Sizing Of Hybrid Energy Storage System Research Articles

Optimal sizing and cost analysis of hybrid energy storage system for EVs using metaheuristic PSO and firefly algorithms

This paper presents a single-objective function optimization method for the optimal sizing and cost of a hybrid energy storage system (HESS) that integrates lithium-ion batteries (LIB) and supercapacitors (SC) for electric vehicle (EV) applications. The study introduces a comprehensive framework for EV modeling, incorporating simulated EV data to enhance accuracy. A key achievement involves adapting the modified–WLTC driving cycle, iteratively employed in EV simulations to accurately capture the spectrum of power and energy profiles within the designated range, ensuring adherence to BMW-i3's top speed requisites. The proposed method's validity is established by comparing optimization results using Particle Swarm Optimization (PSO) and Firefly Algorithm (FA), indicating comparable HESS sizing and cost outcomes. Notably, the PSO algorithm demonstrates superior accuracy and computational efficiency. Through PSO, the optimal LIB-SC HESS weight is determined at 160 kg with an optimal cost of $27,660, while FA yields 161 kg and $28,270, surpassing LIB-Only EV models by approximately 21 % in improved sizing. This research significantly contributes by establishing a robust EV modeling paradigm, employing simulated data for optimization, and successfully implementing PSO to determine optimal HESS parameters, advancing the field of efficient EV energy storage and promoting cost-effective, sustainable electric transportation.

A self-adaptive joint optimization framework for marine hybrid energy storage system design considering load fluctuation characteristics

Recently, with the development of new energy technologies, all-electric ships (AESs) with hybrid energy storage system (HESS) are becoming a promising solution to reduce fuel consumption and emissions. However, the high maneuverability of ships during the actual navigation places higher performance requirements on the HESS, which presents a nonlinear and multi-objective challenge for the HESS design. Therefore, it is necessary to consider the coupling between HESS sizing and energy management strategy (EMS). In this paper, a self-adaptive joint optimization framework (SJOF) for marine HESS design considering load fluctuation characteristics is proposed, which can find the optimal decision solution with excellent system economic and battery life performance for AES HESS design. Based on the rain flow counting (RFC) method, a multi-objective joint optimization method considering life cycle cost (LCC) and battery degradation index (BDI) is introduced into SJOF. Besides, a novel EMS including the self-adaptive segmentation mechanism (SSM) and power allocation is proposed, which can achieve the most efficient energy scheduling.

Optimal sizing of battery-supercapacitor energy storage systems for trams using improved PSO algorithm

A hybrid energy storage system (HESS) of tram composed of different energy storage elements (ESEs) is gradually being adopted, leveraging the advantages of each ESE. The optimal sizing of HESS with a reasonable combination of different ESEs has become an important issue in improving energy management efficiency. Therefore, the optimal sizing method of battery-supercapacitor energy storage systems for trams is developed to investigate the optimal configuration of ESEs based on a constant power threshold. Firstly, the optimal sizing model of HESS taking size, mass, and cost of ESEs as a comprehensive objective function is established. Then, an improved particle swarm optimization (PSO) algorithm with a competition mechanism is developed for obtaining the optimal configurations of ESEs. Finally, Guangzhou Haizhu tram is taken as an example to verify the developed method. The results show that the optimal HESS with different ESEs has advantages in reducing the size, mass, and cost, and exerting the charging and discharging capacity of ESEs.

Cost-Effective Size of HESS Incorporated in VSG With Enhanced Performance of Battery Providing Implicit Inertia

Virtual synchronous generator (VSG) incorporates hybrid energy storage system (HESS) comprising supercapacitor (SC) and battery energy storage system (BESS) for frequency support services (FSS). In this work, new aspects namely implicit contribution of BESS in inertia and subjection of higher magnitude stress factors (SFs) on BESS during FSS are observed, while HESS is sized by existing methodology. Least square estimation and novel performance evaluation model are used to quantify the BESS implicit contribution in inertia and decrease in its desirable performance due to higher SFs, respectively. The resulting computations are utilized in reducing the SC size and marginally increasing the BESS size that leads to reduction in SFs on it. The estimated HESS size also demonstrates lower investment cost (INVC). Further, the implicit contribution by BESS in inertia and reduction in SFs with its estimated size is validated at Power Hardware-In-Loop (PHIL) level.

