Browse Topic: Optimization

Items (7,350)

From LLM to Deep Learning: Efficient Simulation of Last-Mile Energy Behavior in Campus Communities

2026-01-0461To be published on 04/07/2026

Communities are critical nodes in the urban energy network, integrating energy, transportation, and building systems to enhance overall energy efficiency. Accurate management of these systems depends on characterizing individual energy-demand behaviors. However, traditional last-mile behavioral models often fail to capture the complex and non-linear nature of human decision-making, creating a need for advanced simulation techniques. Although agent-based simulations powered by Large Language Models (LLMs) have demonstrated considerable efficacy across diverse domains, their substantial computational demands—specifically in terms of token consumption—pose pronounced constraints in large-scale simulations involving tens of thousands of agents, considerably limiting their practical applicability. To address this challenge, a deep learning-based approach is proposed for single-step prediction of last-mile behavior. A hybrid architecture, consisting of an MLP encoder and a Transformer

Yang, Zhifeng, Chen, Yongjian, Ou, Shiqi(Shawn)

Energy-Aware Traffic Signal Optimization for Sustainable Urban Networks Using Valid Action Policies

2026-01-0462To be published on 04/07/2026

Optimizing signal control across urban networks is crucial for reducing energy consumption and improving traffic efficiency. Advances in Vehicle-to-Everything (V2X) technologies have enabled deep reinforcement learning (DRL) to demonstrate significant promise in traffic signal control. However, many existing studies focus primarily on agent performance while neglecting practical operational constraints, which can lead to fairness issues in traffic control. Moreover, prior work often treats energy optimization as a by-product of efficiency. To address these gaps, this study proposes a multi-agent DRL framework to mitigate peak-hour congestion and reduce excessive emissions in urban networks. Building on this framework, a variant of Independent Proximal Policy Optimization (IPPO), termed Global State Independent PPO (GS-IPPO), is developed to enhance IPPO's coordination capability. To further improve practical applicability and fairness, this study incorporates constraints imposed by the

Qian, Wangrui, Jiang, Shuyan, Qi, Hao, Ou, Shiqi(Shawn)

Optimizing EV Battery Models for Full Vehicle Simulations

2026-01-0389To be published on 04/07/2026

The increasing adoption of electric vehicles (EVs) demands accurate yet computationally efficient battery models that can be integrated into full vehicle simulations. At the cell level, mechanical battery models often employ fine-scale elements to capture localized deformation and failure phenomena. While such detailed discretization enables high-fidelity predictions, it also imposes significant computational costs that become prohibitive when scaling up to pack-level or full-vehicle crash and durability simulations. This research addresses the challenge by systematically simplifying cell-level mechanical models to reduce computational burden while preserving predictive accuracy. We propose an approach in which larger elements and reduced complexity representations are introduced without compromising the model’s ability to replicate experimentally observed behaviors. The methodology emphasizes model validation against targeted loading conditions, ensuring that the essential mechanics

Sahraei, Elham, Brar, Bavneet, Parmar, Dhruv, Muralidharan, Umachandran

Propulsion System Design Considerations for HEV versus EV Applications

2026-01-0641To be published on 04/07/2026

The design and development of EVs and HEVs has become a growing issue recently due to concerns about pollution and dependence on non-renewable fossil fuels. Accordingly, General Motors (GM) has an evolving vehicle electrification plan over the past several decades and into the future to deliver low-cost and efficient EVs and HEVs. Propulsion system requirements for the applications of EV and HEVs are quite different and therefore, the design principles and directions are also distinct between these cases. From micro-hybrid and full plug-in hybrid applications to full EV applications, design requirements, strategies and outcomes can widely vary. Continuous and peak duty are substantially different depending on the application of the vehicle. Motor operational duty is significantly higher for EV compared to the electric motor of a hybrid electric vehicle. Motor torque, power and efficiency requirements are also higher for EV motors, which greatly influences the choice of motor type and

Momen, Faizul, Jensen, William, Das, Shuvajit, Chowdhury, Mazharul, Alam, Khorshed, Anwar, Mohammad, Reinhart, Timothy

Target Cascading Kinematics and Compliance Tables to Suspension Design

2026-01-0584To be published on 04/07/2026

During the initial design phase, automotive Original Equipment Manufacturers (OEMs) require the adaptability to examine various suspension system architectures while maintaining focus on the specific performance objectives. Those requirements are expressed by Kinematics and Compliance (K&C) look-up tables and represent the footprint of what the suspension should look like in real-world applications. However, translating those requirements into the full geometric hardpoint layout is not straightforward. This process often relies on trial-and-error approaches, making it time consuming and requiring significant expertise. This challenge, known as "target cascading," remains a major hurdle for many engineers. The main objective of this paper is to cascade the suspension requirements from K&C look-up tables to hardpoint locations by adopting an automatic workflow and ensuring respect for constructive and feasibility constraints. Design space exploration was conducted using a robust

Brigida, Piero, Di Carlo, Paolo, Di Gioia, Niccolò, Geluk, Theo, Tong, Son, Alirand, Marc, Gorgoretti, Davide, Occhineri, Marco, Tassini, Nicola, Berzi, Lorenzo

Understanding Adversarial Transferability in Vision-Language Models for Autonomous Driving: A Cross-Architecture Analysis

2026-01-0170To be published on 04/07/2026

Vision-language models (VLMs) offer a promising end-to-end approach for autonomous driving, but their robustness to physical adversarial attacks remains an unaddressed gap for safety-critical deployment. This paper introduces a systematic framework for evaluating these attacks using the Dolphins VLM and CARLA simulator. We employ Natural Evolution Strategies (NES), a black-box optimization method, to generate adversarial patches without internal model access. Our approach uses semantic similarity loss and Expectation over Transformation (EoT) to ensure physical realizability under varying viewing conditions. Patches are strategically placed on realistic advertising infrastructure (bus shelter panels and highway billboards), reflecting a plausible threat model. We establish two complementary scenarios: crosswalk pedestrian detection suppression and highway steering manipulation. Our evaluation reveals critical vulnerabilities. Adversarial patches achieved a 76.0% overall attack success

Fernandez, David, MohajerAnsari, Pedram, Salarpour, Amir, Pese, Mert D.

