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The optimization and further development of automated driving functions offers great potential to relieve the driver in various driving situations and increase road safety. Simulative testing in particular is an indispensable tool in this process, allowing conclusions to be drawn about the design of automated driving functions at a very early stage of development. In this context, the use of driving simulators provides support so that the driving functions of tomorrow can be experienced in a very safe and reproducible environment. The focus of the acceptance and optimization of automated driving functions is particularly on vehicle lateral control functions. As part of this paper, a test person study was carried out regarding manual vehicle lateral control on the dynamic vehicle road simulator at the Institute of Automotive Engineering.

Increasing urbanisation and the growing environmental awareness in society require new and innovative vehicle concepts. In the present work, the design freedoms of additive manufacturing (AM) are used to develop a front axle wheel suspension for a novel modular vehicle concept. The development of the suspension components is based on a new method using industry standard load cases for the strength design of the components. To design the chassis components, first the available installation space is determined and a suitable configuration of the chassis components is defined. Furthermore, numerical methods are used to identify component geometries that are suitable for the force flow. The optimisation setup is selected in a way that allows to integrate information, energy and material-carrying conductors into the suspension arms. The conductors even serve as load-bearing structures because of the matching design of the components.

With the automotive industry's increasing focus on electromobility and the growing share of electric cars, new challenges are arising for the development of electric motors. The requirements for torque and power of traction motors are constantly growing, while installation space, costs and weight are increasingly becoming limiting factors. Moreover, there is an inherent conflict in the design between power density and efficiency of an electric motor. Thus, a main focus in today's development lies on space-saving and yet effective and innovative cooling systems. This paper presents an approach for a multi-physical optimization that combines the domains of electromagnetics and thermodynamics. Based on a reference machine, this simulative study examins a total of nine different stator cooling concepts varying the cooling duct positions and end-winding cooling concepts.

Noise, vibration and harshness (NVH) is one of the most important performance evaluation aspect of electric motors. Among the different causes of the NVH issues of electrical drives, the high-frequency spatial and temporal harmonics of the electrical drive system is of great importance. To reduce the tonal noise of the electric motors, harmonic injection methods can be applied. However, a lot of the existing related work focuses more on improving the optimization process of the parameter settings of the injected current/flux/voltage, which are usually limited to some specific working conditions. The applicability and effectivity of the algorithm to the whole frequency/speed range are not investigated. In this paper, a multi-domain pipeline of harmonic injection controller design for a permanent magnet synchronous motor (PMSM) is proposed.

With the increasing importance of electrified powertrains, electric motors and gear boxes become an important NVH source especially regarding whining noises in the high frequency range. Engine encapsulation noise treatments become often necessary and present some implementation, modeling as well as optimization issues due to complex environments with contact uncertainties, pass-throughs and critical uncovered areas. Relying purely on mass spring systems is often a too massive and relatively unefficient solution whenever the uncovered areas are dominant. Coverage is key and often a combination of hybrid backfoamed porous stiff shells with integral foams for highly complex shapes offer an optimized trade-off between acoustic performance, weight and costs.

The global transportation industry, and road freight in particular, faces formidable challenges in reducing Greenhouse Gas (GHG) emissions; both Europe and the US have already enabled legislation with CO2 / GHG reduction targets. In Europe, targets are set on a fleet level basis: a CO2 baseline has already been established using Heavy Duty Vehicle (HDV) data collected and analyzed by the European Environment Agency (EEA) in 2019/2020. This baseline data has been published as the reference for the required CO2 reductions. More recently, the EU has proposed a Zero Emissions Vehicle definition of 3g CO2/t-km. The Zero Emissions Vehicle (ZEV) designation is expected to be key to a number of market instruments that improve the economics and practicality of hydrogen trucks. This paper assesses the permissible amount of carbon-based fuel in hydrogen fueled vehicles – the Pilot Energy Ratio (PER) – for each regulated subgroup of HDVs in the baseline data set.

For battery-electric vehicles (BEVs), the climate control and the driving range are crucial criteria in the ongoing electrification of automobiles in Europe towards the targeted carbon neutrality of the automotive industry. The thermal management system makes an important contribution to the energy efficiency and the cabin comfort of the vehicle. In addition to the system architecture, the refrigerant is crucial to achieve high cooling and heating performance while maintaining high efficiency and thus low energy consumption. Due to the high efficiency requirements for the vehicle, future system architectures will largely be heat pump systems. The alternative refrigerant R-474A based on the molecule R-1132(E) achieved top performance for both parameters in various system and vehicle tests.

