Efficient Energy Management Strategy Research Articles

Comparative Study of Time Series Analysis Algorithms Suitable for Short-Term Forecasting in Implementing Demand Response Based on AMI

This paper compares four time series forecasting algorithms—ARIMA, SARIMA, LSTM, and SVM—suitable for short-term load forecasting using Advanced Metering Infrastructure (AMI) data. The primary focus is on evaluating the applicability and performance of these forecasting models in predicting electricity consumption patterns, which is a critical component for implementing effective demand response (DR) strategies. The study provides a comprehensive analysis of the predictive accuracy, computational efficiency, and scalability of each algorithm using a dataset of real-time electricity consumption collected from AMI systems over a designated period. Through extensive experiments, we demonstrate that each algorithm has distinct strengths and weaknesses depending on the characteristics of the dataset. Specifically, SVM exhibited superior performance in handling nonlinear patterns and high volatility, while SARIMA effectively captured seasonal trends. LSTM showed potential in modeling complex temporal dependencies but was sensitive to hyperparameter settings and required a substantial amount of training data. This research offers practical guidelines for selecting the optimal forecasting model based on data characteristics and application needs, contributing to the development of more efficient and dynamic energy management strategies. The findings highlight the importance of integrating advanced forecasting techniques into smart grid systems to enhance the reliability and responsiveness of DR programs. This study lays a solid foundation for future research on integrating these forecasting models into real-world AMI applications to support effective demand response and grid stability.

An efficient and resilient energy management strategy for hybrid microgrids inspired by the honey badger's behavior

This manuscript introduces a highly efficient hybrid renewable energy system (HRES) that combines photovoltaic (PV) panels and wind turbines (WTs) as primary power sources, supplemented by three backup systems: batteries, hydrogen energy storage systems (HESSs), and supercapacitors (SCs). This system employs a multi-objective optimization algorithm to dynamically identify the best energy management system (EMS). The performance of the proposed EMS across different operational scenarios is assessed using seven distinct algorithms to identify the best solution, particularly emphasizing two main objective functions: operating cost and loss of power supply probability (LPSP). Furthermore, the study rigorously evaluates the performance of these algorithms against nine benchmarks to determine the most appropriate algorithm for the HRES under consideration. In the 1st scenario, power demand is met using both primary and backup energy sources. The honey badger algorithm (HBA) proved its cost-effectiveness and efficiency by achieving the lowest overall operating cost and the highest efficiency of 95.893 %. Additionally, its computation time was notably faster, being 70 s–488 s quicker than the other algorithms. The 2nd scenario arises when power demand exceeds the available generation capacity because of the charging limits of all energy storage systems (ESSs), which prevents them from providing power to the load. The HBA achieved the lowest cost and matched the highest efficiency score of 96.691 %. Furthermore, HBA provided the quickest optimization, completing the process 400 s–450 s faster than the other techniques. In the 3rd scenario, the generated power surpasses the current load demand, and the ESS components are fully charged therefore, the proposed EMS efficiently directs excess power to a dummy load, preventing overcharging of storage components and enhancing grid stability. The HBA consistently exceeded the performance of other algorithms, achieving the lowest average cost, maintaining the highest efficiency of 95.305 %, and solving the optimization problem 300 s–500 s faster than the alternatives. Consequently, these results highlight HBA's flexibility and effectiveness, positioning it as a reliable choice for optimizing energy systems across various operational scenarios.

A CNNbased energy management strategy for a Hybrid energy storage system in electric vehicles

When approaching a large-scale performance, the choice, size, and administration of an Energy Storage System (ESS) for an Electric Vehicle (EV) are crucial. As the peak-to-average power demand ratio is relatively large, particularly for an urban ride that is frequently marked by rapid deceleration as well as acceleration, the complementary characteristics of the battery and Ultra-Capacitor (UC)render this arrangement a viable Hybrid energy storage system (HESS) for EV. Enhanced dynamic responsiveness, increased miles per charge, and extended battery life provided by the HESS increase the Electric Vehicle (EV’s) effectiveness. The primary objective of the study that has been suggested is to create a smart Energy Management Strategy (EMS) for EVs. A battery package and a properly sized Ultra-Capacitor (UC) together give the required high power along with energy density. This research proposes a CNN-based power management technique to aid in efficient EMS. Additionally, by adjusting the Convolution Neural Network (CNN) classifier’s weights through Improved Honey Badger Optimization (IHBO), the adaptive approach of the Standard HBO Algorithm, the performance of the classifier is improved. In MATLAB, the suggested CNN-based model for BESS EM is simulated and experimental assessment and analysis are done in terms of converter current and battery SoC. By contrasting the suggested approach against several standardized models, the performance analysis of the proposed work is assessed to validate its performance.

