Battery-supercapacitor Hybrid Energy Storage System Research Articles

Capacity Sizing of Embedded Control Battery–Supercapacitor Hybrid Energy Storage System

A battery–supercapacitor hybrid energy storage system is investigated as a solution to reduce the high-power delivery stress on the battery. An optimally-sized system can further enhance the storage and cost efficiency. This paper discusses several possible problems in the sizing of a battery–supercapacitor hybrid energy storage system for practical applications. A sizing method that utilises data collected from a fully active embedded control hybrid energy system is proposed. The feasibility of the method is then tested on three load profiles that represent the load demand of inter- and intra-applications with a battery–supercapacitor hybrid energy storage system. The result is compared to a battery-only single energy storage system. The results verified that the number of batteries required in the hybrid energy storage system is reduced by at least 50% compared to the battery-only single energy storage system.

A Novel Multiport DC-DC Converter for Enhancing the Design and Performance of Battery–Supercapacitor Hybrid Energy Storage Systems for Unmanned Aerial Vehicles

This paper proposes an integrated multiport non-isolated DC–DC converter system for integrating battery–supercapacitor hybrid energy storage with photovoltaics for solar-powered unmanned aerial vehicles applications. Compared to the traditional topologies used, the proposed converter allows a size reduction of at least 20% of the supercapacitor by maximizing the utilization of the rated energy capacity. In addition, by proposing to use a phase-shifted carrier modulation technique, the inductors’ current ripple is reduced, which enables a further reduction in the inductor size. These improvements in capability and performance of the proposed topology are experimentally validated on a 500 W PV/battery–supercapacitor integrated power system prototype.

Parameter Matching Methods for Li Battery–Supercapacitor Hybrid Energy Storage Systems in Electric Buses

The parameter matching of composite energy storage systems will affect the realization of control strategy. In this study, the effective energy and power utilizations of an energy storage source were defined. With the miniaturization of a composite energy storage system as the optimization goal, the linear programming simplex method was employed to obtain the optimized masses of Li batteries and supercapacitors under the constraints of maximum speed, climbing gradient, acceleration time and cost-effectiveness. As the module numbers shall be integers, the matching results were modified in combination with the graphical method. Owing to the influences of parameter matching schemes on the overall performance and battery life, the critical points of constraints were analyzed and the most appropriate matching numerical points of the composite energy storage system were identified. Simulation and experimental analysis were conducted under practical urban road conditions in China. The results show that the proposed matching method delivers accurate results. Compared with conventional electric buses, the mileage and overall performance of the prototype bus are improved.

A Model Predictive Control Strategy for Performance Improvement of Hybrid Energy Storage Systems in DC Microgrids

Filtration-based (FB) power/current allocation of battery-supercapacitor (SC) hybrid energy storage systems (HESSs) is the most common approach in DC microgrid (MG) applications. In this approach, a low-pass or a high-pass filter is utilized to decompose the input power/current of HESS into high-frequency and low-frequency components and then assign the high-frequency parts to SC. Moreover, to avoid the state of charge violation (SoC) of SC, this approach requires a rule-based supervisory controller which may result in the discontinuous operation of SC. This paper first provides a small-signal stability analysis to investigate the impact of an FB current allocation system on the dynamic stability of an islanded DC MG in which a grid-forming HESS supplies a constant power load (CPL). Then, it shows that the continuous operation of SC is essential if the grid-forming HESS is loaded by large CPLs. To address this issue, this paper proposes a model predictive control (MPC) strategy that works in tandem with a high-pass filter to perform the current assignment between the battery and SC. This approach automatically restores the SoC of SC after sudden load changes and limits its SoC variation in a predefined range, so that ensure the continuous operation of SC. As a result, the proposed FB-MPC method indirectly enables the MG’s proportional-integral (PI) voltage controller to operate with higher gain values leading to better transient response and voltage quality. The performance of the proposed approach is then validated by simulating the system in MATLAB/Simulink.

