Control Of Hybrid Energy Storage System Research Articles

Design of PV, Battery, and Supercapacitor-Based Bidirectional DC-DC Converter Using Fuzzy Logic Controller for HESS in DC Microgrid

Renewable energy sources (RES) are becoming more popular globally as a reaction to critical energy concerns. Modern energy management technologies are used to maximize their efficiency while preserving the reliability of the grid. A hybrid energy storage system (HESS) connects to the DC microgrid through the bidirectional converter, allowing energy to be transferred among the battery and supercapacitor (SC). In this paper, a fuzzy logic control (FLC) technique is developed for PV-based DC microgrid systems that use both batteries and SCs. The proposed method uses the unbalanced energy from the battery pack to enhance the overall effectiveness of the HESS. The FLC approach is performed to validate under conditions of variable irradiance using MATLAB Simulink. When sudden changes in irradiance occur, the proposed FLC brings the voltage back to the desired level in terms of transient response like 33 ms settling times and 19% overshoot values. The results exhibit that the proposed method is more efficient in terms of time response, power output, increasing battery life, and ensuring a continuous supply of the PV system.

Hybrid energy storage system control strategy to smooth power fluctuations in microgrids containing photovoltaics

Abstract The use of a hybrid energy storage system (HESS) consisting of lithium-ion batteries and supercapacitors (SCs) to smooth the power imbalance between the photovoltaics and the load is a widespread solution, and a reasonable probabilistic allocation of the batteries and SCs affects the performance of the HESS. This paper focuses on developing a simple and effective power allocation strategy for the HESS. The moving average filtering (MAF) is considered to effectively decompose the low-frequency components as well as the high-frequency components in the signal and is simple to implement, and we spoil the low-frequency components decomposed by MAF several times to obtain more excellent low-frequency components; moreover, during HESS charging, when the state of charge (SOC) of the SC is high, we reduce the SC’s charging power by decreasing the number of times of MAF usage while ensuring the performance. During the HESS discharging, when the SOC of the SC is low, this strategy is also used to reduce the SC’s discharging power to ensure the SC’s safe operation. The proposed approach is fully validated, and it has excellent performance.

HESS energy configuration strategy based on load regulation for wind power

Abstract Energy storage systems are particularly suitable for renewable energy sources, such as wind power, because these renewable energy sources are volatile. The hybrid energy storage system (HESS) considers the advantages of multiple energy storage systems and is considered very promising; therefore, the energy configuration strategy of an excellent HESS that considers both cost and performance is crucial. This article based on HESS consists of battery and supercapacitor (SC), additional consideration of user load; thus, by adjusting user translatable load and reducible load, the pressure of wind power and HESS can be alleviated to take into account the cost and performance of HESS. Further, a simple and easy-to-implement energy configuration strategy for HESS is proposed, which takes into account that the energy stored in the battery is almost the energy stored in the HESS and calculates the battery energy and then the SC energy through the gap between the load and the wind power. We used three sets of cases: without HESS, with HESS but without load control, and with HESS and load control. The results show that, compared with HESS without load control, HESS with load control can achieve lower HESS cost, wind abandonment rate, and load power shortage rate, which is impossible to combine with traditional strategies.

Hybrid energy storage system control and capacity allocation considering battery state of charge self-recovery and capacity attenuation in wind farm

Hybrid energy storage system (HESS) can cope with the complexity of wind power. But frequent charging and discharging will accelerate its life loss, and affect the long-term wind power smoothing effect and economy of HESS. Firstly, for the operational control of HESS, a bi-objective model predictive control (MPC) -weighted moving average (WMA) strategy for energy storage target power controlling is proposed, considering both energy storage state of charge (SOC) self-recovery and grid-connected power stabilization. The strategy can quickly adjust the SOC of HESS in the wind power smoothing process and reduce the battery's life loss. Then, since the energy storage capacity determines its power smoothing ability, this paper proposes a battery life model considering the effective capacity attenuation caused by calendar aging, and introduces it into the HESS cost calculation model to optimize the capacity allocation. Finally, combined with the wind power data, the simulation verifies that the proposed strategy can effectively balance the contradiction between energy storage lifetime and wind power smoothing effect. Simultaneously, the HESS optimized capacity allocation results considering battery's effective capacity attenuation can ensure the long-term wind power smoothing effect and better HESS operational states, contributing to the long-term and stable operation of wind-storage combined system.