Reductions in powertrain noise have led to an increased proportion of road noise, prompting various studies aimed at mitigating it. Road excitation primarily traverses through the vehicle suspension system, necessitating careful optimization of the characteristics of bushings at connection points. However, optimizing at the vehicle assembly stage is both time-consuming and costly. Therefore, it is essential to proceed with optimization at the subsystem level using appropriate objective functions. In this study, the blocked force and energy-based index derived from complex power were used to optimize the NVH performance. Calculating the complex power in each bushing enables computing the power flow, thereby providing a basis for evaluating the NVH performance. Through stiffness injection, the frequency response functions (FRF) of the system can be predicted according to arbitrary changes in the bushing stiffness.

Let’s start with the uncomfortable truth, climate change is happening, and the automotive industrial network is one of the main industries contributing to greenhouse gas emissions. SKF is an energy intensive business – directly using energy, mainly in the form of electricity and gas, in its operations around the world. In addition, SKF utilizes materials, predominantly steel, and services which can be energy and carbon intensive – such as transports and raw material in production and processing. The combined impact of these direct and indirect energy uses (scope 1, 2 and 3 upstream) generates an excess of over two million metric tons of CO2e per year. This figure would however be significantly higher were it not for the actions SKF has taken to reduce both energy and carbon intensity. In 2000, we were one of the first companies to actually start to report and set climate targets.

A major issue of battery electric vehicles (BEV) is optimizing driving range and energy consumption. Under actual driving, transient thermal and electrical performance changes could deteriorate the battery cells and pack. These performances can be investigated and controlled efficiently with a thermal management system (TMS) via model-based development. A complete battery pack contains multiple cells, bricks, and modules with numerous coolant pipes and flow channels. However, such an early modeling stage requires detailed cell geometry and specifications to estimate the thermal and electrochemical energies of the cell, module, and pack. To capture the dynamic performance changes of the LIB pack under real driving cycles, the thermal energy flow between the pack and its TMS must be well predicted. This study presents a BTMS model development and validation method for a 75-kWh battery pack used in mass-production, mid-size battery SUV under WLTC.

Vehicle powertrain electrification is considered one of the main measures adopted by vehicle manufacturers to achieve the CO2 emissions targets. Although the development of vehicles with hybrid and plug-in hybrid powertrains is based on existing platforms, the complexity of the system is significantly increased. As a result, the demands of testing during the development and calibration stages is getting significantly higher. To compensate that, high-fidelity simulation models are used as a cost-effective solution. This paper aims to present the methodology followed for the development of a rule-based energy management controller for a plug-in hybrid electric vehicle (PHEV), and to describe the experimental campaign carried out with this passenger car. The controller is implemented in a vehicle simulation model that is parametrized to replicate the operation of the vehicle.

Broadband active noise control algorithms require high-performance so multi-channel control to ensure high performance, which results in very high computational power and expensive DSP. When the control filter update part need a huge computational power of the algorithm is separated and calculated by the server, it is possible to reduce cost by using a low-cost DSP in a local vehicle, and a performance improvement algorithm requiring a high computational power can be applied to the server. In order to achieve the above goal, this study analyzed the maximum delay time when communication speed is low and studied response measures to ensure data integrity at the receiving location considering situations where communication speed delay and data errors occur.

The high-frequency whining noise produced by motors in modern electric vehicles causes a significant issue, leading to annoyance among passengers. This noise becomes even more noticeable due to the quiet nature of electric vehicles, which lack other noises to mask the high-frequency whining noise. To improve the noise caused by motors, it is essential to optimize various motor design parameters. However, this task requires expert knowledge and a considerable time investment. In this study, we explored the application of artificial intelligence to optimize the NVH performance of motors during the design phase. Firstly, we selected and modeled three benchmark motor types using Motor-CAD. Machine learning models were trained using Design of Experiment methods to simulate batch runs of Motor-CAD inputs and outputs.

Traditionally, Electric Machine design has primarily focused on factors like efficiency, packaging, and cost, often neglecting the critical aspects of Noise, Vibration, and Harshness (NVH) in the early decision-making stages. This disconnect between E-Machine design teams and NVH teams has consistently posed a challenge. This paper introduces an innovative workflow that unifies these previously separate domains, facilitating comprehensive optimization by seamlessly integrating NVH considerations with other E-Machine objectives, such as electromagnetic compatibility (EMC). This paper highlights AVL's approach in achieving this transformation and demonstrates how this integrated approach sets a new standard for E-Machine design. The presented approach relies on AI-driven algorithms and computational tools.