Parameter Matching Method of a Battery-Supercapacitor Hybrid Energy Storage System for Electric Vehicles

To satisfy the high-rate power demand fluctuations in the complicated driving cycle, electric vehicle (EV) energy storage systems should have both high power density and high energy density. In order to obtain better energy and power performances, a combination of battery and supercapacitor are utilized in this work to form a semi-active hybrid energy storage system (HESS). A parameter matching method of battery-supercapacitor HESS for electric vehicles (EVs) is proposed. This method can meet the performance indicators of EVs in terms of power and energy for parameter matching. The result shows that optimized parameter matching is obtained by reducing the weight and cost.

A comparative study between lithium-ion battery and Pb-acid battery-supercapacitor hybrid energy storage system for frequency control and Energy management of islanded microgrids

Among various storage technologies used for the energy storage systems, the supercapacitors, the Pb-acid-Batteries (PABs) and the lithium-Batteries (LBs) are widely used for microgrid applications. The supercapacitors with high-power density are suitable for fast power regulations; conversely, the PABs have high-energy density, which makes them suitable for long-term energy management. Since the PABs and the supercapacitor can complement each other deficiencies, their combination as a hybrid energy storage system is used. However, the LB has both high-energy and high-power densities. Therefore, an LB Energy Storage System (LBESS) can similarly function like a Pb-acid battery-supercapacitor-hybrid-ESS (PSHESS). This paper tends to determine which one is technically and economically more suitable for applications in islanded microgrids. For this purpose, a frequency control and energy management scheme is proposed. It maintains the balance between demand and supply, and also keeps the microgrid frequency within safe operational limits using the least needed sizes for the energy storage systems. Simulation results reveal the costs of LIBESS and PSHESS would be $140325.93 and $209408.37, respectively, which shows that the cost of the PSHESS is $69082.44 or almost 49.2 % more than the cost of the LIBESS. This indicates that the LBESS is more cost-effective than the PSHESS.

A Study of the Energy Exchange within a Hybrid Energy Storage System and a Comparison of the Capacities, Lifetimes, and Costs of Different Systems

By combining the advantages of different energy storage technologies, the hybrid energy storage system (HESS) can satisfy the multiple requirements of prosumer systems. However, the required capacity of the HESS is larger than that of the single-battery energy storage system (ESS). This paper investigates the energy exchange within the HESS caused by the phase shift of the low-pass filter controller and its relevant impact on the HESS. The results show that unnecessary energy exchange results in an oversized capacity and increased energy loss. In addition, the increase in the time constant of the low-pass filter controller leads to a larger phase shift, further contributing to the increases in the total capacity and energy loss. Furthermore, this paper compares the single-battery ESS, the battery-supercapacitor HESS, and the battery-flywheel HESS implemented in a household-prosumer system along with a renewable energy source (RES). The comparison of the ESS combinations demonstrates the differences between their power flows, the required capacities of their individual energy storage devices (ESDs), their energy losses, their battery lifetimes, and their project costs. The results indicate that techno-economic analysis should be performed carefully to select the appropriate ESS solution for specific household-prosumer systems.

Research on control strategy of battery-supercapacitor hybrid energy storage system based on droop control

Abstract The hybrid energy storage system can compensate the bus power fluctuation caused by the output power and load variation of the generator set in the Direct Current (DC) microgrid. In the current control strategy, the voltage droop method is used to control the non-high-frequency components of the battery to suppress the power fluctuation of the bus and the integral droop method is used to control the components of the supercapacitor to suppress the high-frequency power fluctuation. The effect of this strategy is similar to that of a filter, which automatically divides the ripple power into high frequency and non-high-frequency ripple components. However, when the voltage drop method is adopted, the difference of battery State of charge (SOC) in the parallel hybrid energy storage unit is not considered and the non-high-frequency fluctuation components subjected to by the battery are not redistributed, which is not beneficial to the avoidance of battery overcharge and over discharge. It is considered that the voltage drop coefficient will be affected by SOC, the voltage drop coefficient is divided by the n power of SOC to get a new voltage drop coefficient, and then the improved voltage drop method related to the voltage drop coefficient SOC is obtained, and then a new coordinated control strategy is obtained by combining the integral drop method. Simulation and experimental results show that the proposed control strategy can reallocate the non-high-frequency power according to the SOC of the battery and suppress the bus power fluctuation at the same time, so as to realize the SOC balance of different energy storage cells, and the bus voltage fluctuation is small during the whole process.

