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Abstract This article discusses a multi-item planning and scheduling problem in a job-shop system with consideration of energy consumption. Planning is considered by a set of periods, each one is characterized by a demand, energy, and length. Scheduling is determined by the sequences of jobs on available resources. A Mixed-Integer Linear Programming (MILP) problem is formulated to integrate planning and scheduling, it is considered as an NP-difficult problem. A Genetic Algorithm (GA) is then developed to solve the MILP, and then a hybridized approach of simulated annealing with genetic algorithm (HGASA) is presented to optimize the results. Finally, numerical results are presented and analyzed to evaluate the effectiveness of the proposed algorithms.

Abstract The equivalent consumption minimization strategy (ECMS) is an instantaneous optimization method implemented online for hybrid electric vehicles (HEVs) to improve fuel economy. To fulfill the near-optimal performance of ECMS, equivalent factors (EFs) must be well tuned for different powertrains and driving cycles. This study proposes a hierarchical offline optimization framework which tunes the penalty value of state of charge (SOC) balance in the outer layer and optimizes EFs based on heuristic algorithms in the inner layer. A comprehensive analysis is conducted to evaluate three heuristic algorithms, including the genetic algorithm (GA), the nonlinear-inertia-decreasing particle swarm optimization algorithm (NLPSO), and the novel firefly algorithm (FA). The traversal optimization method (TOM) is chosen as the benchmark. Besides, a sensitivity analysis is carried out to reveal the impact of the penalty value on the battery SOC balance.

Abstract Electric power steering (EPS) systems have become the most advantageous steering system used in vehicles. They provide better fuel efficiency and a more compact design over traditional hydraulic power steering (HPS) systems. However, EPS systems are afflicted with unwanted noise and vibration that can undermine the safety of drivers. This article presents a mathematical framework for vibration analysis in a column-type EPS system. The steering column is modeled as a continuous clamped column. The equations of motion are derived using Hamilton’s principle, and explicit expressions are presented for the frequency and transmissibility equations. A three-degrees-of-freedom (3-DOF) dynamic model is also presented by an approximation of the stiffness, damping, and mass of the steering column. The results of the proposed analytical models are validated using ANSYS simulation.

Abstract An energy management strategy (EMS) has an essential role in ameliorating the efficiency and lifetime of the powertrain components in a hybrid fuel cell vehicle (HFCV). The EMS of intelligent HFCVs is equipped with advanced data-driven techniques to efficiently distribute the power flow among the power sources, which have heterogeneous energetic characteristics. Decentralized EMSs provide higher modularity (plug and play) and reliability compared to the centralized data-driven strategies. Modularity is the specification that promotes the discovery of new components in a powertrain system without the need for reconfiguration. Hence, this article puts forward a decentralized reinforcement learning (Dec-RL) framework for designing an EMS in a heavy-duty HFCV. The studied powertrain is composed of two parallel fuel cell systems (FCSs) and a battery pack.

Abstract Automotive subframe is a critical chassis component as it connects with the suspension, drive units, and vehicle body. All the vibration from the uneven road profile and drive units are passed through the subframe to the vehicle body. OEMs usually have specific component-level drive point dynamic stiffness (DPDS) requirements for subframe suppliers to achieve their full vehicle NVH goals. Traditionally, the DPDS improvement for subframes welded with multiple stamping pieces is done by thickness and shape optimization. The thickness optimization usually ends up with a huge mass penalty since the stamping panel thickness has to be changed uniformly not locally. Structure shape and section changes normally only work for small improvements due to the layout limitations. Tuned rubber mass damper (TRMD) has been widely used in the automotive industry to improve the vehicle NVH performance thanks to the minimum mass it adds to the original structure.

Abstract Mechanical friction simulations offer a valuable tool in the development of internal combustion engines for the evaluation of optimization studies in terms of time efficiency. However, system modeling and evaluation of model performance may be highly complex. A high number of interacting submodels and parameters as well as a limited model transparency contribute to uncertainties in the modeling process. In particular, model calibration and validation are complicated by the unknown effect of parameters on the model output. This article presents an advanced and model-independent methodology for identifying sensitive parameters of engine friction. This allows the user to investigate an unlimited number of parameters of a model whose structure and properties are prior unknown.

Abstract Automotive interior coatings for flexible and rigid substrates represent an important segment within automotive coating space. These coatings are used to protect plastic substrates from mechanical and chemical damage, in addition to providing colour and design aesthetics. These coatings are expected to resist aggressive chemicals, fluids, and stains while maintaining their long-term physical appearance and mechanical integrity. Designing such coatings, therefore, poses significant challenges to the formulators in effectively balancing these properties. Among many factors affecting coating properties, the cross-link density (XLD) and solubility parameter (δ) of coatings are the most predominant factors.

