In recent years, the push towards electrifying transportation has gained significant traction, with battery-electric vehicles (BEVs) emerging as a viable alternative. However, the widespread adoption of BEVs faces multiple challenges, such as limited driving range, making powertrain efficiency improvements crucial. One approach to improve powertrain energy efficiency is to adjust the DC-link voltage using a DC-DC converter between the battery and inverter. Here, it is necessary to address the losses introduced by the DC-DC converter. This paper presents a dynamic programming approach to optimize the DC-link voltage, taking into account the battery terminal voltage variation and its impact on the overall powertrain losses. We also examine the energy efficiency gains of IGBT-based and silicon carbide (SiC) MOSFET-based adjustable DC-link voltage powertrains during WLTC driving cycles through PLECS and Matlab/Simulink simulations. The findings indicate that both IGBT and MOSFET-based adjustable DC-link voltage powertrains can enhance the WLTC drive-cycle efficiency up to 2.51% and 3.25% compared to conventional IGBT and MOSFET-based powertrains, respectively.
The capacity and voltage rating of battery packs for electric vehicles or stationary energy storages are increasing, which challenge battery management and monitoring. Breaking the larger pack into smaller modules and using power electronics to achieve dynamic reconfiguration can be a solution. Reconfigurable batteries come with their own set of problems, including many sensors and complex monitoring systems, high-bandwidth communication interfaces, and additional costs. Online parameter estimation methods can simplify or omit many of these problems and reduce the cost and footprint of the system. However, most methods require many sensors or can only estimate a subset of the elements in the module’s equivalent circuit model (ECM). This paper proposes a simple decoupling technique to derive individual modules’ voltage and current profiles from the output measurements without direct measurement at the modules. The determined profiles can achieve a high sampling rate with minimum communication between the battery management system (BMS) and the modules. With accurate profiles, an estimation technique can easily determine the parameters of the modules. Provided simulations and experiments confirm this claim by estimating the parameters of a first-order ECM with a parallel capacitor. The proposed technique reduces the number of sensors from 2N + 2 to only two at the pack’s output terminals.
Accurately estimating the state of health (SoH) of batteries is indispensable for the safety, reliability, and optimal energy and power management of electric vehicles. However, from a data-driven perspective, complications, such as dynamic vehicle operating conditions, stochastic user behaviors, and cell-to-cell variations, make the estimation task challenging. This work develops a data-driven multi-model fusion method for SoH estimation under arbitrary usage profiles. All possible operating conditions are categorized into six scenarios. For each scenario, an appropriate feature set is extracted to indicate the SoH. Based on the obtained features, four machine learning algorithms are applied individually to train SoH estimation models using time-series data. In addition to the estimates at the current time step, a histogram data-based and online adaptive model is taken from previous work for predicting the next-step SoH. Then, a Kalman filter is applied to systematically fuse the results of all the estimation and prediction models. Experimental data collected from different types of batteries operated under diverse profiles verify the effectiveness and practicability of the developed method, as well as its superiority over individual models.
Rolling contact fatigue is a common failure mode in gears and bearings. However, this failure mode is getting greater attention due to the increasing tendency to use lower viscosity lubricants to reduce losses. Though several types of research have been done over the past decades, there are still scopes for further investigations. This study aims to study the effect of the slide to roll ratios (SRR), surface roughness and surface treatment on wear and pitting behaviour under realistic contact conditions. Fatigue and wear damages were quantified by studying the surface topography alteration at different contact cycle intervals. It was found that under boundary lubrication, initiation of micropitting took place in almost all test runs. However, once the adhesive wear mechanism activated at a higher contact cycle, the initially formed micropitted area started to wipe off. Moreover, for an extended test period and high sliding, wear volume is almost similar irrespective of SRR. Later, a surface treatment was studied, which was found effective in delaying the micropitting initiation by improving the tribological parameters compared to the untreated samples.
Gear designers make efforts to ensure the gear product will survive failure and fulfil the durability and other design requirements. The current article studies different gear design considerations, discusses how to select a microgeometry design accordingly, and investigates the effect of changing the design on the resulting fatigue life. The presented study provides engineers and researchers with a good understanding of the different practical design considerations and limitations. The considerations of dependency, tooth microgeometry asymmetry, peak-peak transmission error (PPTE), and misalignment variation are studied and discussed. PPTE is an important factor to study, which acts as an excitation source for Noise, Vibration, and Harshness (NVH). MASTA software tool is used for modelling the gear system and obtaining the misalignment values at different load levels. LDP software tool is used for performing the required gear design calculations and obtaining the generated stresses and PPTE. The macro- and microgeometry designs are discussed, calculations of different cases are performed, and comparisons are presented. The article concludes with a brief discussion that emphasizes the use of the studied considerations and explains under what circumstances should these considerations be adopted to meet the design requirements. The design requirements can conflict with each other, and then a compromised microgeometry design should be obtained. Changing the microgeometry design can significantly affect the predicted fatigue life and damage.
