The impact of ageing when using various State of Charge (SOC) levels for an electrified vehicle is investigated in this article. An extensive test series is conducted on Li-ion cells, based on graphite and NMC/LMO electrode materials. Lifetime cycling tests are conducted during a period of three years in various 10% SOC intervals, during which the degradation as function of number of cycles is established. An empirical battery model is designed from the degradation trajectories of the test result. An electric vehicle model is used to derive the load profiles for the ageing model. The result showed that, when only considering ageing from different types of driving in small Depth of Discharges (DODs), using a reduced charge level of 50% SOC increased the lifetime expectancy of the vehicle battery by 44–130%. When accounting for the calendar ageing as well, this proved to be a large part of the total ageing. By keeping the battery at 15% SOC during parking and limiting the time at high SOC, the contribution from the calendar ageing could be substantially reduced.

Since battery cost represents a substantial part of an electric vehicle’s (EV) total cost, the degradation of EV battery and how it is affected by being used in vehicle-to-grid (V2G) applications is a concern and the main argument against implementing V2G. Battery degradation is too complex in terms of non-linearity for practical optimization of V2G scheduling. This paper develops a mixed-integer linear programming (MILP) model to optimize the V2G scheduling of an EV, considering a detailed battery degradation model for calendar aging and cycle aging. In the developed model, calendar aging is affected by the state of charge, battery age, and temperature. The cycle aging is affected by temperature, C-rate, and the energy throughput of the battery. A case study is performed to minimize the annual operational cost of an EV with V2G capability for two different years of electricity cost and ambient temperature data. The results of the developed model are compared with four different cases: immediate charging, smart charging algorithms without V2G, V2G without degradation cost, and V2G with degradation cost as the objective function. It is shown that the developed V2G model achieves a slightly increased cycle aging due to usage in V2G. However, it reduces the overall scheduling cost of the EV by 48-88% compared to the immediate charging and by 10-73% compared to the smart charging. Furthermore, neglecting battery degradation in optimal V2G could lead to a 28% increase in scheduling cost.

The long and tiring discussion of who are the best drivers, men or women, is not answered in this article. This article, though, sheds some light on the actual differences that can be seen in how men and women drive. In this study, GPS-recorded driving dynamics data from 123 drivers, 48 women and 75 men, are analysed and drivers are categorised as aggressive, normal or gentle. A total of 10% of the drivers was categorised as aggressive, with an even distribution between the genders. For the gentle drivers, 11% of the drivers, the men dominated. The driving style investigation was extended to utilise machine learning, confirming the results from statistical tools. As driving style highly impacts a vehicle’s fuel consumption, while switching over to battery electric vehicles it is important to investigate how the different driving styles impact battery utilisation. Two Li-ion battery cell types were tested utilising the same load cycle with three levels of current amplitude, to represent accelerations for the three drive categories. While one cell type was insensitive to the current amplitude, the highly energy-optimised cell proved to be sensitive to higher current amplitudes, corresponding to a more aggressive driving style. Thus, the amplitude of the dynamic current can for some cells be a factor that needs to be considered for lifetime predictions, while it can be neglected for other cells.

The development of Sodium-ion battery technologies and materials is moving rapidly forward, and several companies are on the verge of commercialising their products. An important question is what knowledge and synergies that can be drawn from Lithium-ion batteries. This work has investigated whether the Doyle-Fuller-Newman model (DFN model) [1] can be used for simulating the insertion and extraction of mobile sodium ion in hard carbon. Previous work indicates that this should be the case [2]–[4]. It has been shown that the insertion process of sodium in hard carbon does not follow the same process as for lithium in hard carbon [5]. The sodium insertion in hard carbon is suggested to be a combination of capacitive adsorption, intercalation and nanopore filling [6]–[8]. Hence, the question is if capacitive adsorption and nanopore filling can be simulated as an intercalation process. In addition, the sodium-ion has a lower charge density than lithium-ion, leading to different properties for the electrolyte and the interfacial species [9], [10]. One of the issues with the DFN model is to measure, calculate or estimate the material and electrode properties needed. As a first step in investigating the above assumption, an additional assumption made is that the needed parameters can be extracted using similar methods as for Li-ion batteries. With this starting point, a parameter sensitivity analysis is made for simulating mobile sodium in hard carbon with the DFN model. References [1] M. Doyle, T. Fuller, and J. Newman, “Modeling of galvanostatic charge and discharge of the lithium/ polymer/insertion cell,” J. Electrochem. Soc., vol. 140, no. 6, pp. 1526–1533, 1993, doi: 10.1149/1.2221597. [2] K. Chayambuka, M. Jiang, G. Mulder, D. L. Danilov, and P. H. L. Notten, “Physics-based modeling of sodium-ion batteries part I: Experimental parameter determination,” Electrochim. Acta, vol. 404, p. 139726, Feb. 2022, doi: 10.1016/J.ELECTACTA.2021.139726. [3] K. Chayambuka, G. Mulder, D. L. Danilov, and P. H. L. Notten, “Physics-based modeling of sodium-ion batteries part II. Model and validation,” Electrochim. Acta, vol. 404, p. 139764, Feb. 2022, doi: 10.1016/J.ELECTACTA.2021.139764. [4] C. M. Doyle, “Peer Reviewed Title: Design and Simulation of Lithium Rechargeable Batteries,” 2010. Accessed: Feb. 01, 2021. [Online]. Available: http://www.escholarship.org/uc/item/6j87z0sp [5] H. D. Asfaw, C. W. Tai, M. Valvo, and R. Younesi, “Facile synthesis of hard carbon microspheres from polyphenols for sodium-ion batteries: insight into local structure and interfacial kinetics,” Mater. Today Energy, vol. 18, p. 100505, Dec. 2020, doi: 10.1016/j.mtener.2020.100505. [6] J. Y. Hwang, S. T. Myung, and Y. K. Sun, “Sodium-ion batteries: Present and future,” Chemical Society Reviews, vol. 46, no. 12. Royal Society of Chemistry, pp. 3529–3614, Jun. 21, 2017. doi: 10.1039/c6cs00776g. [7] D. Chen et al., “Hard carbon for sodium storage: mechanism and optimization strategies toward commercialization,” Energy Environ. Sci., vol. 14, no. 4, pp. 2244–2262, Apr. 2021, doi: 10.1039/D0EE03916K. [8] C. Bommier, T. W. Surta, M. Dolgos, and X. Ji, “New Mechanistic Insights on Na-Ion Storage in Nongraphitizable Carbon,” Nano Lett., vol. 15, no. 9, pp. 5888–5892, Sep. 2015, doi: 10.1021/ACS.NANOLETT.5B01969/ASSET/IMAGES/LARGE/NL-2015-01969W_0002.JPEG. [9] R. Mogensen, S. Colbin, and R. Younesi, “An attempt to formulate non‐carbonate electrolytes for sodium‐ion batteries,” Batter. Supercaps, p. batt.202000252, Dec. 2020, doi: 10.1002/batt.202000252. [10] L. A. Ma, A. J. Naylor, L. Nyholm, and R. Younesi, “Strategies for Mitigating Dissolution of Solid Electrolyte Interphases in Sodium-Ion Batteries,” Angew. Chemie - Int. Ed., 2020, doi: 10.1002/anie.202013803.