An improved multi-timescale coordinated control strategy for an integrated energy system with a hybrid energy storage system

In view of the complex energy coupling and fluctuation of renewable energy sources in the integrated energy system, this paper proposes an improved multi-timescale coordinated control strategy for an integrated energy system (IES) with a hybrid energy storage system (HESS). The power response characteristics of various devices in IES at different time scales are analyzed which shows that the multi-timescale coordinated control method can be well coupled with the IES including HESS. The advantages of HESS over single energy storage system in stabilizing power fluctuation and extending energy storage life are compared and analyzed while the control method of supercapacitor under multi-time scale coordinated control strategy is proposed. The primary energy consumption, operating costs, carbon dioxide emissions and load shedding costs are considered the optimization objectives in the day-ahead rolling optimization stage according to the day-ahead forecast renewable energy (RE) data. In the intraday rolling adjusting stage, the proposed method can reduce the effect of the RE day-ahead forecasting errors to achieve the intraday energy scheduling balance and ensure the safe operation of the devices of IES. Considering the background of IES with a high proportion of renewable energy, this paper makes innovative use of the advantages of HESS to improve the power response characteristic of the system. The high-frequency part of RE is balanced by HESS in the real-time coordinated control stage. A typical structure of IES is applied and the simulation results are analyzed to show the performance of the proposed method in this paper. Moreover, general rules for sizing of HESS are also proposed. The influence of the hybrid energy storage capacity allocation ratio on battery capacity loss and carbon emission reduction is discussed. The Pareto front including the configuration and cost of supercapacitor has been obtained. As shown in the case, the application of HESS in the IES can improve the power response characteristic, extend the lithium-ion battery (LiB) life and has more benefits in reducing carbon emissions.

Optimization of Hybrid Energy Storage System Sizing With Considering Energy Management Strategy for High-Power Pulsed Load in Aircraft

High-power pulsed load (HPPL) such as airborne laser weapon and radar poses a severe challenge to aircraft electrical power system. Since peak power cannot be satisfied only by generator, hybrid energy storage system (HESS) should be well sized to match their energetic performances versus instantaneous power requirement and improve reliability of electrical power system. To deal with these challenges, a sizing optimization for HESS with considering energy management strategy (EMS) based on virtual-droop control in aircraft is proposed in this paper. By analyzing the spectrum characteristics of HPPL profile, splitting frequency between different types of energy storage devices is specially analyzed and designed to give full play to HESS. HESS weight, loss and parameters in EMS are taken into account in this multi-objective optimization based on Non-dominated Sorting Genetic Algorithms II (NSGA-II). Series of Pareto fronts can be obtained through the optimization process, and the most suitable solutions of different HESS combinations are selected by a decision-making means which combines subjective and objective weighting methods. Finally, optimization results of four different combinations are compared. Simulation results are obtained in MATLAB/Simulink environment to prove the rationality and applicability of the optimal sizing tool. The research has resulted in a solution of HESS and EMS optimization for HPPL used in aircraft.

Optimal sizing and operation of hybrid energy storage systems in co‐phase traction power supply system considering battery degradation

AbstractTo recycle regenerative braking energy (RBE) while reducing demand charge in electrified railway, a co‐phase power supply system with hybrid energy storage system (HESS) is implemented. However, the dynamical degradation characteristic of battery is necessary to be considered in optimal operation of HESS. A bi‐level model considering battery degradation is proposed to obtain optimal sizing and operation of HESS. The proposed model includes a novel real‐time energy management strategy (EMS) using average power as thresholds, to effectively reduce the demand charge and energy consumption charge. Thresholds are dynamically adjusted with the consideration of battery capacity degradation. A real measured load profile from Beijing‐Shanghai high speed railway is studied. The results demonstrate that the proposed EMS performs better than previous EMS in cost saving. The dynamically adjusted thresholds of EMS are proved to be essential under the consideration of battery degradation. With the optimal sizing of the HESS, the traction substation can achieve 8.69% annual saving of demand charge and recycle 52.33% of the RBE. The results also show that a traction substation equipped with the HESS yields higher economic benefit than the energy storage systems equipped with only a battery or a supercapacitor.

