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Review

A Comprehensive Overview of Basic Research on Human Thermal Management in Future Mobility: Considerations, Challenges, and Methods

by
Ju Yeong Kwon
1,
Jung Kyung Kim
2,
Hyunjin Lee
2,
Dongchan Lee
3 and
Da Young Ju
1,*
1
Department of AI Design & Design Science, Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul 02707, Republic of Korea
2
School of Mechanical Engineering, Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul 02707, Republic of Korea
3
Department of Mechanical and Information Engineering, University of Seoul, 163 Seoulsiripdaero, Dongdaemun-gu, Seoul 02504, Republic of Korea
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(9), 7335; https://doi.org/10.3390/su15097335
Submission received: 15 March 2023 / Revised: 13 April 2023 / Accepted: 27 April 2023 / Published: 28 April 2023
(This article belongs to the Special Issue Electric-Drive Vehicles and Beyond: Assessing the Impact of Disruptive Technologies for Road Transportation)
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Versions Notes

Abstract

:
Thermal management in automobiles is necessary to minimize energy usage while achieving a high level of occupant thermal perception. As the freedom of in-vehicle activity increases with autonomous driving, considering convenience becomes even more important, and, at the same time, the need for thermal management in electric vehicles is expected to increase. While it is necessary to consider the characteristics of the future mobility environment, there is still a lack of research that takes into account these changes in thermal management and proposes future research directions. Therefore, the purpose of this study is to explore basic research directions based on national R&D project cases and to provide a comprehensive overview. The environmental changes that need to be considered in thermal management research include the diversification of future transportation types and usage purpose diversification, the characteristics of electric vehicles, changes in the interior and exterior design of autonomous vehicles, personalized air conditioning environments, and dynamic thermal management according to occupant in-vehicle activity. This study provides an understanding of the overall field, and can help identify challenges, solutions, and ideas. Although this study provides conceptual considerations for research directions, future research is needed to identify detailed factors related to technology, environment, and human factors.

1. Introduction

In the future mobility environment, transparent materials with high transparency, such as glass and transparent displays, are attracting attention as core structures for the exterior materials of automobiles [1,2,3,4,5]. PBV (Purpose Built Vehicle) concepts presented by companies such as Hyundai Motor, Hyundai Mobis, and Toyota emphasize openness and comfortable spaciousness with transparent glass windows being the main material of the vehicle body [3,4,5]. In addition, these concepts apply smart windows with display functions that allow interaction between users and the inside/outside of the vehicle. Many changes are expected not only in the exterior materials for automotive applications but also in the interior and in-vehicle experience [6,7], including in the application of various types of electronic components within the vehicle to support in-vehicle infotainment activities [8,9]. If wide glass windows are applied to the vehicle body, it will show a different aspect of indoor temperature change from that of conventional vehicles. Moreover, as the increase in the use of electronic components leads to increased heat dissipation, new perspectives on thermal management inside the vehicle are needed, along with reliable results and predictive models for indoor temperature change and occupant thermal perception.
It is probable that autonomous vehicles will rely on electric power as their primary energy source [10]. Research on thermal management and HVAC (heating, ventilation, and air conditioning) for future modes of transportation, including electric vehicles, is being conducted [11,12,13,14,15]. However, the focus of the research is mainly on overcoming the technical limitations of the system, reducing energy consumption of electric energy through thermal energy conversion, and measuring and predicting thermal perception during normal driving conditions. Nonetheless, it is difficult to find investigations on the factors and considerations for integrated future mobility environmental changes in thermal management for various types of future modes of transportation, including electric vehicles. Along with various environmental factors previously considered, it is a critical research topic in the field of future mobility to optimize the individual occupant’s thermal perception to enhance comfort and satisfaction for interior and exterior spaces of future modes of transportation, which will undergo changes in space function and design, as well as in-vehicle activities of occupants.
Unlike internal combustion engine vehicles, electric vehicles require independent heating mechanisms such as PTC (positive temperature coefficient) heaters and fuel heaters for interior heating. When the air conditioning system of a fully electric vehicle operates, the energy used for vehicle driving is consumed by the separate heating and cooling mechanisms, causing a significant decrease in the vehicle’s electric efficiency [16,17,18,19].
Therefore, researchers have intensively studied the method to improve the efficiency of a thermal management system in electric vehicles. Lee et al. [20] investigated the cooling performance of a coolant-source heat pump with a triple-fluid heat exchanger, reporting that the proposed system can improve the driving range of FCEVs by 10.8% compared to the conventional one under cold ambient condition. Kim et al. [21] reported that battery electric vehicles (BEVs) using an R134a heat pump system to generate heat can increase the driving range by 10–30% compared to the convectional heating system using PTC heater. Tian et al. [22] proposed an integrated electric vehicle thermal management system (EVTMS) which recovers the waste heat from the motor to heat the cabin with a heat pump system, extending the electric vehicle driving range by approximately 32%. Ma et al. [23] suggested battery energy control strategies, reporting that approximately 30% of energy consumption reduction is obtained under different control modes. Wang et al. [24] developed a novel thermal control system by storing the heat when the vehicle’s battery is charging and then releasing the heat during driving. It showed that the driving range is increased by approximately 20%. As mentioned in the above studies, enhancing thermal management efficiency can improve the mileage and performance of the electric or fuel cell vehicles. Therefore, it is crucial to improve the efficiency of the heat pump system used for cabin HVAC in a vehicle.
Along with improving the efficiency of the heat pump system, it is also important to develop a control strategy for the heat pump system to reduce energy consumption while maintaining the high thermal comfort of passengers. Therefore, a new type of air conditioning technology is needed to provide occupants with a comfortable thermal perception level similar to that of traditional air conditioning systems, while consuming relatively low energy and maximizing occupant satisfaction according to future mobility environments. Evaluating the thermal perception of occupants is necessary for optimal temperature control in electric vehicles. Automotive manufacturers are researching and developing thermal management technology for electric vehicles to enhance convenience and driving range. Hyundai Motor Company is studying a new concept heating system and an AI-based personalized air conditioning control logic to reduce energy consumption [25].
To provide an optimized HVAC environment in various situations, it is necessary to consider the characteristics of the changing future mobility environment. Future mobility vehicles such as PBV can be utilized for various purposes such as cafes, hospitals, workspaces, and living spaces [3,4,5]. Future mobility services such as robotaxi, commercial services, and logistics services are performed in various outdoor environments and carry various types of occupants and cargo. Thus, an adaptive HVAC environment creation, according to outdoor environment, occupants, or cargo loaded on the vehicle is required.
As autonomous vehicles become increasingly used for a variety of purposes, the range of in-vehicle activities of occupants is expected to widen [26,27], leading to changes in the interior components and design [28,29]. The interior of autonomous vehicles is designed to be adaptable with features such as moving consoles, rotating seats, and slim cockpits, making significant design changes likely [28,29]. Thus, new concepts for the design of air conditioning systems, ducts, and air vents need to be developed [30,31,32,33,34,35,36]. The design of air conditioning systems, ducts, and air vents are factors that affect thermal management and occupant thermal perception, so predictive models for energy efficiency and thermal perception should be developed based on the new designs.
To establish strategies for improving occupant thermal perception satisfaction, a comprehensive and interdisciplinary approach is required that considers not only technical factors, such as energy efficiency, but also environmental and human factors that have not been considered in conventional vehicle thermal management and air conditioning systems, as well as the impact of future mobility environment changes. This study aims to propose a comprehensive overview by presenting the direction of basic research on human thermal management for future mobility based on a national R&D project. To achieve this goal, a multidisciplinary research team with expertise in HVAC and thermal management systems, solar radiation and human heat transfer simulation, biosignal measurement and biomedical engineering, and HMI (human machine interface) and mobility interior design has been assembled. The expected outcomes of this project include presenting an optimal integrated local cooling and heating control strategy, developing a local cooling and heating load prediction model, and deriving a cabin design proposal for future mobility vehicles based on the prediction model.
The main contributions of this study are as follows:
  • Identifying and proposing new perspectives on research directions and their necessity in the field of thermal management that have not been considered in previous studies by recognizing and presenting the changing trends in future mobility environments.
  • Providing assistance in identifying challenges and generating ideas in the field.
  • Offering practical alternatives by presenting specific research methods and contents as well as challenges, which are valuable for practical applications in the field.
  • If the research and development of the presented perspectives in this study are pursued, it can help to build an efficient thermal management system by enabling more sensitive adaptation to occupants’ preferences and environmental changes.

