Simulation and Experimental Validation of a 1D Cabin Thermal Model for Electric Trucks with Enhanced Insulation and Heating Panels

Abstract

To reduce emissions in the existing transportation system and lower carbon dioxide (CO₂) output, battery electric vehicles (BEVs) offer a promising approach due to their higher energy efficiency. However, their driving range still falls short compared to conventional vehicles. Optimizing the heating, ventilation, and air conditioning (HVAC) system can help save energy and improve passenger comfort. This study investigates an advanced thermal management system for an electric truck cabin with heating panels and added insulation. A one-dimensional (1D) cabin thermal model was also developed and validated with experimental data. The model integrates insulation, heating panels, and a 1D comfort simulation. It is functional mock-up unit (FMU) compatible and connects to larger system simulations and real-time applications. The results show that energy consumption can be reduced by up to 50% with these thermal measures. In the future, further research and new approaches will be necessary to identify even more efficient subsystems and cost-effective solutions.

1. Introduction

The electrification of the transport system helps achieve zero local emissions and contributes to the reduction of global carbon dioxide (CO₂) emissions, which are currently very high on busy roads around the world [1]. This transition is also required by the European Union as part of the Green Deal [2], where greenhouse gas reduction targets for the transportation sector have been formulated.

In recent years, interest in the electrification of vehicles has expanded beyond passenger cars to include heavy-duty vehicles such as trucks and buses [3,4,5,6,7]. Evaluating the economic feasibility of battery electric trucks (BETs) using total cost of ownership (TCO) and levelized cost of driving (LCOD) models, Samet et al. (2024) determine that BETs are already cost-competitive for short distances [3]. However, for long-haul applications and vehicles over 40 t gross weight—particularly in colder climates—BETs face greater technical and financial challenges. Solutions include technological improvements, expansion of fast-charging infrastructure, and stronger supportive policy measures. Other studies investigate regional opportunities for BEVs. According to Mu et al. (2024), BETs in China currently outperform fuel cell trucks (FCTs) economically, although the cost gap narrows with increasing range and future cell improvements [4]. Other aspects were also investigated in Bhardwaj and Mostofi (2022), where the technical, economic, and environmental dimensions of battery electric trucks were reviewed, with a focus on comparing fast charging and battery swapping methods [5]. The study finds that despite high upfront costs, BETs can reduce total ownership costs and emissions compared to diesel trucks, though challenges remain in terms of infrastructure, standardization, and large-scale adoption. The technical feasibility of BETs for short and long deliveries in Germany, based on real-world data, was investigated in Link and Plötz (2022) [6]. The results show a high potential for electrification, particularly for rigid solo trucks, and recommend optimizing battery sizing, adopting overnight depot charging, and adapting tour routes, alongside immediate planning for fleet electrification. The study by Liimatainen et al. (2019) investigates the usability of BETs in regions such as Switzerland and Finland, highlighting the potential benefits of improvements in BEV technology and charging infrastructure [7]. However, the advantages of electrification for long-haul transport in these contexts are limited. The study also calls for further research on truck charging, grid impacts, and international comparisons to optimize electrification strategies.

However, in BEVs, conditioning the cabin uses energy from the battery, which reduces the maximum driving range at both low and high temperatures. Therefore, optimizing the HVAC system of BEVs is one of the main research focuses in this field [8,9]. According to Haiyan et al. (2022), which analyzes the factors influencing the driving range of BEVs, the main factors identified are high driving speed and low ambient temperature, both of which increase energy demand from the HVAC system [8]. The study therefore recommends that certification of the maximum range of BEVs should include heating scenarios at low temperatures in the testing procedures. Li and Wang (2025) outline the main technologies of distributed thermal management systems and present the heating and cooling demands of each subsystem [9]. The study compares different thermal management approaches, highlighting their respective strengths and limitations, and further examines various heating and cooling methods. It concludes that integrated thermal management systems, particularly those using heat pump air conditioning and system coupling, improve overall efficiency.