Design of semi-actively controlled battery-supercapacitor hybrid energy storage system

For vehicle propulsion, electric vehicles (EVs) utilize lithium ion (Li-ion) based battery storage system. Electric powertrain provides high Efficiency, but Li-ion cells liable to degradation with increase in temperature and load. While a larger energy storage system (ESS) produces more power, but it also increases the ESS's cost, volume, and size. A hybrid energy storage system (HESS) based on batteries and supercapacitor can be utilized to minimize total ESS size and improve performance during heavy loading conditions, with the supercapacitor provide power during charging/discharging peak power. Battery and supercapacitor are connected together with the help of a DC-DC Converter. HESS is a single-unit battery and supercapacitor that combines the benefits of high battery energy density and high supercapacitor power density. Whereas Sizing of HESS is a challenging task because of adding supercapacitor it increases overall size and cost of ESS, to address this issue in this study particle swarm optimization (PSO) is used to optimize HESS size and increase battery cycle life (BCL). The MATLAB/SIMULINK environment is used to model both the Battery Energy Storage System (BESS) and the Hybrid Energy Storage System (HESS). Optimized results are used to compare battery cycle life of BESS and HESS. The optimization findings under the Urban Dynamometer Driving Schedule (UDDS) are compared to the battery-only ESS results, which show a 8.927 percent increase in battery cycle life.

A novel long-term power forecasting based smart grid hybrid energy storage system optimal sizing method considering uncertainties

With the penetration of renewable generation, the reliability of modern power systems is increasingly challenged. This is especially true for power systems with comparatively low inertia, such as smart grids. To mitigate the impact of renewable intermittency on smart grid operation, a hybrid energy storage system (HESS) is widely employed. Nonetheless, the proper sizing of HESS in smart grids remains a technical challenge. Most of the existing energy storage sizing methods rely on historical data or deterministic renewable generation/load forecasting. Their results can be unconvincing with the presence of uncertainties. To figure out the optimal size of HESS in smart grids using probability-based long-term forecasting, this paper proposes a novel sizing approach with an improved forecasting accuracy. It considers the impact of uncertainties and the life cycle cost of HESS.

Sizing of a Plug-In Hybrid Electric Vehicle with the Hybrid Energy Storage System

For plug-in hybrid electric vehicle (PHEV), using a hybrid energy storage system (HESS) instead of a single battery system can prolong the battery life and reduce the vehicle cost. To develop a PHEV with HESS, it is a key link to obtain the optimal size of the power supply and energy system that can meet the load requirements of a driving cycle. Since little effort has been dedicated to simultaneously performing the component sizing of PHEV and HESS, this paper proposes an approach based on the particle swarm optimization (PSO) algorithm to simultaneously determine the sizes of the engine, motor, battery and supercapacitor (SC) in a PHEV with HESS. The drivetrain cost is minimized in a different all-electric range (AER)—and depends on the battery type—while ensuring the driving performance requirements. In addition, the effects of the power system and drive cycle on the component sizes were analyzed and compared. The simulation results show that the cost of the PHEV drivetrain with the Ni-MH battery/SC HESS is reduced by up to 12.21% when compared to the drivetrain with the Li-ion battery/SC HESS. The drivetrain cost is reduced by 8.79% when compared to analysis-based optimization. The type of power supply system and drive cycle can significantly affect the optimization results.

