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Future transport will change drastically with the introduction of automated vehicles. Here, Autonomous Mobility on Demand (AMoD) will play a major role, requiring a radical change of vehicle design, with many different conceivable concepts. This technology shift holds high potentials and high risks. Uncertainties about future usage profiles, operator and customer requirements have to be dealt with. An approach to elicit initial requirements for future vehicle concepts considering the entire ecosystem is introduced. The applicability is shown for a specific urban mobility scenario.

A major part of urban private vehicles needs to be electrified in order to meet the German climate protection goals in the transport sector. Therefore, charging strategies must be developed and the geographical distribution of infrastructure has to be planned accordingly. We propose a new method to combine a multi agent traffic simulation (MATSim) with real-world vehicle distribution and measured charging behavior. The results of our simulation were analyzed for the exemplary use case of Berlin’s private passenger cars. Three different charging strategies were developed and analyzed: charging at home, at work and during leisure activities. Charging at home was suitable to serve all agents’ charging demand with low charging power (7.4 kW). Charging at work only served 56.23 % of the agents with a charging power of 11 kW, but required fewer charging points. Charging during leisure activities served from 46.36 % to 81.92 % of the agents, depending on the chosen charging power and user behavior.

Many cities have announced ambitious plans to introduce zero-emission electric bus systems. The transformation process to electric bus systems opens up a vast design space as different charging strategies, charging technologies and battery types are available. Therefore, a profound assessment strategy is necessary to find a “most suitable system solution” under given strategic and operational requirements.In this study, we present a new methodology for conceptual design of urban electric bus systems. First, the available e-bus technologies are analysed with a special focus on charging systems, battery technology and aging. Relational functional analysis is used to derive a suitable simulation model. Based on the operational requirements, an energetic simulation of the e-bus is carried out, and the required battery capacity is obtained. Subsequently, the design space is reduced by applying a qualitative cost- technology compatibility matrix taking cost and battery aging into account. The applicability of the model is shown for an exemplary realistic operational scenario to identify three most expedient concepts, which are finally validated with an in-depth analysis.

Meta-heuristic and hyperheuristic algorithms are milestones that make the topology optimization practical for dynamic and nonlinear problems. However, researchers are continuing to improve these methods to get better results. Plastic behavior, complex deformation, and load-dependent material properties of the vehicle components during a crash event are challenging issues that are faced in this context. This research focuses on enhancing search efficiency of the hybrid cellular automata (HCA) algorithm with the aim of improving the energy absorption of vehicle structures exposed to high-impact collisions. An attempt is made to utilize an ideal amount of material in the structures to obtain a more uniform distribution of the plastic strain. Thus, the design is based on this criterion throughout the whole structure during the entire collision time. The variable neighborhood radius concept realizes an intelligent search strategy for the modified HCA (MHCA) algorithm. This innovation, applied here to topology optimization design, makes the MHCA algorithm more functional to controlling plastic strain energy. To confirm this, the crash analysis is performed using the finite-element software package LS-DYNA. An additional benefit of using this method is the quick and stable convergence while the energy absorption relative to the mass fraction is remarkably improved.

Submitted to ABMTRANS'21 – 10th International Workshop on Agent-based Mobility, Traffic and Transportation Models, Methodologies and Applications The decarbonization of transport is one major challenge in the upcoming years. One possible solution is the use of battery electric vehicles (BEV). While electric passenger cars and their charging strategies are already in series production, battery electric trucks and their charging strategies are still mostly in the prototype stage. The range limitations of battery electric trucks represent a new challenge for logistics. Therefore, we introduce a methodology for determining charging strategies for freight transport vehicles based on transport simulation results. We analyze the results of an agent-based transport simulation (MATSim) and evaluate different settings of normal and fast charging points. We found for a case study dealing with the food retailing in Berlin, that for a fleet with 279 vehicles in 16 depots 214 normal and 61 fast charging points are sufficient to complete approx. 90% of the tours with BEV. If the vehicles share their charging points, only 71 fast charging points with 400 kW are sufficient. With higher charging power the share of charged vehicles hardly increases. With 29 additional high performance opportunity chargers within the city, all tours can be operated by battery electric trucks.

