Optimal Energy Management Strategy Research Articles

Optimized Energy Management Strategy for an Autonomous DC Microgrid Integrating PV/Wind/Battery/Diesel-Based Hybrid PSO-GA-LADRC Through SAPF

This study focuses on microgrid systems incorporating hybrid renewable energy sources (HRESs) with battery energy storage (BES), both essential for ensuring reliable and consistent operation in off-grid standalone systems. The proposed system includes solar energy, a wind energy source with a synchronous turbine, and BES. Hybrid particle swarm optimizer (PSO) and a genetic algorithm (GA) combined with active disturbance rejection control (ADRC) (PSO-GA-ADRC) are developed to regulate both the frequency and amplitude of the AC bus voltage via a load-side converter (LSC) under various operating conditions. This approach further enables efficient management of accessible generation and general consumption through a bidirectional battery-side converter (BSC). Additionally, the proposed method also enhances power quality across the AC link via mentoring the photovoltaic (PV) inverter to function as shunt active power filter (SAPF), providing the desired harmonic-current element to nonlinear local loads as well. Equipped with an extended state observer (ESO), the hybrid PSO-GA-ADRC provides efficient estimation of and compensation for disturbances such as modeling errors and parameter fluctuations, providing a stable control solution for interior voltage and current control loops. The positive results from hardware-in-the-loop (HIL) experimental results confirm the effectiveness and robustness of this control strategy in maintaining stable voltage and current in real-world scenarios.

Multi-agent based optimal sizing of hybrid renewable energy systems and their significance in sustainable energy development

This paper delves into the enhancement and optimization of on-grid renewable energy systems using a variety of renewable energy sources, with a particular focus on large-scale applications designed to meet the energy demand of a certain load. As global concerns surrounding climate change continue to mount, the urgency of replacing traditional fossil fuel-based power generation with cleaner, more cost-effective and dependable alternatives becomes increasingly apparent. In this context, a comprehensive investigation is conducted on grid connected hybrid energy system that combines photovoltaic, wind, and fuel cell technologies. The study employs three state-of-the-art optimization algorithms, namely Walrus Optimization Algorithm (WaOA), Coati Optimization Algorithm (COA), and Osprey Optimization Algorithm (OOA) to determine the optimal system size and energy management strategies, all aimed at minimizing the cost of energy (COE) for grid-based electricity. The results of the optimization process are compared with the results obtained from the utilization of the Particle swarm optimization (PSO) and Grey Wolf optimizer (GWO). The findings of this study underscore both the practical feasibility and the critical importance of adopting on-grid renewable energy systems to decrease the dependence on traditional energy sources within the grid. The proposed WaOA succeeded to reach the optimal solution of the optimal design process with a COE of 0.51758129611 $//kwh while keeping the loss of power supply probability (LPSP), the reliability index, at 7.303681e-19. The practical recommendations and forward-looking insights provided within this research hold the potential to foster sustainable development and effectively mitigate carbon emissions in the future.

Energy Consumption and Energy Costs Based on Time-of- Day Tariffs in Seafood Processing Units in Kerala, India

Energy conservation in seafood processing is crucial due to the highly energy-intensive nature of its main stages such as pre-processing, processing, and storage, which must operate under cold chain conditions. The variability of energy tariffs based on Time of Day (TOD) significantly impacts the economic sustainability of these operations. This study evaluates the electrical energy consumption patterns of seafood processing units in South India, categorized by production capacity. Primary data were collected from 10 seafood processing units, assessing energy consumption during Normal Load Time (NLT), Peak Load Time (PLT), and Off-Peak Load Time (OLT) over a one-year period. Resultsrevealed that smaller capacity units (<30 T/D) accounted for 56% of total production during PLT, despite energy tariffs being highest during this time.These low-capacity units also exhibited higher mean energy consumption and costs compared to larger units, suggesting inefficiencies. The findings highlight the need for optimized energy management strategies, such as rescheduling production to lower-tariff periods and adopting more energy-efficient technologies. Implementing such measures could reduce energy costs and improve the overall profitability of seafood processing operations, particularly for smaller units.

