Comparison of Charging Strategies of Electric Vehicles Using Local Power Production to Minimize Carbon Emissions

Hertrampf, Florian; Späthe, Steffen; Apel, Sebastian

doi:10.1109/iotais47347.2019.8980453

Cited by 1 publication

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2) Heat Flow Model: Based on the first principle of thermodynamics, we estimate the battery temperature from heat flows into and out of the battery. The power loss Qloss,n at the battery's internal resistance as given in (6), is assumed to be completely dissipated into heat. Regardless of the sign of p n , the battery temperature increases, i.e.…”

Section: B Thermal Modelmentioning

confidence: 99%

“…via machine learning approaches [37]. 6 The causes of battery aging can be separated into cyclic aging and calendar aging. Battery cycling, i.e.…”

Section: Battery Aging Modelmentioning

confidence: 99%

“…i) minimizing EV operating costs [1], [2], ii) prolonging battery life [3], [4], iii) supporting the power grid, i.e. Vehicle-to-Grid (V2G) [5], [6], or iv) combinations of the above [7], [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Schwenk¹,

Meisenbacher²,

Briegel³

et al. 2020

Preprint

View full text Add to dashboard Cite

Smart charging of Electric Vehicles (EVs) reduces operating costs, allows more sustainable battery usage, and promotes the rise of electric mobility. In addition, bidirectional charging and improved connectivity enables 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 on-board 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 requires precise thermal models for charging power greater than 7 kW, ii) disregarding battery aging underestimates EVs' operating costs 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.

show abstract

Section: B Thermal Modelmentioning

confidence: 99%

“…via machine learning approaches [37]. 6 The causes of battery aging can be separated into cyclic aging and calendar aging. Battery cycling, i.e.…”

Section: Battery Aging Modelmentioning

confidence: 99%

See 1 more Smart Citation

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

Schwenk¹,

Meisenbacher²,

Briegel³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

Comparison of Charging Strategies of Electric Vehicles Using Local Power Production to Minimize Carbon Emissions

Cited by 1 publication

References 3 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

Contact Info

Product

Resources

About