Development of Smart Climate Control with Multiple Level Auto Calibrations

In this work, HVAC (Heating, Ventilation and Air Conditioning) systems applied in university buildings with control based on PMV (Predicted Mean Vote) and aPMV (adaptive Predicted Mean Vote) indexes are discussed. The building’s thermal behavior with complex topology, in transient thermal conditions, for summer and winter conditions is simulated by software. The university building is divided into 124 spaces, on two levels with an area of 5931 m2, and is composed of 201 transparent surfaces and 1740 opaque surfaces. There are 86 compartments equipped with HVAC systems. The simulation considers the actual occupation and ventilation cycles, the external environmental variables, the internal HVAC system and the occupants’ and building’s characteristics. In this work, a new HVAC control system, designed to simultaneously obtain better occupants’ thermal comfort levels according to category C of ISO 7730 with less energy consumption, is presented. This new HVAC system with aPMV index control is numerically implemented, and its performance is compared with the performance of the same HVAC system with the usual PMV index control. Both HVAC control systems turn on only when the PMV index or the aPMV index reaches values below −0.7, in winter conditions, and when the PMV index or the aPMV index reaches values above +0.7, in summer conditions. In accordance with the results obtained, the HVAC system guarantees negative PMV and aPMV indexes in winter conditions and positive PMV and aPMV indexes in summer conditions. The energy consumption level is higher in winter conditions than in summer conditions for compartments with shading, and it is lower in winter conditions than in summer conditions for compartments exposed to direct solar radiation. The consumption level is higher using the PMV control than with the aPMV control. Air temperature, in accordance with Portuguese standards, is higher than 20 °C in winter conditions and lower than 27 °C in summer conditions. In Mediterranean climates, the HVAC systems with aPMV control provide better occupants’ thermal comfort levels and less energy consumption than the HVAC system with PMV control.

A unified thermal management framework for electric vehicles: Design and test bench implementation

<div class="section abstract"><div class="htmlview paragraph">One of the practical consequences of the development of low CO₂ emission cars is that many of the traditional NVH sound engineering processes no longer apply and must be revisited.</div><div class="htmlview paragraph">Different and new sound sources, new constraints on vehicle body design (e.g., due to weight) and new sound perception characteristics make that the NVH knowledge built on generations of internal combustion-powered vehicles cannot be simply transferred to Hybrid and Electric Vehicles (HEV). Hence, the applicability of tools must be reviewed and extensions need to be developed where necessary.</div><div class="htmlview paragraph">This paper focuses on sound synthesis tools as developed for ICE-powered vehicles. Because of the missing masking effect and the missing intake and exhaust noise of the Internal Combustion Engine (ICE) in electric vehicles, on one hand electric vehicles are quieter than traditional vehicles. On the other hand, other components such as HVAC system, alternator, vacuum pump, power-steering pump, cooling systems, transmission systems, inverter noise, etc., become more important and generate new complex sound signatures. Typically, their interior noise is well-defined by high-frequency noise components which can be subjectively experienced as annoying, hence the increased importance put to sound quality aspects and perceptual-relevant NVH studies.</div><div class="htmlview paragraph">In the paper, a Virtual Car Sound (VCS) synthesis technique for HEV is designed. This approach turns out to be a very helpful engineering tool to design a brand specific sound and allows reproducing typical sound features of HEV during real-time driving simulation based on a Sound Quality Equivalent Model (SQE). Several SQE models were constructed for different powertrains of HEV in various engine conditions. Finally, the results of the sound metrics and listening tests indicate a comparison between the synthesized sounds and the original sound recordings.</div></div>

<div class="section abstract"><div class="htmlview paragraph">The ongoing global demand for greater energy efficiency plays an essential role in vehicle development, especially in the case of electric vehicles (EVs). The thermal management of the full vehicle is becoming increasingly important, since the Heating, Ventilation, and Air Conditioning (HVAC) system has a significant impact on the EV range. Therefore the EV design requires new guidelines for thermal management optimization.</div><div class="htmlview paragraph">In this paper, an advanced method is proposed to identify the most influential cabin design factors which affect the cabin thermal behavior during a cool down drive cycle in hot environmental conditions. These parameters could be optimized to reduce the energy consumption and to increase the robustness of the vehicle thermal response.</div><div class="htmlview paragraph">The structured Taguchi’s <i>Design for Six Sigma</i> (DFSS) approach was coupled with CFD-Thermal FE simulations, thanks to increased availability of HPC. The first control factors selected were related to the thermal capacity of the panel duct, dashboard, interior door panels and seats. Surface IR emissivity and solar radiation absorptivity of these components were then added to the study. Car glass with absorptive and reflective glazing were finally included in the study. The design space of 18 vehicle configurations was simulated in spring and hot summer conditions, with steady state thermal simulations. A 2-step optimization was then conducted, trying first to increase the robustness of the cabin response and, secondly, to reduce the equivalent temperature actually felt by passengers. The <i>Verify</i> phase was then conducted on the <i>Best Engineering</i> design emerged by the 2-step optimization, through quasi-transient CFD-Thermal FE analyses. The thermal results were then sent to a CFD 1D energy prediction model, confirming the HV battery energy saving and the extended range reached during the cool down drive cycle.</div></div>