2. Materials and Methods

To provide important factors to be considered and solutions to address them in the field of thermal management in future mobility, this study explored three perspectives: considerations, challenges, and methods. Although the importance of thermal management factors and their impact may vary depending on the six levels of automation distinguished by the Society of Automotive Engineers (SAE), ranging from non-automation to fully autonomous driving without the need for a driver, this research covers key aspects that should be considered in all manual and autonomous driving situations in the future mobility environment. Prior to the advanced level of autonomous driving where driver intervention is minimized, in partial and conditional autonomous driving situations, both driving and non-driving situations should be considered due to the occurrence of control transition. Due to the relationship between driving performance, energy efficiency, and occupants’ comfort in terms of thermal management [14,25], research that considers driving performance for optimal thermal management is needed. However, this study highlights the lack of research that considers the significant changes in occupants’ in-vehicle experience due to changes in the vehicle body and interior space, which is a crucial aspect that has been overlooked in previous studies. Therefore, this study focused on the importance of occupants’ thermal perception and suggests that it will become increasingly important in the future. As a result, this study has the limitation of not proposing direct factors for maximizing the performance and efficiency of thermal management for electric and future vehicles. Further research is necessary to identify detailed factors related to thermal management in terms of technical, environmental, and personal factors that were not covered in this study.
Based on the current status of research and development in the field of an individual occupant’s thermal perception optimization in the future mobility environment, considerations are presented and suggestions for future research are proposed. Furthermore, research methods are proposed depending on the direction of future research. To this end, key areas to be considered for integrated HVAC control strategy research were identified based on research background and objectives, as shown in Table 1. Technological, environmental, and human factors can be derived from each area to establish integrated thermal management design criteria.
Despite the prediction that many changes will occur and a lot of research has been conducted on the future mobility environment, including the changes in autonomous driving technology and user experience in the field of automotive thermal management, studies considering and mentioning various situations and conditions of using transportation and validated research are difficult to find. Related research can play an important role and serve as a criterion in designing cabin interiors for future transportation, where many changes are expected to occur. This study provides an understanding of the entire field, helps to identify key issues and challenges, and can provide solutions. It can also generate new research ideas.

3. Related Works and Challenges

As shown in Table 1, various aspects of thermal management research are being conducted. In this section, we present previous studies related to thermal perception and thermal comfort in Table 2, depending on the scope of the research. We focused on previous studies that considered influencing factors based on the importance of thermal perception, and excluded previous studies that focused on technical efficiency and effectiveness. While research on thermal perception in electric vehicles is being continuously conducted, it is difficult to find previous studies on thermal perception that consider the characteristics of future vehicles such as autonomous driving vehicles.
Nastase et al. [54] proposed the development of methods for assessing thermal comfort in electric vehicles’ personal comfort systems, with a focus on dynamic vehicle environments, based on a literature review of factors influencing thermal comfort. Future research needs to include factors that consider the future mobility environment when considering factors that influence thermal comfort. Lahlou et al. [55] proposed an optimal control method that considers both driving distance and thermal comfort in electric vehicles and performed simulations. In the future, predicting thermal perception in new mobility environments will require experimental verification through both simulation and physical experiments. Zhou et al. [58] investigated thermal comfort in a passenger car under various conditions during summer using actual outdoor driving conditions. If this method is applied in the future mobility environment, experimental research that considers various situations of passengers in actual driving environments is necessary. Li et al. [53] proposed a comprehensive numerical model that considers thermal comfort for different body parts of occupants, which is particularly essential for energy-saving electric vehicles. The importance of predicting thermal comfort for various body parts in a dynamic environment was emphasized, which will be necessary in the future mobility environment as autonomous vehicles will consider positions other than the typical sitting position, and interior arrangements will differ. Warey et al. [66] proposed machine learning models for the real-time prediction of thermal comfort under any boundary conditions. To improve the accuracy of thermal perception prediction, research comparing simulation and actual vehicle results is needed. Lee et al. [71] developed a personalized overall thermal sensation (OS) model to evaluate the thermopsychological effect of local radiant heating and to simulate the OS. Gehringer et al. [37] introduced the collaborative project UNICARagil and provided the development process and thermal management system, including the HVAC system, for an autonomous battery electric vehicle. They predicted thermal comfort through simulations for large vehicle models, considering future vehicle architecture sizes. However, the results did not consider the design of interior concepts, and future evaluation and simulation result verification can be performed in the final vehicle prototype. This study’s distinctiveness is proposing a specific verification method considering scenarios, including passengers’ situations and the necessity of cabin design for thermal management, alongside interior design. Lahlou et al. [59] proposed a real-time method that involves estimating the energy needed for both traction and thermal comfort during the intended journey based on online calculations, taking into account traffic and weather forecasts.
Research that specifically proposes factors affecting thermal perception related to the characteristics of future cars is still not actively pursued. Although not focused on thermal perception, König et al. [61] presented HVAC consumption considering the size of the vehicle and weather scenarios, taking into account autonomous and electric vehicle concepts. Zheng et al. [62] provided an overview of the thermal control design utilized by Zoox, an autonomous, electric robotaxi start-up company. The paper covers the challenges and solutions related to the thermal control design, as well as the high-level control design strategies. Previous studies have also explored seat ventilation for thermal comfort in autonomous vehicles [67].
It is difficult to find research that proposes considerations for optimizing HVAC systems from an integrated perspective, considering various factors in the future mobility environment. Additionally, research that considers specific scenarios reflecting multiple factors is also scarce. This study comprehensively proposes considerations and specific methods for HVAC system optimization.

4. Considerations

Research on automotive thermal management is being conducted to minimize energy consumption while achieving a high level of occupant convenience [15,25]. Especially with the possibility of autonomous driving in the future, the importance of considering occupant convenience will increase as the freedom of in-vehicle activity is expected to be higher. At the same time, the need for thermal management in electric vehicles will also increase, making it crucial to propose research and solutions in this field. From this perspective, this study suggests future mobility thermal management considerations that can address challenging issues in this field, which can serve as an important foundation for future research development (Figure 1).
In the future mobility environment, it is expected that various types of transportation will emerge [3,4,5,73,74,75]. Personalized air vehicles such as PBVs (Purpose Built Vehicles) based on skateboards that can be used for various purposes, such as a vehicle-sharing service, as well as UAMs (Urban Air Mobility), a three-dimensional future transportation that can solve road traffic congestion and environmental problems [3,4,5,74,75,76], are representative examples of new modes of transportation. Research on user experience in UAM-related applications is still lacking compared to autonomous vehicles, but there is a high need for research considering user experience and thermal perception in the future research field. In particular, advanced autonomous vehicles, which are defined as living spaces with a variety of usage scenarios, are expected to be commercialized around 2030, and it is a reality that requires preparation [77,78]. Future transportation such as PBVs can be used for various purposes such as cafes, hospitals, workplaces, and living spaces [3,4,5]. Hyundai Motor Company is developing PBVs as customized spaces that can accommodate all aspects of life or can be utilized for delivery and logistics services. Toyota has presented a mobility service platform through the e-Palette concept model, an autonomous electric vehicle. As the types and usage purposes of transportation diversify, new transportation types and thermal management strategies need to be established.
Autonomous vehicles such as PBVs are expected to rely on electricity as their energy source. One important factor in the competitiveness of electric vehicles is thermal management technology [25]. Due to the characteristics of electric vehicles, which rely entirely on energy from batteries, the development of thermal management technology can maximize their performance and efficiency [25]. Especially when designing future vehicles that require high occupant convenience, increasing both occupant convenience and energy efficiency at the same time can be an important design factor to address when designing electric vehicles.
Research and development of the characteristics and related technologies of glass windows for exterior surfaces of cars have been conducted to improve energy efficiency [25,78,79,80]. However, research is still being conducted based on the typical design of traditional internal combustion engine and electric vehicles, and therefore studies should be conducted considering future car exterior designs with a wider glass window area in box-shaped vehicle. In particular, many concepts have been proposed for applying transparent displays to the exterior of vehicles [29,34], and the display function of smart windows is expected to be utilized in existing parts such as door trims, windshields, and ceilings. However, it is difficult to find research on thermal management related to vehicles with transparent displays applied to their exteriors.
In addition, various types and applications of displays supporting in-vehicle activities will be applied to the interior [3,4,5,28,29,33,34,35,40,41,42,43]. As the heat dissipated from the display can affect the interior thermal management of the vehicle, the purpose, size, and location of displays applied inside the cabin must be considered comprehensively. Along with the significant change in interior design due to the expansion of display applications, slimming the cockpit is also one of the design changes. With the development of electric vehicles and autonomous driving technology, the shape of the cockpit can change, allowing for foldable steering wheels or their removal. Therefore, the arrangement and design of air vents applied to the cockpit must also be changed. Additionally, although not directly related to the air conditioning system, various in-vehicle activities such as sleep, rest, meals, work, and video viewing can diversify the interior layout and occupant posture, which can also affect the air conditioning system’s design. For example, appropriate air vent placement may differ when an occupant is sleeping, emphasizing the importance of creating a comfortable environment for sleep.
Ultimately, personalized air conditioning environments tailored to individual preferences are necessary as the importance of occupant experiences and activities increases. Furthermore, since thermal perception can vary among individuals under the same thermal environment [56,60], it is essential to provide an individual-specific interior thermal environment in the future mobility scenario where personalization and automation services become crucial.
To accommodate various usage scenarios of future transportation, dynamic thermal management is essential. Dynamic thermal management, which controls the flow and distribution of heat, has been studied previously [81,82], but future research should consider and verify the changes in future mobility environments as one of the various factors to be considered in dynamic thermal management. Previous studies have considered temporal control, which controls the flow and distribution of heat according to the driving conditions and time, and spatial control, which controls the flow and distribution of heat at various points in space. However, in the future, spatial usage and circumstances should be comprehensively considered. As thermal comfort varies with the occupant’s position or body parts [83,84,85], understanding the thermal comfort requirements for various body parts of occupants is necessary to effectively provide suitable thermal environments. Therefore, research on thermal comfort for various body parts according to occupant posture (e.g., lying down, facing each other by rotating the seat, etc.) is necessary.

