Electric Energy Management Research Articles

Adaptive Multi-Agent Reinforcement Learning for Optimizing Dynamic Electric Vehicle Charging Networks in Thailand

The rapid growth of electric vehicles (EVs) necessitates efficient management of dynamic EV charging networks to optimize resource utilization and enhance service reliability. This paper explores the application of adaptive multi-agent reinforcement learning (MARL) to address the complexities of EV charging infrastructure in Thailand. By employing MARL, multiple autonomous agents learn to optimize charging strategies based on real-time data by adapting to fluctuating demand and varying electricity prices. Building upon previous research that applied MARL to static network configurations, this study extends the application to dynamic and real-world scenarios, integrating real-time data to refine agent learning processes and also evaluating the effectiveness of adaptive MARL in maximizing rewards and improving operational efficiency compared to traditional methods. Experimental results indicate that MARL-based strategies increased efficiency by 20% and reduced energy costs by 15% relative to conventional algorithms. Key findings demonstrate the potential of extending MARL in transforming EV charging network management, highlighting its benefits for stakeholders, including EV owners, operators, and utility providers. This research contributes insights into advancing electric mobility and energy management in Thailand through innovative AI-driven approaches. The implications of this study include significant improvements in the reliability and cost-effectiveness of EV charging networks, fostering greater adoption of electric vehicles and supporting sustainable energy initiatives. Future research directions include enhancing MARL adaptability and scalability as well as integrating predictive analytics for proactive network optimization and sustainability. These advancements promise to further refine the efficacy of EV charging networks, ensuring that they meet the growing demands of Thailand’s evolving electric mobility landscape.

Electric vehicle power system in intelligent manufacturing based on soft computing optimization

Soft computing technology has attracted extensive attention in computer engineering and automatic control domains because it can deal with uncertainties, fuzziness, and complex practical problems. This study adopts a Genetic Algorithm (GA) in soft computing technology to realize the cooperative optimization of electric vehicle's dynamic and economic performance. The advantage of soft computing technology lies in its adaptability to uncertainty, fuzziness, and complex practical problems, making GA an effective tool for solving complex optimization problems. Firstly, the electric vehicles' power system structure and energy management strategy are investigated and analyzed. Secondly, the improved non-dominated sorting genetic algorithm II (NSGA-II) is selected to optimize the parameters of electric vehicles because of its simple operation and high optimization accuracy. Thirdly, NSGA-II is used to construct the electric vehicles' power and energy configuration, with power and economic performance as the main optimization objectives. Meanwhile, a fuzzy logic controller is designed to adjust the parameters of GA online, so that the optimization process is closer to the actual operating conditions. Finally, the relevant variables are selected to achieve the optimization goal, the optimization objective function and constraint conditions are established, and the model is simulated and evaluated. The results show that the optimized electric vehicle's acceleration time is remarkably reduced, the dynamic performance is improved by more than 7 %, and the power loss is reduced by 5 %. In addition, compared with the current multi-objective optimization model, this model enables electric vehicles to travel longer distances under the same power. This study provides a new idea and method for the performance optimization of electric vehicles. Moreover, it offers a valuable reference for the innovation and development of electric vehicle technology in the intelligent manufacturing field. This study indicates that electric vehicles could be more efficient, energy-saving, and environmentally friendly to serve people's travel needs in the future.

An improved data-driven predictive optimal control approach for designing hybrid electric vehicle energy management strategies

This paper proposes an improved “prediction + optimal control” method for energy management in hybrid electric vehicles equipped with planetary gears. A differentiable predictor and a differentiable optimal controller are developed using supervised learning and reinforcement learning approaches, respectively. Three training steps are performed for the initial predictor, the optimal controller, and the final predictor. This method improves the traditional energy management predictive optimal control approach by incorporating an additional step of retraining the differentiable predictor. This adjustment ensures that the predictor does not blindly improve its performance based on evaluation criterion irrelevant to energy management, which was commonly used in previous studies. Instead, it focuses on enhancing the overall performance of energy management under the “prediction + optimal control” framework. The approach introduced in this paper is compared with the globally optimal dynamic programming results and traditional predictive optimal control methods on the Next Generation Simulation (NGSIM) data. Our method outperforms traditional approaches in energy management on both the training dataset and the test dataset. This further illustrates that the conventional practice of presumptuously optimizing predictors in “prediction + optimal control” methods can be improved using the proposed method.

