Energy Efficiency Of Vehicles Research Articles

Enhancing energy recovery in automotive suspension systems by utilizing time-delay

This study introduces time-delay active control technology to with the aim of enhancing the system's energy harvesting potential and ride smoothness. The time-delay active suspension represents a nonlinear, multivariable system, and stability analysis in this context is intricate. The mainly challenge lies in effectively leveraging time delay to augment the system's energy collection potential while harmonizing the vehicle's ride comfort and energy efficiency. Addressing this issue, our study proposes enhancing the suspension's energy recovery capability through time-delay control. Initially, the impact of time-delay control parameters on energy collection ability under various operational conditions was analyzed, establishing that appropriate time-delay parameters can enhance energy harvesting capacity. Subsequently, to balance the relationship between vehicle comfort and energy efficiency, the research introduces a method for constructing an optimized objective function based on linear equivalent excitation, thereby quantifying the relationship between system time-domain vibrational response, time-delay control parameters, and external excitations. This approach enables the comprehensive optimization of the suspension system's comfort, safety, and energy efficiency. The control system's stability was analyzed using cluster treatment of characteristic roots method. Finally, simulations and experiments were conducted to evaluate the effectiveness of time-delay active across different scenarios, confirming the efficacy of the proposed control methodology.

Energy efficient waste collection vehicle routing system using time series prediction

Energy efficient waste collection vehicle routing system using time series prediction

Experimental study and exergy analysis of electric vehicle single-stage compression secondary throttling CO2 air-source heat pump system in cold climate

A novel CO2 air-source heat pump system (ASHPs) with a secondary throttling based on a single-stage compression was proposed to further improve the heating energy efficiency of electric vehicles (EVs) for heating in cold climates. The heating characteristics of the secondary throttling system (STS) and conventional throttling system (CTS) at different indoor air supply temperatures were compared. The STS performance characteristics were investigated under varying ambient temperature, outdoor air velocity, and exergy analysis was performed on the system and its components. A fitting correlation equation for optimal discharge pressure was obtained based on experimental data. The results demonstrate that implementing STS significantly reduces CO2 ASHPs energy consumption (Wcom), enhances the coefficient of performance (COP), and reduces exergy destruction. When the indoor air supply temperature reaches 45°C, using STS can result in an 8.1% reduction in Wcom, a 9.4% increase in COP, and a 6.1% improvement in the exergy efficiency (Exη) compared to that of CTS. Furthermore, the advantages of STS become more pronounced with higher indoor target air supply temperatures. As the ambient temperature decreases, the exergy efficiency of the STS reaching the target air supply temperature decreases. The velocity of outdoor air significantly affects the STS performance, and increasing outdoor air velocity can effectively enhance the Exη of STS. The optimal discharge pressure fitting equation for the STS was obtained through multiple linear regression analysis. These findings can provide ideas for architectural optimization and design of CO2 ASHPs for EVs, which are important for further improving system performance.

Extend the EV Range through Dynamic Scheduling of Battery: Present and Future Techniques

The pursuit of extending the driving range and improving the energy efficiency of electric vehicles (EVs) is a critical objective in advancing sustainable transportation. Central to this pursuit is the battery management system (BMS), which ensures the operational integrity and optimal performance of the EV's battery. Traditional BMSs have largely been conservative, relying on static parameters and predefined rules, which often do not fully exploit the battery's capacity or adapt to the dynamic nature of driving conditions. This has resulted in EVs that do not optimize their range, either underutilizing their energy or risking premature depletion. This paper introduces a dynamic scheduling approach for battery usage in EVs, a paradigm shifts from traditional BMS algorithms that are deterministic and linear, to one that is adaptable and predictive. The proposed dynamic scheduling method utilizes data analytics, machine learning, and real-time monitoring to anticipate and adapt to varying driving conditions, traffic patterns, driver behavior, and route topography. The objective is not just to respond to the battery's current state but to manage the energy distribution proactively, optimizing the use of the stored energy and enhancing the vehicle's range on a single charge. The paper explores the technological advancements enabling dynamic scheduling, the benefits of such a system, and the challenges it may encounter. It is posited that dynamic scheduling represents a necessary evolution in battery management, capable of significantly boosting the range and desirability of EVs. Finally, the paper proposes a novel system and method that leverage real-time data and machine learning to implement an effective energy management strategy for dynamic scheduling, which could markedly improve the range of an EV. The implications of this approach suggest a future where EVs can meet and exceed the range expectations of consumers, thereby accelerating the transition to electric mobility.