Finite-horizon energy allocation scheme in energy harvesting-based linear wireless sensor network

Linear wireless sensor networks (LWSNs) are a specialized topology of wireless sensor networks (WSNs) widely used for environmental monitoring. Traditional WSNs rely on batteries for energy supply, limiting their performance due to battery capacity constraints, while renewable energy harvesting technology is an effective approach to alleviating the battery capacity bottleneck. However, the stochastic nature of renewable energy makes designing an efficient energy management scheme for network performance improvement a compelling research problem. In this paper, we investigate the problem of maximizing throughput over a finite-horizon time period for an energy harvesting-based linear wireless sensor network (EH-LWSN). The solution to the original problem is very complex, and this complexity mainly arises from two factors. First, the optimal energy allocation scheme has temporal coupling, i.e., the current optimal strategy relies on the energy harvested in the future. Second, the optimal energy allocation scheme has spatial coupling, i.e., the current optimal strategy of any node relies on the available energy of other nodes in the network. To address these challenges, we propose an iterative energy allocation algorithm for EH-LWSN. Firstly, we theoretically prove the optimality of the algorithm and analyze the time complexity of the algorithm. Next, we design the corresponding distributed version and consider the case of estimating the energy harvest. Finally, through experiments using a real-world renewable energy dataset, the results show that the proposed algorithm outperforms the other two heuristics energy allocation schemes in terms of network throughput.

AI-powered deep learning for sustainable industry 4.0 and internet of things: Enhancing energy management in smart buildings

With the increasing demand for energy in urban areas, there is a pressing need to manage energy consumption more effectively. Buildings, especially commercial and industrial ones, are major consumers of energy. Implementing AI-powered solutions can help monitor, predict, and reduce energy usage, leading to substantial cost savings and more efficient energy use. Integrating Industry 4.0 and the Internet of Things (IoT) into smart buildings presents a significant opportunity for enhancing energy management through advanced technologies. The increasing demand for energy in urban areas necessitates the development of more efficient energy management strategies, particularly within smart buildings, which are significant energy consumers. This study proposes an AI-powered deep learning framework utilizing Convolutional Neural Networks (CNNs) to enhance energy management in smart buildings, leveraging the ASHRAE - Great Energy Predictor III dataset. The proposed framework leverages deep learning techniques to analyze historical energy data, weather conditions, and building characteristics to forecast future energy usage accurately. Additionally, the framework includes anomaly detection mechanisms to identify inefficiencies and faults in the energy management system. By optimizing energy consumption in real-time and implementing demand response strategies, the framework aims to reduce energy costs and enhance the overall efficiency of smart buildings. The proposed CNN-IoT-based energy management framework in smart buildings demonstrates significant advancements over existing methods, achieving an accuracy of 88 %. These performance metrics indicate a substantial improvement in prediction accuracy and efficiency compared to existing approaches such as SVM, ELM, and LSTM.

Machine learning-based predicting of PCM-integrated building thermal performance: An application under various weather conditions in Morocco

As is now stands, the building sector is among the “big three” significant energy consumer and greenhouse gas contributor in the world. Consequently, there has been a growing movement towards the development and adoption of renewable energy sources, energy efficient measures and energy management strategies. Phase change materials (PCMs) are considered as an alluring option for energy efficiency measures in building. In this paper, a case study building, based on typical residential construction in Morocco, was selected to assess the potential energy savings of adding PCM to the roof for twenty-four cities using the dynamic simulation tool, TRNSYS. Thereafter, thirteen machine learning techniques, including ANN, DT, SVM, ELM, GB, RF, TB, GLRM, GPR, LR, GAM, KRRM and LRR were assessed for predicting the hourly heating, cooling, and total energy consumptions. The models were trained and tested on a dataset that was gathered from the simulations in twenty-four locations in Morocco. The outdoor dry-bulb temperature, the relative humidity, the wind velocity, the wind direction, and the total solar radiation were considered as the key features. The obtained results revealed that using PCM, can effectively lower the total energy demand for all the cities under study, with exception for very cold climate given by Ifrane and Midelt, where the annual total energy consumption shows an increasing trend. Furthermore, comparing the statistical results for each machine learning model, it is found that the SVM model consistently outperforms all models and can be successfully employed to predict the hourly network's outputs of the building.