A novel power management algorithm for a residential grid-connected PV system with battery-supercapacitor storage for increased self-consumption and self-sufficiency

The increasing penetration of renewable energy technologies causes major problems in the power network, as their generation cannot be totally predicted. Along with fluctuations in the generation of renewables due to weather uncertainties, storage is very important for mitigating several problems that may arise, affecting the stability and reliability of the grid. In particular, in recent years there has been an emphasis on residential storage applications (behind-the-meter storage), with the aim of increasing the energy self-consumption and therefore reducing electricity bills. The proposed model consists of a 3 kWp rooftop solar photovoltaic (PV) system connected to the grid through converters and a battery-supercapacitor hybrid energy storage system. The model is developed and simulated in the MATLAB/Simulink software environment, based on mathematical analysis and average modeling. The supercapacitor handles rapid changes that occur within 0.2 s, and this can relieve the battery stress and extend the battery lifetime. The building’s electricity demand is satisfied through the PV, hybrid energy storage and/or grid. A new filtration-based power management algorithm (PMA) is proposed here, prioritizing the utilization of the PV and battery-supercapacitor instead of the grid, thus achieving a reduced power exchange between the building and the grid and increasing the PV self-consumption and self-sufficiency of the building. The dynamic performance of the proposed model is verified through several simulations over short time periods (10–30 s) for different scenarios that could occur. The obtained results show that the model works properly and responds extremely fast during the different mode transitions, exhibiting a very fast DC-bus voltage regulation with a very small ripple voltage of up to 5 V (a maximum of ± 0.625%). Additionally, both battery and supercapacitor remain between their minimum and maximum limits. Finally, an effective power sharing is achieved between the PV, the battery-supercapacitor storage, the building load and the grid.

A distributed rule‐based power management strategy in a photovoltaic/hybrid energy storage based on an active compensation filtering technique

This paper proposes a distributed rule-based power management strategy for dynamic power balancing and power smoothing in a photovoltaic (PV)/battery-supercapacitor hybrid energy storage system. The system contains a PV system, a battery-supercapacitor hybrid energy storage system (HESS), and a group of loads. Firstly, an active compensation technique is proposed which improves the efficiency of the power smoothing filter. Then, a distributed supervisory control technique is employed that prevents the BESS and SC from SOC violation while maintaining the balance between generation and load. To this end, the system components are divided into three different reactive agents including an HESS agent, a PV agent, and a load agent. These agents react to the system changes by switching their operational mode upon satisfying a predefined rule. To analyse the hybrid dynamical behaviour of the agents and design the supervisory controllers, the agents are modelled in hybrid automata frameworks. It is shown that the proposed distributed approach reduces the complexity of the supervisory control system and increases its scalability compared to its equivalent centralized method. Finally, the performance of the proposed approach is validated using a test system simulated in MATLAB/Simulink.

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

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%.

Accurate modelling and analysis of battery–supercapacitor hybrid energy storage system in DC microgrid systems

Accurate modelling and analysis of battery–supercapacitor hybrid energy storage system in DC microgrid systems

Energy storage sizing in plug-in Electric Vehicles: Driving cycle uncertainty effect analysis and machine learning based sizing framework