Day-ahead optimal scheduling considering thermal and electrical energy management in smart homes with photovoltaic–thermal systems

The environmental impact on the energy sector has become a significant concern, necessitating the implementation of Home Energy Management Systems (HEMS) to enhance the energy efficiency of buildings, reduce costs and greenhouse gas emissions, and ensure user comfort. This paper presents a novel approach to provide optimal day-ahead energy management plans in smart homes with Photovoltaic/Thermal (PVT) systems, aiming to achieve a balance between energy cost and user comfort. This multi-objective problem employs the Non-dominated Sorting Genetic Algorithm III as the optimization algorithm and the Nonlinear Auto-regressive with External Input to forecast the day-ahead meteorological variables, which serve as inputs to predict the PVT electrical and heat production in the thermal resistance model. The HEMS benefits from the time-of-use tariff due to the flexibility provided by the energy storage from a battery bank and a boiler. Furthermore, it performs a load scheduling for 10 controllable loads based on three feature parameters to characterize occupant behavior. A study case analysis revealed a cost reduction of approximately 66% in the solution close to the knee of the Pareto curve (S3 solution). The environmental impact on the energy sector has become a The PVT heat production was sufficient to meet the thermal demand of the showers. The proposed hybrid battery management model effectively eliminated the export of electricity to the grid, reducing consumption during peak periods and the maximum peak demand.

Electric vehicle fast charging station energy management system for radial distribution network with a photo-voltaic distributed generator (PV-DG)

As the demand for electric vehicles (EVs) increases in all countries, automobile companies are launching different types of EVs. Installation of EVCS in the present power system network without modification can cause an extra burden on the present network. However, this extra burden can be reduced if EVCS gets a supply of renewable energy resources. This paper proposes energy management schemes by installing a photo-voltaic distributed generator (PV-DG) and energy storage system (ESS) at an EVCS. An IEEE-33 bus system for the Saudi Arabia United Kingdom rural location has been selected to introduce real-time energy management schemes. In this radial distribution network (RDN), three EVCS of different capacities are installed at different locations to maintain the stability of the network. The power flow program is used in the MATLAB environment to check the stability of the network. Accurate solar data, traffic patterns, and EV charging patterns in Saudi Arabia are collected and used for energy management at EVCS. Three cases are used for comparison: EVCS annual charges, EVCS annual benefits, and grid annual fuel charges. All proposed cases are also simulated in MATLAB using MATPOWER6 to check the impact on voltage profile and power line flows.

CLEMD, a circuit-level electrical measurements dataset for electrical energy management

Enhancing energy efficiency in commercial buildings is crucial for reducing energy consumption. Achieving this goal requires careful monitoring and analysis of the energy usage patterns exhibited by different devices. Nonetheless, gathering data from individual appliances in commercial buildings presents difficulties due to the large number of appliances, complex installations, and costs. This paper presents the Circuits-Level Electrical Measurements Dataset (CLEMD). The measurement was conducted at the main switchboard to a set of distribution boards instead of measuring at the individual loads. The data is gathered from an institutional setting. It consists of 42 records of vital electrical parameters including voltage, current, frequency, real power, reactive power, apparent power, power factor, and odd harmonics for electrical currents. The device deployed in the measurement were industry-grade and had a high sampling rate of 200 kHz. The measurements were done over a 40-day period, from September 16 2023 to October 25 2023. CLEMD is the first Malaysian public dataset on circuit-level electricity consumption and offers analysis opportunities in different research areas such as electricity load disaggregation at circuit level, circuit identification, load profile forecasting, and pattern recognition.