Enhanced energy management system for isolated microgrid with diesel generators, renewable generation, and energy storages

This paper presents an enhanced control and energy management strategy for the remote isolated power system. The presented control method includes the hybrid energy storage system control with a supercapacitor voltage restoration loop. This method improves the transient response and extends the battery lifespan while maintaining the SC voltage at a defined range of operation. To maximize the usage of renewable energy and reduce the diesel generator’s frequent turn-on and off operation, the novel battery state of charge stop control is included in the energy management system. This improves the lifetime of diesel generators and reduces maintenance costs. In the proposed control method, a stabilizing control method is also introduced which helps in a seamless transition from grid-connected mode to islanded operating mode. The effectiveness of the proposed EMS and control method is validated by real-time simulation studies.

An investigation into hybrid energy storage system control and power distribution for hybrid electric vehicles

This study aims to develop a hybrid energy storage system (HESS), targeting a commercialised Hybrid Electric Vehicle model (Hyundai Sonata), that consists of battery and supercapacitor cells, to evaluate its benefits on the battery's health and vehicle's performance. The different possible configurations of combining the two energy storage devices are discussed, while a semi-active configuration is considered for the purpose of simulation and testing, where the supercapacitor is connected to a bidirectional DC-DC converter, implemented with a newly developed control logic for the HESS. The analysis of the HESS is done by modelling the vehicle powertrain on the simulation software Ricardo IGNITE, while adapting the WLTC Class 2 drive cycle. The results are validated by comparing HESS model to the sole battery model, which showed the working status of the battery is stabilised by the addition of the supercapacitor in the HESS model during both the traction and regenerative modes. There is a significant reduction in the battery peak current, 15.26% and 20.54% for the charge and discharge current, respectively. Other improvements include a 0.43% increase on the average battery SOC, a 6.8% decrease in the battery's maximum temperature and 47.56% decrease in the average battery power. On the other hand, there was a 0.46% increase on vehicle's average L/100 km fuel consumption, due to losses in the supercapacitor system. Various other results are also compared in the discussion section, justifying the addition of the supercapacitor. The investigation finishes off by conducting a parametric study based on the number of supercapacitor modules in parallel, and comparing results such as current, power, fuel consumption and battery SOC. The results show that 2 supercapacitor modules provided the optimal results in terms of battery's current and power behaviour.

Train-Network-HESS Integrated Optimization for Long-Distance AC Urban Rail Transit to Minimize the Comprehensive Cost

In order to solve the problem of the serious stray current existing in DC urban rail transit, and to improve the utilization rate of the regenerative braking energy (RBE) generated by locomotive, an AC traction power supply system (TPSS) for long-distance urban rail transit is proposed, in which the network power flow distribution is affected by the train trajectory, the parameters of network and hybrid energy storage system (HESS) configuration. For achieving the minimum comprehensive cost including the electricity cost and the life cycle cost (LCC) of HESS, this paper aims to develop a Train-Network-HESS integrated model to optimize network power flow distribution. The train trajectory is illustrated based on time-space conversion method and HESS control strategy is discussed to increase the utilization of the RBE. Furthermore, the optimized configuration of the AC TPSS for urban rail transit is obtained by the hybrid GA-PSO algorithm. Finally, the effectiveness of the proposed model is verified by detailed case studies and the comprehensive cost can be reduced up to 39.08% compared with conventional TPSS.

Dual-Source Bidirectional Quasi-Z-Source Inverter Development for Off-Road Electric Vehicles

In this paper, a battery pack and a supercapacitor bank hybrid energy storage system (HESS) with a new control configuration is proposed for electric vehicles (EVs). A bidirectional quasi-Z-source inverter (Bq-ZSI) and a bidirectional DC-DC converter are used in the powertrain of the EV. The scheme of the control for the proposed HESS Bq-ZSI using finite control set model predictive control (FCS-MPC) is first deduced to enhance the dynamic performance. With the idea of managing battery degradation mitigation, the fractional-order PI (FOPI) controller is then applied and associated with a filtering technique. The Opal-RT-based real-time simulation is next executed to verify the performance and effectiveness of the proposed HESS control strategy. As a result, the proposed HESS Bq-ZSI with this control scheme provides a quick response to the mechanical load and stable DC link voltage under the studied driving cycle. Moreover, the comparative results also show that the proposed HESS Bq-ZSI equipped with the new control configuration enables the reduction of the root-mean-square value, the mean value, and the standard deviation by 57%, 59%, and 27%, respectively, of the battery current compared to the battery-based inverter. Thus, the proposed HESS Bq-ZSI using these types of controllers can help to improve the EV system performance.