Hardware-in-the-Loop Implementation of an Optimized Energy Management Strategy for Range-Extended Electric Trucks

The reliance of the commercial transportation industry on fossil fuels has long contributed to pollutant and greenhouse gas emissions. Since full electrification of medium- and heavy-duty vehicles faces limitations due to the large battery capacity required for extended driving ranges, this study explores a Range-Extended Electric Vehicle (REEV) for medium-duty Class 6 pick-up and delivery trucks. This hybrid architecture combines an electric powertrain with an internal combustion engine range-extender. Maximizing the efficiency of REEVs requires an Energy Management Strategy (EMS) to optimally split the power between the two power sources. In this work, a hierarchical EMS is developed through model-based design and validated via Hardware-In-The-Loop (HIL) simulations. The proposed EMS demonstrated a 7% reduction in fuel consumption compared to a baseline control strategy, while maintaining emissions and engine start frequency comparable to a benchmark globally optimal EMS obtained with dynamic programming. Furthermore, HIL results confirmed the strategy’s real-time implementation feasibility, highlighting the practical viability of the controller. This research underscores the potential of REEVs in significantly reducing emissions and fuel consumption, as well as providing a sustainable alternative for medium-duty truck applications.

Development of optimal energy management strategy for proton exchange membrane fuel cell-battery hybrid system for drone propulsion

Drones powered by fuel cells have been developed to ensure long flight distances. Due to the low dynamic responsiveness of proton exchange membrane fuel cell (PEMFC) systems, they must be integrated with batteries to fully meet the power demand profiles of drones. In this study, a dynamic model of a PEMFC-battery hybrid system for drone propulsion is developed in MATLAB-Simulink® to establish the optimal energy management strategy (EMS) for ensuring efficient and safe operation. The proposed PEMFC system comprises two modules of an open-cathode PEMFC stack, which are connected in parallel. To estimate the dynamic behavior and performance of PEMFC, one-dimensional dynamic model of PEMFC considering two-phase transport through the gas diffusion layer is developed and simulated. A zero-dimensional dynamic model of the battery was also developed based on an equivalent circuit model. The resulting PEMFC-battery hybrid system model was simulated to determine the power required for the propulsion of the drone according to a flight scenario comprising five phases: takeoff hover, climb, cruise, descent, and landing hover. Three different EMSs are developed and simulated to define the optimal one for the PEMFC-battery hybrid system by comparing the total hydrogen consumption and the final SOC of the battery As a result, the EMS that equally splits the required power between two PEMFCs exhibits the lowest hydrogen consumption during the flight simulation. This study helps provide the optimal system design and control scheme of drones powered by fuel cells.

Data-driven energy utilization for plug-in hybrid electric bus with driving patten application and battery health considerations

The efficient utilization of energy is a crucial consideration for plug-in hybrid electric buses (PHEB). However, achieving the optimal energy management strategy (EMS) for PHEB necessitates the harmonious optimization of both driving conditions and battery status. Consequently, an innovative EMS that integrates the data-driven prediction of the driving cycles and battery health status. Explicitly, the bi-directional long short-term memory algorithm is employed to reconstruct and train the health status data of power batteries, enabling the accurate estimation of their State of Health (SOH) values. Moreover, a database of historical driving behavior is meticulously collected through a real-world PHEB platform, and the driving cycles for a certain while are predicted with the clustering algorithm. Then, this invaluable information on the battery status and driving cycles is seamlessly introduced into an adaptive-network-based fuzzy inference system (ANFIS) to regulate of the energy conversion factor in the following equivalent consumption minimization strategy (ECMS). Besides, the offline global optimization of equivalent fuel consumption with the Pontryagin's minimum principle (PMP) is preliminarily conducted to generate the pre-established energy conversion factors, facilitating convenient access to the engine and motor's reasonable power distribution range. Finally, simulations and experiments have validated the effectiveness of the proposed EMS, demonstrating a 23.57 % reduction in battery SOC depletion and a 10.53 % decrease in fuel consumption compared to traditional ECMS methods. More importantly, the proposed method has been validated for its executability in embedded devices through real-world bench experiments. This research holds significant reference value for energy utilization and practical implementation in the PHEB, thereby contributing to the advancement of knowledge in this field.

Dual-Source Cooperative Optimized Energy Management Strategy for Fuel Cell Tractor Considering Drive Efficiency and Power Allocation

