Ultracapacitor Research Articles

Enhanced Model of Hybrid Controller for Smooth Switching of Energy Sources Used in Electric Vehicle Application

The sharing of load current to battery and ultracapacitor (UC) of the multiple energy storage structure (MESS) according to the vehicle dynamics is the main obstacle in the electric vehicles (EVs) application. In this paper, a control strategy technique is projected, to split the current between two sources based on the EVs requirement. A conventional/intelligent controller is used here to produce pulses to the DC-DC converter's corresponding load on the motor. A math condition-based controller (CBC) is designed by considering four individual math functions based on the speed condition of the motor, which is used to produce regulated pulse signals of the switches present in converters. The combination of CBC plus conventional/intelligent controllers makes a new hybrid controller, to achieve the main objective of the proposed work. The performance of the designed control strategy is investigated with four modes based on changed loads. Two different hybrid controllers CBC plus fuzzy logic (FLC) and CBC with proportional integral derivative (PID), and implemented and a comparative analysis was also made based on different time domain specifications by taking the speed curve as a reference.

Cooperative Application of Onboard Energy Storage and Stationary Energy Storage in Rail Transit Based on Genetic Algorithm

The transition towards environmentally friendly transportation solutions has prompted a focused exploration of energy-saving technologies within railway transit systems. Energy Storage Systems (ESS) in railway transit for Regenerative Braking Energy (RBE) recovery has gained prominence in pursuing sustainable transportation solutions. To achieve the dual-objective optimization of energy saving and investment, this paper proposes the collaborative operation of Onboard Energy-Storage Systems (OESS) and Stationary Energy-Storage Systems (SESS). In the meantime, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied to optimize the ESS capacity and reduce its redundancy. The simulation is programmed in MATLAB. The results show that the corporation of OESS and SESS offers superior benefits (70 kWh energy saving within 30 min operation) compared to using SESS alone. Moreover, the OESS plays a significant role, emphasizing its significance in saving energy and investment, therefore presenting a win–win scenario. It is recommended that the capacity of OESS be designed to be two to three times that of SESS. The findings contribute to the ongoing efforts in developing more sustainable and energy-efficient transportation solutions, with implications for the railway industry’s investment and broader initiatives in energy saving for sustainable urban mobility.

Unsymmetrical Dual-Input Isolated High Gain Bidirectional DC–DC Converter With Inherent Current Sharing for Light EV Applications

In hybrid Electric Vehicles (EVs), multi-input DC-DC converters with different sources of energy storage are preferred to enhance the EV market penetration. The proposed new topology is a modified SEPIC converter with integrated boost cell, capable to handle the battery and ultracapacitor with different terminal voltage. It is also having single source transition capability. This multi-input DC-DC converter provides the high voltage gain, input-output isolation and bidirectional power flow feature. The essential of regeneration in the EV is demonstrated to achieve the better energy utilization. The current sharing between the energy storage systems (battery and ultracapacitor (UC)) is inherently provided by the topological structure of the proposed converter. Hence, it eliminates inner current control loop requirement. Accordingly, cost and control complexity is minimized with improved storage utility. Therefore, the proposed converter’s functional capabilities are suitable for battery/UC based Light EV applications. The operating performance is evaluated on a 250 W hardware prototype to validate the functional capability of the proposed converter.

An efficient energy management scheme using rule-based swarm intelligence approach to support pulsed load via solar-powered battery-ultracapacitor hybrid energy system

This work presents an energy management scheme (EMS) based on a rule-based grasshopper optimization algorithm (RB-GOA) for a solar-powered battery-ultracapacitor hybrid system. The main objective is to efficiently meet pulsed load (PL) demands and extract maximum energy from the photovoltaic (PV) array. The proposed approach establishes a simple IF-THEN set of rules to define the search space, including PV, battery bank (BB), and ultracapacitor (UC) constraints. GOA then dynamically allocates power shares among PV, BB, and UC to meet PL demand based on these rules and search space. A comprehensive study is conducted to evaluate and compare the performance of the proposed technique with other well-known swarm intelligence techniques (SITs) such as the cuckoo search algorithm (CSA), gray wolf optimization (GWO), and salp swarm algorithm (SSA). Evaluation is carried out for various cases, including PV alone without any energy storage device, variable PV with a constant load, variable PV with PL cases, and PV with maximum power point tracking (MPPT). Comparative analysis shows that the proposed technique outperforms the other SITs in terms of reducing power surges caused by PV power or load transition, oscillation mitigation, and MPP tracking. Specifically, for the variable PV with constant load case, it reduces the power surge by 26%, 22%, and 8% compared to CSA, GWO, and SSA, respectively. It also mitigates oscillations twice as fast as CSA and GWO and more than three times as fast as SSA. Moreover, it reduces the power surge by 9 times compared to CSA and GWO and by 6 times compared to SSA in variable PV with the PL case. Furthermore, its MPP tracking speed is approximately 29% to 61% faster than its counterparts, regardless of weather conditions. The results demonstrate that the proposed EMS is superior to other SITs in keeping a stable output across PL demand, reducing power surges, and minimizing oscillations while maximizing the usage of PV energy.