Optimal Scheduling of FTPSS With PV and HESS Considering the Online Degradation of Battery Capacity

To improve the power quality (PQ) and eliminate the neutral zone (NZ), a flexible traction power supply system (FTPSS) was proposed to provide flexible interfaces for hybrid energy storage system (HESS) and photovoltaic (PV). However, in order to realize the optimal scheduling of FTPSS, it is necessary to further study the high cost of HESS and battery available capacity. In this study, a two-layer model with the lowest comprehensive cost as the goal is proposed, which includes the cost of investment, replacement, operation and maintenance (O&M), and electricity. In the upper layer, the HESS sizing and replacement strategy are performed to achieve the lowest daily comprehensive cost within the project period. In the lower layer, based on the piecewise linear method, the battery capacity degradation is formulated as a linear mathematical model concerning the depth of discharge (DOD). Then, with the aim to achieve the lowest electricity charge, a mixed-integer linear programming (MILP) model is formulated by associating PV, regenerative braking energy (RBE), and HESS. Sparrow search algorithm (SSA) with CPLEX solver embedded is utilized to solve this two-layer nonlinear model. Finally, the simulation results show that the proposed model can achieve a 13.55% cost reduction.

Probabilistic Forecasting Based Sizing and Control of Hybrid Energy Storage for Wind Power Smoothing

With the increasing wind power integration, the security and economy of the power system operations are greatly influenced by the intermittency and fluctuation of wind power. Due to the flexible operational modes for charging/discharging, the hybrid energy storage system (HESS) is composed of battery energy storage system and super-capacitor can effectively mitigate the wind power uncertainty. This paper proposes a probabilistic forecasting-based HESS sizing and control scheme to cost-effectively smooth wind power fluctuations. First, probabilistic wind power forecasting is combined with multivariate Gaussian copula to generate temporally correlated wind power scenarios. Then, an adaptive variational mode decomposition (VMD) method is proposed to extract the frequency components of each wind scenario and determine the pre-scheduled power of wind-HESS system adaptively. Finally, a two-stage stochastic optimization model is constructed to determine the capacity and correct the pre-scheduled power of HESS with the objective of minimizing total cost. Case studies based on actual data from a Danish wind farm demonstrate that the proposed HESS sizing and control scheme can significantly reduce the installation cost and operation cost of HESS and prominently smooth wind power fluctuations.

Optimal Hybrid Energy Storage System Planning of Community Multi-Energy System Based on Two-Stage Stochastic Programming

The multi-energy system (MES) provides a good environment for the local consumption of renewable energy such as wind and solar power because of its high operational flexibility. In the MES, the hybrid energy storage system (HESS) composed of the battery and thermal storage tank plays an important role in enhancing reliability, economics, and operational flexibility. Hence, determining the optimal size of HESS in the MES is a critical problem but has not received enough attention. In light of this problem, this paper focuses on the optimal HESS planning problem in the community MES (CMES) under diverse uncertainties. Firstly, a two-stage stochastic planning model is proposed for the CMES to coordinate the optimal long-term HESS allocation and the short-term system operation. The thermal inertia in the heating network, space heating demand, and domestic hot water demand is utilized to reduce both the planning and operational cost. Secondly, a deterministic equivalence is proposed for the two-stage planning model to convert it into a mixed-integer linear programming model, which is then solved by off-the-shelf solvers. Finally, simulation results verify the effectiveness of the proposed method. The results reveal that the HESS can enhance the operational flexibility of the CMES but only needs a very few investment costs and prove that the thermal inertia in the CMES can reduce the investment cost of HESS, the fuel, and the operational maintenance cost.