1.1. Previous Research in the Field

In order to maximize the range of an electric vehicle, efficient thermal management is essential. Numerous strategies have been proposed in the literature. The authors of Zhu et al. (2025) review recent integrated thermal management systems for BEVs, investigating energy efficiency and system performance [10]. The main conclusion is that using two thermal systems can reduce energy usage by approximately 10%, while full integration of HVAC, battery, and motor systems can save up to approximately 20%. The study also highlights that more intelligent control strategies are necessary; instead of a PID controller, approaches such as model predictive control (MPC) and neural networks are more effective. It also stresses the importance of having common evaluation methods and flexible, modular system designs. As shown in Guo et al. (2023), an integrated thermal management topology for range-extended electric vehicles can effectively recover waste heat from the range extender and electric drive system to heat both the battery and cabin, thereby improving overall energy utilization [11]. A heating power control method based on model predictive control is proposed, and the feasibility and superiority of the strategy are verified by the co-simulation of Simulink and AMEsim. Compared with the independent thermal management strategy, the integrated approach shortens battery heating time by 39.1%, saves 2.04 kWh of electricity, and maintains cabin comfort. Furthermore, the model predictive control strategy reduces heating energy consumption by 20.95% and lowers total energy consumption for propulsion and thermal management by 2.84%. Schaut and Sawodny (2020) present a predictive thermal management model for battery electric vehicle cabins that minimizes energy use while maintaining passenger comfort [12]. The model has several innovations: it uses a linear-quadratic model predictive control framework and integrates thermal comfort evaluation via the Predicted Mean Vote (PMV) model. This enables accurate modeling of comfort, eliminating the need for fixed temperature setpoints. Additionally, the model accounts for CO₂ levels, humidity, HVAC dynamics, and system constraints, making real-time implementation possible. Dvorak et al. (2025), however, present another way to experimentally evaluate an air–air heat exchanger and improved cabin insulation as an alternative approach to reducing HVAC energy demand in electric trucks [13]. The results demonstrate that these measures can lower power consumption by up to 22% while maintaining acceptable CO₂ levels and passenger comfort, thereby helping to extend BEV driving range.

Many studies aim to maximize comfort and minimize energy demand using heating panels. This application has been investigated and modeled in various ways [14,15,16,17]. Liew et al. (2024) develops and validates electric radiant (ER) heaters for improving cabin thermal management in electric vehicles (EVs) [14]. The proposed simulation model was experimentally validated and demonstrated good accuracy for heating performance. Material selection, particularly for the insulator and base structure, was identified as critical: the combination of rigid polyurethane (PU) foam insulation with an expanded polypropylene (EPP 30) base achieved the best performance, with approximately 47% radiant power efficiency—over 25% higher than the baseline design. The study shows that ER heaters can enhance comfort, reduce HVAC loads, and achieve up to 45% energy savings, highlighting their potential as a promising solution for EV thermal management. Frohner et al. (2015) present an infrared (IR) heating system for electric vehicles, using conductive coatings to directly warm passengers and reduce reliance on conventional positive temperature coefficient (PTC) heaters, which can cut range by up to 50% [15]. Simulations and vehicle tests showed that IR heating provides comfort at much lower power (∼200 W) compared to convective heating (∼4.5 kW), though it cannot fully warm the cabin air. A hybrid approach with low-level convective heating (∼2 kW) achieved the best results, reducing heating demand by up to 50% and highlighting the potential of IR coatings for efficient EV cabin heating. In Gellai et al. (2024), the authors present a numerical analysis of the thermal performance of a truck cabin [16]. The preliminary numerical analysis indicates the energy saving potential of around 30% for this innovative cabin thermal concept, as compared to the original energy demand for the traditional cabin heating configuration. The energy efficiency measure that was analysed here for the heating case is the use of infrared (IR) heating panels, which act directly on the occupants’ body surface and reduce the needed cabin temperature for sensing neutral indoor comfort. Depending on the operating conditions and the cabin configuration, the estimated energy saving potential is between 15% and 36% of the thermal energy demand for heating (winter case) and between 6% and 14% for cooling (summer case). Improving HVAC and cabin efficiency in BEVs is a key research area. Some studies have presented model-based control strategies, integrated thermal systems, and energy-efficient heating methods. The work of Cvok et al. [17] presents a multi-objective optimization framework for a BEV cabin heating system that balances thermal comfort and energy efficiency. NSGA-II and a Dymola [18]–MATLAB R2021b–ModeFrontier simulation setup were used to optimize the blower and infrared heating panels, achieving up to 30% energy savings. In this work, PMV-based comfort modeling and control-oriented simulation were applied. HVAC power demand was reduced without compromising user comfort by using an effective thermal management strategy.

1.2. Current Study Novelty and Focus

Previous research aimed to improve the efficiency and modeling accuracy of thermal systems. Thermal measures such as heating panels and insulation were typically investigated separately, rather than as part of an integrated system where all components interact. In contrast, this study introduces an advanced electric truck cabin in which new components—such as surface heating panels, and enhanced insulation—are integrated and evaluated together. A one-dimensional (1D) model of the cabin is developed, where all components interact with one another to represent the full thermal system. The 1D simulation approach offers several advantages: it reduces computational load compared to 3D CFD (Computational Fluid Dynamics) models, allows for fast concept evaluation, and supports system-level integration. Despite the growing interest in electric vehicle thermal management, no publicly available study to date has presented a validated 1D model that incorporates all these measures in a unified framework—particularly for commercial electric trucks. In addition to simulation, the model was validated against experimental data from climatic chamber and laboratory tests. Furthermore, the 1D model has been structured to support FMU (Functional Mock-up Unit) export, enabling its integration into higher-level control system simulations or vehicle co-simulation environments—a critical step toward real-world application and development efficiency.