5. Methods Applied in Research Project

This section presents foundational research based on research project cases, providing the direction and methodology for future research, and offering help in identifying challenges and deriving solutions through a convergence research approach (Figure 2). The ongoing three-phase research topics and processes in this research project are presented based on the main areas related to the purpose of this study (Table 1) and considerations. This section mainly focuses on describing detailed research topics, contents, and methodologies for each phase of the research project.

5.1. Key Concepts and Methods Applied in Research Project

5.1.1. Localized Thermal Stimulation

Infrared radiant heating based on a flexible coating-based heating device installed on the interior walls of vehicles, which utilizes local thermal stimulation, has been shown to be more efficient than conventional convective heating systems. In simulation experiments, the high efficiency of infrared radiation heating resulted in a nearly 50% reduction in energy consumption for the vehicle heating system [86,87]. Not only the temporal changes in localized thermal stimulation, but also the spatial changes in the stimulated body part can affect dynamic thermal perception. Since the distribution of cold and warm receptors in the skin varies depending on the body part, the skin thermosensitivity to thermal stimulation can differ for cold and warm stimuli [83]. Therefore, understanding the dynamic thermal perception characteristics based on the distribution of skin temperature in various body parts can be utilized as a new dimension of future mobility thermal management, as thermal comfort may change with the occupant’s position or body part being stimulated [84,85,86,88].

5.1.2. Measurement of Dynamic Thermal Perception

As a technology for analyzing occupants’ thermal perception, models of thermophysiology and thermopsychology have been developed, taking into account human responses to thermal environments [89]. The thermophysiological model reflects the mechanism by which the body regulates temperature in response to external thermal stimuli on various parts of the body, while the thermopsychological model quantifies the degree of cold or heat perceived subjectively by a person based on skin temperature and its rate of change on various parts of the body [90,91]. Research has been conducted to objectively and quantitatively predict thermal comfort, a psychological indicator, using physiological indicators based on non-invasive sensors [92,93,94]. Although there is a clear correlation between physiological indicators, such as skin temperature, heart rate, and respiration rate measured in a laboratory environmental chamber, and thermal comfort, this is a static average correlation derived from a static state. Therefore, there are limitations in evaluating and predicting the subjective comfort of future mobility occupants in a dynamic environment. Furthermore, technology for the evaluation of an individual occupant’s thermal comfort and prediction models based on non-contact methods such as cameras, rather than sensors attached to or fixed to the human body, are not yet established. In this project, we aim to study a new occupant-centric thermal management strategy that considers dynamic thermal perception of the human body in the transient thermal environment of future mobility. This innovative concept of a new future mobility thermal management technology that reduces energy consumption while improving dynamic thermal perception satisfaction by optimizing spatial control that cools or heats local body parts and temporal control that optimizes thermal stimuli over time compared to existing air conditioning methods requires basic research for leadership in related fields.

5.1.3. On-Board Environmental Chamber and Simulation

Based on fundamental research on dynamic thermal sensation of the human body, if we can establish indicators that can objectively and quantitatively evaluate individual occupants’ subjective thermal sensation in on-board environments, and if we can identify spatiotemporal patterns of local thermal stimuli that maximize thermal comfort satisfaction through experiments and simulations under various interior and exterior conditions, we can ultimately derive optimal designs for cabin environment management and cabin design that maximize thermal comfort satisfaction for future mobility occupants (Figure 3). Model predictive control (MPC), which is based on weather data suitable for spatial scales of vehicle size and temporal scales for air conditioning control, has been recognized as an important technology for the vehicle energy management system (VEMS). While it has not been attempted much due to the difficulty of securing prediction data, this project aims to implement one of the important technologies of VEMS by predicting vehicle-specific weather data and optimizing local HVAC equipment. In particular, as the use of glass materials increases in ground transportation such as EV, PBV, and UAM, which are expected to be future transportation means, as well as in the UAM aircraft, the impact of solar radiation is expected to increase. Solar radiation is an important factor that affects not only the on-board thermal environment and thermal load but also occupants’ thermal sensation [85]. Predicting and evaluating the effects of solar radiation, which has significant spatiotemporal variability, is a challenging issue. However, for the sake of maximizing thermal comfort satisfaction and managing the thermal environment for future mobility occupants, basic research on vehicle-specific weather data-based local HVAC control technology is required.

5.2. Research Objectives and Process

5.2.1. Research Objectives by Research Phase

The aim of this project is to establish spatiotemporal control strategies for a local air conditioning system that considers the dynamic thermal perception of future mobility passengers and to develop a cabin design proposal. To achieve this, we will perform fundamental research on the concepts and implementation of spatiotemporal control patterns for dynamic thermal perception and thermal stimuli of passengers in future mobility environments. In Phase 1, we will devise strategies to enhance the satisfaction of future mobility passengers’ dynamic thermal perception. In Phase 2, we will evaluate and verify the improvement in dynamic thermal perception in future mobility passengers based on spatiotemporal control patterns of local thermal stimuli under various environmental conditions, and in an on-board environmental chamber. In the final Phase 3, our goal is to propose a cabin design and optimal control strategy for the local air conditioning system to maximize the satisfaction of future mobility passengers’ dynamic thermal perception in accordance with the roadmap of future mobility (Figure 4).