Improving the Education and Training Policies of the Agri-Food and Forestry Sectors: Identifying New Strategies to Meet the Needs of the Sector and Farm-to-Fork Priorities

The current European agri-food and forestry (AFF) systems are perceived to be moving too slowly towards more sustainable agriculture, forestry, food and bio-based value chains. The European Green Deal and Farm to Fork (FtF) Strategy stress the importance of the sustainable transition of food systems that emphasize resilience and justice along food chains. In this direction, education and training (ET) are given a major role, constituting one of the pillars of the Agricultural Knowledge and Innovation Systems (AKIS) framework. This study aims to propose an extended version of the AKIS framework to focus on the transition of policies concerning the ET pillar and to use this framework as a conceptual background with which to identify strategies for the improvement of ET policies in the AFF sectors. Data collection was undertaken through a round of workshops, and the data were analyzed using a thematic analysis approach. The results revealed that the need for a high-quality educational policy and the need to enhance collaboration, entrepreneurship and innovative learning methods were among the most important for the sector, where urgent changes in pace and an approach in ET are necessary for the entire value chain, from farm to fork. These results emphasize that pivoting the transition of ET systems toward achieving the Green Deal, FtF and new CAP objectives requires the development of policies that support student-centered and interdisciplinary education, while also being flexible and supported by non-formal and lifelong learning approaches.

Study of hybrid energy storage system with energy management for electric vehicle applications

This paper conducts an in-depth study on the on-board energy storage system for electric vehicles. We analyze the advantages and disadvantages of domestic and foreign energy storage systems including battery, flywheel, superconductor and supercapacitor. We propose a hybrid energy storage system composed of battery and supercapacitor as the on-board power supply system. It adopts a two-phase staggered parallel bidirectional DC-DC converter as the main control circuit of the storage system. And we adopt the power outer loop and current inner loop control strategy. Then the rate limiter prevents the sudden change of battery power and realizes that the instantaneous power is provided by the supercapacitor individually. The purpose is to extend the battery life and achieve the purpose of reasonable and efficient utilization of energy. Finally, the simulation verification is carried out by Matlab/Simulink. And the results show that the energy management strategy is more efficient for both power and energy management, which meets the design expectation and achieves better experimental results.

Realization of ultracapacitor as sole energy storage device in induction motor drive electric vehicle with modified state timing based field weakening control algorithm

This article employs the concept of realizing an electric vehicle (EV) driven by an induction motor (IM) with an ultracapacitor (UC) as a sole energy storage device for a short distance range in city drive. In battery-driven EVs, the performance of batteries will extensively degrade during frequent start, stop, acceleration and deceleration of the vehicle. However, UC can withstand that circumstance without any degradation. High charge/discharge efficiency, high power density, high temperature withstand capabilities and quick charging capability allows UC to run the EV for an extended range, including the regenerative braking technique. Apart from source, EVs also require efficient drive control for a smooth vehicle operation in the entire driving range. To achieve the vehicle characteristics, it is preferred to operate the propulsion drive in the field weakening (FW) region over the constant torque region. This paper also proposes a Modified State Timing SVPWM Based Control Algorithm for the FW region operation of IM for EV applications. The proposed algorithm is designed to overcome the torque ripple issue of the conventional SVPWM Based Control Algorithm in the FW region. The reduction in torque ripple is achieved by imposing current and voltage limits on the motor d and q axis by considering a maximum modulation index of 1.2732 and with an additional current loop for mitigating the transients in the torque due to sudden variation of the load. The simulation/hardware studies show a smooth operation of UC-driven EV for an entire driving range with a 78.57 percent improvement in torque ripple in the FW region by the proposed algorithm compared with the conventional FW control algorithm. The real time Lab prototype of UC driven EV has been developed and the proposed algorithm has been validated in real-time using the STM32F407VGTx ARM Cortex M4 microcontroller.

