Motion sickness (MS) is a common issue for vehicle passengers, characterized by symptoms such as nausea, dizziness, and discomfort due to sensory conflicts between the visual and vestibular systems. This study aims to identify and quantify the modulating factors contributing to MS in vehicles within the Malaysian context. A systematic questionnaire was conducted, incorporating subjective feedback from 634 participants. The results highlight key factors like seating position, visual activities, and environmental conditions that influence MS susceptibility showing the most prominent factors are from Visual Activity with a mean of 3.44 among the others contributing most susceptibility from the activities of Reading (83.2%) and Writing (79.2%). Older people and females seemed to report greater susceptibility to motion sickness. This vulnerability is shown to increase with age as those 45+ years and above show the highest susceptibility, reporting a 69% rate on curvy roads and 66% during reading, in contrast with those between 18-29 who report a 52% and 50% susceptibility rate. Females again reported consistently higher rates than males, with the difference being larger in situations involving rear-facing seating or tasks requiring significant visual focus, such as reading (female 63% vs. male 46%) and driving on curvy roads (female 68% vs. male 50%). These factors of rear-facing seating and tasks involving intense visual focus, like reading or watching videos, were the strongest modulators identified, with a strong aggravating influence on symptoms across all groups. The findings provide crucial baselines for engineering future vehicle layouts, optimizing cabin climate systems, and designing targeted mitigation systems required to support human comfort and accelerate Automated Vehicle (AV) adoption in developing transport infrastructures.
The performance of a proton exchange membrane fuel cell (PEMFC) is strongly influenced by its operating conditions. In this study, a one-dimensional (1-D) mathematical model is developed to investigate PEMFC performance while explicitly accounting for water transport within the fuel cell during operation. The results demonstrate that the cell output performance is significantly affected by key operating parameters, including operating temperature, operating pressure, membrane thickness, exchange current density, and charge transfer coefficient. In addition, gas species transport and water management are incorporated into the model. The developed model is further applied to evaluate and analyze the dynamic performance characteristics of a hydrogen fuel cell electric motorcycle, including hydrogen consumption, water generation, and energy efficiency under the World Motorcycle Test Cycle (WMTC). This study provides a meaningful framework for predicting and designing the dynamic performance of hydrogen fuel cell electric motorcycles under realistic operating conditions.
Crashworthiness is a critical requirement for lightweight structures in automotive, electric vehicle and aerospace applications, where efficient energy absorption and controlled force transmission are essential. This review looks at more than 100 experimental, numerical, and analytical studies published between 2013 and 2025. It focuses on how materials, geometry, manufacturing processes, and loading conditions work together to affect the crashworthiness of thin-walled structures. Using a PRISMA inspired systematic narrative review combined with bibliometric and thematic analyses, key performance metrics: peak crushing force (PCF), mean crushing force (MCF), energy absorption (EA), specific energy absorption (SEA) and crushing force efficiency (CFE) are evaluated across metals, polymers, composites and hybrid systems, as well as non-tapered, tapered geometries under quasi-static and dynamic loading. The reviewed studies show that crashworthiness is best improved through a good combination of material, geometry, manufacturing quality and loading conditions. Hybrid structures are particularly promising, but their performance depends heavily on the interface quality, shape, and testing conditions.
The electrification of two-wheelers (E2Ws) is increasingly recognised as a high-impact decarbonisation strategy in emerging Asian markets, where motorcycles are the dominant mode of transport. However, the literature remains fragmented, and behavioural studies consistently report a persistent gap between stated adoption intentions and actual purchase behaviour. This study provides a robust empirical proxy to address these limitations through a two-phase hybrid approach: a Scoping Review and Bibliometric Analysis (ScoRBA) of 278 Scopus-indexed articles, synthesised using the PAGER framework, followed by empirical validation via a multi-model machine learning approach applied to a stated-preference dataset of 6,040 respondents from Solo, Indonesia. Bibliometric mapping identified three core socio-technical research clusters. The empirical analysis revealed a finding that departs substantially from prevailing assumptions: Perceived E-bike Quality, not financial incentives or operational costs, emerged as the dominant predictor of adoption. While initially identified via a baseline Decision Tree, this dominance was robustly validated across advanced ensemble algorithms and confirmed via SHAP analysis (Mean |SHAP Value| = 17.88%). Correctly situated within the Technology Perception dimension of the Technology Acceptance Model (TAM), this variable's dominance implies a sequential cognitive architecture: technology credibility must be established before economic evaluation becomes relevant. Consequently, policymakers in similar motorcycle-dominated transitional markets should prioritise quality certification and demonstration programmes before deploying purchase subsidies at scale.
