Digital Twin-Based Intelligent System for Thermal Conditioning of Engines and Vehicles with Phase Change Thermal Energy Storage

Abstract

The development of modern transport energy systems is driven by increasing demands for energy efficiency, environmental sustainability, and operational reliability of vehicles. One of the most critical challenges in internal combustion engine operation is the cold-start condition, which results in increased fuel consumption, intensified component wear, and elevated emissions. Under these conditions, the development of intelligent thermal conditioning systems capable of accelerating engine warm-up and maintaining optimal thermal regimes becomes essential. This study proposes an intelligent engine and vehicle thermal conditioning system based on the integration of digital twin technology and phase-change thermal (PCM) energy storage. A digital twin architecture of the engine thermal conditioning system is developed to enable the integration of monitoring, simulation and predictive control of engine thermal processes. A mathematical model of the thermal conditioning system describing the dynamic temperature behavior of the engine, coolant, engine oil and PCM-based thermal energy storage units is formulated. A model predictive control strategy is implemented within the digital twin environment to support decision-making and optimization of engine thermal conditioning processes. Simulation and experimental results demonstrate that the proposed system can reduce engine warm-up time by 17.8–68.4%, decrease fuel consumption during the cold start phase by approximately 19.5–56.25%, and reduce harmful emissions. These findings confirm the potential of integrating digital twin technologies, predictive control and phase change thermal energy storage for improving the energy efficiency and environmental performance of modern transport power systems.

1. Introduction

The development of modern transport technologies is characterized by increasing requirements for energy efficiency, environmental sustainability, and operational reliability of vehicles. The tightening of international environmental regulations and the global transition toward low-carbon transport systems require improving the efficiency of energy conversion in vehicle power units and optimizing their operating modes [1,2,3]. In this context, rational management of thermal processes occurring in internal combustion engines and related vehicle subsystems becomes particularly important.

The thermal state of the engine has a significant influence on its operational characteristics, including start-up stability, fuel efficiency, mechanical losses, component durability, and the level of exhaust gas emissions [4,5,6]. The engine temperature regime determines the efficiency of fuel combustion, heat transfer, and lubrication processes, which directly affect the overall energy efficiency of the power unit. The cold-start operating mode is particularly critical. When operating outside the optimal temperature range, the effective engine power may decrease by 10–15%, fuel consumption may increase by 20–30%, and harmful emissions may rise by up to 40–60% compared with nominal operating conditions [7,8]. In addition, cold starts are accompanied by increased friction losses, higher viscosity of lubricants, and increased hydraulic resistance in the cooling and lubrication systems.

To accelerate engine warm-up and stabilize its temperature regime, various thermal conditioning systems are employed, including thermostat-based cooling system regulation, electric preheaters, auxiliary heating devices, and technologies for waste heat recovery from exhaust gases [9,10,11]. However, most of these systems operate based on fixed control algorithms and are characterized by considerable thermal inertia, increased energy consumption, and limited adaptability to changing operating conditions [12,13,14,15]. Additional difficulties arise when vehicles are operated at low ambient temperatures, where increased viscosity of engine oils, reduced fuel evaporation, and a significant increase in engine warm-up time are observed [16,17,18].

In recent years, significant attention has been devoted to thermal energy storage technologies, particularly systems based on phase change materials (PCM). These materials exhibit high latent heat of phase transition and the ability to store significant amounts of thermal energy at a nearly constant temperature. Consequently, they are considered a promising solution for improving the efficiency of thermal conditioning in transport systems [11,12,13]. The integration of PCM-based thermal storage units into the engine cooling system makes it possible to accumulate thermal energy during engine operation and subsequently use it to accelerate engine warm-up and stabilize temperature regimes.

In parallel with the development of thermal energy storage technologies, digital methods for controlling complex technical systems have been rapidly advancing. One of the most promising concepts is the Digital Twin technology, which represents a dynamic virtual model of a physical system synchronized with the real object based on monitoring data and enabling simulation, prediction, and optimization of operational processes [14,15,16]. In recent years, digital twins have been widely applied in manufacturing systems, the energy sector, and transport engineering for tasks such as diagnostics, optimization, and predictive maintenance of equipment [19,20,21,22,23,24,25].

Despite significant progress in the development of thermal conditioning systems for vehicle power units, thermal energy storage technologies, and digital twin technologies, these research areas have largely evolved independently of each other [25,26,27,28,29,30]. Most studies focus either on improving engine cooling and thermal conditioning systems or on investigating PCM materials as means of thermal energy storage. At the same time, digital twin technologies are predominantly applied for monitoring and diagnostics of transport systems, primarily electric powertrains.

2. Background and Related Work

Contemporary research in the field of thermal conditioning of transport systems can be conditionally divided into two main directions: the application of thermal energy storage technologies and the development of digital methods for modeling and control of engineering systems.

One of the most promising approaches is the use of phase change materials for thermal energy storage. Owing to their high energy storage density, PCM materials are widely investigated for stabilizing the temperature regimes of vehicle power units [11,12,31]. Early studies were primarily focused on examining the feasibility of using PCM for thermal buffering of individual components of transport systems. In particular, the review by Jankowski and McCluskey [19] demonstrates that PCM materials can be effectively applied to stabilize the temperature regimes of various vehicle components. However, such systems are mainly considered as autonomous thermal storage units without integration into comprehensive control systems.

Subsequent studies proposed solutions aimed at accelerating the warm-up of engines and individual components of fuel systems. For example, Gumus and Ugurlu [20] investigated the feasibility of using PCM for the preheating of the evaporator and pressure regulator in a gaseous fuel supply system. Park et al. [21] experimentally studied a thermal energy storage system for a diesel engine, demonstrating the possibility of reducing engine warm-up time and lowering emissions during the cold-start phase. Later studies have also confirmed the effectiveness of PCM-based thermal storage units in accelerating the warm-up of vehicle power units and improving the energy efficiency of transport systems [22,23]. However, most of these studies consider PCM systems as independent thermal storage elements without integration into intelligent thermal conditioning systems of the vehicle.

At the same time, research related to the application of digital twin technologies has been actively developing. Digital twins make it possible to integrate mathematical models of physical processes, monitoring data, and intelligent control algorithms into a unified cyber-physical architecture [14,15,16]. In transport engineering, such technologies are widely applied for analyzing the operation of battery systems in electric vehicles and optimizing battery management systems. For example, Wu et al. [24] proposed a digital twin concept for a battery system that integrates physical models, operational data, and artificial intelligence methods. Li et al. [25] developed a cloud-based battery management system based on a digital twin, designed for real-time estimation of the state of charge and health condition of batteries.

In recent years, digital twins have also begun to be applied for the analysis of thermal processes in electric transport systems [26,27]. For example, Shi et al. [26] proposed a digital twin of the thermal conditioning system of an electric vehicle, enabling the optimization of energy flows based on experimental data. Other studies focus on the development of multiphysics digital twins of electric powertrains and electric motors [28].

However, an analysis of the current scientific literature indicates that research on PCM-based thermal energy storage systems and digital twins of transport systems rarely overlaps. Most studies devoted to PCM storage units consider them as autonomous thermal elements for solving local problems of thermal buffering or accelerating engine warm-up. At the same time, research on digital twins is mainly focused on monitoring the technical condition, diagnostics, and optimization of the operation of electric transport systems.

A comparative analysis of existing studies in the field of thermal conditioning, PCM-based thermal energy storage, and digital twin technologies in transport systems is presented in

Table A1. Comparison of existing studies on thermal conditioning, PCM-based heat storage and digital twin technologies in transport systems.

