Empirical Capacity Measurements of Electric Vehicles Subject to Battery Degradation From V2G Services

Thingvad, Andreas; Calearo, Lisa; Andersen, Peter Bach; Marinelli, Mattia

doi:10.1109/tvt.2021.3093161

Cited by 60 publications

(53 citation statements)

References 30 publications

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“…In this article we consider both definitions. When considering the battery capacity in Wh, the discharge capacity is usually preferred because the charging capacity accounts also for the heat dissipation in the battery internal resistance [14]. Without disassembling the battery from the EV a full discharge cycle cannot be performed because of BMS limitations on minimum discharge SOC levels.…”

Section: B Empirical Capacity Methodsmentioning

confidence: 99%

“…Calendar aging is a function of time, temperature and SOC, and occurs even when the battery is not used. Cycle aging is a function of the active usage, in terms of full equivalent charge cycles at a certain temperature and current C-rate [14], [15]. To maintain the battery lifespan of EVs, BMSs can restrict capacity usage by introducing energy reserves [16].…”

Section: A Ev Battery Capacitymentioning

confidence: 99%

“…Another way to determine capacity is an empirical method, used for the first time on EV battery packs in our previous work [14], but only for 24 kWh Nissan EVs. The method consists of a full charge of the battery without disassembling it from the vehicle and violating the warranty.…”

Section: Introductionmentioning

confidence: 99%

“…• The empirical capacity method presented in [14] is extended and validated by assessing the effect of on-board charging to complement the DC charging for six Nissan vehicles with different battery size, chemistry, and usage. • The validity of BMS measurements, both instantaneous current and voltage, is assessed and compared to the EE measurements.…”

Section: Introductionmentioning

confidence: 99%

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Validation of electric vehicle battery capacity measurements with on-board charger

Calearo¹,

Ziras²,

Thingvad³

et al. 2022

Preprint

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<p>Battery degradation is a main concern for electric vehicle (EV) users and a reliable capacity estimation is of major importance. Every EV battery management system (BMS) provides a variety of information, including measured current and voltage, and estimated capacity of the battery. However, the estimations are subject to the car manufacturer methodology and the measurement accuracy is unknown. This article uses extensive measurements from six diverse EVs to compare and assess capacity estimation with three different methods: (1) reading the capacity estimation from the BMS through the central area network (CAN) bus, (2) using an empirical method with external current measurements and (3) using the same empirical method with measurements coming from the BMS. The empirical method relies on a full discharge and charge cycle and measuring the total current flowing into the battery. We show that the use of BMS current measurements provides consistent capacity estimation (a difference of approximately one percent) and can circumvent the need for costly experimental equipment and DC chargers. This data can simplify the empirical method by only using an on-board diagnostics port (OBDII) reader and an AC charger, as the car measures the current directly at the battery terminals. We compare the capacity based on the current measurements with the internal BMS estimation of the capacity, and provide insight on how that can be interpreted.</p>

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Section: B Empirical Capacity Methodsmentioning

confidence: 99%

Section: A Ev Battery Capacitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Validation of electric vehicle battery capacity measurements with on-board charger

Calearo¹,

Ziras²,

Thingvad³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…However, the still rather early research on the V2G impacts on battery degradation currently does not come to a consensus on the impact of such services on the battery lifetime. On one side, various research has not identified additional wear of batteries when V2G services are provided [28][29][30], while on the other hand, the literature clearly revealed additional battery degradation when participating in V2G [28,[31][32][33][34]. As all of these studies are still at a rather early stage and much more investigation is required to obtain reliable results on the impact of V2G activities on the traction battery of EVs, no reimbursement for the additional use of the EV battery is applied in this study.…”

Section: Vehicle-to-grid Impactmentioning

confidence: 99%

Vehicle to Grid Impacts on the Total Cost of Ownership for Electric Vehicle Drivers

Huber

Clerck

Cauwer

et al. 2021

WEVJ

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Electric vehicles (EV) are foreseen as one major technology toward decarbonizing the mobility sector. At the same time, Vehicle to Grid (V2G) technology opens a new market for EV owners. This article identifies the impacts of providing V2G services on the Total Cost of Ownership (TCO) of EVs. Thus, we studied EVs in private, semi-public and public charging cases, considering two different V2G revenue streams. The included V2G services were: (i) local load balancing to balance the peaks and valleys of the electricity demands of buildings and (ii) an imbalance service to enhance grid stability. In this paper, the impact of these two V2G services is quantified and considered in the TCO calculations. To the authors’ knowledge, no comparable study incorporating the same V2G services exists in the literature. The TCO is calculated with real-life data for four different EVs currently available in the market. As a result, the V2G TCO ranges from €33.167 to €61.436 over an average of nine years for the Flanders region (Belgium).

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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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Empirical Capacity Measurements of Electric Vehicles Subject to Battery Degradation From V2G Services

Cited by 60 publications

References 30 publications

Validation of electric vehicle battery capacity measurements with on-board charger

Validation of electric vehicle battery capacity measurements with on-board charger

Vehicle to Grid Impacts on the Total Cost of Ownership for Electric Vehicle Drivers

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

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