Journal
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
Volume 6, Issue 2, Pages 478-487Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TTE.2020.2986675
Keywords
Batteries; State of charge; Integrated circuit modeling; Optimization; Electric vehicle charging; Computational modeling; Battery modeling; dc fast charging; electric vehicle; second-order cone programming; smart charging
Funding
- Engineering and Physical Sciences Research Council [EP/S000887/1, EP/S031901/1]
- Innovate UK [104229]
- Oxford Martin Programme on Integrating Renewable Energy
- EPSRC [EP/S000887/1, EP/S031901/1] Funding Source: UKRI
- Innovate UK [104229] Funding Source: UKRI
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This article proposes a new convex optimization strategy for coordinating electric vehicle charging, which accounts for battery voltage rise and the associated limits on maximum charging power. Optimization strategies for coordinating electric vehicle charging commonly neglect the increase in battery voltage, which occurs as the battery is charged. However, battery voltage rise is an important consideration since it imposes limits on the maximum charging power. This is particularly relevant for dc fast charging, where the maximum charging power may be severely limited, even at the moderate state of charge levels. First, a reduced-order battery circuit model is developed, which retains the nonlinear relationship between the state of charge and maximum charging power. Using this model, limits on the battery output voltage and battery charging power are formulated as the second-order cone constraints. These constraints are integrated with a linearized power flow model for three-phase unbalanced distribution networks. This provides a new multiperiod optimization strategy for electric vehicle smart charging. The resulting optimization is a second-order cone program and, thus, can be solved in polynomial time by standard solvers. A receding horizon implementation allows the charging schedule to be updated online, without requiring prior information about when vehicles will arrive.
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