for the calibration of a state-of-the-art physical model is hereby presented, focusing on high-energy batteries, which themselves require a careful focus on the high-frequency resistance of the impedance response. In this work, the common assumption of the infinite conductivity of the current collectors is questioned, presenting an improved methodology for simulating the pure resistance of the cell. This enables us to assign the proper contribution value to current collectors’ resistance and, in turn, not to underestimate electrolyte conductivity, thereby preserving the physical relation between electrolyte conductivity and diffusivity and avoiding physical inconsistencies between impedance spectra and charge–discharge curves. The methodology is applied to the calibration of the model on a commercial sample, demonstrating the reliability and physical consistency of the solution with a set of discharge curves, EIS, and a dynamic driving cycle under a wide range of operating conditions.

Despite its commercial level, lithium-ion battery ageing is still a hot topic in the research community. Indeed, the relation between ageing and the operating conditions and loads in a real-life operation is under investigation and the corresponding residual life prediction is still a challenging task. In this work, a combined approach with experiments and modeling is applied. A wide driving cycle campaign is carried out, involving 12 different profiles to test different combinations of temperature, C-rate, SoC and DoD, whose values were defined after a literature review about the most common values in a real application, and highlight their effect on battery ageing. Two calendar ageing campaigns at the same temperatures are also conducted to quantify the acceleration of the degradation rate due to cycling and analyse if the degradation path is different. For the interpretation of the results, the P2D model is employed. No explicit description of the main degradation mechanisms is applied. Indeed, the degradation is described through the variation of the physical parameters of the usual model implementation, like a reduction of lithium inventory or active electrode material or a variation of reaction rates and diffusion constants. A specific sequence of tests has been developed as check-up procedure. As detailed in ¹, it is a trade-off between testing time and the operating conditions that maximise the identifiability of the parameters of the model. Therefore, the parameter identification problem is performed periodically upon check-ups and parameters trend over time/cycles is derived. Since it is a physical model, every parameter has a physical meaning and its evolution can be associated to a certain range of degradation phenomena. In this way, the model can support the identification of the most likely phenomena that are ongoing in the battery and the value of the parameters constitutes a new concept of state of health, more comprehensive than the residual capacity value. Finally, the model interpretation is proved in two ways. First, a validation dataset is provided, to evaluate the capabilities of the calibrated model in reproducing the behaviour of an aged cell under different operating conditions with respect to the check-up procedure. Then, some cells undergo post-mortem analyses to verify the origin of degradation and measure the values of the physical parameters, like optical and chemical analyses, q-OCP discharges of harvested electrodes in coin cell setup for thermodynamic parameters or EIS for kinetic parameters. This approach, though not suitable on-board, provides a novel physics-based method for a reliable analysis of lithium-ion batteries degradation. ¹ G. Sordi, C. Rabissi, A. Casalegno, Reliable thermal-physical modeling of lithium-ion batteries: consistency between high-frequency impedance and ion transport. Under Review

Lithium ion-battery (LIB) technology, featuring upstanding energy and power density, satisfying lifetime, high round-trip efficiency and very fast dynamics in a reasonably economic package, rapidly became the undisputed ruler of portable power and it is now the main driver of the electrification of transportation sector. However, despite the fully commercial development, understanding and predicting degradation of such devices is still a great challenge for the scientific and technical community, especially when dealing with real-life-operation induced aging. The reusability of such devices in a circular economy perspective is a hot topic to the sector to improve sustainability, but requires understanding “how” batteries are faded rather than only “how much” they are, to enable a physically consistent and second-life-related estimation of residual lifetime. In the present activity, a detailed analysis of high-power LFP (lithium iron phosphate) cells samples operated on IVECO hybrid buses are performed to assess their possible reusability, in the frame of a joint research cooperation. A large batch of cells with different ages (from 9 years old to brand new) and different positions inside the modules are investigated. State-of-art electrochemical diagnostics are performed embedded in a multi-measurement optimized protocol (full discharge, pulse test, electrochemical impedance spectroscopy) developed after a sensitivity optimization for a model-based improved parameter identification [1], as visible in Figure 1. Residual performances are analyzed and compared with limited on-board data collected from vehicles BMS and analyzed by means of an appositely-improved physical modelling platform. Both capacity and power capabilities have sensibly decreased over the cells lifetime (see Figure 2 for the full discharges at 0.1C, 25°C) with a clear pattern attributable to cells position beneath the modules, prompting the importance of a homogeneous thermal management during operation. Power-based state of health of the samples is higher than a capacity-based SoH, foreseeing a possible reuse of the samples in a power-intensive second-life. Residual performance interestingly feature a high consistency with data recently published in [2], relative to similar system despite operated in a completely different geographic area; this strengthens the generalizability of the results. Aged cells diagnostics were interpreted with a previously developed Newman P2D physical model provided with heath exchange, to identify the degradation mechanisms through the EIS-based parameter identification procedure [1] involving both thermodynamic and kinetic aspects. Interpretation of thermodynamic aging modes has been conducted on all the tested cell samples, based on 0.1C thermodynamic analysis and the differential voltage. Incremental capacity curves of aged cells have been reproduced with a PSO-based (particle-swarm-optimization) identification of three ageing parameters: loss of lithium inventory (LLI), mainly corresponding to solid electrolyte interphase (SEI) growth, and loss of active materials at negative (LAMn) and positive (LAMp) electrode, indicating loss of actives sites for the electrochemical reactions. Despite the severely low residual capacity, the model reproduces satisfyingly the features of the aged cells. Interestingly, a linear trend could be identified as common beneath all the samples involved in the analysis, indicating common aging mechanism appearing with different magnitude. As visible in Figure 3, the trend points out a two-steps aging path: (I) a first pathway mainly leading to LLI close to 20%, followed by (II) a second pathway with both LLI and LAMn occurring at the same time. In the literature, similar observations have been already performed by some authors in previous works, with a lower extent of degradation [3,4]. They agree in this interpretation: the first stage is usually associated to the growth of the SEI layer, while the second stage is usually associated to the onset of lithium plating (due to a dense SEI which inhibits the lithium intercalation into graphite). Confirmation of such interpretation is obtained by means of model based interpretation on non-equilibrium measurements, permitting estimation of physical parameters value and enabling identification of aging mechanisms, together with ex-situ measurements based on electrochemical and morpho chemical analyses. Additionally, several consistencies between on-line measurements, such as EIS, and equilibrium capacity loss of cells have been identified, theoretically discussed and tested under further accelerated aging tests, enabling possible strategies of SoH implementation based on fast and informative electrochemical measurements. References [1]Rabissi et al., doi.org/10.1016/j.est.2022.106435 [2] K. Ramirez-Meyers et al., doi.org/10.1016/j.est.2022.106472 [3] E. Sarasketa-Zabala et al., doi.org/10.1021/jp510071d [4] X.G. Yang et al., https://doi.org/10.1016/j.jpowsour.2017.05.110 Figure 1