The performance of hydraulic braking systems is strongly influenced by the condition of brake fluid, particularly boiling point and tendency to form vapor bubbles that may lead to vapor lock. Glycol-based brake fluids, such as DOT 4 and 5.1, are hygroscopic, causing moisture content to increase over their service life. Therefore, this study aimed to examine the effect of moisture percentage on boiling point and bubble formation characteristics of DOT 4 and 5.1 brake fluid. An experimental method was applied by adding distilled water to brake fluid at varying concentrations of 0%, 1%, 2%, 3%, and 4%, followed by gradual heating. The boiling point was recorded using a temperature sensor connected to a data logger. At the same time, bubble formation was visually observed during the heating process using a high-speed camera with a frame rate of 240 fps. The recorded images during the analysis were subsequently examined using ImageJ processing software. The results showed that increasing moisture content led to a significant reduction in boiling point for both types of brake fluid. At identical temperature levels, fluids with higher moisture content indicated larger bubble volume formation. These results indicated that moisture content played a critical role in degrading the thermal performance of brake fluid and increased the risk of vapor lock in automotive braking systems.
The controller area network (CAN) communication protocol used in vehicles relies on fixed message identifiers, which makes it vulnerable against frame injection and replay attacks. This study proposes an efficient lightweight hardware method that randomizes the identifier while preserving the priority rules that control bus arbitration. The design is implemented in a hardware description language (Verilog) and uses a linear feedback shift register (LFSR) as the randomization engine. The upper four bits of the identifier are kept unchanged to retain priority, where the lower seven bits are randomized. The module supports reseeding from a cryptographically secure random source. However, for the baseline statistical evaluation, reseeding was intentionally disabled to measure the intrinsic distribution. The design was evaluated using Xilinx Vivado environment. Statistical analysis was performed on 8,188 randomized ID, achieving a Shannon entropy of 6.999978 bits (maximum 7), and a chi‑square goodness‑of‑fit test that showed no detectable deviation from a uniform distribution ( = 0.2482, -value ≈ 1). Synthesis to a Artix-7 field‑programmable device reported only 15 lookup tables and 23 flip-flops (<0.1% of resources), with a maximum operating frequency of 482 MHz, indicating a minimal hardware footprint. The mechanism was further validated on a physical CAN testbed confirming protection against replay and spoofing attempts, while the mechanism added no measurable bus or timing overhead. These results show that simple, hardware‑level identifier randomization can strengthen in‑vehicle communication while keeping arbitration behaviour intact and without requiring protocol changes.
The issue of global warming and the increasing concentration of carbon dioxide (CO₂) represents a significant environmental challenge, with the transportation sector contributing approximately 23% of global greenhouse gas emissions. One of the crucial problems is the operation of Over-Dimension Over-Load (ODOL) trucks, which generate serious negative environmental and social impacts. This study conducts a socio-environmental evaluation of ODOL trucks from the perspectives of carbon emissions and carbon tax, and further analyzes the acceptance of the Zero ODOL and Carbon Tax policies in Indonesia. The technical evaluation involves simulates fuel consumption, CO₂ emissions, and carbon tax burdens based on ODOL truck travel data. Meanwhile, the social evaluation is conducted through a survey of two respondent groups, namely truck drivers (97 respondents) and the general public (214 respondents), using a questionnaire that integrates constructs from the Health Belief Model (HBM), risk perception, user cost, law enforcement knowledge (LEK), and the Policy Acceptance Model (PAM). The technical findings indicate that ODOL trucks have higher fuel consumption, CO₂ emissions, and carbon tax burdens compared to non-ODOL trucks. From the social perspective, acceptance of the Zero ODOL policy is influenced by different determinants across the two groups. For drivers, policy acceptance is highly sensitive to economic-based instruments such as carbon tax and knowledge of sanctions. In contrast, the general public is more driven by safety perception, traffic order, and the social impacts of road disturbances. These findings emphasize the importance of tailored policy implementation strategies, where economic incentive–based approaches are more effective for drivers, while safety- and public order–based approaches are more resonant for the public.