Transportation electrification has been considered as one of the promising solutions towards clean environment. On-board Energy Storage System (ESS) has significant influence on the cost and reliability of Electric Vehicles (EVs). Furthermore, the choice of the ESS in terms of type and size, i.e., its capacity heavily depends on the driving patterns which, usually, vary significantly depending on factors like driver characteristics, geographical locations and traffic congestion. Hence, it is crucial to understand the effect of driving patterns/cycles on the sizing of ESS in order to avoid over/under-sizing. In this context, we investigate the effect of driving cycle uncertainties on the optimal sizing of ESS in EVs. We specifically focus on the uncertainty arising from the unpredictability of the future state of speed and acceleration and model the same as a scaling factor on velocity-magnitudes and time-windows of the velocity profile. Subsequently, (i) we derive the quantitative relationship between uncertainty and corresponding change in ESS size based on four thousand test driving cycles; (ii) we develop a framework which provides a guideline towards the choice of appropriate sizing of ESS in EVs subject to uncertainty in driving cycle; and (iii) we propose a machine-learning based framework that can enable a data-driven approach for ESS sizing with lower computational requirement. We choose a city bus application to illustrate our approach and consider three different ESS configurations: (i) Li-ion battery alone, (ii) Supercapacitor alone, and (iii) Battery–Supercapacitor hybrid ESS.

Adaptive Split-Frequency Quantitative Power Allocation for Hybrid Energy Storage Systems

As the two classical power allocation methods in battery-supercapacitor hybrid energy storage systems, split-frequency methods and power-level methods have been developed separately for many years. In this article, we made an attempt to integrate the advantages of the two methods and proposed an adaptive frequency-split-based quantitative power allocation strategy. First, an adaptive power preallocation is quantitatively determined according to the state of charge (SoC) of batteries and supercapacitors. Then, a windowed fast Fourier transform (FFT)-based power spectrum calculation algorithm is designed to derive the power level corresponding to each sampling frequency. By mapping the preallocated power to the power spectrum, the split frequency is adaptively computed. Finally, the power allocation is implemented through a low-pass filter (LPF) with the derived split frequency. Extensive experiments verify that the proposed method provides an improved performance in suppressing the dc bus voltage fluctuations and protecting batteries when compared with existing methods.

Toward Optimal Risk-Averse Configuration for HESS With CGANs-Based PV Scenario Generation

In this paper, an optimal risk-averse configuration framework for hybrid energy storage system (HESS) is proposed in planning utility-scale photovoltaic (PV) plants with conditional generative adversarial networks (CGANs)-based PV scenario generation. Other than most existing economy-based methods, we focus on frequency-based method to size a battery-supercapacitor HESS for mitigating the PV generation fluctuations in two time scales. We explore for the first time the potential of CGANs to generate sufficient PV scenarios through learning for experimental data preparation. For satisfying the fluctuation restrictions strictly, a data-driven frequency-based batteries optimization is developed, combining the flexible low-pass filter with wavelet package transform to guide the behaviors of both battery and supercapacitor for every individual scenario. Moreover, to hedge against risk exposure imposed by uncertain PV resource, we employ conditional value-at-risk to perform the optimal risk-averse configuration to meet the fluctuation mitigating requirements and minimize the expected configuration as well as the risk. Case studies are provided to verify the reasonableness and the efficiency of the proposed method.

Optimal sizing and sensitivity analysis of a battery-supercapacitor energy storage system for electric vehicles

This paper presents a sizing method with sensitivity analysis for battery-supercapacitor hybrid energy storage systems (HESSs) to minimize vehicle-lifetime costs. An optimization framework is proposed to solve joint energy management-sizing optimization. Sensitivity analysis is performed using eight parameters of the vehicle, HESS system and components as sensitive factors. We explain why HESS sizing is sensitive to each factor by discussing the change of optimal HESS size and costs with varying factor values. The relative importance of each factor in practical engineering is quantified and compared. Results show that battery degradation accounts for around 89% of HESS costs; among eight sensitive factors, vehicle driving range has the biggest impact on HESS costs with a calculated impact degree of 1.243. By analyzing comprehensive factors in optimization of HESS sizing, it is expected to provide general a sizing guide applicable to various application scenarios of HESS in electric vehicles.