Energy-Efficient Train Control with Onboard Energy Storage Systems considering Stochastic Regenerative Braking Energy

Energy-Efficient Train Control with Onboard Energy Storage Systems considering Stochastic Regenerative Braking Energy

Energy Management Strategy with Regenerative-Breaking Recovery of Mixed Storage Systems for Electric Vehicles

Abstract The present paper addresses the energy management (EM) strategy between batteries and ultracapacitors (UCs) in a dual-propulsion urban electric vehicle (EV). The use of two propulsion machines proves advantageous for high-performance EVs facing spatial constraints. Allocating load power requirements among the propulsion machines and energy storage components poses a significant challenge in this design. In this paper, the control strategy presents managing the energy flow between the converters and the two brushless DC motors (BLDCs) motors via the DC link in order to maintain the energy demand of the EV coming from the dynamics of the latter. For this, power control is carried out by a management algorithm. This management is based on the power requested/generated by the two machines (BLDCs), the state of charge of the batteries (SOCBat) and the state of charge of the ultracapacitors (SOCUC). The bidirectional DC-DC converter is controlled with current to ensure the functioning of the motor or the generator of the vehicle. We also integrate the controls of the DC bus and BLDC. Additionally, the recovered energy during braking is stored in the battery or in the UC depending on the operating conditions.

Wireless Power Transfer-Based Voltage Equalizer for Scalable Cell-String Charging

Wireless power transfer (WPT)-based voltage equalizer (VE) shows unique advantages due to its physical isolation. Meanwhile, the markets of high-power fast charging have imposed significant needs for high-voltage wireless charging systems. However, a WPT-based charging equalizer cannot support a large-scale cell-string. This paper proposes a wireless charging VE with modularity to balance cascaded cell-strings automatically using multiple coils. The WPT system's intrinsic high-frequency power is used to power the voltage multiplier (VM). Only two diodes and one capacitor are required for an extra cell, which enables fewer components and a simple structure. Circuit analysis and a one-transmitter-two-receiver charging equalization experiment validate the system's operating principles. The voltage gaps among eight ultra-capacitor (UC) cells are equalized from 1.1 V to 0.01 V, with an overall efficiency of 76.5% under a 35.2 V output. Non-ideal conditions with coupling mismatch and frequency shifts are also analyzed to reveal the system's characteristics. It contributes several ways to providing general modular wireless charging equalization solutions for large-scale cell-string with lower cost and higher scalability.

Modeling and Capacity Configuration Optimization of CRH5 EMU On-Board Energy Storage System

Modeling and Capacity Configuration Optimization of CRH5 EMU On-Board Energy Storage System

Review on Battery State Estimation and Management Solutions for Next-Generation Connected Vehicles

The transport sector is tackling the challenge of reducing vehicle pollutant emissions and carbon footprints by means of a shift to electrified powertrains, i.e., battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). However, electrified vehicles pose new issues associated with the design and energy management for the efficient use of onboard energy storage systems (ESSs). Thus, strong attention should be devoted to ensuring the safety and efficient operation of the ESSs. In this framework, a dedicated battery management system (BMS) is required to contemporaneously optimize the battery’s state of charge (SoC) and to increase the battery’s lifespan through tight control of its state of health (SoH). Despite the advancements in the modern onboard BMS, more detailed data-driven algorithms for SoC, SoH, and fault diagnosis cannot be implemented due to limited computing capabilities. To overcome such limitations, the conceptualization and/or implementation of BMS in-cloud applications are under investigation. The present study hence aims to produce a new and comprehensive review of the advancements in battery management solutions in terms of functionality, usability, and drawbacks, with specific attention to cloud-based BMS solutions as well as SoC and SoH prediction and estimation. Current gaps and challenges are addressed considering V2X connectivity to fully exploit the latest cloud-based solutions.

Prostate cancer diagnosed and staged using UV-irradiated urine samples and a paper-based analytical device

The early detection of prostate cancer (CaP), one of the most common cancers in males, improves treatment efficacy. However, current methods to detect CaP are limited by specificity and sensitivity or require an invasive and unpleasant procedure. Therefore, there is a need for a novel, fast, and noninvasive CaP detection method. This proof-of-concept study aimed to diagnose CaP from urine samples by detecting the UV-induced fluorescent clusters formed in situ that reflect a urine composition specific to CaP patients using fluorescence spectroscopy. The UV-induced clusters formed upon irradiation by UV light with a wavelength of 254 nm were detected at excitation and emission wavelengths of 400 nm and 460 nm. Fifteen patients with CaP were correctly distinguished from 5 controls. The different stages of CaP could be further determined using molecular imprinting technology. Additionally, a paper-based analytical device was developed that enabled the correct identification of stage I cancer patients by fluorescent spectroscopy detection and the naked eye. In conclusion, this novel, easy-to-operate, point-of-care assay can enable early diagnosis of CaP with high accuracy and judgement of the disease stage. The simplicity, as well as versatility of the proposed method, will enable its application also to other diseases and disorders.