Abstract Electric motors constitute a critical component of an electric vehicle powertrain. An improved motor design can help improve the overall performance of the drivetrain of an electric vehicle making it more compact and power dense. In this article, the electromagnetic torque output of a double V-shaped traction IPMSM is maximized by geometry optimization, while considering overall material cost minimization as the second objective. A robust and flexible parametric model of the IPMSM is developed in ANSYS Maxwell 2D. Various parameters are defined in the rotor and stator geometries to perform an effective multi-objective parametric design optimization. Advanced sensitivity analysis, surrogate modeling, and optimization capabilities of ANSYS optiSlang software are leveraged in the optimization. Furthermore, a demagnetization analysis is performed to evaluate the robustness of the optimized design.

Abstract The Formula SAE (FSAE) is an international engineering competition where a Formula style race car is designed and built by students from worldwide universities. According to FSAE regulation, an air restrictor with circular cross section of 20 mm for gasoline-fuelled and 19 mm for E-85-fuelled vehicles is to be incorporated between the throttle valve and engine inlet. The sole purpose of this regulation is to limit the airflow to the engine used. The only sequence allowed is throttle valve, restrictor and engine inlet. A new approach of combining ram theory and acoustic theory methods are investigated to increase the performance of the engine by designing an optimized intake runner for a particular engine speed range and an optimized plenum volume in this range. Engine performance characteristics such as brake power, brake torque and volumetric efficiency are taken into considerations.

Abstract Optimization is essential in real-life mechanical engineering problems that mostly are nonlinear, depend on mixed decision variables, and are usually subject to constraints. However, most of the studied problems are modelled assuming continuous variables. A limited number of studies have been devoted to cases with mixed variables. Moreover, there is a lack of algorithm treating mixed variable problems properly. This article introduces a hybrid algorithm that can handle constrained problems depending on continuous or mixed variables. The proposed algorithm combines two meta-heuristics, Jaya and particle swarm optimization (PSO). PSO is one of the most popular methods to solve nonlinear problems, and Jaya is a novel parameter-free optimization algorithm. This new hybrid optimization algorithm is proposed in order to improve the convergence speed and to investigate what improvements it will bring to optimization problem solutions.

Abstract The two-wheel system design is widely used in various mobile tools, such as remote-control vehicles and robots, due to its simplicity and stability. However, the specific wheel and body models in the real world can be complex, and the control accuracy of existing algorithms may not meet practical requirements. To address this issue, we propose a double inverted pendulum on mobile device (DIPM) model to improve control performances and reduce calculations. The model is based on the kinetic and potential energy of the DIPM system, known as the Euler-Lagrange equation, and is composed of three second-order nonlinear differential equations derived by specifying Lagrange. We also propose a stable feedback control method for mobile device drive systems. Our experiments compare several mainstream reinforcement learning (RL) methods, including linear quadratic regulator (LQR) and iterative linear quadratic regulator (ILQR), as well as Q-learning, SARSA, DQN (Deep Q Network), and AC.

Abstract Integrated control for automated vehicles in platoons with nonlinear coupled dynamics is developed in this article. A nonlinear MPC approach is used to address the multi-input multi-output (MIMO) nature of the problem, the nonlinear vehicle dynamics, and the platoon constraints. The control actions are determined by using model-based prediction in conjunction with constrained optimization. Two distinct scenarios are then simulated. The first scenario consists of the multivehicle merging into an existing platoon in a controlled environment in the absence of noise, whereas the effects of external disturbances, modeling errors, and measurement noise are simulated in the second scenario. An extended Kalman filter (EKF) is utilized to estimate the system states under the sensor and process noise effectively.

Abstract Maximum lifetime and minimum manufacturing cost for new products are the primary goals of companies for competitiveness. These two objectives are contradictory and the geometric dimensions of the products directly control them. In addition, the earlier design errors of new products are predicted, the easier and more inexpensive their rectification becomes. To achieve these objectives, we propose in this article a novel model that makes it possible to solve the problem of optimizing the lifespan and the manufacturing cost of new products during the phase of their design. The prediction of the life of the products is carried out by an energy damage method implemented on the finite element (FE) calculation by using the ABAQUS software. The manufacturing cost prediction is carried out by applying the ABC cost estimation analytical method. In addition, the optimization problem is solved by the method of genetic algorithms.

