This study proposes a phase space reconstruction-based fuzzy identification method to improve the prediction accuracy of chaotic time series. First, the delay time and embedding dimension are determined using mutual information and the Cao method, respectively, to reconstruct the phase space. Second, the particle swarm optimization algorithm is applied to optimize the membership function parameters of the T-S fuzzy model, enabling a more rational partition of the premise space. Finally, the conclusion parameters of the fuzzy model are identified via the recursive least squares method. Experimental validation using two benchmark chaotic time series and a real-world wind speed series demonstrates that the proposed model achieves high prediction accuracy. The results confirm the effectiveness and practical applicability of the method for modeling and forecasting chaotic systems.
To address issues of inaccurate parameter identification and large state of charge (SOC) estimation error for lithium-ion batteries in electric vehicle (EV) applications, we proposed a parameter identification and corresponding SOC estimation method based on a trust region least squares (TRLS) algorithm. First, a second-order resistor-capacitor (RC) equivalent circuit model for the lithium-ion battery was established. To effectively describe the dynamic characteristics of the lithium-ion battery, discharge experiments were designed and conducted to collect the data of terminal voltage, current, and time. The initial parameters of this model were then obtained through a segmented identification based on the Levenberg-Marquardt (LM) algorithm. Subsequently, an improved parameter identification algorithm based on the TRLS was constructed. This algorithm conducted the iterative optimization of model parameters by searching for the best solution within the trust region, thereby achieving precise identification of model parameters. Finally, the SOC estimation was achieved using the TRLS-based optimization solution of model parameters in conjunction with the dual extended Kalman filter (DEKF) algorithm. With the proposed TRLS-based method, the Mean Absolute Error (MAE) values of the estimated SOC were 0.0031, 0.0066, 0.0052, and 0.0044 under the static open-circuit voltage (OCV), the dynamic Urban Dynamometer Driving Schedule (UDDS), Hybrid Pulse Power Characterization (HPPC), and Dynamic Stress Test (DST) conditions, respectively. The comparative results successfully verified the reliability and effectiveness of the proposed TRLS-based method, which demonstrated significant potential and advantages for battery management applications.
In this study, we evaluated three renewable-based microgrid configurations designed to strengthen energy security and long-term sustainability. Configuration 1 integrates a photovoltaic (PV) array and wind turbines (WT) with a battery energy storage system (BESS). Configuration 2 replaces BESS with a hydrogen energy storage system (HESS), offering extended storage capacity and improved energy availability. Configuration 3 combines BESS and HESS, leveraging the advantages of short-term and long-term storage to create a more resilient energy system. The objective function was formulated to minimize the total annual cost (TAC) while optimizing energy generation, storage efficiency, and overall system reliability. A robust and efficient transient search algorithm (TSA) was employed to determine the optimal capacity of the microgrid configurations. A comprehensive cost analysis revealed that configuration 1 was the most cost-effective. However, incorporating HESS in configuration 2 resulted in a 41.3% increase in cost due to the investment required for electrolyzers, storage tanks, and fuel cells. Configuration 3, which integrates battery and hydrogen storage, incurred an additional 3.2% cost compared to configuration 2 and 45.8% compared to configuration 1. Despite the higher cost, this hybrid system enhances energy resilience by efficiently balancing short-term and long-term storage solutions.
Biodiesel is a viable alternative to non-renewable fossil fuels. However, its commercialization faces challenges due to the high costs of conventional feedstocks. This study investigated the influence of reaction parameters-catalyst concentration, reaction temperature, and reaction time-on the yield of synthesized biodiesel from waste cooking oil using snail shell and plantain peel wastes as catalysts. Snail shells were calcined at 800 °C to produce CaO and characterized by X-ray fluorescence. Organic KOH, extracted from plantain peel ash burned at 750 °C, impregnated the CaO to form a composite KOH/CaO catalyst. Using low-quality feedstock (acid value of 8.8 mg KOH/g oil), the oil underwent acid-esterification followed by transesterification under varied conditions. The optimal parameters, determined via response surface methodology (RSM), were 7% wt/wt catalyst concentration, 60.51 °C reaction temperature, and 1.93 h reaction time, achieving a maximum biodiesel yield of 92.23%. The best experimental yield, 92.10%, was obtained using a 2:1 green KOH/CaO ratio. Physicochemical properties and biodiesel composition conformed to ASTM D6751 standards. The highest fatty acid to methyl ester conversion rate, 93.86%, was achieved using the optimized snail shell catalyst. This research demonstrates that converting waste into biodiesel is not only feasible but also adds value to waste materials.
