Multi-Objective Coordination and System Integration in Next-Generation Vehicle Design

1. Introduction

Electrification and increasing vehicle intelligence are changing how vehicles are designed and developed. Vehicle performance increasingly depends on coordination among chassis, sensing, and control systems. The International Energy Agency’s Global EV Outlook 2026 discusses electric vehicles, power batteries, and charging infrastructure together, highlighting the need to consider these elements as a whole [1]. The development of autonomous driving also requires engineers to address human behavior, cognition, trust, and human–machine interactions so that automated functions can meet users’ needs [2]. Therefore, vehicle design must address both energy consumption and safety, while also considering interactions with the environment and society, in the pursuit of continuous performance improvement and sustainable development.

These changes also require vehicle design to accommodate a wider range of performance requirements. For example, structural weight reduction must account for load-carrying capacity and fatigue life; motion planning must consider the vehicle’s ability to execute planned motions, as well as occupant comfort; and electric powertrain design must coordinate energy efficiency, dynamic performance, and packaging constraints, etc. These requirements often interact, and improving one aspect may introduce limitations elsewhere. Design assessment must therefore consider both component performance and its effects on overall vehicle behavior under the intended requirements and operating conditions.

As vehicle functions expand, coordination among mechanical, electrical, and control systems becomes more complex. Model-based systems engineering (MBSE) can connect design requirements, system configurations, and verification activities, supporting collaboration across engineering disciplines [3]. In practical development, engineers need to optimize individual components, assess whether they work together effectively when installed in the vehicle, and use simulation and testing to determine whether the vehicle meets its intended requirements.

This collection of papers published in Designs, titled “Designs of New Generation Vehicles”, includes nine articles covering LiDAR, occupant posture monitoring, motion paths, chassis materials and structures, electric powertrains, and bidirectional charging. This editorial presents these studies under three themes: sensing and occupant experience, chassis materials and structural optimization, and electric powertrain and energy system integration. Through these cases, we discuss how improvements in components and methods affect vehicle performance and what verification is needed to assess their practical value.

2. Sensing and Occupant Experience

Onboard sensing systems need to be designed around the vehicle’s intended use. LiDAR offers several measurement and scanning approaches, each with its own advantages and technical challenges [4]. For example, when LiDAR and cameras operate together, their relative positions and orientations must be accurately calibrated to support data fusion [5]. Sensor assessment should therefore consider detection performance, installation conditions, calibration requirements, and system integration together.

Bridgelall (Contribution 1) combines patent analysis and scientometric methods to identify 188 relevant patents granted in the United States between 2018 and 2024. The patents are classified into five areas: beam generation, beam steering, field-of-view expansion, signal quality improvement, and cost and size reductions. Cost and size reductions receive considerable attention in this sample, while solid-state approaches, modular design, and integrated manufacturing are also key areas of interest. This study helps clarify the LiDAR technology landscape and informs design choices. For automotive applications, detection performance and the feasibility of vehicle integration need to be considered together. Patent counts, however, reflect research and development priorities within the selected sample; the maturity and cost advantages of specific approaches still need to be demonstrated in practical applications.

Sensing also provides information about occupants inside the vehicle. Vergnano et al. (Contribution 2) propose a standardized sensor interface installed between the seat rails and the vehicle body that can accommodate different vehicles and seats. The device uses four load cells to estimate the center of pressure and incorporates inertial measurements to compensate for vehicle acceleration, allowing normal sitting, forward leaning, and lateral leaning postures to be identified. Static tests and vehicle tests on a closed course demonstrate posture recognition capability, although dynamic performance depends on the individual, posture, and changes in loading; lateral leaning remains more difficult to distinguish. The adaptable mounting interface provides a flexible means of acquiring occupant state information and may support subsequent research on occupant restraint control. Further testing is needed to determine whether it improves crash protection.

Occupant experience depends on both cabin design and the driving experience. Research on comfort in automated driving needs to consider vehicle characteristics, automation systems, and individual differences among occupants [6]. Motion and behavior planning involves coordination among paths, trajectories, and driving decisions [7]. Parlangeli (Contribution 6) addresses curvature discontinuities at the junctions between straight lines and circular arcs in Dubins paths by developing smooth transition functions with tunable curvature and constraints on its derivatives. Geometric constructions and numerical examples illustrate connections between initial and final positions and orientations, as well as the handling of intermediate waypoints, offering an approach to balancing path length and motion smoothness. The study primarily relies on numerical analysis under kinematic assumptions such as planar motion and constant forward speed. Accurate path tracking by a real vehicle and any improvement in occupant comfort still require verification that accounts for control and vehicle dynamics.