The Safety, Operation, and Energy Efficiency of Rail Vehicles—A Case Study for Poland

The objective of the article was to describe the importance of a good technically and economically planned process for purchasing a rail vehicle. Compliance with this process with safety standards and energy efficiency is crucial regarding effectiveness in the long-life cycle cost of a rail vehicle. Methods that were used focused on audit and document analysis. In the result based on a specific audit of a railway company, some non-compliances were found, and it was methodically proven that they are significant risk factors in terms of performing such processes in the future. Major conclusions regarded the importance of fulfilling legal requirements of SMS for purchase purposes, involvement of a safety engineer post in this process, and usage of technical feedback regarding previous operation of railway vehicles and operational findings from the past. The transport policy of the EU and other developed countries sets ambitious goals for reducing energy consumption in transportation activities, which is linked to the aim of reducing environmental burdens.

Thermal management improvement in electric vehicles through design optimization of coolant distributor valves

On battery electric vehicles a precise control of heat flows is required for passenger comfort and for both performance and safety of battery packs. This is typically achieved with a thermal management system which employs some type of liquid coolant to transfer heat. Coolant distributor valves are used to regulate coolant flows through the exchangers, for example to recover heat from hot sources, thus to increase vehicle energy efficiency. The development of this type of component is high-priced and the design is generally complex since several operating functions and manufacturing constraints should be satisfied. Despite this complexity and the positive impact such valves have on the thermal management of electric vehicles, a few works are currently available in the literature on design procedures for this component, where only a partial overview of the topic is provided and a comprehensive design methodology considering the effective operating conditions is still missing. In this work we construct a physical model of the valve for torque, internal leakage and pressure drop, and we build an optimization procedure to improve the global performance of the valve. We compare the obtained designs with a simpler approach that optimizes performance parameters independently and we show that designs which severely under-perform with respect to the uncontrolled performance parameters can be produced if an over-simplified design approach is followed. We then show with a global vehicle thermal model that the impact of valve design on global vehicle performance crucially depends on the thermal exchanges at vehicle level and, thus, on its operating conditions. In particular, the impact is considerable when mixing of cold and hot flows through the valve occurs. For a specific example vehicle evaluated in a type-3a cycle of the Worldwide Harmonized Light Vehicle Test Procedure, a valve determined with the proposed optimization procedure allows us to obtain in this condition an increase of about 1 km in the vehicle driving range with respect to a valve defined with the simpler approach.

A Comparative Study of Vehicle Velocity Prediction for Hybrid Electric Vehicles Based on a Neural Network

Vehicle velocity prediction (VVP) plays a pivotal role in determining the power demand of hybrid electric vehicles, which is crucial for establishing effective energy management strategies and, subsequently, improving the fuel economy. Neural networks (NNs) have emerged as a powerful tool for VVP, due to their robustness and non-linear mapping capabilities. This paper describes a comprehensive exploration of NN-based VVP methods employing both qualitative theory analysis and quantitative numerical simulations. The used methodology involved the extraction of key feature parameters for model inputs through the utilization of Pearson correlation coefficients and the random forest (RF) method. Subsequently, three distinct NN-based VVP models were constructed comprising the following: a backpropagation neural network (BPNN) model, a long short-term memory (LSTM) model, and a generative pre-training (GPT) model. Simulation experiments were conducted to investigate various factors, such as the feature parameters, sliding window length, and prediction horizon, and the prediction accuracy and computation time were identified as key performance metrics for VVP. Finally, the relationship between the model inputs and velocity prediction performance was revealed through various comparative analyses. This study not only facilitated the identification of an optimal NN model configuration to balance prediction accuracy and computation time, but also serves as a foundational step toward enhancing the energy efficiency of hybrid electric vehicles.