A cabin climate control system, often referred to as a heating, ventilation, and air conditioning (HVAC) system, is one of the largest auxiliary loads of an electric vehicle (EV), and the real‐time optimal control of HVAC brings a significant energy‐saving potential. In this article, a linear‐time‐varying (LTV) model predictive control (MPC)‐based approach is presented for energy‐efficient cabin climate control of EVs. A modification is made to the cost function in the considered MPC problem to simplify the Hessian matrix in utilizing quadratic programming for real‐time computation. A rigorous parametric study is conducted to determine optimal weighting factors that work robustly under various operating conditions. Then, the performance of the proposed LTV‐MPC controller is compared against a rule‐based (RB) controller and a nonlinear economic MPC (NEMPC) benchmark. Compared with the RB controller benchmark, the LTV‐MPC reaches the target cabin temperature at least 69 s faster with 3.2% to 15% less HVAC system energy consumption, and the averaged cabin temperature difference is 0.7°C at most. Compared with the NEMPC, the LTV‐MPC controller can achieve comparable performance in temperature regulation and energy consumption with fast computation time: the maximum differences in temperature and energy consumption are 0.4°C and 2.6%, respectively, and the computational time is reduced 72.4% on average with the LTV‐MPC.

Estimating the HVAC energy consumption of plug-in electric vehicles

<div class="section abstract"><div class="htmlview paragraph">With the advent of electric vehicles and advance trends in power train there is an increasing demand in improving the overall cabin comfort level. This is especially true when it comes to electric vehicles as the major source of sound other than the external flows would be from the HVAC systems for the vehicle. The noise source can be further divided on structural noise and flow induced noise; the paper would be focusing on prediction of flow induced noise levels. Flow induced noise by the HVAC’s would be critical since there are significant advancements in the cabin insulations/materials from externally generated noise sources.</div><div class="htmlview paragraph">Automotive HVAC system consist of complex flow paths, blower, flaps, ducts, and vents which are the main source of noise generation in HVAC systems. With packaging space beneath the IP been premium, changes proposed to improve the noise levels are expensive and are understood at later level/phase of product development cycle. This paper puts forward an approach to evaluate the air induced noise levels for HVAC systems/components which can be used in early phase of development cycle. Computational Aeroacoustics (CAA) is proposed to be used to predict the sound generation and propagation level for the given microphone positions. The noise source can be identified and improved in early phase of development cycle which if done in later phase would require an expensive change.</div><div class="htmlview paragraph">The process for prediction of noise has been validated and tested with good correlations between the two approaches.</div></div>

Efficient management of heating, ventilation, and air conditioning (HVAC) systems is paramount for mitigating energy consumption and enhancing climate resilience in modern infrastructure. This review explores the significance of leveraging project management tools to optimize energy efficiency within HVAC operations, thus fostering a pathway towards climate resilience. The integration of project management methodologies offers a systematic approach to address challenges associated with energy consumption in HVAC systems, thereby facilitating informed decision-making processes. Firstly, the review delves into the pressing need for energy conservation in HVAC operations amidst escalating concerns over climate change and its adverse impacts. The imperative to minimize energy consumption in buildings, particularly through HVAC systems, is highlighted as a pivotal step towards achieving climate resilience and sustainability goals. Subsequently, the review outlines the role of project management tools in orchestrating effective strategies to enhance energy efficiency within HVAC operations. By employing techniques such as project scheduling, risk management, and resource allocation, project managers can streamline the implementation of energy-efficient measures, thereby optimizing HVAC system performance and reducing carbon emissions. Moreover, the review underscores the importance of data analytics and technological advancements in augmenting the efficacy of project management tools for energy efficiency in HVAC operations. Leveraging real-time data monitoring, predictive analytics, and IoT-enabled devices enables proactive maintenance and continuous optimization of HVAC systems, thereby maximizing energy savings and bolstering climate resilience. Furthermore, the review elucidates the potential benefits and challenges associated with the adoption of project management tools for energy efficiency in HVAC operations. While improved cost-effectiveness, environmental sustainability, and operational performance emerge as primary benefits, challenges such as initial investment costs, technological complexities, and organizational inertia necessitate careful consideration. This review advocates for the integration of project management tools as a viable approach to foster energy efficiency in HVAC operations, thereby paving the way towards enhanced climate resilience and sustainability in built environments. By embracing innovative methodologies and technological solutions, stakeholders can mitigate energy consumption, reduce greenhouse gas emissions, and fortify infrastructure against the impacts of climate change. Keywords: HVAC, Climate Resilience, Project Management, Energy, Review.