Reference/Research Focus	Digital Twin/Digital Prototype	PCM/Thermal Energy Storage	Experimental Validation	Limitations Relative to the Proposed Study
[19]/Review of PCM applications for vehicle thermal buffering	No	Yes	No (review)	No digital twin architecture and no intelligent control of engine thermal conditioning
[20]/PCM-based preheating of LPG evaporator and regulator	No	Yes	Yes	Local thermal solution for a single component without system-level integration
[21]/PCM heat storage system for diesel engine warm-up	No	Yes	Yes	Experimental PCM system without predictive digital modelling
[22]/Vehicle warm-up improvement using PCM thermal storage	No	Yes	Yes	No digital twin integration and no predictive thermal control
[23]/PCM heat storage for hybrid engine warm-up	No	Yes	Yes	PCM-based thermal storage studied separately from digital twin architecture
[24]/Battery digital twin for smart battery management	Yes	No	Conceptual/analytical	Digital twin applied to batteries, not to engine thermal conditioning
[25]/Cloud-based digital twin battery management system	Yes	No	Yes	Focus on battery systems rather than vehicle thermal conditioning
[15]/Review of digital twin applications in engineering	Yes (review)	No	No	No focus on thermal conditioning or transport energy systems
[26]/Digital twin for EV thermal conditioning	Yes	No	Yes	Focus on electric vehicles without PCM thermal storage
[27]/Digital prototype for EV thermal system design	Partial	No	Conceptual	Concept-phase modelling without real-time digital twin architecture
[28]/Digital twin for cooling system condition monitoring	Yes	No	Yes	Focus on monitoring rather than intelligent thermal conditioning
[29]/Multiphysics digital twin for electric motor thermal analysis	Yes	No	Yes	Motor-focused system without integration with engine thermal conditioning
[30]/Predictive digital twins for thermal systems	Yes	No	Simulation	Does not consider PCM-based thermal storage for engine systems

(Appendix A), with specific information sources indicated.

Thus, the conducted literature review reveals the existence of a research gap associated with the lack of integrated approaches that combine the digital twin architecture of a vehicle energy system, intelligent control of thermal processes, and thermal energy storage technologies based on phase change materials.

Taking into account the identified research gap, the aim of this study is to develop an intelligent thermal conditioning system for engines and vehicles based on digital twin technology and thermal energy storage using phase change materials.

To achieve this objective, the following tasks are addressed:

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Analysis of thermal processes in thermal conditioning systems of vehicle power units;

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Development of the digital twin architecture for the engine thermal conditioning system;

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Development of mathematical models of heat transfer considering PCM-based thermal storage units;

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Development of algorithms for intelligent control of thermal regimes;

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Evaluation of the efficiency of the proposed system based on modeling and experimental studies.

The scientific novelty of this work lies in the development of an integrated concept of an intelligent thermal conditioning system for engines and vehicles that combines digital modeling based on digital twin technology, predictive control of thermal processes, and thermal energy storage systems based on phase change materials within a unified cyber-physical architecture.

The proposed approach provides adaptive control of the thermal regimes of vehicle power units, reduces engine warm-up time, and improves the overall energy efficiency of vehicle thermal conditioning systems.

3. Materials and Methods

In this study, the digital twin (DT) of the intelligent thermal conditioning system for engines and vehicles is developed based on the five-component model proposed by Tao F. [32], which includes the physical object, its virtual counterpart, DT data, the service and control layer, and communication channels between the components. Unlike classical digital twins, which are mainly oriented toward monitoring and diagnostics, in this work the DT is considered as a tool for the design, improvement, and intelligent control of a thermal conditioning system operating based on thermal energy storage using phase change materials.

The proposed approach is aimed at improving the efficiency of thermal conditioning of engines and vehicles under real operating conditions and covers the main stages of their life cycle: system structure formation, parameter refinement, experimental validation, adaptation to operating conditions, and subsequent intelligent control. A distinctive feature of the study is the use of phase change thermal energy storage units to ensure the following key operating modes: pre-start heating of the engine and passenger compartment, accelerated post-start warm-up, maintenance of a rational thermal state during operation, and accumulation of thermal energy for subsequent thermal conditioning cycles.

For vehicle operation systems, the digital twin should be based on information modules that utilize data on the technical condition and energy parameters of the engine and the vehicle throughout their entire life cycle. Such a structure should enable the analysis, assessment, and prediction of the thermal state of the engine and the vehicle, as well as support decision-making for controlling thermal regimes, considering the properties of phase change thermal energy storage units and operating conditions.

A distinctive feature of this study is the use of phase change thermal energy storage units to ensure reliable thermal conditioning during the following vehicle operating processes:

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Pre-start heating of the engine (coolant and engine oil up to +50 °C) and the passenger compartment (cabin);

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Post-start (accelerated) warm-up (coolant and engine oil from +50 °C to approximately +85 °C);

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Maintenance of rational thermal parameters during operation and charging of phase change thermal energy storage units for subsequent thermal conditioning cycles.

The methodological framework of this study is based on a systems approach applied both at the stages of design and modernization and under real vehicle operating conditions. For stationary operational processes, the digital twin of the thermal conditioning system is illustrated in Figure 1. This scheme adequately represents the functioning of an already implemented system; however, it is limited in terms of further modernization. For the stages of system design and improvement, an extended DT architecture (Figure 2) is proposed, which is also oriented toward engineering and technological development.

The functional capabilities of the system components at the stages of design and improvement are presented in

Table 1. Functional capabilities of system components at the design and improvement stages of the digital twin model of an intelligent thermal conditioning system for engines and vehicles based on thermal energy storage technology.

Component Identifier	Functional Purpose
A0	Generation of Initial Data and Evaluation of the Operational Efficiency of an Intelligent Thermal conditioning System for Engines and Vehicles Based on a Digital Twin and Thermal Energy Storage Technologies.
A1	Selection and integration of thermal energy storage materials; design of phase-change thermal energy storage units for specified engine and vehicle operating conditions; experimental investigation of materials and system components using laboratory test benches; calculation of PCM mass and structural parameters of the storage units; development of a digital twin database.
A2	Arrangement and installation of thermal energy storage units and components of the thermal conditioning system directly on the engine and vehicle; experimental investigation of the system on power units; updating the digital twin database.
A3	Development of a physical prototype of the thermal conditioning system and its adaptation to real operating conditions of the engine and vehicle.
A4	Development of subsystems for monitoring, diagnostics, and prediction of operating parameters of engines and vehicles equipped with a thermal conditioning system; development and adaptation of virtual software platforms for integration into a digital production environment; organization of continuous condition monitoring of the system.
A5	Development of computational algorithms and software tools; systematization of thermal conditioning system configurations; modeling and analysis based on mathematical models; development of the digital twin database.
A6	Systematization of databases and verification of compliance with design, technological, and operational requirements imposed on the thermal conditioning system, the engine, and the vehicle; implementation of step-by-step interactive optimization procedures.

. At these stages, the following processes are performed: the formation of initial data and efficiency criteria; the selection and experimental investigation of thermal energy storage materials; the design and installation of thermal storage units; the development of a physical prototype; the creation of monitoring, diagnostics, and prediction subsystems; the development of computational algorithms and software tools; as well as the verification of databases and design solutions.

The presented structure ensures consistent integration of experimental data, simulation models and decision-support algorithms within the digital twin framework.

The relationships between the digital twin components for the operation, design, and system improvement modes are presented in

Table 2. Interrelationships among digital twin components for steady-state operation processes within the life cycle (Figure 1) and for the design and system improvement stages (Figure 2, Table 1).