Ultra-Low Latency Video Pipeline Optimization for Perception-Centric Autonomous Systems

2026-01-0022To be published on 04/07/2026

Autonomous platforms such as self-driving vehicles, advanced driver-assistance systems (ADAS), and intelligent aerial drones demand real-time video perception systems capable of delivering actionable visual information at ultra-low latency. High-resolution vision pipelines are often hindered by delays introduced at multiple stages-sensor acquisition, video encoding, data transmission, decoding, and display-undermining the responsiveness required for safety-critical decision making. This study introduces a holistic system-level optimization framework that systematically reduces end-to-end video latency while maintaining image fidelity and perception accuracy. The proposed approach integrates hardware-accelerated encoding, zero-copy direct memory access (DMA), lightweight UDP-based RTP transport, and GPU-accelerated decoding into a unified pipeline. By minimizing redundant memory copies and software bottlenecks, the system achieves seamless data flow across hardware and software

Indrakanti, Rama Kiran Kumar

Numerical Analysis of Operating Parameters Coupled Effects on Flow Instability Mechanisms at Supercritical Pressure

2026-01-0134To be published on 04/07/2026

Supercritical fluids (SCFs) are widely utilized in advanced energy systems due to their exceptional heat transfer characteristics. However, flow instability, particularly near pseudo-critical conditions, poses a significant challenge to their effective applications. Despite extensive research, the coupled effects of thermal and fluid dynamic mechanisms on flow instability remain insufficiently understood, particularly in supercritical water (SCW) systems. This study identifies the onset of flow instability (OFI) in the negative slope region of pressure drop curve during steady-state conditions, analyzing the relationship between pressure drop and mass flow rate. Transient analyses were then conducted to investigate the influence of operating parameters on oscillatory phenomena such as pressure drop oscillations (PDO), density wave oscillations (DWO), and thermal oscillations (ThO). The results show that higher mass flow rates and heat fluxes, reduce pressure oscillations by up to 20

Rehman, Attiq Ur, Ahmad, Hassaan, Du, Xiaocheng, Kanwal, Lubna

Ensemble of Experts for Generating Digital Parking Maps from Remote Sensing Images

2026-01-0107To be published on 04/07/2026

Being a vital component of transportation, parking and parking-related research are gaining increasing attention from the intelligent transportation and automotive industry research communities. A digital parking map with precise parking spot geospatial information is crucial for research such as automatic valet parking, parking spot recommendations, and parking route optimization. Currently, these maps are generated through costly and resource-intensive human annotation. To address the challenges of insufficient parking maps and the high costs of manual map creation, researchers have proposed the following two strategies for generating parking maps. The SLAM-based map generation for indoor parking lots and the remote sensing image-based map generation for outdoor parking lot. Our work mainly focuses on the outdoor high-definition parking map generation from remote sensing images. These images often suffer from occlusion, inconsistent resolution, and varying luminosity conditions. To

Shukla, Ajitesh, Cao, Xiaofei, Liu, Yongkang, Takeuchi, Yusuke, Sisbot, Akin

High Order Control Lyapunov Function - Control Barrier Function - Quadratic Programming Based Autonomous Driving Controller for Bicyclist Safety

2026-01-0034To be published on 04/07/2026

Ensuring the safety of vulnerable road users is a critical challenge in the development of advanced autonomous driving systems. Among vulnerable road users, bicyclists present unique characteristics that make their safety both critical and manageable. Compared to pedestrians, vehicles often travel at significantly higher relative speeds when interacting with bicyclists, yet bicyclist motion is generally more predictable and governed by clear traffic rules, offering opportunities for model-based control strategies. To address bicyclist safety in complex traffic environments, this study proposes a High-Order Control Lyapunov Function–High-Order Control Barrier Function–Quadratic Programming (HOCLF-HOCBF-QP) control framework. By using this approach, CLFs enforce system stability, ensuring the vehicle follows a desired trajectory, while CBFs define safety boundaries, preventing collisions between the ego-vehicle and other road users. The optimal control input is then obtained by solving a

Chen, Haochong, Cao, Xincheng, Guvenc, Levent, Aksun Guvenc, Bilin

Design development and optimization of hybrid cross car beam for performance

2026-01-0513To be published on 04/07/2026

The cross-car beam (CCB) within the instrument panel (IP) is a multifunctional structural element that supports safety, vibration control and modular integration in automotive design. The reduction of mass without compromising structural integrity plays a vital role in this endeavor. This study presents the design and optimization of design intent model of magnesium beam to meet the performance requirements Vs study model of hybrid cross car beam using magnesium steering column bracket, steel and plastic material to achieve reduced mass and enhanced stiffness while meeting performance targets. Advanced Computer Aided Engineering (CAE) techniques were employed, including topology optimization, lattice optimization, bracket sensitivity studies as well as shape & gauge optimization. Performed benchmarking against industry models such as Tesla Model Y observed hybrid material with structural simplification. The final hybrid beam design demonstrated overall cost reduction, while satisfying

Didgur, Gulzarahmed, McAdams, Ian, Viswaraj, Obuliraj

From algorithm to engineering judgment: navigating topology optimization's limits in crash applications

2026-01-0189To be published on 04/07/2026

The non-linear nature of crash applications has led to many designs being developed through extensive trial and error based on the intuitions of the design engineer, as such using topology optimization for crash applications can provide major improvements in cost, weight, and passenger safety. Topology optimization is known for creating stiff, lightweight structures, however its application to crash scenarios must be handled carefully. Compliance minimization, the most common optimization objective, can yield misleading designs that prioritize undesirable qualities when developing structures for crash applications. In this paper the design optimization process of a passenger seat assembly subject to both an enforced displacement phase into a crash deceleration phase is discussed Due to the conflicting nature of compliance minimization and enforced displacement, the design was split into two types of regions; sacrificial, which are regions manually designed to absorb the majority of the

Orr, Mathew, Shi, Yifan, Lee, Jake, Gray, Savannah, Park, Taeil, Wotten, Erik, LeFrancois, Richard, Huang, Yuhao, Patel, Anuj, Kim, Hansu, Burns, Nicholas, Jalayer, Shayan, Grant, Robert, Kok, Leo, Hansen, Eric, Kim, Il Yong

Optimization Strategies of Gliding Arc Plasma-Based Ammonia Cracker towards On-Board Applications