A Master-Slave Salp Swarm Algorithm Optimizer for Hybrid Energy Storage System Control Strategy in Electric Vehicles

Pure electric vehicles provide an enticing ecofriendly alternative to traditional fossil fuel combustion engine locomotives. Batteries have primarily been used to store energy in electric vehicles; however, peak load demand and transient power leading to decreased battery lifespan have bred interest in hybrid energy storage systems in electric vehicles. Management of energy drawn from a hybrid energy storage system (HESS) in electric vehicles is a real-time multistage optimization problem aimed at minimizing energy consumption while aptly distributing energy drawn from the battery and capacitor to enhance the battery life cycle. This paper explores the feasibility of a master-slave salp swarm optimization algorithm (MSSSA) (metaheuristic algorithm) in a HESS control strategy for electric vehicles. Introducing a master-slave learning approach to the salp swarm algorithm (SSA) improves its performance by increasing its convergence rate while maintaining a balance between exploration and exploitation phases of the algorithm. A comparison of the MSSSA results with the SSA (salp swarm algorithm), DA (dynamic algorithm), WOA (whale optimization algorithm), MFO (moth flame optimization algorithm), GA (genetic algorithm), and PSO (particle swarm optimization algorithm) on benchmark test functions and dynamic program simulation of an electric vehicle’s HESS control strategy and shows preeminence of the MSSSA control strategy for HESS.

Design and Analysis of Sliding-Mode Artificial Neural Network Control Strategy for Hybrid PV-Battery-Supercapacitor System

Nowadays, the growing integration of renewable energy sources poses several challenges to electrical energy systems. The latter need be controlled by grid rules to ensure their stability and maintain the efficiency of renewable energy consumption. In this context, a novel HESS (hybrid energy storage system) control strategy, combining the PV (photovoltaic) generator with FLC (fuzzy logic control), SC (super-capacitor), and lithium-ion battery modules, is advanced. The proposed energy control rests on monitoring of the low-frequency and high-frequency electrical power components of the mismatch between power demand and generation, while applying the error component of the lithium-ion battery current. On accounting for the climatic condition and load variation considerations, the SC undertakes to momentarily absorb the high-frequency power component, while the low-frequency component is diverted to the lithium-ion battery. To improve the storage system’s performance, lifetime, and avoid load total disconnection during sudden variations, we consider equipping the envisioned energy control design with controllers of SM and ANN types. The MATLAB/Simulink based simulation results turn out to testify well the investigated HESS control scheme’s outstanding performance and efficiency in terms of DC bus voltage rapid regulation, thereby enhancing the battery’s lifetime and ensuring the PV system’s continuous flow.

Multiobjective Optimization for a Li-Ion Battery and Supercapacitor Hybrid Energy Storage Electric Vehicle

The acceptance of hybrid energy storage system (HESS) Electric vehicles (EVs) is increasing rapidly because they produce zero emissions and have a higher energy efficiency. Due to the nonlinear and strong coupling relationships between the sizing parameters of the HESS components and the control strategy parameters and EV’s performances, energy consumption rate, running range and HESS cost, how to design the HESS EVs for different preferences is a key problem. How to get the real time performances from the HESS EV is a difficulty. The multiobjective optimization for the HESS EV considering the real time performances and the HESS cost is a solution. A Li-ion battery (BT) semi-active HESS and optimal energy control strategy were proposed for an EV. The multiobjectives include energy consumption over 100 km, acceleration time from 0–100 km per hour, maximum speed, running range and HESS cost of the EV. According to the degrees of impact on the multiobjectives, the scaled factors of BT capacity, the series number of Li-ion BTs, the series number of super-capacitors (SCs), the parallel number of SCs, and charge power of the SCs were chosen as the optimization variables. Two sets of different weights were used to simulate the multiobjective optimization problem in the ADVISOR software linked with MATLAB software. The simulation results show that some of the multiobjectives are sensitive to their weights. HESS EVs meeting different preferences can be designed through the weights of different objectives. Compared with the direct optimization algorithm, the genetic algorithm (GA) has a stronger optimization ability, and the single objective is more sensitive to its corresponding weight. The proposed optimization method is practical for a Li-ion BT and SC HESS EV design.