An innovative approach of optimizing size and cost of hybrid energy storage system with state of charge regulation for stand-alone direct current microgrids

• This paper proposed a mathematical model for multi-objective optimization of hybrid energy storage system (HESS) in detail. • A novel penalty cost function for the HESS and a new SOC regulation concept for supercapacitor storage system have been introduced. • The proposed model was analysed under a case study. • An optimal HESS sizing was achieved to obtain the minimum total investment cost of HESS including the future replacement costs. • The best value for the energy ratio between supercapacitor storage and battery storage for the considered load profile was found as 25%. This study proposed a novel approach to optimize size and cost of hybrid energy storage systems (HESS) based on a solar photovoltaic (PV) fed stand-alone DC microgrid, while considering the state of charge (SOC) of both batteries and supercapacitors to assure the long life of batteries and well-being during the operation. The sizing strategy is combined with an optimization model and a HESS Assessment Algorithm. The strategy differs from traditional strategies since it comprises of not only the system cost, but also SOC regulation of both batteries and supercapacitors. A unique penalty cost function depending on the unutilized solar power generation and unserved demand power after HESS is fully charged or discharged has been introduced. By this penalty cost function, SOC of HESS can be kept within a safer margin to guarantee the system stability. Also, system availability can be assured by penalizing the unserved demand power. A new SOC regulation concept for supercapacitors has been introduced. SOC of supercapacitors is maintained in between a predefined focused band to ensure the supercapacitors availability to absorb or deliver sudden power fluctuations. The lifetime of HESS and the solar PV system are considered in the objective function to find the optimum sizing of HESS. The simulation was performed under Hambantota Solar Park weather conditions using MATLAB. Multi-objective genetic algorithm is used to optimize the objective functions. Comparing with a traditional sizing method, it shows that the novel optimal sizing strategy can reduce the BS system capacity by 18% by the hybridization of supercapacitors. The best value for the energy ratio between supercapacitors and batteries, for the considered load profile was found to be 25%. Results prove that the number of penalties, surplus solar power generations and unserved demand power were made minimum while obtaining an optimal sizing of HESS with a minimum system investment cost.

Sizing a battery-supercapacitor energy storage system with battery degradation consideration for high-performance electric vehicles

This paper presents sizing guides and energy management (EM) benchmarks of battery-supercapacitor (SC) hybrid energy storage system (HESS) in electric vehicle (EV) applications. We explain how to optimize the HESS size in order to minimize battery degradation and financial costs in EVs. We also illustrate the optimal EM benchmarks that can minimize battery degradation with whatever EM technique implemented. By decoupling the EM problem from the sizing one, we reveal the general trends of battery degradation with HESS size, which are irrelevant to design parameters of EVs and specifications of batteries and SCs. The vehicle-lifetime battery replacements and HESS costs are discussed with HESS sizing method. The efficacy of the proposed sizing guides and EM benchmarks is tested in the case study of a sports EV. Results show that the optimally sized HESS can extend battery lifetime by 37% as compared with the battery-only energy storage system and can reduce vehicle-lifetime HESS costs by up to 39% as compared with the unoptimized HESS designs, respectively.

Electric Vehicle Powertrain and Fuzzy Control Multi-Objective Optimization, Considering Dual Hybrid Energy Storage Systems

The hybrid energy storage system (HESS), which is composed of battery and ultracapacitor, is established to enhance the performance of an electric vehicle (EV). Moreover, several studies highlight the gains of split the traction power demand among electric motors (EMs) with different characteristics and drivetrain configurations, allowing these to operate at higher efficiency. In this paper, we unite the advantages of the multi EM drive train with the HESS power split and propose a novel two HESS system, in which each HESS is in charge of one of the drivetrain configurations (front and rear system). The EV is driven by two in-wheel EM at the front wheels and a single EM assembled with a differential transmission that moves the rear wheels. In this paper, a multiobjective optimization, based on a genetic algorithm (GA), is formulated to minimize the HESS sizing and maximize the driving range of the vehicle. Also employing the fuzzy control, this strategy is responsible to split the power between the front and rear wheels systems in a more reasonable way to satisfy the demands of better performance. The complete strategy has been developed under the FTP-75 (urban), HWFET (highway) and the US06 (high speed and required acceleration) driving cycles using the MATLAB/Simulink software environment. As compared to a similar EV with a single HESS system, the proposed dual-HESS configuration was able to improve the driving range in 145.15 km also decreasing 23.93% of the HESS mass.