Key Novel Contributions:

• Integrated system-level modeling: The study introduces a 1D model that brings together multiple thermal measures, like insulation and heating panels. It offers a complete understanding of the system.
• Comfort measurement and validation: Linear comfort simulation is implemented within the 1D simulation, providing a robust assessment of passenger comfort.
• Electric Truck Focus: The study highlights the thermal aspects of electric truck cabins and fills a key gap in current research. An electric truck cabin with these types of measures was investigated and validated with real measurement data. The measurements made it possible to build an accurate 1D model and support further development.
• FMU compatibility: The model is ready to be exported as a FMU, making it easy to integrate into larger system simulations or real-time applications—an important step toward practical deployment.

In Figure 1, an overview of the workflow for the simulation and validation of the electric cabin is presented. There are two main workflows. The first is simulation and data processing, which begins with vehicle specification. Following that, the simulation models are established, alternative thermal measures are analyzed, the concept is identified, concept cabin simulations are conducted, and finally, a FMU is created. The second workflow is focused on measurements and supports the simulation process. Laboratory measurements of the original cabin help to establish the simulation models. Additional laboratory measurements with different thermal measures assist in analyzing these alternatives. Finally, climatic chamber measurements of both the original and the selected thermal measures are used to validate the simulation.

In summary, this work offers a meaningful and practical contribution to the field of EV thermal management by bridging the gap between individual components and full-system implementation, especially in the underexplored area of electric trucks.

2. Physical Background

For the thermal management analysis, an electric truck cabin equipped with heat panels and insulation was used. This truck cabin served as the basis for developing and parameterizing the 1D Dymola [18] model. In Section 2.1, the experimental truck cabin and its thermal measurements are introduced. In Section 2.2, the thermal 1D model developed in Dymola is explained.

2.1. Measurement of the Prototype Cabin

The 1D thermal model of the truck cabin was validated against experimental data to assess its accuracy in predicting power consumption, interior cabin temperature, and comfort value. Validation of power consumption and cabin temperature was performed using two controlled test environments: a laboratory and a climatic chamber. The comfort value validation was conducted only in the climatic chamber.

Two experimental setups were used for validating the simulation model:

• Laboratory measurements: The cabin was placed in a room-temperature environment and actively heated.
• Climatic chamber measurements: The cabin interior was held at room temperature while the external environment was cooled.

The laboratory and climatic chamber available within the framework of this project were not capable of simulating dynamic scenarios. The primary objective of the simulations and measurements in this study was to provide a first-order assessment of the new thermal measures, such as insulation and heating panels, under controlled steady-state conditions.

For this study, a conventional truck cabin was used, whose thermal management system was developed and equipped with various energy-saving thermal measures. The cabin is approximately 2.5 m × 3 m × 1.5 m in size and is designed as a two-seat configuration. Heat from the cabin’s interior tends to escape to the outside through gaps and by conduction through the walls. To tackle this issue, insulation material can be added to the inner surfaces of the cabin. In our experiments, we used 25 mm thick Armaflex insulation mats, which were applied to the sides, bottom, and top of the interior, as well as to the front exterior of the vehicle. Figure 2 provides a visual representation of how these insulation measures were implemented.

The truck cabin is equipped with heating panels, as shown in Figure 3 using a thermal camera, where the bright yellow areas correspond to panels reaching their maximum temperature. The emissivity ($\epsilon$) of the plastic surfaces was set to 0.95 for the thermal camera measurements, consistent with typical values for common plastics [19]. The functional seat heating is also visible, providing full comfort to the driver. Heating is provided around both the driver and the passenger seat. Because of the heating panels, the driver’s comfort can be achieved more quickly, and the overall cabin temperature can be kept lower, which helps saving energy [16].

Comfort tests were conducted to assess the effectiveness of heating panels at maintaining occupant comfort in a colder cabin environment. The cabin was held at 16 °C instead of the typical 22 °C, while the heating panels reached surface temperatures of approximately 50–75 °C, as observed with a thermal camera. Because of the soft layer covering the panels, no burning sensation was reported despite the high surface temperature. However, an inconsistent temperature distribution was observed on the panel surfaces, which is attributed to the way the panels are manufactured. The participants were asked to rate their level of comfort in the head, body, and leg areas after spending ten minutes in the cabin. The overall comfort value was then defined as the average of the participants’ ratings. For each case, 9–10 individuals took part in the comfort evaluation. Participant ages ranged from 25 to 50 years, and the gender distribution was kept at approximately 50% male and 50% female for every cabin setup. Clothing typically consisted of T-shirts and jeans; in each case, one participant additionally wore a sweater or jacket, but this had no distinguishable effect on perceived comfort, highlighting the subjective nature of thermal perception. A comfort survey was used to document the findings.

The cabin, with and without thermal measures, was evaluated in both a room-temperature laboratory and a climatic chamber. In the laboratory, the cabin interior was heated to generate a temperature gradient between the inside and outside, and the corresponding energy requirements were assessed.

