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This paper proposes a new integrated chassis control (ICC) using a predictive model-based control (MPC) for optimal allocation of sub-chassis control systems where a predictive model has 6 Degree of Freedom (DoF) for rigid body dynamics. The 6 DoF predictive vehicle model consists of longitudinal, lateral, vertical, roll, pitch, and yaw motions while previous MPC research uses a 3 DoF maximally predictive model such as longitudinal, lateral and yaw motions. The sub-chassis control systems in this paper include four wheel individual braking torque control, four wheel individual driving torque control and four corner active suspension control. Intermediate control inputs for sub-chassis control systems are simplified as wheel slip ratio changes for driving and braking controls and vertical suspension force changes for an active suspension control.

This paper presents an adaptation method of equivalent factor in equivalent consumption minimization strategy (ECMS) of fuel cell hybrid electric vehicle (FCHEV) using hilly road information. Instantaneous optimization approach such as ECMS is one of real-time controllers. Furthermore, it is widely accepted that ECMS achieves near-optimum results with the selection of the appropriate equivalent factor. However, a lack of hilly road information no longer guarantees near-optimum results as well as charge-sustaining of ECMS under hilly road conditions. In this paper, first, an optimal control problem is formulated to derive ECMS analytical solution based on simplified models. Then, we proposed updating method of equivalent factor based on sensitivity analysis. The proposed method tries to mimic the globally optimal equivalent factor trajectory extracted from dynamic programming solutions.

This paper investigates an optimal design of a diesel engine and after-treatment systems for a series hybrid electric forklift application. A holistic modeling approach is developed in GT-Suite® to establish a model-based hardware definition for a diesel engine and an after-treatment system to accurately predict engine performance and emissions. The used engine model is validated with the experimental data. The engine design parameters including compression ratio, boost level, air-fuel ratio (AFR), injection timing, and injection pressure are optimized at a single operating point for the series hybrid electric vehicle, together with the performance of the after-treatment components. The engine and after-treatment models are then coupled with a series hybrid electric powertrain to evaluate the performance of the forklift in the standard VDI 2198 drive cycle.

The series-parallel hybrid electric vehicle(HEV), which employs a planetary gear set to combine one internal combustion engine(ICE) and two electric motors(EMs), can take advantages of both series and parallel hybrid system. The efficient powertrain operating point of the system can be obtained by the instantaneous optimization of equivalent fuel consumption. However, heavy computational requirements and variable constraints of the optimization process make it difficult to build real-time control strategy. To overcome the difficulty, this study suggests the control strategy which divides the optimization process into 2 stages. In the first stage, a target of charge/discharge power is determined based on equivalent fuel consumption, then in the second stage, an engine operating point is determined taking power transfer efficiency into account.

A previously developed CFD-based optimization tool is utilized to find optimal engine operating conditions with respect to fuel consumption and emissions. The optimization algorithm employed is based on the steepest descent method where an adaptive cost function is minimized along each line search using an effective backtracking strategy. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space. The application of this optimization tool is demonstrated for the Sulzer S20, a central-injection, non-road DI diesel engine. The optimization parameters are the start of injection of the two pulses of a split injection system, the duration of each pulse, the exhaust gas recirculation rate, the boost pressure and the compression ratio.

A global optimization method has been developed for an engine simulation code and utilized in the search of optimal fuel injection strategies. This method uses a Lagrange interpolation function which interpolates engine output data generated at the vertices and the intermediate points of the input parameters. This interpolation function is then used to find a global minimum over the entire parameter set, which in turn becomes the starting point of a CFD-based optimization. The CFD optimization is based on a steepest descent method with an adaptive cost function, where the line searches are performed with a fast-converging backtracking algorithm. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space.

This paper attempts to integrate a derivative-free probability analysis method to Reliability-Based Robust Design Optimization (RBRDO). The Eigenvector Dimension Reduction (EDR) method is used for the probability analysis method. It has been demonstrated that the EDR method is more accurate and efficient than the Second-Order Reliability Method (SORM) for reliability and quality assessment. Moreover, it can simultaneously evaluate both reliability and quality without any extra expense. Two practical engineering problems (vehicle side impact and layered bonding plates) are used to demonstrate the effectiveness of the EDR method.