This paper introduces an improved real-time Model Predictive Climate Control (MPCC) technique to reduce power consumption in Heating, Ventilation and Air Conditioning (HVAC) system of the Battery Electric Vehicles (BEVs). In the presented control technique, the fan, heating and cooling power usage is optimized considering the effect of the ambient temperature. Moreover, the need of a physical temperature measurement sensor is eliminated using an ambient temperature observer. In a typical BEV, the HVAC load is significant and it influences the overall vehicle performance and driving range. Therefore, a real-time control system capable of maintaining desired cabin temperature while achieving maximum HVAC efficiency is highly desirable. In the proposed MPCC, the optimum split of the battery power between the heating\cooling and the fan power is identified as a function of ambient temperature through an offline optimization process and used in the form of a lookup-table for real-time implementation. It is demonstrated that the proposed MPCC improves energy consumption efficiency of the entire HVAC system up to 5% by dynamically adapting to the variations in the ambient temperature. Furthermore, owing to the online temperature observation process, the proposed HVAC control system allows elimination of ambient temperature sensor and corresponding maintenance efforts. In this paper, the performance of the proposed MPCC is evaluated over a single-zone HVAC model of a BEV in MATLAB\Simulink <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> environment.

The main objective of this on-site study is to use a full-scale HVAC (Heating, Ventilating and Air-Conditioning) system installed in an office building in Taiwan for comparing the power consumption, energy-saving, and CO2reduction by variable primary flow (VPF) chilled water systems for controlling the HVAC. The on-site experimental results indicate that average power consumptions are 44,935 kWh for the constant volume (CV) chilled water systems, and 39,025 kWh for the VPF system. Approximately 19% of the maximum energy-saving rate can be achieved if the HVAC system is operated as a VPF system. Additionally, the reduced quantity of accumulated CO2emission varies from 0 to 44,186 kg with 0.623 kg-CO2kwh-1emission factor during the office hours of 08:00 (a.m.) to 20:00 (p.m.). The results demonstrate that switching the operation of an office building HVAC system from CV to VPF will significantly enhance energy savings and CO2reduction. This study is will offer useful information for evaluating an indoor environmental policy with respect to energy savings and CO2emission reduction for office HVACs used in subtropical regions.

Almost every building in the United States has a HVAC (Heating, ventilation, and air conditioning) system that relies on balancing Velocity Pressure and calculated CFM to properly control the atmosphere in a building. This is ensured and tested by Field Engineers whose job it is to maintain the efficiency and proper output of these HVAC systems. These systems are used to improve indoor air quality, control moisture and condensation, and contaminants while satisfying comfort needs of occupants. Just with any mechanical system, HVAC systems function efficiently with multiple formulas derived from calculus and physics. Every building is equipped with a system that needs to meet a certain threshold of air movement, measured in Cubic Feet per Minute (CFM), that creates a stable and comfortable indoor atmosphere by testing and balancing these systems with Velocity Pressure. Velocity Pressure is the base reading it takes to understand and adjust air movement within a HVAC system to guarantee the overall best quality of an indoor air environment. Once a measurement of Velocity Pressure, measured in inches of water column (in-WC) using a tool called a Flow Hood or Straight Pitot Tube, is taken then you can start to calculate flow readings and test if a fan is producing the correct amount of CFM that the system was built to produce and support. This testing is done by using a series of calculus and algebraic equations that takes area and velocity into consideration. When solving these formulas, it can then be determined if a HVAC system and its fans are producing the ideal amount of airflow and air quality to accurately produce the precise environment for any building.

