Energy Performance Optimization Research Articles

Ontology-Based Data Representation Prototype for Indoor Air Quality, Building Energy Performance, and Health Data Computation

This study investigates the data integration of IoT-enabled sensor networks, emphasizing energy performance and personalized indoor air quality (IAQ) solutions to improve indoor environments, energy efficiency, and sustainability. Ontologies—structured frameworks that standardize data representation and enable interoperability—are the tools for interpreting complex IAQ data for optimal energy rate plans and health situations. Our methodology follows the well-established three-phase engineering approach. We present the design of a prototype with essential classes, which is proposed to integrate IAQ data with health conditions, enhancing real-time monitoring and automated decision making for optimal energy performance for smart buildings. Our research goal is to define the most essential classes, arranging them hierarchically to create a prototype for data computing covering IAQ, energy performance, and health aspects. This ontological framework, covering all three aspects, addresses a current research gap. Results demonstrate the minimum viable product with 78 classes for a smart home IoT system, providing tailored indoor climate control based on user health profiles and energy performance. This prototype represents a significant advancement in sustainable building and IAQ management, promising improved building energy performance, occupant health, and comfort. Future research will validate this framework through extensive testing in real-world environments.

Optimal Semi-Transparent Photovoltaic (STPV) window based on energy performance, daylighting quality, and occupant satisfaction – A case study of private office in Chengdu China

With the global warming, energy crisis, and the increasing application of glazing curtain walls, semi-transparent photovoltaic (STPV) systems have garnered significant attention for their excellent energy performance and daylight quality. The cell coverage ratio (CCR) of an STPV window impacts energy performance, daylight quality, and occupants’ satisfaction simultaneously, though these impacts can be conflicting. Therefore, this study investigates the optimal CCR of STPV for optimal daylight, energy performance, and occupants' satisfaction in Chengdu City, China through a case study. A private office room was selected as the base model. Energy performance, and daylighting quality according to the different CCR (10% to 50%) of STPV were analyzed using Honeybee in Grasshopper (GH), which is a Plugin for Rhinoceros. Occupants’ satisfaction was assessed through a survey conducted with ClimateStudio (CS) and Virtual reality (VR). The results indicated that the optimal CCR of STPV for this room is as follows: East (30%), West (10%), North (10%), and South (40%), based on energy performance, daylight quality, and occupants’ satisfaction.

Review of energy-efficient HVAC technologies for sustainable buildings

This review paper provides a comprehensive examination of energy-efficient Heating, Ventilation, and Air Conditioning (HVAC) technologies for sustainable buildings. The focus is on exploring the latest advancements, benefits, challenges, and future prospects in the realm of HVAC systems designed to enhance energy efficiency and promote sustainability in building operations. The review begins with an overview of the importance of energy-efficient HVAC technologies in the context of sustainable building design and operation. It underscores the critical role of HVAC systems in reducing energy consumption, minimizing environmental impact, and enhancing occupant comfort and well-being. The paper delves into various categories of energy-efficient HVAC technologies, including advanced heating and cooling systems, ventilation strategies, and smart controls. Each category is examined in detail, highlighting key features, performance metrics, and real-world applications. Furthermore, the review evaluates the benefits associated with energy-efficient HVAC technologies, such as significant energy savings, reduced operating costs, improved indoor air quality, and enhanced building resilience. It also discusses the potential challenges and barriers to adoption, such as high upfront costs, complexity of implementation, and retrofitting existing buildings. The paper explores emerging trends and innovations in the field of energy-efficient HVAC technologies, including the integration of renewable energy sources, smart sensors and actuators, and predictive maintenance algorithms. These advancements have the potential to further optimize energy performance, increase system reliability, and enable proactive management of HVAC systems. In conclusion, the review emphasizes the importance of energy-efficient HVAC technologies as a cornerstone of sustainable building practices. It underscores the need for continued research, innovation, and collaboration among stakeholders to overcome existing challenges and unlock the full potential of energy-efficient HVAC systems in contributing to a more sustainable built environment. Overall, this comprehensive review provides valuable insights into the current state of energy-efficient HVAC technologies, their applications, benefits, and challenges, while also highlighting opportunities for future advancements and their crucial role in achieving sustainability goals in the building sector.