2026-01-0292To be published on 04/07/2026

Ammonia has emerged as a viable hydrogen energy carrier owing to its superior hydrogen density, mature industrial utilization. However, ammonia faces critical challenges including inadequate ignition characteristics and sluggish combustion kinetics, necessitating supplementary high-reactivity fuels for optimizing combustion. Onboard ammonia cracking technology resolves this problem through on-demand hydrogen in-time production. Among existing decomposition methods, gliding arc plasma (GAP) demonstrates exceptional promise for on-board crackers given its decent hydrogen conversion rate and transient response capability. Prevailing research predominantly employs experimental methodologies wherein macroscopic parameters inadequately proxy microscopic electric field characteristics, thereby impeding mechanistic insight. This study establishes a quasi-1D numerical model to interrogate how electric field parameters modulate ammonia-to-hydrogen conversion yield and system energy efficiency in

Dong, Guangyu, LI, Xian, ZHOU, Yanxiong, XU, Jie, Li, Liguang

Optimizing joint efficiency in stainless steel-aluminum alloy FSW joints through advanced parameter control

2026-01-0228To be published on 04/07/2026

The present work investigates the friction stir welding (FSW) of dissimilar materials, specifically stainless steel 304 and aluminum alloy AA2024-T3, to analyze their tensile strength behavior. The significant challenges inherent in joining these materials, stemming from their vastly divergent thermal and mechanical properties, are addressed through a structured experimental and statistical methodology. The L18 Taguchi mixed orthogonal array was employed to systematically evaluate the influence of three critical process parameters: tool rotational speed, welding speed, and tool pin offset. Eighteen experimental trials were conducted using a butt joint configuration. The analysis identified tool rotational speed as the most influential parameter affecting the joint integrity. A combination of 450 rpm rotational speed, 20 mm/min welding speed, and a 1.5 mm pin offset toward the aluminum side yielded the optimum results, achieving a peak ultimate tensile strength (UTS) of 344.7 MPa and a

Mir, Fayaz Ahmad, Khan, Noor Zaman, Pali, Harveer Singh

Novel Steel Tubular Side Sill Reinforcement Design and Validation for Enhanced Crash Performance in EVs

2026-01-0478To be published on 04/07/2026

Increased use of Electrical vehicles (EVs) brings in unique structural design challenges particularly in Side Impact scenarios when the battery pack is near the sill region therefore elevating the possibility of Electrical Module penetration and triggering thermal runway event resulting from the crash. Thus, Electric Vehicles present unique structural design challenges. This study intends to evaluate a novel tubular structure design for side sill reinforcement and its performance benefits over a traditional aluminum side sill reinforcement currently commonly utilized for battery protection in EV vehicles body components. The proposed Steel side sill design mainly focuses on structural rigidity and improved load transfer during side pole impact crash cases and address manufacturing complexity and cost reduction opportunity. Multiple iterations for Steel side sill reinforcement design were carried out using finite element analysis with software like LSDYNA Hyper works to propose optimal

Kusnoorkar, Harsha, Khutorsky, Alex, Penumetsa, Vivek, Koraddi, Basavaraj

Mechanical Performance and Deformation Mechanisms of Hybrid Cylindrical TPMS Lattices with Gradient Wall Thickness

2026-01-0484To be published on 04/07/2026

Lattice metamaterials possess lightweight, high strength, and excellent energy absorption, making them promising for engineering applications. Their mechanical properties can be tuned by cell topology and structural design. This study proposes a multi-degree-of-freedom hybrid strategy for cylindrical TPMS lattices. Three uniform types (Primitive, Gyroid, IWP) were fabricated and validated through experiments and simulations. Radial, axial, and circumferential hybridizations were then explored, revealing distinct deformation and energy absorption mechanisms: CMA structures showed multi-stage behavior, while CMR and CMC offered longer plateau stages and higher stresses. Inspired by biological architectures, gradient wall thickness designs were introduced, significantly improving performance. Notably, CMR-GPI213 and CMC-PIG312 increased specific energy absorption by 24% and 14.8%, and CMA-GIP312 reduced peak force by 37.7% without sacrificing absorption. These results demonstrate that

Liu, Zhe, Guo, Pengbo, Zhong, Gaoshuo, Lian, Yuehui, Li, Youguang

Efficient casting system design through simulation for AlCoCrFeNi high-entropy alloy

2026-01-0176To be published on 04/07/2026

This study depicts the design, simulation, and fabrication of an equiatomic Al–Co–Cr–Fe–Ni high entropy alloy (HEA) through casting using Siemens NX CAD and ADSTEFAN simulation software. The part and gating system, including the sprue, runner, pouring cup, and riser, were modeled to generate the STL file and optimization was done through iterative simulation. Fluid flow and solidification analyses were performed to simulate the filling temperature, cold shut, and soundness of the casting. The optimized gating design is expected to provide uniformity in the temperature distribution, avoid defects during solidification and to eliminate shrinkage cavities in the cast alloy. HEA was fabricated by utilizing the validated gating system using vacuum induction melting of high-purity elemental granules. Further microstructural investigation using methods like SEM and EDS showed a uniformity of Al, Co, Cr, Fe, and Ni in near-equiatomic proportions. The optimization of riser-fed design

Vashistha, Saurabh, Rawat PhD, Pankaj, singh PhD, Shailesh, Pathak, Sunil

Rapid Prediction Method for Crankshaft dynamic balancing Based on SolidWorks

2026-01-0494To be published on 04/07/2026

As the core component of high-speed power systems such as internal combustion engines and compressors, the crankshaft’s dynamic balancing performance is a key factor influencing overall machine vibration, noise and operation reliability of the whole machine. In response to the problem whereby the traditional dynamic balancing correction method relies on empirical formulas and repeated trial weights, resulting in a long design cycle, this paper proposes a rapid prediction and optimized design method for the dynamic balancing performance of crankshafts based on SolidWorks. The method first determines the center of mass and the moment of inertia of the crankshaft through mass properties analysis, and then uses the motion simulation module for dynamic simulation to accurately predict the imbalance force and moment. In order to overcome the common numerical convergence difficulties and oscillations in the result curves in simulation, this study introduces a series of optimization strategies

Yang, Minghao, Zheng, Yuqing, Ma, Honghui, Chen, Hao

Development and Application of CAE Methods for Predicting Snow Accumulation on Floor Undercovers During Snowy Road Driving

