On Calendar-Based Scheduling for User-Friendly Charging of Plug-In Electric Vehicles

Schwenk, Karl; Faix, Manuel; Mikut, Ralf; Hagenmeyer, Veit; Appino, Riccardo Remo

doi:10.1109/cavs.2019.8887782

Cited by 8 publications

(14 citation statements)

References 16 publications

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“…Note that (16) only accounts for aging caused throughout the charging event. To include battery aging for trips in between charging events in future work, a superordinate scheme as in [36] could determine the optimal target energy e N .…”

Section: Optimization Schemementioning

confidence: 99%

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Integrating Battery Aging in the Optimization for Bidirectional Charging of Electric Vehicles

Schwenk

Meisenbacher

Briegel

et al. 2021

IEEE Trans. Smart Grid

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Smart charging of Electric Vehicles (EVs) reduces operating cost, allows more sustainable battery usage, and promotes the rise of electric mobility. In addition, bidirectional charging and improved connectivity enable efficient power grid support. Today, however, uncoordinated charging, e.g. governed by users' habits, is still the norm. Thus, the impact of upcoming smart charging applications is mostly unexplored. We aim to estimate the expenses inherent with smart charging, e.g. battery aging costs, and give suggestions for further research. Using typical onboard sensor data we concisely model and validate an EV battery. We then integrate the battery model into a realistic smart charging use case and compare it with measurements of real EV charging. The results show that i) the temperature dependence of battery aging calls for precise thermal models for charging power greater than 7 kW, ii) disregarding battery aging underestimates EVs' operating cost by approx. 30%, and iii) the profitability of Vehicle-to-Grid (V2G) services based on bidirectional power flow, e.g. energy arbitrage, depends on battery aging costs and the electricity price spread.

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Section: Optimization Schemementioning

confidence: 99%

“…11 We specify e 0 as the battery energy at the beginning of the charging event in (14d). The desired battery energy at departure e N is determined by the user in (14e); prospectively, a previous calculation of e N as in [36] can also be used.…”

Section: Optimization Schemementioning

confidence: 99%

Integrating Battery Aging in the Optimization for Bidirectional Charging of Electric Vehicles

Schwenk

Meisenbacher

Briegel

et al. 2021

IEEE Trans. Smart Grid

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“…Yang et al [14] thought it should use some other ways to stimulate users to participate in system optimization services. Based on the users' charging demand, Schwenk et al [15] proposed a calendar-based charging strategy to stimulate users' charging behavior to realize reasonable guidance of users' charging. To some extent, matching users' travel demand can also improve the market penetration of EVs.…”

Section: Introductionmentioning

confidence: 99%

Dynamic-Area-Based Shortest-Path Algorithm for Intelligent Charging Guidance of Electric Vehicles

et al. 2020

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With the increasing popularization and competition of electric vehicles (EVs), EV users often have anxiety on their trip to find better charging stations with less travel distance. An intelligent charging guidance strategy and two algorithms were proposed to alleviate this problem. First, based on the next destination of EV users’ trip, the strategy established a dynamic-area model to match charging stations with users’ travel demand intelligently. In the dynamic area, the Dijkstra algorithm is used to find the charging station with the shortest trip. Then, the area extension algorithm and the charging station attribution algorithm were developed to improve the robustness of the dynamic area. The two algorithms can automatically adjust the area size according to the number of charging stations in the dynamic area to reduce the number of nodes traversed by the Dijkstra algorithm. Finally, simulation examples were used to verify the effectiveness of the proposed model and algorithms. The results showed that the proposed intelligent charging guidance strategy can meet the travel demand of users. It is a promising technique in smart cities to find better travel trips with less travel distance and less computed time.

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“…For EV users also the power supply interaction changes, as not only home appliances but also mobility requires electric energy. Hence, a convenient and cost-efficient charging process is hard to achieve without automation technology, when using an EV on a daily basis [6].…”

Section: Introductionmentioning

confidence: 99%

“…Electric mobility also poses new issues for car manufacturers: During charging and discharging of EV batteries a degradation (battery aging) occurs [15], that correlates with a value depreciation of the entire EV. However, EV users' satisfaction requires reliable and value-stable products, which car manufacturers aim to achieve by offering services, such as charging assistants [6]. The provided charging strategies target simplified and sustainable EV usage by considering individual customer needs and battery aging.…”

Section: Introductionmentioning

confidence: 99%

Data-driven charging strategies for grid-beneficial, customer-oriented and battery-preserving electric mobility

Schwenk¹,

Harr²,

Großmann³

et al. 2019

Preprint

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On Calendar-Based Scheduling for User-Friendly Charging of Plug-In Electric Vehicles

Cited by 8 publications

References 16 publications

Integrating Battery Aging in the Optimization for Bidirectional Charging of Electric Vehicles

Integrating Battery Aging in the Optimization for Bidirectional Charging of Electric Vehicles

Dynamic-Area-Based Shortest-Path Algorithm for Intelligent Charging Guidance of Electric Vehicles

Data-driven charging strategies for grid-beneficial, customer-oriented and battery-preserving electric mobility

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