In this contribution a motion estimation approach for the autonomous flight of tethered airfoils is presented. Accurate motion data are essential for the airborne wind energy sector to optimize the harvested wind energy and for the manufacturer of tethered airfoils to optimize the kite design based on measurement data. We propose an estimation based on tether angle measurements from the ground unit and inertial sensor data from the airfoil. In contrast to existing approaches, we account for the issue of tether sag, which renders tether angle measurements temporarily inaccurate. We formulate a Kalman Filter which adaptively shifts the fusion weight to the measurement with the higher certainty. The proposed estimation method is evaluated in simulations, and a proof of concept is given on experimental data, for which the proposed method yields a three times smaller estimation error than a fixed-weight solution.

Decarbonisation is a major challenge for the coming decades, for all industries, including the transport sector. Battery electric vehicles are a potential solution for the transport sector to reduce its carbon impact. Asides from the question whether there is sufficient supply of electric vehicles for freight transport, it is also unclear whether battery-powered trucks meet the practical requirements, especially in terms of their driving range. To investigate this, synthetic tours were generated by solving a Vehicle Routing Problem (VRP). This also generates the fleet size and composition depending on a set of different vehicle types. The network with underlying traffic conditions comes from an publicly available transport model. The generated tours are then simulated with an open-source transport simulation (MATSim), for both diesel and battery electric vehicles (BEVs). In a sensitivity study, two different purchase prices were considered for calculating vehicle costs. The case study uses a model of the food retailing industry for the city of Berlin. 56% of the tours can be driven without recharging. When recharged one time, 90% of the tours are suitable for BEVs. The costs for transporting the goods will increase by 17 to 23% depending on the assumption for the purchase prices for the BEVs. Using a well-to-wheel calculation, the electrification of all tours leads to a reduction of greenhouse gas (GHG) emissions by 26 to 96% depending on the assumed electricity production. DFG, 398051144, Analyse von Strategien zur vollständigen Dekarbonisierung des urbanen Verkehrs

We introduce the concept of social sustainability, intertwined with ecological and economic aspects, to the field of service robots and comparable automation technology. It takes a first step towards a comprehensive guideline that operationalizes and applies social sustainability. By applying this guideline to the project MURMEL we offer a concept that collects and rates social key issues to visualize their individual importance. Social sustainability is an important and often overlooked aspect of sustainable technology development which should be considered in the early development phase.

Electric vehicles with a distributed drive train configuration provide great possibilities for the improvement of the vehicle dynamics, handling, safety as well as efficiency. In this paper, an energy-efficient torque allocation scheme is proposed for the improvement of traction efficiency and braking energy recovery. In traction conditions, the traction distribution is developed using an objective function of minimizing power loss of four electric motors. In braking conditions, aiming at guaranteeing the braking stability and recapturing the braking energy as much as possible, the changeable distribution of braking torque is obtained based on the ideal front-rear braking force distribution curve, while complying with braking regulations of Economic Commission for Europe. The proposed allocation scheme does not rely on the complex online computation. It is obtained via an offline optimization procedure and utilized for online allocation by simple interpolation. The low calculation effort makes it easy to implement the algorithm on real vehicles. Additionally, a conventional torque allocation is introduced as a contrasting approach. Finally, the simulations are conducted in CarSim and MATLAB/Simulink environment. The results demonstrate that the energy-efficient torque allocation scheme considerably improves the vehicle efficiency and increases the braking energy recovery compared with the conventional approach.