In the climatic chamber, a lower ambient temperature was maintained while the cabin was held at room temperature. This configuration enabled the assessment of passenger comfort and the performance of the heating panels. Environmental parameters were monitored using the instruments illustrated in Figure 4. Fifteen temperature sensors, represented by black dots, were installed at various points of the HVAC system, around the seat at the leg, body, and head areas on both the driver and passenger sides, as well as at the air outlet, air inlet, cabin center, and in the ambient environment. Two humidity sensors, shown as blue circles, were placed inside and outside the cabin, while pressure sensors, shown as green circles, were also installed inside and outside the cabin. The global radiation sensor, indicated by a yellow circle, was installed on the cabin exterior surface to monitor whether global radiation was relevant.

2.2. 1D Model

The thermal behavior of the truck cabin has already been investigated in previous studies [16,17]. The thermal behavior of the cabin can be investigated through the temporal variation of the average cabin temperature $T_{\mathrm{cab}}$ [K]. This dynamic behavior is modeled by establishing a energy balance between the input heat power $P_{\mathrm{in}}$ [W] (delivered via convection or radiation) and the heat losses $P_{\mathrm{loss}}$ [W]:

$$m·c_p·{\displaystyle \frac{d{T}_{\mathrm{cab}}}{dt}}=P_{\mathrm{in}}-P_{\mathrm{loss}}$$

(1)

where m [kg] and $c_p$ [J kg⁻¹ K⁻¹] represent the mass and specific heat capacity of the air inside the cabin, respectively.

The heat power input $P_{\mathrm{in}}$ [W] is provided by the HVAC system and consists of the heated air inflow ($P_{\mathrm{air}\_\mathrm{in}}$ [W]), the fan power ($P_{\mathrm{fan}}$ [W]), the radiative power from the heat panels ($P_{\mathrm{rad}}$ [W]), and solar radiation ($P_{\mathrm{sun}}$ [W]). The total heat loss $P_{\mathrm{loss}}$ [W] comprises four components. The first is the transmission loss $P_{\mathrm{trans}}$ [W], which depends on the cabin geometry and the insulation properties of the cabin walls. This is characterized by the heat transfer coefficient G [J K⁻¹], which is a function of the insulation thickness $d_{\mathrm{cwi}}$ [m]. The second component is the outlet air loss ($P_{\mathrm{air}\_\mathrm{out}}$ [W]). The third is the pre-conditioning power ($P_{\mathrm{pc}}$ [W]), which involves heating and cooling the thermal masses within the cabin. The fourth is the dehumidification power ($P_{\mathrm{dh}}$ [W]), which is part of the HVAC model for cabin cooling. Additionally, ventilation heat loss can be reduced by using an air–air heat exchanger, as introduced in Ref. [13]. The heat balance terms from Equation (1) can be written as:

$$P_{\mathrm{in}}=\dot{V}\rho c_p(T_2-T_1)+P_{\mathrm{fan}}+P_{\mathrm{rad}}+P_{\mathrm{sun}}$$

(2)

$$P_{\mathrm{loss}}=G\left(d_{\mathrm{cwi}}\right)(T_{\mathrm{cab}}-T_{\mathrm{amb}})+\dot{V}\rho c_p(T_{\mathrm{cab}}-T_{\mathrm{amb}})+P_{\mathrm{pc}}+P_{\mathrm{dh}}$$

(3)

Here, $T_1$ [K] and $T_2$ [K] refer to the air temperatures before and after the evaporator and heater core, respectively. $T_{\mathrm{amb}}$ [K] is the ambient temperature, and $T_{\mathrm{out}}$ [K] is the output air temperature from the cabin. $\dot{V}$ [m³] and $\rho$ [kg m⁻³] are the volumetric flow rate and the density of the incoming air, respectively.

The main purpose of this study is to investigate and validate the thermal model of the cabin, the HVAC model, and the PMV model, rather than the complete system including the control strategy. The focus is on demonstrating that PMV, power consumption, and cabin temperature can be independently calculated with this model, provided the key control inputs remain unchanged. To achieve this objective, a 1D model was developed.

The 1D model of the advanced electric truck cabin was established in Dymola using TIL Library [20]. The model consists of two main parts that effectively simulate the entire thermal management of the cabin. The cabin air, fans, evaporator and heater core, air channels and heat panels, along with a comfort model, were included.

The second part involves measurement data. Many input values were taken from real measurements, such as ambient temperature, radiation, and blower percentage. During the validation process, modifications were applied to better simulate real-life behavior. The heater core and cabin air were thermally connected not only by the inlet air, and this effect is not negligible. Since the heater core is placed under the dashboard, the cabin is also heated through conduction. The separation of the model into two parts makes it possible to export or create FMUs that can be connected with other truck models. This can be seen in Figure 5, where the Dymola model is connected with the validation setup. As illustrated in Figure 5, the measurement data used as input for the model include ambient temperature and humidity; the set temperature, represented by the average measured cabin temperature; blower request in percentage; coolant temperature and flow rate; recirculation and mix flap positions; heating panel power level (In percentage) and the insulation rate.

