Battery lifetime for different driving cycles of EVs

Barcellona, Simone; Brenna, Morris; Foiadelli, Federica; Longo, M.; Piegari, Luigi

doi:10.1109/rtsi.2015.7325138

Cited by 17 publications

(9 citation statements)

References 7 publications

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“…Complex models are available in the literature to take into account the battery dynamic [15][16][17][18][19][20], thermal behaviour [21][22][23], and ageing aspects [24][25][26][27][28][29][30][31]. An ad hoc methodology for a correct battery system sizing is required.…”

Section: Introductionmentioning

confidence: 99%

Economic viability for residential battery storage systems in grid‐connected PV plants

Barcellona

Piegari

Musolino

et al. 2017

IET Renewable Power Generation

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Today's residential battery energy storage systems (BESSs) are off the shelf products used to increase the selfconsumption of residential photovoltaic (PV) plants and to reduce the losses related to energy transfer in distribution grids. This work investigates the economic viability of adding a BESS to a residential grid-connected PV plant by using a methodology for optimising the size of the BESS. The identification of the optimal size which minimises the total cost of the system is not trivial; indeed, it is a trade-off between OPEX and CAPEX, which are mainly affected by the battery technology, usage profile, expected lifetime, and efficiency. Here, an analysis of the opportunity to install a storage system together with a grid-connected residential PV plant is performed. Three typical low-voltage prosumers (Italy, Switzerland, and the UK) are investigated in order to take into account the different legislative and tariff framework over Europe. Numerical results reported here show that present costs of storages are still too high to allow an economic convenience of the storage installation. Moreover, an indication of the necessary incentives to allow the spreading of these systems is given.

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Section: Introductionmentioning

confidence: 99%

Economic viability for residential battery storage systems in grid‐connected PV plants

Barcellona

Piegari

Musolino

et al. 2017

IET Renewable Power Generation

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“…To analyze the capacity fading over the time through DWT analysis, three different Li-ion cells were cycled, repeating two different drive cycles selected. [38] Specifically, the two drive tests are: the urban SC03 Test Drive, introduced by the Supplemental Federal Test Procedure (SFTP) to represent the engine load associated with use of air conditioning in vehicles certified over the FTP75 test cycle. [39] It corresponds to a mileage of 12.8 km at a maximum speed of 88.2 km/h and a duration of 596 seconds.…”

Section: Bev Test Drive Profilesmentioning

confidence: 99%

A Data‐Driven Method based on Discrete Wavelet Transform for online Li‐Ion Battery State‐of‐Health Prediction and Monitoring

Pelosi,

Gallorini,

Ottaviano

et al. 2024

Batteries & Supercaps

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Transportation electrification is accelerating the clean energy transition. Due to high efficiencies and energy density, Li‐ion batteries (LIBs) are used as on‐board energy carrier for battery electric vehicles (BEVs). Nevertheless, LIBs are subject to rapid degradation due to fast‐charging, mechanical, electrical and thermal factors. Thus, state‐of‐health (SoH) prediction is of utmost importance for extending LIB lifespan. In this paper, an online accurate and easy‐of‐implementation battery SoH prediction and monitoring method is presented for BEV applications. The method is based on the application of discrete wavelet transform (DWT) analysis to voltage profiles, measured while driving. Specifically, an extensive cycle aging experimental campaign on NCR 18650 cells was performed, applying two typical US test drives (urban and extra‐urban drive cycle, respectively) to the cells at different SoH. Moreover, tests carried out on LIBs at different temperatures demonstrated that temperature effect on the implemented DWT‐based method can be distinguished and separated from cycle aging effect. The proposed method allows a real‐time SoH estimation showing a good accuracy (MAE, ME and RMSE respectively result in 0.917, 2.897 and 1.32) without requiring high computational efforts. This allows to predict LIB aging also during the driving, continuously providing SoH information to avoid LIB fast degradation.

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“…BEV range is calculated by dividing the stated capacity of the battery by the energy consumption value determined in the speed profile test [10]. This is likely to underestimate actual BEV energy consumption due to the combination of unrealistic energy consumption from the drive cycle tests and an oversimplification of a consistent battery capacity over time when it is known that lithium-ion (Li-ion) cells lose capacity with time and use [16][17][18][19]. In recognition of the increasingly large differences between the NEDC cycles and real-world values, the EU is planning to introduce the new Worldwide Harmonized Light Vehicles Test Procedure (WLTP) in 2017 [20].…”

Section: Research Articlementioning

confidence: 99%

Limitations of testing standards for battery electric vehicles: accessories, energy usage, and range

Oakley

McHenry²,

Bräunl

2016

IET Electrical Systems in Transportation

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Battery lifetime for different driving cycles of EVs

Cited by 17 publications

References 7 publications

Economic viability for residential battery storage systems in grid‐connected PV plants

Economic viability for residential battery storage systems in grid‐connected PV plants

A Data‐Driven Method based on Discrete Wavelet Transform for online Li‐Ion Battery State‐of‐Health Prediction and Monitoring

Limitations of testing standards for battery electric vehicles: accessories, energy usage, and range

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