Comprehensive Study of Fuel Cell Hybrid Electric Vehicles: Classification, Topologies, and Control System Comparisons

The utilization of fuel cells (FC) in automotive technology has experienced significant growth in recent years. Fuel cell hybrid electric vehicles (FCHEVs) are powered by a combination of fuel cells, batteries, and/or ultracapacitors (UCs). By integrating power converters with these power sources, the FCHEV system can overcome the limitations of using them separately. The performance of an FCHEV is influenced by the efficiency of the power electronics converter controller, as well as the technical efficiency of the power sources. FCHEVs need intricate energy management systems (EMSs) to function effectively. Poor EMS can lead to low efficiency and accelerated fuel cell and battery degradation. The literature discusses various types of EMSs such as equivalent consumption minimization strategy, classical PI controller, fuzzy logic controller, and mutative fuzzy logic controller (MFLC). It also discusses a systematic categorization of FCHEV topologies and delves into the unique characteristics of these topologies. Furthermore, it provides an in-depth comparative study of EMSs applied in FCHEVs, encompassing rule-based, optimization-based, and advanced learning-based approaches. However, comparing different EMSs can be challenging due to the varying vehicle and system parameters, which might lead to false claims being made regarding system performance. This review aims to categorize and discuss the various topologies of FCHEVs, highlighting their pros and cons, and comparing several EMSs based on performance metrics such as state of charge (SOC) and FC deterioration. This paper seeks a deeper comprehension of the recent advancements in EMSs for FCHEVs. It offers insights that can facilitate a more comprehensive grasp of the current state of research in this field, aiding researchers in staying up to date with the latest developments.

Implementation of a P&E management system for a dual-source EV powered by different batteries

There is an era started in the field of power energy (PE) management in the electric vehicles (EVs) application cloud, which impacts the smart grid in significant ways and necessitates the collaboration of multiple branches of engineering. Most of the problems with EVs stem from their limited range, which can be improved by incorporating additional forms of energy storage. This paper makes an effort to bring a fresh viewpoint to the description of the power and energy management problem facing EV, taking into account all the needs of such a vehicle. Using a novel power and energy management system, the proposed methodology enables a systematic approach to this multidisciplinary problem. Implementing a power and energy management system for a dual-source EV using lead-acid batteries and ultra-capacitors (UCs) exemplifies the novel framework's capabilities. The electronic power development architecture is outlined in detail, along with the implementing modular blocks that make up the entire system architecture.

Influence of Ultracapacitor and Plug-In Electric Vehicle for Frequency Regulation of Hybrid Power System Utilizing Artificial Gorilla Troops Optimizer Algorithm

The proposed research work considers the effect of ultracapacitor (UC) and plug-in electric vehicle (PEV) inside a distributed energy sources based on three-area hybrid power system. Recently developed artificial gorilla troops optimizer (GTO) algorithm-based fractional order tilt integral derivative (FOTID) controller is suggested for load frequency control (LFC) of a three-area hybrid power system. By setting up a TID controller for the hybrid system’s frequency regulation, the algorithms’ realistic use is attempted via showcasing its immense potential. The results of the GTO-based TID regulator are observed better over GTO-based PID. Next, by considering a modification of system settings and under existence of intermittent behavior of renewable energy sources, it is evident that the influence of UC and PEV is more ethical compared to the conventional regulator-based frequency control. Additionally, it is seen that the suggested GTO-based FOTID controller along with the UC and PEV outperforms GTO-based TID controller in terms of lesser overshoots/settling times and fast responses.

Slime Mould Algorithm (SMA) and Adaptive Neuro-Fuzzy Inference (ANFIS)-Based Energy Management of FCHEV Under Uncertainty

This manuscript proposes a hybrid technique for optimal energy management (EM) of fuel cell (FC) and ultra-capacitor (UC) hybrid electric vehicles (FCHEVs) under uncertainty. The proposed method is a joint execution of the Slime Mould Algorithm (SMA) and the Adaptive Neuro-fuzzy Inference System (ANFIS), otherwise called the SMA-ANFIS system. The main aim of the proposed system is to achieve and regulate the steady state of DC bus voltage with minimal steady-state error (SSE) under different load conditions by using the proposed SMA-ANFIS technique. FC supplies power during vehicular operation and it is used to regulate DC bus voltage to the chosen value and recharge, and UC supplies current optimally through the proposed technique. The optimal EMS performs and controls the power flows through the associated power converters to accomplish some power allocation and energy efficiency levels. Finally, the proposed method is done in the MATLAB platform, and the performance is compared with other methods.

