Multi-Objective Optimization of a Battery-Supercapacitor Hybrid Energy Storage System Based on the Concept of Cyber-Physical System

Abstract

Optimal operation of energy storage systems plays an important role in enhancing their lifetime and efficiency. This paper combines the concepts of the cyber–physical system (CPS) and multi-objective optimization into the control structure of the hybrid energy storage system (HESS). Owing to the time-varying characteristics of HESS, combining real-time data with physical models via CPS can significantly promote the performance of HESS. The multi-objective optimization model designed in this paper can improve the utilization of supercapacitors, reduce energy consumption, and prevent the state of charge (SOC) of HESS from exceeding the limitation. The new control scheme takes the characteristics of the components of HESS into account and is beneficial in reducing battery short-term power cycling and high discharge currents. The rain-flow counting algorithm is applied for battery life prediction to quantify the benefits of the HESS under the control scheme proposed. A much better power-sharing relationship between the supercapacitor and the lithium–ion battery (LiB) can be observed from the SIMULINK results and the case study with our new control scheme. Moreover, compared to the traditional low-pass filter control method, the battery lifetime is quantifiably increased from 3.51 years to 10.20 years while the energy efficiency is improved by 1.56%.

Evaluation of Multi-Objective Optimization Techniques for Resilience Enhancement of Electric Vehicles

“…Equation (7) implies that energy allocated to each EV should be less than or equal to the amount of energy required by that EV. Finally, Equation (8) implies that the total amount of allocated energy to all EVs should be less than or equal to the available energy (E avail ).…”

“…All Pareto optimal solutions are considered equally good if no preference information is provided [3]. Due to the ability to provide acceptable solutions for various complex real-world problems, MOO is used in several fields, including power systems and microgrids [4][5][6][7][8].…”

“…A multi-objective genetic algorithm is used to solve the planning problem. A MOO problem is formulated [8] for a hybrid storage system for maximizing the utilization, reducing energy consumption, and preventing overcharging of the energy storage system.…”

An Adaptive Energy Optimization Method of Hybrid Battery-Supercapacitor Storage System for Uncertain Demand

“…Most of the aforementioned literature focuses on suppressing power fluctuation based on the method of storage device limits, to prevent overcharging or over-discharging for the energy-storage system in a grid-connected DC microgrid. However, there are few studies on the power balance of off-grid DC microgrid connected to the fluctuation load, the adaptive adjustment of storage energy SoC, and the operation optimal of an off-grid DC microgrid [20][21][22]. In [20], a management scheme is proposed for a hybrid energy-storage system with battery and supercapacitor energy storage as the core, and the designed scheme has adaptive characteristics.…”

“…Another study, Ref. [22], proposed a first-order low-pass filtering method to filter DG power fluctuations, but the sensitivity of low-pass filtering to the typical characteristics of power fluctuations, such as pulsating loads, and its applicability are slightly poorer. The power balance of an off-grid DC microgrid and the suppression of DG fluctuations are quite different.…”