Optimizing Energy Efficiency in Commercial Buildings through Advanced Building Strategies

The pressing issue of climate change and its correlation with greenhouse gas emissions has propelled a heightened emphasis on energy-efficient building practices, particularly within the commercial sector. This study delves into advanced design strategies aimed at maximizing energy efficiency while upholding quality and comfort standards. By scrutinizing a functioning commercial building in Gurugram, Haryana, data was gathered to construct an energy model using eQUEST. Analysis encompassed various building facets such as envelope components (e.g., walls, roofs, and windows), HVAC systems, lighting, and occupancy patterns. The outcomes furnish valuable insights into pivotal factors influencing energy efficiency and propose tailored strategies for integration into commercial building design. This methodical approach, informed by case evaluation, seeks to optimize energy performance and cost-effectiveness across diverse building types and climates.

Towards a Digital Representation of Building Systems Controls

Energy monitoring and performance optimization processes play a significant role in ensuring energy-efficient building operation. However, the current implementation of these processes requires substantial time and effort due to the fragmented and non-digital nature of technical building equipment information. To address these challenges, we demonstrate the application of the PLCont methodology within an ongoing monitoring process in a real building. PLCont utilizes Semantic Web Technologies and established RDF ontologies to describe the control topology. The digital representation of control functions aligns closely with the IEC standard 61131-3, and the PLCont ontology establishes a connection between the topology and the control functions. We compare the conventional approach and the PLCont methodol-ogy using the supply air temperature control of an Air Handling Unit (AHU) as a use case. A graphical user interface built upon the PLCont backend provides a comprehensive represen-tation of the system's functionality. It displays the system topology as a digital HVAC schema, the function code exported from the PLC, time-series data plots, and textual descriptions of implemented functions. We demonstrate how this approach enhances transparency and facil-itates the identification and elimination of faulty system behavior, ultimately contributing to the optimization and energy efficiency of building systems.

Energy and economic performance optimization of a window with variable transparency shape-stabilized PCM in hot summer and cold winter climate zone

Improving the thermal performance of windows is important for building energy efficiency. Filling windows with variable transparency shape-stabilized phase change materials (VTSS-PCM) improves the thermal inertia of windows while avoiding the leakage of PCM. In this paper, a new type of VTSS-PCM window was proposed, tested, simulated and optimized in hot summer and cold winter climate zone. A numerical model of the VTSS-PCM window was built, and the model was validated using experimental tests. On this basis, three key parameters of VTSS-PCM were investigated and optimized. Finally, the energy and economic performance of the optimized VTSS-PCM window were compared with a typical hollow glass window. The results showed that the total annual unfavourable heat transfer (TAHT) and the annual investment cost (AIC) of the VTSS-PCM window obtained from the optimization were 118.16 kWh/m2 and 8.53 CNY/m2, respectively. Compared with the hollow glass window, the VTSS-PCM window reduced the TAHT by 30.14% and the total annual cost by 28.39%. The VTSS-PCM window produced a better development potential in terms of energy performance and economic performance. This study provided a reference for the application of the VTSS-PCM window in hot summer and cold winter regions of China.

