Online Rectangle Packing Algorithm for Swapped Battery Charging Dispatch Model Considering Continuous Charging Power

Bai, Jiawen; Ding, Tao; Jia, Wenhao; Zhu, Shanying; Bai, Linquan; Li, Fangxing

doi:10.1109/tase.2022.3220280

Cited by 6 publications

(4 citation statements)

References 38 publications

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“…The selected vehicle is dispatched to charge, serve the order or remain idle (see lines [16][17]. The (s i t−1 , a i t−1 , r i t−1 , s i t ) quadruple corresponding to the previous state is then collected (see lines [18][19].…”

Section: Summary Of the Overall Algorithmmentioning

confidence: 99%

“…Prior research has endeavored to provide pertinent recommendations for EV users, focusing primarily on factors such as charging time cost [9][10][11][12][13] and electricity price [14][15][16]. Battery swap station recommendations have also gained attention, often emphasizing path or electricity cost due to the notably shorter time associated with swapping compared to traditional charging stations [17][18][19]. Furthermore, a subset of recent studies has begun to incorporate charging path optimization into the decision-making process for selecting charging stations while considering energy constraints and recuperation [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

PROLIFIC: Deep Reinforcement Learning for Efficient EV Fleet Scheduling and Charging

Ma,

Zhang,

Duan

et al. 2023

Sustainability

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Electric vehicles (EVs) are becoming increasingly popular in ride-hailing services, but their slow charging speed negatively affects service efficiency. To address this challenge, we propose PROLIFIC, a deep reinforcement learning-based approach for efficient EV scheduling and charging in ride-hailing services. The objective of PROLIFIC is to minimize passenger waiting time and charging time cost. PROLIFIC formulates the EV scheduling problem as a Markov decision process and integrates a distributed charging scheduling management model and a centralized order dispatching model. By using a distributed deep Q-network, the agents can share charging and EV supply information to make efficient interactions between charging and dispatch decisions. This approach reduces the curse of dimensionality problem and improves the training efficiency of the neural network. The proposed approach is validated in three typical scenarios with different spatiotemporal distribution characteristics of passenger order, and the results demonstrate that PROLIFIC significantly reduces the passenger waiting time and charging time cost in all three scenarios compared to baseline algorithms.

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Section: Summary Of the Overall Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PROLIFIC: Deep Reinforcement Learning for Efficient EV Fleet Scheduling and Charging

Ma,

Zhang,

Duan

et al. 2023

Sustainability

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show abstract

“…Hence, Resilience, or performance and stability in the face of such uncertainty, is not guaranteed by conventional controllers such as PI. Liion batteries have been critical parts of energy and mobility advancements in recent years in different applications in the sky [14] and on the earth [15], [16]. However, the resilience and autonomy of battery chargers against unexpected faults have not been well discussed.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Resilience in Battery Charging through Co-Simulation with Reinforcement Learning

Seyedi,

Sheida,

Siner

et al. 2024

Preprint

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Controlling the charging and discharging procedures of Lithium-Ion Batteries is of paramount importance as violating safety constraints, such as current deviations, can lead to significant damage to the battery or circuit or interruption in service. Thus, it is crucial to employ a robust controller capable of handling uncertainties and unexpected scenarios. PI controllers have become prevalent in recent years for managing battery dynamics, but they exhibit limited robustness in unpredictable situations. In this paper, we propose a Reinforcement Learning (RL) driven control method as a substitute for the PI controller. The agent is trained using a co-simulation approach with simultaneous employment of Python and Matlab, ensuring an accurate estimation of the environment and, consequently, enhanced performance. A prototype of the proposed controller is developed using dSPACE rapid control prototyper. The performance is compared with the benchmark controller (PI) across different fault scenarios, considering three criteria: overshoot, undershoot, and stabilization time. The comparative analysis reveals that, in most scenarios, the RL agent outperforms the PI controller, exhibiting a remarkable 50% reduction in both overshoot and undershoot compared to the benchmark controller. This research contributes to advancing battery control systems by introducing an RL-based controller that proves to be a more robust alternative, delivering improved performance in the face of uncertainties and fault scenarios.

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“…In recent years, many researchers have addressed the Rectangle Packing problem by using various optimization algorithms [2]. Due to the complexity of the Rectangle Packing problem, these algorithms cannot guarantee to obtain the optimal global solution.…”

Section: Introductionmentioning

confidence: 99%

Differential evolutionary algorithm based on principal component analysis for the satellite bandwidth resource scheduling problem

Wang,

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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This paper focuses on the research of the communication satellite bandwidth resource scheduling problem. Satellite resource scheduling refers to adjusting the order of task execution within a certain scheduling time to complete as many satellite tasks as possible while satisfying bandwidth and time constraints. Traditional algorithms cannot meet the time requirements in the process of satellite communication resource scheduling. Therefore, this paper proposes a differential evolution algorithm based on principal component analysis (PCA) and combines it with the Lowest Horizontal Line Algorithm (LHLA) to solve this problem. The method was validated on two datasets at 20 dimensions and 40 dimensions, and compared with other traditional DE algorithms. The experimental results showed that the method obtained better solutions.

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Online Rectangle Packing Algorithm for Swapped Battery Charging Dispatch Model Considering Continuous Charging Power

Cited by 6 publications

References 38 publications

PROLIFIC: Deep Reinforcement Learning for Efficient EV Fleet Scheduling and Charging

PROLIFIC: Deep Reinforcement Learning for Efficient EV Fleet Scheduling and Charging

Enhanced Resilience in Battery Charging through Co-Simulation with Reinforcement Learning

Differential evolutionary algorithm based on principal component analysis for the satellite bandwidth resource scheduling problem

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