Interior permanent magnet machines are a popular solution for traction applications. Within the frame of this paper, an air-cooled spoke interior permanent magnet machine is designed and optimized with respect to the maximum average torque and minimum magnet mass. Several performance constraints, such as the permissible torque ripple and the relative thermal loading, have been considered in the design workflow. A metamodel, (or surrogate model) optimization is utilized with the help of Ansys OptiSLang tool. Within an initial sensitivity study, the metamodel predicts the optimal design space which shortens the cumulative optimization time from typical 1-2 weeks to approximately one day. It is shown that this simplification marginally reduces the solution accuracy (±4 %). The obtained Pareto front is verified using the finite element analysis through Ansys Motor-CAD proving that the selected optimized design meets all set requirements. The novelty of the proposed design is reflected in the combination of pole shaping strategy and rotor mass minimization by implementation of inter pole holes.
Automotive gears are facing stringent requirements regarding weight and functional surfaces, especially in view of the electric powertrain. To achieve these demands, powder metallurgy gears need to be finished using grinding, and in certain cases, mechano-chemical treatments. With regards to the latter, five different triboconditioning strategies based on vibratory tub finishing and/or centrifugal barrel finishing were considered and their effects on the surface integrity and friction behavior were investigated. Triboconditioning improved the surface roughness after grinding and resulted in higher compressive residual stresses. Additionally, microscopic observations of the surface topography were carried out. The lowest friction coefficients were observed for triboconditioning with a doped material (tribofilm) on the finished surface.
The unsteady flow around a travelling vehicle induces fluctuating aerodynamic loads. Automotive manufacturers usually set targets on the time-averaged lift forces to ensure good straight-line stability performance at high speeds. These targets are generally sufficient in preventing unstable vehicle designs. Yet, small changes in averaged values occasionally yield unexpectedly large differences in the stability performance, indicating that the changes in averaged normal loads cannot solely explain these differences. The unsteady aerodynamic effects on driving stability are, therefore, an interesting topic to study. The objective of the present work is to investigate the differences in wake dynamics and fluctuating aerodynamic loads for two variants of a roof spoiler on a sports utility vehicle: a baseline that was known to cause stability issues and an improved design which resolved them. The vehicle designs were investigated using accurate time-resolved CFD simulations for a set of crosswind conditions. The unsteady aerodynamic response was coupled to a vehicle dynamics model to analyse the resulting impact on driving stability. It was shown that in crosswinds the baseline spoiler, contrary to the improved spoiler, has bi-stable wake dynamics that induce lift force fluctuations at frequencies close to the 1st natural frequency of the rear suspension.
Resistive cabin heaters can significantly reduce the driving range of battery electric vehicles in cold climate conditions. Heat pump solutions can mitigate this drawback, but these are also complemented with resistive heaters which are often unnecessary in warmer climates. This paper investigates different drivetrain-loss-heating techniques, which can be used as redundancy or as a replacement for the resistive heater. With the help of different software tools, the achievable electric drive unit (EDU) losses, considering the motor and inverter losses, of a Volkswagen ID.3 are simulated. When driving at lower speeds or standstill, the EDU losses can be regulated via the stator current magnitude. As demonstrated, this method increases the torque ripple, but the generated heat losses, varying from 5.8 kW to 7.9 kW, are sufficient to fulfill the cold climate heating requirements. When operated at standstill, a declutched motor can achieve comparable heat losses, but disconnectors are seldomly used in battery electric vehicles. When using balanced three-phase DC currents at standstill, the heat losses vary from 4.6 kW to 5.4 kW depending on the rotor position, which might not be sufficient to fulfill the required heating capacity at cold climates.
A synchronous reluctance machine has been selected and optimized for commercial vehicle power takeoff application. The meta-modeling optimization process has been covered in detail. To reduce execution time, skewing has not been included in the optimization workflow. The penalty is elevated torque ripple of unskewed design (13.9%), which has been mitigated in the post-optimization process with the use of asymmetric rotor poles (7.5%), equal length segment skewing (6.2%), variable-length segment skewing (4.2%), and continuous rotor skewing (2.8%). Due to the increased production cost mostly related to winding insertion, stator skewing has not been considered as an option. A detailed comparative study has been conducted to illustrate the benefits and drawbacks of each rotor skewing alternative. The novel contribution presented in the paper is a new variable-length rotor segment skewing. This solution merges the qualities of continuous skewing (minimal torque ripple) and segmented skewing (reduced production cost).
The global market for autonomous robotics platforms has grown rapidly due to the advent of drones, mobile robots, and driverless cars, while the mass media coverage examining the progress of robotics and autonomous systems field is widespread [...]