Figure 5 also presents the Dymola model, which consists of four main parts: Environment, HVAC model, Cabin model, and PMV model, all of which are connected to each other. The TIL Library [20] was used in this Dymola 1D simulation. Since it is a paid licensed model, a more detailed description and parameters have been added to describe the model structure and provide the main parameters of the model. The Environment block represents the environmental conditions for the model, namely ambient temperature and humidity. Solar radiation can also be included; however, in this study it was neglected because validation of solar radiation under arbitrary environmental conditions was not possible. The HVAC model draws airflow from the environment under its corresponding ambient conditions. It is constructed using components from the TIL Library. The boundary conditions are defined using TIL.GasComponents.Boundaries.Boundary, which specifies the air temperature, humidity, and pressure. This boundary is connected to TIL.GasComponents.Fans.SimpleFan, configured with a fan efficiency of 40%. The model input is the blower power, which determines the air volume flow using an air volume factor. This efficiency value is representative of typical automotive HVAC blowers, whose efficiencies generally range between 30% and 60% [21]. The model calculates the shaft power, which is fully added to the fluid energy balance, assuming a drive efficiency of 95%. A simple fan model was selected because detailed pressure calculations were not required in this study; the primary focus was on overall efficiency, as the fan itself has only a minor influence. The fan is connected to a pressure-drop model, representing losses in the original air channels. Next is the evaporator, which, in the heating case presented here, is inactive and therefore does not thermally affect the airflow. After the evaporator, a mixing flap valve regulates the proportion of airflow that passes through the heater core. The heater core is modeled using TIL.HeatExchangers.MPET.MoistAirLiquid.CrossFlowHX. This heat exchanger is used on both the glycol-water side and the moist air side. The model is configured with a fin-side heat transfer coefficient of $\alpha =500$ W/m²K and a tube-side heat transfer coefficient of $\alpha =5000$ W/m²K. The geometry of the exchanger is used to calculate wall conductance, and the tube-side pressure drop is assumed to be zero. The tube-side pressure corresponds to the liquid coolant pressure drop, which was neglected in this simulation since only the coolant flow rate was relevant for calculating the cabin heat demand, while the pressure drop itself was not. The other main parameters of the heater core model are listed in

Table 1. Heater core parameters.

Parameter	Value	Unit
Port cross-sectional area (non-circular)	$3.45\times {10}^{-6}$	m2
Port perimeter (non-circular)	$7.6\times {10}^{-3}$	m
Louver pitch	$1.1\times {10}^{-3}$	m
$n_{\mathrm{Passes}}$	2	–
Fin thickness	$1.0\times {10}^{-4}$	m
$n_{\mathrm{PortsPerTube}}$	5	–
Heat exchanger height	0.200	m
Heat exchanger width	0.300	m
Heat exchanger depth	0.030	m
$n_{\mathrm{TubesPerPass}}$	{9, 9}	–
Port diameter	$8.0\times {10}^{-3}$	m
Louver angle	90	°
Tube thickness	$8.0\times {10}^{-3}$	m
Louver length	$1.5\times {10}^{-3}$	m
Fin pitch	$1.5\times {10}^{-3}$	m

.

Finally, the HVAC model is connected to the Cabin model, which interacts with the PMV model to evaluate thermal comfort. The main cabin characteristics used in the simulations are summarized in

Table 2. Main cabin characteristics used in the simulations.

Quantity	Value	Unit
Surface area of cabin	12	m2
Volume of cabin	2.4	m3
Heat capacity of cabin interior	65,000	J/K
Heat capacity of cabin walls	21,000	J/K
Heat capacity of air channels	1040	J/K

. These were determined from the cabin geometry and material properties using the CAD (Computer-Aided Design) model. By combining material properties with the volumes of the individual components, parameters such as surface area, cabin volume, and heat capacities were obtained. The thermal resistance of the cabin walls was also estimated; however, this value can only approximate reality due to the varying surface areas and materials within the cabin. The simulations could capture the full complexity of truck operation; for example by imposing realistic driving and environmental condition profiles, however here they were assumed to be constant.

These models are essential for capturing steady-state heat transfer behaviour and provide the basis for representing thermal interactions within the system in the absence of more detailed transient dynamics.

In the simulation, a 1D comfort model was used. As described in Ref. [16], and shown in Figure 6, the passenger’s comfort was calculated using a linear mapping which can be expressed as:

$$\mathrm{PMV}={\displaystyle \frac{T_{\mathrm{surface}}+T_{\mathrm{cabin}\_\mathrm{air}}-2·T_{\mathrm{comfort}\_\mathrm{point}}}{T_{\mathrm{PMV}\_\mathrm{step}}}}$$

(4)

