Performance Of Hybrid Energy Storage System Research Articles

Hybrid method based energy management of electric vehicles using battery-super capacitor energy storage

This paper presents a hybrid technique for managing the Energy Management of a hybrid Energy Storage System (HESS), like Battery, Supercapacitor (SC), and integrated charging in Electric Vehicle (EV). The proposed hybrid method combines the Namib Beetle Optimization (NBO) and Quantum Neural Networks (QNN) technique and is commonly known as the NBO-QNN approach. The proposed energy management technique reduces EV power use and maximizes battery life. QNN forecasts and combines power supply and charge levels to fulfill load needs. EV energy management uses NBO to regulate the output voltage, generate references, and regulate current continuously. Higher energy density battery and power density SC meet vehicle needs. An uncontrolled rectifier with a DC-to-DC buck converter balances charging and ensures energy transmission. The Proposed technique is implemented using the MATLAB platform, and its performance is compared to existing methods. HESS performance is evaluated by comparing it to existing systems. The research shows that the proposed strategy reduces the primary and secondary source stress, enhances performance of charging unit, and extends the life of battery. Furthermore, the NBO-QNN technique outperforms other existing methods, such as the Cooperation Search Algorithm (CSA), Latent Semantic Analysis (LSA), and Grasshopper Optimization Algorithm (GOA). The proposed method displays the best output in all existing Cooperation Search Algorithm (CSA), Latent Semantic Analysis (LSA), and Grasshopper Optimization Algorithm (GOA) methods. The result concludes that the NBO-QNN approach based on THD value is less than existing methods.

Active hybrid energy storage management in a wind-dominated standalone system with robust fractional-order controller optimized by Gases Brownian Motion Optimization Algorithm

Accessibility to sustainable and reliable electrical energy for remote area power supply (RAPS) systems under high penetration levels of wind energy requires adopting appropriate technologies and controllers. In recent years, energy storage has gained significant attention for smoothing inherent voltage and power fluctuations of standalone systems. This paper presents a filtration-based fractional-order PI (FOPI) controller optimized by Gases Brownian Motion Optimization (GBMO) algorithm to manage an active battery-supercapacitor hybrid energy storage system (HESS) in a wind-dominated RAPS system. The GBMO algorithm has a high accuracy and convergence rate among optimization algorithms introduced in recent years. Moreover, a fuzzy logic controller (FLC) guarantees the wind turbine generator's maximum power point tracking (MPPT) at any wind condition while mitigating its fluctuating torque. The proposed RAPS system uses the advantages of the FOPI controller, the GBMO optimization algorithm, the FLC-based MPPT algorithm, and a high-pass filter to manage precise coordination between different components of the RAPS system. The high-pass filter decouples high and low-frequency voltage oscillations to modify the HESS performance and relieve battery stress, thereby extending its lifespan. The proposed controller's performance and robustness are verified in comparison with an optimal PI controller based on GBMO under turbulent wind speed and load step changes in a detailed model of the system. The results demonstrate the superiority of the optimal FOPI controller over the optimal PI controller.

Capacity Configuration Method of Hybrid Energy Storage Participating in AGC Based on Improved Meta-Model Optimization Algorithm

To improve the performance and economy of the hybrid energy storage system (HESS) coordinating thermal generators to participate in automatic generation control (AGC), a HESS bi-layer capacity configuration model that considers the control strategy and net benefits of HESS is proposed. In addition, an improved mode-pursuing sampling (MPS) optimization algorithm based on meta-model is presented to improve the accuracy of model solving. In the lower layer, to improve the performance of HESS participating in AGC, a model predictive control (MPC) strategy is presented to distribute HESS power reasonably. Based on this, the upper layer develops a life-cycle net benefit model of HESS participating in AGC to improve its economy. The bi-layer model realizes iterative optimization of HESS capacity and operation through parameter transmission. Furthermore, to improve the solution accuracy of the bi-layer model, the convergence speed of the MPS algorithm is improved, so that the global search and local convergence speed can be taken into account. The case study results show that the bi-layer model can comprehensively consider the interaction between the economy and operating strategy of HESS. The proposed MPC strategy has better frequency regulation performance and the improved MPS algorithm has better solution performance.