To solve the problems of the low driving efficiency of a fuel cell tractor power source and the high hydrogen consumption caused by the irrational power allocation of the energy source, the power system was divided into two parts, power source and energy source, and a dual-source cooperative optimization energy management strategy was proposed. Firstly, a general energy efficiency optimization method was designed for the power source composed of a traction motor and PTO motor, and the energy source was composed of a fuel cell and power battery. Secondly, the unified objective function and constraint conditions were established, and the instantaneous optimization algorithm was used to construct the weight factor. The instantaneous optimal drive efficiency energy management strategy and the instantaneous optimal equivalent hydrogen consumption energy management strategy were designed, respectively. Finally, with the demand power as the transfer parameter, the instantaneous optimal drive efficiency energy management strategy and the instantaneous optimal equivalent hydrogen consumption energy management strategy were integrated to form a dual-source collaborative optimal energy management strategy. In order to verify the effectiveness of the proposed strategy, a rule-based energy management strategy was developed as a comparison strategy and tested in an HIL test under plowing and rotary plowing conditions. The results show that the average fuel cell efficiency of the proposed strategy increased by 7.86% and 8.17%, respectively, and the proposed strategy’s equivalent hydrogen consumption decreased by 24.21% and 9.82%, respectively, compared with the comparison strategy under the two conditions. It can significantly reduce the SOC fluctuation of the power battery and extend the service life of the power battery.

Optimal power management for hybrid electrical vehicle

This paper introduces an innovative optimal energy management strategy tailored for a hybrid electric-powered hydraulic excavator system. The aim is to bolster power performance, extend the lifespan of power sources, and optimize hydrogen usage. In this system, the fuel cell serves as the primary power source, while integrating supercapacitors and batteries offers benefits such as enhancing power performance and storing regenerative energy for future use. To efficiently distribute power among these three sources and maximize the utilization of regenerative energy, we propose an energy management strategy. This strategy combines fuzzy logic control with a rule-based algorithm. Moreover, we optimize the parameters of the fuzzy logic system using a combination of the backtracking search algorithm and sequential dynamic programming. This optimization aims to reduce hydrogen consumption and prolong the lifespan of the power sources. Simulation results demonstrate that our proposed energy management strategy significantly enhances vehicle performance, improves fuel economy of the hybrid electric-powered hydraulic excavator system, and enhances the durability and efficiency of the battery and supercapacitor systems within the fuel cell system.

Battery health-considered energy management strategy for a dual-motor two-speed battery electric vehicle based on a hybrid soft actor-critic algorithm with memory function

Energy management strategies (EMSs) ease mileage anxiety and improve the health performance of battery electric vehicles (BEVs). This study proposes a soft actor-critic (SAC)-based EMS for dual-motor two-speed BEV to minimize electrical energy consumption and battery capacity losses. In addition, two optimization tricks are employed to optimize the original SAC. The linear mapping trick is integrated into the actor-network of SAC to enable agents to search for optimal EMS in a discrete (driving mode)-continuous (torque distribution) hybrid action space. The SAC-based EMS is modeled as a partially observable Markov decision process (POMDP) to obtain more information about the real state of the environment. Based on this, another optimization trick, the long short-term memory (LSTM) network, is integrated into the actor and critic of SAC to make full use of both historical and current environmental information. Simulation results show that the proposed method learns the optimal EMS, its converged episodes are shortened to the 97th, the health performance of the battery is the closest to the dynamic programming (DP)-based EMS, and maintains battery operating at a range of 30–40°C. In addition, the proposed EMS has the best adaptability in test cycles, reaching 99.42% and 99.29% of DP-based EMS, respectively.

Optimal energy management strategy based on neural network algorithm for fuel cell hybrid vehicle considering fuel cell lifetime and fuel consumption

Optimal energy management strategy based on neural network algorithm for fuel cell hybrid vehicle considering fuel cell lifetime and fuel consumption

Research on approximate optimal energy management and multi-objective optimization of connected automated range-extended electric vehicle

In order to attain the optimal allocation of energy between auxiliary power unit (APU) and battery of the connected automated range-extended electric vehicle, from a multi-scale perspective, an Approximate Optimal Energy Management Strategy (AOEMS) has been proposed. Firstly, a composite determination method was designed based on prediction and parameter identification to divide the operating condition types, which could be as an operating condition prediction and feed forward control method to determine the approximate optimal power distribution between APU and battery. This method keeps APU operating at the optimal operating point/area as much as possible without predicting the accurate vehicle speed sequence. Then, an adaptive method based on V2X information was used to adjust the key threshold parameters in real-time using a fuzzy logic controller which reduces the algorithm complexity with prediction and division of the operating conditions types, which has significantly improved the robustness and overall performance of the optimization. Finally, the simulation and experimental results thoroughly indicated that the proposed AOEMS can better balance the performances, as anticipated, enhancing economy, reducing emissions, and extending battery lifewere effectively maintained in equilibrium.

