Development and Evaluation of a Battery Lifetime Extending Charging Algorithm for an Electric Vehicle Fleet

Rücker, Fabian; Bremer, Ilka; Linden, Sebastian; Badeda, Julia; Sauer, Dirk Uwe

doi:10.1016/j.egypro.2016.10.118

Cited by 15 publications

(15 citation statements)

References 4 publications

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“…The references [2][3][4][5][6][7][8][9][10][11][12] studied the centralized and decentralized overnight charging scheduling of large and small-scale EV fleet in order to minimize different objectives including V2G. The references [2][3][4]8] investigated the battery life prolongation through an optimized use of the charging/discharging process of the battery by minimizing the operational cost. References [5,6] managed a large number of EVs in order to minimize cost with respect to special grid constraints.…”

Section: Optimization Problem Formulationmentioning

confidence: 99%

“…The results of this mono-objective optimization for minimizing the battery aging cost presented in Figure 7 show that the optimal charging power tends to increase gradually. This seems logical according to the aging fitness function defined in Equation (8). The optimal charging power profile chosen by the optimization algorithm leads to a delayed charge with lower values of SoC and charging power to minimize calendar aging and battery overheating respectively.…”

Section: Optimization Of the Aging Cost For One Eb Chargingmentioning

confidence: 99%

“…In order to reduce EBs battery aging, a smart use is needed so that the batteries operate in the most favorable conditions. Centralized and decentralized overnight charging scheduling for large and small-scale electric vehicle fleets has already been studied in order to minimize different objectives including V2G [2][3][4][5][6][7][8][9][10][11][12]. A centralized charging scheduling is operated by a central controller whereas decentralized charging scheduling is managed by individual users that optimize their own charging profiles.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging

et al. 2019

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Electro-mobility is increasing significantly in the urban public transport and continues to face important challenges. Electric bus fleets require high performance and extended longevity of lithium-ion battery at highly variable temperature and in different operating conditions. On the other hand, bus operators are more concerned about reducing operation and maintenance costs, which affects the battery aging cost and represents a significant economic parameter for the deployment of electric bus fleets. This paper introduces a methodological approach to manage overnight charging of an electric bus fleet. This approach identifies an optimal charging strategy that minimizes the battery aging cost (the cost of replacing the battery spread over the battery lifetime). The optimization constraints are related to the bus operating conditions, the electric vehicle supply equipment, and the power grid. The optimization evaluates the fitness function through the coupled modeling of electro-thermal and aging properties of lithium-ion batteries. Simulation results indicate a significant reduction in the battery capacity loss over 10 years of operation for the optimal charging strategy compared to three typical charging strategies.

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Section: Optimization Problem Formulationmentioning

confidence: 99%

Section: Optimization Of the Aging Cost For One Eb Chargingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging

et al. 2019

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“…To optimize for EV availability, EVs with lower SoC are charged with higher priority [19]. Furthermore, an optimal green charging is reached, if the EV is only charged to necessary capacity [7]. Other charging aims take the energy price into account [7,19,33].…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, an optimal green charging is reached, if the EV is only charged to necessary capacity [7]. Other charging aims take the energy price into account [7,19,33]. Depending on these aims, the overall cost and power peaks in the grid can be reduced [7,33].…”

Section: Related Workmentioning

confidence: 99%

Seamless Electromobility

Eider

Sellner

Berl

et al. 2017

Proceedings of the Eighth International Conference on Future Energy Systems

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e existing electromobility (EM) is still in its edgling stage and multiple challenges have to be overcome to make Electric Vehicles (EVs) as convenient as combustion engine vehicles. Users and Electric Vehicle Fleet Operators (EFOs) want their EVs to be charged and ready for use at all times. is straightforward goal, however, is counteracted from various sides: e range of the EV depends on the status and depletion of the EV ba ery which is in uenced by EV use and charging characteristics. Also, most convenient charging from the user's point of view, might unfortunately lead to problems in the power grid. As in the case of a power peak in the evening when EV users return from work and simultaneously plug in their EVs for charging. Last but not least, the mass of EV ba eries are an untapped potential to store electricity from intermi ent renewable energy sources. In this paper, we propose a novel approach to tackle this multilayered problem from di erent perspectives. Using on-board EV data and grid prediction models, we build up an information model as a foundation for a back end service containing EFO and Charging Station Provider (CSP) logic as well as a central Advanced Drivers Assistant System (ADAS). ese components connect to both battery management and user interfaces suggesting various routing and driving behaviour alternatives customized and incentivized for the current user pro le optimizing above mentioned goals. CCS CONCEPTS •Applied computing →Transportation; •Hardware →Smart grid; Energy distribution; •Social and professional topics →User characteristics; •So ware and its engineering →So ware architectures;

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Energy Autonomous Sweat‐Based Wearable Systems

et al. 2021

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The continuous operation of wearable electronics demands reliable sources of energy, currently met through Li‐ion batteries and various energy harvesters. These solutions are being used out of necessity despite potential safety issues and unsustainable environmental impact. Safe and sustainable energy sources can boost the use of wearables systems in diverse applications such as health monitoring, prosthetics, and sports. In this regard, sweat‐ and sweat‐equivalent‐based studies have attracted tremendous attention through the demonstration of energy‐generating biofuel cells, promising power densities as high as 3.5 mW cm−2, storage using sweat‐electrolyte‐based supercapacitors with energy and power densities of 1.36 Wh kg−1 and 329.70 W kg−1, respectively, and sweat‐activated batteries with an impressive energy density of 67 Ah kg−1. A combination of these energy generating, and storage devices can lead to fully energy‐autonomous wearables capable of providing sustainable power in the µW to mW range, which is sufficient to operate both sensing and communication devices. Here, a comprehensive review covering these advances, addressing future challenges and potential solutions related to fully energy‐autonomous wearables is presented, with emphasis on sweat‐based energy storage and energy generation elements along with sweat‐based sensors as applications.

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Development and Evaluation of a Battery Lifetime Extending Charging Algorithm for an Electric Vehicle Fleet

Cited by 15 publications

References 4 publications

Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging

Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging

Seamless Electromobility

Energy Autonomous Sweat‐Based Wearable Systems

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