New Optimal Control Algorithms for Battery-Supercapacitor HESS Based on Wirtinger-Based Integral Inequality

This paper focuses on the design of new optimal control algorithms for battery-supercapacitor hybrid energy storage system (HESS) with input saturation and time delay. Different from the existing research results, a new state feedback system to track the output DC bus voltage and battery current is developed via the state-space averaged model method. By fully considering the state delay information, a proper Lyapunov–Krasovskii functional (LKF) is constructed. In order to reduce the inherent conservation, Wirtinger-based integral inequality is firstly employed to establish novel stability criteria of battery-supercapacitor HESS. Meanwhile, for the sake of shortening the transition time and realizing the smooth transition of some key variables during the load switch, new optimal control algorithms with actuator saturation can be achieved in terms of linear matrix inequalities (LMIs). Finally, numerical results are presented to verify the effectiveness and superiority of the analysis results.

Optimal Energy Management of Battery Supercapacitor aided Solar PV Powered Agricultural Feed Mill using Pontryagin's Minimum Principle

The commercialization of renewable energy sources is often obstructed due to the cost of energy storages such as batteries, which are unavoidable due to intermittent nature of these energy sources. Average power density and low cycle life of batteries lead to frequent replacement and put additional economic burden toward the replacement cost at its end-of-life. The battery supercapacitor-hybrid energy storage system (BS-HESS) with efficient energy management system (EMS) has shown the potential to improve the battery life in various applications. Here, we exploit the Pontryagin's minimum principle (PMP) for optimal power-split between battery and supercapacitor in HESS for a stand-alone photovoltaic assisted agricultural feed mill such that the battery life is improved. Also, we employ a new technique called hybrid maximum power point tracking, by introducing an extra term in the performance index, to take care of the effects of partial shading and provide fast response to sudden change in solar irradiance. We verify the effectiveness of the proposed PMP-based EMS via simulation and comparison with conventional methods. Simulation results show that the HESS along with PMP-based EMS can improve the battery cycle life compared to rule-based EMS and battery-only storage. Finally, we implement the proposed PMP-based EMS in hardware prototype to prove its practicality. The results obtained are satisfactory.

Adaptive Online Power Management for More Electric Aircraft With Hybrid Energy Storage Systems

More electric aircraft (MEA) has become the trend of future advanced aircraft for its potential to be more efficient and reliable. The optimal power management, thus, plays an important role in MEA, especially when using hybrid energy storage systems (HESSs). In this article, we propose a novel adaptive online power management (AOPM) algorithm for MEA, which aims to minimize the power fluctuation of the generators based on the battery–supercapacitor HESS. The problem is first formulated as a constrained stochastic programming problem. We then present an online algorithm to approximately solve the problem using the Lyapunov optimization method, which does not require any statistics and future knowledge of the electricity demand. We further propose the AOPM algorithm for MEA by incorporating an adaptive strategy with the online algorithm. Trace-driven simulation results demonstrate the effectiveness, efficiency, and adaptability of the proposed power management algorithm for MEA.

A Decentralized Automatic Load Power Allocation Strategy for Hybrid Energy Storage System

A decentralized improved I-V droop control strategy for battery-supercapacitor (SC) hybrid energy storage system (HESS) is proposed in this paper. The dynamic power sharing between battery and SC is realized by replacing the constant droop coefficient in I-V droop control with virtual impedance, i.e. virtual inductance for battery side converter and virtual resistance for SC side converter. Besides, by injecting the virtual inductance in the battery side converter, negligible DC bus voltage deviation can be achieved without extra voltage compensator. Moreover, the state-of-charge (SoC) recovery is also considered to extend the service life of the HESS. Furthermore, in the proposed regulated power system, since the power allocation, DC bus stability and SoC recovery are decoupled from each other, the design of control parameters is simple. The corresponding design guideline is demonstrated in this paper. Finally, to verify the accuracy and feasibility of the theoretical analyses, hardware in the loop simulations have been conducted.