Advancing Sustainable Building Practices: Intelligent Methods for Enhancing Heating and Cooling Energy Efficiency

Our work is dedicated to enhancing sustainability through improved energy efficiency in buildings, with a specific focus on heating and cooling control and the optimization of thermal comfort of occupants. With an energy consumption of more than 60% in buildings, HVAC systems are the biggest energy users. By integrating advanced technology, data algorithms, and digital twins, our study aims to optimize energy performance effectively. We have developed a Neural Network-based Model Predictive Control (NNMPC) to achieve this goal. Leveraging technologies such as MQTT communication, Wi-Fi modules, and field-programmable gate arrays will enhance scalability and flexibility. Our findings demonstrate the efficacy of the NNMPC system deployed on the PYNQ board for reducing sensible thermal energy usage for both cooling and heating purposes. Compared to traditional On/Off control systems, the NNMPC achieved an impressive 40.8% reduction in heating energy consumption and a 37.8% decrease in cooling energy consumption in 2006. In comparison to the On/Off technique, the NNMPC demonstrated a 25.6% reduction in annual heating energy consumption and a 28.8% drop in annual cooling energy consumption in the simulated year of 2017. We observed that, across all strategies and platforms, there were no instances where the Predicted Mean Vote (PMV) fell below −0.5. However, a significant proportion of PMV values (ranging from 65% to 83%) were observed between −0.5 and 0.5, signifying a high level of occupant comfort. Additionally, for PMV values between 0.5 and 1.0, percentages ranged from 16% to 33% for both years. Importantly, the NNMPC exhibited notable efficiency in maintaining occupants’ comfort within this range, requiring less energy while ensuring highly satisfactory environments.

A neural network framework for optimizing parallel computing in cloud servers

Energy efficiency has become a major focus in optimizing hardware resource usage for Cloud servers. One approach widely employed to enhance the execution of parallel applications is thread-level parallelism (TLP) exploitation. This technique leverages multiple threads to improve computational efficiency and performance. However, the increasing heterogeneity of resources in cloud environments and the complexity of selecting the optimal configuration for each application pose a significant challenge to cloud users due to the massive number of possible configurations and the need to effectively harness TLP in diverse hardware setups to achieve optimal energy efficiency and performance. To address this challenge, we propose TLP-Allocator, an artificial neural network (ANN) optimization strategy that uses hardware and software metrics to build and train an ANN model. It predicts worker node and thread count combinations that provide optimal energy-delay product (EDP) results. In experiments using ten well-known applications on a private cloud with heterogeneous resources, we show that TLP-Allocator predicts combinations that yield EDP values close to the best achieved by an exhaustive search. It also improves the overall EDP by 38.2% compared to state-of-the-art workloads scheduling on cloud environments.

Multi-objective optimization of energy and daylight performance for school envelopes in desert, semi-arid, and mediterranean climates of Iran

Considering global warming as a critical challenge to human life, performance optimization of the building envelope can play a noticeable role to reduce a building's environmental footprint. Meanwhile, shading systems are considered as sustainable passive solutions to contribute to building energy efficiency, and daylight control in early-stage design. Nonetheless, a paucity of research has examined the various shading strategies in diverse climates, particularly arid desert and steppe regions, in conjunction with educational buildings. The present study aims to evaluate the role of fixed exterior shading systems (FESSs), and window-to-wall ratio (WWR) on building’ thermal and daylight performance in Iran with desert, semi-arid, and Mediterranean climates. Pareto Frontier and weighted-sum method for multi-objective optimization, and sensitivity analysis were used to study the optimum solutions, and the relationship between the design variables and the performance metrics. Three objective metrics including Spatial Daylight Autonomy (sDA), Annual Sunlight Exposure (ASE), and Energy Use Intensity (EUI), were defined as performance metrics. Shadings include vertical louver, horizontal louver, light shelf, overhang, and egg-crate. The results showed that EUI was reduced via FESS-integrated façade by 25.22%, 20.84%, 19.14%, 14.06%, and 13.47%, in Yazd, Bushehr, Kerman, Rasht, and Mashhad as selected studied cities, respectively. Based on ASE, the horizontal louver surpasses the other systems by 100% ASE value reduction in all climate zones except for Rasht. sDA level is reduced in all climate zones considering five studied FESSs excluding horizontal louver in Yazd and Rasht and overhang in Mashhad by 100% sDA level. Building energy performance simulation is validated by ASHRAE140-2020.

