On large action space in EV charging scheduling optimization

Jiang, Zhaoyu; Jia, Qing‐Shan; Guan, Xiaohong

doi:10.1007/s11432-020-3106-7

Cited by 13 publications

(2 citation statements)

References 38 publications

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“…These methods are usually easy to implement, but they rely on some assumptions on the system and lack of performance guarantee. In light of this, event-based optimisation (Jiang, Jia, & Guan, 2022;Long & Jia, 2021b;Long, Tang, & Jia, 2017), ordinal optimisation (Long, Jia, Wang, & Yang, 2021) and hierarchical optimisation methods (Huang, Jia, & Guan, 2016) have been proposed recently to put forward new ideas for solving the problem quickly and efficiently.…”

Section: Smart Charging Of Electric Vehiclesmentioning

confidence: 99%

Control for smart systems: Challenges and trends in smart cities

Jia

Panetto

Macchi

et al. 2022

Annual Reviews in Control

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Section: Smart Charging Of Electric Vehiclesmentioning

confidence: 99%

Control for smart systems: Challenges and trends in smart cities

Jia

Panetto

Macchi

et al. 2022

Annual Reviews in Control

Self Cite

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“…In summary, among the main functions to be performed on batteries by a BMS are charge and discharge control [ 12 ], thermal management [ 13 ], battery equalization [ 14 ], fault diagnosis [ 15 , 16 , 17 ], data acquisition, communication [ 18 ], and the estimation of the battery’s state of charge [ 19 , 20 ], state of energy (SoE) [ 21 ], state of power (SoP) [ 22 ], and state of health (SoH) [ 23 ]. Especially in top-priority safety-critical cases, they require the application of safeguarding functions to the system, including battery disconnection operations from generation or consumption sources [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Distributed Intelligent Battery Management System Using a Real-World Cloud Computing System

García

Quiles

Correcher

2023

Sensors

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In this work, a decentralized but synchronized real-world system for smart battery management was designed by using a general controller with cloud computing capability, four charge regulators, and a set of sensorized battery monitors with networking and Bluetooth capabilities. Currently, for real-world applications, battery management systems (BMSs) can be used in the form of distributed control systems where general controllers, charge regulators, and smart monitors and sensors are integrated, such as those proposed in this work, which allow more precise estimations of a large set of important parameters, such as the state of charge (SOC), state of health (SOH), current, voltage, and temperature, seeking the safety and the extension of the useful life of energy storage systems based on battery banks. The system used is a paradigmatic real-world example of the so-called intelligent battery management systems. One of the contributions made in this work is the realization of a distributed design of a BMS, which adds the benefit of increased system security compared to a fully centralized BMS structure. Another research contribution made in this work is the development of a methodical modeling procedure based on Petri Nets, which establishes, in a visible, organized, and precise way, the set of conditions that will determine the operation of the BMS. If this modeling is not carried out, the threshold values and their conditions remain scattered, not very transparent, and difficult to deal with in an aggregate way.

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