PV Powered Hybrid Energy Storage System Control Using Bidirectional and Boost Converters

The increase of renewable energy generation and the integration with the grid brings some difficulties on the power network worldwide. The main problems on the energy providers are surplus or unregulated energy generation like in partially cloudy or fully sunny days. The main faults on the consumer side can be surplus or unregulated energy demand and in such cases, storage requirements exist for this energy to regulate the energy demand and thereby having a hybrid energy system. In this paper, the focus is on the active power control using a hybrid energy storage system (HESS) on the energy generation side by applying bidirectional power converters and maximum power point tracking (MPPT)control using a boost converter. Supercapacitor (SC) technology is proposed in such a hybrid storage system. A photovoltaic powered hybrid system storing the energy in a battery, and an SC is used to solve the big problems in the source and load side. The HESS is designed using bidirectional converters, and the working principles are given. Also, control algorithms for bidirectional converters are investigated with a detailed literature review to define the best solution for the proposed system. The HESS is designed in MATLAB/Simulink using bidirectional converters and simulated for resistive, battery, SC loads, and also combinations of these loads. Further, a newly proposed power-sharing method is investigated to increase the amount of energy stored with SC. Simulation results of the proposed system given and examined to verify the applicability of the idea.

RETRACTED ARTICLE: PMP hybrid energy storage system control technology for hybrid electric vehicles

RETRACTED ARTICLE: PMP hybrid energy storage system control technology for hybrid electric vehicles

Composite Non-Linear Control of Hybrid Energy-Storage System in Electric Vehicle

The underlying circuit control is a key problem of the hybrid energy-storage system (HESS) in electric vehicles (EV). In this paper, a composite non-linear control strategy (CNC) is proposed for the accurate tracking current/voltage of the fully-active HESS by combining the exact feedback linearization method and the sliding mode variable structure control technology. Firstly, by analyzing the circuit characteristics of HESS, the affine non-linear model of fully-active HESS is derived. Then, a rule-based energy management strategy (EMS) is designed to generate the reference current value. Finally, the HESS is linearized by the exact feedback linearization method, and the proposed CNC strategy is developed combined with sliding mode variable structure control technology to ensure fast response, high performance, and robustness. At the same time, the stability proof based on the Lyapunov method is given. Moreover, the performance of the CNC strategy is thoroughly investigated and compared with simulation studies with the traditional PI control and a modified sliding mode control, and its effectiveness under different driving conditions is fully verified.

A Coordination Control between Battery and Supercapacitor Energy Storage Systems to Segregate Power for On-Grid Application

Abstract The present work describes a control methodology for a hybrid energy storage system (HESS) to improve its transient performance under dynamic load conditions. The proposed coordination control enhanced life cycle performance by segregating the power between battery energy storage systems (BESS) and a supercapacitor (SC). The BESS and SC are connected parallel to each other, and two individual DC–DC bidirectional converters connect them to a common DC bus. The coordination control is established between the controllers of BESS and the SC of HESS, which helps to utilise the usable energy capacity of the HESS. The charging/discharging current of the BESS is controlled within the allowable safety range based on the slope and magnitude of the BESS current. The high-frequency power component is handled by the SC, which helps to reduce the extra exhaustion on the BESS during operation with a higher current. The proposed coordination control of HESS is validated through simulation and the results show the effectiveness of the proposed controller.

Optimal Design for Super Capacitor/Battery Power Management Applied in Electric Vehicle Applications: A Hybrid Methodology

This manuscript presents an optimal control system for energy management of the hybrid energy storage system (HESS) as battery and super capacitor (SC) on electric vehicles (EVs). The proposed system is parallel execution of levy flight with Tunicate swarm optimization. The proposed method is improved by levy flight distribution; hence, it is named Improved Tunicate swarm optimization (ITSA). Here, the HESS method calculates the super capacitor reference voltage in terms of load dynamics and improves the power. At first, compute the super capacitor reference voltage assuming real-time load dynamics. Furthermore, diminish the range of battery power magnitude and power loss concurrently. In the proposed system, the Improved Tunicate swarm optimization is merged for generating the probable HESS control signal data set. Furthermore, the proposed method gains the super capacitor voltage, amount of battery current, variation of battery current. Through the proposed method, the hybrid energy storage system parameters are enhanced, and the proposed system gives a reliable solution. The proposed method is performed on the MATLAB/Simulink work platform. The quality of a hybrid energy storage system is compared with that of other methods.

