Economic Payback Time of Battery Pack Replacement for Hybrid and Plug-In Hybrid Electric Vehicles

Anselma, Pier Giuseppe; Kollmeyer, Phillip J.; Feraco, Stefano; Bonfitto, Angelo; Belingardi, Giovanni; Emadi, Ali; Amati, Nicola; Tonoli, Andrea

doi:10.1109/tte.2022.3202792

Cited by 5 publications

(2 citation statements)

References 38 publications

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“…The battery open-circuit voltage (V OC (SoC)) and its internal resistance (R eq (SoC)) are modeled as functions of SoC [30] and scaled with the number of cells in parallel and series.…”

Section: Vehicle Modelmentioning

confidence: 99%

Route Planning for Electric Vehicles Including Driving Style, HVAC, Payload and Battery Health

Ponso,

Bonfitto,

Belingardi

2023

Energies

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The increasing environmental awareness paired with the rise of global warming effects has led, in the past few years, to an increase in the sales of electric vehicles (EVs), partly but not only, caused by governmental incentives. A significant roadblock in the mass transition to EVs can be found in the so-called range anxiety: not only do EVs have, generally, considerably shorter ranges than their internal combustion engine vehicle (ICEV) equivalents, but recharge takes significantly longer than does filling up a gas tank, and charging stations are less widespread than are petrol stations. To counteract this, EV manufacturers are developing route planners which select the best route to go from A to B according to the range of the vehicle and the availability of charging stations. These tools are indeed powerful but do not account for the state of health (SoH) of the battery or for temperature conditions, two factors which may severely degrade the range of an EV. This article presents an innovative route planning method which takes into account SoH, temperature and driving style and selects, along the planned route, the charging stations among those which can be reached with the energy of the battery. To verify its proper operativity, simulations were conducted, highlighting the risk of running out of battery before destination, considering if the route is planned based on the declared range, and taking into account battery SoH, external temperature and driving style.

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“…The battery open-circuit voltage (V OC (SoC)) and its internal resistance (R eq (SoC)) are modeled as functions of SoC [30] and scaled with the number of cells in parallel and series.…”

Section: Vehicle Modelmentioning

confidence: 99%

Route Planning for Electric Vehicles Including Driving Style, HVAC, Payload and Battery Health

Ponso,

Bonfitto,

Belingardi

2023

Energies

Self Cite

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“…Different charging C−rates are considered. As a matter of fact, such a novel approach aims to fill the gap with the different approaches presented in the literature on battery aging and battery state of health assessment, having a significant impact on battery economy, as illustrated in [23][24][25][26][27][28][29]. Through dynamic modeling of the EV battery system, the state of charge (SoC) evolution is determined for different charging C−rates, considering both real discharging and charging current profiles.…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of Fast−Charging Effect on Aging of Electric Vehicle Li−Ion Batteries

Pelosi,

Longo,

Zaninelli

et al. 2023

Energies

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A huge increase in fast−charging stations will be necessary for the transition to EVs. Nevertheless, charging a battery pack at a higher C−rate impacts its state of health, accelerating its degradation. The present paper proposes a different and innovative approach that considers the daily routine of an EV Li−ion battery based on a standard driving cycle, including charging phases when the depth of discharge is 90%. Through dynamic modeling of the EV battery system, the state of charge evolution is determined for different charging C−rates, considering both real discharging and charging current profiles. Finally, by applying a suitable post−processing procedure, aging test features are defined, each being related to a specific EV battery working mode, including charging at a particular C−rate, considering the global battery operation during its lifespan. It is demonstrated that, according to the implemented procedure, fast−charging cycles at 50 kW reduce battery lifespan by about 17% with respect to charge in a 22 kW three−phase AC column, in parity with the discharge rate. Thus, this work can provide a deep insight into the expected massive penetration of electric vehicles, providing an estimate of battery useful life based on charging conditions.

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Energy management strategy for electro-hydraulic hybrid electric vehicles considering optimal mode switching: A soft actor-critic approach trained on a multi-modal driving cycle

Zhou,

Zhang,

Zhang

et al. 2024

Energy

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Economic Payback Time of Battery Pack Replacement for Hybrid and Plug-In Hybrid Electric Vehicles

Cited by 5 publications

References 38 publications

Route Planning for Electric Vehicles Including Driving Style, HVAC, Payload and Battery Health

Route Planning for Electric Vehicles Including Driving Style, HVAC, Payload and Battery Health

Experimental Investigation of Fast−Charging Effect on Aging of Electric Vehicle Li−Ion Batteries

Energy management strategy for electro-hydraulic hybrid electric vehicles considering optimal mode switching: A soft actor-critic approach trained on a multi-modal driving cycle

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