Optimal Filter-Based Energy Management for Hybrid Energy Storage Systems with Energy Consumption Minimization

The proposed optimal energy management system balances the energy flows among the energy consumption by accelerating trains, energy production from decelerating trains, energy from wind and solar photovoltaic (PV) energy systems, energy storage systems, and the energy exchange with a traditional electrical grid. In this paper, an AC optimal power flow (AC-OPF) problem is formulated by optimizing the total cost of operation of a railroad electrical system. The railroad system considered in this paper is composed of renewable energy resources such as wind and solar PV systems, regenerative braking capabilities, and hybrid energy storage systems. The hybrid energy storage systems include storage batteries and supercapacitors. The uncertainties associated with wind and solar PV powers are handled using probability distribution functions. The proposed optimization problem is solved using the differential evolution algorithm (DEA). The simulation results show the suitability and effectiveness of proposed approach.

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An Optimal Power and Energy Management by Hybrid Energy Storage Systems in Microgrids

Energies ◽

10.3390/en10111909 ◽

2017 ◽

Vol 10 (11) ◽

pp. 1909 ◽

Cited By ~ 12

Author(s):

Alessandro Serpi ◽

Mario Porru ◽

Alfonso Damiano

Keyword(s):

Energy Storage ◽

Energy Management ◽

Storage Systems ◽

Energy Storage Systems ◽

Hybrid Energy ◽

Hybrid Energy Storage ◽

Optimal Power ◽

Power And Energy

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A Study of Control Methodologies for the Trade-Off between Battery Aging and Energy Consumption on Electric Vehicles with Hybrid Energy Storage Systems

Energies ◽

10.3390/en15020600 ◽

2022 ◽

Vol 15 (2) ◽

pp. 600

Author(s):

Kevin Mallon ◽

Francis Assadian

Keyword(s):

Energy Consumption ◽

Energy Storage ◽

Electric Vehicle ◽

Energy Management ◽

Storage Systems ◽

Storage System ◽

Energy Storage System ◽

Trade Off ◽

Hybrid Energy ◽

Hybrid Energy Storage

Hybrid and electric vehicle batteries deteriorate from use due to irreversible internal chemical and mechanical changes, resulting in decreased capacity and efficiency of the energy storage system. This article investigates the modeling and control of a lithium-ion battery and ultracapacitor hybrid energy storage system for an electric vehicle for improved battery lifespan and energy consumption. By developing a control-oriented aging model for the energy storage components and integrating the aging models into an energy management system, the trade-off between battery degradation and energy consumption can be minimized. This article produces an optimal aging-aware energy management strategy that controls both battery and ultracapacitor aging and compares these results to strategies that control only battery aging, strategies that control battery aging factors but not aging itself, and non-optimal benchmark strategies. A case study on an electric bus with variously-sized hybrid energy storage systems shows that a strategy designed to control battery aging, ultracapacitor aging, and energy losses simultaneously can achieve a 28.2% increase to battery lifespan while requiring only a 7.0% decrease in fuel economy.

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Development of Machine Learning Methods in Hybrid Energy Storage Systems in Electric Vehicles

Mathematical Problems in Engineering ◽

10.1155/2022/3693263 ◽

2022 ◽

Vol 2022 ◽

pp. 1-8

Author(s):

Tzu-Chia Chen ◽

Fouad Jameel Ibrahim Alazzawi ◽

John William Grimaldo Guerrero ◽

Paitoon Chetthamrongchai ◽

Aleksei Dorofeev ◽

...

Keyword(s):

Machine Learning ◽

Energy Storage ◽

Energy Management ◽

Management System ◽

Storage Systems ◽

Energy Management System ◽

Energy Storage Systems ◽

Hybrid Energy ◽

Hybrid Energy Storage ◽

Machine Learning Methods

The hybrid energy storage systems are a practical tool to solve the issues in single energy storage systems in terms of specific power supply and high specific energy. These systems are especially applicable in electric and hybrid vehicles. Applying a dynamic and coherent strategy plays a key role in managing a hybrid energy storage system. The data obtained while driving and information collected from energy storage systems can be used to analyze the performance of the provided energy management method. Most existing energy management models follow predetermined rules that are unsuitable for vehicles moving in different modes and conditions. Therefore, it is so advantageous to provide an energy management system that can learn from the environment and the driving cycle and send the needed data to a control system for optimal management. In this research, the machine learning method and its application in increasing the efficiency of a hybrid energy storage management system are applied. In this regard, the energy management system is designed based on machine learning methods so that the system can learn to take the necessary actions in different situations directly and without the use of predicted select and run the predefined rules. The advantage of this method is accurate and effective control with high efficiency through direct interaction with the environment around the system. The numerical results show that the proposed machine learning method can achieve the least mean square error in all strategies.

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