Electric three-wheelers are increasingly adopted in developing countries as sustainable alternatives to internal combustion engine-based transport, yet their design and optimization require accurate modelling tools for improved adoption. This study develops a comprehensive MATLAB/Simulink model for evaluating the dynamic performance and energy efficiency of a three-wheel electric vehicle under standard driving cycle conditions. The model integrates a 3 kW brushless DC (BLDC) motor, a 48 V 100 Ah lithium-ion battery (NMC), and an H-bridge inverter with regenerative braking. The motor electrical parameters, battery internal resistance, C-rate, and inverter switching characteristics were incorporated to ensure reproducibility. A proportional-integral (PI) controller is employed to emulate driver torque demand. Vehicle performance is assessed using four standard drive cycles, such as the federal test procedure (FTP-75), the new European driving cycle (NEDC), the worldwide harmonized light vehicles test procedure (WLTP Class 3), and the urban dynamometer driving schedule (UDDS). Results show that WLTP Class 3 provides the most realistic representation of energy consumption for urban three-wheelers owing to its wider speed range, more dynamic acceleration patterns, and mixed urban–suburban characteristics. In comparison, FTP-75 and UDDS emphasise low-speed stop-and-go operation, while NEDC reflects smoother peri-urban driving. A mass-based analysis further demonstrates a strong positive correlation between vehicle weight and energy consumption, offering insight for chassis design, payload limits, and operational planning. The proposed framework provides a scalable, cost-effective tool that supports EV retrofit programs and guides manufacturers in optimising motor sizing, controller tuning, and battery capacity for three-wheel electric mobility in developing regions.
As the share of new energy generation increases, its intermittent and uncertain nature threatens the stability of power systems. This study introduces a dynamic scheduling approach for wind-solar storage-charging hybrid power stations utilizing digital twin technology. By constructing an accurate virtual model of physical entities, the approach enables real-time monitoring, simulation analysis, and intelligent optimal control of the power system, providing a new solution for improving power station operation efficiency and power grid stability. In this application scenario, the digital twin system integrates and analyzes the operation data of the power station and optimizes the node voltage control and peak shaving control strategies of the energy storage power station. Regarding node voltage regulation, the reactive power output is dynamically fine-tuned in line with energy storage’s charge-discharge attributes, with the ideal charge-discharge power identified via the particle swarm optimization (PSO) algorithm and digital twin simulation capability. Simulation results show that the node voltage fluctuation of the traditional strategy is ± 5% with 8 over-limits, while the proposed strategy reduces it to ± 2% with 1 over-limit. In peak shaving control, through digital twin simulation and PSO for energy storage capacity configuration, the optimal charging and discharging plan is determined, reducing the grid peak-valley difference from 30% of the traditional strategy to 15%. In conclusion, this strategy has significant advantages in improving the efficiency of new energy power stations and power grid stability, providing technical support for building a clean and efficient energy system.
In this paper, we explored gas pressure as a determining factor influencing the performance, efficiency, and durability of Proton Exchange Membrane Fuel Cells (PEMFCs). An extensive simulation model was developed over operating pressures ranging from 1 to 4 atm at 353 K under fully humidified conditions. The findings indicated that increasing pressure by 1 atm to 2 atm enhances net efficiency by 17% (48.5 to 56.8%) and, correspondingly, maximum current density increases to 1.40 A cm-2 and the stack life is estimated to be even longer, to almost 6,000 hours. Nonetheless, beyond a pressure of 4 atm, the returns are non-linear as compressor penalties become nearly 5-fold over baseline and long-term stability decreases to 5,100 hours. These results highlight the ideal operating range of 2–3 atm, and efficiency, durability, and water balance were optimized at the same time, which is consistent with and like earlier experimental works. The article gives practical information on pressure-tuning measures of PEMFCs in vehicle, marine, and non-portable energy systems.
As the connection between the upgraded power grid and users, the microgrid can not only control the output of the local distributed power but also realize a low-carbon transition of users in order to achieve the carbon reduction target. With the aim of reducing operating costs and carbon emissions at the same time, this paper proposes a sustainable economic scheduling strategy combining a ladder-type reward and punishment carbon trading mechanism with generalized energy storage. Initially, the charge and discharge process is analyzed in detail for the energy storage system, and mathematical models relating the actual energy storage system with the virtual energy storage system in the microgrid are established. The composition of the energy supplied to the microgrid and the emission intensity of different power supply sources are analyzed, and a multi-ladder carbon trading mechanism with rewards and penalties is proposed. Subsequently, the typical wind power and photovoltaic power generation cases are simulated by an improved generative adversarial network, which considers the randomness of renewable energy. Eventually, based on MATLAB, a simulation verification is carried out on the proposed scheduling scheme, and the experimental results show that the proposed scheme can effectively reduce operating costs and carbon emissions by 2.03% and 14.08%, respectively, which improves the economy and sustainability of the microgrid.