2026-01-0482To be published on 04/07/2026

The automotive industry is increasingly challenged to address environmental concerns, particularly the goal of achieving carbon neutrality (CN). Japan has committed to CN by 2050 and aims for 100% electric vehicle (EV) sales by 2035. A key factor influencing EV adoption is driving range, which is significantly affected by driving resistance—especially aerodynamic drag. Underbody covers (undercovers) are widely used to improve aerodynamic performance, but they are exposed to various environmental stresses, including water intrusion, stone chipping, and snow accumulation during winter driving. While previous studies have effectively simulated water and chipping stresses, research specifically targeting snow stress has been limited. This study proposes a novel simulation approach to evaluate snow stress on undercovers, focusing on slushy road conditions commonly found in Japan’s snowy regions. A particle-based method was employed to model the interaction between slush and underbody

Matsuura, Tadashi, Annen, Teruyuki, Harada, Takeyuki, Ueno, Shigeki, Asai, Mikio, Watanabe, Haruyuki

Computational Modeling and Optimization of Shape Memory Polymer-Based Energy Absorbers

2026-01-0573To be published on 04/07/2026

Shape memory polymers (SMPs) provide tunable thermomechanical properties and enable the design of recoverable crash structures for automotive applications. This paper introduces a computational framework for the design and optimization of SMP-based crash absorbers with periodic auxetic microstructures. A finite element (FE) model is developed and validated against experimental data reported in the literature. The model is used to simulate crushing and recovery behavior under different temperature conditions. A parametric study is performed by varying key microstructural features, including wall thickness, cell size, and cell shape. Structural performance is evaluated in terms of specific energy absorption (SEA), peak force, and recoverability under axial crush loading. To efficiently explore the high-dimensional design space, surrogate models based on machine learning are constructed, and multi-objective optimization is carried out to identify Pareto-optimal designs that balance

Zhu, Yingbo, Zhu, Feng, Deb, Anindya

Integrating Optimization and Model Fidelity Selection Methods for Real-Time Driver-in-the-Loop Vehicle Simulators

2026-01-0621To be published on 04/07/2026

Automotive OEMs perform extensive prototype testing to configure vehicles for objective criteria (performance), and subjective criteria (handling and comfort). To reduce testing time and costs, OEMs rely on real-time Driver-In-the-Loop Simulators (DIL) running complex Multi-Body Dynamics (MBD) models. Recent advances in simulation technology have increased model accuracy but also operating costs, possibly limiting the viability of real-time DIL applications. Running high fidelity MBD models in real-time is computationally intensive and often requires re-configuration, CAE model de-contenting, solver setting optimization, which can introduce significant analysis errors. This presents a core challenge: selecting model fidelity levels that result in computationally efficient simulations, while maintaining sufficient predictive accuracy. This study introduces a methodology that integrates optimization algorithms with decision-making techniques to select the right fidelity within a

Balchanos, Michael, Emara, Mariam, Zarate Villazon, Angel, Mavris, Dimitri

CAE-based Electro-Thermal Characterization and Design Optimization of Automotive Inverter Busbars

2026-01-0642To be published on 04/07/2026

High power density demands in automotive inverters need precise thermal management to ensure efficiency, reliability and extended component lifetime. This paper presents a systematic, integrated electro-thermal workflow utilizing Computer-Aided Engineering (CAE) tools to characterize and optimize internal inverter components by directly linking electrical performance to thermal behavior. The methodology comprises three main steps: first, detailed frequency-dependent electrical parameters (RLCG, Z, S) and current density maps are extracted using computational calculations by ANSYS HFSS and ANSYS Q3D Extractor. Second, these parameters inform comprehensive power loss calculations (conduction, dielectric, switching), which are then mapped onto detailed 3D thermal fields using Computational Fluid Dynamics (CFD) through ANSYS Icepak. Finally, the results are analyzed to identify critical components and inform targeted design modifications. The workflow's effectiveness is demonstrated

Oliveira, Felipe, Aguiar, Cláudio, Justino, Marco, Tarrago, Vinicius, Almeida, Oberti

Path Following Strategy for Multi-Axle Vehicles via Nash Game-Based Coordinated Control of AFS and DYC

2026-01-0629To be published on 04/07/2026

Due to their complex structure and large inertia, multi-axle hub electric drive vehicles often suffer from response delays and insufficient control sensitivity. To improve path tracking accuracy and handling stability under high-speed and low-adhesion conditions, this paper proposes a coordinated control strategy that integrates a three-dimensional stability assessment with a Nash game-based approach for active front steering (AFS) and direct yaw moment control (DYC) systems. A path tracking control model tailored for multi-axle vehicles is developed, and a robust Tubular Model Predictive Control (Tube-MPC) method is employed to enhance trajectory tracking performance under model uncertainties and external disturbances. Within the prediction horizon of Tube-MPC, the system continuously evaluates whether the vehicle state remains within the stable region. If the state exceeds this region, a Nash game-based coordination mechanism is activated to formulate a multi-objective control game

Lv, Xin, Liu, Zuyang, Shen, Yanhua, Wang, Kaidi

Self-Supervised Gaussian-Based Framework for Robust Static-Dynamic Decomposition and Real-Time Urban Scene Reconstruction

2026-01-0010To be published on 04/07/2026

We propose a self-supervised Gaussian-based framework that enhances the robustness and efficiency of static-dynamic decomposition and high-fidelity reconstruction in autonomous driving scenarios. Existing approaches demonstrate promising performance but remain prone to noisy motion segmentation, overfitting on sparse LiDAR observations, and degraded accuracy in low-texture or distant regions, resulting in unstable geometry. To address these challenges, our method introduces a feature fusion module that jointly exploits image semantics and geometric cues, enabling more reliable motion prior extraction under noise and illumination variations. A cross-sequence geometric consistency constraint is further designed to regularize reconstruction across temporal and viewpoint changes, ensuring physically coherent scene representations. In addition, we formulate an efficient Gaussian parameter optimization strategy that constrains scale and normal updates, preventing unrealistic ellipsoid growth

Feng, Runlei, Wang, Ning, Zhang, Zhihao

Efficient Embedded Control of Nonlinear Systems for Software-Defined Vehicles: A Case Study Using the Inverted Pendulum