Here, the surface temperature ($T_{\mathrm{surface}}$), the cabin air temperature ($T_{\mathrm{cabin}\_\mathrm{air}}$), the comfort point temperature ($T_{\mathrm{comfort}\_\mathrm{point}}$)—defined as the temperature at which both air and surface temperatures result in PMV = 0—and the step value ($T_{\mathrm{PMV}\_\mathrm{step}}$), which determines the sensitivity of the PMV value, all influence the comfort result. For example, if the comfort point is 22 °C and either the cabin air or surface temperature increases by 4 °C, the PMV rises from 0 to 1. The linear 1D comfort model provides an average value of the comfort sensation for a group of people and, in doing so, considers and averages all comfort-influencing factors such as clothing, metabolism, air speed, vertical stratification, and radiation asymmetry. This approach is similar to the comfort chart by Bedford and Liese, which illustrates the dependency of comfort on the relationship between wall surface temperature and air temperature, and has been used in many studies to estimate comfort [14,15,16,22]. This tool is not intended to make individual assumptions about comfort; instead, it provides a generalized and universally valid estimation of the average comfort sensation, which can be applied in digital twin applications for fast assessments, as investigated in this study. The surface temperature was calculated from available geometric data (CAD model) and the final installation locations of the heat panels (see Figure 3). From the driver’s perspective, a simplified geometric distribution of surrounding surfaces was created to approximate the extent of direct heating. Approximately 25% of the passenger’s surroundings are covered by heat panels, whose temperature is known and time-dependent, while the remaining 75% corresponds to cabin surfaces that follow the thermal behavior of the interior materials, modeled via their heat capacity. This approach provides an approximate yet computationally efficient estimation of passenger thermal comfort, allowing for fast execution within the overall system simulation.

3. Results

The measurement results and simulation data are presented in this section. Section 3.1 presents the simulation and measurement data and compares them. Section 3.2 presents the evaluation of energy-saving thermal measures, such as additional insulation and heating panels with reduced interior temperature. Section 3.3 presents a comparison between the simulation results and comfort survey data. Section 3.4 presents a comparison of laboratory and climatic chamber measurement data, demonstrating the comparability of these two test environments.

3.1. Model Validation

The Dymola 1D model was validated using measurement data. Three main aspects were considered in the validation: the power consumption of the cabin, the interior cabin temperature, and the PMV comfort values. In this section, the validation of power consumption and temperature is presented. Figure 7 shows four diagrams comparing the measurement data with the simulation results. At the beginning of the experiment, the model deviates from the measurements. These transient phases occur because the system’s initial conditions and complexities are not fully captured in the simulation. However, after the initial period, the simulation closely follows the measured values, which is most important when evaluating the effectiveness of thermal measures. During phases of high power demand, the difference can reach several hundred watts, but in steady-state operation the deviation decreases to less than 50 W. A similar pattern is observed in the temperature results: at the beginning, there is a 3–5 °C difference, but in steady-state conditions the maximum deviation is about 1 °C. In diagrams C and D of Figure 7, both measurement and simulation results show fluctuations. These correspond to the comfort assessment phase, during which the cabin door was opened and closed several times. This caused colder air to enter the cabin, lowering the average temperature. In response, the control system activated heating; however, this typically overshoots in a conditioned cabin, causing the temperature to rise rapidly before the heating power is reduced almost to zero. Since the model was not designed to capture this situation exactly, the deviations in both power and temperature are larger during this phase. The blower power input was also used as an input for the simulation (see Figure 5); therefore, the model was partly able to reproduce these fluctuations in both temperature and power.

3.2. Evaluation of Thermal Measures

In the climatic chamber, the cabin’s thermal performance was assessed using three different configurations:

• Original cabin with 22 °C cabin air temperature,
• Cabin with additional insulation and 22 °C air temperature,
• Cabin with additional insulation and heating panels, with a lowered air temperature of 16 °C.

These thermal measures are compared during the steady-state phase, where no transient processes occur and the thermal capacitances are in equilibrium.

The climatic chamber test results, comparing the three cabin configurations, are shown in Figure 8. These results represent 1–2 h average values during the steady-state phase. This phase occurs approximately 2–3 h after the start of the measurement, when the system reaches thermal equilibrium. The baseline (original) configuration consumed the most energy, requiring 1854 W at 0 °C and 3357 W at −10 °C. The configuration with additional insulation reduced energy consumption to 1514 W at 0 °C, representing an 18% reduction, and to 2438 W at −10 °C, a 27% reduction. With only 919 W at 0 °C (a 50% reduction) and 2117 W at −10 °C (a 37% reduction), the heating panel setup achieved the highest energy efficiency while maintaining the cabin temperature at 16 °C. These findings show that while both thermal strategies result in notable energy savings, heating panels offer the greatest improvement, particularly during colder conditions.