Abstract Brake squeal reduces comfort for the vehicle occupants, damages the reputation of the respective manufacturer, and can lead to financial losses due to cost-intensive repair measures. Mode coupling is mainly held responsible for brake squeal today. Two adjacent eigenfrequencies converge and coalesce due to a changing bifurcation parameter. Several approaches have been developed to suppress brake squeal through structural changes. The main objective is to increase the distance of coupling eigenfrequencies. This work proposes a novel approach to structural modifications and sizing optimization aiming for a start at shifting a single component eigenfrequency. Locations suitable for structural changes are derived such that surrounding modes do not significantly change under the modifications. The positions of modifications are determined through a novel sensitivity calculation of the eigenmode to be shifted in frequency.

Abstract This article presents a novel approach to optimize the placement of light detection and ranging (LiDAR) sensors in autonomous driving vehicles using machine learning. As autonomous driving technology advances, LiDAR sensors play a crucial role in providing accurate collision data for environmental perception. The proposed method employs the deep deterministic policy gradient (DDPG) algorithm, which takes the vehicle’s surface geometry as input and generates optimized 3D sensor positions with predicted high visibility. Through extensive experiments on various vehicle shapes and a rectangular cuboid, the effectiveness and adaptability of the proposed method are demonstrated. Importantly, the trained network can efficiently evaluate new vehicle shapes without the need for re-optimization, representing a significant improvement over classical methods such as genetic algorithms.

Abstract Traditionally, vehicle subframe mass optimization process is achieved by an iterative process, which is usually conducted with virtual test using an initial flexible suspension structure while satisfying compliance constraints via multibody dynamic simulation software. The optimization process is typically performed via a multibody dynamic simulation software, and ideally, with an adequate flexible subframe model, the design problem is formulated and solved via a traditional optimization procedure. In reality with a complex model, optimization is in general extremely cumbersome, time-consuming, and with no guarantee of an optimal solution.

Abstract Nowadays, with the great advancement of automobile intellectualization, vehicle integrated dynamic control is increasingly becoming a hot research field. For vehicle stability, this article focuses on the coordinated control of Direct Yaw-moment Control (DYC) and Active Front Steering (AFS). First of all, the nominal control variables (yaw rate and sideslip angle) are designed based on the linear two Degrees of Freedom (2 DOF) vehicle model, in which the phase difference between the actual and nominal variables has been pointed out due to the approximate substitution with first-order time-delay transfer function. Secondly, considering the uncertainty of cornering stiffness per axle, and increasing robustness of the system, the Optimal Guaranteed Cost Control (OGCC) theory is adopted to design the coordinated controller.

Abstract Lightweight and high-strength high-pressure die casting (HPDC) aluminum has been widely used in automotive components such as the cylinder block, lower crankcase extension, transmission case, and drive unit. Die cast parts have good surface finishes with relatively higher material strength in the casting skin than the center core material, maintain consistent features and tolerance, and maximize metal yield, therefore making it the most cost-effective casting process for mass production of aluminum parts. However, due to the rapid filling rates, the HPDC process tends to form large porosity and oxides because of the entrapped gas and solidification shrinkage, thereby deteriorating the mechanical properties of the casting parts.

Abstract Electrochemical impedance spectroscopy (EIS) is widely used to diagnose the state of health (SOH) of lithium-ion batteries. One of the essential steps for the diagnosis is to analyze EIS with an equivalent circuit model (ECM) to understand the changes of the internal physical and chemical processes. Due to numerous equivalent circuit elements in the ECM, existing parameter identification methods often fail to meet the requirements in terms of identification accuracy or convergence speed. Therefore, this article proposes a novel impedance model parameter identification method based on the random mutation differential evolution (RMDE) algorithm. Compared with methods such as nonlinear least squares, it does not depend on the initial values of the parameters. The method is compared with chaos particle swarm optimization (CPSO) algorithm and genetic algorithm (GA), showing advantages in many aspects.

Abstract In this article, a novel hybrid metaheuristic based on passing vehicle search (PVS) cultural algorithm (CA) is proposed. This contribution has a twofold aim: First is to present the new hybrid PVS-CA. Second is to prove the effectiveness of the proposed algorithm for adaptive signal timing optimization. For this, a system that can adapt efficiently to the real-time traffic situation based on priority signal control is developed. Hence, Transit Signal Priority (TSP) techniques have been used to adjust signal phasing in order to serve emergency vehicles (EVs) and manage the tram priority in a coordinated tram intersection. The system used in this study provides cyclic signal operation based on a real-time control approach, including an optimization process and a database to manage the sensor data from detectors for real-time predictions of EV and tram arrival time.