5.2.2. Research Process

The specific implementation strategies and methods for each step of the research process are as follows.
  • Phase 1: Development of strategies for enhancing the dynamic thermal perception satisfaction of future mobility occupants.
    • Development of in-vehicle scenarios based on the characteristics of future transportation technologies and vehicle types: analyzing a technology roadmap for future mobility that considers changes in the mobility environment, not just as a simple means of transportation; categorizing various types of future mobility vehicles by their characteristics, different from traditional internal combustion engine vehicles, and investigating their characteristics accordingly; identifying situations and factors that affect passenger thermal comfort based on dynamic thermal perception of individual passengers; developing in-vehicle scenarios that are linked to the spatiotemporal control environment of local thermal stimuli, based on the functions and interior/exterior design features of autonomous electric vehicles, user behavioral characteristics, and user-centered space comfort.
    • Establishment of a dynamic thermal perception evaluation method based on non-invasive and non-contact biosignal measurements, and derivation of quantitative evaluation indices: We establish a quantitative evaluation method for a passenger’s dynamic thermal perception based on biosignals such as vasomotor response under the skin, skin temperature, heart rate, respiratory rate, etc. Input variables include biosignals (changes in core body temperature, changes in skin and breath temperatures, changes in heart rate and respiratory rate) and external environmental variables (temperature, relative humidity). We derive a personalized thermal perception prediction model using an artificial neural network through training with subjective responses regarding thermal comfort.
    • Developing a method for estimating external weather data suitable for the spatial scale of a vehicle: The types of external weather data (temperature, humidity, solar radiation, wind speed, etc.) that affect the internal thermal environment and thermal load of a vehicle are determined. Various weather sensors are used to measure external weather data for the vehicle. A method for estimating weather data using the measured data from sensors and obtained data from weather forecast is studied, taking into account the spatial scale required for local air conditioning control.
    • Research on pre-active heat pump control technology based on predicted cooling and heating load: A strategy is derived for pre-changing the operating mode in charge of the expected cooling and heating load. Optimal operating modes are set for each expected load in the long-term and short-term phases. A cooling and heating control mode is studied that considers future environmental changes based on operational information linked to navigation, traffic information, weather forecast, and vehicle speed alike.
  • Phase 2: Dynamic evaluation and verification of thermal perception of future mobility occupants based on the spatiotemporal control patterns of local thermal stimulation.
    • Quantitative and objective evaluation of dynamic thermal perception levels based on the spatiotemporal control patterns of local thermal stimulation through usability, acceptability, and affective evaluations. Using a virtual human thermal comfort simulation and onboard environmental chamber experiments based on participant evaluations, we aim to quantify and objectify the thermal perception levels of occupants of future mobility vehicles according to spatiotemporal control patterns of local heating and cooling, and identify factors that affect thermal perception. To accomplish this, we consider various situations and factors that affect thermal perception, such as future mobility driving scenarios, weather conditions outside the vehicle, the interior space structure of future mobility vehicles (PBV and UAM), occupant thermal and psychological characteristics, and spatiotemporal changes in local heating and cooling.
    • Improving the performance of a virtual human thermal perception simulator: developing a program that simulates the dynamic thermal sensation of occupants during local heating and cooling by adding a skin thermal receptor response model for local cold and warm stimuli, and reflecting the cold and warm sensitivity of each body part based on a thermal physiology/psychology model-based virtual human thermal perception simulator.
    • Establishing an artificial neural network computational model for predicting dynamic thermal sensation based on unconstrained and non-contact physiological signal measurements: The imaging-based monitoring system responsible for collecting and analyzing biological signals is connected to the cloud-based artificial neural network. The monitoring system sends various biological signals, thermal sensation response values of occupants, and computation requests to the cloud where the main algorithm is stored. Depending on the type of data, the artificial neural network performs learning or prediction computations for occupants’ thermal sensation and sends the results back to the monitoring system. The monitoring system and future mobility are connected through the Internet of Things. The thermal comfort prediction results sent from the monitoring system to the future mobility are used as a basis for controlling the in-vehicle environment.
    • Research on a vehicle exterior weather prediction method considering the temporal scale required for local heating and cooling control: Research a machine learning method to predict future data based on past and present weather data. Determine the time range and interval required for local heating and cooling control and produce appropriate prediction data. Develop a numerical analysis model and deep learning model that estimate the in-vehicle environment and thermal load, considering the vehicle’s external weather and occupant information. Complete a simulation method for vehicle interior environments by additionally considering the impact of air conditioning equipment for local heating and cooling control.
    • Selection of a local heating and cooling system for improving dynamic thermal sensation: Compare and analyze the performance and characteristics of various local heating and cooling systems based on radiation, conduction, and convection. Select a local heating and cooling system that maximizes dynamic thermal sensation while complying with the roadmap of future mobility.
  • Phase 3: Design proposals for cabin by type considering the future mobility roadmap and dynamic thermal perception of occupants.
    • Developing a machine learning model using unrestricted and non-contact biosignal measurement data: The software for the monitoring system for biosignal collection and analysis consists of two programs (Table 3). Program 1 in Table 3 detects changes in core body temperature, and its result is a continuous real number, while previous studies predicting core body temperature used categorical classification for ease of prediction. Dorsal hand vein diameters were measured using near-infrared cameras, and indoor temperature, vein diameter, skin temperature, and heart rate were used as input variables for machine learning. The random forest algorithm was used for predicting core body temperature.
    • Produce customized weather environment data for future mobility and utilize it for optimal control of local air conditioning equipment; analyze the driving environment according to the air conditioning equipment design proposal for each type of future mobility, and produce customized weather environment data for future mobility based on that analysis. Analyze the changes in the in-vehicle environment according to the air conditioning system control logic and investigate its effect on occupant thermal perception and optimal driving distance. Develop an optimal control strategy for the local air conditioning system considering the dynamic thermal perception of occupants using a virtual human thermal comfort simulator linked to external weather environment data.
    • Develop a control system for implementing local and global air conditioning simultaneously, considering dynamic thermal perception; conduct research on multiple heat exchangers, multiple air conditioning temperature systems, and ejector technology to implement local and global air conditioning simultaneously through a single heat pump. Develop a system control logic optimized for each load condition by comparing and analyzing the loads of local and global air conditioning.
    • Develop a system control logic for local and global air conditioning systems by simulating the performance of heat pumps to develop control logic that maximizes energy efficiency by using the predicted cooling and heating load data, and simulates the performance of heat pumps according to driving conditions.

6. Discussion

This study presents not only the research direction but also specific research stages, content, and methods based on a project case. Although there have been previous studies on thermal comfort according to the characteristics of future mobility environments, it is difficult to find studies that provide a contextual understanding of the factors affecting thermal comfort or suggest a convergent research approach for optimizing HVAC systems. This study aims to provide a comprehensive and convergent approach by considering the changing characteristics of future vehicles, such as their shape and size, and the correlation between thermal management and thermal perception. Some studies attempt to apply this approach [37,62] and suggest that it is necessary to secure cabin design strategies along with HVAC systems to increase energy efficiency while improving thermal comfort.
To propose specific research stages, content, and methods, key concepts and methods to be applied in the project were selected. For vehicles with energy-saving properties, such as electric cars, it is important to determine the requirements for thermal comfort based on localized thermal stimulation [53]. Since the internal thermal environment of a vehicle is highly non-uniform and transient, consistent thermal perception and demand cannot be assumed [53,95]. Thus, this study considers the dynamic environment to be important and designs the research accordingly; the following studies also emphasize this concept [53,54,55]. To predict thermal perception in a new space, such as a future mobility environment, new data are required, and this study designs the research by utilizing an on-board environmental chamber. Although this study proposes a chamber environment that allows for experiments to be conducted under various scenarios, it may also be necessary to obtain and verify data from actual driving situations, as suggested in previous studies [58].
Based on the main concepts and methods, this project aims to propose a local air conditioning system and cabin design strategy that considers dynamic thermal perception. The research is divided into three stages, and to identify the factors that affect thermal perception in future mobility environments that require prior research, the study proposes a future mobility technology roadmap and vehicle interior scenarios. Here, scenarios can include indoor behavior characteristics such as work, rest, sleep, conversation, and entertainment activities in a vehicle, as well as vehicle architecture features, user factors, and technical and environmental factors. Various studies have been conducted on vehicle usage scenarios in future mobility environments, and Oehl et al. [96] presented a collection of affective use cases and concepts that can direct research efforts towards the creation of empathetic fully autonomous vehicles. In the study, various ideas were presented, such as proposing entertainment that matches the user’s mood and preferences. Lee et al. [97] presented design requirements based on categories such as passenger, activity type, interaction type, user interface, interior space, information and contents, and system functionality. Considering these research trends and content, it is necessary to integrate the functions applied in a vehicle’s interior, interior design elements, and user behavior characteristics. Moreover, external factors such as transportation and weather predictions have been considered previously [59], and it is also possible to consider elements that can affect thermal comfort, such as seat ventilation [68], in addition to the air conditioning system. To apply this project in actual fieldwork in the future, it is necessary to conduct simulation verification along with experiments in the chamber environment to propose a strategy based on iterative verification. This research did not present specific future mobility architecture characteristics, but when designing scenarios and verifying them through experiments, multi-dimensional research is needed, considering space characteristics, interior layout, and interior design. As there is currently no integrated research combining thermal environment and cabin design, this study proposes these considerations, but there is a need for specific experimental design methods according to the vehicle functions and characteristics in future research.

7. Conclusions

In future mobility environments, the diversification of transportation means, energy efficiency, and passenger comfort of electric and autonomous vehicles are crucial, necessitating a focus on thermal management research. This includes considering changes in vehicle interior and exterior design, personalized dynamic thermal management, and a comprehensive analysis of factors affecting thermal perception to establish strategies for improving passenger thermal comfort satisfaction. However, despite the active development of concepts and research related to future mobility, research on thermal management in these environments is difficult to find, highlighting the urgent need for interdisciplinary research. While existing studies have mentioned the need to consider future mobility environments and autonomous vehicles, few studies have considered thermal management in the broader context of automobile use. Against this backdrop, this study proposes a basic research direction for human thermal management in future mobility based on a project case. Because of the increased thermal comfort of the passengers thanks to the control using dynamic thermal perception, the heating load for the HVAC system can be decreased, reducing the energy consumption of the heat pump system. This technology is significantly important for future mobility to improve mileage and energy storage in a battery because the majority of future mobility is expected to be powered by electricity. Through this project, optimal integrated localized cooling and heating control strategies and cabin design can be developed. The considerations and methods proposed in this study can provide guidance for future thermal management research, and the results of this project can help adapt to changes in passenger preferences and environmental conditions, contributing to the development of more efficient and convenient cooling and heating systems. Future studies can build upon the methods presented in this study to further refine specific considerations for technical, environmental, and human factors.

