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Electrified drives will change significantly in the wake of the further introduction of automated driving functions. Precise drive dimensioning, taking automated driving into account, opens up further potential in terms of drive operation and efficiency as well as optimal component design. Central element for unlocking the dimensioning potentials is the knowledge about the driving functions and their application. In this paper the implications of automated driving on the drive and component design are discussed. A process and a virtual toolchain for electric drive development from concept optimization to detailed component dimensioning is presented. The process is subdivided into a concept optimization part for finding the optimal drive topology and layout and a detailed prototype dimensioning process, where the final detailed drive dimensioning is carried out.

Encapsulations of E-drive systems are gaining importance in electric mobility, since they are simple measure to improve the noise behavior of the drive. Current experimental evaluation methods however pose substantial challenges for the test personnel and are associated with considerable effort in both time and cost. Evaluating the encapsulation on an e-drive test bed, for example, requires a functional e-drive and test bed resources. Evaluations in the vehicle on the other hand make objective assessments difficult and are subject to increasingly limited availability of prototype vehicles fit for NVH testing. To overcome these challenges, AVL has developed a new experimental evaluation method for the NVH efficiency of e-drive encapsulations. In this method, the e-drive is freely suspended in a semi-anechoic chamber and its structure is excited using shakers while the radiated noise with and without encapsulation is measured.

With the modernization of agriculture, the application of unmanned agricultural special vehicles is becoming increasingly widespread, which helps to improve agricultural production efficiency and reduce labor. Vehicle path-tracking control is an important link in achieving intelligent driving of vehicles. This paper designs a controller that combines path tracking with vehicle lateral stability for four-wheel steer/drive agricultural special electric vehicles. First, based on a simplified three-degrees-of-freedom vehicle dynamics model, a model predictive control (MPC) controller is used to calculate the front and rear axle angles. Then, according to the Ackermann steering principle, the four-wheel independent angles are calculated using the front and rear axle angles to achieve tracking of the target trajectory.

Update typo from: 4.3.9 Spray frequency: (1) application per hour (Moderate Test: eight (8) applications per hour) to: 4.3.9 Spray frequency: (1) application per hour (Moderate Test: (1) application every (8) hours)

The steer-by-wire (SBW) system, an integral component of the drive-by-wire chassis responsible for controlling the lateral motion of a vehicle, plays a pivotal role in enhancing vehicle safety. However, it poses a unique challenge concerning steering wheel return control, primarily due to its fundamental characteristic of severing the mechanical connection between the steering wheel and the turning wheel. This disconnect results in the inability to directly transmit the self-aligning torque to the steering wheel, giving rise to complications in ensuring a seamless return process. In order to realize precise control of steering wheel return, solving the problem of insufficient low-speed return and high-speed return overshoot of the steering wheel of the SBW system, this paper proposes a steering wheel active return control strategy for SBW system based on the backstepping control method.

Recently, the market share of electric vehicles is becoming increasingly obvious. It is expected that electric vehicles are quieter than fuel vehicles. Actually, without the cover of low-frequency engine noise, the high-frequency noise of electric vehicles is more prominent, which seriously affect the perceived sound quality. The present work is related tonal noise resulted from electric drive system (EDS), which is one of the fundamental noise sources for battery electric vehicle (BEV). The dominant noise sources observed in the vehicle interior are 26th and 36th orders for reducer and drive motor separately. Poor vibration isolation of right mounting system is the fundamental cause identification of EDS noise which has been investigated with objective measurements and simulation tools. Dynamic stiffness analysis is carried out to optimize the passive bracket. An engineering solution is implemented to enhance bracket to improve resonance effect.

Considering the current trend towards the electrification of commercial vehicles, the development of Beam eAxle solutions has become necessary. The utilization of an electric drive unit in heavy-duty solid axle-based commercial vehicles presents unique and demanding challenges. These include the necessity for elevated peak and continuous torque while meeting packaging constraints, structural integrity requirements, and extended service life. One such solution was developed by BorgWarner to address these challenges. This paper offers a comprehensive overview of the design and development process undertaken for this Dual Motor Beam eAxle system. This includes the initial comparison of various eAxle solutions, the specifications of components selected for this design, and the initial results from dyno and vehicle development.

Owing to its remarkable simplicity, high torque density, and expansive speed range, the switched reluctance motor (SRM) garners significant attention in the automotive industry, particularly in propelling electric vehicles. Nevertheless, the most prominent challenge faced by SRMs in their role as hub motors for electric vehicles is the unbalanced radial electromagnetic forces resulting from air gap eccentricity, which will induce motor vibration and noise, even seriously jeopardizing the driving safety of electric vehicles. Building upon the foundation of a nonlinear model for the SRM, this paper presents an analytical approach to assess the unbalanced radial forces arising from both radial and tilt air gap eccentricities. Furthermore, a test platform is constructed to validate the effectiveness of the proposed method, and comparative analyses of experiments, finite element simulations, and numerical analytical results are provided.