Smart Energy Systems Based on Next-Generation Power Electronic Devices

Power electronics plays a key role in the management and conversion of electrical energy in a variety of applications, including the use of renewable energy sources such as solar, wind and hydrogen energy, as well as in electric vehicles, industrial technologies, homes and smart grids. These technologies are essential for the successful implementation of the green transition, as they help reduce carbon emissions and promote the production and consumption of cleaner and more sustainable energy. The present work presents a new generation of power electronic devices and systems, which includes the following main aspects: advances in semiconductor technologies, such as the use of silicon carbide (SiC) and gallium nitride (GaN); nanomaterials for the realization of magnetic components; using a modular principle to construct power electronic devices; applying artificial intelligence techniques to device lifecycle design; and the environmental aspects of design. The new materials allow the devices to operate at higher voltages, temperatures and frequencies, making them ideal for high-power applications and high-frequency operation. In addition, the development of integrated and modular power electronic systems that combine energy management, diagnostics and communication capabilities contributes to the more intelligent and efficient management of energy resources. This includes integration with the Internet of Things (IoT) and artificial intelligence (AI) for automated task solving and work optimization.

Research on driving condition recognition in hybrid electric vehicle energy management strategy

Abstract The driving conditions encountered by vehicles during operation are complex and varied, and the ECMS (Equivalent fuel Consumption Minimization Strategy) based on minimum equivalent fuel consumption has poor adaptability to different driving conditions. To optimize the adaptability of the ECMS and improve the overall vehicle economy, this paper proposes an operating condition recognition model based on the Back Propagation neural network algorithm to optimize the ECMS energy management control strategy. Firstly, three types of driving conditions were identified: urban, suburban, and high-speed conditions. A complete vehicle model was constructed on the Matlab/Simulink platform. The efficacy of the strategy, before and after optimization, is verified under the WLTP (Worldwide Harmonized Light Vehicles Test Procedure) driving cycle. Simulation results show that the optimized ECMS can better adjust the power apportionment among the engine and motor, reducing the fuel consumption per hundred kilometers by 4.01% and thereby enhancing the overall vehicle economy.

The implementation of an optimized neural network in a hybrid system for energy management

In the face of increasing global energy demand and volatile energy prices, many countries are searching for solutions to ensure their energy independence. One of the most popular solutions is to incorporate renewable energy sources in their energy systems. While there are many advantages to integrating renewable energy sources, it is important to note that their intermittent operation can present challenges. Energy storage and smart grid management systems are key solutions to overcome these challenges and ensure sustainable, reliable use of renewable energy sources. In this article, we present an intelligent electrical energy management system for hybrid energy systems. This management system is based on a multi-layer neural network that has undergone an architecture optimization phase to improve the accuracy of real-time energy management and simplify its implementation. The management model that was built demonstrated highly good performance across a range of test circumstances.

Electric vehicle charger energy management by considering several sources and equalizing battery charging

This article proposes a novel energy management structure for electric vehicles, consisting of a supercapacitor and two types of batteries, to improve efficiency and navigable distance. The key features of a suitable energy storage system include high power and energy density, low cost and weight, minimal maintenance, and long life. Although batteries are the primary power storage source in electric vehicles, they have power limitations. Therefore, a high-power-density supercapacitor is added to the battery to create a hybrid energy storage system, reducing stress on the battery and increasing its lifespan. The article presents a new structure for hybrid storage systems based on the existence of a main battery, a replaceable battery, a supercapacitor, and a DC-DC converter. An active charge equalizer circuit for the main battery in standby mode and its control system are also introduced. A control method for dividing power between different sources under different conditions is presented, and the design parameters of the energy storage system are optimized using a genetic algorithm to minimize mass and losses. The performance of the proposed system is evaluated through simulation in MATLAB software and tested in a small-scale laboratory sample. The article also provides a detailed analysis of different working modes, including electric motor acceleration, low-speed mode, high-speed mode, and electric regenerative braking mode. Additionally, a control method for energy management between batteries and supercapacitors is introduced, aiming to increase efficiency. The results show that the proposed hybrid energy storage system can effectively improve the efficiency of electric vehicles.