During design development phases, automotive components undergo a strict validation process aiming to demonstrate requested levels of performance and durability. In some cases, specific developments encounter a major blocking point : decoupling systems responsible for optimal acoustic performances. On the one hand, damping rubbers need to be soft to comply with noise, vibration & harshness criteria. However, softness would provoke such high amplitudes during vibration endurance tests that components would suffer from failures. On the other hand, stiffer rubbers, designed for durability purposes, would fail to meet noise compliance. The rubber design development goes through a double-faced dilemma : design with acceptable trade-off between NVH and durability, and efficient ways to develop compliant designs. This paper illustrates two case studies where different methodologies are applied to validate decoupling systems from both acoustic and reliability perspectives.

Computer modelling, virtual prototyping and simulation is widely used in the automotive industry to optimize the development process. While the use of CAE is widespread, on its own it lacks the ability to provide observable acoustics or tactile vibrations for decision makers to assess, and hence optimize the customer experience. Subjective assessment using Driver-in-Loop simulators to experience data has been shown to improve the quality of vehicles and reduce development time and uncertainty. Efficient development processes require a seamless interface from detailed CAE simulation to subjective evaluations suitable for high level decision makers. In the context of perceived vehicle vibration, the need for a bridge between complex CAE data and realistic subjective evaluation of tactile response is most compelling. A suite of VI-grade noise and vibration simulators have been developed to meet this challenge.

In order to improve the obstacle avoidance ability of autonomous vehicles in complex traffic environments, speed planning, path planning, and tracking control are integrated into one optimization problem. An integrated vehicle trajectory planning and tracking control method combining a pseudo-time-to-collision (PTC) risk assessment model and model predictive control (MPC) is proposed. First, a risk assessment model with PTC probability is proposed by considering the differentiation of the risk on the relative motion states of the self and front vehicles, and the obstacle vehicles in the lateral and longitudinal directions. Then, a three-degrees-of-freedom vehicle dynamics model is established, and the MPC cost function and constraints are constructed from the perspective of the road environment as well as the stability and comfort of the ego-vehicle, combined with the PTC risk assessment model to optimize the control.

The analysis presented in this document demonstrates the mathematical model approach for determining the rotation of a door about the hinge axis. Additional results from the model are the torque due to gravity about the axis, opening force, and the door hold open check link force. Vector mechanics, equations of a plane, and parametric equations were utilized to develop this model, which only requires coordinate points as inputs. This model allows for various hinge axis angles and door rotation angles to quickly be analyzed. Vehicle pitch and roll angles may also be input along with door mass to determine the torque about the hinge axis. The vector calculations to determine the moment arm of the door check link and its resulting force are demonstrated for both a standard check link design and an alternate check link design that has the link connected to a slider translated along a shaft.

Vehicles equipped with articulated steering systems have advantages such as low energy consumption, simple structure, and excellent maneuverability. However, due to the specific characteristics of the system, these vehicles often face challenges in terms of lateral stability. Addressing this issue, this paper leverages the precise and independently controllable wheel torques of a hub motor-driven vehicle. First, an equivalent double-slider model is selected as the dynamic control model, and the control object is rationalized. Subsequently, based on the model predictive control method and considering control accuracy and robustness, a weight-variable adaptive model predictive control approach is proposed. This method addresses the optimization challenges of multiple systems, constraints, and objectives, achieving adaptive control of stability, maneuverability, tire slip ratio, and articulation angle along with individual wheel torques during the entire steering process of the vehicle.

Accurately predicting real-world vehicle energy consumption is essential for optimizing vehicle designs, enhancing energy efficiency, and developing effective energy management strategies. This paper presents a data-driven approach that utilizes machine learning techniques and a comprehensive dataset of vehicle parameters and environmental factors to create precise energy consumption prediction models. The methodology involves recording real-world vehicle data using data loggers to extract information from the CAN bus systems for ICE and hybrid electric, as well as hydrogen and battery fuel cell vehicles. Data cleaning and cycle-based analysis are employed to process the dataset for accurate energy consumption prediction. This includes cycle detection and analysis using methods from statistics and signal processing, and then pattern recognition based on these metrics.