These three studies address external environmental sensing, occupant state recognition, and vehicle motion planning, respectively. Future research could explore the use of occupant posture and individual needs in motion planning to better adapt vehicle behavior to occupants. This requires consideration of sensor errors, planning assumptions, and the vehicle’s actual execution capability, followed by co-testing to assess the outcome.

3. Chassis Materials and Structural Optimization

The chassis and its associated structures carry loads, constrain motion, and influence vehicle vibration. Topology optimization adjusts material distribution to identify suitable structural configurations under specified loads and constraints [8]. Research on electric vehicle bodies also shows that weight reduction affects load paths and crash response [9]. Component optimization should therefore consider the component’s role in the vehicle and the relationships among weight, load-carrying capacity, stiffness, and other requirements.

The ability of materials to retain their properties during long-term use is an important issue in chassis design. Imberti et al. (Contribution 3) investigate the durability of a magnetorheological fluid intended for automotive braking. The authors use cumulative dissipated energy to represent braking duty and conduct a bench test with braking energy equivalent to 100,000 km of use. Rheological tests and microscopic observations compare the material properties before and after aging. Under the conditions tested, shear stress in the presence of a magnetic field does not decrease but rather rises to approximately twice that of the original fluid. The authors relate this change to particle refinement and changes in microstructure. These results provide evidence on the fluid’s long-term behavior and show that design and control approaches need to account for changes in material properties during use. The energy-equivalent bench results still require further verification through longer-duration tests, thermal cycling, and vehicle-level durability testing.

Xu et al. (Contribution 7) investigate weight reduction in a snowplow mounting frame. The authors analyze the loads, perform topology optimization, convert the optimized result into a manufacturable structure, and verify it using finite element analysis. The frame mass is reduced from 177.52 kg to 137.77 kg, a reduction of 22.39%. Maximum stress and deformation increase after weight reduction, but the maximum stress remains below the adopted allowable value, and deformation remains within the range considered acceptable in the study. Under the specified loads, the fatigue analysis predicts a life of at least one million cycles. This case shows that weight reduction requires simultaneous checks of load-carrying capacity, stiffness, and fatigue life. The effects of actual operating loads, connection details, and manufacturing variations on service life still need to be confirmed through prototype and field testing.

Structural optimization should also address vehicle dynamics. Gulino et al. (Contribution 8) investigate in-wheel suspension for urban micromobility vehicles such as bicycles and electric-assist bicycles, linking spoke and rim geometry to stiffness distribution and vertical vibration assessment. Trapezoidal spokes combined with a locally thickened rim reduce the coefficient of variation in stiffness with rotational angle to below 2%. Simulations also show a reduction in the discomfort peak near the low-speed critical velocity. However, the optimized mean stiffness remains relatively high, and vibration isolation remains limited at higher speeds or on rough roads. This indicates that improvements in stiffness uniformity need to be considered alongside overall compliance. The study connects component geometry with comfort assessment, while the dynamic benefits of the optimized design still require prototype testing.

When a design involves many variables and performance objectives, efficient screening of candidate solutions becomes a further challenge. Multi-objective reinforcement learning can investigate trade-offs among objectives and learn strategies based on decision preferences [10]. De Santanna et al. (Contribution 4) apply dominance-based multi-objective reinforcement learning to the offline optimization of a double-wishbone suspension. A neural network surrogate model evaluates design parameters, and multibody simulation then checks the candidate solutions. In a case with 30 design variables and 14 objectives, the method outperforms two comparison methods according to the study’s k-optimality ranking while reducing the number of surrogate model evaluations. The results suggest that learning methods can assist in the search for complex suspension designs. However, the benefits in this case primarily concern solution evaluation and sample efficiency, with no advantage in total computation time. Further work should assess the effects of surrogate model error, changes in suspension configuration, and manufacturing variations.

These four studies provide different tools for chassis design through materials research, structural analysis, vibration assessment, and optimization methods. Material tests help identify changes in properties; structural analysis checks load transfer and load-carrying capacity; vehicle dynamics models assess how component changes affect driving behavior; and learning methods assist the screening of complex designs. Combining these activities in practical development helps assess the benefits and costs of a design change. Scheme selection should also consider whether test loads represent actual use, whether fatigue margins are sufficient, and how large model errors may be, with continued verification through testing.