A Gallium Nitride Based High Bandwidth, Extended Single-Ended Primary Inductance Converter, for Fast Balancing of Ultracapacitors

Ultracapacitors (UCs) are the preferred choice for specific power requirements. Series and parallel of UCs are required to match the required voltage and power, as the available voltage range is 2.5 to 3 VDC. Passive and active balancing is inevitable here, while some demand, high bandwidth control for fast balancing and stable operations. A novel Extended-Single Ended Primary Inductance Converter (E-SEPIC) is used to actively balance the UCs. A new, simple and effective method is formulated to achieve a higher Degree of Balancing of UCs. A two-stage E-SEPIC with Silicon (Si) and Gallium Nitride (GaN) switch based are demonstrated with an input of 12 VDC to balance up to 3 VDC UCs at the output for high bandwidth and high-power density properties. An effective method of emulating impedance of UCs using Hybrid Electro-chemical Impedance Spectroscopy is evolved. The devised novel method of balancing is effective in height of balancing with GaN based converter exhibiting higher bandwidth of around 30 kHz around 18 times better, with increased efficiency of 12% with an improved power-density of 8.25 times in comparison with that of Si based. This contribution of GaN properties in electrical performance enhancements has influenced the design of Power Electronics applications.

Ultracapacitor as selectable energy buffer in electric vehicle application

A method of ultracapacitor integration with a regenerative braking system for use in electric drive trains is presented in this paper. An ultracapacitor (UC) is an intermediary to store and provide energy on the DC bus in certain scenarios, such as during acceleration and regenerative braking. UC is used to avoid direct inrush loading on the battery, which the electric drive system may cause. The energy from UC is injected into the DC bus in compliment to the battery, for which a dedicated power source switch called the “Main Charge Controller” (MCC) is responsible. The charge discharge of UC is done through a bidirectional buck-boost converter (BDBBC). The use of UC to offload high energy demand from the battery and store energy recovered during regenerative braking helps improve the vehicle's life and per-charge range. The stored energy in UC can be obtained from the battery at the start. UC can also be charged at the time of charging. The design and the presented control are ideal for lightweight electric vehicles. The system uses a 39.9 kJ ultracapacitor, formed from market-available 50F 2.7 V units in a 52S configuration, storage, and a 6kWh battery. The simulated vehicle in battery only case provides a minimum of 154 km range in rough conditions, in best case scenario it provides a 200 km range. The proposed design provides an increased extended range compared to simple battery-based regenerative braking, in the case of this work the hybrid solution provides an addition of more than 30 km of additional range compared to a battery-only solution which is simulated for all the same conditions except without the addition of the UC and its control and handling system, resulting in a total range of 230 km. The speed control response achieved by the user is also very fast. Electric Vehicles have range limitations due to storage technology which is a high entry barrier for consumers looking for reliable vehicles without range anxiety, as storage technology progresses adoption will become easier. This work is an attempt at using hybrid storage and selective use of it, for improved range and sharper speed response. However, this work does not explore the aging of the storage components due to increased operational load and thermal stresses or application to alternate battery chemistries. This work is primarily focused on the vehicles that use lithium-based batteries.

Energy efficient power cap configurations through Pareto front analysis and machine learning categorization

The growing demand for more computing resources has increased the overall energy consumption of computer systems. To support this increasing demand, power and energy consumption must be considered as a constraint on software execution. Modern architectures provide tools for managing the power constraints of a system directly. The Intel Power Cap is a relatively new tool developed to give users fine-grained control over power usage at the central processing unit (CPU) level. The complexity of these tools, in addition to the high variety of modern heterogeneous architectures, hinders predictions of the energy consumption and the performance of any target software. The application of power capping technologies usually leads to the bi-objective optimization problem for energy efficiency and execution time but optimal power constraints could also produce exceeding performance losses. Thus, methods and tools are needed to calculate the proper parameters for power capping technologies, and to optimize energy efficiency. We propose a methodology to analyze the performance and the energy efficiency trade-offs using this power cap technology for a given application. A Pareto front is extracted for the multi-objective performance and energy problem, which represents multiple feasible configurations for both objectives. An extensive experimentation is carried out to categorize the different applications to determine the overall optimal power cap configurations. We propose the use of machine learning (ML) clustering techniques to categorize each application in the target architecture. The use of ML allows us to automate the process and simplifies the effort required to solve the optimization problem. A practical case is presented where we categorize the applications using ML techniques, with the possibility of adding a new application into an existing categorization.