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%.

Impact of Power Sharing Method on Battery Life Extension in HESS for Grid Ancillary Services

Hybrid energy storage system (HESS) based on Li-ion and supercapacitor (SC) can play a potential role to stabilize the grid by providing the fast frequency ancillary services. The SC helps to reduce the battery charge/discharge stress and, hence, assists to extend the battery lifespan. The power sharing method (PSM) is the heart in control part to improve the HESS performance and reduce the battery stress. This paper proposes a hybrid PSM and investigates its impact on battery life extension along with its relation to the system design and regulation signal. The performance of hybrid PSM is compared with three other PSMs (low-pass filter, first and second rule based) in a 10 MW/10 MWh full-active parallel HESS for frequency regulation service in two networks: U.K. (national grid) and USA (PJM). Considering maximum possible battery lifetime upto 25 years, result shows that the hybrid PSM approach allows a degree of the better performance for both grid while the sharing of SC is kept maximum 2.0% and 2.5% (for U.S. and U.K. grid, respectively) of the HESS capacity. This study also analyses the impact of PSM on shared capacity and design of HESS for different grid regulation signals.

The application of hybrid energy storage system with electrified continuously variable transmission in battery electric vehicle

Due to the fact that demand for battery power increasing dramatically with the fast development of battery electric vehicles (BEVs), and poor power density prevents batteries absorbing more braking energy, leads to the so-called range phobia and presents a significant barrier to BEV commercialization. As one of the promising solutions, a supercapacitor-based hybrid energy storage system (HESS) is fast becoming the automotive industry trend, and there are widespread attempts of integrating multi-speed transmission in BEVs to improve electric machine efficiency. As part of the response to the above barriers, an electrified continuously variable transmission (CVT) is proposed along with HESS for BEV in this study. The improvements of passenger BEV in terms of vehicle dynamic performance, energy saving, and battery life extending through applying electrified CVT and HESS will be investigated. The impact of CVT on battery health improvement via HESS will also be analyzed. Specifically, a designed gear ratio varying control strategy is developed for electrified CVT to improve energy efficiency and vehicle performance. A vehicle performance comparison of electrified CVT and traditional single speed BEV is provided. Additionally, the potential benefit of the HESS and CVT combination to driving range, battery life and manufacturing/user cost is reported. Results reveals that the proposed CVT not only provides considerable benefit to BEV dynamic and economic performance, it also shows potential to improve the performance of HESS in terms of extending battery lifetime.

A Model Predictive Current Controlled Bidirectional Three-Level DC/DC Converter for Hybrid Energy Storage System in DC Microgrids

This letter proposes a new three-level dc/dc converter configuration for a hybrid energy storage system (HESS) in dc microgrids. It effectively integrates different energy storage devices (ESDs), such as battery and ultracapacitor (UC), using one converter with bidirectional power flow. Furthermore, the proposed converter provides the flexibility of independent regulation of different ESDs with significantly reduced inductor current ripple due to the availability of three voltage levels. The voltage ratings of power semiconductors employed in this converter are also reduced. To further enhance the performance of HESS, a constant switching frequency based model predictive current control is employed for HESS regulation. The design guideline and operating principle of the proposed converter are discussed. Experimental results are presented to verify the efficacy of the proposed converter and control.

Power allocation smoothing strategy for hybrid energy storage system based on Markov decision process

The hybrid energy storage system (HESS) in electric vehicle (EV) requires power allocation for optimal performance. Recent researches show that the Markov decision process (MDP) provides promising characteristics for the energy management. However, the power fluctuation, which is rarely considered, can significantly affect the performance of HESS. The paper adopts the bilinear interpolation to smooth the MDP-based strategy. In this way, the power fluctuation can be mitigated, meanwhile the computation cost is not greatly increased. Given the battery and the ultracapacitor, two types of DC/DC converters are employed and the model of the HESS including the battery capacity degradation is introduced. Considering the energy loss and the energy reserve in the HESS, the reward function is built. Utilizing the cumulative reward function, the effect of ultracapacitor pack size on the performance of power allocation is analyzed in detail, the appropriate ultracapacitor size can be obtained further. Simulation results show that MDP strategy with the bilinear interpolation can not only reduce the energy loss by 5–10%, but also prolong the battery cycle life. Finally, the experimental workbench is built. The master-slave strategy is utilized to achieve the power allocation and DC/DC converter control. The proposed strategy is verified by the experimental results.