Digital Twin Component	Main Interactions with the Functional Modules of the System	Purpose of the Interaction
V1—Physical object (engine and thermal conditioning system)	A1, A2, A3, A0	Development of the physical system configuration, integration of thermal energy storage units, and experimental validation of operating parameters.
V2—Digital Twin Model	A5, A4, A1, A2, A3, A0	Modeling of thermal processes, integration of experimental data and computational algorithms for the analysis and prediction of thermal operating conditions.
V3—Digital Twin Data Layer	A6, A4, A5, A1, A2, A3, A0	Development, organization, and continuous updating of databases containing experimental data, system parameters, and simulation results.
V4—Service Layer and Decision Support System	A4, A5, A0	Data analysis, system condition diagnostics, prediction of operating parameters, and generation of control decisions.
V5—Communication Infrastructure	A4, A5, A3, A0	Data exchange between the physical system, the digital twin, and user interfaces; ensuring synchronization between models and monitoring data.

.

The presented structure ensures the coordinated integration of experimental data, mathematical models, and decision-support algorithms within the framework of digital twins.

Within the development of the intelligent thermal conditioning system, three conceptual directions were identified. The first is related to the specification and maintenance of the required thermal parameters of the engine and the vehicle based on a combination of technical equipment, monitoring systems, and software platforms for remote control, diagnostics, and prediction [33]. The second direction is focused on stabilizing the temperature of the coolant and engine oil during the inter-shift period under outdoor vehicle storage at low ambient temperatures, which makes it possible to reduce energy consumption and eliminate inefficient and environmentally unfavorable warm-up methods [34,35]. The third direction is associated with accelerated pre-start and post-start thermal conditioning of the engine, passenger compartment, and catalytic converter to improve environmental performance and reduce the time required for the power unit to reach an efficient operating regime [33,36,37].

The development of such systems based on phase change thermal energy storage units and digital twin technologies is considered one of the most promising directions for improving the efficiency of vehicle operation and their functional systems [33,34,35,36,37]. In this context, the intelligent thermal conditioning system includes two fundamental blocks: the energy block and the information block.

3.1. Energy Structure of the Intelligent Thermal Conditioning System

The formalized scheme of the energy component of the system is presented in Figure 3.

The scheme includes the following main subsystems: an accelerated warm-up subsystem; a waste exhaust heat recovery subsystem with a phase change thermal energy storage unit; a contact thermal energy storage unit; engine oil and coolant storage units with integrated phase change elements; as well as a catalytic converter thermal energy storage unit. Structurally, the thermal conditioning system is integrated into the engine cooling and lubrication systems, thereby influencing the parameters of the engine operating process [33,34,35,36,37]. The system provides pre-start and post-start heating of the coolant, engine oil, and elements of the emission control system to temperatures that allow the engine to be loaded and subsequently brought to its operating thermal state. In addition, the system maintains the required thermal regime during the inter-shift period in accordance with operational requirements and the design features of the engine.

The main operating principle of the system is based on the accumulation of thermal energy directly within the vehicle using phase change thermal energy storage units. The primary energy sources include the heat released during fuel combustion, the heat of exhaust gases, as well as the heat dissipated by the engine through convection and radiation. This energy, which is normally lost under conventional operating conditions, is recovered and returned to the system, where it is used for subsequent thermal conditioning of the engine and the vehicle.

For the formation of experimental phase change thermal energy storage units, the following heat storage materials were used: high-density polyethylene (low-pressure grade T-3) for use in the thermal energy storage unit; paraffin (a mixture of aliphatic hydrocarbons of the C_nH_2n+2 series) for use in the contact thermal energy storage unit and in the coolant and engine oil storage units; as well as hydroquinone C₆H₄(OH)₂ and sodium hydroxide NaOH in the phase change thermal energy storage unit of the exhaust gas aftertreatment system [34,35,36,37,38,39]. The typical thermophysical properties of these substances are presented in

Table 3. Typical thermophysical properties of thermal energy storage materials used in the intelligent thermal conditioning system.

Substance	Melting Temperature, °C	Latent Heat of Fusion, kJ/kg	Thermal Conductivity, W/(m·K)
High-density polyethylene (HDPE), grade T-3	130–135	200–230	0.40–0.50
Paraffin wax (a mixture of alkanes CnH2n+2)	47–65 (depending on the composition)	180–220	0.20–0.30
Hydroquinone C6H4(OH)2	170–173	140–160	0.20–0.30
Sodium hydroxide (caustic soda, NaOH)	318–323	160–180	0.50–0.60

.

These substances ensure coordinated integration and systemic interaction of experimental data, models, and decision-support algorithms within the digital twin framework of the thermal conditioning system. Thermophysical properties of the selected phase change materials indicate their suitability for thermal energy storage applications in engine thermal conditioning systems. Paraffin-based PCM and polymeric materials provide high latent heat values and stable phase transition temperatures, while inorganic materials such as sodium hydroxide offer higher thermal conductivity and heat storage density [34,35,36,39].

The most energy-intensive process is the phase transition of the thermal energy storage material, which enables the accumulation of a significant amount of thermal energy. The stored heat is subsequently used to heat the coolant, engine oil, engine block, and catalytic converter. The contact thermal energy storage unit is implemented as a thermally insulated coating with sectional containers filled with PCM materials, allowing the thermal state of the engine to be maintained for an extended period after shutdown [34,38,39,40]. Similar principles are implemented for the engine oil and coolant storage units, as well as for the system designed for accelerated heating of the catalytic converter.

From the perspective of energy analysis, the object of study represents a complex multicomponent system with multiple sources, levels, and directions of thermal energy transformation. Therefore, the following methods were used in the study: system analysis, morphological synthesis, set theory, mathematical statistics, regression analysis, graph theory, object-oriented design, as well as classical heat transfer laws, the principles of the theory of working processes of thermal machines, methods of exergy analysis, and the theory of thermal energy storage [34,38,40,41,42,43,44].

3.2. Digital Twin Architecture

The architecture of the intelligent thermal conditioning system based on the DT is shown in Figure 4 and includes four hierarchical levels:

• Physical layer, including the real thermal conditioning system, its equipment, sensors, actuators, controllers, and data acquisition interfaces.
• Data preparation and management layer, ensuring the collection, transmission, storage, integration, visualization, and analytical processing of information.
• Digital twin layer, where thermal process modeling, system state analysis, operational scenario evaluation, diagnostics, and prediction are performed.
• Application service layer, implementing user interfaces, decision-support systems, databases, and tools for optimization, monitoring, and remote control.

The presented schematic representation of the architectural structure of the intelligent thermal conditioning system ensures the integration of physical processes, monitoring data, mathematical models, and decision-support algorithms within the digital twin framework.

A distinctive feature of the proposed architecture is the seamless integration of the physical model and the digital twin within a unified thermal conditioning cycle. This is particularly important because real engine warm-up processes under operating conditions are characterized by high variability and multifactorial influences and therefore cannot be reliably investigated using experimental methods alone [33,34,45,46,47,48,49]. For this reason, an experimental–analytical approach is employed in this study, in which physical testing is complemented by digital modeling [33,34,50,51], decision-making within digital technologies, and remote monitoring.

3.3. Mathematical and Information Descriptions of the Problem and the Digital Twin Model

The mathematical model of the thermal conditioning system based on phase change thermal energy storage units is founded on a previously developed mathematical model based on the simulation of working processes in an internal combustion engine. For this implementation, the volumetric balance method was applied [34,35,36,42]. The developed algorithms consider the specific features of engine thermal conditioning processes using the thermal conditioning system and the operating modes of its application. The key parameters of the mathematical model of the system, which determine its efficiency, include the temperature of the coolant in the engine cooling system, as well as the temperature and efficiency of the catalytic converter.

To improve the efficiency of engine thermal conditioning processes and optimize the operation of the thermal energy storage system, the present study employed developed algorithms for rational control based on the corresponding models. This approach is based on the use of a digital twin of the engine thermal conditioning system, which enables the prediction of changes in the thermal state of the engine and its associated subsystems within a defined prediction horizon. In contrast to conventional control algorithms based on static or proportional–integral control laws, the applied method provides dynamic optimization of the control process while considering the established constraints of the thermal conditioning system, the predicted operating conditions, and the thermal inertia of system components.