Optimization of sizing and frequency control in battery/supercapacitor hybrid energy storage system for fuel cell ship

The fuel cell is generally coupled with the hybrid energy storage system (HESS) to improve power system dynamic performance and prolong the fuel cell lifetime. Therefore, the sizing of HESS and design of energy management strategy (EMS) have already become key research points. Based on support vector machine and frequency control, a novel EMS is proposed. As the sizing of HESS and the design of energy management strategy have a strong inner link, a multi-objective optimization method for the HESS and EMS is proposed. After that, simulations are used to compare the performance of the optimal hybrid power system. Compared with the different hybrid power system structures, the optimal HESS can meet power demand and reduce the cost of the energy storage device. Compared with the rule-based energy management strategy, the energy consumption of the optimal hybrid power system reduces 5.4%, and improves power quality and prolongs the device life. The results indicate that the proposed method can achieve excellent performance and is easily applied.

A Real-Time Energy Management and Speed Controller for an Electric Vehicle Powered by a Hybrid Energy Storage System

A real-time unified speed control and power flow management system for an electric vehicle (EV) powered by a battery-supercapacitor hybrid energy storage system (HESS) is developed following a nonlinear control system technique. In view of the coupling between energy management and HESS sizing, a HESS sizing model is developed in this article to optimally determine the size of HESS to serve an EV using the controller designed. The objectives of the controller are to track the set speed of the vehicle with globally exponential stability and to make use of the HESS wisely to reduce battery stress. The design provides a compound controller by exploiting the physical origins of the vehicles’ power demand. The controller and HESS sizing system designed are simulated on a standard urban dynamometer driving schedule and a recorded actual city driving cycle for a full-size EV to demonstrate their effectiveness.

Virtual Capacity of Hybrid Energy Storage Systems Using Adaptive State of Charge Range Control for Smoothing Renewable Intermittency

This paper presents a method for improving capability of a Hybrid Energy Storage System (HESS) comprised of a battery and supercapacitor (SC), for smoothing power fluctuations of renewable energy sources by adaptively controlling the state of charge (SOC) allocation range using automatic SOC management. The proposed method secures the preset SOC range of both battery and SC for power smoothing applications of renewables and also has the ability to significantly smooth power fluctuations using the virtual capacity concept of an energy storage system (ESS) by extending the SOC usage range. The bode plots of the proposed method are analyzed to observe the coverage of the power spectrum in the frequency domain in each storage based on the case studies. A criterion of HESS sizing, both battery and SC, is also developed based on the SOC range, smoothing time constant, and the power rating of renewables. The proposed method for the integration of HESS on renewable energy sources is verified with simulation results and characteristic analysis using PSCAD/EMTDC and MATLAB software.

Design criteria for the optimal sizing of a hybrid energy storage system in PV household-prosumers to maximize self-consumption and self-sufficiency

Batteries of photovoltaic (PV) household-prosumers undergo many fast, partial charge/discharge cycles because of the short-term fluctuations of household load and PV profiles. This negatively affects battery lifetime and can increase project cost involving energy storage systems (ESSs). To address this problem, this research developed an innovative analytical technique that assesses the techno-economic impact of battery-aging mechanisms and their influence on the optimal sizing of a hybrid energy storage system (HESS) for prosumers so as to minimize the total energy supply cost. This technique, implemented in a dynamic model of the integrated system, designs battery degradation, supercapacitor (SC) behaviour, converter hardware implementation, and power management strategy (PMS). The results, as reflected in technical short and long-term assessments, showed a potential improvement in self-consumption and self-sufficiency ratios due to PV, battery, SC, and the extension of battery lifetime in various PV-ESS sizing scenarios. The optimal PV-battery configuration was then determined by a techno-economic assessment, as well as the most suitable HESS sizing, while preserving the previous ratios of optimal PV-battery configuration, though with a lower life-cycle cost. The optimal PV-battery configuration was found to depend on the long-term power fluctuations of input profiles, whereas short-term fluctuations determined the optimal HESS sizing.