Modeling, Analysis, and Control of an Integrated Hybrid Energy Storage System

In traditional hybrid energy storage system (HESS), separated bidirectional dc/dc converter is usually used to interface energy storage system with dc bus. Though it has an advantage in flexibility of control system design using separated power converters, it results in more switches, auxiliary components, and cost for the whole system development. Particularly, since the energy storage system (ESS) designed for high dynamic compensation (e.g., supercapacitor) is mainly used to provide high-dynamic compensation in transient state process, the utilization rate of the corresponding interface dc/dc converter is relatively low. On the other hand, the dc bus voltage compensation performance will be discounted due to the inherent time delay for the current reference signal production of HESS with outer loop voltage control. In this paper, to address these two issues, an integrated topology of the hybrid energy storage system is proposed, and an improved control method that has the ability to enhance the control performance of HESS to compensate the dc bus voltage is developed too. The experimental test results are provided to validate the correctness and effectiveness of the proposed methods.

SMES/battery hybrid energy storage system based on bidirectional Z‐source inverter for electric vehicles

This study proposes a novel hybrid energy storage system (HESS) composed of a battery pack and a superconducting magnetic energy storage (SMES) for electric vehicle. Typically, the SMES has a higher power density and lower energy density than other energy storage devices, while battery has higher energy density. Thus, the overall HESS performance can be increased in power and energy density by combining SMES and batteries. The proposed HESS is designed based on bidirectional Z-source inverter (ZSI). Compared to other SMES/battery-based HESS topologies that are two stage designs (including DC/DC and AC/DC converters), in this topology, SMES and battery can be incorporated into the Z-source network which results in lower cost and improved HESS performance. Furthermore, the battery converter has been eliminated due to the buck/boost feature of the ZSI. The fuzzy control method and filters are used to distribute power between the SMES and battery. This study also describes the proposed HESS performance principles and its operation in different modes.

Control strategy for AC-DC microgrid with hybrid energy storage under different operating modes

In this paper, a control strategy is proposed for renewable-interfaced hybrid energy storage system (HESS) under grid connected/islanding conditions. A second harmonic based phased locked loop (PLL) is employed for effective synchronization/resynchronization of the microgrid system under contingency conditions. The operation and management of the microgrid system under both these modes are accomplished by an efficient adaptive power management algorithm. A quantitative analysis on the HESS performance is provided in order to investigate the effectiveness of the proposed approach. Further, small signal average models are derived to comment on the system stability. This approach address seamless transfer between the various sub-modes of the system along with additional services such as power quality enhancement and power dispatch between various sources. The effectiveness of the proposed scheme is verified by both simulation and experimental investigations.

The battery-supercapacitor hybrid energy storage system in electric vehicle applications: A case study

The hybrid energy storage system (HESS), which combines the functionalities of supercapacitors (SCs) and batteries, has been widely studied to extend the batteries' lifespan. The battery degradation cost and the electricity cost should be simultaneously considered in the HESS optimization. However, the continuous decline in the price of lithium batteries may weaken the effectiveness of HESS, as the battery degradation cost becomes less important. This paper analyzes the influence of different temperatures and battery prices on the integrated optimization of HESS, including the optimization of SC size and energy management strategy (EMS) for electric vehicle (EV) applications. Based on an average temperature, the HESS performance is examined considering a wide range of battery prices (from $143/kWh in 2028 to $257/kWh in 2018). Simulation results show that both the SC sizing and EMS optimization results are robust to the temperature and the battery price. In addition, the total cost of HESS for customers is shown to be 12% less than a battery energy storage system, even at low battery prices. The HESS is therefore validated to be effective in EV applications in the near future.