2026-01-0075To be published on 04/07/2026

Software-defined vehicles (SDVs) are reshaping automotive control architectures by shifting intelligence to embedded systems, where computational efficiency is paramount. This paper presents the implementation of multiple control strategies such as PID, LQR, and MPC for the inverted pendulum on a cart problem, a canonical example of nonlinear and unstable system control. The objective is to demonstrate how sophisticated control algorithms can be optimized for execution on microcontrollers with limited processing power and memory, reflecting the constraints typical of embedded platforms in SDVs. Each control strategy is implemented with careful consideration of algorithmic complexity, real-time responsiveness, and resource utilization. Performance is evaluated across key metrics including stability, convergence time, and computational load, enabling a comparative analysis that highlights trade-offs between control fidelity and hardware efficiency. The results provide actionable insights

Vupparige, Varun, Pandya, Vidit

Optimal Control Analysis of Minimum-Time Maneuvers for Vehicles Recovering from Instability

2026-01-0218To be published on 04/07/2026

Vehicles may enter highly unstable dynamic states due to lateral collisions, sudden loss of grip, or extreme steering disturbances. When such instability arises in congested road sections where obstacle avoidance is required, the safety risk to both the ego vehicle and surrounding traffic escalates significantly. In such scenarios, the vehicle must not only regain stability but also navigate the roadway in the shortest feasible time to prevent secondary collisions. This paper investigates the minimum-time maneuver of a vehicle starting from an unstable dynamic condition and constrained to travel within prescribed road boundaries. A single-track vehicle model with combined-slip nonlinear tire model is employed to capture the vehicle dynamics under high slip conditions. Phase-plane analysis is conducted to reveal how control inputs reshape the system’s vector field and influence the possibility and speed of stability recovery. An optimal control problem is formulated to compute the

Leng, Jiatong, Yu, Liangyao, Wang, Yongxin, You, Weijie, Li, Ziang, Jin, Zhipeng

Solving the Right Problem with Multi-Robot Formations

2026-01-0260To be published on 04/07/2026

Formation control simplifies minimizing multi-robot cost functions by encoding a cost function as a shape the robots maintain. However, by reducing complex cost functions to formations, discrepancies arise between maintaining the shape and minimizing the original cost function. For example, a Diamond or Box formation shape is often used for protecting all members of the formation. When more information about the surrounding environment becomes available, a static shape often no longer minimizes the original protection cost. We propose a formation planner to reduce mismatch between a formation and the cost function while still leveraging efficient formation controllers. Our formation planner is a two-step optimization problem that identifies desired relative robot positions. We first solve a constrained problem to estimate non-linear and non-differentiable costs with a weighted sum of surrogate cost functions. We theoretically analyze this problem and identify situations where weights

Cornwall, Chaz, Bos, Jeremy

Comparative Analysis of Flux Linkage Characterization Methods for Automotive Permanent Magnet Traction Motors: A Critical Review and Performance Evaluation Framework

2026-01-0441To be published on 04/07/2026

Accurate flux linkage characterization is essential for the design, control, performance and efficiency optimization of permanent magnet (PM) traction motors in automotive applications. Precise knowledge of flux linkage across varying load, speed, and temperature conditions directly impacts torque production, field-weakening capability, overall drive system efficiency and torque security. This paper presents a critical review and classification of flux-linkage characterization methods, encompassing offline laboratory mapping, standstill signal injection, self-commissioning inverter-only routines, and online real-time estimation. Each method exhibits distinct trade-offs in terms of accuracy, robustness to inverter nonlinearities, temperature adaptability, cost, and scalability for production and in-vehicle use. With the increasing complexity of automotive electrified traction systems, understanding these trade-offs is crucial for optimal motor design and control. To enable systematic

Khan, Ahmad Arshan, Haddad, Reemon, Kim, Jay, Hermann, Justin, Mohamadian, Mustafa

Innovation Powered by High-Fidelity Multiphysics Modeling: A Holistic System Optimization of BorgWarner’s Next Generation Integrated Drive Modules

2026-01-0497To be published on 04/07/2026

In the stringent market of BEV, the development of integrated Drive Modules (iDM) fitting to environmental and customer needs is mandatory. It is important to extract the best from the less. To achieve those goals, a deep insight of complex multiphysics phenomena occurring in an iDM has been achieved by accurate and validated models. This engineering methodology is applied through the development of BorgWarner products, comprising non-exhaustively iDM 180-HF, Externally Excited Synchronous Machine and Multi-Level Inverter. The paper will review the methodology development for deeper understanding involving in-house technical excellence and complemented by strategic partnerships with academic institutions and start-ups. It will present the approach of integrating advanced multiphysics models with high-quality experimental validations, specifically on loss evaluation on electrical machines and inverters. Complex models involving multiphysics such as thermal/fluid coupling or electric

Leblay PhD, Arnaud, Bossi, Adrien, Bourniche, Eric, DAVID, Pascal, Nanjundaswamy, Harsha

Numerical Framework for Predicting Power Loss and Oil Flow Dynamics in Dip-Lubricated Gear Systems

2026-01-0411To be published on 04/07/2026

Oil churning and windage power losses in dip-lubricated gearboxes can significantly improve overall transmission efficiency, particularly at high rotational speeds. As modern gearbox systems are pushed toward higher efficiency and reliability, understanding and predicting these losses becomes increasingly important. In addition to energy dissipation, the associated multiphase flow phenomena—such as oil splashing, thin film formation along gear surfaces, and aeration of the sump—strongly influence lubrication effectiveness, heat transfer, and component durability. Capturing these effects requires a robust numerical strategy that can resolve both power loss mechanisms and multiphase flow dynamics with sufficient accuracy. In this study, a single spur gear is numerically analyzed under varying oil depths and rotational speeds to quantify total power loss and investigate oil flow patterns. The computational approach employs a volume-of-fluid multiphase framework, and the predictions are

Mahyawansi, Pratik J., Haria, Hiral, Pandey, Ashutosh, KHAJEH HOSSEINI D, NAVVAB

Multi-Physics Topology Optimization and Machine Learning Surrogates for Lightweight Cast Automotive Parts

2026-01-0510To be published on 04/07/2026

This paper proposes an integrated optimization framework for lightweight cast vehicle structures by combining Multi-Physics Topology Optimization (MPTO) with machine learning (ML)-driven surrogate modeling. Conventional structural optimization approaches often rely on simplified load cases—such as 3 g inertial loading, frame torsion, or bending—rather than capturing the true complexity of performance-critical conditions like crashworthiness and fatigue durability. As a result, designs optimized for a single criterion frequently degrade in other performance areas, leading to costly redesigns or over-engineered solutions. The proposed methodology introduces MPTO through four structured steps that progressively build design fidelity. Step 1 establishes a baseline primitive topology optimization workflow using a standardized package checklist, ensuring that the design domain, boundary conditions, constraints, and key manufacturing rules are consistently defined as the foundation for

