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Current batteries of battery electric vehicles (BEVs) require a battery management system (BMS) in order to enable a safe and long-lasting operation. The main functions of the battery management systems are a continuous monitoring of the voltage of each cell, a continuous monitoring of the battery temperature, the control of the charge current and the discharge current as well as the prevention of both a deep discharge and an overcharging. For the realization of these functions, different architectures are possible, ranging from an individual intelligent system at each cell up to a realization of the whole BMS within one central computing unit for the whole vehicle. This paper investigates and structures different architectural possibilities, discusses analysis possibilities and presents approaches for the synthesis of sensible architectures such as BMS. A concept synthesis for the start-up and shut-down of the high-voltage system is presented by comparing three different integrated pre- and discharging circuits and using a Hardware-in-the-Loop (HiL) program as an example. Finally, a topology consisting of three switches and two resistors (3S2R2) turns out to be the best one, due to the number of components, safety and price.

In the field of inner-city cargo transportation, solutions such as electrified cargo trailers are increasingly being used. To provide an intelligent drivetrain control system that improves driving dynamics and enables safety, it is necessary to know the characteristics of the trailer system. This includes the behavior of the tires. Existing investigations of bicycle tires focus on camber-angle-dependent models. However, in most trailers, a rigid mounting of the tires without camber is used. For this reason, a bicycle tire model is created within the scope of this study using real measurement data that represent a 20 in tire with typical wheel loads and without camber. The measurements were collected with the mobile tire measurement laboratory of the Bern University of Applied Sciences on an asphalt test site under real conditions. Crosstalk occurring in the measurement hub during the data collection was successfully corrected using a matrix method. With help of the so-called Magic Formula, a tire model was created that can be used for driving dynamics simulations and controller design.

Bicycle-drawn cargo trailers with an electric drive to enable the transportation of high cargo loads are used as part of the last-mile logistics. Depending on the load, the total mass of a trailer can vary between approx. 50 and 250 kg, potentially more than the mass of the towing bicycle. This can result in major changes in acceleration and braking behavior of the overall system. While existing systems are designed primarily to provide sufficient power, improvements are needed in the powertrain control system in terms of driver safety and comfort. Hence, we propose a novel prototype that allows measurement of the tensile force in the drawbar which can subsequently be used to design a superior control system. In this context, a sinusoidal force input from the cyclist to the trailer according to the cadence of the cyclist is observed. The novelty of this research is to analyze whether torque impulses of the cyclist can be reduced with the help of Model Predictive Control (MPC). In addition, the powertrain of the trailer is intended to support the braking process of the system with regenerative braking. In the context of this research, a first MPC controller design is carried out and analyzed with the help of a Hardware-in-the-Loop (HIL) approach where the microcontroller of the power electronics is included as hardware to ensure the vehicle dynamics control interacts properly with the lower-level field-oriented control. The battery and motor subsystems are simulated in a Typhoon HIL 604, which is supplemented by a vehicle dynamics model of the trailer that is integrated as a Functional Mock-Up Unit (FMU). First results indicate that the MPC longitudinal dynamics controller supports the driver during acceleration, attenuates the sinusoidal oscillations and reduces the force with which the trailer pushes the bicycle during braking.

Currently, a consensus in the scientific community can be observed that it is necessary to reduce the carbon footprint and the use of fossil resources in order to ensure the ongoing well-being of humanity and our planet. Battery electric vehicles (BEVs) can contribute to this reduction, as they can use energy from sustainable sources as well as store it in order to enable individual mobility. Still, as long as sustainable energy is not available in abundance and a share of our energy still is generated using fossil sources, it is important to consider the energy consumption of these BEVs in greater detail. BEVs may actually consume more energy than necessary due to an architecture borrowed from non-BEVs, due to their drive-train topology, due to many individual product development issues and last but not least because they are not operated at their highest efficiency. This paper addresses the evaluation of a specific sustainable product development process for BEVs. The study is based on detailed energy consumption simulations of smaller BEVs with different drive train technologies. A general consideration of sustainability and utility based on the design choices, as well as of societal consequences, leads to requirements and challenges for sustainable product development. A digital product development process is described, which addresses these challenges.

