Multi-objective electric vehicle scheduling considering customer and system objectives

Maigha, Maigha; Crow, Mariesa

doi:10.1109/ptc.2017.7981275

Cited by 11 publications

(14 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, and the battery cost, for each HEV are 0.2 and $300/kWh, respectively [21]. In the second stage, the values of , and are shown in Table IV.…”

Section: Resultsmentioning

confidence: 99%

“…In [19], [20], aiming at minimizing the charging costs, proper policies and charging strategies to determine the optimal scheduling considering historical data were developed. To fairly address the optimal scheduling of EVs, the economic charging and battery degradation were co-optimized in [21] via the Pareto front technique. The EV charging rates were modeled via partial differential equations under three conditions: 1) EVs receive energy from the grid, 2) EVs are connected to the grid but not charging, and 3) EVs deliver energy to the grid, was considered in [22].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Efficient Automation of an HEV Heterogeneous Fleet Using a Two-Stage Methodology

Cerna

Pourakbari‐Kasmaei

Lehtonen

et al. 2019

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

An influential factor in enhancing the attendance services, mainly in commercial and emergency sectors, is the vehicular technology used to transport people, goods, or equipment. Although hybrid electric vehicles (HEVs) represent a sustainable transport alternative, the existing technical limitations such as battery and fuel capacities, and autonomy, among others, highlight the provision of an efficient automation tool. The tool can serve to enhance the operational performance of the HEV by selecting the proper driving mode (on fuel or electricity), and the navigation strategies to the delivery and charging points in urban areas. This paper proposes a two-stage methodology that allows the HEVs operators to automate the operational performance of a heterogeneous HEV fleet on a city map. Each stage is handled by its corresponding optimization model. In the first stage, the total navigation time and the battery lifetime of the fleet during the operation are optimized. In this stage, constraints related to charge-sustaining/charge-depleting modes, state of charge (SoC) of the HEVs battery, and deliveries schedules are taken into account. To this end, operating strategies related to the performance of different types of existing HEV technologies are anonymously considered. In the second stage, the best operating strategy among all the operating strategies is selected while considering the capacity of HEVs to deliver a given quantity of goods. Moreover, uncertainties during the HEV navigation are simulated considering the change in traffic density of the urban roads as a function of the levels of service (LOS). Results show that the proposed methodology establishes an efficient operational scheme for a HEVs fleet, ensuring a significant reduction of energy usage as well as mitigating the CO2 emissions.

show abstract

“…, and the battery cost, for each HEV are 0.2 and $300/kWh, respectively [21]. In the second stage, the values of , and are shown in Table IV.…”

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Efficient Automation of an HEV Heterogeneous Fleet Using a Two-Stage Methodology

Cerna

Pourakbari‐Kasmaei

Lehtonen

et al. 2019

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

show abstract

“…And there should at least exist one t to hold following inequation which is to maintain that the duration is enough for the whole charging process. t ≥ t original + t ch arg ing , ∃ t = t e − t s (22) where t original is the time cost of original routes and t ch arg ing is the time cost of charging process. As for constrains of EVs' batteries, its SOC should be less than 30% according to the flow chart.…”

Section: B Charging Time Selectionmentioning

confidence: 99%

“…In [21], a real-time distributed control approach based on EV users' travel behaviors was built, which can smooth the daily grid load profile and ensure EV users' charging demand. A multi-objective optimization strategy was proposed which considered economic charging, minimizing battery degradation and maintaining system load profile in [22]. However, these researches always ignore the influence of fast charging navigation which can help EV users to choose reasonable routes and time to charge.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Navigation Strategy of EV Fast Charging Based on Dynamic Scene

Xia

Chen

et al. 2019

IEEE Access

View full text Add to dashboard Cite

With the development of the EV industry, the growing demand for EV fast charging load has brought enormous impact on the power system. Due to the unbalance of charging time distribution and the high power of EV fast charging, the difference between peak and valley of the load curve will widen. In this paper, a hierarchical navigation strategy (HNS) based on dynamic traffic/temperature data is proposed to decrease the EV fast charging load at peak hours and the time and energy cost during the charging process. The upper layer of the HNS is charging time selection. The optimal selections of charging time, which is based on the habits of EV users, is proposed in this layer. It aims at providing efficient time slots for charging, which can decentralize the fast charging demand and decrease the EV users' time cost. The underlayer is the route selection layer, which is based on the priority coding genetic algorithm. It proposes the optimal charging routes to decrease EV users' energy cost and time cost. At the same time, the peak charging load can also be shaved due to the decline in energy cost. The case study under the scene with realistic traffic, temperature, and power grid information shows that the proposed HNS can shave the peak load of the power grid and decrease the energy/time cost during the EV fast charging process. Therefore, the effectiveness of the HNS is proved. INDEX TERMS EV fast charging, peak shaving, hierarchical navigation strategy, dynamic data, priority coding genetic algorithm.

show abstract

“…In the first case by considering the capacity of the transformer to achieve an aggregate load profile and secondly decrease the on-off switching of EVs. (Maigha and Crow, 2017) due to charging and discharging, the life of a battery is degraded so it is important to maximize battery life degradation. System performance depends on load demand, so if the EVs are charging in high demand hours then it gives the worst impact on network performance.…”

Section: Introductionmentioning

confidence: 99%

Strategic Approach for Ancillary Services of Electric Vehicle Battery in Weekend Days

2020

Contemporary Issues in Computing

View full text Add to dashboard Cite

Multi-objective electric vehicle scheduling considering customer and system objectives

Cited by 11 publications

References 19 publications

Efficient Automation of an HEV Heterogeneous Fleet Using a Two-Stage Methodology

Efficient Automation of an HEV Heterogeneous Fleet Using a Two-Stage Methodology

A Hierarchical Navigation Strategy of EV Fast Charging Based on Dynamic Scene

Strategic Approach for Ancillary Services of Electric Vehicle Battery in Weekend Days

Contact Info

Product

Resources

About