Numerical Simulation of Graphene-Nanoplatelet Nanofluid Convection in Millimeter-Sized Automotive Radiator

The depletion of fossil fuels and environmental considerations in transportation sector motivate the researchers to enhance the efficiency and performance of the automotive systems. However, the poor thermal performance of conventional coolant poses a limitation in the development of energy-efficient vehicle due to the cooling constraint. In the present study, a comprehensive numerical study is conducted to scrutinize the convective performance of graphene nanoplatelets (GnP) nanofluid in millimeter-sized automotive radiator, aiming to enhance the understandings on the underlying physical significance of the suspension of graphene-based nanoparticle in water for the performance enhancement of automotive radiator. The temperature-dependent thermophysical properties of GnP-water nanofluid is predicted via existing correlations, while a modified viscosity correlation is developed. ANSYS Fluent is employed in the present numerical simulation to investigate the effects of various pertinent parameters such as Reynolds number, nanoparticles aspect ratio, tube aspect ratio and tube hydraulic diameter on the heat transfer performance of the radiator. Double precision and second-order upwind scheme with inclusion of viscous heating, and convergent criteria of 10˗6 are adopted for the present simulation. It is observed that the convective performance of the radiator is significantly enhanced by increasing Reynolds number and nanoparticle volume fraction while decreasing the aspect ratios of nanoparticle and radiator tube, with an enhancement rate as much as 1816%. Therefore, it is evident that the suspension of GnP intensifies the heat transfer performance of millimeter-sized automotive radiator, which could possibly lead to a more efficient radiator that is smaller and lighter.

Enhancing platoon performance: A novel approach to speed and direction control using V2X communication

Autonomous vehicle platoons have become an increasingly popular solution to enhance driving system performance and facilitate the safe integration of these vehicles on the roads. To ensure efficient platoon traffic, it is essential to effectively manage both speed and direction within this group of vehicles. It is in this context that this research is situated. Our primary objective is to enhance the performance of speed and direction controllers, as the ability of vehicles to adjust these parameters in a coordinated manner is crucial for the success of platoon traffic. To achieve this, we have developed a novel speed control approach based on neural networks and fuzzy logic, utilizing V2X communication. By incorporating environmental parameters through vehicle-to-vehicle communication and considering the specific goals of the platoon, our neuro-fuzzy system can accurately calculate optimal speeds and directions for the vehicles. Our experiments have demonstrated the effectiveness of this approach compared to traditional and advanced methods, improving both energy efficiency and temporal coordination of autonomous vehicles within the platoon.

Assessing the Value of Information Technology for the Decarbonization of Freight Transport

In its first part, the paper gives a summary of the levels of emissions resulting from each mode of transport by use of statistics at the European and global levels. It also summarizes the main measures and policies that have been put forward for the reduction of freight transport greenhouse gas (GHG) emissions. To facilitate the subsequent analyses, these measures and policies are classified into four categories, i.e., affecting the “demand” for freight transport services, shifting freight to less or no carbon-intensive modes, improving the energy efficiency of existing freight vehicles, and transitioning to “clean” fuels including electricity and hydrogen. For each of these categories, their current or potential use of information technology (IT) applications is presented, and key examples of such applications are mentioned. In the final part, a multicriteria analysis is performed with the help of two expert panels which are asked to assess, on a 5-level Likert-type scale, the extent of IT use in each of the four categories. The results show an important or very important contribution of IT in all categories except “improving the energy efficiency of current vehicles”. The highest contribution (with scores higher than 4) appears to be in the category “shifting freight to less carbon-intensive modes”.

Promoting Green Energy Vehicle: A Global Warming Solution

Given the escalating concerns surrounding global warming, nations worldwide are confronted with the urgent need to curb greenhouse gas emissions. Transitioning to energy-efficient vehicles has emerged as a promising solution to mitigate environmental harm. This paper aims to delve into the present-day dynamics and incentives associated with these eco-friendly vehicles. Specifically, it scrutinizes governmental incentives that promote their acquisition, corporate ingenuity in their manufacturing and distribution, consumer allure factors, and the medias role in portraying both traditional auto industry constraints and affirmative narratives about these sustainable vehicles. Furthermore, the analysis sheds light on predicaments tied to energy vehicles, such as challenges in electricity infrastructure and potential overuse. Findings reveal that prevailing policies across nations remain imperfect, with some strategies focusing on bolstering the effectiveness of green vehicle directives while others are tailored to stimulate consumer buying tendencies. To secure a sustainable vehicular future, an integrated effort among governmental agencies, media outlets, and businesses is paramount.