The paper solves the problem of increasing the accuracy of measuring torque and use of an electronic torque meter as a feedback sensor of synchronous and induction machines of an electric traction drive. Relevance of the problem and methods for its solving by indirect means using simulation models of electric machines are considered. The theoretical development of the meter is based on the energy model of electric machines in the form of differential equations for active and reactive power balance. This eliminates the direct influence of instability and nonlinearity of inductive elements and takes into account electrical, magnetic, mechanical, and harmonic losses using the simplest algorithms. Key variables of the model in the form of total active power and angular velocity are measured directly, and the high nominal efficiency of traction machines (95%) provides a wide tolerance range for calculating total losses.

Electrified vehicles represent mobility’s future, but they impose challenging and diverse requirements like range and performance. To meet these requirements, various components, such as battery cells, electric drives, fuel cells, and hydrogen vessels need to be integrated into a drive and storage system that optimizes the key performance indicators (KPI). However, finding the best combination of components is a multifaceted problem in the early phases of development. Therefore, advanced simulation tools and processes are essential for satisfying the customer´s expectation. EDAG Engineering GmbH has developed a flat storage platform, which is suitable for both, BEV and FCEV. The platform allows for the flexible and modular integration of batteries and hydrogen vessels. However, package space is limited and the impact of the design choices regarding the vehicle’s KPI need to be considered.

As part of the U.S. Environmental Protection Agency’s (EPA’s) continuing assessment of advanced light-duty automotive technologies in support of regulatory and compliance programs, a development project was started to study various test methods to benchmark Electric Drive Units (EDUs) consisting of an electric motor, inverter and a speed-reduction gearset. Several test methods were identified for consideration, including both in-vehicle testing of the complete EDU and stand-alone testing of the EDU and its subcomponents after removal from the vehicle. In all test methods explored, sweeps of speed and torque test points were conducted while collecting key EDU data required to determine efficiency, including motor torque and speed, direct current (DC) battery voltage and current into the inverter, and three-phase alternating current (AC) phase voltages and currents out of the inverter and into the electric motor.

Accurate prediction temperature variation of electric drive transmission (EDT) can effectively monitor its abnormal temperature rise that may occur under high speed and heavy load working conditions, so as to ensure the vehicles’ safe operation. In this paper, combined with real temperature and input/output characteristic data collected from EDT test platform under different working conditions, a spatio-temporal relationship dynamic graph convolution neural network based on least square method (OLS-DRGCN) for temperature prediction is proposed. Firstly, OLS is used to estimate the EDT’s internal temperature based on partial sensor information as the input of OLS-DRGCN. Secondly, the spatial dependence relationship of each temperature node is dynamically learned through node embedding and the dynamic thermal network topology of EDT is constructed. Meanwhile, the timing rule of each temperature node is obtained through the gated recurrent unit.

The rise of Software-Defined Vehicles (SDV) has rapidly advanced the development of Advanced Driver Assistance Systems (ADAS), Autonomous Vehicle (AV), and Battery Electric Vehicle (BEV) technology. While AVs need power to compute data from perception to controls, BEVs need the efficiency to optimize their electric driving range and stand out compared to traditional Internal Combustion Engine (ICE) vehicles. AVs possess certain shortcomings in the current world, but SAE Level 2+ (L2+) Automated Vehicles are the focus of all major Original Equipment Manufacturers (OEMs). The most common form of an SDV today is the amalgamation of AV and BEV technology on the same platform which is prominently available in most OEM’s lineups. As the compute and sensing architectures for L2+ automated vehicles lean towards a computationally expensive centralized design, it may hamper the most important purchasing factor of a BEV, the electric driving range.

An analytic first-order fuel consumption model is developed for FWD 2-motor HEV vehicles which on average achieve 36% EPA Combined efficiency. The premise of this paper is that this is primarily the result of new functionality specific to HEV. Detailed benchmarking data show that in such an HEV the engine not only provides traction power but simultaneously charges the battery. This combined operation of engine and electric powertrain is unique to HEV and is studied using their linear transfer functions. Charging by the engine enables extended electric driving at low traction power, which reduces engine running time and the associated overhead. The analysis predicts an engine duty cycle proportional to the traction power and inversely proportional to the engine output power: the electric driving is limited by the engine’s ability to deliver the required traction work.

The hybrid system's thermal strategy is centered around controlling the cooling of the motor, inverter, DCDC and evaporator. In this electric drive circuit system, the water temperature sensor is positioned at the radiator outlet rather than within it. Consequently, when determining the required air volume for radiator cooling and water demand for sub-components of the electric drive circuit, an estimation of the inlet water temperature becomes necessary. This estimation relies on a heat transfer formula that converts heat released by circuit sub-components into their contribution to temperature rise within the circuit plus the outlet temperature from the previous round through the radiator to determine inlet water temperature. The inverter's heat transfer power depends on voltage and current levels. Adjusting motor torque leads to rapid changes in current flow while maintaining a low speed for optimal flow rate through the electric drive pump.