The research will enhance the forecasting of the lithium-ion battery degradation to facilitate more secure and sustainable energy storage in the electric vehicle. An analytical framework that is hybrid in nature, incorporating both statistical analysis and artificial neural network (ANN) modeling, was developed and verified using a long time dataset of INR21700- M50T cells being cycled in realistic urban driving profiles according to the Urban Dynamometer Driving Schedule (UDDS). The indicators of key degradation were first described using statistical analysis where it was found that there were strong negative relationships between capacity retention and capacity C-rate (Pearson r = -0.83) and internal resistance (r = -0.71). Based on these findings, a feedforward neural network, whose features were selected using ReliefF algorithm, was built which was used to model nonlinear aging behavior at lower input dimensionality. ANN inputs were chosen as the 2 most powerful features low-frequency impedance at 0.01 Hz and internal resistance. The resulting model had a high predictive performance of a root mean squared error (RMSE) less than 1.2% and a coefficient of determination (R2) greater than 0.97 on the original data. These results underscore the fact that combining data-based feature relevance analysis with machine learning is useful in improving the accuracy of prediction as well as the interpretability of the model. The obtained results demonstrate that combining statistically supported feature relevance analysis with reduced-input ANN modeling can improve both predictive capability and model interpretability for battery degradation estimation. The proposed hybrid framework provides a computationally efficient approach for lithium-ion battery state-of-health prediction under the investigated dataset conditions and may support future development of simplified battery management system strategies.
The advancement of connected autonomous vehicle (CAV) technologies has significantly accelerated the development and deployment of vehicle-to-everything (V2X) communication systems, which are essential for enhancing traffic connectivity and safety. Among the prominent applications, red-light violation warning (RLVW) and green light optimal speed advisory (GLOSA) systems have emerged as key innovations, drawing interdisciplinary interest from computer science, intelligent transport systems (ITS), civil engineering, and electronics. The RLVW system uses CAV capabilities to monitor and analyse driver braking behavior in response to traffic signal changes, aiming to reduce red-light violations and improve intersection safety. In contrast, the GLOSA system offers speed recommendations and estimates time to the next green signal, thereby contributing to reduced fuel consumption, lower CO₂ emissions, and enhanced driving efficiency. Both systems rely on signal phase and timing (SPaT) and map data message (MAP) protocols to transmit real-time traffic signal information and intersection geometry from roadside units (RSUs) to on-board units (OBUs). This paper presents a comprehensive review of the operational principles, benefits, and limitations of RLVW and GLOSA systems and identifies key research gaps that warrant further investigation to support the future evolution of V2X-enabled traffic management solutions.
This work evaluates the impact of manufacturing method and fiber orientation on the mechanical properties of carbon fiber-reinforced polymer (CFRP) for automotive applications. CFRP composites were fabricated using vacuum bagging (VB), vacuum-assisted resin infusion (VARI), and hand lay-up (HLU) processes. Composites in each method were manufactured with 0° and 90° fiber orientations for compressive and tensile tests, and ±45° for in-plane shear response by tensile test. Short beam and V-notched beam tests were performed to determine the interlaminar shear and shear properties. Microstructural characterization was performed on the manufactured composites and the fracture specimens after testing. Unlike previous studies that mainly focused on selected mechanical properties or a single manufacturing route, this study provides a comprehensive comparative assessment of HLU, VB, and VARI unidirectional CFRP laminates by integrating mechanical characterization, CT-scan defect analysis, SEM observations, and finite element validation. The findings reveal that superior laminate compactness and tensile-related properties achieved by VARI do not necessarily translate into higher interlaminar shear strength, providing new insight into the role of manufacturing-induced laminate architecture on composite performance. The study results showed that composites manufactured with vacuum infusion using 0° and ±45° fiber direction had higher tensile strength and stiffness than those fabricated with vacuum bagging and hand lay-up. The ultimate tensile strengths of the 0° CFRP composites for HLU, VB, and VARI specimens are 507.72 ± 52.14 MPa, 685.69 ± 62.65 MPa, and 774.31 ± 58.18 MPa, respectively. Meanwhile, for the 45° CFRP composite specimens, the tensile strength values were measured as 20.85 ± 0.82 MPa for HLU, 21.20 ± 0.45 MPa for VB, and 22.18 ± 0.81 MPa for VARI. However, at 90° fiber direction, the manufacturing method did not significantly affect the tensile strength, although the tensile modulus was still affected by the method used. The compressive strength results of the 0° composites showed that hand lay-up specimens (124.8 ± 13.1 MPa) had the highest values, while vacuum infusion specimens had the highest compressive strength at 90° fiber direction (44.60 ± 0.82 MPa). The vacuum infusion composites had lower shear (15.31 ± 1.01 MPa) and interlaminar shear strength (13.68 ± 0.85 MPa), indicating that the high fiber volume fraction did not significantly affect this behavior. However, it has a significant effect on composite stiffness, where the tensile (39.31 ± 4.58 GPa) and shear (1.50 ± 0.15 GPa) modulus values of these composites are the highest. Microstructural evaluation showed that the improvement of resin distribution and fiber/matrix bonding in vacuum infusion composites contributed to the improvement in mechanical properties.
