Incremental Capacity Analysis Applied on Electric Vehicles for Battery State-of-Health Estimation

Schaltz, Erik; Stroe, Daniel‐Ioan; Nørregaard, Kjeld; Ingvardsen, Lasse Stenhoj; Christensen, Andreas Elkjær

doi:10.1109/tia.2021.3052454

Cited by 74 publications

(30 citation statements)

References 11 publications

(14 reference statements)

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“…The ICA method is conventionally used on battery cells, however it needs to be feasible on pack or EV level and not only on individual cell level. Nickel Manganese Cobalt (NMC) based cells for the BMW i3 EV has been tested both on cell level and on car level and the results shows consistent characteristic peaks and valleys of the ICA on car and cell level [16,18].…”

Section: Introductionmentioning

confidence: 99%

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

Thingvad

Calearo

Andersen

et al. 2021

IEEE Trans. Veh. Technol.

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Section: Introductionmentioning

confidence: 99%

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

Thingvad

Calearo

Andersen

et al. 2021

IEEE Trans. Veh. Technol.

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“…4) The health of the AEV's battery is also one of the concerns on the development of the vehicle [34].…”

Section: Discussionmentioning

confidence: 99%

An Integrated Framework on Autonomous-EV Charging and Autonomous Valet Parking (AVP) Management System

Zhang¹,

Xia

Liu

et al. 2022

IEEE Trans. Transp. Electrific.

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Autonomous vehicles (AVs) transform traditional commuting by decreasing congestion, improving road safety, and naturally integrate better with electric controls for flexible implementation of autonomous driving technologies. Indeed, electric-powered AVs or autonomous electric vehicles (AEVs) are benefiting each other in many aspects.While autonomy brings great efficiency in driving as well as battery use, EVs require less maintenance and drastically cut fuel costs. With AVs, a pivotal concern is within the realm of long-range Autonomous Valet Parking (LAVP), such as diverse customer demands on parking (or drop-off / pick-up) for various journey planning. On the other hand, electric-powered AVs are typically with limited cruising range, and locating convenient charging services are also among the major impediments. As of yet, recent studies have started to investigate EV charging and LAVP in isolation as they rarely consider a joint optimization on user trip and energy refueling. Rather, we target in this work the integration of vehicle charging with autonomy in the sense of a systemic approach. Specifically, we propose an integrated AEV charging and LAVP management scheme, to resolve critical decision-making on convenient charging and parking management upon customer requirements during their journeys. The proposed scheme jointly considers charging reservations as well as parking duration at car parks (CPs), aiming to enable accurate predictions on future charging (and parking) states at CPs. Results show the advantage of our proposal

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“…Thus, the charging IC curves are analyzed and the health features are extracted from these curves. Derive the charging IC curve, namely

( Schaltz et al., 2021 ). It is enumerated using finite difference over single time step, as:

where

and

denote charging capacity and voltage, and

means the sampling step.…”

Section: Step-by-step Methods Detailsmentioning

confidence: 99%

Protocol for state-of-health prediction of lithium-ion batteries based on machine learning

Shu

Shen

et al. 2022

STAR Protocols

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Incremental Capacity Analysis Applied on Electric Vehicles for Battery State-of-Health Estimation

Cited by 74 publications

References 11 publications

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

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

An Integrated Framework on Autonomous-EV Charging and Autonomous Valet Parking (AVP) Management System

Protocol for state-of-health prediction of lithium-ion batteries based on machine learning

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