Optimal energy management strategy for electric vehicles and electricity distribution system based on quantile deep learning with enhanced African vulture optimization

The increasing fluctuation in electricity prices due to unpredictable demand loads on the power grid has underscored the importance of finding optimal scheduling solutions for electric vehicles (EVs). Integrating EVs into the distribution system (DS) is critical for efficiently managing their energy needs. However, current EV scheduling approaches face challenges. Centralized solutions are often unreliable and complex, while existing decentralized methods struggle to efficiently schedule EVs in large networks with smart energy DS. To address these challenges, we propose a hybrid technique called Quantile Deep Learning with Enhanced African Vulture Optimization (Quantum VultureNet) for energy management systems (EMS) involving EVs and DS. This approach combines the African Vultures Optimization algorithm with elite mutation, learning based on dynamic opposition, and chaotic map-based population initialization. By enhancing exploration and exploitation capabilities, this hybrid method aims to improve the scheduling of EVs, particularly in dynamic environments. The primary goal of the Quantum VultureNet technique is to minimize system costs and power losses while optimizing energy management. The proposed system considers factors such as bidirectional energy trading, EV fleet arrival times in driving plans, Photo Voltaic uncertainty impacts on energy management systems, and factors affecting energy selling back to the grid. The model was implemented in MATLAB and compared with conventional methods. Simulation results demonstrate that the Quantum VultureNet technique effectively finds near-global optimum solutions with reduced computational complexity. This indicates the effectiveness of the proposed approach for EMS in EVs integrated with DS.

Energy management strategy of series–parallel hybrid transmission integrating map information and personalized driving characteristics

The integration of multi-source intelligent and connected information during a driving trip, along with its online application to globally optimized energy management strategies, has emerged as a crucial technical approach for enhancing the energy-saving effectiveness of hybrid transmissions. However, the action mode of such information and the optimization calculation efficiency of existing dynamic programming (DP) methods limit the online application of the aforementioned strategies with global optimization capabilities. To address these problems, the present study proposes a hierarchical energy management strategy that follows the reference trajectory of the battery state of charge (SoC) and comprehensively considers the multi-source information on the driving trip. First, a global speed prediction model based on personalized driving characteristics is proposed to obtain an accurate driving cycle input for the space-domain DP method. Second, the aforementioned tasks as well as the working-mode decision of the hybrid transmission and the multi-power-source torque distribution calculation tasks are deployed in the dual-core controller. Finally, the hierarchical energy management strategy is verified via vehicle testing. Compared with the DP strategy, the proposed strategy has an energy-saving potential of 4.17% that is yet to be realized. Furthermore, compared with the charge-depleting and charge-sustaining (CD–CS) strategy, the proposed strategy reduces fuel consumption by 0.38 L per 100 km, and its energy-saving effect is significant. This study is the first to apply the DP method to the vehicle controller, thereby facilitating the online application of energy management strategies with global optimization capabilities.

Energy Management Considering both Efficiency Optimization and Lifetime Balance of Multi-stack FCS

Abstract With the increase of energy transformation and environmental protection awarenesses, fuel cells, as a clean and efficient energy conversion technology, have been widely adopted in transportation and stationary power supply applications. A single fuel cell has a limited power capability. On the other hand, the multiple fuel cells are combined into a multi-stack fuel cell system (MFCS), which provides a high power output and modular design. However, the energy management of such multi-stack systems faces challenges such as uneven power distribution, inconsistent stack life, and low overall efficiency. Current multi-stack fuel cell control systems often optimize single objectives, either efficiency or lifetime balance produce the optimized energy management strategy. This may not suffice for the overall system benefit. For example, if only efficiency is optimized by energy management, the failure to consider lifetime balance may result in uneven life degradation for each fuel cell, and vice versa. To address the above issues, this paper proposes an energy management strategy for multi-stack fuel cell systems that considers dual objectives, which can simultaneously ensure the optimal co-optimization of the output efficiency and stack life for each stack, thereby improving the overall operating efficiency and lifetime of the system. The effectiveness and practicality of the proposed energy management control strategy were verified on a 60kW dual-stack PEMFC fixed power generation system developed by SeeEx.

Optimal sizing and energy management strategy for an office building with photovoltaic, heat pump, thermal tank, and electric vehicle under time-of-use tariff

The increasing demand for sustainable energy solutions is driving the integration of various renewable energy technologies. Integrating electric vehicle batteries, photovoltaics, heat pumps, and thermal tanks into building energy systems offers a promising approach to enhancing energy flexibility and efficiency. However, research on such integrated energy systems is still in its infancy. Optimizing the configuration and operation of the system may lead to significant cost savings and improved energy management. A coupled model and simulation-based optimization approach are used to optimize the thermal tank capacity and key operational parameters to minimize the annual total cost. The results show that the integrated energy system reduces the annual total cost by 36.35% and monthly electricity bills by up to 69.26% compared to the base system. As for energy flexibility, only 13.91% of electricity demand is supplied by the grid during peak periods, and the monthly load coefficient, load cover ratio, and photovoltaic self-consumption ratio of the integrated energy system are significantly improved. This study demonstrates the economic and operational benefits of integrating various renewable energy technologies into building energy systems and provides new insights into their design and optimization.