ОЦЕНКА ЭКОЛОГИЧЕСКОЙ БЕЗОПАСНОСТИ И ЭНЕРГЕТИЧЕСКОЙ ЭФФЕКТИВНОСТИ ТРАНСПОРТНЫХ СРЕДСТВ, ОБСЛУЖИВАЮЩИХ МАРШРУТЫ ГОРОДСКОГО НАЗЕМНОГО ТРАНСПОРТНОГО КОМПЛЕКСА

The urban passenger transport complex is the most important part of the infrastructure of modern cities and provides a number of key functions. The condition for the effective operation of the transport complex is the balanced development of the subsystems included in its structure, which in turn necessitates the formation of a methodological framework that determines the conditions for such development. Based on the indicated provisions, the purpose of the study is formulated, which is to increase the efficiency of the operation of vehicles serving the routes of the urban ground passenger transport complex, based on the results of an assessment of their environmental safety and energy efficiency. To achieve this goal, it is necessary to solve a number of tasks: – study and analysis of the content of research papers, regulatory and technical documents in the field of environmental safety assessment of energy efficiency of vehicles; – development of a methodology for assessing and comparative analysis of environmental safety and energy efficiency, vehicles serving urban public transport routes; – assessment of environmental safety of various categories of vehicles serving the routes of the urban ground passenger transport complex; – generalization and analysis of the results obtained, formulation of conclusions and practical recommendations. During the study, an approach was used that assumes: modeling of energy consumption parameters of vehicles implementing various technologies of fuel and energy supply and a comparative assessment of indicators determining their energy efficiency and environmental safety in traffic conditions on urban passenger routes. Based on the implemented approach, the methods of system and statistical analysis of the initial information, mathematical modeling and methods of multi-criteria evaluation of the results are used in the work. The main result of the study is the numerical values of the indicators of energy efficiency and environmental safety of vehicles implementing various technologies of fuel and energy supply in the conditions of urban ground passenger transport complex, which is the scientific novelty of the study. The use of the obtained values in assessing the efficiency of passenger transportation by vehicles of various categories allows for the formation of a fleet of vehicles based on the results of a multi-criteria analysis. One of the directions of further research involving the use of the results obtained is the development of a comprehensive methodology for determining the optimal structure and coordinated formation of subsystems that make up the urban ground passenger transport complex.

Advanced Equivalent Consumption Minimization Strategy for Fuel Cell Hybrid Electric Vehicles

This paper aims to develop an Advanced Equivalent Consumption Minimization Strategy (AECMS) for efficient fuel cell hybrid electric vehicles (FCHEV). The Equivalent Consumption Minimization Strategy (ECMS) is one of the most feasible solutions for hybrid electric vehicles as it has less complexity and low computational cost in making the optimal control decision. However, typical ECMS designs with fixed gain use could not enhance an optimal energy control when dealing with multi decisive factors for highly non-linear and uncertain systems like FCHEVs. To address this control challenge, the AECMS is developed as the combination of the ECMS and an adaptive equivalence factor to optimally split the energy flow between the fuel cell and battery in the FCHEV. First, a cost function is formulated based on two key objectives which are power consumptions of the fuel cell and battery, the equivalence factor and a set of soft and hard constraints, considering the number of fuel cell start/stop, fuel cell optimal operating range, state of charge (SoC) range, temperature ranges and power limits. Second, the adaptive equivalence factor is newly designed as the combination of states and dynamics of the fuel cell and battery and corresponding weighting factors. Third, fuzzy inferences are derived to adjust online the weighting factors in an adaptive manner according to the working conditions of the energy sources, subsequently improving the overall vehicle energy efficiency and maintaining the battery SoC while prolonging their lifetime. Numerical simulations using a FCHEV model have been carried out under UDDS and HWFET drive cycles to illustrate the superior performance of the AECMS against the traditional ECMS.

Research and application of heat recovery and automation monitoring system for new energy vehicle motor equipment

In order to improve the energy efficiency of pure electric vehicles, the author proposes a thermal energy recovery and automation monitoring system for motor equipment based on new energy vehicles. Based on the analysis of the battery thermal management model, a series heat exchange model between the motor and the battery was established. An integrated thermal management system control strategy based on motor waste heat recovery was proposed for electric vehicles in low temperature environments, and simulation and validation were conducted at different environmental temperatures (-10~0 ?). The simulation results show that this configuration can shorten the heating time by 111-343 seconds compared to traditional battery thermal management systems during the heating stage. During the insulation stage, frequent activation of positive temperature coefficient thermistor can be avoided and the battery temperature can be maintained around 20?C, which is not only conducive to extending the battery life but also reduces the comprehensive energy consumption by 4.39-7.70%. On the other hand, this configuration can reduce the impact of low temperature on comprehensive energy consumption from 9.77-2.07%. It is proved that heat recovery can effectively alleviate the range anxiety caused by ambient temperature.

