Electric Vehicle Operation Scheduling Optimization Considering Electrochemical Characteristics of Li-Ion Batteries

Lyu, Mingyu; Han, Bing; Lu, Shaofeng

doi:10.1109/yac51587.2020.9337681

Cited by 1 publication

(1 citation statement)

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“…The fast charging process will stop once the EV battery is charged to the upper limit of the state of charge (SOC) for the sake of time saving and overcharge protection [11]. Although charging control and management can improve CS operators' profits from an operating perspective [20,21], EVs are assumed to be charged at rated power in this paper in consideration of the immature technology and the convenience of modeling. When the SOC is below the lower limit of the state of charge (SOC) during the travel, EV will warn the owner of emergency charging to avoid the adverse consequences.…”

Section: Charging Behavior Analysismentioning

confidence: 99%

Multistage and Dynamic Layout Optimization for Electric Vehicle Charging Stations Based on the Behavior Analysis of Travelers

Chen

Feng

Han

et al. 2021

WEVJ

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Electric vehicles (EV) are growing fast in recent years with the widespread concern about carbon neutrality. The development of charging infrastructures needs to be in phase with EV both in terms of quantity and charging time to decrease the range anxiety of EV users and resource waste. This paper proposed a multistage and dynamic layout optimization model based on mixed integer linear programming (MILP) for EV charging stations (CSs) to minimize the total social costs (TSC) consisting of the detour cost of EV users and the construction, relocation, and operating cost of CSs. The charging satisfaction coefficient and M/M/S/K model of queuing theory has been introduced to determine the desirable charging supply. The spatial-temporal distribution of charging demand was modeled based on the behavior analysis of travelers and over the discrete-time intervals for a day. Comparison studies based on the Sioux Falls network reveal that TSC with a multistage optimization strategy will drop 8.79% from that with a one-time optimization strategy. Charging service quality, relocation cost, and road network scales have a significant impact on the optimization results according to the sensitivity analysis.

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Section: Charging Behavior Analysismentioning

confidence: 99%