Adaptive energy management strategy for high-speed railway hybrid energy storage system based on double-layer fuzzy logic control

Luo, Jiaming; Gao, Shibin; Wei, Xiaoguang; Tian, Zhongbei

doi:10.1016/j.ijepes.2023.109739

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…This indicator is calculated with the ampere-hour integration, as seen in Equation ( 11) or an extended Kalman filter [93,[98][99][100][101][102][103].…”

Section: State Of Charge (Soc)mentioning

confidence: 99%

“…It is considered an interesting indicator as a better efficiency indicates better functioning of the system. This indicator is calculated by dividing the output energy or the energy used and the input energy or the total energy, as seen in Equation ( 13) [82,98,100,106].…”

Section: • Ah Throughput Batterymentioning

confidence: 99%

“…This indicator is calculated by considering the cycle life and the depth of discharge, as seen in Equation ( 15) [100,111].…”

Section: • Remaining Lifementioning

confidence: 99%

“…These kinds of vehicle, to be socially accepted, need to provide a very similar execution to gasoil ones, which is translated to a need for the energy necessary to achieve similar speeds and a high autonomy joined to the irregularity the load always presents, which makes the combination of a HESS and EMS the perfect tool. Although it is more frequently seen in electrical cars and buses, there are also cases for motorcycles and even high-speed trains [97,100].…”

Section: Applicationsmentioning

confidence: 99%

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Optimizing Performance of Hybrid Electrochemical Energy Storage Systems through Effective Control: A Comprehensive Review

Clemente,

Arias,

Gevorkov

et al. 2024

Electronics

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The implementation of energy storage system (ESS) technology with an appropriate control system can enhance the resilience and economic performance of power systems. However, none of the storage options available today can perform at their best in every situation. As a matter of fact, an isolated storage solution’s energy and power density, lifespan, cost, and response time are its primary performance constraints. Batteries are the essential energy storage component used in electric mobility, industries, and household applications nowadays. In general, the battery energy storage systems (BESS) currently available on the market are based on a homogeneous type of electrochemical battery. However, a hybrid energy storage system (HESS) based on a mixture of various types of electrochemical batteries can potentially provide a better option for high-performance electric cars, heavy-duty electric vehicles, industries, and residential purposes. A hybrid energy storage system combines two or more electrochemical energy storage systems to provide a more reliable and efficient energy storage solution. At the same time, the integration of multiple energy storage systems in an HESS requires advanced control strategies to ensure optimal performance and longevity of the system. This review paper aims to provide a comprehensive overview of the control systems used in HESSs for a wide range of applications. An overview of the various control strategies used in HESSs is offered, including traditional control methods such as proportional–integral–derivative (PID) control, and advanced control methods such as model predictive control (MPC), droop control (DC), sliding mode control (SMC), rule-based control (RBC), fuzzy logic control (FLC), and artificial neural network (ANN) control are discussed. The paper also highlights the recent developments in HESS control systems, including the use of machine learning techniques such as deep reinforcement learning (DRL) and genetic algorithms (GA). The paper provides not only a description and classification of various control approaches but also a comparison between control strategies from the evaluation of performance point of view. The review concludes by summarizing the key findings and future research directions for HESS control systems, which is directly linked to the research on machine learning and the mix of different control type strategies.

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“…This indicator is calculated with the ampere-hour integration, as seen in Equation ( 11) or an extended Kalman filter [93,[98][99][100][101][102][103].…”

Section: State Of Charge (Soc)mentioning

confidence: 99%