Kim, Hyosig, Senkowski, Andres, Gona, Kiran, Saroha, Lalit, Boraiah, Mahesh

Leveraging Large Language Models for CAD Automation and Multi-Objective Optimization of Automotive Heat Exchangers

2026-01-0506To be published on 04/07/2026

The design of automotive heat exchangers is a critical challenge, requiring the careful balance of thermal performance, aerodynamic efficiency, structural integrity, and manufacturability. Traditional design processes rely heavily on manual CAD modeling and iterative simulations, which are both time-intensive and computationally demanding. Recent advances in artificial intelligence, particularly in Large Language Models (LLMs) and Transformer-based architectures, open new possibilities for automating these tasks. This work introduces a novel framework that integrates LLMs with CAD kernels to generate parametric heat exchanger geometries directly from natural language descriptions. The models are automatically exported into standard formats for computational fluid dynamics simulations and seamlessly incorporated within a multi-objective optimization pipeline. By coupling natural language-driven CAD automation with advanced optimization techniques, the framework enables rapid exploration

Chaudhari, Prathamesh, Tovar, Andres

Multi-stage Packaging and Routing Optimization for Automotive Component Layout, Wire Harness and Duct Route Design

2026-01-0507To be published on 04/07/2026

Modern vehicle design involves complex considerations and tradeoffs between system integration and layout which have a direct impact on performance, efficiency, and cost. The placement of equipment including control boards, motors, and fans as well as the routing of ducts and wire harnesses poses a time-consuming and intricate problem for design engineers. This paper presents an automated methodology to determine the optimal component packaging configuration, duct routing, and wire harnessing layout to maximize component packing density and minimize the total routing length. A two-stage optimization framework has been developed where the first stage packages the components within the design space with considerations for space utilization, component overlap, proximity relationships, point-to-point accessibility, and component mounting. The second stage implements a custom A-star path-finding algorithm and gradient based optimization to determine the optimal route layout between port

LeFrancois, Richard, Kim, Il Yong

Modelling of Le Mans Daytona Hybrid Hypercar Powertrain System

2026-01-0270To be published on 04/07/2026

This study presents the development, validation, and application of a full-vehicle simulation model of the BMW M Hybrid V8 LMDh Hypercar, designed to investigate and optimise hybrid energy recovery strategies in compliance with FIA and IMSA 2025 technical regulations. With increasing regulatory and sustainability demands in endurance racing, the deployment and recovery of electric energy using MGU-K have become pivotal for both performance and fuel efficiency. A physics-based simulation model was constructed in GT-SUITE, incorporating a map based internal combustion engine, transmission, and 50 kW rear-axle hybrid system. Complementary to this, a novel telemetry extraction method using Optical Character Recognition (OCR) in MATLAB and Python was developed to generate real-world drive cycles from on-board race footage. The model was validated using telemetry data. Subsequently, multiple energy deployment strategies were evaluated under varying race scenarios to identify optimal control

Buysse, Alexander, Samuel, Stephen

Multi-Joint Topology Optimization Method with Interface Geometry Control

2026-01-0503To be published on 04/07/2026

Topology optimization (TO) has become a reliable and lightweight design approach which was widely adopted in multiple industrial applications. Over the past decades, TO has advanced through three major development stages to increase its practicality and application scope: single-material topology optimization (SMTO), multi-material topology optimization (MMTO), and multi-joint topology optimization (MJTO). SMTO involves only one candidate material, whereas MMTO takes multiple candidate materials into consideration, which widen the application scope of TO. In terms of MJTO, it not only considers multiple candidate materials but also considers the bonding material between dissimilar candidate materials, improving practicality over MMTO. However, prior MJTO methods overlook the geometry of dissimilar material interfaces, a factor that may impact material bonding effect. In this paper, a novel MJTO algorithm is introduced to enable explicit control over interface geometry in MJTO. Central

Shi, Yifan, Kim, Il Yong

In-situ Decoupling and Stiffness Optimization of Resiliently Coupled Assemblies Using FRF-Based Substructuring

2026-01-0227To be published on 04/07/2026

Road shake remains one of the most critical NVH challenges in light- and heavy-duty body-on-frame trucks, directly shaping ride comfort and perceived vehicle quality. At the heart of this issue are the body mounts, which govern the transfer of vibration between frame and body. Optimizing these mounts for improved road shake performance is therefore a key objective in NVH engineering. Traditionally, such optimization has relied on finite element analysis (FEA). While highly accurate, FEA—especially when performed with commercial solvers such as NASTRAN—demands enormous computational resources, with complete simulations often requiring hundreds of hours. Each design iteration multiplies this cost, restricting the pace and efficiency of NVH development. Recent methods such as response surface modeling (RSM) and machine learning (ML) have aimed to reduce the burden, yet they remain dependent on large libraries of pre-computed FEA data, inheriting much of the same expense and inflexibility

Haider, Syed, Abbas, Ahmad, Jahangir, Yawar, Maddali, Ramakanth

CFD Investigation of Oil Ingestion and Aeration in a V6 Engine Oil Pan During Vehicle Maneuvers

2026-01-0279To be published on 04/07/2026

A computational investigation was carried out using SimericsMP+ to analyze oil distribution and aeration behavior in a V6 engine oil pan during severe vehicle maneuvers. The model accounted for the crankshaft/camshaft rotations and piston motions, which allows for capturing realistic oil distribution in cylinder head drainbacks, engine bay and sump after initializing the crankcase with prescribed oil levels to establish baseline aeration prior to applying dynamic maneuver profiles. Of particular interest was the response of the main oil gallery (MOG) pressure and the exposure of the oil pickup tube during kickoff conditions at multiple fill levels. Both a baseline configuration and a modified sump featuring a containment “doghouse” were examined. Results obtained from the kickoff maneuver show complete uncovering of the pickup tube in the baseline design, leading to unstable lubrication. The first doghouse design only delayed pickup tube uncovering briefly, as oil pooled at the rear