Author Contributions

Conceptualization, J.K.K. and D.Y.J.; methodology, J.Y.K., J.K.K., H.L., D.L. and D.Y.J.; validation, J.K.K. and D.Y.J.; investigation, J.Y.K., J.K.K., H.L., D.L. and D.Y.J.; resources, J.Y.K., J.K.K., H.L., D.L. and D.Y.J.; data curation, J.Y.K., J.K.K., H.L., D.L. and D.Y.J.; writing—original draft preparation, J.Y.K., J.K.K., H.L., D.L. and D.Y.J.; writing—review and editing, J.Y.K. and D.Y.J.; visualization, J.Y.K. and D.Y.J.; supervision, J.K.K. and D.Y.J.; project administration, J.K.K., H.L., D.L. and D.Y.J.; funding acquisition, J.K.K., H.L., D.L. and D.Y.J. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1A4A5018891).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Choi, T.H.; Lee, H.W.; Ha, J.U.; Jeoung, S.K. Automotive Display Technology for Future Cars. AUTO J. J. Korean Soc. Automot. Eng. 2022, 44, 61–66. [Google Scholar]
  2. Koo, S. An Observation on Implications from Curtain Wall Structures for Electrified Mobility Body Designs. Trans. KSAE 2022, 30, 889–899. [Google Scholar] [CrossRef]
  3. The Dream of Future Mobility: Hyundai Mobis M.Vision TO and HI. Available online: https://tech.hyundaimotorgroup.com/article/the-dream-of-future-mobility-hyundai-mobis-m-vision-to-and-hi/ (accessed on 1 March 2023).
  4. Hub and PBV: The Essence of the Future Mobility. Available online: https://tech.hyundaimotorgroup.com/article/hub-and-pbv-the-essence-of-the-future-mobility/ (accessed on 1 April 2022).
  5. Toyota Reveals the Full Scope of the Evolved E-Palette. Available online: https://toyotatimes.jp/en/toyota_news/115.html (accessed on 1 April 2022).
  6. Forget the Wheel. Available online: https://www.deloitte.com/global/en/our-thinking/insights/industry/automotive/reinventing-autonomous-vehicle-interiors.html (accessed on 1 April 2022).
  7. Strömberg, H.; Pettersson, I.; Andersson, J.; Rydström, A.; Dey, D.; Klingegård, M.; Forlizzi, J. Designing for social experiences with and within autonomous vehicles—exploring methodological directions. Des. Sci. 2018, 4, E13. [Google Scholar] [CrossRef]
  8. LG Display Unveils Next-generation Automotive Displays and Solutions at CES 2023. Available online: https://news.lgdisplay.com/en/2023/01/lg-display-unveils-next-generation-automotive-displays-and-solutions-at-ces-2023/ (accessed on 1 March 2023).
  9. Murali, P.K.; Kaboli, M.; Dahiya, R. Intelligent In-Vehicle Interaction Technologies. Adv. Intell. Syst. 2022, 4, 2100122. [Google Scholar] [CrossRef]
  10. Zhuge, C.; Wang, C. Integrated modelling of autonomous electric vehicle diffusion: From review to conceptual design. Transp. Res. D Transp. Environ. 2021, 91, 102679. [Google Scholar] [CrossRef]
  11. Singh, R. Vehicle Thermal Management Using Heat Pipes. In Proceedings of the 2019 IEEE CPMT Symposium Japan (ICSJ), Kyoto, Japan, 18–20 November 2019. [Google Scholar]
  12. Amini, M.R.; Wang, H.; Gong, X.; Liao-McPherson, D.; Kolmanovsky, I.; Sun, J. Cabin and battery thermal management of connected and automated HEVs for improved energy efficiency using hierarchical model predictive control. IEEE Trans. Control Syst. Technol. 2019, 28, 1711–1726. [Google Scholar] [CrossRef]
  13. Amini, M.R.; Kolmanovsky, I.; Sun, J. Hierarchical MPC for robust eco-cooling of connected and automated vehicles and its application to electric vehicle battery thermal management. IEEE Trans. Control Syst. Technol. 2020, 29, 316–328. [Google Scholar] [CrossRef]
  14. Cvok, I.; Škugor, B.; Deur, J. Control trajectory optimisation and optimal control of an electric vehicle HVAC system for favourable efficiency and thermal comfort. Optim. Eng. 2021, 22, 83–102. [Google Scholar] [CrossRef]
  15. Basciotti, D.; Dvorak, D.; Gellai, I. A novel methodology for evaluating the impact of energy efficiency measures on the cabin thermal comfort of electric vehicles. Energies 2020, 13, 3872. [Google Scholar] [CrossRef]
  16. Clodic, D.; Zgheib, E.; Mortada, S. Impacts of heating and cooling on electrified vehicles. In Proceedings of the 4th European Workshop—Mobile Air Conditioning and Vehicle Thermal Systems, Torino, Italy, 1–2 December 2011. [Google Scholar]
  17. Torregrosa, B.; Payá, J.; Corberán, J.M. Modelling of mobile airconditioning systems for electric vehicles. In Proceedings of the 4th European Workshop—Mobile Air Conditioning and Vehicle Thermal Systems, Torino, Italy, 2 December 2011. [Google Scholar]
  18. Qi, Z. Advances on air conditioning and heat pump system in electric vehicles–A review. Renew. Sustain. Energy Rev. 2014, 38, 754–764. [Google Scholar] [CrossRef]
  19. Kang, B.H.; Lee, H.J. A review of recent research on automotive HVAC systems for EVs. Int. J. Air-Cond. Refrig. 2017, 25, 1730003. [Google Scholar] [CrossRef]
  20. Lee, H.; Lee, D.; Kim, Y. Heating performance of a cool-ant-source heat pump using waste heat from stack and electric devices in fuel cell electric vehicles under cold conditions. Energy Convers. Manag. 2022, 252, 115092. [Google Scholar] [CrossRef]
  21. Kim, K.Y.; Kim, S.C.; Kim, M.S. Experimental studies on the heating perfor-mance of the PTC heater and heat pump combined system in fuel cells and electric vehicles. Int. J. Automot. Technol. 2012, 13, 971–977. [Google Scholar] [CrossRef]
  22. Tian, Z.; Gan, W.; Zhang, X.; Gu, B.; Yang, L. Investigation on an integrated thermal man-agement system with battery cooling and motor waste heat recovery for electric vehicle. Appl. Therm. Eng. 2018, 136, 16–27. [Google Scholar] [CrossRef]
  23. Ma, J.; Sun, Y.; Zhang, S. Experimental investigation on energy consumption of power battery integrated thermal management system. Energy 2023, 270, 126860. [Google Scholar] [CrossRef]
  24. Wang, M.; Wolfe, E.; Craig, T.; Laclair, T.J.; Gao, Z.; Abdelaziz, O. Design and Testing of a Thermal Storage System for Electric Vehicle Cabin Heating; SAE Technical Paper 2016-01-0248; Oak Ridge National Laborator: Oak Ridge, TN, USA, 2016. [Google Scholar] [CrossRef]
  25. Future EV Heat Management Technology: How Far Has It Come? Available online: http://team-hyundai.com/view.php?code=110 (accessed on 1 March 2023).
  26. Lee, S.C.; Nadri, C.; Sanghavi, H.; Jeon, M. Exploring user needs and design requirements in fully automated vehicles. In Proceedings of the Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, New York, NY, USA, 25 April 2020. [Google Scholar] [CrossRef]
  27. Pfleging, B.; Rang, M.; Broy, N. Investigating user needs for non-driving-related activities during automated driving. In Proceedings of the 15th International Conference on Mobile and Ubiquitous Multimedia, New York, NY, USA, 12 December 2016. [Google Scholar] [CrossRef]
  28. A New Way to Travel. Available online: https://www.volvocars.com/intl/v/cars/concept-models/360c (accessed on 1 March 2023).
  29. XIM18 (eXperience in Motion 18) Demonstrator Vehicle. Available online: https://www.yanfeng.com/en/xim18 (accessed on 1 March 2023).
  30. Manju, O. Future Trends of EV’s Integrated Thermal Management and Technology. Auto J. J. Korean Soc. Automot. Eng. 2021, 43, 49–52. [Google Scholar]
  31. Breathing Diamond System for Autonomous Vehicles | Future Technology Series. Available online: https://www.youtube.com/watch?v=Mic8mOBtQCw (accessed on 1 March 2023).
  32. Shin, G.Y.; Kwon, D.H.; Kim, M.H.; Han, S.S. A Study on HVAC sliming concept to secure cabin space. In Proceedings of the KSAE Spring Conference Proceedings, Gwangju, Korea, 27–29 May 2021. [Google Scholar]
  33. LIFESTYLE VEHICLE. Available online: https://www.canoo.com/canoo/ (accessed on 1 March 2023).
  34. Bosch Presents Tomorrow’s Mobility Today. Available online: https://www.bosch-presse.de/pressportal/de/en/bosch-presents-tomorrows-mobility-today-178432.html (accessed on 1 April 2022).
  35. Why Do Many Self-Driving Cars Look Like Toasters on Wheels? Available online: https://www.wired.com/story/self-driving-cars-look-toasters-wheels/ (accessed on 1 April 2022).
  36. Gehringer, D.; Keilhoff, D.; Kuthada, T.; Wiedemann, J. UNICARagil: Challenges in thermal management for autonomous vehicle concepts. In Proceedings of the 12th FKFS Conference, Stuttgart, Germany, 1–2 October 2019. [Google Scholar]
  37. Gehringer, D.; Kuthada, T.; Wagner, A. Thermal Management System of the UNICARagil Vehicles—A Comprehensive Overview. World Electr. Veh. J. 2023, 14, 6. [Google Scholar] [CrossRef]