A new real-time optimal energy management strategy for range extended electric vehicles considering battery degradation and engine start-up

A new real-time optimal energy management strategy for range extended electric vehicles considering battery degradation and engine start-up

SVM classifier based energy management strategy for dual‐source fuel cell hybrid electric vehicles

AbstractThis study proposes an optimal energy management strategy for dual‐source fuel cell hybrid electric vehicles (FCHEV) utilizing the support vector machine (SVM) classifier. The goal is to optimize power distribution between fuel cells and batteries to enhance vehicle's performance and efficiency. The SVM classifier is trained using a dataset of driving conditions and corresponding optimal power distribution (OPD) values obtained through simulation. The trained classifier predicts real‐time OPD based on driving conditions. In comparison to existing literature, this study conducts a comparative analysis of energy management control strategies like model predictive control (MPC), fuzzy, equivalent consumption minimization strategy (ECMS), proportional‐integral (PI) control, and state machine control (SMC) strategy for FCHEVs using the MATLAB/SIMULINK platform and real‐world driving dataset. The proposed strategy is then tested in a real‐time EV simulator to verify its efficacy. Additionally, this study introduces the SVM classifier technique for selecting the optimal energy management strategy for FCHEVs. Performance analysis using SVM reveals that the MPC control strategy offers the highest efficiency compared to other techniques based on selected features, achieving an average performance of 95% through cross‐validation. This analysis demonstrates the most cost‐effective and fuel‐efficient utilization of electricity flow in a modern energy‐efficient environment.

Multi-level optimal energy management strategy for a grid tied microgrid considering uncertainty in weather conditions and load

Microgrids require efficient energy management systems to optimize the operation of microgrid sources and achieve economic efficiency. Bi-level energy management model is proposed in this paper to minimize the operational cost of a grid-tied microgrid under load variations and uncertainties in renewable sources while satisfying the various technical constraints. The first level is day ahead scheduling of generation units based on day ahead forecasting of renewable energy sources and load demand. In this paper, a recent meta-heuristic algorithm called Coronavirus Herd Immunity Optimizer (CHIO) is used to solve the problem of day-ahead scheduling of batteries, which is a complex constrained non-linear optimization problem, while the Lagrange multiplier method is used to determine the set-point of the Diesel Generator (DG). The second level of the proposed EMS is rescheduling and updating the set-points of sources in real-time according to the actual solar irradiance, wind speed, load, and grid tariff. In this paper, a novel real-time strategy is proposed to keep the economic operation during real-time under uncertainties. The obtained results show that the CHIO-based bi-level EMS demonstrates an optimal economic operation for a grid-connected microgrid in real-time when there are uncertainties in weather, utility tariffs, and load forecasts.

Optimal Energy Management Strategy Based on Driving Pattern Recognition for a Dual-Motor Dual-Source Electric Vehicle

Optimal Energy Management Strategy Based on Driving Pattern Recognition for a Dual-Motor Dual-Source Electric Vehicle

Optimal Energy Management Strategy for Repeat Path Operating Fuel Cell Hybrid Tram

This study focuses on minimizing fuel consumption of a fuel cell hybrid tram, operated with electric power from both the fuel cell stack and the energy storage system, by optimizing energy distribution between distinct energy sources. In the field of fuel cell hybrid system application, dealing with real-world optimal control implementation becomes more important. Some ‘online control’ strategies optimize energy management by measuring the current battery’s state and planning for future cycles. However, its dependence on stochastic processes remains a limitation for adapting ‘online control’ even when driving in the same way. In order to optimize energy distribution robustly during the tram’s repetitive cycle operation, we develop a practical control map with a fuel cell hybrid tram simulation model and conduct energy distribution. The control map is based on a mathematical equivalent consumption minimization strategy (ECMS) equation reflecting the characteristics of the fuel cell stack and electric cells. The comparison of fuel consumption with another practical control strategy optimized for a specific railway cycle shows that the suggested map-based optimal control achieves a reduction in fuel consumption while satisfying a boundary condition.