4. Electric Powertrains and Bidirectional Charging

Electric powertrain design requires component selection based on vehicle-level requirements and the definition of corresponding operating and control strategies. A review of dual-motor powertrains discusses the relationships among electromechanical configurations, component matching, and control methods [11], showing that energy efficiency and dynamic performance depend on coordination among the system’s parts. For a specific vehicle, cost, packaging space, manufacturing, and maintenance conditions must also be considered to make the design practical.

Hazizi et al. (Contribution 5) develop a powertrain for a Shell Eco-marathon Urban Concept vehicle, determining power requirements from the competition route and driving requirements. Within cost and packaging constraints, the authors select a 1.5 kW brushless DC motor, a lithium battery pack with 12 series-connected groups of eight parallel cells, and an adjustable two-stage chain transmission. Longitudinal vehicle dynamics and battery models are then used for simulation-based assessment. The study connects vehicle-level requirements, component selection, and packaging and provides construction information to support reproduction by other researchers. However, the assessment does not yet fully account for regenerative braking, detailed motor efficiency characteristics, or thermal dynamics. Energy efficiency therefore requires further confirmation using complete operating conditions and measured results.

Bidirectional charging strengthens the connection between vehicle energy systems and the electricity grid. Supplying power from a vehicle to the grid requires solutions for bidirectional power conversion, control, and equipment interfaces [12]. Scarlatache et al. (Contribution 9) propose an MBSE framework that first represents requirements, use cases, and logical architecture in System Modeling Language (SysML), then manually maps them to the physical architecture and connects executable simulation models. Short-duration simulations demonstrate transitions among charging, standby, and vehicle-to-grid operation, as well as the relationships among requirements, architecture, and models. The process helps organize development across engineering disciplines, but model correspondence between tools still requires manual maintenance. The modeled operating sequences and short-duration simulations do not yet cover all requirements.

These two studies illustrate a specific electric powertrain configuration method and a development process for bidirectional charging. Reviews identify integration among models, information exchange between tools, and traceability from requirements to verification results as important issues in MBSE [3]. Model simplification and tool integration also affect the industrial application of early verification methods [13]. Further development therefore needs to clarify parameter sources, interface requirements, and verification conditions, while extending coverage to thermal effects, aging, and abnormal operating conditions. Cross-checks among model calculations, hardware-in-the-loop testing, and vehicle tests can provide a more complete basis for determining whether a system design meets its requirements.

5. Conclusions and Outlook

The nine articles in this collection address different vehicles and components, covering sensing, motion planning, chassis materials and structures, electric powertrains, and bidirectional charging. Together, they suggest that vehicle design should start with actual tasks and operating conditions, account for interacting performance requirements, and examine how component improvements affect the system. Patent analysis, sensor interface design, durability testing, path construction, optimization methods, and system modeling serve different purposes. Their value should be assessed in terms of the problems they address and the conclusions supported by the available analyses and tests.

Future research should first broaden test conditions to better represent actual use. Occupant posture, road unevenness, operating loads, and material aging all affect vehicle behavior. Testing across a wider range of operating conditions and users, together with analysis of sensing and modeling errors, can help establish where a design is applicable. For occupant-related design, further work could also investigate how state monitoring results can inform motion planning, with the benefits assessed through vehicle testing and user evaluation.

Coordination across engineering disciplines and development stages also needs to be strengthened. Structural optimization should be followed by assessment of vehicle dynamics; the design scheme selected by learning methods should be checked against engineering models and design constraints; and energy system configurations should remain consistent with requirements, interfaces, and verification models. Models should be updated using test results to reflect changes in materials, manufacturing variations, and actual operation. For vehicle concepts still under exploration, such as flying cars and multimodal mobility platforms [14,15], these design approaches may offer useful guidance, but their applicability needs to be verified against the dynamics and task conditions of each platform.

Finally, research findings require progressively more extensive and sufficient verification. This collection includes technology trend and numerical analyses, structural and system simulations, material bench tests, and vehicle tests on closed courses, with different scopes of verification across the studies. Further work should combine analysis with testing and address long-term operation and abnormal conditions to clarify which methods can be applied more widely and the conditions needed to do so. We thank all the authors and reviewers for their contributions to this collection and hope that these studies will provide further guidance on performance coordination, system integration, and engineering verification in next-generation vehicle design.