Cooperative Control of SFCL and SMES-Battery HESS for Mitigating Effect of Ground Faults in DC Microgrids

DC fault is one of the most important and critical challenges for stable operation of DC microgrids. Resistive type superconducting fault current limiter (R-SFCL) is an ideal protective solution to limit DC fault current. However, the effect of only using SFCL may further deteriorate the DC bus voltage stability. This paper first analyzes the effect of R-SFCL on DC microgrid under different operation modes and different fault locations, and then presents a coordinated control method of SMES-battery hybrid energy storage system (HESS) and SFCL in ground fault. This method can not only reduce the fault current level but also regulate the DC bus voltage. Finally, the comparative study is conducted in PSCAD/EMTDC, the feasibility and superiority of the proposed method is verified.

A control strategy for battery/supercapacitor hybrid energy storage system

In DC microgrid (MG), the hybrid energy storage system (HESS) of battery and supercapacitor (SC) has the important function of buffering power impact, which comes from renewable energy sources (RES) and loads. This paper proposes a HESS control strategy with DC bus voltage self-recovery function. High and low frequency power decomposition based on virtual droop control, and DC bus drop voltage is compensated by added proportional integral regulation. The relationship between DC bus voltage recovery and super-capacitor (SC) state of charge (SoC) recovery is analyzed. The system can realize stable energy storage, supply under frequent load power impact. The effectiveness of the proposed control strategy is verified by simulation in MATLAB/Simulink.

Energy management for hybrid energy storage system in electric vehicle: A cyber-physical system perspective

Adoption of the hybrid energy storage system (HESS) brings a bright perspective to improve the total economy of plug-in hybrid electric vehicles (PHEVs). This paper proposes a novel energy management method to improve the total economy of PHEV by exploiting the energy storage capability of HESS. Firstly, A cyber-physical energy management framework that enables the synergistic scheduling of fuel engine, battery, and supercapacitor is designed to derive the optimal power distribution strategy for PHEV with HESS. Then, an optimization-based energy management model is established to distribute the vehicle power requirement between the fuel engine and driving motor. The reduction of fuel cost and the mitigation of the battery aging phenomenon are designed as the optimization objectives. In a further stage, an online power distribution algorithm is designed for the optimal control of HESS, where the supercapacitor is used to prolong the battery life. The qualitative and quantitative analyses indicate that both the fuel economy and battery aging cost are sensitive to the power system topology and power distribution algorithm. With the proposed methods, the PHEV total economy can be improved by 8.9% and 6.9% compared to the conventional PHEV structure and power distribution strategy while guarantee the vehicle dynamic performance.

Electric vehicle battery-ultracapacitor hybrid energy storage system and drivetrain optimization for a real-world urban driving scenario

A battery has normally a high energy density with low power density, while an ultracapacitor has a high power density but a low energy density. Therefore, this paper has been proposed to associate more than one storage technology generating a hybrid energy storage system (HESS), which has battery and ultracapacitor, whose objective is to improve the electric vehicle (EV) driving range. The HESS parameters have been evaluated in a configuration of EV powered by two in-wheel electric motors, coupled straight into the front wheels, and by a unique EM, connected to a differential transmission to drive the rear wheels. Moreover, this paper considers a real-world drive cycle based on the urban driving behavior of Campinas city, one of the most populous cities in Brazil. Aiming to minimize the HESS size and enhance the EV driving range, an optimization problem was formulated and solved using a genetic algorithm technique, in which the EV drivetrain parameters and HESS components and control are optimized. Finally, the obtained Pareto frontier defines the optimum EV configurations, in which the best-selected configurations were able to perform up to 188 km with a 418 kg HESS (maximum drive range solution), or 82.75 km with a 146.58 kg HESS (minimum HESS solution) and 319 km with a 188.43 kg HESS (best trade-off solution), without presenting performance losses.