The authors present a method to reverse engineer 3D printer specific machine instructions (GCode) to a point cloud representation and then a STL (Stereolithography) file format. GCode is a machine code that is used for 3D printing among other applications, such as CNC routers. Such code files contain instructions for the 3D printer to move and control its actuator, in case of Fused Deposition Modeling (FDM), the printhead that extrudes semi-molten plastics. The reverse engineering method presented here is based on the digital simulation of the extrusion process of FDM type 3D printing. The reconstructed models and pointclouds do not accommodate for hollow structures, such as holes or cavities. The implementation is performed in Python and relies on open source software and libraries, such as Matplotlib and OpenCV. The reconstruction is performed on the model's extrusion boundary and considers mechanical imprecision. The complete reconstruction mechanism is available as a RESTful (Representational State Transfer) Web service.

Purpose – This paper aims to reports the further developments of an optical sensing technique, relying on Mie scattered and reflected light, from the ice surface and volume, to determine the ice accretion rate as well as the ice type. Design/methodology/approach – By measuring the optical intensity of the backscattered and reflected light, the paper demonstrates that it is possible to obtain information on the onset of icing as well as determine the thickness and type of ice accreted on the leading edge of a wing in real time. Findings – This work is important in the design and development of optical direct ice detection sensors for aerospace applications. Practical implications – This work is aimed at showing a new approach to ice detection. Originality/value – Original concept follow on paper from pervious publication.

The reduction of the computational effort is desirable for the simulation of marine ecosystem models. Using a marine ecosystem model, the assessment and the validation of annual periodic solutions (i.e., steady annual cycles) against observational data are crucial to identify biogeochemical processes, which, for example, influence the global carbon cycle. For marine ecosystem models, the transport matrix method (TMM) already lowers the runtime of the simulation significantly and enables the application of larger time steps straightforwardly. However, the selection of an appropriate time step is a challenging compromise between accuracy and shortening the runtime. Using an automatic time step adjustment during the computation of a steady annual cycle with the TMM, we present in this paper different algorithms applying either an adaptive step size control or decreasing time steps in order to use the time step always as large as possible without any manual selection. For these methods and a variety of marine ecosystem models of different complexity, the accuracy of the computed steady annual cycle achieved the same accuracy as solutions obtained with a fixed time step. Depending on the complexity of the marine ecosystem model, the application of the methods shortened the runtime significantly. Due to the certain overhead of the adaptive method, the computational effort may be higher in special cases using the adaptive step size control. The presented methods represent computational efficient methods for the simulation of marine ecosystem models using the TMM but without any manual selection of the time step.

Marine ecosystem models are an important tool to assess the role of the ocean biota in climate change and to identify relevant biogeochemical processes by validating the model outputs against observational data. For the assessment of the marine ecosystem models, the existence and uniqueness of an annual periodic solution (i.e., a steady annual cycle) is desirable. To analyze the uniqueness of a steady annual cycle, we performed a larger number of simulations starting from different initial concentrations for a hierarchy of biogeochemical models with an increasing complexity. The numerical results suggested that the simulations finished always with the same steady annual cycle regardless of the initial concentration. Due to numerical instabilities, some inadmissible approximations of the steady annual cycle, however, occurred in some cases for the three most complex biogeochemical models. Our numerical results indicate a unique steady annual cycle for practical applications.

Parameter identification for marine ecosystem models is important for the assessment and validation of marine ecosystem models against observational data. The surrogate-based optimization (SBO) is a computationally efficient method to optimize complex models. SBO replaces the computationally expensive (high-fidelity) model by a surrogate constructed from a less accurate but computationally cheaper (low-fidelity) model in combination with an appropriate correction approach, which improves the accuracy of the low-fidelity model. To construct a computationally cheap low-fidelity model, we tested three different approaches to compute an approximation of the annual periodic solution (i.e., a steady annual cycle) of a marine ecosystem model: firstly, a reduced number of spin-up iterations (several decades instead of millennia), secondly, an artificial neural network (ANN) approximating the steady annual cycle and, finally, a combination of both approaches. Except for the low-fidelity model using only the ANN, the SBO yielded a solution close to the target and reduced the computational effort significantly. If an ANN approximating appropriately a marine ecosystem model is available, the SBO using this ANN as low-fidelity model presents a promising and computational efficient method for the validation.

The reduction of computational costs for marine ecosystem models is important for the investigation and detection of the relevant biogeochemical processes because such models are computationally expensive. In order to lower these computational costs by means of larger time steps we investigated the accuracy of steady annual cycles (i.e., an annual periodic solution) calculated with different time steps. We compared the accuracy for a hierarchy of biogeochemical models showing an increasing complexity and computed the steady annual cycles with offline simulations that are based on the transport matrix approach. For each of these biogeochemical models, we obtained practically the same solution even though larger time steps. This indicates that larger time steps shortened the runtime with an acceptable loss of accuracy.