Gear microgeometry is important as it can change the tooth flank contact pattern effectively. In gear design, it is important to minimize the peak-peak transmission error (PPTE) for lower Noise, Vibration and Harshness (NVH), and to minimize the generated stresses for higher durability that can help to avoid tooth failure. In the current article, a structured approach is developed that combines the advantages of applying meta-models and robust optimization. For the Design of Experiments (DOE), the LDP software tool was used to obtain the objective functions of PPTE, contact stress, and root stresses at all design points. Probability distribution and worst-case scenario measures were applied to weigh the objective functions and make them robust against torque. The generated data were used in MATLAB to build meta-models for each objective function using squared exponential Gaussian regression. The generated meta-models were then used in a multi-objective optimization algorithm to obtain a Pareto set of solutions. These solutions were examined and ranked from the highest to the lowest, based on the weights of the PPTE and stress safety factors. Using Rank 1 design, the gains of -25, -7, +3 and -5 % for the PPTE, contact stress, gear 1 root stress and gear 2 root stress respectively were obtained. However, these gains can be different in other design cases because these gains were calculated using Rank 1 design as compared to the manually selected benchmarked design which mainly depends on the engineer's experience. The developed approach can save time and help to obtain a unique optimal design solution in a structured format.
Implementation of methods for perceived quality evaluation is an integral part of the automotive manufacturers’ strategic development plans. The correct definition of perceived quality requirements is one of the significant factors influencing customer’s purchase intention. This study seeks to understand how customers perceive and prioritize attributes that are associated with the geometrical and materials quality of a premium car market segment. We applied the Perceived Quality Attributes Importance Ranking (PQAIR) methodology to understand the importance of different perceived quality attributes form a customer perspective. Such an understanding can contribute to the effectiveness of the design processes in the early product development phases. This approach is tested on 144 respondents representing customer’s target group and performed in collaboration with China Euro Vehicle Technology (CEVT) technical experts. Our results verify the rationality and feasibility of the applied method and indicate the improvement of engineering practices regarding complex product development.
Accurately predicting batteries’ ageing trajectory and remaining useful life is not only required to ensure safe and reliable operation of electric vehicles (EVs) but is also the fundamental step towards health-conscious use and residual value assessment of the battery. The non-linearity, wide range of operating conditions, and cell to cell variations make battery health prediction challenging. This paper proposes a prediction framework that is based on a combination of global models offline developed by different machine learning methods and cell individualised models that are online adapted. For any format of raw data collected under diverse operating conditions, statistic properties of histograms can be still extracted and used as features to learn battery ageing. Our framework is trained and tested on three large datasets, one being retrieved from 7296 plug-in hybrid EVs. While the best global models achieve 0.93% mean absolute percentage error (MAPE) on laboratory data and 1.41% MAPE on the real-world fleet data, the adaptation algorithm further reduced the errors by up to 13.7%, all requiring low computational power and memory. Overall, this work proves the feasibility and benefits of using histogram data and also highlights how online adaptation can be used to improve predictions.
Autonomous Vehicles [AVs] will emerge as a powerful catalyst, thus forming a potentially disruptive technology opening doors to technological, socio-cultural and legal challenges. Present-day research is mainly focused ontechnological orientation, lacking the predominant social and behavioural linkages. NZ being a testbed of innovative technologies, is facing significant challenges in growing population and traffic congestions. Auckland ranks 47th globally in terms of traffic congestion costing $1.3b a year to the economy. Therefore, harnessing AV technology will be a significant step to remain competitive. The people’s readiness for acceptance influences AVs successful implementation. In this background, it is quintessential to ascertain and evaluate critical factors affecting the successful deployment of AVs in NZ. This exploratory research study is a part of a larger project of realizing a trust dynamics and governance framework for humanizing driverless technology. It is being carried out in collaboration with BMW NZ Group using Autonomous Level 2 Vehicle in Auckland on high, medium and low-density roads with level 3 functions mostly. Drawing on systematic qualitative evidence, this study attempted to find out key determinants/variables affecting user acceptance, including anthropomorphism, training, feedback, safety, security, privacy, customization and adaptive automation for AVs deployment in NZ and their role in garnering users’ trust besides suggesting an HMI Autonomous Driving Events Relationship Framework for AVs
Crosswinds affect vehicle driving stability and their influence increase with driving speed. To improve high speed driving stability, interdisciplinary research using unsteady aerodynamics and vehicle dynamics is necessary. The current demands of faster development times require robust virtual methods for assessing stability performance in early design phases. This paper employs a numerical one-way coupling between the two disciplines and uses a variety of realistic crosswind gust profiles for the aerodynamic simulations to output representative forces and moments on three vehicle dynamic models of different fidelity levels, ranging from a one-track model to a full multi-body dynamic model of a sports utility vehicle. An investigation on required model fidelity was conducted along with a sensitivity study to find key aerodynamic and vehicle dynamic characteristics to minimise the yaw velocity and lateral acceleration response during crosswinds. Transient aerodynamic simulations were used to model crosswind gusts at high speeds. Analysis of the forces and moments showed that rapid changing gusts generate overshoots in the yaw moment, due to the phase delay of the flow between the front and rear of the vehicle. A methodology for modelling this phase delay is proposed. The response of the vehicle was captured equally well by the enhanced model (mid-level fidelity) and the full multi-body dynamic model, while the simplest one-track model failed to emulate the correct vehicle response. The sensitivity study showed the importance of the positioning of the centre of gravity, the aerodynamic coefficient of yaw moment, wheel base, vehicle mass and yaw inertia. In addition, the axles' side force steer gradients and other suspension parameters revealed potential in improving crosswind stability.
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