For the power measurement, the coolant inlet and outlet temperatures were measured with PT100 resistance temperature detectors in a 4-wire setup, and the coolant volume flow was recorded using an IFM SM6000 (ifm electronic GmbH, Essen, Germany) magnetic-inductive flow meter, as shown in Figure 4. The PT100 Class A (otom^® Group GmbH, Bräunlingen, Germany) sensors have an accuracy of approximately $\pm (0.15+0.002T)$ °C over the measured range, and the flow meter has an accuracy of $\pm 0.8\%$ of the measured value. Air temperatures were measured with Type-K thermocouples with Class 1 tolerance ($\pm 1.5$ °C). Due to this relatively low accuracy, the values from several thermocouples were averaged to determine and control the cabin temperature. Measurement errors were mitigated by employing the same sensors and data acquisition system for all measurements and by averaging over longer periods when the control inputs and temperatures were steady. The data logger sampled all signals at 10 Hz and exported an average value each second. The main takeaway of the measurements is on the differences between cases, which helps to reduce the impact of sensor errors. However, the effects of the mentioned sensor uncertainties on the results of Equation (2) are illustrated in Figure 8.

3.3. Comfort Analysis

The comfort of the cabin was measured using the PMV value. The PMV values obtained from occupant feedback during climatic chamber measurements are compared with the results of a linear 1D comfort model simulation. The main purpose of this comparison is to ensure that the cabin provides the same level of comfort at 16 °C with heating panels as it does at 22 °C without heating panels.

Figure 9 shows the PMV values for individual body parts based on user feedback, the whole-body average PMV, and the PMV results from the thermal simulation at −10 °C. The three cases described in the previous chapter are compared. The PMV scale ranges from −3 to +3, where −3 indicates very uncomfortably cold and +3 indicates very uncomfortably hot. Participants were asked to rate the comfort of different body regions, including the head and neck, upper body, thighs, lower legs and feet, left arm, and right arm. The diagram shows the average values of the participants’ responses.

Comfort perception depends on a person’s thermal sensation, clothing and the distance from the panels, but general observations can still be made based on the results. The left arm, which was oriented toward the door window, consistently felt colder than other body parts because windows are typically the coldest surfaces in the cabin. The head and neck regions were generally warmer and more comfortable due to the horizontal layering of warm air within the cabin.

The whole-body PMV averages in each case fell between −0.5 and 0, indicating a generally comfortable environment for most participants. For the original cabin case, the reported average value and the simulation result was nearly identical. However, the simulated comfort was marginally lower in the insulated case, likely because the interior included a mattress and insulation foam, which enhanced thermal comfort compared to the original metal and plastic surfaces. The insulation foam has a much higher emissivity value, making it feel warmer than raw plastic or metal surfaces. This difference can be observed in the subjective comfort feedback, where the test passenger felt more comfortable in the insulated cabin than in the original cabin, even at the same measured temperature. This effect was not captured by the linear thermal model. In the third case, using the heating panels, the simulated and the average reported PMV values also closely matched. This suggests that the simulation effectively reflects the measured results. Both the participants’ reports and the simulations prove, that the use of heating panels make it possible to maintain comfort levels with lower cabin air temperature.

3.4. Comparison of Laboratory and Climatic Chamber Measurements

Heating energy consumption measurements were conducted both in the laboratory and in the climatic chamber. The laboratory measurements offer a significantly more energy efficient solution compared to the climatic chamber, since the latter requires a considerable amount of energy to be conditioned. In this section, it is investigated, whether measurements without conditioned ambient environment can provide HVAC consumption estimation. Both the original and the insulated cases are depicted in Figure 10. The measurement results are represented by the data points, some of which were obtained in the climatic chamber and others in the lab. The diagram shows the heating energy consumption as a function of the temperature difference between the ambient and average cabin temperature. For both the insulated and uninsulated case, the results follow a similar trend regardless of the environment. This is indicated by the points falling mainly on the same line. In conclusion, heating the cabin in an environment without temperature control can provide a good estimation of energy consumption in a real life (climatic chamber) scenario. Therefore, the effect of insulation can also be approximated without using the climatic chamber. The energy consumption changes exponentially with the temperature difference, and the difference between the original and insulated cases also increases with increasing temperature difference. At $\Delta T$ = 30 °C, the original cabin needed approximately 3050 W, while the insulated cabin required just approximately 2200 W of heating energy. The effect of insulation on the consumption is negligible under a 10 °C temperature difference.

However, to investigate other effects, such as humidity and solar radiation, or assess the comfort with different thermal measures, the climatic chamber has to be used. Therefore, the laboratory measurements can only reduce the number of experiments in the climatic chamber. Still, it is a viable option to reduce the cost of development.

4. Discussion

This study shows that insulation and heating panels can improve the thermal efficiency of a cabin in an electric truck. Simulation and measurement results also demonstrate that they are effective thermal measures to decrease the energy demand of a cabin while maintaining the same thermal comfort for the passengers.

The validated 1D Dymola thermal model could predict the cabin’s average temperature and the energy demand of the cabin HVAC system in both laboratory and climatic chamber environments, despite its limitations and simplifications. It can be used for initial system evaluation, rapid simulation, and as an FMU in system-level co-simulation and control development, due to its low computational requirements.