This paper presents a Karush–Kuhn–Tuckert (KKT) based global optimization algorithm for solving torque allocation, which aims at improving the stability performance of distributed drive electric vehicles (DDEVs). The driver desired traction and direct yaw moment, from the driver model and the direct yaw moment controller, are taken as the generalized force that needs to be distributed among four wheels. An optimization term is introduced from the perspective of the tire grip margin to construct a stability objective function. According to KKT conditions, the nonlinear objective function is transformed into the eigenvalue problem, thereby making the solving process independent of the initial guess of the optimal solutions. Afterwards, to guarantee the global minima acquisition, two phases of optimization are designed, namely the preliminary and secondary optimization. The preliminary optimization is developed regardless of the realistic boundary of the actuators, while the secondary optimization takes the physical constraints into account. Additionally, an active-set method is introduced as a comparative allocation algorithm. The proposed KKT-based algorithm and active-set method are applied to a detailed vehicle model, which is built in CarSim combining with Simulink. The simulation under a double-lane-change test is carried out to evaluate the proposed algorithm. The results show that the KKT-based algorithm is able to find the global optimum and accurately allocate the generalized force. More importantly, it highly reduces the computational efforts, providing the potential for the real vehicle application.

This paper presents a vehicle dynamic stability controller for distributed-drive electric vehicles. A hierarchical control structure is adopted for the proposed controller. An upper controller is designed on the basis of integrated model-matching control. It consists of a feedforward component plus a feedback component to calculate the desired external yaw moment to achieve the desired vehicle motion. The feedforward control aims at compensating the effect caused by the variation in the linear cornering stiffnesses of the tyres during the life cycle of the tyres. It provides a rapid response under common driving conditions. The linear cornering stiffnesses of the tyres are estimated in real time by the adaptive forgetting-factor recursive least-squares method. Since many vehicle parameters have strongly non-linear and time-varying characteristics, adaptive sliding mode control is used as the feedback component to make the controller robust against systematic uncertainties. To combine the outputs of feedforward and feedback together and to avoid probable conflict, a weight gain coefficient is obtained. Additionally, a conventional sliding-mode controller is introduced as a comparative upper control strategy. The lower controller is utilized to allocate the required yaw moment and traction to the four independent motors, taking into account the tyre grip margins. Simulations for a low- g manoeuvre and a high- g manoeuvre are carried out to evaluate the proposed control algorithm. The results show that the proposed vehicle stability controller can significantly stabilize the vehicle motion and greatly reduce the driver’s workload in comparison with with the conventional sliding-mode controller.

With the ongoing electrification of bus fleets in metropolitan areas appropriate charging strategies will play an increasingly important role for sector coupling solutions. With regards to the energy and transportation systems, this paper identifies the transactions of market entities involved in charging processes and defines respective function roles and responsibilities in joint market operations. Therefore, bus depot characteristics and processes are assessed and appropriate charging possibilities presented. A methodology for service-centric Virtual Power Plant operations is introduced that enables enhanced charging strategies. Here, a multi-period optimization model for the Virtual Power Plant operator is proposed allowing the integration of electric bus fleets as an active element in power plant portfolio operations. The solution approach is validated in numerical simulations under various testing conditions, showing the benefits of utilizing electric buses as mobile energy sources in power plant portfolio and power system operations.

The performance of an airborne wind energy system crucially depends on the aerodynamic, structural dynamic and flight dynamic properties of the tethered wing. In the scope of this paper, flight dynamic properties are determined against reproducible steering inputs. Current design methods for highly flexible membrane wings have achieved a mature product level by combining iterative testing with empirical and intuitive variation of wing parameters. However, for significant further improvements, experimental data of high quality is indispensable. We have developed a tow test setup for reproducible measurement of the dynamic properties of different types of tethered membrane wings. The test procedure is based on repeatable automated maneuvers with the entire kite system under realistic conditions. By measuring line forces and line angles, we determine the aerodynamic coefficients and the lift-to-drag ratio as functions of the length ratio between power and steering lines. This non-dimensional parameter characterizes the angle-of-attack of the wing and is varied automatically by the control unit on the towed test bench. During each towing run, several test cycles are executed such that mean values can be determined and errors can be minimized. We can conclude from this study that an objective measurement of specific dynamic properties of highly flexible membrane wings is feasible. The presented tow test method is suitable for quantitatively assessing and comparing different wing designs. The method represents an essential milestone for the development and characterization of tethered membrane wings as well as for the validation and improvement of simulation models. On the basis of this work, more complex maneuvers and a full degree of automation can be implemented in subsequent work. It can also be used for aerodynamic parameter identification.

