TY - JOUR
T1 - Dynamic Charging Optimization for Mobile Charging Stations in Internet of Things
AU - Chen, Huwei
AU - Su, Zhou
AU - Hui, Yilong
AU - Hui, Hui
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2018/9/14
Y1 - 2018/9/14
N2 - The increasing use of Internet of Things (IoTs) has brought more advantages in supplying power to electric vehicles (EVs). With the help of IoTs, EVs can be charged more easily by mobile charging stations (MCSs) compared with the fixed charging stations (FCSs). However, previous works in the management of power supply in FCSs have not been properly applied in MCSs, e.g., dynamic of EV users' arrival and variable power supply from MCSs in IoTs. In this paper, we study how to manage MCSs' supply power in IoTs under the condition that MCSs supply multiple kinds of power. First, considering the randomness of power supply and dynamic of EV users' arrival, we develop the dynamic framework of power supply and the economic model. Then, aiming to maximize the long-term average profits of MCSs, a stochastic optimization problem is formulated to decide the optimal strategy of power management. Based on the Lyapunov optimization theory, a Lyapunov-based online distributed algorithm is proposed to obtain the optimal solutions. Meanwhile, the performance of our proposed algorithm is analyzed and simulation results validate the effectiveness of our proposal.
AB - The increasing use of Internet of Things (IoTs) has brought more advantages in supplying power to electric vehicles (EVs). With the help of IoTs, EVs can be charged more easily by mobile charging stations (MCSs) compared with the fixed charging stations (FCSs). However, previous works in the management of power supply in FCSs have not been properly applied in MCSs, e.g., dynamic of EV users' arrival and variable power supply from MCSs in IoTs. In this paper, we study how to manage MCSs' supply power in IoTs under the condition that MCSs supply multiple kinds of power. First, considering the randomness of power supply and dynamic of EV users' arrival, we develop the dynamic framework of power supply and the economic model. Then, aiming to maximize the long-term average profits of MCSs, a stochastic optimization problem is formulated to decide the optimal strategy of power management. Based on the Lyapunov optimization theory, a Lyapunov-based online distributed algorithm is proposed to obtain the optimal solutions. Meanwhile, the performance of our proposed algorithm is analyzed and simulation results validate the effectiveness of our proposal.
KW - Internet of Things
KW - Lyapunov optimization
KW - electric vehicles
KW - mobile charging stations
KW - online distributed algorithm
UR - https://www.scopus.com/pages/publications/85053287347
U2 - 10.1109/ACCESS.2018.2868937
DO - 10.1109/ACCESS.2018.2868937
M3 - 文章
AN - SCOPUS:85053287347
SN - 2169-3536
VL - 6
SP - 53509
EP - 53520
JO - IEEE Access
JF - IEEE Access
M1 - 8466586
ER -