Jia, Kun, Rahman, Ashique, Pandey, Ashutosh

Topology Optimization of Multibody Structures Considering Inertial Loads

2026-01-0501To be published on 04/07/2026

Topology optimization (TO) of dynamic structures has traditionally focused on single-body components subject to harmonic and impact loads, with limited extension to multibody dynamics. This work presents a novel framework for topology optimization of multibody dynamic systems considering inertial loads. The method integrates density-based topology optimization with nonlinear multibody dynamics to capture the distribution of mass, stiffness, and inertia across interconnected rigid and flexible components. Inertial forces, arising from accelerations and joint interactions, are explicitly incorporated into the optimization formulation to ensure dynamically consistent designs. Numerical examples demonstrate how accounting for inertial effects leads to lightweight, dynamically efficient multibody structures that outperform conventional static- or harmonic-based designs. The proposed approach establishes a foundation for inertia-aware topology optimization of multibody systems, with

Gupta, Aakash, Tovar, Andres

Exploring Bionic Twisted TPMS Designs for Improved Energy Absorption

2026-01-0243To be published on 04/07/2026

Triply periodic minimal surface (TPMS) lattice structures have emerged as a promising class of customizable mechanical metamaterials owing to their outstanding potential in lightweight design and multifunctional engineering applications. However, previous studies have mainly concentrated on global parameters such as wall thickness, cell size, and periodicity, while the local deformation and pre-deformation characteristics of unit cells, particularly the twist effects along specific directions, have not been systematically investigated. To address this gap, a bioinspired twisted design strategy was proposed in this study, in which controllable pre-twist was introduced along the build direction to tailor the energy absorption behavior of lattice structures. Twisted lattice specimens with different twist angles were fabricated using selective laser melting, and their mechanical properties were comprehensively examined through numerical simulations and experimental validation. The results

Liu, Zhe, Lian, Yuehui, Li, Youguang, Guo, Pengbo, Zhong, Gaoshuo

AI-Powered Requirements Engineering & Design Optimization of Electric Powertrains

2026-01-0500To be published on 04/07/2026

The development of electric vehicle powertrains is driven by diverse and often conflicting requirements. In early development phases, these requirements are often vague, incomplete, and continuously refined and subject to change as development progresses. Moreover, powertrain designs must be competitive regarding multiple key performance indicators (KPIs) such as performance, cost, energy efficiency, and package integration. This challenges engineers to concurrently develop the powertrain design alongside the requirements on which the design is based on. Managing this combination of uncertain requirements and multi-KPI design optimization represents a complex challenge in automotive engineering. The present work introduces a requirements engineering approach based on OPED (Optimization of Electric Drives). OPED digitalizes the transition from requirements to technical solutions by integrating parametric system models with an AI-based evolutionary optimization algorithm. This enables

Hofstetter, Martin, Lechleitner, Dominik

Influences of Process Parameters on Residual Stress and Tensile Performance of Riveted Joints

2026-01-0183To be published on 04/07/2026

The mechanical integrity and performance of riveted joints critically influence the reliability and safety of automotive structures. To investigate the effect of residual stress on the tensile behavior of riveted joints, this study firstly established a finite element model of the riveting process using Abaqus software. The model was validated using the thick-walled cylinder theory, and the distribution of residual stress around the rivet hole was subsequently analyzed. Orthogonal experiments were subsequently conducted to examine the influence of key process parameters including rivet spacing, hole diameter, and upset head height on tensile performance, yielding simulation results for both radial residual stress and maximum tensile load. Based on the finite element analysis results, XGBoost and Kriging surrogate models were established using four key parameters as input variables, namely rivet spacing, hole diameter, upset head height, and arrangement pattern, with radial residual

Shen, yunlong, Zhan, Zhenfei, Tang, Weiqin, Yang, Yutong, Xiao, yong, HUANG, SHIYAO

Optimization of Road Reference Profiles based on Highway Design for Connected and Automated Vehicles

2026-01-0096To be published on 04/07/2026

Advances in Connected and Automated Vehicles (CAVs) have developed a level in which high-definition maps can be used to improve road safety. Data compactness and robustness on road characterization is essential for the proper handling of vehicles under curves. In this paper, an optimization scheme that relates highway-design road curvature and optimal speed of travel is defined to safely navigate through a given road. The scheme is divided in two main steps. First a nonlinear optimization problem, in which curvature profiles are fitted from a model that based on street design standards as per the American Association of State Highway and Transportation Officials (AASHTO). Secondly, the optimized curvature profile is subject to a secondary optimization problem that uses vehicle dynamics for both constraints and objective function derivation. Guidance reference parameters such as curvature and velocity, at different levels of friction are analyzed. Results show that, even in sparse

Jacome, Ricardo Osmar, Stolle, Cody, Grispos, George

Fast and Robust CFD Simulation Method for Water Management in Automotive Interior Design

2026-01-0487To be published on 04/07/2026

Knowledge of the behavior of fluid flow over and into narrow gaps is important in many industrial applications, particularly in the automotive sector. Evaluating the potential of water entering narrow pathways and towards components that can be damaged by it is of great importance. CFD simulations can help in evaluating the potential of water getting into such gaps. Meshless tools are used in a variety of simulation scenarios of fluid flow, especially due to their ability to easily simulate free surfaces with strong curvatures. In previous work, a validated simulation setup was created in the meshless simulation tool Meshfree from Fraunhofer ITWM for simulating water entering in small gaps. Currently, the simulation time of the setup needs to be sped up especially for industrial use cases, where simulation times higher than 6-10 days were encountered. Because of these long simulation times a fast and robust CFD simulation method was developed to be able to simulate even industrial use

Zrnic, Dino, Konstantinovics, Athena, Kospasch, Alexander, Rugerri, Evelyn, Loy, Michael, Bäder, Dirk, Michel, Isabel

Multi-material topology optimization using difference-based equivalent static load method

2026-01-0173To be published on 04/07/2026

Topology optimization (TO) has become a powerful tool for generating lightweight structural designs. TO has been widely applied to linear static problems, where analytical sensitivities are easy to obtain. However, crashworthiness design requires nonlinear dynamic analysis, for which analytical sensitivities are generally not available. To extend TO into crash problems, approximation methods such as the Equivalent Static Load (ESL) method have been developed. ESL replaces the nonlinear problem with a series of linear static subproblems, ensuring that the displacement fields match at certain time steps. These subproblems can then be efficiently solved using standard TO techniques. A key limitation of ESL is that it relies on the initial mesh for all subproblems, which reduces accuracy for highly nonlinear crash responses. To address this, Triller proposed the difference-based ESL (DiESL) method, which updates the mesh in each subproblem to the deformed configuration, therefore improving

Huang, Yuhao, Kim, Il Yong

A study on Crack propagation characteristics of interlaced holes for key automotive components.