Occupancy modeling on non-intrusive indoor environmental data through machine learning

The primary drivers of energy consumption within buildings are the occupants. Non-intrusive Internet of Things (IoT) technology can be utilized to detect occupancy and optimize energy performance while preserving the privacy of building occupants. This study explores the suitability of various indoor environmental data for occupancy detection in office rooms. Data was collected utilizing an IoT sensory device, recording CO2 concentration, air temperature, relative humidity, indoor air quality, sound pressure level, and illuminance in two double-occupied office rooms at a campus building in Munich, Germany. Machine learning models, namely Random Forest, XGBoost, and dense neural networks were trained on the dataset, and the models’ feature importance was analyzed through post-hoc explainability testing. The best features were then incorporated in a sliding window of 15 min to consider temporal dependencies in the models. The study also compared two hyperparameter optimization techniques: Random and Bayesian optimization.The results of this study indicate that with six days of ground truth data of sound pressure level, CO2, and illuminance, accuracy and F1-score for occupancy detection of above 0.95 and 0.93 can be respectively achieved.

Thermal performance analysis of hollow bricks integrated phase change materials for various climate zones

AbstractThis comprehensive research addresses the significant challenge of building‐related energy consumption in Morocco. Our innovative approach involves integrating phase change materials (PCMs) into hollow bricks, strategically addressing the diverse climate zones prevalent in the country. The primary focus is on enhancing energy efficiency within structures. Leveraging detailed simulations and employing the enthalpy‐porosity approach, our study models the impact of PCMs on internal temperatures. Optimal outcomes are achieved by partially filling brick holes with PCMs in specific configurations, demonstrating the materials' ability to adapt to varying conditions. A noteworthy finding is the 2–3‐h phase shift observed in cold zones, indicating the potential for PCMs to effectively regulate temperatures. Equally significant is their capability to maintain a constant internal temperature of 26°C in hot zones, even amidst extreme external conditions reaching up to 47°C. This resilience underscores the novel and tailored thermal regulation potential of PCMs. Beyond the technical insights, our research highlights the paramount importance of considering regional climates in PCM applications' implementation. This awareness is crucial for optimizing energy performance in buildings and ensuring sustainability. In essence, this study contributes valuable knowledge and practical implications for the strategic deployment of PCMs to enhance building energy efficiency, emphasizing the need for context‐specific solutions in diverse environmental conditions.

GREEN ARCHITECTURE AND ENERGY EFFICIENCY: A REVIEW OF INNOVATIVE DESIGN AND CONSTRUCTION TECHNIQUES

Green architecture and energy efficiency have become imperative considerations in contemporary construction practices, driven by the escalating global concern over environmental degradation and energy consumption. This paper provides a comprehensive review of innovative design and construction techniques employed in green architecture to enhance energy efficiency. The study delves into the integration of sustainable design principles, emphasizing the importance of minimizing the ecological footprint of buildings. Various strategies such as passive design, green roofs, and efficient insulation systems are explored to create structures that harness natural resources intelligently. These techniques not only reduce energy consumption but also contribute to a healthier indoor environment. Innovative construction materials play a pivotal role in green architecture, with a focus on utilizing recycled and locally sourced materials. The paper scrutinizes emerging technologies like bio-concrete and self-healing materials, which not only enhance structural resilience but also embody eco-friendly attributes. Furthermore, the utilization of advanced building systems, such as smart grids and energy-efficient HVAC systems, is discussed for optimizing energy performance throughout the lifecycle of a building. The review also highlights the significance of renewable energy sources, with an emphasis on incorporating solar panels, wind turbines, and other sustainable energy solutions into architectural designs. The integration of these technologies not only promotes energy self-sufficiency but also contributes to the overall reduction of greenhouse gas emissions. In conclusion, this paper encapsulates a comprehensive exploration of the symbiotic relationship between green architecture and energy efficiency. By examining cutting-edge design and construction techniques, the study seeks to provide insights that can inform and inspire future practices in the construction industry, fostering a sustainable approach that addresses both environmental concerns and the growing demand for energy-efficient infrastructure.
 Keywords: Energy, Green Architecture, Construction Technique, Innovative Design, Review.