For the purpose of utilizing electric bus fleets in metropolitan areas and with regard to providing active energy management at depots, a profound understanding of the transactions between the market entities involved in the charging process is given. The paper examines sophisticated charging strategies with energy procurements in joint market operation. Here, operation procedures and characteristics of a depot including the physical layout and utilization of appropriate charging infrastructure are investigated. A comprehensive model framework for a virtual power plant (VPP) is formulated and developed to integrate electric bus fleets in the power plant portfolio, enabling the provision of power system services. The proposed methodology is verified in numerical analysis by providing optimized dispatch schedules in day-ahead and intraday market operations.

\ud \ud This paper proposes a fast model predictive control allocation (MPCA) approach to minimize the tire slip power loss on contact patches for distributed drive electric vehicles (DDEV). In this strategy, two assumptions are set up from a practical focus: (1) the vehicle acceleration and yaw rate are measurable by global position system (GPS)/ inertial navigation system (INS) and inertial measurement unit (IMU), respectively; (2) the longitudinal velocity, road adhesion factor, and vehicle yaw rate are arranged to be “already known” by advanced estimators. For the strategy design, a CarSim-embedded driver model and a linear quadratic regulator (LQR) based direct yaw moment controller, are respectively applied to calculate the desired longitudinal traction and yaw moment as a virtual input first. Then, a MPCA method is proposed to reasonably distribute the virtual input among four in-wheel motors in order to optimize the tire slip power loss and vehicle stability performance. To accurately characterize tire slip power loss in MPCA, a tire slip estimator is established for tire slip information acquirement. Moreover, addressing on the heavily computational challenge in MPCA, a modified continuation/generalized minimal residual (C/GMRES) algorithm is employed. Since the traditional C/GMRES algorithm cannot directly solve the inequality constraint problem, the barrier functions are applied for transforming the inequality constraints to equivalent cost. According to Pontryagin’s minimum principle (PMP) conditions, the existence and uniqueness for solution of the modified C/GMRES algorithm are strictly proved. Subsequently, a Karush–Kuhn–Tucker​ (KKT) condition based approach is developed to fast gain the optimally initial solution in C/GMRES algorithm for extending application. Finally, numerical simulation validations are implemented and demonstrate that the proposed MPCA can ensure the compatibility between the tire slip power loss reduction and vehicle stability in a computationally efficient way.

Many public transport authorities have a great interest in introducing zero-emission electric buses. However, the transformation process from diesel to electric bus systems opens up a vast design space which seems prohibitive for a systematic decision making process. We present a holistic design methodology to identify the ‘most suitable system solution’ under given strategic and operational requirements. The relevant vehicle technologies and charging systems are analysed and structured using a morphological matrix. A modular simulation model is introduced which takes technical and operational aspects into account. The model can be used to determine a feasible electric bus system. The technology selection is based on a detailed economic analysis which is conducted by means of a total cost of ownership (TCO) model. To cope with uncertainties in forecasting, a stochastic modelling of critical input parameters is applied and three different future scenarios are evaluated. The applicability of the model was verified in a pilot project in Berlin and the methodology was applied to a realistic operational scenario. Our results indicate that electric bus systems are technically feasible and can become economically competitive from the year 2025 under the conditions examined.

Electric vehicles mainly work in urban conditions. Due to frequent acceleration and deceleration, the driving and braking torques vary in a large range as well as the motor efficiency. Distributed drive electric vehicles allow for an independent and accurate torque control of each wheel. Based on that, a novel driving and regenerative braking regulation (DRBR) with respect to energy-saving is developed. Firstly, economy-based objective function is established to optimize the vehicle efficiency under driving conditions. Its solutions, traction distribution coefficient, are derived under the constraints of desired driving torque, current RPM and motor property. Then according to the ideal braking force distribution curve and ECE regulations, the regenerative braking regulation is proposed combining friction brake and motor brake. Therefore it can guarantee both the braking stability and the energy recovery. Finally, the driving and braking regulation is verified with computer simulations based on the worldwide harmonized light vehicles test procedures. The simulation results indicate that the presented approach improves vehicle efficiency significantly.