This paper presents a simulation-based system-level evaluation of a four-source hybrid electric vehicle integrating a LiFePO4 battery, a PEMFC, a VIPV, and a compressed-air energy storage subsystem. A detailed MATLAB/Simulink model is developed using a common DC-bus architecture and four independent in-wheel hub motors with a total rated power of 12 kW. A rule-based energy management system is used to control how power is shared among different sources, maintain the battery's charge, and regulate the DC-bus voltage. The vehicle's performance is evaluated using the WLTP Class 2 driving cycle, and seven hybrid configurations are systematically compared under the same operating conditions. Simulation results confirm accurate tracking of the reference velocity profile and stable DC-bus regulation at 225 ± 3 V across all configurations. The measured specific energy consumption is approximately 5.05 kWh/100 km, including regenerative braking. Energy flow analysis shows that the battery provides short bursts of power and recovers energy from braking. In contrast, the fuel cell offers a consistent power source, which is especially useful for long-distance travel. With a 50 L hydrogen tank at 350 bar, the fuel cell extends the estimated driving range from about 95 km in battery-only operation to approximately 513 km. The integration of VIPV and compressed-air subsystems provides additional auxiliary contributions, increasing the total achievable range to roughly 606 km under full battery utilization, while improving current smoothing and transient load support. Parametric assessment of hydrogen and compressed-air storage systems reveals that hydrogen storage capacity is the principal determinant of operational duration, while compressed air provides only a modest increase in range, though it is useful for short-term support. These findings validate the technical viability of four-source hybridization and elucidate the complementary functions of electrochemical, photovoltaic, and pneumatic energy sources within a practical multi-motor vehicle framework.
Unlocking the dynamic performance of electric vehicles is often limited by the voltage constraints of the battery system. This study proposes an optimized control strategy for Interior Permanent Magnet Synchronous Motors based on an analytical formulation of direct-axis current trajectories to maximize speed extension while maintaining torque smoothness under strict battery voltage constraints, utilizing parameters characteristic of commercial C-segment electric vehicles (e.g., VinFast VF e34). Through a comprehensive simulation framework, the research investigates a coordinated Field-Oriented Control scheme integrated with a Flux-Weakening strategy through direct-axis current adjustment to reconcile the conflict between high-speed operation (up to 6 times the base speed of 100 rad/s) and power quality. The analysis identifies a critical operating point at a direct-axis current of -25 A, which effectively prevents voltage saturation while maintaining torque smoothness. The results demonstrate that, when evaluated against the baseline Field-Oriented Control without flux-weakening at 600 rad/s, this specific trajectory significantly reduces torque ripple by 8.6 Nm and suppresses Total Harmonic Distortion to a negligible 0.19%. This combined mitigation contributes to the high-speed operating capability by preventing system oscillations and preserving linear voltage modulation at this upper speed limit. These findings provide a validated guideline for enhancing powertrain stability and mechanical lifespan in modern electric mobility.
Efficient battery thermal management systems (BTMS) are essential for ensuring the safety and performance of lithium-ion batteries in electric vehicles. This study numerically investigates the influence of serpentine channel curvature on the thermal and hydraulic characteristics of a liquid-cooled prismatic battery module. Four channel designs were evaluated: a base case and three serpentine configurations with curvature values of 0.075 mm⁻¹, 0.1 mm⁻¹, and 0.15 mm⁻¹. Simulations were conducted under steady-state and transient conditions with discharge rates of 0.5C–2C and mass flow rates of 2.41 × 10⁻³ – 3.61 × 10⁻² kg/s. The results show that higher curvature and mass flow rates reduce maximum battery temperature and improve temperature uniformity, but at the expense of increased pressure drop and pumping power. At 3.61 × 10⁻² kg/s, the base-case channel exhibited a 28% increase in pressure drop compared to 2.41 × 10⁻² kg/s, while the 0.15 mm⁻¹ channel recorded up to a 60% rise under the same condition. Transient analysis revealed that curved channels enhanced heat dissipation, achieving up to 8.56% higher cooling performance than the base case. These findings highlight the trade-off between thermal improvement and hydraulic penalty, providing valuable guidance for optimizing liquid-cooled BTMS in electric vehicle applications.