Lithium-ion batteries are essential for electric vehicles and energy storage systems; nevertheless, inconsistent temperature distributions jeopardize their safety and reliability, potentially resulting in thermal runaway. Thus, precise temperature forecasting models are crucial for enhancing performance and guaranteeing operational safety. In this research, we present a hybrid framework that combines the Wavelet Scattering Transform (WST) with Long Short-Term Memory (LSTM) neural networks to predict battery temperature distributions. The methodology was validated using datasets from the NASA Ames Prognostics Center of Excellence (PCoE), which included authentic cycle data for voltage, current, and temperature. To guarantee reliability, model predictions were calibrated and assessed using three methods: RAW outputs, bias correction, and Poly2. The proposed WST-LSTM framework showed improved stability and consistency relative to the raw-signal LSTM baseline, supporting its suitability for thermal-safety evaluation and battery-management applications. The proposed framework was implemented in MATLAB/Simulink for an electric-vehicle battery model, thereby validating its applicability to real-world automotive platforms. This study emphasizes forecasting temperature at a specific sensor point rather than reconstructing the temperature field. The suggested WST-LSTM model predicted the temperature trajectory at this location utilizing electrical and environmental signals.
For an uncertain microgrid with integrated electric vehicles (EVs), a distributionally robust scheduling algorithm is proposed. First, Monte Carlo simulation is adopted to generate the uncertain region of charging/discharging scenarios for EVs, and a hybrid scenario set for renewable energy and normal load is generated via FCM clustering. In the day-ahead scheduling stage, with the feasibility of the hybrid scenario set and its probability-weighted performance index of the economic cost as the objective function, the optimal power output of the microgrid equipment is calculated to achieve optimal performance of hybrid scenarios by using the column-and-constraint generation algorithm. Subsequently, a robustness test is conducted to ensure the feasibility of the day-ahead optimal solution for any scenario. In the intraday scheduling stage, real-time data on renewable generation, normal load, and electric vehicle are utilized to optimize power adjustment of the day-ahead solution. Results show that the proposed method improves the economic performance of the microgrid system. Simulation cases verify the effectiveness of the proposed method.
To address the disadvantages of both lithium-ion batteries (low power density) and supercapacitors (low energy density), lithium-ion capacitors (LICs) were created as a hybrid energy storage system. In this study, the biomass-derived activated carbon from corncob (CACN) was prepared by adopting physical and chemical activation processes under different nitrogen gas flow rates (N2). The material was activated with KOH and then pyrolyzed at 700 °C under varying N2 flow rates from 200 to 400 standard cubic centimeters per minute (sccm). Scanning electron microscopy (SEM) images revealed that the surface morphology appears to have a highly porous structure. The highest specific surface area (SSA) (1936 m2 g−1) was achieved following activation at 300 sccm, confirmed by the Brunauer–Emmett–Teller (BET) results. X-ray diffraction (XRD) and Raman spectroscopy indicated that the crystal structure was amorphous. Through electrochemical tests, the quasi-rectangular cyclic-voltammogram (CV) graph of the LIC coin-cell constructed by CACN (LICN) has similar characteristics to capacitors and batteries. The best coulombic stability was found for LIC with CACN activated at 300 sccm, presenting the highest energy and power density (10.79 Wh kg−1 and 526.39 W kg−1) by charge-discharge (CD) tests. This optimization of carbon activation enables us to achieve the optimum physical characteristics and electrochemical performance of activated carbon and LIC, respectively.
In this study, we evaluated the feasibility of water buffalo ( Bubalus bubalis) manure as a substrate for biogas production under tropical environmental conditions in southeastern Mexico. The manure was collected from a livestock unit in Chiapas and processed in a 64 m³ lagoon-type anaerobic digester. The substrate was characterized through proximate and elemental analyses, and its chemical formulas and theoretical biogas composition were estimated. Additionally, physicochemical parameters (pH, oxidation-reduction potential (ORP), electrical conductivity (EC), total dissolved solids (TDS), and chemical oxygen demand (COD)) and biogas composition (CH4, CO2, H2S) were monitored throughout the study. Our results indicated that buffalo manure exhibits favorable properties for anaerobic digestion, with high moisture content (>80%), adequate volatile solids, and adequate carbon and nitrogen contents. Over 14 weeks of operation, the system maintained stable conditions of pH (7.7–8.0) and redox potential (−180 to −280 mV), reflecting an efficient transition toward the methanogenic phase. COD removal efficiency reached 86.6%, with a degradation constant of k = 0.147 week−1. The biogas produced showed a stable CH4 content (58–60% v/v) and low H2S concentrations (<22 ppm), which were eliminated after filtration, achieving calorific values up to 25.51 MJ/m³, highlighting its potential contribution to decentralized renewable energy systems in tropical rural regions.