Optimal Rule-Interposing Reinforcement Learning-Based Energy Management of Series—Parallel-Connected Hybrid Electric Vehicles

P2–P3 series–parallel hybrid electric vehicles exhibit complex configurations with multiple power sources and operational modes, presenting a difficulty in developing efficient energy management strategies. This paper takes a P2–P3 series–parallel hybrid power system-KunTye 2DHT system as the research object and proposes a deep reinforcement learning framework based on pre-optimized energy management to improve the energy consumption performance of the hybrid electric vehicles. Firstly, a control-oriented model is established based on its system configuration and characteristics. Then, the optimal distribution of the motor energy under different operating modes is pre-optimized, which aims to reduce the energy management task’s dimensionality by equating two motors as an equivalent motor. Subsequently, based on real-time traffic information under connected conditions, deep reinforcement learning is utilized to optimize the optimal operating modes of the hybrid system and the optimal distribution between the engine and equivalent motors. Combining the pre-optimized results, the optimal energy distribution between the engine and the two motors in the system is achieved. Finally, performance comparisons are made between the predictive control and the traditional Dynamic Programming and Adaptive Equivalent Consumption Minimization Strategy, revealing the proposed optimization algorithm’s promising potential in reducing fuel consumption.

Optimizing multi-objective design, planning, and operation for sustainable energy sharing districts considering electrochemical battery longevity

The urgent need to address the global warming crisis has led to a paradigm shift in energy production from traditional fossil fuels to renewable sources such as geothermal, solar, and wind. This transition necessitates efficient energy management strategies to enhance renewable energy utilization, reduce microgrid dependency, and establish a more stable power grid. This study explores the novel integration of interactive energy sharing networks utilizing electrochemical battery storage, emphasizing detailed modeling of battery degradation, smart energy management, and multi-criteria decision-making. This research involves an innovative method combining Monte Carlo Tree Search technique and a multi-criteria approach for energy management in district communities. The research delves into the complexities of integrated multi-energy systems, considering structural designs, advanced modeling methods, and multi-criteria predictions. It also employs powerful machine learning methods to predict battery depreciation, demonstrating their superiority over standard semi-empirical models. The study also proposes deterministic and stochastic methods for intelligent energy management, considering unpredictable factors like weather and energy demand profiles. The research incorporates a comprehensive examination of interactive energy sharing networks, encompassing renewable-to-vehicle and vehicle-to-building interactions. Through an in-depth analysis this research highlights the advantages of multi-agent systems, time-of-use energy shifting, demand profile flattening, and micro-grid resilience.

Knowledge-network-embedded deep reinforcement learning: An innovative way to high-efficiently develop an energy management strategy for the integrated energy system with renewable energy sources and multiple energy storage systems

To achieve efficient energy management in complex integrated energy systems (IESs) with renewable energy sources (RESs) and multiple energy storage systems (ESSs), the study aims to propose a novel approach. Evolutionary-based methods are difficult to find the optimal scheme, while deep reinforcement learning (DRL)-based methods face problems with low training efficiency. To address the limitations, a knowledge-network-embedded DRL method is proposed, which combines the fast-searching capability of genetic algorithm (GA) with the environment-aware decision-making capability of DRL, with the aim of developing a highly efficient energy management strategy in improving user comfort and reducing operating costs. GA is used to solve the IES optimization model for obtaining a solution set that serves as domain knowledge, and a knowledge network is constructed using deep learning (DL). Then, DRL uses this network as an actor network to explore the optimal strategy. The use of GA helps the DRL to enhance training efficiency and find an optimal strategy. Simulations show significant improvements in cost reduction (5.7 %) and user comfort (4.2 %), along with improved training efficiency (58.3 %) compared to baseline DRLs.