Real-Time Load Forecasting and Adaptive Control in Smart Grids Using a Hybrid Neuro-Fuzzy Approach

The transition to smart grids is revolutionizing the management and distribution of electrical energy. Nowadays, power systems must precisely estimate real-time loads and use adaptive regulation to operate in the era of sustainable energy. To address these issues, this paper presents a new approach—a hybrid neuro-fuzzy system—that combines neural networks with fuzzy logic. We use neural networks’ adaptability to describe complex load patterns and fuzzy logic’s interpretability to fine-tune control techniques in our approach. Our improved load forecasting system can now respond to changes in real-time due to the combination of these two powerful methodologies. Developing, training, and implementing the forecasting and control system are detailed in this article, which also explores the theoretical underpinnings of our hybrid neuro-fuzzy approach. We demonstrate how the technology improves grid stability and the accuracy of load forecasts by using adaptive control methods. Furthermore, comprehensive simulations confirm the proposed technology, showcasing its smooth integration with smart grid infrastructure. Better energy management is just the beginning of what our method can accomplish; it also paves the way for a more sustainable energy future that is easier on the planet and its inhabitants. In conclusion, this study’s innovative approach to adaptive control and real-time load forecasting advances smart grid technology, which, in turn, improves sustainability and energy efficiency.

Study of the Development Potential of Geospatial-Based PSEL (Waste Processing into Electrical Energy) Areas

Since 1989, most of the waste processing in DKI Jakarta Province has ended at the Bantar Gebang TPST, causing the available capacity to accommodate waste to approach the maximum limit. One alternative processing that has a high level of efficiency in reducing the volume of waste is the PSEL (Waste to Electrical Energy Management) facility. Planning PSEL facilities in urban areas requires consideration from all aspects. While the space utilization in DKI Jakarta is quite dense and there are no practical guidelines in determining the location of PSEL facilities, it is necessary to conduct a comprehensive study, namely a study of the potential development of PSEL areas. The purpose of this study is to obtain the location and spatial pattern of the potential development of the PSEL area and obtain the characteristics of the area with the 'Recommended' category as the location of PSEL facility development. This study uses geospatial analysis with the quantitative Weighted Overlay method, where geospatial modeling is carried out by inputting the weight of each criterion (aspects that have an influence on determining the location of PSEL facilities) obtained from the results of the Fuzzy Analytic Hierarchy Process (FAHP) analysis assisted by a questionnaire tool. The results of the study obtained 2 (two) areas that have High Category status with a total area of 1.52 ha located in Cilincing District and Kelapa Gading District, North Jakarta with the condition that the first area occupies an improper designation zone (Industrial and Warehousing Zone) while the second area has a radius distance of less than 500 m to the nearest settlement and tourist location.

HP3O algorithm-based all electric ship energy management strategy integrating demand-side adjustment

To tackle the energy management challenge that integrates power generation scheduling and demand-side adjustment for all-electric ship in uncertain marine environment, a hybrid penalized proximal policy optimization algorithm (HP3O)-based energy management strategy is proposed. First, demand-side adjustment, which involves adjusting the power of the ship's electric propulsion motors and flexible service loads, is integrated into the energy management problem. Second, HP3O algorithm is employed to obtain both continuous and discrete variables simultaneously. It utilizes a continuous actor network to obtain continuous variables, such as the generator's power and ship cruising speed, while employing a discrete actor network to determine discrete variables, i.e., the on/off status of the generators. Third, to handle complex constraints reasonably, the energy management problem is formulated as a constrained Markov decision process (CMDP), and an action mask mechanism is also integrated into the energy management framework to make agent's actions more reliable. The simulation results of an all-electric cruise ship validate the effectiveness and superiority of the proposed strategy in achieving near-optimal scheduling while satisfying operation constraints. Furthermore, a case study on a hybrid diesel-electric ferry confirms its generalization performance.