  38. Cao, S.; Tang, P.; Sun, X. Driver take-over reaction in autonomous vehicles with rotatable seats. Safety 2020, 6, 34. [Google Scholar] [CrossRef]
  39. Sun, X.; Cao, S.; Tang, P. Shaping driver-vehicle interaction in autonomous vehicles: How the new in-vehicle systems match the human needs. Appl. Ergon. 2021, 90, 103238. [Google Scholar] [CrossRef]
  40. Lagonda Vision Concept—A New Kind of Luxury Mobility. Available online: http://www2.astonmartin.com/live/news/2018/03/06/lagonda-vision-concept---a-new-kind-of-luxury-mobility (accessed on 1 April 2022).
  41. Caballero-Bruno, I.; Wohllebe, T.; Töpfer, D.; Hernández-Castellano, P.M. The effect of seating recline on sleep quality, comfort and pressure distribution in moving autonomous vehicles. Appl. Ergon. 2022, 105, 103844. [Google Scholar] [CrossRef]
  42. Audi Grandsphere Concept. Available online: https://www.audi-mediacenter.com/en/presskits/audi-grandsphere-concept-14169 (accessed on 1 April 2022).
  43. Cadillac Introduces InnerSpace Autonomous Concept at CES 2022. Available online: https://media.cadillac.com/media/us/en/cadillac/home.detail.html/content/Pages/news/us/en/2022/jan/ces/0105-cadillac-halo.html (accessed on 1 April 2022).
  44. Ive, H.P.; Sirkin, D.; Miller, D.; Li, J.; Ju, W. Don’t make me turn this seat around! Driver and passenger activities and positions in autonomous cars. In Proceedings of the AutomotiveUI ’15: The 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, New York, NY, USA, 1 September 2015. [Google Scholar] [CrossRef]
  45. Jorlöv, S.; Bohman, K.; Larsson, A. Seating positions and activities in highly automated cars–a qualitative study of future automated driving scenarios. In Proceedings of the International Research Conference on the Biomechanics of Impact, Antwerp, Belgium, 13–15 September 2017. [Google Scholar]
  46. Pettersson, I.; Karlsson, I.M. Setting the stage for autonomous cars: A pilot study of future autonomous driving experiences. IET Intell. Transp. Syst. 2015, 9, 694–701. [Google Scholar] [CrossRef]
  47. Detjen, H.; Pfleging, B.; Schneegass, S. A wizard of oz field study to understand non-driving-related activities, trust, and acceptance of automated vehicles. In Proceedings of the 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’20), Association for Computing Machinery, New York, NY, USA, 20 September 2020. [Google Scholar] [CrossRef]
  48. Ataya, A.; Kim, W.; Elsharkawy, A.; Kim, S. How to interact with a fully autonomous vehicle: Naturalistic ways for drivers to intervene in the vehicle system while performing non-driving related tasks. Sensors 2021, 21, 2206. [Google Scholar] [CrossRef] [PubMed]
  49. Lee, J.; Lee, D.; Park, Y.; Lee, S.; Ha, T. Autonomous vehicles can be shared, but a feeling of ownership is important: Examination of the influential factors for intention to use autonomous vehicles. Transp. Res. Part C Emerg. Technol. 2019, 107, 411–422. [Google Scholar] [CrossRef]
  50. Ju, Y.J.; Lim, J.R.; Jeon, E.S. Prediction of AI-based personal thermal comfort in a car using machine-learning algorithm. Electronics 2022, 11, 340. [Google Scholar] [CrossRef]
  51. AVT Expo Interview: How to Create a Safe, Personal Space in Future Avs. Available online: https://www.autonomousvehicleinternational.com/features/avt-expo-interview-how-to-create-a-safe-personal-space-in-future-avs.html (accessed on 1 April 2022).
  52. MX191. Available online: https://www.toyota-boshoku.com/global/special/ces2020/mx191.html (accessed on 1 May 2022).
  53. Li, W.; Chen, J.; Lan, F.; Zheng, X.; Zeng, W. Numerical projection on occupant thermal comfort via dynamic responses to human thermoregulation. Int. J. Automot. Technol. 2022, 23, 193–203. [Google Scholar] [CrossRef]
  54. Nastase, I.; Danca, P.; Bode, F.; Croitoru, C.; Fechete, L.; Sandu, M.; Coşoiu, C.I. A regard on the thermal comfort theories from the standpoint of Electric Vehicle design—Review and perspectives. Energy Rep. 2022, 8, 10501–10517. [Google Scholar] [CrossRef]
  55. Lahlou, A.; Ossart, F.; Boudard, E.; Roy, F.; Bakhouya, M. Optimal management of thermal comfort and driving range in electric vehicles. Energies 2020, 13, 4471. [Google Scholar] [CrossRef]
  56. Lajunen, A.; Yang, Y.; Emadi, A. Review of cabin thermal management for electrified passenger vehicles. IEEE Trans. Veh. Technol. 2020, 69, 6025–6040. [Google Scholar] [CrossRef]
  57. Devonshire, J.M.; Sayer, J.R. Radiant heat and thermal comfort in vehicles. Hum. Factors 2005, 47, 827–839. [Google Scholar] [CrossRef]
  58. Zhou, X.; Lai, D.; Chen, Q. Experimental investigation of thermal comfort in a passenger car under driving conditions. Build Environ. 2019, 149, 109–119. [Google Scholar] [CrossRef]
  59. Lahlou, A.; Ossart, F.; Boudard, E.; Roy, F.; Bakhouya, M. A real-time approach for thermal comfort management in electric vehicles. Energies 2020, 13, 4006. [Google Scholar] [CrossRef]
  60. Simion, M.; Socaciu, L.; Unguresan, P. Factors which influence the thermal comfort inside of vehicles. Energy Procedia 2016, 85, 472–480. [Google Scholar] [CrossRef]
  61. König, A.; Mayer, S.; Nicoletti, L.; Tumphart, S.; Lienkamp, M. The Impact of HVAC on the Development of Autonomous and Electric Vehicle Concepts. Energies 2022, 15, 441. [Google Scholar] [CrossRef]
  62. Zheng, Q.; Mueller, P. An Overview of the Zoox Smart Automatic Thermal Control Design—An Autonomous Electric Taxi. In Proceedings of the 2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI), Tianjin, China, 4 January 2022. [Google Scholar] [CrossRef]
  63. Metzmacher, H.; Wölki, D.; Schmidt, C.; van Treeck, C. Personalized Air-Conditioning in Electric Vehicles Using Sensor Fusion and Model Predictive Control. In Proceedings of the Energy and Thermal Management, Air-Conditioning, and Waste Heat Utilization: 2nd ETA Conference, Berlin, Germany, 22–23 November 2018. [Google Scholar] [CrossRef]
  64. Peng, Q.; Du, Q. Progress in heat pump air conditioning systems for electric vehicles—A review. Energies 2016, 9, 240. [Google Scholar] [CrossRef]
  65. Chen, K.H.; Bozeman, J.; Wanf, M.; Ghosh, D.; Wolfe, E.; Chowdhury, S. Energy efficiency impact of localized cooling/heating for electric vehicle. SAE Int. J. Passeng. Cars-Mech. Syst. 2015, 7. [Google Scholar] [CrossRef]
  66. Stuke, P.; Bengler, K. Interior air conditioning for electric vehicle EVA: New approach on vehicle interior cooling to increase comfort and reduce energy consumption. In Proceedings of the COFAT 2014, München, Germany, 18 March 2014. [Google Scholar]
  67. Kipp, M.; Rolle, A.; Bengler, K. An Innovative Seat Ventilation Concept: Does the Seat Provide Overall Thermal Comfort in Autonomous Vehicles? In Proceedings of the 21st Congress of the International Ergonomics Association (IEA 2021), 13–18 June 2021. [Google Scholar] [CrossRef]
  68. Warey, A.; Kaushik, S.; Khalighi, B.; Cruse, M.; Venkatesan, G. Data-driven prediction of vehicle cabin thermal comfort: Using machine learning and high-fidelity simulation results. Int. J. Heat. Mass. Transf. 2022, 148, 119083. [Google Scholar] [CrossRef]
  69. Tian, Z.; Qian, C.; Gu, B.; Yang, L.; Liu, F. Electric vehicle air conditioning system performance prediction based on artificial neural network. Appl. Therm. Eng. 2015, 89, 101–114. [Google Scholar] [CrossRef]
  70. Xu, X.; Zhao, L.; Yang, Z. Prediction Models of Overall Thermal Sensation and Comfort in Vehicle Cabin Based on Field Experiments. SSRN 2022, 4274030. [Google Scholar] [CrossRef]
  71. Lee, Y.; Lee, H.; Kang, B.H.; Kim, J.K. Machine learning-based personal thermal comfort model for electric vehicles with local infrared radiant warmers. J. Mech. Sci. Technol. 2021, 35, 3239–3247. [Google Scholar] [CrossRef]
  72. Su, H.; Jia, Y. Study of human comfort in autonomous vehicles using wearable sensors. IEEE Trans. Intell. Transp. Syst. 2021, 23, 11490–11504. [Google Scholar] [CrossRef]
  73. Straubinger, A.; Rothfeld, R.; Shamiyeh, M.; Büchter, K.D.; Kaiser, J.; Plötner, K.O. An overview of current research and developments in urban air mobility–Setting the scene for UAM introduction. J. Air Transp. Manag. 2020, 87, 101852. [Google Scholar] [CrossRef]
  74. Hyundai Motor Presents Smart Mobility Solution ‘UAM-PBV-Hub’ to Vitalize Future Cities. Available online: https://www.hyundainews.com/en-us/releases/2947 (accessed on 1 May 2022).