The application of additional insulation represents a relatively simple thermal measure to implement, as most OEMs already incorporate insulation within the cabin structure. Therefore, its extended use is recommended. Simulation and measurement data indicate that the energy-saving potential of additional insulation is approximately 20% at 0 °C and nearly 30% at −10 °C.

The heating panels demonstrated the highest energy-saving potential in this study. The main advantage of using heating panels is their ability to maintain passenger comfort even when the cabin air temperature is lower than usual. The study shows that a cabin air temperature of 16 °C can be maintained without a loss of thermal comfort for the passengers. This results in significant energy savings: at 0 °C, energy demand was reduced by approximately 50%, and at −10 °C, by around 37%, compared to the original setup. Heating panels are not yet widely used by OEMs, mainly because, compared to insulation, they represent a more complex system. However, their implementation is worth considering due to their high energy-saving potential.

The comfort assessment, specifically the determination of the PMV value, was an important part of this study due to the use of heating panels. While the comfort study involving participants provided relatively good evaluations of comfort across different body regions, the simulation was limited to a simplified method. Despite this simplicity, after an iterative validation process, the model could be used to approximate the actual comfort values. However, the simulation could not capture variations in comfort across different body regions, nor the vertical temperature differences within the cabin.

To prepare for the climatic chamber measurements and conserve resources, most of the measurements were conducted in a laboratory environment. Analyzing the data showed that the laboratory and climatic chamber measurements were comparable in terms of energy consumption and temperature differences between the cabin and the environment. This confirms that heating tests conducted in a laboratory can provide reliable and comparable results, especially for steady-state energy evaluation. However, for comfort measurements and the determination of the influence of other conditions—such as radiant heat, humidity, and external airflow—the laboratory setup was not sufficient and lacked the necessary capabilities.

Proposed solution could be complementary to other HVAC optimization methods, as it can be applied as a separate, additional layer. For existing fleets, retrofitting our approach is more practical than installing a heat pump, while still capturing meaningful savings. For example, controlled chassis-dynamometer testing of otherwise similar BEVs found that switching from a PTC heater to a heat-pump system improved cold-weather range retention by approximately 1–15% depending on drive cycle and temperature (with the largest gain at 0 °C), reflecting lower HVAC energy use overall [23]. Likewise, better control strategies such as MPC for automotive HVAC have been shown to cut energy use by about 8–9% compared to conventional on/off or PI baselines in both experiments and simulations, while maintaining passenger comfort [24]. Where heat pumps or MPC already exist, the method presented in this work can further reduce energy use by coordinating setpoints and schedules, rather than replacing those systems.

Notwithstanding the positive results, it’s critical to acknowledge some limitations. The model and experimental setup assumed a steady-state ambient temperature and a stationary vehicle, and therefore did not simulate real driving conditions with fluctuating ambient temperatures, solar radiation, different driving speeds, and the transient behavior of the HVAC system. Idealized boundary conditions were used, primarily to evaluate the effectiveness of the applied thermal measures. On the other hand, the comfort model and measurements were also idealized, as feedback was collected from a small group of participants under controlled environmental conditions. The linear 1D comfort model is a simplified comfort model that is suitable for applications where only an overall comfort value is required. For example, it can be applied in real-life digital twin scenarios where a quick estimation of thermal comfort is needed. However, the model is not capable of capturing local variations in thermal sensation, such as differences across body parts, or accounting for variations in clothing and individual characteristics.

The results and prototype setup are limited to a specific truck cabin design; therefore, generalizing these results to other vehicle types or configurations should be done cautiously.

5. Conclusions and Outlook

This paper presents a measurement series and a validated 1D simulation approach for an electric truck cabin. The effects of additional thermal insulation and the use of heating panels with reduced cabin interior temperatures were investigated and compared to the original setup. Comfort evaluation was also introduced during both measurement and simulation, as it was important to ensure the same level of comfort for the passenger. Simulation and measurement data indicate that the energy-saving potential of additional insulation is approximately 20% at 0 °C and nearly 30% at −10 °C. The main advantage of using heating panels is their ability to maintain passenger comfort even when the cabin air temperature is lower than usual. The study shows that a cabin air temperature of 16 °C can be maintained without a loss of thermal comfort for the passengers. This results in significant energy savings: at 0 °C, energy demand was reduced by approximately 50%, and at −10 °C, by around 37%, compared to the original setup. By applying these thermal measures, the HVAC system’s power consumption can be reduced by up to 50%, which directly contributes to extending the maximum driving range of battery electric vehicles.

In future work, the setup should be tested under variable environmental conditions and dynamic driving cycles. The simulation should increase spatial resolution within the cabin, and comfort evaluation should be conducted with a broader participant base. To further support the use of these thermal measures, a cost–benefit analysis is needed to evaluate their economic feasibility. However, the results show that even simple thermal strategies can improve the efficiency of HVAC systems. This is particularly relevant for BEVs, as the HVAC system draws energy directly from the battery, reducing the available driving range. Further research is required to improve the overall energy efficiency of BEVs, making them more sustainable and attractive to a wider range of customers.