An innovative method for building electricity energy management in smart homes based on electric vehicle energy capacity

The surging demand for electricity, fueled by environmental concerns, economic considerations, and the integration of distributed energy resources, underscores the need for innovative approaches to smart home energy management. This research introduces a novel optimization algorithm that leverages electric vehicles (EVs) as integral components, addressing the intricate dynamics of household load management. The study’s significance lies in optimizing energy consumption, reducing costs, and enhancing power grid reliability. Three distinct modes of smart home load management are investigated, ranging from no household load management to load outages, with a focus on the time-of-use (ToU) tariff impact, inclining block rate (IBR) pricing, and the combined effect of ToU and IBR on load management outcomes. The algorithm, a multi-objective approach, minimizes the peak demand and optimizes cost factors, resulting in a 7.9% reduction in integrated payment costs. Notably, EVs play a pivotal role in load planning, showcasing a 16.4% reduction in peak loads and a 7.9% decrease in payment expenses. Numerical results affirm the algorithm’s adaptability, even under load interruptions, preventing excessive increases in paid costs. Incorporating dynamic pricing structures like inclining block rates alongside the time of use reveals a 7.9% reduction in payment costs and a 16.4% decrease in peak loads. In conclusion, this research provides a robust optimization framework for smart home energy management, demonstrating economic benefits, peak load reduction potential, and enhanced reliability through strategic EV integration and dynamic pricing.

Robust Collaborative Scheduling Strategy for Multi-Microgrids of Renewable Energy Based on a Non-Cooperative Game and Profit Allocation Mechanism

The Multiple Microgrid System (MMG) facilitates synergistic complementarity among various energy sources, reduces carbon emissions, and promotes the integration of renewable energy generation. In this context, we propose a two-stage robust cooperative scheduling model for MMGs based on non-cooperative game theory and a benefit allocation mechanism. In the first stage, considering electricity price fluctuations and uncertainties in wind and solar power outputs, a robust optimization approach is applied to establish an electric energy management model for MMGs. This model enables point-to-point energy sharing among microgrids. In the second stage, addressing the benefit allocation problem for shared electric energy, we introduce a Cost Reduction Ratio Distribution (CRRD) model based on non-cooperative game theory. The generalized Nash equilibrium is utilized to determine the benefit distribution for shared electric energy. Finally, through case studies, the proposed model is validated, ensuring fair returns for each microgrid. The results indicate that the proposed model optimizes the operational states of individual microgrids, reduces operational costs for each microgrid, and lowers the overall total operational costs of the MMG system. Additionally, an investigation is conducted into the impact of electricity price uncertainty coefficients and confidence levels of wind and solar uncertainties on the operational costs of microgrids.

Coordinated Optimization of Logistics Electric Fleet and Energy Management System of Constrained Energy Hub

Coordinated Optimization of Logistics Electric Fleet and Energy Management System of Constrained Energy Hub

OPTIMISATION OF HYBRID ELECTRIC VEHICLE ENERGY MANAGEMENT USING MACHINE LEARNING

The focus of the research is on the optimization of the hybrid electric vehicle energy management using machine learning. The objective of this research is to develop the algorithm by using Support Vector Machine (SVM) and to identify the optimal operation mode for SHEV based on the power demand use by using the algorithm. First of all, this research focus on the series hybrid electric vehicle and the vehicle used is an all-terrain vehicle (ATV). Suitable formula will be used to complete the modeling of the vehicle in Energetic Macroscopic Representation (EMR) form and the model constructed by using Matlab Simulink. Then, Support Vector Machine is used to optimize the energy management in the vehicle. The training data from New European Driving Cycle (NEDC) and Worldwide harmonized Light vehicles Test Cycles (WLTC) with 3 classes will put be inside the SVM to undergo training and then the optimal operation modes for each driving cycle will be obtained by using Linear SVM. Then, the obtained results which are the predicted operation mode using the driving cycles and are plotted in the graph. The pattern of the graph is analyzed and then the best predicted operation mode with the highest accuracy among all the driving cycles is chosen. The trained model of the driving cycle with the highest accuracy is used to predict the optimal operation mode for ATV so that to have higher efficiency in energy management by using the Classification Learner inside the Matlab.