  75. Bernhart, W.; Hasenberg, J.P.; Schickram, S.; Kirchhoff, R. Purpose-built mobility vehicles—A new breed of cars just around the corner. In Vehicles of Tomorrow 2019; Liebl, J., Ed.; Springer Vieweg: Wiesbaden, Germany, 2021; pp. 11–30. [Google Scholar] [CrossRef]
  76. PBV∙Hub. Available online: https://tech.hyundaimotorgroup.com/mobility-device/pbv-hub/ (accessed on 1 May 2022).
  77. Litman, T. Autonomous Vehicle Implementation Predictions; Victoria Transport Policy Institute: Victoria, BC, Canada, 2017. [Google Scholar]
  78. Private Autonomous Vehicles: The Other Side of the Robo-Taxi Stor. Available online: https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/private-autonomous-vehicles-the-other-side-of-the-robo-taxi-story (accessed on 1 May 2022).
  79. Marshall, G.J.; Mahony, C.P.; Rhodes, M.J.; Daniewicz, S.R.; Tsolas, N.; Thompson, S.M. Thermal management of vehicle cabins, external surfaces, and onboard electronics: An overview. Engineering 2019, 5, 954–969. [Google Scholar] [CrossRef]
  80. Ozeki, Y.; Takabayashi, T.; Tanabe, S.I. Effects of spectral properties of glass on the thermal comfort of car occupants. In Elsevier Ergonomics Book Series; Tochihara, Y.L., Ohnaka, T., Eds.; Elsevier: Amsterdam, The Netherlands, 2005; Volume 3, pp. 289–297. [Google Scholar] [CrossRef]
  81. Skadron, K.; Abdelzaher, T.; Stan, M.R. Control-theoretic techniques and thermal-RC modeling for accurate and localized dynamic thermal management. In Proceedings of the Eighth International Symposium on High Performance Computer Architecture, Cambridge, MA, USA, 2–6 February 2002. [Google Scholar] [CrossRef]
  82. Loyd, K.A. Development of a Dynamic Thermal Model for the Rear Electric Motor System on the Ohio State EcoCAR Mobility Challenge Vehicle. Ph.D. Thesis, The Ohio State University, Columbus, OH, USA, 2021. [Google Scholar]
  83. Luo, M.; Wang, Z.; Zhang, H.; Arens, E.; Filingeri, D.; Jin, L.; Ghahramani, A.; Chen, W.; He, Y.; Si, B. High-density thermal sensitivity maps of the human body. Build. Environ. 2020, 167, 106435. [Google Scholar] [CrossRef]
  84. Bandi, P.; Manelil, N.P.; Maiya, M.P.; Tiwari, S.; Thangamani, A.; Tamalapakula, J.L. Influence of flow and thermal characteristics on thermal comfort inside an automobile cabin under the effect of solar radiation. Appl. Therm. Eng. 2022, 203, 117946. [Google Scholar] [CrossRef]
  85. Rolle, A.; Schmandt, B.; Guinet, C.; Bengler, C. How can the thermal sensation be objectively determined in order to analyse different vehicle air conditioning concepts. In Proceedings of the Windsor Conference, Windsor, UK, 16–19 April 2020. [Google Scholar]
  86. Danca, P.; Vartires, A.; Dogeanu, A. An overview of current methods for thermal comfort assessment in vehicle cabin. Energy Procedia 2016, 85, 162–169. [Google Scholar] [CrossRef]
  87. Bauml, T.; Dvorak, D.; Frohner, A.; Simic, D. Simulation and measurement of an energy efficient infrared radiation heating of a full electric vehicle. In Proceedings of the 2014 IEEE Vehicle Power and Propulsion Conference (VPPC), Coimbra, Portugal, 27–30 October 2014. [Google Scholar] [CrossRef]
  88. Prasad, B.; Lee, Y.; Kang, B.H.; Kim, J.K.; Lee, H. Development of a thermal physiological model to analyze the effect of local radiant heaters in electric vehicles. J. Mech. Sci. Technol. 2019, 33, 3577–3584. [Google Scholar] [CrossRef]
  89. Alahmer, A.; Mayyas, A.; Mayyas, A.A.; Omar, M.A.; Shan, D. Vehicular thermal comfort models; a comprehensive review. Appl. Therm. Eng. 2011, 31, 995–1002. [Google Scholar] [CrossRef]
  90. Zhang, H.; Arens, E.; Huizenga, C.; Han, T. Thermal sensation and comfort models for non-uniform and transient environments: Part I: Local sensation of individual body parts. Build. Environ. 2010, 45, 380–388. [Google Scholar] [CrossRef]
  91. Tanabe, S.I.; Kobayashi, K.; Nakano, J.; Ozeki, Y.; Konishi, M. Evaluation of thermal comfort using combined multi-node thermoregulation (65MN) and radiation models and computational fluid dynamics (CFD). Energy Build. 2002, 34, 637–646. [Google Scholar] [CrossRef]
  92. Buller, M.J.; Tharion, W.J.; Cheuvront, S.N.; Montain, S.J.; Kenefick, R.W.; Castellani, J.; Latzka, W.A.; Roberts, W.S.; Richter, M.; Jenkins, O.C.; et al. Estimation of human core temperature from sequential heart rate observations. Physiol. Meas. 2013, 34, 781. [Google Scholar] [CrossRef]
  93. Fakir, M.H.; Kim, J.K. Prediction of individual thermal sensation from exhaled breath temperature using a smart face mask. Build. Environ. 2022, 207, 108507. [Google Scholar] [CrossRef]
  94. Shourav, M.K.; Salsabila, S.; Lee, J.Y.; Kim, J.K. Estimation of core body temperature by near-infrared imaging of vein diameter change in the dorsal hand. Biomed. Opt. Express 2021, 12, 4700–4712. [Google Scholar] [CrossRef] [PubMed]
  95. Zhou, X.; Lai, D.; Chen, Q. Thermal sensation model for driver in a passenger car with changing solar radiation. Energy Build. 2020, 183, 107219. [Google Scholar] [CrossRef]
  96. Oehl, M.; Ihme, K.; Pape, A.A.; Vukelić, M.; Braun, M. Affective use cases for empathic vehicles in highly automated driving: Results of an expert workshop. In Proceedings of the HCI in Mobility, Transport, and Automotive Systems. Automated Driving and In-Vehicle Experience Design: Second International Conference, MobiTAS 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, 19–24 July 2020. [Google Scholar]
  97. Lee, S.C.; Nadri, C.; Sanghavi, H.; Jeon, M. Eliciting user needs and design requirements for user experience in fully automated vehicles. Int. J. Hum.-Comput. Interact. 2022, 38, 227–239. [Google Scholar] [CrossRef]
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Figure 1. Considerations for thermal management research in future mobility environments.
Figure 1. Considerations for thermal management research in future mobility environments.
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Figure 2. Convergence research framework.
Figure 2. Convergence research framework.
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Figure 3. On-board environmental chamber.
Figure 3. On-board environmental chamber.
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Figure 4. Considerations for thermal management research in future mobility environments.
Figure 4. Considerations for thermal management research in future mobility environments.
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Table 1. The main areas related to the purpose of this study.
Table 1. The main areas related to the purpose of this study.
ClassificationTopicRelated WorksRelated Works on Thermal Management for Future MobilityChallenges
The interior and exterior characteristics of future transportationExterior characteristics[2,3,4,5,33,34,35][36,37]
  • In recent years, a concept of a box-shaped vehicle with large glass windows has been widely announced. Although the shape and material that could affect thermal management have been mentioned, it is difficult to find research that reflects, in detail, the characteristics of the future mobility environment and exterior changes, or the development contents that have been published.
Interior characteristics[3,4,5,28,29,33,34,35,38,39,40,41,42,43][25,31,32,36,37]
  • Although many cabin design concepts have been announced and research is actively being conducted on them, there are a lack of studies presenting cabin designs that reflect the direction of future mobility interior changes and are linked to thermal management. While concepts related to the cooling and heating system technologies and designs have been introduced and researched, with significant changes in the interior, consideration is needed for the placement, structure, and design of ducts and air vents.
Future transportation user experienceMobility service and in-vehicle activity[6,7,26,27,42,43,44,45,46,47,48][49,50,51,52]
  • There is active research on the preferences and acceptability of in-vehicle activities, interior design directions, and vehicle usage scenarios. However, research on thermal perception and thermal management in relation to these aspects is lacking. Although such research is insufficient, there is an emphasis on individualized air conditioning concepts in the context of mobility services.