The digital twin was used as a mathematical model for predicting the thermal processes of the thermal conditioning system, including the temperature dynamics of the engine coolant and engine oil, as well as the temperatures of the phase change thermal energy storage units. The task of ensuring thermal conditioning and maintaining the optimal temperature state of engines and vehicles, based on information about the actual parameters of their technical condition, was formulated in the digital twin models as the construction of a functional relationship within the processes of monitoring the parameters of the technical condition.

The main objective of the applied system control algorithm was to determine the sequence of control actions that ensure minimization of the deviation of the engine temperature from the specified optimal regime while minimizing energy consumption using phase change thermal energy storage units. The objective function was formulated as follows (for possible prediction of thermal conditioning parameters—in parentheses), considering the stages of thermal conditioning of the engine and the vehicle:

$$\left\{\begin{array}{l}{\overline{J}}_{ST}={\displaystyle \sum _{n=1}^{Ni}\left[w_{a1}{\left(Q\left(T_{edt,n}\right)-Q\left(T_o\right)\right)}^2+w_{a2}\overline{H}{t}_n^2\right]=S_{Qopt}}\\ F_t\left(\overline{H}t,t,\Delta t,{\overline{X}}_i\left(t\right),{\overline{X}}_i\left(t-\Delta t\right),\dots ,{\overline{X}}_i\left(t-n_k\Delta t\right),DR{K}_{ti}\right)=S_{Qopt}\\ \left(\begin{array}{l}{F}_{(t+ki\Delta t)}\left(\overline{H}\left(t+k_i\Delta t\right),t,\Delta t,k,{\overline{X}}_i\left(t+k_i\Delta t\right),{\overline{X}}_i\left(t+(k-1)\Delta t\right),\dots ,{\overline{X}}_i\left(t+(k-n_k)\Delta t\right)\right)\\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}=S_{Qopt}\left(t+k_i\Delta t\right)\end{array}\right)\\ {\mathsf{\Omega }}_l^{m_i}{\left(e_Q,r\right)}^j=S_{Qopt}\\ \\ S_{Qopt}=F\left(Q_1+Q_2+Q_3+Q_4\right)\\ Q_1=\left(Q_{HA}+Q_{HA1}+\left[Q_{KHA}+Q_{NMO}+Q_{NC}\right]\right)\to Q\left(T_o\right)\\ Q_2=Q_{AP}+\left(Q_{STP}=F\left(Q_C+Q_{MO}\right)\right)+Q_{1T}+Q_{HA1}\\ Q_3=Q_{\Sigma }\to \left(Q_{HA}+Q_{HA1}+Q_{KHA}+Q_{NMO}+Q_{NC}\right)\\ Q_4={\displaystyle \sum _{n=1}^{Ni}\left(Q_{HA};Q_{HA1};Q_{KHA};Q_{NMO};Q_{NC}\right)}\end{array}\right.$$

(1)

To substantiate the overall methodology for the formation and evaluation of methods for ensuring the optimal temperature state of an engine equipped with a vehicle thermal conditioning system under operating conditions, information on the technical condition of the objects at different stages of their life cycle was used in this study. A distinctive feature of the methodology is the systems approach to representing the investigated objects, based on the identification of key system processes according to their objective functions, the determination of the main functional elements, feedback loops (as parameters for process control), and the interactions with the external environment. The application of system principles [34,38] made it possible to represent the process of thermal conditioning of the engine and the vehicle under operating conditions as a set of four interrelated main processes.

In previous studies, the approach of representing engines and vehicles within a thermal conditioning system based on phase change thermal energy storage units as the main system objects has not been applied. In this study, the description is based on monitoring data of the actual technical condition of the processes involved in thermal conditioning, structured according to the following principle:

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Engine warm-up processes without engine start during pre-start thermal conditioning (Q₁): transformation of thermal energy accumulated in the thermal energy storage material and in the elements of the thermal conditioning system during the preparation of the engine and the vehicle for operation without starting the engine.

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Engine warm-up processes during post-start thermal conditioning (Q₂): conversion of fuel energy into thermal and mechanical energy of the engine; transformation of the latent heat of phase transition in the system elements into thermal energy for additional heating of the engine heat-transfer media. In parallel, heating of the vehicle passenger compartment is carried out through the engine cooling system, and accelerated heating of the catalytic converter is provided by the corresponding thermal energy storage unit of the exhaust gas aftertreatment system.

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Processes of heat accumulation in the phase change thermal energy storage units of the system during the charging mode (Q₃): conversion of fuel energy into thermal and mechanical energy of the engine, as well as accumulation of thermal energy from exhaust gases and the operating engine in the heat storage material of the system components (charging processes of the thermal energy storage units).

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Processes of cooling of the heat storage substances in the thermal energy storage units of the system during the storage mode together with the engine and the vehicle in the surrounding environment (Q₄): dissipation of thermal energy accumulated in the heat storage material through the system components during vehicle parking (storage). This stage characterizes the processes of preserving the thermal energy of the engine.

In the digital twin, these equations are used not only for the calculation of steady-state parameters but also as the basis for predictive control. The implementation of this function in the monitoring subsystem is ensured through synchronization of the model with vehicle data via the CAN bus (or with data from additional trackers for auxiliary sensors), as well as through the monitoring system for coolant and engine oil temperatures, remote data processing at the operator’s workstation of the control system, control units, and regulation of heat transfer fluid flow using control elements of the thermal conditioning system. Thus, classical heat balance equations become the foundation for algorithmic control of thermal regimes.

The digital twin of the thermal conditioning system includes models of the internal combustion engine, the vehicle, phase change thermal energy storage units, the exhaust heat recovery device, contact thermal energy storage units, coolant and engine oil storage units, the passenger compartment heating heat exchanger, the catalytic converter, the catalytic converter thermal energy storage unit, as well as auxiliary accelerated warm-up elements, valves, and actuators. The corresponding block diagrams of the component and process models are presented in Figure 5 and Figure 6.

Formally, the DT of the thermal conditioning system is described by a system of information Equation (2), which integrates the models of the system components and the models of the thermal conditioning processes of the engine and the vehicle [34,35,36,42].

$$\left\{\begin{array}{l}D{S}_{STPEV}={\displaystyle \sum _{i=1}^{a}IC{E}_i}\cup {\displaystyle \sum _{j=1}^{b}VE{H}_j}\cup {\displaystyle \sum _{k=1}^{c}PCP{A}_k}\cup {\displaystyle \sum _{l=1}^{d}EGHR{U}_l}\\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\cup {\displaystyle \sum _{m=1}^{f}PCHS{M}_m}\cup {\displaystyle \sum _{n=1}^{h}CTPCT{A}_n}\cup {\displaystyle \sum _{o=1}^{r}CR{A}_o}\cup {\displaystyle \sum _{p=1}^{s}EOR{A}_p}\\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\cup {\displaystyle \sum _{q=1}^{t}PCV{C}_r}\cup {\displaystyle \sum _{r=1}^{u}EEGC{C}_s}\cup {\displaystyle \sum _{s=1}^{v}TAEGC{C}_t}\cup {\displaystyle \sum _{t=1}^{w}CEAT{P}_u}\\ D{S}_{EVTCP}={\displaystyle \sum _{i=1}^{a}ETC{S}_i}\cup {\displaystyle \sum _{j=1}^{b}ICEW{P}_j}\cup {\displaystyle \sum _{k=1}^{c}TCMP{P}_k}\cup {\displaystyle \sum _{l=1}^{d}TC{C}_l}\\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\cup {\displaystyle \sum _{m=1}^{f}TCM{V}_m}\cup {\displaystyle \sum _{n=1}^{h}TCD{E}_n}\cup {\displaystyle \sum _{o=1}^{r}TCD{O}_o}\cup {\displaystyle \sum _{p=1}^{s}OCTPCT{A}_p}\\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\cup {\displaystyle \sum _{q=1}^{t}OPCT{A}_r}\cup {\displaystyle \sum _{r=1}^{u}OEOC{R}_s}\cup {\displaystyle \sum _{s=1}^{v}OEAW{S}_t}\\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\cup {\displaystyle \sum _{t=1}^{w}OCCTA{O}_u}\cup {\displaystyle \sum _{u=1}^{x}OEGHR{S}_u}\end{array}\right.$$

(2)

3.4. Implementation, Data, and Verification

For the implementation of the DT, remote monitoring technologies, software tools for visualization and analytical processing, digital communication tools, databases, and control algorithms were used. The implementation process included the development of digital models of equipment, the organization of real-time data acquisition and transmission, synchronization of the physical and virtual environments, the implementation of diagnostic and decision-support algorithms, as well as analytical data processing using a remote monitoring platform and associated software [33,38,50].