In the last decade, considerable advances have been made in reliability-based design optimization (RBDO). One assumption in RBDO is that the complete information of input uncertainties are known. However, this assumption is not valid in practical engineering applications, due to the lack of sufficient data. In practical engineering design, information concerning uncertainty parameters is usually in the form of finite samples. Existing methods in uncertainty based design optimization cannot handle design problems involving epistemic uncertainty with a shortage of information. Recently, a novel method referred to as Bayesian Reliability-Based Design Optimization (BRBDO) was proposed to properly handle design problems when engaging both epistemic and aleatory uncertainties [1]. However, when a design problem involves a large number of epistemic variables, the computation task for BRBDO becomes extremely expensive.

Supervisory control strategy of a hybrid electric vehicle (HEV) provides target powers and operating points of an internal combustion engine and an electric motor. To promise efficient driving of the HEV, it is needed to find the proper values of control parameters which are used in the strategy. However, it is very difficult to find the optimal values of the parameters by doing experimental tests, since there are plural parameters which have dependent relationship between each other. Furthermore variation of the test results makes it difficult to extract the effect of a specific parameter change. In this study, a model based parameter optimization method is introduced. A vehicle simulation model having the most of dynamics related to fuel consumption was developed and validated with various experimental data from real vehicles. And then, the supervisory control logic including the control parameters was connected to the vehicle model.

Optimal fuel injection strategies are obtained with a micro-genetic algorithm and an adaptive gradient method for a nonroad, medium-speed DI diesel engine equipped with a multi-orifice, asynchronous fuel injection system. The gradient optimization utilizes a fast-converging backtracking algorithm and an adaptive cost function which is based on the penalty method, where the penalty coefficient is increased after every line search. The micro-genetic algorithm uses parameter combinations of the best two individuals in each generation until a local convergence is achieved, and then generates a random population to continue the global search. The optimizations have been performed for a two pulse fuel injection strategy where the optimization parameters are the injection timings and the nozzle orifice diameters.

This paper describes the design, development and validation of a muffler for reducing exhaust noise from a commercial tele-handler. It also describes the procedure for modeling and optimizing the exhaust muffler along with experimental measurement for correlating the sound transmission loss (STL). The design and tuning of the tele-handler muffler was based on several factors including overall performance, cost, weight, available space, and ease of manufacturing. The analysis for predicting the STL was conducted using the commercial software LMS Virtual Lab (LMS-VL), while the experimental validation was carried out in the laboratory using the two load setup. First, in order to gain confidence in the applicability of LMS-VL, the STL of some simple expansion mufflers with and without extended inlet/outlet and perforations was considered. The STL of these mufflers were predicted using the traditional plane wave transfer matrix approach.

Due to technological evolutions and social demands, motor vehicles are requested to be enhanced in terms of occupant safety and comfort. As a result, many countries are reinforcing crash regulations and new car assessment programs. Automotive seats are essential parts for providing passenger safety and comfort and have become most important. Many automotive companies concentrate on optimization of the seat structure. This paper presents an overview of the recent evolution of the seat structures and gives a development procedure covering seat frame design, optimization and validation. Through the study, a competitive frame design is drawn as a case result and a design guideline and a standard development procedure is established

A technical approach to reduce NOx and to minimize the fuel consumption caused by the DeNOx aftertreatment system was introduced. The NEDC mode test of the HMC (Hyundai Motor Company) DeNOx system was done with a Euro 5 vehicle (ETW (Equivalent Test Weight) = 1,810 kg, 143 kW, 430 N⋅m), which resulted in that the Euro 6 legislation standards were met. The NOx and HC emissions were, respectively, measured to be 0.059 g/km and 0.087 g/km with the hydrothermal-aged catalysts, and CO₂ was increased by ≺ 4%.

This paper reflects an efficient and comprehensive approach for vehicle sound optimization integrated into the entire development process. It shows the benefits of early consideration of typical vehicle NVH features and of intensive interaction of P/T and vehicle responsibilities. The process presented here considers the typical restriction that acoustically representative prototypes of engines and vehicles are not available simultaneously at the early development phase. For process optimization at this stage, a method for vehicle interior noise estimation is developed, which bases on measurements from the P/T test bench only, while the vehicle transfer behavior for airborne and structure-borne noise is assumed to be similar to a favorable existing vehicle. This method enables to start with the pre- optimization of the pure P/T and its components by focusing on such approaches which are mainly relevant for the vehicle interior noise.