Revolutionizing power grid loss prediction with advanced hybrid time series deep learning model

Accurate prediction of grid loss in power distribution networks is pivotal for efficient energy management and pricing strategies. Traditional forecasting approaches often struggle to capture the complex temporal dynamics and external influences inherent in grid loss data. In response, this research presents a novel hybrid time-series deep learning model: Gated Recurrent Units with Temporal Convolutional Networks (GRU-TCN), designed to enhance grid loss prediction accuracy. The proposed model integrates the temporal sensitivity of GRU with the local context awareness of TCN, exploiting their complementary strengths. A learnable attention mechanism fuses the outputs of both architectures, enabling the model to discern significant features for accurate prediction. The model is evaluated using well-established metrics across distinct temporal phases: training, testing, and future projection. Results showcase Resulting in encouraging Figures for mean absolute error, root mean squared error, and mean absolute percentage error, the model’s capacity to capture both long-term trends and transitory patterns. The GRU-TCN hybrid model represents a pioneering approach to power grid loss prediction, offering a flexible and precise tool for energy management. This research not only advances predictive accuracy but also lays the foundation for a smarter and more sustainable energy ecosystem, poised to transform the landscape of energy forecasting.

A novel strategy for real-time optimal scheduling of grid-tied microgrid considering load management and uncertainties

This paper proposes an efficient bi-level energy management strategy (EMS) to optimize the operation cost of a grid-connected microgrid, considering the system operational constraints and uncertainties for renewable energy sources and load demand. The first level is optimal day-ahead scheduling based on two stages: the first stage is finding the optimal operating points of sources during the next day while the second one is controllable loads management. 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. Also, a recent meta-heuristic algorithm called Honey Badger Algorithm (HBA) is used to solve the problem of day-ahead scheduling of batteries, which is a complex constrained non-linear optimization problem. Results obtained demonstrate that the HBA based bi-level EMS provides the real-time optimal economic operation of a grid tied microgrid under uncertainties in weather, utility tariff and load forecasts.

An efficient energy management strategy based on heuristic dynamic programming specialized for hybrid electric unmanned delivery aerial vehicles

Large-sized unmanned delivery aerial vehicles (UDAVs) stand out as prominent vertical take-off and landing aircraft, benefiting from integrating a hybrid electric propulsion system (HEPS) to enhance the power-to-weight ratio. Despite this advancement, UDAVs encounter challenges regarding range limitations and the need for swift computational solutions. Addressing these issues necessitates a computationally efficient energy management strategy (EMS) that optimizes power distribution to properly plan engine working points, ultimately aimed at enhancing fuel economy to increase flight range. This study proposes a heuristic dynamic programming (HDP)-based EMS tailored for the HEPS of a large-sized UDAV. In this strategy, the HDP framework is adopted to improve computational efficiency by iteratively updating control variables. To account for the specific flight characteristics, a flight state identifier incorporating selected flight command flags applicable to UDAVs is designed. This identifier aids in predicting velocity changes for different flight states by amalgamating corresponding prediction networks into an integrated velocity prediction network (IVPN). Substituting the model network with IVPN in the HDP framework significantly enhances prediction accuracy and guides power distribution towards optimal results, signifying an efficient EMS specialized for UDAVs. The proposed strategy is evaluated against baseline strategies in designed flight scenarios, highlighting a remarkable 51.37% improvement in prediction accuracy, a noteworthy 6.04% reduction in equivalent fuel consumption, and an impressive 95.81% reduction in global computation time. Finally, the actual applicability of the proposed strategy is validated in a hardware-in-loop test. Consequently, the proposed strategy can provide theoretical support for the efficient energy management of diverse large-sized UDAVs.

Advanced efficient energy management strategy based on state machine control for multi-sources PV-PEMFC-batteries system

This article offers a PV-PEMFC-batteries energy management strategy (EMS) that aims to meet the following goals: keep the DC link steady at the standard value, increase battery lifespan, and meet power demand. The suggested multi-source renewable system (MSRS) is made to meet load demand while using extra power to fill batteries. The major energy source for the MSRS is photovoltaic, and fuzzy logic MPPT is used to guarantee that the PV operates at optimal efficiency under a variety of irradiation conditions. The suggested state machine control consists of 15 steps. It prioritizes the proton exchange membrane fuel cell (PEMFC) as a secondary source for charging the battery when power is abundant and the state of charge (SOC) is low. The MSRS is made feasible by meticulously coordinating control and power management. The MSRS is made achievable by carefully orchestrated control and electricity management. The efficacy of the proposed system was evaluated under different solar irradiance and load conditions. The study demonstrates that implementing the SMC led to an average improvement of 2.3% in the overall efficiency of the system when compared to conventional control techniques. The maximum efficiency was observed when the system was operating under high load conditions, specifically when the state of charge (SOC) was greater than the maximum state of charge (SOCmax). The average efficiency achieved under these conditions was 97.2%. In addition, the MSRS successfully maintained power supply to the load for long durations, achieving an average sustained power of 96.5% over a period of 7.5 s. The validity of the modeling and management techniques mentioned in this study are confirmed by simulation results utilizing the MATLAB/Simulink (version: 2016, link: https://in.mathworks.com/products/simulink.html) software tools. These findings show that the proposed SMC is effective at managing energy resources in MSRS, resulting in improved system efficiency and reliability.

