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

被引：49

作者：

Schwenk, Karl ^{[1
,2
]}

Meisenbacher, Stefan ^{[2
]}

Briegel, Benjamin ^{[1
]}

Harr, Tim ^{[1
]}

Hagenmeyer, Veit ^{[2
]}

Mikut, Ralf ^{[2
]}

机构：

[1] Mercedes Benz AG, eDrive Innovat Dept, D-71063 Sindelfingen, Germany

[2] Karlsruhe Inst Technol, Inst Automat & Appl Informat, D-76131 Karlsruhe, Germany

来源：

IEEE TRANSACTIONS ON SMART GRID | 2021年 / 12卷 / 06期

关键词：

Batteries; Aging; Data models; Vehicle-to-grid; State of charge; Temperature sensors; Temperature dependence; Electric vehicle charging; artificial neural network (ANN); vehicle-to-grid; optimization; smart charging; electric vehicles; energy arbitrage; PRICE; MODEL; STRATEGIES;

D O I：

10.1109/TSG.2021.3099206

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：5135 / 5145

页数：11

共 44 条

[1]

[Anonymous], 2019, EVALUATION E MOBILIT

[2]

Ayadi F, 2020, 8TH INTERNATIONAL CONFERENCE ON SMART GRID (ICSMARTGRID2020), P183, DOI [10.1109/icSmartGrid49881.2020.9144695, 10.1109/icsmartgrid49881.2020.9144695]

[3] Cost Projection of State of the Art Lithium-Ion Batteries for Electric Vehicles Up to 2030 [J].

Berckmans, Gert ;

Messagie, Maarten ;

Smekens, Jelle ;

Omar, Noshin ;

Vanhaverbeke, Lieselot ;

Van Mierlo, Joeri .

ENERGIES, 2017, 10 (09)

[4]

Berecibar M, 2019, NATURE, V568, P325, DOI 10.1038/d41586-019-01138-1

[5] Estimating the grid payments necessary to compensate additional costs to prospective electric vehicle owners who provide vehicle-to-grid ancillary services [J].

Bishop, Justin D. K. ;

Axon, Colin J. ;

Bonilla, David ;

Banister, David .

ENERGY, 2016, 94 :715-727

[6] Should we reinforce the grid? Cost and emission optimization of electric vehicle charging under different transformer limits [J].

Brinkel, N. B. G. ;

Schram, W. L. ;

AlSkaif, T. A. ;

Lampropoulos, I ;

van Sark, W. G. J. H. M. .

APPLIED ENERGY, 2020, 276

[7] Determination of the Selling Price Offered by Electricity Suppliers to Electric Vehicle Users [J].

Carrion, Miguel .

IEEE TRANSACTIONS ON SMART GRID, 2019, 10 (06) :6655-6666

[8]

Chollet F., 2015, Keras

[9] The case for Bi-directional charging of electric vehicles in low voltage distribution networks [J].

Crozier, Constance ;

Morstyn, Thomas ;

Deakin, Matthew ;

McCulloch, Malcolm .

APPLIED ENERGY, 2020, 259

[10] Mathematical Modeling for Economic Evaluation of Electric Vehicle to Smart Grid Interaction [J].

Das, Rajib ;

Thirugnanam, Kannan ;

Kumar, Praveen ;

Lavudiya, Rajender ;

Singh, Mukesh .

IEEE TRANSACTIONS ON SMART GRID, 2014, 5 (02) :712-721

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