Improvement in energy performance from the construction of inlet guide vane and diffuser vane geometries in an axial-flow pump

Advanced inlet guide vane (IGV) and diffuser vane (DV) geometries were constructed in an effort to increase the energy performance of an axial-flow pump at the best efficiency point (BEP). DV setting angles were also investigated to increase energy performance at the off-design points. By integrating the advantages of an adjustable IGV, combinations of adjustable IGV and DV geometries were constructed and thoroughly analyzed by way of energy loss. The asymmetrical geometry of the IGV, upgraded through the use of a hydrofoil profile, resulted in higher hydraulic performance compared to that of the reference model. The efficiency and total head at the BEP increased significantly with the implementation of the new DV, by 1.456% and 5.756% over those of the reference model, respectively. Using the new DV reduced the unsteady turbulent flow behind the trailing edge of the DV under all flow rate conditions, resulting in a reduction in vibration and noise. The positive setting angles of the DV increased the energy performance in the high-flow-rate region, whereas the negative DV setting angles produced a good performance in the low-flow-rate region. Combining an adjustable IGV with an adjustable DV model resulted in a significant increase in the total head, with more optimal energy performance provided by the positive IGV setting angles. At the BEP and under high-flow-rate conditions, the low-velocity zone is closely related to high entropy generation. Furthermore, these high-entropy generation regions follow the trajectory of the low-velocity zones. However, the low-velocity zone is not strongly associated with the high-entropy generation region when operating under low-flow-rate conditions.

Investigating adaptive sampling strategies for optimal building energy performance using artificial neural networks and kriging surrogate models

Recent studies highlight surrogate models as efficient alternatives to energy simulation tools, reducing computational burden. However, accurate metamodels often require extensive evaluations, leading to high computational costs. Adaptive sampling approaches offer a promising solution by constructing accurate surrogate models at a lower computational expense compared to static sampling. The selection of an appropriate adaptive sampling criterion is crucial in sequential sampling strategies, as it determines how new sample points are chosen based on previous iterations. However, concerns remain regarding the impact of the sampling infill criterion on surrogate model accuracy. Additionally, a lack of studies exists comparing artificial neural networks (ANN) and Kriging models in terms of potential computational improvements through sequential sampling. This study aims to explore the potential of adaptive sampling strategies to estimate optimal building energy consumption within a reasonable computational budget, thus reducing the number of energy simulation model runs required. Furthermore, a comprehensive comparison between sequential sampling techniques employing ANN-based and Kriging-based surrogate models is conducted. Four different sampling criteria are evaluated to understand the relationship between sampling strategies and accuracy. The findings contribute to a more efficient resolution of building optimization problems. The results indicate that the choice of a sampling strategy depends on the model's requirements and the specific problem. Each strategy has distinct strengths and weaknesses, necessitating careful consideration of the model's needs. Selecting the appropriate sampling strategy improves model accuracy and reduces training time. Adaptive sampling methods yield significant improvements, saving 25 %–68 % of simulation samples compared to static approaches.

Intelligent Renewable Energy Source Allocation Scheme for Internet of Things Enabled PHEVs Systems