The adequacy of the obtained calculated and experimental relationships was evaluated using the root mean square deviation, maximum absolute deviation, multiple correlation coefficient, and the Fisher criterion [34,35,36,38]. The verification was carried out based on parameters such as fuel consumption, warm-up duration, coolant and engine oil temperatures, PCM material temperature, vehicle speed, and emission levels. For coolant temperature and fuel consumption, the maximum deviation of the calculated results from the experimental data within the investigated temporal and technological intervals of the thermal conditioning modes did not exceed 5%, which is considered acceptable given the achieved efficiency of the thermal conditioning process. The obtained results confirm the sufficient adequacy of the developed mathematical models and the possibility of their further application for the analysis of engine pre-start and post-start warm-up processes.

The features of the technologies and the architectural scheme of the data acquisition and transmission processes used in the intelligent thermal conditioning system based on thermal energy storage technology and the digital twin approach are described in detail in a separate publication by the authors [50].

3.5. Influence of Modeling Assumptions on Model Accuracy

During the development of the mathematical model of the engine thermal conditioning system, several simplifying assumptions were introduced in order to reduce computational complexity and ensure the applicability of the model within a digital twin environment for predictive analysis. A complete list of assumptions is provided in Appendix B. At the same time, to assess the reliability of the obtained results, it is necessary to analyse the potential impact of the most significant assumptions on the accuracy of thermal process modelling.

The most influential assumptions affecting the calculation results include:

• The assumption of constant heat transfer coefficients;
• Neglecting heat losses in connecting pipelines;
• One-dimensional representation of the phase transition front in the phase change thermal energy storage unit.

3.5.1. Assumption of Constant Heat Transfer Coefficients

In the developed model, heat transfer coefficients in cooling circuits and heat exchangers are treated as constant values defined for representative engine operating conditions. In real operating environments, these coefficients may vary depending on coolant flow rate, temperature levels, and engine operating mode.

Sensitivity analysis indicates that variations in heat transfer coefficients within ±10–15% result in changes in the calculated coolant temperature not exceeding 2–3%. Such deviations fall within the overall uncertainty of thermal process modeling and do not significantly affect the final simulation results.

3.5.2. Neglecting Heat Losses in Pipelines

In the model, heat losses in the connecting pipelines of the thermal conditioning system were neglected. This assumption is justified by the relatively short length of the pipelines and the presence of thermal insulation, which substantially reduces heat dissipation.

Additional calculations show that accounting for these heat losses leads to variations in the predicted coolant temperature of less than 1–2%. Therefore, the influence of this factor on the dynamics of thermal processes is considered minor.

3.5.3. One-Dimensional Representation of the Phase Change Process

The melting process of the phase change material in the thermal energy storage unit was described using a one-dimensional moving boundary model. In practice, phase change processes are inherently three-dimensional and may involve complex convective flows within the molten phase.

However, analysis of heat transfer mechanisms indicates that the dominant heat flow occurs along the principal temperature gradient between the heat exchanger and the phase change material. Therefore, the one-dimensional approximation provides an adequate description of the heat storage and release dynamics.

Comparison of simulation results with experimental data confirms that the adopted modeling approach ensures acceptable accuracy in describing thermal energy storage processes.

3.5.4. Overall Assessment of Assumptions Impact

In general, the conducted analysis demonstrates that the influence of the considered assumptions on simulation results is limited and does not lead to significant deviations in the predicted thermal parameters of the system.

Comparison between simulation and experimental data shows that the deviation of coolant temperature predictions does not exceed 4.75%, while the deviation in fuel consumption is approximately 4.6% (overall within 5%). These values confirm the adequacy of the developed mathematical model for analyzing thermal processes in the engine and its thermal conditioning system.

Thus, the adopted simplifying assumptions provide a reasonable balance between modeling accuracy and computational efficiency, which is essential for the effective implementation of the model within a digital twin framework for transport energy systems.

3.6. Construction of a DT of Individual Components of the Thermal Conditioning System for Engines and Vehicles Operating on Thermal Storage Technology

Based on the developed digital twin models for stationary operation, design, and improvement of the intelligent thermal conditioning system, it has been established that its most significant element is the energy subsystem, which ensures the accumulation, redistribution, and utilization of heat under various operating modes of the engine and the vehicle.

It is shown that the integration of phase change thermal energy storage units into the cooling, lubrication, exhaust heat recovery, and exhaust aftertreatment circuits makes it possible to form a unified intelligent thermal conditioning system capable of operating either in a coordinated manner according to a unified control algorithm or through individual subsystems depending on operational tasks and conditions [34,35,38,42]. Figure 7 illustrates the implementation of the developed approach using one of the elements of the thermal conditioning system—the phase change thermal energy storage unit of the engine and the vehicle—as an example.

The developed digital twin architecture ensures the systemic integration of physical processes, monitoring data, computational models, and control decisions. This enables not only the analysis of the current thermal state of the engine but also the evaluation of alternative operating modes, diagnostics, prediction, and optimization of thermal processes in real time [34,35,38,50].

It has been established that the use of the digital twin is particularly effective in tasks where pure experimental investigation is either difficult or insufficiently informative. In particular, the digital twin makes it possible to reproduce thermal regimes that are not accessible for direct bench testing, as well as to investigate the influence of the design parameters of thermal energy storage units, charging/discharging modes, external conditions, and control algorithms on the efficiency of the entire system.

The developed system of component and process models makes it possible to solve both design and configuration problems, as well as tasks related to the analysis of thermal, exergy, and energy processes. This provides the possibility of selecting a rational configuration of the thermal conditioning system depending on the objectives of thermal preparation, the type of engine, the vehicle, and the operating conditions.

As a result of the study, it has been confirmed that the digital twin of the intelligent thermal conditioning system can be considered an integrating platform for:

-

The formation and refinement of the system structure;

-

The selection and justification of the parameters of PCM materials and thermal energy storage units;

-

The evaluation of the efficiency of various thermal conditioning schemes;

-

The synchronization of the physical object and the digital model in real time within remote monitoring processes;

-

The implementation of intelligent control of thermal regimes.

The obtained results indicate that the proposed methodology and DT architecture provide a basis for the further transition from individual thermal conditioning systems to integrated intelligent vehicle operation systems throughout the entire life cycle.

4. Results of the Study

This section presents the main results of the study in a structured and logically consistent manner. First, the general characteristics and functional architecture of the developed intelligent thermal conditioning system are described. Next, the formation of the energy structure and the results of numerical simulation of thermal processes are presented. This is followed by the experimental verification of system performance and a detailed analysis of thermal energy storage processes and control algorithms. Finally, the adequacy of the developed mathematical model and the practical efficiency of the proposed system under various operating conditions are assessed. Such an organization allows for a consistent transition from system design and modeling to experimental validation and practical interpretation of the obtained results.