Indonesia’s Free Nutritious Meals Program (MBG), launched in January 2025, aims to deliver daily meals to up to 80 million beneficiaries but faces persistent logistical challenges related to geographic dispersion, vehicle limitations, and food safety risks. Conventional delivery vehicles are often incompatible with Indonesia’s diverse terrain, resulting in inefficiencies, food spoilage, and service delays. Therefore, this article advances an evidence-based design argument that positions Design Thinking, augmented by Kansei Engineering principles, as a practical design logic for addressing these nationally significant challenges through real-world vehicle adaptation. Grounded in stakeholder engagement, field observation, and iterative prototyping, a Toyota GUN125-based multi-purpose logistics vehicle was developed to support end-to-end MBG meal distribution. Key design interventions include a reconfigurable rear cabin with a three-way door system, a dedicated food trolley, reinforced suspension, and ergonomically optimized loading and unloading mechanisms. These features were derived from operational pain points and translated into engineering solutions through an iterative Design Thinking process. Field validation along a 25 km distribution route provides empirical support for the proposed design. Performance evidence indicates 98.7% operational uptime, zero thermal breaches, food waste reduced to 2% from a 28% baseline, and a 42% reduction in delivery cycle time from 5.5 hours to 3.2 hours. The vehicle configuration supports a distribution capacity of up to 612 meals per school and achieved Kansei post-test scores averaging 4.6 out of 5 for reliability and user-friendliness. The integrated evidence demonstrates how user-centered automotive engineering, grounded in operational realities, can enhance public service logistics performance. The proposed vehicle concept offers a scalable and locally adaptable platform aligned with MBG objectives and provides a transferable design perspective for other perishable goods distribution systems.
Traffic crashes remain a critical safety challenge, with Indonesia experiencing 73,446 fatalities annually. This study develops an integrated Z-Score and Bayesian Network framework to analyze causal interactions between human and environmental factors influencing crash severity on toll roads. Z-Score analysis of 450 crash records (2022–2025) identified five statistically significant blackspot segments, with KM 430–431 exhibiting the highest concentration (Z = 4.036, n = 91). A Bayesian Network model constructed using K2 structure learning and Expectation-Maximization parameter estimation achieved 86.2% classification accuracy, surpassing previous international applications (78–82%). Conditional probability analysis revealed that straight-downhill segments exhibited 3.3-fold higher fatal crash probability than straight-level segments (0.083 vs. 0.025), while night-time conditions increased fatal risk by 57%. Sensitivity analysis demonstrated that crash type (weighted index = 0.282) and accident cause (0.214) exerted strongest influence on severity outcomes. Human error constituted 83% of crashes but showed moderate sensitivity, indicating that severe outcomes emerge from interactions between human factors and adverse conditions rather than isolated factors. Findings support prioritizing enhanced lighting and speed management on curved-downhill segments during night-time, alongside rear-end collision prevention strategies. This validated framework enables evidence based, proactive crash management and intervention prioritization for toll road safety in developing countries.
Global warming, increasing temperatures, and air pollution have become significant challenges in the past decade due to traditional emissions. Therefore, using green energy, especially electric vehicles and electric motorcycles, is the key solution to protecting the environment. Electric motorcycles are widely used in many countries due to their convenience, ease of use, and flexibility. Thus, modeling and simulating electric motorcycles are crucial for accurately calculating and designing the battery pack energy requirements. In this study, electric motorcycles were modeled and simulated to investigate energy characteristics under driving cycle test using Matlab/Simulink software. The results show the electric motorcycle dynamics and energy consumption, the influence of electric motorcycle mass, aerodynamic drag, the quality of the road, road slope angle on the electric motor power, and operating ambient temperature on the battery behavior in the heat generation. In addition, the characteristics of batteries and suitability for selecting of battery required power were compared under various batteries and proposed the best battery for the electric motorcycle. The battery trademark of the A123 (pouch) model was selected as the most suitable for the required battery pack owing to superior characteristics compared to other batteries, with the insight characteristics of high capacity of 19.5 Ah, continuous current of 19.5 A, mass of battery pack of 9.45 kg, and number of cells of 19, with total average energy consumption of 28.23 Wh km−1. This study is significant for the design and precise calculation of the battery's required power for new electric motorcycles.