Decomposed particle swarm optimization for optimal scheduling in energy hub considering battery lifetime

The energy hub has become pivotal in optimizing multi-carrier energy systems, aiming to enhance the flexibility and efficiency of the overall infrastructure. By incorporating energy storage within these hubs, it becomes feasible to dynamically coordinate the balance between energy generation and consumption. This coordination considers diverse energy pricing structures and local renewable energy generation, leading to substantial reductions in energy expenditures. To further amplify the long-term utilization of batteries, it is essential to account for the associated costs related to their lifespan during the optimization process. Nonetheless, optimizing the operation of an interconnected energy hub system while factoring in battery lifespan introduces a complex challenge, encompassing various limitations, spanning multiple time frames, and involving non-convex aspects. This article introduces an inventive strategy termed the “decomposition technique,” which entails amalgamating particle swarm optimization with a numerical approach. By disassembling the complex optimization predicament into more manageable sub-problems, particularly the organization of storage and other elements within the energy hub system, this approach capitalizes on the individual strengths of particle swarm optimization and the interior point method. To assess the efficacy of this proposed method, it is applied across various scenarios, encompassing a two-hub system and a three-hub system spanning a 24-h timeframe. Through comparison with analytical methods, the results obtained affirm the technique's capability to converge remarkably close to the global minimum. Moreover, the computational efficiency of this approach enables its online implementation, with a reduced time horizon, and ensures real-time optimization. The success of this approach opens up potential applications in diverse energy hub systems and emphasizes its adaptability and scalability. Using the strengths of particle swarm optimization and the interior point method, this technique provides an effective solution for optimizing energy hub systems while considering battery lifetime costs. It enables near-optimal performance while overcoming limitations related to convergence and computation time. The findings of this research contribute to the broader field of energy optimization and pave the way for more efficient and sustainable energy management strategies. The decomposition technique yielded a significant 12% reduction in total energy cost compared to conventional particle swarm optimization. In the three-hub system, the decomposition technique showcased a 15% increase in computational efficiency, affirming its adaptability for larger systems. Considering battery lifetime costs, excluding them led to a 6% boost in energy savings but resulted in a 2% rise in overall system cost, emphasizing the need for a balanced optimization approach.

Weighted double Q-learning based eco-driving control for intelligent connected plug-in hybrid electric vehicle platoon with incorporation of driving style recognition

Driving style poses significant impacts on eco-driving performance of vehicles, especially for those with hybrid powertrains. By incorporating driving style recognition, an efficient velocity planning and energy management strategy is developed for a platoon of intelligent connected plug-in hybrid electric vehicles (PHEVs). Firstly, a high-fidelity driving style classification and recognition model is established based on the agglomerative hierarchical clustering algorithm, and then the support vector machine algorithm is employed to recognize the driving style. Next, a multi-objective velocity planning problem is formulated with the consideration of the fuel economy, driving adaptability and comfort, following performance, and driving safety optimization as the optimization objective, and is then solved based on orthogonal collocation direct transcription and the interior-point methods. Finally, an energy management strategy incorporating model predictive control and weighted double Q-learning algorithm is built. The simulation results demonstrated that the velocity planning algorithm incorporating driving styles achieved preferable adaptability and comfort. The proposed strategy can achieve up to most 98.88 % energy economy of the stochastic dynamic programming for the following PHEVs, and reduce the overall fuel consumption by 27.39 % for the platoon, comparing to that without driving style incorporation.

