Joint Optimization of Delay-Tolerant Autonomous Electric Vehicles Charge Scheduling and Station Battery Degradation

Cao, Yihua; Li, Demin; Zhang, Yihong; Chen, Xuemin

doi:10.1109/jiot.2020.2992133

Cited by 26 publications

(9 citation statements)

References 29 publications

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“…Tuchnitz et al [29] define user comfort as avoiding the trouble to give input to a system that coordinates EV chargingthis benefit is questionable since the EVs are not compensated. In an autonomous EV, Cao et al [120] find a tradeoff between the total electricity cost and the waiting time to charge the vehicle. Zhang et al [123] aim to minimize the time that the EV owner spends getting access to the charging point and waiting for the EV to charge.…”

Section: Management Of User Discomfortmentioning

confidence: 99%

“…ii) EV FLEET For fleets of self-driving EVs, minimization targets include charging costs [124], charging times [120], time spent not carrying passengers [110] and battery exhaustion [147].…”

Section: B) Charging I) Carmentioning

confidence: 99%

“…Temesgene et al [88] perform an optimization at virtual small cells that jointly minimizes harvested energy and the volume of dropped traffic. Cao et al [120] minimize the distance travelled, and thus the energy consumption, of a battery powered mobile wireless sensor charger. Faraci et al [75] go further, using a fleet of drones as the mobile wireless charger, and using only locally generated wind power for charging the drones.…”

Section: Buildingmentioning

confidence: 99%

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Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

2022

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The transition to renewable production and smart grids is driving a massive investment to battery storages, and reinforcement learning (RL) has recently emerged as a potentially disruptive technology for their control and optimization of battery storage systems. A surge of papers has appeared in the last two years applying reinforcement learning to the optimization of battery storages in buildings, energy communities, energy harvesting Internet of Things networks, renewable generation, microgrids, electric vehicles and plug-in hybrid electric vehicles. This article reviews these applications through 4 different perspectives. Firstly, the type of optimization problem is analyzed; the literature can be divided to approaches that optimize either financial targets or energy efficiency. Secondly, the approaches for handling user comfort are analyzed for applications that may impact a human user. Thirdly, this paper discusses the approach to model and reduce battery degradation. Fourthly, the articles are categorized by application context and applications likely to attract a high amount of research are identified. The paper concludes with a list of unresolved challenges.

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Section: Management Of User Discomfortmentioning

confidence: 99%

Section: B) Charging I) Carmentioning

confidence: 99%

Section: Buildingmentioning

confidence: 99%

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Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

2022

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“…A review of the research on the integration of SAEVs with the power grid is presented in [26], which highlights smart charging strategies as one of the major interaction points. A comprehensive mathematical model is proposed in [27] to minimise the electricity cost of autonomous electric vehicles, the degradation of the charging station batteries, and the user dissatisfaction due to additional delay with the constraint of satisfying a given demand for V2G services. This work assumes that vehicles stop during ongoing trips at intermediate charging stations for recharging, whereas in this study we assumed that charging occurs only at the end of a trip without causing any discomfort to passengers, as detouring was not allowed.…”

Section: Charging Optimisation Of Shared Fleetsmentioning

confidence: 99%

An Integrated Optimisation-Simulation Framework for Scalable Smart Charging and Relocation of Shared Autonomous Electric Vehicles

2021

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Ride-hailing with autonomous electric vehicles and shared autonomous electric vehicle (SAEV) systems are expected to become widely used within this decade. These electrified vehicles can be key enablers of the shift to intermittent renewable energy by providing electricity storage to the grid and offering demand flexibility. In order to accomplish this goal, practical smart charging strategies for fleets of SAEVs must be developed. In this work, we present a scalable, flexible, and practical approach to optimise the operation of SAEVs including smart charging based on dynamic electricity prices. Our approach integrates independent optimisation modules with a simulation model to overcome the complexity and scalability limitations of previous works. We tested our solution on real transport and electricity data over four weeks using a publicly available dataset of taxi trips from New York City. Our approach can significantly lower charging costs and carbon emissions when compared to an uncoordinated charging strategy, and can lead to beneficial synergies for fleet operators, passengers, and the power grid.

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“…Price signals are utilized to guide EVs to charge in charging stations to meet the predefined electricity demand. The charging station can incorporate renewable energy into the power system [9]. The charging station decides EV charging amount and brings the arbitrage to electricity market with regulation service as an aggregator [10].…”

Section: Introductionmentioning

confidence: 99%

Robust Charging Schedule for Autonomous Electric Vehicles With Uncertain Covariates

Cao

Wang

2021

IEEE Access

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Autonomous electric vehicles (AEVs) will become an inevitable trend in the future transportation network and have an important impact on the power grid. It is difficult to find the optimal distributed charging solution for AEVs to minimize the system cost with some uncertainties. In this paper, we investigate an AEVs charging and discharging problem with vehicle-to-grid (V2G) services. We aim to minimize the total electricity cost and battery degradation cost of AEVs and charging station batteries with V2G services, which takes the random arrival and departure of AEVs into account. We first propose a distributed charging framework of AEVs and charging stations by clustering method with the constraint of limited AEVs for each charging station in a region and formulate a distributed offline optimization problem. Then we formulate a distributed online charging optimization problem and propose a distributed online AEV charging scheduling (DOAS) algorithm to get an optimal charging solution. To study a more practical case, we reformulate the distributed online optimization problem with the uncertainties from base loads, renewable energy and charging demands. Furthermore, to improve the time efficiency of DOAS algorithm, we reduce the dimension of the distributed problem and design a dimension-reduction DOAS (DDOAS) algorithm. To seek a robust solution with some uncertainties, we propose a DDOAS algorithm with DRO based on Wasserstein distance (DDODW). Simulation results show that DOAS and DDOAS algorithms can have a close-to-optimal charging cost and a significantly less battery degradation cost of charging stations, compared with centralized online charging scheduling algorithm and DDOAS algorithm is more time-efficient than DOAS algorithm. The proposed DDODW algorithm can provide a robust solution for the energy schedule.

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Joint Optimization of Delay-Tolerant Autonomous Electric Vehicles Charge Scheduling and Station Battery Degradation

Cited by 26 publications

References 29 publications

Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

An Integrated Optimisation-Simulation Framework for Scalable Smart Charging and Relocation of Shared Autonomous Electric Vehicles

Robust Charging Schedule for Autonomous Electric Vehicles With Uncertain Covariates

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