Plug-in hybrid electric vehicles (PHEVs) have received a lot of interest because of their ability to cut fuel usage by using electricity from the grid as an alternative energy source. PHEVs differ from typical hybrid electric vehicles (HEVs) in terms of increased battery capacity, the availability of plug-in recharging, and improved battery state of charge (SOC) management systems. Efficient energy consumption and excellent performance are critical issues for hybrid powertrains. The estimation of SOC in PHEVs is a difficult NP-hard problem that is frequently tackled utilising metaheuristic techniques. In this paper, we offer an enhanced dingo optimisation with deep learning-based prediction (EDOA-DLP) model specifically tailored for PHEVs. The EDOA-DLP model includes an efficient SOC estimate algorithm for real-time energy management. The EDOA-DLP technique's performance has been thoroughly proven through considerable experimentation in a variety of circumstances. The results show that the proposed EDOA-DLP method beats other current methods in terms of predicting SOC accurately and reaching the targeted goals for PHEVs. Overall, this research presents a unique EDOA-DLP SOC prediction approach for PHEVs, with promising results in terms of energy management and performance optimisation. The experimental results highlight the EDOA-DLP model's improved performance. Overall, this research presents a unique EDOA-DLP SOC prediction approach for PHEVs, with promising results in terms of energy management and performance optimisation. The experimental results illustrate the EDOA-DLP model's improved performance when compared to competing methodologies, highlighting its potential to improve the efficiency and efficacy of PHEV systems.

Regression-based sensitivity analysis and multi-objective optimisation of energy performance and thermal comfort: Building envelope design in hot arid urban context

Previous research has established that energy demand of buildings in street canyons are affected by the surrounding urban physical characteristics. This suggests that it is possible to optimise the energy performance of buildings by acting on the physical urban parameters that potentially influence their overall energy consumption. Unfortunately, few studies have addressed this issue. Precisely, improving buildings thermal behaviour and energy demand pattern throughout sensitivity-based optimisation by focusing on the building’s envelope design without neglecting the effect of urban context, remains a relatively under-explored research topic. The present study attempts to fill this gap by implementing a combined method based on sensitivity analysis and multi-objective optimisation of energy demand and thermal comfort for an existing residential building subjected to hot arid climatic conditions. The proposed method utilises Latin hypercube sampling with multivariate multiple linear regression to identify the relative importance of design parameters on building performance criteria, namely, the annual energy demand and the integrated summer thermal discomfort. Non-dominated sorting genetic algorithm II (NSGA-II) is then applied to simultaneously optimise the investigated building performance criteria with regards to the identified relevant envelope parameters. The results of sensitivity analysis indicate that window characteristics, thermal insulation, and roof reflectance are the most influential factors. The optimisation results, in turn, show promising improvement potential with a wide range of design solutions that can ensure better energy performance as well as better thermal comfort. The annual energy demand could be significantly improved by 13.2% up to 49.5% while discomfort degrees could be decreased by over 26%.

Deployment of real-time building automation system-integrated inverse-model-based fault detection and diagnostics algorithms

The complex operation of HVAC systems in large commercial buildings warrants regular implementation of advanced analytical approaches to operations and maintenance, and subsequent corrective measures to improve and maintain optimal energy performance. Despite the established capabilities of data-driven fault detection and diagnostics (FDD) to identify suboptimal controls policies and mechanical faults resulting in poor energy performance, few attempts have been made to deploy scalable solutions around these approaches. Furthermore, real-time BAS-integrated FDD methods are predominantly rule-based, offering limited insights to faults with gradual negative impacts to energy performance. This paper demonstrates the application of various established data-driven, inverse-model-based FDD methodologies in a BAS-integrated environment. Traditionally implemented sparingly, the novelty of recursive and automatic execution of advanced FDD methodologies, facilitated through a direct data pipeline to an existing BAS, capitalizes on the BAS’s real-time monitoring capabilities to enable continuously refreshed inverse model generation that can capture the gradual degradation of building performance, and provide up-to-date actionable visualizations and key performance indicators (KPI) to building operators. Since deployment, the application has successfully identified a scheduling fault on two separate occasions in a case study building in Ottawa, Canada, and the visualizations were presented to the building operators who resolved the issues.