4.1. General Characteristics of the Developed Thermal Conditioning System

As a result of the research conducted, an intelligent thermal conditioning system for engines and vehicles has been developed, operating based on the integration of digital twin technology and thermal energy storage using PCM. The main objective of the development was to increase the efficiency of engine thermal conditioning processes, reduce fuel consumption during the cold-start stage (pre-start and post-start thermal conditioning), and decrease harmful emissions into the atmosphere.

The developed system represents a comprehensive cyber-physical system that integrates the physical infrastructure of engine thermal conditioning and a digital model implemented in the form of a digital twin. The architecture of the system is designed to ensure continuous interaction between the real processes occurring in the vehicle engine and its thermal subsystems and their virtual representation in the digital environment.

Functionally, the system includes several interconnected subsystems:

-

A subsystem for the accumulation and storage of waste thermal energy using thermal energy storage units based on phase change materials;

-

A subsystem for the recovery of waste heat from engine exhaust gases;

-

A subsystem for accelerated warm-up of the engine and its functional components;

-

A subsystem for remote monitoring, analysis of the technical condition parameters and the thermal state of the vehicle engine, as well as its parametric diagnostics;

-

An intelligent subsystem for the control of thermal regimes implemented based on a digital twin.

A distinctive feature of the developed system is the use of a digital twin to integrate monitoring data, mathematical models of thermal processes, and intelligent control algorithms into a unified thermal conditioning system for the engine based on a thermal conditioning system incorporating phase change thermal energy storage units.

The digital twin performs several key functions:

-

Modeling of engine thermal processes;

-

Prediction of temperature regimes of the engine and its subsystems;

-

Optimization of thermal flow control;

-

Analysis of the operational efficiency of the thermal conditioning system.

The developed digital twin models make it possible to predict changes in the temperature of the engine coolant and engine oil, as well as the temperatures of the phase change thermal energy storage units under various modes of thermal conditioning and vehicle operation in real operating conditions.

The integration of the digital twin with the thermal conditioning system based on phase change thermal energy storage units enables adaptive control of thermal processes, which is particularly important for vehicle operation under variable and complex climatic and operational conditions.

4.2. Formation of the Energy Structure of the Thermal Conditioning System

One of the key objectives of the study was the formation of the energy structure of the engine thermal conditioning system. For this purpose, a formalized scheme of the energy subsystem was developed to ensure the efficient utilization of thermal flows generated during engine operation. The energy subsystem of the thermal conditioning system includes the following main elements:

-

A heat exchanger for the recovery of exhaust gas heat;

-

A phase change thermal energy storage unit;

-

A contact thermal energy storage unit of the engine;

-

A storage-type coolant accumulator;

-

A storage-type engine oil accumulator;

-

A subsystem for accelerated heating of the vehicle catalytic converter.

The primary source of thermal energy in the system is the heat released during fuel combustion in the engine cylinders. In conventional systems, a significant portion of this energy is lost with exhaust gases and is not reused. In the developed system, part of the thermal energy of the exhaust gases is recovered by means of a heat exchanger installed in the engine exhaust system. The heat transfer fluid circulating through the heat exchanger is heated and transfers heat to the phase change material of the thermal energy storage unit.

During the phase transition process, the material can accumulate a significant amount of thermal energy at a relatively constant temperature. This provides a high density of thermal energy storage and allows it to be effectively utilized later. During the subsequent engine start, the accumulated thermal energy is used for accelerated heating of the following elements: the coolant of the engine cooling system, the engine oil of the lubrication system, and the structural components of the engine.

In addition, part of the stored thermal energy is used for accelerated heating of the catalytic converter, which contributes to a faster achievement of the effective operating temperature of the exhaust gas aftertreatment system.

4.3. Results of the Simulation of Thermal Conditioning Processes

To analyze the efficiency of the proposed system, a mathematical model of the thermal processes of the engine and the thermal conditioning system was developed. The model was implemented within the framework of the digital twin and considers the thermal interactions between the main elements of the system. Simulation of the thermal conditioning system based on phase change thermal energy storage units was carried out for several typical engine operating modes: pre-start heating of the engine up to +50 °C; accelerated engine warm-up after start from +50 °C to +85 °C; steady-state engine operation; and the charging and discharging processes of the thermal energy storage units during vehicle storage under ambient conditions.

The simulation results showed that the use of phase change thermal energy storage units significantly improves the dynamics of engine warm-up. When the proposed system is used, the temperature of the coolant can reach 50 °C before the engine starts. This significantly reduces the cold-start period and decreases the negative effects of engine operation at low temperatures. After engine starts, the stored thermal energy accelerates engine warm-up to the operating temperature. The simulation results indicate that the use of thermal energy storage units reduces the time required to reach the engine operating temperature by approximately 12–15% compared with conventional thermal conditioning systems.

4.4. Experimental Verification of the System Efficiency

To confirm the simulation results, experimental studies of the operation of the engine thermal conditioning system were carried out. The experimental setup included an internal combustion engine equipped with phase change thermal energy storage units and an exhaust heat recovery system. During the experiments, the following parameters were measured: coolant temperature, engine oil temperature, temperature of the phase change material in the thermal energy storage unit, engine fuel consumption, and the concentration of harmful substances in the exhaust gases.

The experimental studies were conducted at ambient temperatures ranging from −20 °C to +20 °C, which corresponds to the operating conditions of vehicles during the cold season.

The experimental data obtained confirmed the results of mathematical modeling.

The main experimental results are as follows:

-

Reduction of engine warm-up time by 17.8–68.4%;

-

Reduction of fuel consumption during the warm-up period by 19–56.25%;

-

Reduction of harmful emissions.

In addition to the overall performance indicators, a deeper interpretation of the system efficiency requires a detailed analysis of the thermal energy storage behavior, as well as the evaluation of the implemented control strategies and the adequacy of the developed mathematical model.

4.5. Analysis of Thermal Energy Storage Processes

The conducted analysis of the operation of phase change thermal energy storage units showed that the process of thermal energy storage is characterized by the presence of a temperature plateau in the phase transition region of the material. The presence of such a temperature plateau ensures the stability of the heat transfer fluid temperature, a high density of thermal energy storage, and efficient heat transfer to the engine cooling system.

Experimental studies have demonstrated the high stability of the phase change materials used under multiple charging and discharging cycles.

4.6. Efficiency of the Thermal Conditioning System Control Algorithm

To optimize the thermal operating conditions of the engine, a predictive control algorithm based on the Model Predictive Control (MPC) approach was implemented within the digital twin framework. This algorithm was used as a tool for forecasting the thermal state of the system and generating control recommendations for engine thermal conditioning modes.

The MPC algorithm provides prediction of engine temperature dynamics over a defined prediction horizon and enables the determination of appropriate control actions, including adjustment of coolant flow rate, regulation of pump operation modes, and management of thermal energy storage and utilization processes.

During the conducted study, the algorithm operated as part of a decision-support system: real-time data on the engine and thermal conditioning system were transmitted to the digital twin via the monitoring system (CAN interface), after which control recommendations were generated based on predictive calculations. The final implementation of control actions was carried out by an operator through a control workstation, which was required to ensure safe operation of the experimental setup.

The results of simulation and experimental studies demonstrate that the application of the predictive control approach makes it possible to: reduce the deviation of engine temperature from the optimal value; decrease engine warm-up time; improve the efficiency of stored thermal energy utilization; reduce thermal losses in the thermal conditioning system.

Thus, the implemented predictive control algorithm improves the overall performance of the engine thermal conditioning system and can be considered as a basis for further development of fully automated control systems for thermal management of transport power units.