Novel energy management method for suppressing fuel cell degradation in hydrogen and electric hybrid energy storage systems compensating renewable energy fluctuations

This research investigates an energy management method utilized in a hydrogen and electric hybrid energy storage system (HESS), which is utilized as an ancillary system for renewable energy electricity generation. To suppress the performance degradation of the fuel cell (FC), the newly proposed energy management method deals with main FC degradation causes, such as low humidification and frequent and rapid voltage changes. The entire HESS's performance is demonstrated using the proposed energy management method. In addition, a simulation is conducted to evaluate the proposed energy management method's performance in terms of both suppressing the FC's degradation and ensuring system efficiency. The results of the experiment and simulation show that the proposed energy management method can suppress the FC's harmful working states while maintaining high system efficiency.

An Integrated Energy Management Strategy With Parameter Match Method for Plug-In Hybrid Electric Vehicles

A battery-ultracapacitor hybrid energy storage system (HESS) is a reliable candidate to overcome the drawbacks of a single power source system for its complementary features of power and energy in plug-in hybrid electric vehicle application. In this paper, an integrated energy management strategy procedure, which consists of three layers, is presented to distribute the power of the battery packs and the ultracapacitor packs. First, an HESS parameter match method is developed under different driving cycles with optimization objectives of cost and weight. Second, three-level wavelet transform (WT) algorithm is employed to isolate the different frequency components of power demand. The low-frequency components with less transients and sharps in power demand are provided by extending the battery lifetime, while an ultracapacitor satisfies the high-frequency components for its nice features of high specific power. Finally, fuzzy logic control is used to manage power flows between various components for guaranteeing the HESS performance. The simulation results show that compared with WT-based-only energy management strategy, the proposed procedure can always control the battery currents within 2C rate and reduce the energy consumption of about 6.54%.

Mitigating Power Fluctuations in Electric Ship Propulsion With Hybrid Energy Storage System: Design and Analysis

Shipboard electric propulsion systems experience large power and torque fluctuations on their drive shaft due to propeller rotational motion and waves. This paper explores new solutions to address these fluctuations by integrating a hybrid energy storage system (HESS) and exploring energy management (EM) strategies. The HESS combines battery packs with ultracapacitor banks. Two strategies for real-time EM of HESS are considered: one splits the power demand such that high- and low-frequency power fluctuations are compensated by ultracapacitors and batteries, respectively; another considers the HESS as a single entity and designs an EM strategy to coordinate the operations of the ultracapacitors and batteries. For both strategies, model predictive control is used to address power tracking and energy saving under various operating constraints. To quantitatively analyze the performance of HESS and its associated controls, a propeller and ship dynamic model, which captures the underlying physical behavior, is established to support the control development and system optimization. Power fluctuation mitigation and HESS loss minimization, the main objectives, are evaluated in different sea conditions. Simulation results show that the coordination within HESS provides substantial benefits in terms of reducing fluctuations and losses.

Energy Optimal Control Strategy of PHEV Based on PMP Algorithm

Under the global voice of “energy saving” and the current boom in the development of energy storage technology at home and abroad, energy optimal control of the whole hybrid electric vehicle power system, as one of the core technologies of electric vehicles, is bound to become a hot target of “clean energy” vehicle development and research. This paper considers the constraints to the performance of energy storage system in Parallel Hybrid Electric Vehicle (PHEV), from which lithium-ion battery frequently charges/discharges, PHEV largely consumes energy of fuel, and their are difficulty in energy recovery and other issues in a single cycle; the research uses lithium-ion battery combined with super-capacitor (SC), which is hybrid energy storage system (Li-SC HESS), working together with internal combustion engine (ICE) to drive PHEV. Combined with PSO-PI controller and Li-SC HESS internal power limited management approach, the research proposes the PHEV energy optimal control strategy. It is based on revised Pontryagin’s minimum principle (PMP) algorithm, which establishes the PHEV vehicle simulation model through ADVISOR software and verifies the effectiveness and feasibility. Finally, the results show that the energy optimization control strategy can improve the instantaneity of tracking PHEV minimum fuel consumption track, implement energy saving, and prolong the life of lithium-ion batteries and thereby can improve hybrid energy storage system performance.