FlexNet: A warm start method for deep reinforcement learning in hybrid electric vehicle energy management applications

Deep reinforcement learning (DRL) has been widely studied in the energy management of hybrid electric vehicles (HEVs) for its remarkable energy efficiency improvement compared to conventional methods. However, how to alleviate the time consumption of training a stable reinforcement learning agent still needs to be solved in real-world implementation. This study presents a human expert knowledge encoded ‘warm start’ method with the flexibility to change the neural network architecture. The expert knowledge is encoded in a decision tree which then initializes the weights and bias of the DRL neural network. Compared with another fixed architecture warm start method, the proposed FlexNet exhibits improved learning speed by 60.8 % and 88.8 % in action space 50 and 100, respectively. The energy consumption by the proposed FlexNet EMS method is 12.2 % and 6.4 % better than rule-based and equivalent consumption minimization strategy, respectively. This proposed warm start method can reduce learning time and increase energy efficiency in various energy management applications.

Research on IoT-based hybrid electrical vehicles energy management systems using machine learning-based algorithm

Electric vehicles (EVs) are quickly becoming a staple of smart transportation in applications involving smart cities due to their ability to reduce carbon footprints. However, the widespread use of electric vehicles significantly strains the nation's electrical system. In-depth descriptions of the EV's energy management system (EMS) should highlight the vehicle's powertrain's vital role. The energy for propulsion in electric automobiles comes from a rechargeable battery. The safe and dependable operation of batteries in electric vehicles relies heavily on online surveillance and status estimations of charges. An energy management strategy (EMS) that considers the electric vehicle's battery and ultra-capacitor may lessen the vehicle's reliance on external power sources and extend the battery's lifespan. A machine learning-based mathematical dynamic programming algorithm is used in designing the energy management system to teach the system how to respond appropriately to various situations without resorting to predefined rules. Therefore, this research aims to use Machine Learning to create a Smart Energy Management System for Hybrid Electrical Vehicles (SEMS-HEV) with energy storage. Energy optimization techniques and algorithms are necessary in this setting to reduce expenses and length of charging and appropriately arrange the EV charging process to prevent bursts in the electrical supply that may impact the transmission network. To improve the performance of an energy management system, this study employs an IoT-based smart charging system for scheduling V2G connections for hybrid electrical vehicles. It allows for more precise and effective control and greater efficiency by enabling the system to learn from its surroundings.

Electrifying Green Logistics: A Comparative Life Cycle Assessment of Electric and Internal Combustion Engine Vehicles

Green logistics is an approach aimed at reducing the environmental impact of transport, storage, and distribution practices, through low-emission vehicles, optimized routes, clean energy tech in warehouses, and efficient waste management. These solutions can contribute to achieving the sustainable development goals of the European Green Deal. The main research question of this paper is whether an electric vehicle has a lower environmental impact compared to a gasoline vehicle. This study presents a life cycle assessment (LCA) of an electric vehicle using lithium-ion battery technology (BEV) and compares it to an internal combustion engine vehicle (ICEV), considering the transportable load within the context of Italy. Through a gate-to-grave approach, both vehicles’ life cycle use and disposal phases were evaluated to identify the hotspots of environmental impact. The LCA methodology allows for an objective comparison and the results show that BEV emits slightly less kgCO2eq than ICEVs. The primary contributor to the vehicles’ impact is the dependency of the electric energy primary source from fossil fuels. Therefore, a second analysis was conducted to analyse the benefit of photovoltaic panels to generate the electric energy, showing that it can result in a significant 50% reduction in impact, making the electric vehicle a valid solution for achieving green logistics objectives. However, the questions of electric energy production, management, and distribution together with the supply of raw material and disposal of lithium batteries remain open. This issue raises a concern regarding the BEV in a country like Italy where the lack of recharging points limits the adoption of electric vehicles in green logistics.