2026-01-0186To be published on 04/07/2026

Aiming at the engineering problem that the stress interference of interlaced holes dominates crack propagation and directly affects driving safety in the hole group structure of key components such as chassis and body frame in the automotive field, in order to accurately reveal the quantitative influence of key parameters of interlaced hole placement (crack deflection Angle, longitudinal hole spacing) on the crack evolution of automotive hole group components, This study adopts the approach of numerical simulation combined with variable control to conduct systematic analysis. Taking an aluminum alloy thin plate with two relatively initial cracks (a commonly used structural material for automobiles) as the research object, multiple sets of typical interlocking hole structure variable models of automotive components covering different crack deflection angles and longitudinal hole spacings were constructed in the ABAQUS software. The J-integral was calculated by the circumscribed line

Chen, Yechao, Zhan, Zhenfei, Tang, Weiqin, Yang, Yutong, Gan, Yuan, Yan, Kaibo, HUANG, Shiyao

Thermo-Mechanical Analysis of 1U CubeSat Structures using ANSYS: Insights for Mobility Applications

2026-01-0145To be published on 04/07/2026

Particularly for small satellites like CubeSats, the growing commercialization and downsizing of space technology have brought both new opportunities and problems in the areas of heat management and structural design. A Single Unit (1U) CubeSat structure's design and simulation analysis are presented in this work, with an emphasis on the structure's mechanical and thermal performance during launch and orbit. The CubeSat platform, which is frequently constructed using commercial off-the-shelf (COTS) components, needs robust and lightweight structures that can handle mechanical loads and high temperature changes. In order to conduct mechanical stress loading (MSL) and thermal analysis (TA) on a 3D CubeSat model, this work uses ANSYS in a simulation-driven manner. Geometry modeling, meshing, boundary condition application, and simulation of important space environment elements including microgravity, solar radiation, and launch-induced stresses are all part of the process. The findings

Hussain, Muzammil, Azeem, Naqash, Adeel, Muhammad

Safety-Constrained Control of Nonlinear Mobility Systems Using Reference Governor

2026-01-0033To be published on 04/07/2026

Ensuring safe operation and reliable control of mobility systems remains a significant challenge, particularly for nonlinear and high-dimensional applications subject to external disturbances with hard constraints and limited computational resources in real-time implementations. A reference governor (RG) can enforce constraints using an add-on scheme that preserves the pre-stabilizing controller while balancing the need to satisfy other requirements, including reference tracking and disturbance rejection. Thus, in this paper, we exploit RG-based strategies focusing on nonlinear mobility systems. While the method is generalizable to other applications, such as waypoint following for autonomous driving, the flight dynamics of a quadrotor system with twelve states are used as an example. We implement a disturbance rejection RG to satisfy safety constraints and track set points. To handle nonlinearity, we propose an optimal strategy to quantify the maximum deviation between the nonlinear

Dong, Yilong, Li, Huayi

Real-time Parameter Optimization for Eco-driving Control in Connected and Automated Vehicles Using Reinforcement Learning

2026-01-0041To be published on 04/07/2026

This paper introduces a novel methodology to enhance the energy efficiency of eco-driving controllers in Connected and Automated Vehicles (CAVs) by leveraging Reinforcement Learning (RL) techniques for real-time parameter optimization. Traditional eco-driving strategies rely on fixed control parameters, which limit adaptability across diverse traffic and road conditions. To address this, we apply continuous action space RL algorithms, specifically Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO), to dynamically tune four key parameters within a model predictive control framework that is grounded in Pontryagin’s Maximum Principle (PMP). These parameters influence acceleration, braking, cruising, and intersection-approach behaviors, making them critical for achieving optimal eco-driving performance. Our study employs Argonne National Laboratory’s RoadRunner simulator, a Simulink-based environment designed for high-fidelity CAV analysis, incorporating

Zhang, Yaozhong, Ammourah, Rami, Han, Jihun, Moawad, Ayman, Shen, Daliang, Karbowski, Dominik

Conjugate Heat Transfer Modeling for Predicting Thermal Behavior of X76 Emotor Windings

2026-01-0118To be published on 04/07/2026

A computational study based on a conjugate heat transfer (CHT) method in SimericsMP+ was performed to predict the winding temperatures in an X76 emotor. In this study, the thermal load was represented in the simulation through the solution of electromagnetic equations in SimericsMP+, where heat generation was driven by root-mean-square (RMS) current, while liquid cooling was applied at flow rates ranging from 1 LPM to 6 LPM. Simulations were conducted to measure the temperature on three thermocouple locations on each side of the winding crown and weld regions under steady operation. The computational strategy employed a loosely coupled approach. A fluid-only simulation was first carried out to establish stable flow conditions, followed by coupling with solid conduction where the winding acted as the heat source. The predicted temperature distributions were then compared with test data. Results obtained show good agreement, with differences remaining within an acceptable range, thereby

Jia, Kun, Schlautman, Jeff, Srinivasan, Chiranth

Bearing Fault Diagnosis Method Based on Optimized VMD and Deep Timing Fusion

2026-01-0158To be published on 04/07/2026

In the current field of rolling bearing fault diagnosis, two critical challenges persist. First, it is difficult to effectively extract fault features from nonlinear and non-stationary vibration signals. Second, precise diagnosis remains a challenge, especially when distinguishing between different fault types and capturing incipient faults with weak characteristic information. To address these issues, this paper proposes a novel fault diagnosis method based on adaptively optimized Variational Mode Decomposition (VMD) and Deep Temporal Fusion. First, the method improves the traditional Sparrow Search Algorithm (SSA). It enhances SSA’s global optimization capability through strategies like chaotic population initialization and adaptive perturbation. This improved SSA enables efficient global optimization of VMD’s key parameters. Leveraging these optimized parameters, the method decomposes modal signal components with different center frequencies from the vibration signal. This process

Wen, Chao, Zhong, Hong, Wang, Liangmo, Chen, Yong, Gao, Qiang, Li, Yong

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