An integrated starter and generator (ISG) is a type of electric machine which is mechanically connected to an internal combustion engine (ICE). The ISG is intended to conduct important roles in the hybrid electric vehicle (HEV) such as engine start and stop. Since the HEV has frequent electric vehicle (EV)/HEV mode transition, rapid engine cranking and vibration-free engine stop controls are necessary. In the case of the engine stop, the ISG provides the negative torque output to the ICE which can rapidly escape from its resonance speed. However, the ISG torque is determined by engineering intuition, the amount of energy recovery is hardly considered. Dynamic programming (DP) is an effective solution to find optimal ISG control strategy to maximize energy recovery during engine stop transition. Even though DP is an offline algorithm, the result can be used as a reference to evaluate and improve an existing on-line algorithm.

Power split in Fuel Cell Hybrid Electric Vehicles (FCHEVs) has been controlled using different strategies ranging from rule-based to optimal control. Dynamic Programming (DP) and Model Predictive Control (MPC) are two common optimal control strategies used in optimization of the power split in FCHEVs with a trade-off between global optimality of the solution and online implementation of the controller. In this paper, both control strategies are developed and tested on a FC/battery vehicle model, and the results are compared in terms of total energy consumption. In addition, the effects of the MPC prediction horizon length on the controller performance are studied. Results show that by using the DP strategy, up to 12% less total energy consumption is achieved compared to MPC for a charge sustaining mode in the Urban Dynamometer Driving Schedule (UDDS) drive cycle.

The squeak noise generated during the moving of the door glass has a influence on the performance of vehicles felt by the consumer. In order to improve the noise, it is necessary to understand the principle of a friction vibration. In this paper, it is confirmed that the principle on the waist belt is most closely related to stick-slip and sprag-slip among various vibration characteristics. Stick-slip is expressed by energy accumulation and divergence due to difference in static and dynamic friction coefficient. Sprag-slip define instability of geometric structure due to angle of lips on the belt. In this paper, the physical model and the energy equation are established for the above two phenomena. Stick-slip can be solved by decreasing the difference of the static and dynamic friction coefficient. Sprag-slip is caused by the ratio of compressive and shear stiffness of the lips. The belt uses flocking to ensure durability, not coating.

Engine calibration on the powertrain test bed with transient mode is proposed with dynamic powertrain test bed having low inertia dynamometer. Automated ECU (Engine Control Unit) calibration system is completed with the combination of experimental design software, powertrain test bed, evaluation tools and their electrical interfaces. The process is composed up of the system interface definition, test design using DoE skill, test proceedings by step sequence of connecting systems, measured data collecting, mathematical model and optimization result extraction at the end. All the processes are automated by interfaces between the systems. Acceleration surge is minimized by proposed process by optimizing combustion control labels and tip in driveability is maximized by manipulating torque filter labels of EMS (Engine Management System) logic. Their detailed steps from the problem definition to the verification test results of improved design with vehicle test are presented.

It is common knowledge that body structure is an important factor of road noise performance. Thus, a high stiffness of body system is required, and determining their optimized stiffness and structure is necessary. Therefore, a method for improving body stiffness and validating the relationship between stiffness and road noise through CAE and experimental trials was tested. Furthermore, a guideline for optimizing body structure for road noise performance was suggested.

In order to achieve the proper automobile interior sound, the tailpipe noise of the exhaust system must be considered as a main contributor. This paper describes a study of the achievement of dynamic sound quality through exhaust system design. Firstly, we determined the vehicle's interior sound quality and established a target sound using a subjective assessment of 10 benchmark vehicles. The exhaust noise target is determined by means of transfer path analysis focusing on the noise source and how it's impacted by the muffler design. The exhaust system is commonly modeled as a combination of source strength and impedance. We obtained the source character by the wave decomposition method using two microphones and six loads ultimately leading to an optimized design of the inner muffler structure. Based on this study, we achieved dynamic interior sound and improved exhaust system performance.