Energy, exergy, environmental and economic performance analysis and optimization of a novel CCHP system integrated with ammonia driven MCFC and solar energy

Energy, exergy, environmental and economic performance analysis and optimization of a novel CCHP system integrated with ammonia driven MCFC and solar energy

Adaptive building envelope combining variable transparency shape-stabilized PCM and reflective film: Parameter and energy performance optimization in different climate conditions

Developing an adaptive building envelope (ABE) to cope with drastic changes in the ambient environment contributes to achieving the goal of net-zero emissions by 2050 in the building industry. In this study, two types of ABE, namely, adaptive ventilation and sunlight regulation wall (AVSRW) and adaptive sunlight regulation wall (ASRW), were designed by combining variable transparency shape-stabilized PCM (VTSS-PCM) and reflective film, and the AVSRW had an air cavity to control the heat storage and release of VTSS-PCM. The numerical models of the two ABEs and the massive wall (MW) were developed and validated by a full-scale experimental test. Based on these validated numerical models and weather datasets in four climate zones of China, the energy performances of AVSRW were simulated under the influence of melting temperature (Tm) of VTSS-PCM, thickness (L) of VTSS-PCM, and reflectivity (R) of reflective film. Then, a bi-objective global optimization was conducted to find the optimal performance and design parameters of AVSRW. Finally, the AVSRW and the ASRW with the optimal design parameters were compared with and the MW. The parameters analysis revealed that the combined action of Tm, L, and R on AVSRW determined the trade-off among the heat conducted to the interior, the solar energy stored by VTSS-PCM, and the solar energy reflected by the reflective film. The optimization results showed that, in the climate conditions of Beijing, Changsha, Guangzhou, and Harbin, the optimal values of Tm were 24.02 ℃, 24.37 ℃, 25.86 ℃, and 23.43 ℃, respectively; the optimal values of L were 0.16 m, 0.16 m, 0.09 m, and 0.16 m, respectively; the optimal values of R were 0.23, 0.29, 0.79, and 0.33, respectively. Under the optimal values of Tm, L, and R, compared with MW, the annual ESRs of AVSRW and ASRW were 84.26 % and 38.71 % in Beijing, 81.98 % and 39.45 % in Shanghai, 70.28 % and 55.53 % in Guangzhou, 76.20 % and 29.54 % in Harbin. This study provides the basis for the design and optimization of AVSRW and ASRW in different climate regions.

A deep reinforced markov action learning based hybridized energy management strategy for electric vehicle application

Due to the severe lack of fossil fuels and existing environmental pollution, developing Electric Vehicles (EV) powered by clean, new energy is essential to finding a way to address this problem. The hybrid EV powered by fuel cell is the most practical and suitable option in present days. Several researchers have concluded that a key factor in determining an EV's performance is the hybrid energy management system's output performance. Moreover, proper energy management and performance optimization are the most essential solutions to ensure the normal operation of the hybridized system. In the existing studies, several energy management methodologies are developed using a hybridized energy sources, yet it facing some challenges in terms of high cost, increased system designing complexity, stability issues, and unreliable power supply. Therefore, the proposed work intends to implement a novel Energy Management System (EMS) for EVs with the use of advanced learning algorithms. In the proposed topology, the combination of Proton Exchange Membrane Fuel Cell (PEMFC), battery, and Super-Capacitor (SC) have been used as the major energy sources. Then, a high gain DC-DC converter is utilized to boost the regulate output voltage with reduced stress and loss factors. For improving the performance of converter, a recently developed Gorilla Troop Optimization Algorithm (GTOA) is implemented, which provides the best solution to choose the controlling parameters for generating the switching pulses. Moreover, the Deep Reinforced Markov Action Learning (DRLA) is employed to assure an efficient energy management in EV systems. For validation, the simulation and comparative analyses are carried out in this study with the use of numerous parameters. Index termsElectric Vehicle (EV), Proton Exchange Membrane Fuel Cell (PEMFC), Battery, Super-Capacitor (SC), Renewable Energy Sources (RES), High Gain DC-DC Converter, Gorilla Troop Optimization Algorithm (GTOA), and Deep Reinforced Markov Action Learning (DRLA).