The study develops a mathematical model of vehicle stability with torque redistribution, aimed at ensuring guaranteed stabilization under non-stationary conditions. Unlike existing methods, the approach combines Lyapunov functions with bifurcation analysis to derive analytical stability criteria for vehicles with mechanical differentials and enables the synthesis of adaptive control strategies that integrate differential locking, wheel braking, and dynamic torque redistribution with formal stability guarantees. The model provides accurate calculations of torque redistribution to the inner or outer wheels during vehicle oversteer or understeer, respectively, ensuring motion stabilization and preserves stability even under sharp steering inputs, as confirmed by phase portraits and transient response analyses. The proposed model was implemented and verified. The model can be incorporated into active safety systems of wheeled vehicles to enhance stability on complex surfaces, reduce computational requirements, and ensure compatibility with existing mechanical drivetrains.
Optimizing combustion parameters by incorporating alternative fuels and modifying the engine's mechanical properties is essential to improving the thermal efficiency and performance of modern internal combustion engines. This study examines the impact of HHO gas utilization, variations in compression ratios, various types of spark plugs, and ethanol gasoline blends on the torque and other characteristics of a 4-stroke fuel-injected single cylinder engine. Hydrogen is generated via electrolysis and used as a supplementary fuel. The Taguchi method was employed to create tests involving four variables: HHO percentage, compression ratio, spark plug type, and ethanol mixture. Testing occurred at 5000 RPM under a load of 1800 Watts. The findings indicated that the combination of 20% HHO, a compression ratio of 16.9:1, platinum spark plugs, and E-80 ethanol yielded optimal engine performance, with thermal efficiency reaching 60% at 7500 rpm. Moreover, the results of deposit content analysis after 50 hours of operation indicated that the ideal design produced fewer deposits than RON 92 gasoline.
Plastic waste pyrolysis has emerged as a promising strategy for converting non-recyclable plastics into plastic-derived diesel oil (PDDO), providing a pathway for both waste valorization and alternative fuel production. However, the direct utilization of PDDO in diesel engines remains constrained by suboptimal combustion behaviour and elevated exhaust emissions. While real-time non-surfactant emulsion fuel supply systems (RTES) have been widely investigated for conventional diesel fuels, their application to PDDO has not yet been systematically evaluated in engine operation. This study presents the first implementation of a real-time non-surfactant emulsification system to generate surfactant-free water-in-PDDO emulsions containing 5–15% water by volume. Engine performance and exhaust emissions were experimentally assessed using a 4.5 kW single-cylinder compression-ignition generator at low and high loads. The results indicate that controlled water addition modifies combustion behaviour by improving spray atomization and secondary droplet breakup associated with micro-explosion phenomena. Among the tested blends, the 15% water emulsion (EPO15) provided the most balanced performance, improving brake thermal efficiency by 6.48% while reducing NOx emissions by up to 47.06% compared with the baseline fuel. Exhaust gas temperature was consistently reduced, without substantial deterioration in fuel consumption. These findings demonstrate that RTES can enhance the combustion and emission characteristics of PDDO, supporting its potential application in small-scale compression ignition engine systems.
This paper presents a comprehensive design methodology and mechanical evaluation of an electric motorcycle battery pack that fulfills both international safety standards and Indonesian regulations. As the country with the world’s largest motorcycle fleet, Indonesia faces significant challenges in transitioning to electric mobility—particularly in preventing battery malfunctions and mitigating accident risks. The design process begins by defining minimum technical requirements, ensuring regulatory compliance, and addressing geometric constraints, then proceed with the development of component architectures in accordance with industry best practices. Detailed models of the pack and its subassemblies are subjected to finite element analysis to verify structural integrity under demanding operational conditions. Key performance targets include an IP68 ingress protection rating, sufficient rigidity to withstand vibrational loads, and impact resistance for accident scenarios. Simulation results confirm that the final design maintains mechanical robustness across a variety of load cases. Building on these findings, the paper offers specific recommendations to further enhance pack performance and safety. The described methodology is fully reproducible and provides practical insights for efficient product development, even when resources are limited.