4.7. Assessment of the Adequacy of the Developed Model

To verify the accuracy of the developed mathematical model, a comparison between the simulation results and experimental data was carried out. For the specified operating conditions and the investigated ranges of engine temperatures, vehicle speeds, and fuel consumption, the comparison results showed that the deviation in coolant temperature and fuel consumption did not exceed 5%. Such values are considered acceptable for complex thermodynamic systems and confirm the adequacy of the developed model.

After validating the model accuracy and analyzing the key functional components of the system, it is important to assess the practical applicability and engineering effectiveness of the proposed thermal conditioning system under real operating conditions.

4.8. Practical Efficiency of the Proposed System

The developed thermal conditioning system was tested on various types of vehicles: passenger cars, trucks, and stationary power units. In addition, it can also be applied in hybrid vehicles. The main advantages of the proposed system include reduced engine warm-up time, lower fuel consumption, and decreased emissions of harmful substances (

Table 4. Comparison of the efficiency of the conventional and proposed thermal conditioning systems (using only a phase change thermal energy storage unit) for the G4GC engine (4FS 8.2/9.35) of the KIA CEE’D 2.0 5MT2 vehicle under different warm-up scenarios.

Indicator	Conventional System	Proposed System	Improvement
Engine warm-up time to 85 °C	9.42–21.89 min	8–13 min	17.8–68.4%
Fuel consumption during warm-up	0.16–1.42 kg	0.135–0.99 kg	19.5–56.25%
Coolant temperature at engine start (at 0 °C ambient temperature)	0 °C	50 °C	+50 °C
Time required for the catalytic converter to reach operating temperature	7.3–12.3 min	5.8–6.13 min	20.8–50.2%

). This may contribute to extending the service life of vehicle engines.

Thus, to ensure transport safety in terms of maintaining the optimal thermal state of the engine and the vehicle, the following configuration options are recommended:

-

For engine warm-up from ambient temperature to 50 °C and from 50 °C to 85 °C within the cooling system—the following configuration: the standard system + an accelerated warm-up subsystem + a phase change thermal energy storage unit;

-

To maintain the temperature of the coolant and engine oil of the vehicle engine at approximately 50 °C when the vehicle is stopped under ambient environmental conditions—a thermal energy storage unit + a contact thermal energy storage unit + coolant and engine oil storage units and a separate phase change thermal energy storage unit.

To ensure environmental safety (minimization of harmful impact on the environment), it is advisable to use the following configuration: a thermal energy storage unit of the exhaust gas aftertreatment system + coolant and engine oil storage units and a separate phase change thermal energy storage unit.

To ensure transport comfort, it is advisable to use various means of thermal conditioning of the engine cooling system. The most effective configuration options include: a thermal energy storage unit + a contact thermal energy storage unit + coolant and engine oil storage units and a separate phase change thermal energy storage unit.

4.9. Features of Experimental Studies and Obtained Results for the Thermal Conditioning System Based on Phase Change Thermal Energy Storage

Experimental and analytical investigations of individual components and the thermal conditioning system were conducted using specialized test benches in research laboratories, as well as on stationary power plant engines and vehicles. At different stages of the study (Figure 8, Figure 9 and Figure 10,

Table 5. Comparative analysis of mean time and fuel saving metrics for G4GC engine coolant heating (KIA CEE’D 2.0 5MT2) across various warm-up modes and ambient temperatures.

Heating Modes	Savings		From 40 °C to 85 °C			From 50 °C to 85 °C			From 60 °C to 85 °C
			−5 °C	−10 °C	−20 °C	−5 °C	−10 °C	−20 °C	−5 °C	−10 °C	−20 °C
1—idle warm-up	Time saving,	min.	6	8	15	7	12	20	9	15	23
		%	22.2	17.8	24.2	25.9	26.7	32.3	33.3	33.3	37.1
	Fuel saving,	kg	0.134	0.25	0.46	0.189	0.366	0.55	0.21	0.41	0.596
		%	27.3	29.3	39.66	38.6	42.9	47.4	42.86	48.1	51.4
2—idle warm-up with activated electrical consumers	Time saving,	min.	6	10	17	8	13	21	12	17	29
		%	19.35	19.23	25.37	25.81	25	31.3	38.71	32.69	43.28
	Fuel saving,	kg	0.135	0.277	0.49	0.22	0.359	0.586	0.333	0.48	0.75
		%	19.5	25.45	34.75	31.9	32.99	41.56	48.3	44.12	53.2
3—idle warm-up followed by gradual driving	Time saving,	min.	7	13	21	10	15	25	13	17	27
		%	25.9	43.3	45.65	52.6	50	54.35	68.4	56.67	58.69
	Fuel saving,	kg	0.155	0.368	0.61	0.257	0.485	0.91	0.44	0.617	0.959
		%	20.75	31.2	31.9	34.4	41.2	47.6	58.9	52.38	50.21
4—warm-up during driving	Time saving,	min.	6	10	17	8	13	22	9	16	25
		%	33.3	34.48	38.64	44.4	44.8	50	50	55.17	56.8
	Fuel saving,	kg	0.192	0.369	0.66	0.247	0.478	0.81	0.347	0.53	0.99
		%	27.08	32.37	37.5	34.84	41.9	46.02	48.9	46.49	56.25

), the properties of thermal energy storage materials, system components, and the integrated system were developed and examined. These included phase change thermal storage units for heating coolants and engine oil, contact-type engine heat accumulators, and storage units for coolant and lubricating oil equipped with phase change materials.

Engine-related investigations were performed on stationary 6FS 12/14 engines, including the K-461M1 diesel engine and the K-159M2 gas engine (Yuzhdizelmash, Tokmak, Ukraine). The technological capabilities of intelligent monitoring systems were also evaluated within these test facilities. An information platform was developed to assess methods for maintaining optimal thermal conditions of vehicle engines equipped with thermal conditioning systems. Adaptation studies were conducted on GAZ 2705 GAZelle (GAZ Group, Nizhny Novgorod, Russia) and Lada VAZ-2104 vehicles (AvtoVAZ, Togliatti, Russia). The feasibility of monitoring, diagnostics, and condition prediction under real operating conditions was successfully demonstrated.

In addition, thermal conditioning processes were studied on a GAZ-66-11 truck (GAZ Group, Nizhny Novgorod, Russia) with a ZMZ-66-06 engine (8FS 9.2/8) (Zavolzhsky Motor Plant (ZMZ), Zavolzhye, Russia) and a KIA CEE’D 2.0 5MT2 passenger car (Kia Corporation, Seoul, Republic of Korea) equipped with a G4GC engine (4FS 8.2/9.35) (Hyundai Motor Company, Ulsan, Republic of Korea). These vehicles were fitted with thermal conditioning and remote monitoring systems for experimental purposes. Detailed specifications of the test objects are provided in Appendix C and in references [33,34,35,36,37,41,42,43,50].

To investigate pre-start and post-start warm-up processes, four heating modes were developed and applied: (1) idle warm-up; (2) idle warm-up with activated electrical consumers; (3) idle warm-up followed by gradual driving; and (4) warm-up during driving. For the KIA CEE’D 2.0 5MT2 vehicle (Appendix C.2), a unified monitoring system architecture was designed for CAN-based platforms. Furthermore, an information exchange scheme was implemented to analyze engine operations with thermal storage during start-up and post-start heating.

Experimental results demonstrated substantial fuel savings when phase change thermal storage was applied during pre-start and post-start warm-up. The integration of a PCM-based heat source into the cooling system of the G4GC engine in the KIA CEE’D 2.0 5MT2 vehicle (Figure 7) (Kia Corporation, Seoul, Republic of Korea) reduced warm-up time by 17.8–68.4% and decreased fuel consumption by 19.5–56.25% under various operating conditions and heating modes [33,34,35,36,37,41,42,43,50]. Mode 3 was identified as the most balanced option in terms of warm-up duration, driving distance required for heating, and fuel consumption. This mode combines idle heating with subsequent gradual driving.