Energy efficiency in rail vehicles: analysis of contemporary technologies in reducing energy consumption

The article focuses on the problem of energy intensity in rail vehicles and analyzes current technologies aimed at reducing it. The author discusses innovations and strategies aimed at increasing energy efficiency in rail transportation. He also outlines the challenges of reducing energy consumption in this sector and future development prospects. The main focus of the article is on the problem of energy demand in rail vehicles, with a special emphasis on railroads. The author analyzes the current challenges of high energy consumption in rail transportation and presents various technologies that have been introduced to minimize this consumption. The article includes descriptions of technological innovations such as advanced energy management systems, efficient braking systems that recover energy, and the use of low aerodynamic drag materials. The author also discusses strategies to improve the energy efficiency of rail vehicles, including both existing ones and those planned for market introduction. One of the key points of the article is also a presentation of the challenges of reducing energy consumption in the rail sector. These include the need to adapt existing infrastructure, the financial aspects of introducing new technologies, and acceptance by consumers and operators, among others.

Comparative analysis of electrical models of lith-ium-ion batteries in electric vehicles

Problem. This article addresses the challenge of enhancing the environmental friendliness and energy efficiency of vehicles. It does so by conducting a comparative analysis and identifying ways to improve the electrical models of lithium-ion batteries used in electric vehicles. The study includes an examination of well-known electrical models of lithium-ion rechargeable batteries, such as the Rint model, the RC model, the Thevenin model, and the PNGV model. It identifies key characteristics of lithium-ion batteries in electric vehicles, including state of charge, mass, actual voltage, energy required for recharging, among others. The study also explores models of battery degradation, focusing on capacity reduction and the increase in active resistance. It substantiates directions for improving electrical models of lithium-ion batteries in electric vehicles by considering changes in capacity, internal resistance, polarization resistance, and both calendar and cyclic degradation. Goal. The aim of this work is to enhance the environmental friendliness and energy efficiency of vehicles through a comparative analysis and by determining ways to improve the electrical models of lithium-ion batteries in electric vehicles. Methodology. Our approach to achieving this goal involves using electrical models of lithium-ion batteries in electric vehicles, which describe various parameters such as state of charge, actual voltage during charge/discharge processes, and energy required for recharging. The study encompasses an investigation into the degradation of electric vehicle batteries, including their use in Vehicle to Grid (V2G) technology. Results. The analysis of electrical models of lithium-ion batteries in electric vehicles, aiming to increase their accuracy, considers the following aspects: changes in internal resistance and polarization resistance; capacity variation; and battery degradation. The change in internal resistance and polarization resistance should be considered based on two factors: the state of charge of the battery and the degree of its degradation. While the first factor is relevant primarily when the battery is deeply discharged (SoC<30%), the second factor must be considered at any state of charge. Capacity changes should be accounted for based on calendar and cyclic degradation. It has been determined that the primary causes of degradation in electric vehicle batteries are calendar aging (service life) and aging due to charge/discharge cycles. Contrarily, it is argued that using Vehicle to Grid (V2G) technology can reduce battery degradation by 10%. Originality. The results of this study provide a comprehensive understanding of the electrical models of lithium-ion batteries in electric vehicles and contribute to the improvement of existing models. Practical value. This research enhances the accuracy of current electrical models of lithium-ion batteries in electric vehicles by considering the variable nature of internal resistance and capacity during vehicle operation. It may be valuable in assessing the residual parameters of electric vehicle batteries during their secondary use, such as in the residential sector for solar energy support. The findings can be recommended to scientific and technical professionals involved in developing energy storage systems for electric vehicles.

DC/DC converter in the electric vehicle traction system

DC-DC converters are used in various electrical devices including the traction system of electric vehicles. Electric vehicles use synchronous motors with permanent magnets as drives. Their distinctive feature is the nonlinear dependence of the electromagnetic torque of the motor on its input voltage. One of the ways to improve the energy characteristics of an electric vehicle is to include a DC-DC converter in the engine power supply circuit. The study has examined the main types of DC-DC converters, studied their advantages and disadvantages; a method has been determined to increase the energy efficiency of an electric vehicle by using a multi-channel DC-DC converter as part of its power circuit. This structure makes it possible to reduce the requirements for individual power switches and converter elements, and is also optimal from the point of view of weight and size indicators, since converters of this type do not have a transformer. Based on the review, a multichannel DC-DC converter circuit has been selected for integration into the traction electric drive system. The Tesla Model S has been chosen as the base vehicle. Based on the calculated dynamic characteristics of the electric vehicle, main parameters of the DC-DC converter have been determined, computer simulation of the power plant of an electric vehicle with a DC-DC converter has been carried out in the Matlab Simulink software environment. The use of the converter improves the energy efficiency of vehicles, and is also a better solution compared to installing a battery with a higher voltage.