Research on Energy Hierarchical Management and Optimal Control of Compound Power Electric Vehicle

In response to the challenges posed by the low energy utilization of single-power pure electric vehicles and the limited lifespan of power batteries, this study focuses on the development of a compound power system. This study constructs a composite power system, analyzes the coupling characteristics of multiple systems, and investigates the energy management and optimal control mechanisms. Firstly, a power transmission scheme is designed for a hybrid electric vehicle. Then, a multi-state model is established to assess the electric vehicle’s performance under complex working conditions and explore how these conditions impact system coupling. Next, load power is redistributed using the Haar wavelet theory. The super capacitor is employed to stabilize chaotic and transient components in the required power, with low-frequency components serving as input variables for the controller. Further, power distribution is determined through the application of fuzzy logic theory. Input parameters include the system’s power requirements, power battery status, and super capacitor state of charge. The result is the output of a composite power supply distribution factor. To fully exploit the composite power supply’s potential and optimize the overall system performance, a global optimization control strategy using the dynamic programming algorithm is explored. The optimization objective is to minimize power loss within the composite power system, and the optimal control is calculated through interpolation using the interp function. Finally, a comparative simulation experiment is conducted under UDDS cycle conditions. The results show that the composite power system improved the battery discharge efficiency and reduced the number of discharge cycles and discharge current of the power battery. Under the cyclic working condition of 1369 s, the state of charge of the power battery in the hybrid power system decreases from 0.9 to 0.69, representing a 12.5% increase compared to the single power system. The peak current of the power battery in the hybrid power system decreases by approximately 20 A compared with that in the single power system. Based on dynamic programming optimization, the state of charge of the power battery decreases from 0.9 to 0.724. Compared with that of the single power system, the power consumption of the proposed system increases by 25%, that of the hybrid power fuzzy control system increases by 14.2%, and that of the vehicle decreases by 14.7% after dynamic programming optimization. The multimode energy shunt relationship is solved through efficient and reasonable energy management and optimization strategies. The performance and advantages of the composite energy storage system are fully utilized. This approach provides a new idea for the energy storage scheme of new energy vehicles.

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

The escalating trends in energy consumption and the associated emissions of pollutants in the past century have led to energy depletion and environmental pollution. Achieving comprehensive sustainability requires the optimization of energy efficiency and the implementation of efficient energy management strategies. Artificial intelligence (AI), a prominent machine learning paradigm, has gained significant traction in control applications and found extensive utility in various energy-related domains. The utilization of AI techniques for addressing energy-related challenges is favored due to their aptitude for handling complex and nonlinear data structures. Based on the preliminary inquiries, it has been observed that predictive analytics, prominently driven by artificial neural network (ANN) algorithms, assumes a crucial position in energy management across various sectors. This paper presents a comprehensive bibliometric analysis to gain deeper insights into the progression of AI in energy research from 2003 to 2023. AI models can be used to accurately predict energy consumption, load profiles, and resource planning, ensuring consistent performance and efficient resource utilization. This review article summarizes the existing literature on the implementation of AI in the development of energy management systems. Additionally, it explores the challenges and potential areas of research in applying ANN to energy system management. The study demonstrates that ANN can effectively address integration issues between energy and power systems, such as solar and wind forecasting, power system frequency analysis and control, and transient stability assessment. Based on the comprehensive state-of-the-art study, it can be inferred that the implementation of AI has consistently led to energy reductions exceeding 25%. Furthermore, this article discusses future research directions in this field.

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.

Model predictive control energy management strategy integrating long short-term memory and dynamic programming for fuel cell vehicles

This paper proposes a novel energy management strategy satisfying real-time and highly efficient energy management strategies for fuel cell hybrid electric vehicles (FCHEVs). The strategy is based on model predictive control (MPC), which integrates long short-term memory (LSTM) and dynamic programming (DP). A high-precision powertrain model of the investigated FCHEV is established for subsequent simulations. After training under several typical working conditions, LSTM is designed to forecast future power demands of the entire vehicle. Using the prediction results, the DP algorithm calculates the control scheme based on model predictive control. Considering the economy and durability of power sources, the results of four different control strategies are compared: thermostat, power following, traditional DP, and MPC. The MPC proposed in this paper reduces the total usage cost per 100 km on the test set by 9%, 33.5%, and −4.6%.

A computationally efficient model predictive control energy management strategy for hybrid vehicles considering driving style

A computationally efficient model predictive control energy management strategy for hybrid vehicles considering driving style