Human and environmental factors affecting thermal managementOccupant state and external environment[53,54,55,56,57,58,59,60] 1[36,37,61,62] 2
  • In electric vehicle thermal management research, studies considering human factors, environmental factors, and thermal perception have been conducted. However, comprehensive studies that integrate factors such as future transportation architecture characteristics, usage purposes, occupant conditions, interior environments, and various external environmental factors to identify the influencing factors and levels of impact and present strategies for their application in different situations are difficult to find.
Technological factors in terms of thermal management efficiencyInformation and Communications Technology[11,12,15,55,63] 1[13,42,52,62] 2
  • Concepts proposing personalized and automated integrated HVAC control systems considering thermal perception for mobility services and energy efficiency have been presented and studied. However, research validating these concepts in various in-vehicle activities and conditions is lacking.
Air conditioning technology and system[14,64,65,66] 1[67] 2
  • Research and development are being conducted on various thermal management technologies and air conditioning technologies for energy efficiency in electric vehicles. In addition to the development of cooling and heating systems, the research and development of interior and exterior components with new technologies are also underway, requiring the exploration of factors that affect thermal perception.
Measuring and predicting thermal perceptionMeasurement and prediction methods[15,53,66,67,68,69,70,71] 1[72] 2
  • Research has been conducted in the field of developing optimal heat management control models and conducting simulations of occupant thermal perception, taking into account the thermal perceptions of occupants of conventional internal combustion and electric vehicles. However, research in the autonomous driving environment is still lacking, so it is necessary to explore methods for measuring and predicting occupant thermal perception in the dynamic and changing thermal environments of future mobility.
1 Previous studies on internal combustion engine and electric vehicles. 2 Previous studies considering future mobility environment. Related studies that mention future mobility environment are also included.
Table
Table 2. The main areas related to the purpose of this study.
Table 2. The main areas related to the purpose of this study.
ClassificationMethodsAutonomous DrivingFindings/
Advantages		LimitationsRelated WorksChallenges 1
Measuring
thermal
perception		Literature review-The need for developing thermal comfort assessment methods that consider the dynamic environment of the vehicle that has been presented.As the study was conducted through literature review, various tests based on different factors are necessary through related research.[54]When considering factors that influence thermal comfort, there is a need to include factors related to future mobility environments.
Simulation-The results showed the usefulness of using dynamic programming for thermal comfort management.Further investigation is required to apply the algorithm in scenarios involving cold climates.[55]Research should be conducted on both validation in real physical environments and validation through simulations.
Experimental Investigation-This study assessed the non-uniform and fluctuating thermal conditions inside the vehicle, and discovered a strong correlation between the average skin temperature and the perceived thermal sensation.The sample size of the study was relatively small, and it did not include a diverse range of ages, weights, or other factors.[58]Experimental research that takes into account various situations of passengers in actual driving environments is needed.
Review-This study identified and discussed the factors involved in the study of thermal comfort. This study proposes using measurable and personal factors to determine thermal comfort.There has been no actual validation of factors in terms of their integrated consideration.[60]A comprehensive study should be conducted to determine the level of influence of various factors that affect thermal perception, and to integrate these factors.
Predicting
thermal
perception		Developing a numerical model-A comprehensive numerical model is developed that considers human thermal regulation and dynamic environmental characteristics.Consideration for cooling conditions is necessary, as the study only focused on heating conditions.[53]Research is needed on the thermal requirements of various body parts in a dynamic environment in future mobility scenarios.
Predictions for various body parts will be different in autonomous vehicles due to considerations of postures and interior arrangements other than the normal seated position.
Machine learning models-The machine learning models developed in this study allow real-time prediction of thermal comfort under any boundary conditions.Not all factors influencing occupant thermal comfort were taken into account.[68]To improve the accuracy of predicting thermal perception, research should be conducted to compare and analyze simulation results with actual vehicle data, thereby increasing the reliability of the prediction.
Developing an overall thermal Sensation Model-A personalized overall thermal sensation (OS) model was developed using machine learning to evaluate the thermopsychological effect of local radiant heating and to simulate the OS of occupants in electric vehicles.The experiment was conducted in a cold environmental chamber.[71]In future vehicles, as the degree of freedom of occupant movement increases, it is necessary to collect scenario-specific data for real-time prediction.
Thermal
comfort
control 		Simulation● 2Results on a large vehicle model indicated good thermal comfort.The results were not based on considerations of the interior design.[37]Experimental validation of thermal comfort optimization in various scenario situations considering interior design and passenger conditions in actual physical environments is required. Further research that extends to cabin design for thermal management is also necessary.
Simulation-A real-time approach is proposed, based on online estimation of the energy required for traction and thermal comfort.The simulations were conducted for a limited set of traffic and weather scenarios, and further tests may be needed for changing driving predictions and different weather conditions.[59]To develop a real-time personalized HVAC system, it is necessary to consider various scenarios.
1 Challenges proposed by the researchers of this study based on prior research. 2 ●: A prior research considering scenarios relevant to autonomous driving.
Table
Table 3. Monitoring system software.
Table 3. Monitoring system software.
1. Predictive Program for Core Body Temperature Changes2. Predictive Program for Dynamic Thermal Perception
InputChanges in vascular diameter, heart rate, and respiration rate, etc.Skin temperature, exhaled breath temperature, heart rate, humidity, outdoor temperature, relative humidity, thermal perception response, etc.
OutputChanges in core body temperature.Thermal perception.
ProcessingInstall on monitoring system, utilize output as input for thermal comfort prediction program.Send input from monitoring system to the cloud to predict personal thermal comfort.
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Kwon, J.Y.; Kim, J.K.; Lee, H.; Lee, D.; Ju, D.Y. A Comprehensive Overview of Basic Research on Human Thermal Management in Future Mobility: Considerations, Challenges, and Methods. Sustainability 2023, 15, 7335. https://doi.org/10.3390/su15097335

AMA Style

Kwon JY, Kim JK, Lee H, Lee D, Ju DY. A Comprehensive Overview of Basic Research on Human Thermal Management in Future Mobility: Considerations, Challenges, and Methods. Sustainability. 2023; 15(9):7335. https://doi.org/10.3390/su15097335

Chicago/Turabian Style

Kwon, Ju Yeong, Jung Kyung Kim, Hyunjin Lee, Dongchan Lee, and Da Young Ju. 2023. "A Comprehensive Overview of Basic Research on Human Thermal Management in Future Mobility: Considerations, Challenges, and Methods" Sustainability 15, no. 9: 7335. https://doi.org/10.3390/su15097335

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