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Conventional charging systems for electric and plug-in hybrid vehicles currently use cables to connect to the grid. This methodology creates several disadvantages, including tampering, risk, depreciation and non-value added user efforts. Loose or faulty cables may also create a safety issue. Wireless charging for electric vehicles delivers both a simple, reliable and safe charging process. The system enhances consumer adoption and promotes the integration of electric vehicles into the automotive market. Increased access to the grid enables a higher level of flexibility for storage management, increasing battery longevity.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;The power class of 3.7kW or less is an optimal choice for global standardization and implementation, due to the readily available power installations for potential customers throughout the world. One of the key features for wireless battery chargers are the inexpensive system costs, reduced content and light weight, easing vehicle integration.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;This paper demonstrates a wireless charging design with minimal component content. It includes a car pickup coil with 300 mm side length and low volume and mass 1.5 dm&lt;sup&gt;3&lt;/sup&gt; power interface electronics. After an overview of its hardware requirements, power transfer and efficiency benefits are presented, providing the anticipated horizontal and lateral deviations.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;An intense magnetic field is required to transfer the target power at low volumes between the transfer units. This field heats up any metal object over the transfer coil, similar to an induction oven. Consequently, the system should be powered down whenever a metal object is detected in this area. A Foreign Object Detection (FOD) design has been developed to continuously monitor the critical high field area. Device testing results are also provided.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;Field characteristics are verified alongside the vehicle, ensuring system safety for living beings; compliant with all applicable standards reference limits which is more challenging than the basic limits [&lt;span class="xref"&gt;13&lt;/span&gt;].&lt;/div&gt;&lt;/div&gt;

Increasing interest has been expressed towards intelligent heating, ventilation and air conditioning (HVAC) systems in hospital environments. This article presents a literature review of intelligent HVAC systems used in hospitals. First, we discuss the current state of HVAC systems and common ventilation issues regarding modern hospitals. Second, we describe hospital airflow modelling using computational fluid dynamics. We then define ‘intelligent HVAC systems’ and address challenges concerning their design and implementation. Lastly, possibilities for HVAC system optimization and energy conservation are presented.

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Electric vehicles (EVs) are a promising and proven technology for achieving sustainable mobility with zero carbon emissions, very low noise pollution, and reducing the dependency on fossil fuels.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;Global EV sales have been increasing by ~110 % since 2015, with a significant rise in 2021 (~6.75 mils EV registered) mainly led by China, the US, and Europe, amplifying the EV market share to 8.3% compared to 4.2% in 2020.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;Future developments aimed at designing better batteries and charging technologies that reduce charging time, reduce initial battery cost, and increased flexibility. In India, EVs are emerging significantly due to stringent Carbon di Oxide (CO2) reduction drives, increasing crude oil prices, and the availability of cheaper renewable energy.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;Leveraging government promotional policies, evolving the entire ecosystem, globally advantageous manufacturing costs, and competitive engineering skills form the perfect blend for India. To produce world-class EVs to complement the “Make in India” drive and make India the leading EV engineering and manufacturing hub for global markets.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;This paper reviews India’s EV market potential with a key focus on the EV ecosystem, including EV commodities infrastructure, supplier plans, and government initiatives to enhance EVs in India with statistical data and reports from research papers.&lt;/div&gt;&lt;/div&gt;

During the operation of HVAC (Heating, Ventilation, and Air-Conditioning) systems, precise energy consumption prediction plays an important role in achieving energy savings and optimizing system performance. However, the HVAC system is a complex and dynamic system characterized by a large number of variables that exhibit significant changes over time. Therefore, it is inadequate to rely on a fixed offline model to adapt to the dynamic changes in the system that consume tremendous computation time. To solve this problem, a deep neural network (DNN) model based on Just-in-Time learning with hyperparameter R (RJITL) is proposed in this paper to predict HVAC energy consumption. Firstly, relevant samples are selected using Euclidean distance weighted by Spearman coefficients. Subsequently, local models are constructed using deep neural networks supplemented with optimization techniques to enable real-time rolling energy consumption prediction. Then, the ensemble JITL model mitigates the influence of local features, and improves prediction accuracy. Finally, the local models can be adaptively updated to reduce the training time of the overall model by defining the update rule (hyperparameter R) for the JITL model. Experimental results on energy consumption prediction for the HVAC system show that the proposed DNN-RJITL method achieves an average improvement of 5.17% in accuracy and 41.72% in speed compared to traditional methods.