Research on Carbon Emission Characteristics and Mitigation Pathways in the Vehicle Fuel Cycle: A Case Study of Guangdong Province

This study presents a comprehensive analysis of vehicle ownership, energy consumption, and carbon emissions in Guangdong Province, China, from 2020 to 2035 under different scenarios. Key findings highlight the province’s pursuit of carbon peak goals and provide valuable insights into strategies to achieve them. Vehicle ownership in Guangdong is projected to exceed 48 million by 2035, which represents a doubling from 2020. Under both scenarios, internal combustion engine vehicle ownership will peak around 2030 and then gradually decline, while under the enhanced scenario, electric vehicle ownership will exceed 40% by 2035. Enhanced vehicle energy efficiency and reduced annual mileage will lead to a 17% reduction in gasoline and diesel consumption by 2035 in both scenarios. At the same time, there will be a substantial five- to six-fold increase in electricity consumption for vehicles compared to 2020. Both scenarios peak in carbon emissions before 2030, with the enhanced scenario achieving this peak a year earlier. The enhanced scenario outperforms the baseline, reducing carbon emissions by about 21.2% from the peak and 8% relative to 2020. Pure electric vehicles exhibit a significant advantage in reducing carbon emissions per vehicle compared to their internal combustion engine counterparts. Encouraging new energy vehicles, especially pure electric ones, accelerates the carbon emissions peak and lowers overall peak emissions. Accelerating the adoption of electric vehicles, reducing per-vehicle fuel consumption and annual average mileage, and optimizing transportation modes are crucial for carbon peaking from the vehicle fuel cycle. Policy recommendations focus on promoting new energy vehicles, optimizing transportation, and advancing research and technology.

Application of Neural Network Feedforward in Fuzzy PI Controller for Electric Vehicle Thermal Management System: Modeling and Simulation Studies

The electric vehicle thermal management system (EVTMS) plays a crucial role in ensuring battery efficiency, driving range, and passenger comfort. However, EVTMSs still face unresolved challenges, such as accurate modeling, compensating for temperature variations, and achieving efficient control strategies. Addressing these issues is crucial for enhancing the performance, reliability, and energy efficiency of electric vehicles. Therefore, this study presents a cooling EVTMS model, considering both the battery pack temperature and the cabin comfort, and utilizes the prediction of neural network as a feedforward in a fuzzy PI controller to compensate for the model temperature variations. The simulation results reveal that, compared with PI controller and MPC, the neural network fuzzy PI (NN-Fuzzy PI) controller can well predict and compensate for the system’s nonlinear characteristics as well as the time-delay caused by heat transfer, achieving superior control performance and reducing energy consumption. The battery pack temperature and PMV fluctuations are effectively constrained within [−0.5, 0.5] and [−0.1, 0.1], reducing up to 150% and 164%, and the energy consumption of the pump and compressor are reduced by up to 0.23 and 100.1 KJ, with ranges of 18% and 2.68%. Meanwhile, the neural network feedforward also works effectively in different controllers. The findings of this research can provide valuable insights for TMS engineers to select advanced control strategies.

Development of Effective Infrared Reflective Coatings

The adsorption of surfaces exposed to sunlight results in increased temperatures that can cause physical damage and an increase in energy consumption. Infrared reflective coatings can keep objects cooler and have significant benefits in a wide variety of application by reflecting infrared light and decreasing heat, reducing operating costs, improving energy efficiency in buildings and vehicles, and extending an objects’ lifespan. The main aim of our research was to develop coatings in a RAL7016 Anthracite grey color with minimum heat adsorption in the infrared wavelength range. This was achieved using a combination of infrared transparent and infrared reflective pigment built-in coatings applied on two primers: white and black. Infrared reflectivity or transparency, as well as surface temperature, was investigated as a function of the composition and concentration of pigments. These coatings were characterized by chromatic parameters, by total solar and infrared solar reflectance in the UV, visible, and infrared wavelength range, and by heat reflection. Among the coatings developed, two produced very effective controls for infrared reflectance and transparency, and they could control heat reflectance, resulting in a significant decrease in surface temperature.