System-level results (Figure 3) confirm the significant influence of the intelligent thermal conditioning system based on thermal energy storage on thermal performance indicators. Digital twin simulations demonstrated that the application of the intelligent system under different ambient temperatures improves coolant and oil warm-up characteristics. Calculations show that pre-start and post-start heating times were reduced by 22.9–57.5% and 25–57%, respectively. During long-term storage, heat retention increased by factors of 9–92 and 6.2–61 without engine idling.

The efficiency ranges reported in this study correspond to diverse operational conditions of the thermal management system. Table 5 details the boundary parameters under which the mean minimum and maximum performance enhancements are observed.

The findings indicate that the effectiveness of the proposed system increases substantially as ambient temperatures decrease and specific thermal conditioning sub-systems are engaged. As evidenced by the data in Table 5, the implementation of a thermal management system based exclusively on a phase-change thermal accumulator significantly improves the warm-up time (by 17.8–68.4%) and fuel efficiency (by 19.5–56.25%) of the G4GC test engine (4FS 8.2/9.35) in the KIA CEE’D 2.0 5MT2.

The digital twin also enables prediction of system behavior and thermal processes for engines of different purposes. An example of digital twin–based generation of warm-up characteristics for the 6FS 12/14 Diesel K-461M1 engine (Appendix C) is presented in Figure 11. The model reproduces key thermal preparation stages, excluding heat storage mode.

The main technological stages are summarized as follows:

TS1—Pre-start thermal preparation. Heating from ambient temperature to the target coolant temperature required for reliable engine start-up. In this study, the target temperature was +50 °C, achieved within 862 s using heat supplied from the phase change thermal storage unit.

TS2—Post-start thermal preparation. Heating from +50 °C to the normal operating coolant temperature (+85 °C) within 561 s, achieved through combined engine operation and thermal storage input.

TS3—Steady-state operation with thermal storage charging. After reaching nominal operating temperature, the thermal storage unit is disconnected from the cooling circuit, and charging from exhaust heat is performed for 460 s.

TS4—Completion of charging and steady-state operation. After the thermal storage material reaches 135–150 °C, charging is terminated. Reconnection occurs only if coolant temperature decreases or storage material temperature drops.

A comparison between the conventional warm-up procedure and the thermal conditioning system demonstrates the following improvements. For stage TS1, under identical initial conditions, the warm-up time was reduced by 8.7 min, corresponding to a 37% improvement. For stage TS2, the time reduction reached 13.1 min, or 57.5%. During TS1 operation, no fuel is consumed for engine heating. In TS2 mode, fuel consumption is reduced due to combining heating from fuel combustion and thermal energy supplied by the storage unit. Deviations between calculated and experimental data in predicting thermal conditioning parameters did not exceed 5% relative to measured values.

5. Discussion

The obtained results confirm the high efficiency of the proposed intelligent engine thermal conditioning system based on the integration of digital twin technology and phase change thermal energy storage.

In contrast to conventional thermal conditioning systems, the developed system provides comprehensive control of thermal flows in the engine and the associated subsystems of the vehicle. The main advantage of the proposed approach is the integration of thermal energy storage processes, monitoring of the thermal state, and predictive control into a unified cyber-physical system.

The results of simulation and experimental studies have shown that the application of phase change thermal energy storage units significantly improves the thermal dynamics of the engine during the cold-start stage. This is due to the high thermal energy storage density of phase change materials and their ability to maintain an almost constant temperature during the phase transition process.

The use of thermal energy storage units makes it possible to utilize part of the thermal energy that is irreversibly lost with the exhaust gases in conventional systems. The accumulated energy is used for accelerated heating of the coolant and engine oil during subsequent engine starts.

The obtained results indicate that the application of the proposed system reduces the engine warm-up time by approximately 17.8–68.4% compared with conventional thermal conditioning systems.

The reduction in warm-up time leads to a decrease in fuel consumption during the cold-start period. Experimental studies have shown a reduction in fuel consumption during the warm-up stage of approximately 19.5–56.25%.

In addition, accelerated engine warm-up contributes to a faster attainment of the operating temperature of the catalytic converter. This results in a reduction in harmful emissions, primarily carbon monoxide and unburned hydrocarbons.

Importance is associated with the integration of the thermal conditioning system with digital twin technology. The digital twin provides the possibility of continuous monitoring of the thermal state of the engine and its associated subsystems.

The use of model-based predictive control algorithms (Model Predictive Control, MPC) enables prediction of changes in the thermal state of the system and optimization of the operating modes of the cooling system and thermal energy storage units.

An important advantage of the proposed approach is the ability to adapt the control system to changing vehicle operating conditions.

The developed digital twin makes it possible to simulate the thermal processes of the engine under various driving modes, climatic conditions, and engine operating regimes. This makes it possible to optimize the parameters of the thermal conditioning system both at the design stage and during vehicle operation.

Thus, the proposed approach enables a transition from traditional static thermal conditioning systems to intelligent systems based on the use of digital models and predictive control algorithms.

To evaluate the scientific novelty of the proposed approach, an analysis of contemporary research in the field of thermal conditioning of transport systems, phase change thermal energy storage, and digital twin technologies was carried out (Appendix A).

The literature analysis shows that most existing studies can be broadly divided into two main directions:

• Studies on the application of phase change materials for thermal energy storage;
• Studies on the application of digital twins in transport and energy systems.

In studies devoted to PCM applications, the primary focus is on improving engine warm-up efficiency and stabilizing the temperature regimes of individual components of transport systems.

At the same time, research on digital twins is mainly focused on tasks related to diagnostics of technical condition, equipment monitoring, and optimization of the operation of electric power systems.

The integration of digital twin technologies and phase change thermal energy storage for thermal conditioning systems of internal combustion engines has been scarcely addressed in existing scientific literature.

Table A1 (Appendix A) shows that the present study integrates several research directions that have previously been considered separately.

6. Conclusions

This study presents the development of an intelligent thermal conditioning system for engines and vehicles based on the integration of digital twin technology and phase change thermal energy storage.

The main scientific results of the study can be summarized as follows:

A digital twin architecture of the engine thermal conditioning system has been developed, providing the integration of monitoring, modelling, and control processes of engine thermal regimes.

A formalized structure of the energy subsystem of the thermal conditioning system has been developed, including phase change thermal energy storage units, an exhaust gas heat recovery system, and accelerated engine warm-up subsystems.

A mathematical model of the thermal processes of the engine and the thermal conditioning system has been developed and implemented within the digital twin framework.

A predictive control algorithm for engine thermal regimes based on the Model Predictive Control method has been developed.

The results of modelling and experimental studies demonstrate that the proposed system allows:

• Reducing engine warm-up time by approximately 17.8–68.4%;
• Decreasing fuel consumption during the warm-up phase by 19.5–56.25%;
• Reducing harmful emissions during the cold-start period of the engine.

The obtained results confirm the potential of applying digital twin technologies and phase change thermal energy storage to improve energy efficiency and environmental performance of transport power systems.

Promising directions for further research include the development of more advanced digital twin models of transport systems as well as the application of machine learning methods to enhance the performance of intelligent thermal conditioning systems.

7. Patents

During the research, two Ukrainian invention patents were obtained, related to the topic of this paper and dedicated to the technology of thermal preparation of internal combustion engines:

• Igor Gritsuk et al. System for ensuring optimal coolant temperatures in an internal combustion engine. UA 103729
• Igor Gritsuk et al. System for ensuring optimal coolant temperatures in an internal combustion engine. UA 106525