Electrification of Urban Freight Transport - a Case Study of the Food Retailing Industry

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 Verkehr

Comparative study of two dynamics-model-based estimation algorithms for distributed drive electric vehicles

The effect of vehicle active safety systems is subject to the accurate knowledge of vehicle states. Therefore, it is of great importance to develop a precise and robust estimation approach so as to deal with nonlinear vehicle dynamics systems. In this paper, a planar vehicle model with a simplified tire model is established first. Two advanced model-based estimation algorithms, an unscented Kalman filter and a moving horizon estimation, are developed for distributed drive electric vehicles. Using the proposed algorithms, vehicle longitudinal velocity, lateral velocity, yaw rate as well as lateral tire forces are estimated based on information fusion of standard sensors in today’s typical vehicle and feedback signals from electric motors. Computer simulations are implemented in the environment of CarSim combined with Matlab/Simulink. The performance of both estimators regarding convergence, accuracy, and robustness against an incorrect initial estimate of longitudinal velocity is compared in detail. The comparison results demonstrate that both estimation approaches have favourable coincidence with the corresponding reference values, while the moving horizon estimation is more accurate and robust, and owns faster convergence.DFG, 325093850, Open Access Publizieren 2017 - 2018 / Technische Universität Berli

Identification of parameters for optimization of crash sensitive structures

Un modelo "Crash Box" parametrizado con CATIA y localizado entre el parachoques de un coche y el chasis, se encarga del amortiguamiento de diferentes tipos de impacto. El objetivo es encontrar el espacio de diseño con la mejor combinación de parámetros que optimize la respuesta del modelo. El diseño de experimento se fundamenta en la optimización mediante la implementación de algoritmos genéticos (OptiSlang-ANSYS), modificando los valores de mutación y entrecruzamiento, así como analizando previamente el sistema con un análisis de sensitividad que permite acotar el espacio de búsqueda. Un bucle actualiza los inputs/outputs del sistema y cambia el mallado del modelo automáticamente. Finalmente se comparan los distintos métodos empleados de optimización y se aportan posibles modelos finales de la estructura

A Comprehensive TCO Evaluation Method for Electric Bus Systems Based on Discrete-Event Simulation Including Bus Scheduling and Charging Infrastructure Optimisation

Bus operators around the world are facing the transformation of their fleets from fossil-fuelled to electric buses. Two technologies prevail: Depot charging and opportunity charging at terminal stops. Total cost of ownership (TCO) is an important metric for the decision between the two technologies; however, most TCO studies for electric bus systems rely on generalised route data and simplifying assumptions that may not reflect local conditions. In particular, the need to reschedule vehicle operations to satisfy electric buses’ range and charging time constraints is commonly disregarded. We present a simulation tool based on discrete-event simulation to determine the vehicle, charging infrastructure, energy and staff demand required to electrify real-world bus networks. These results are then passed to a TCO model. A greedy scheduling algorithm is developed to plan vehicle schedules suitable for electric buses. Scheduling and simulation are coupled with a genetic algorithm to determine cost-optimised charging locations for opportunity charging. A case study is carried out in which we analyse the electrification of a metropolitan bus network consisting of 39 lines with 4748 passenger trips per day. The results generally favour opportunity charging over depot charging in terms of TCO; however, under some circumstances, the technologies are on par. This emphasises the need for a detailed analysis of the local bus network in order to make an informed procurement decision.TU Berlin, Open-Access-Mittel – 202

Method for a Multi-Vehicle, Simulation-Based Life Cycle Assessment and Application to Berlin’s Motorized Individual Transport

The transport sector in Germany causes one-quarter of energy-related greenhouse gas emissions. One potential solution to reduce these emissions is the use of battery electric vehicles. Although a number of life cycle assessments have been conducted for these vehicles, the influence of a transport system-wide transition has not been addressed sufficiently. Therefore, we developed a method which combines life cycle assessment with an agent-based transport simulation and synthetic electric-, diesel- and gasoline-powered vehicle models. We use a transport simulation to obtain the number of vehicles, their lifetime mileage and road-specific consumption. Subsequently, we analyze the product systems’ vehicle production, use phase and end-of-life. The results are scaled depending on the covered distance, the vehicle weight and the consumption for the whole life cycle. The results indicate that the sole transition of drive trains is insufficient to significantly lower the greenhouse gas emissions. However, sensitivity analyses demonstrate that there is a considerable potential to reduce greenhouse gas emissions with higher shares of renewable energies, a different vehicle distribution and a higher lifetime mileage. The method facilitates the assessment of the ecological impacts of complete car-based transportation in urban agglomerations and is able to analyze different transport sectors.DFG, 398051144, Analyse von Strategien zur vollständigen Dekarbonisierung des urbanen VerkehrsTU Berlin, Open-Access-Mittel – 202

Electrification of Urban Waste Collection: Introducing a Simulation-Based Methodology for Feasibility, Impact and Cost Analysis

We introduce a multi-agent-based simulation methodology to investigate the feasibility and evaluate environmental and economic sustainability of an electrified urban waste collection. Electrification is a potential solution for transport decarbonization and already widely available for individual and public transport. However, the availability of electrified commercial vehicles like waste collection vehicles is still limited, despite their significant contribution to urban emissions. Moreover, there is a lack of clarity whether electric waste collection vehicles can persist in real word conditions and which system design is required. Therefore, we present a synthetic model for waste collection demand on a per-link basis, using open available data. The tour planning is solved by an open-source algorithm as a capacitated vehicle routing problem (CVRP). This generates plausible tours which handle the demand. The generated tours are simulated with an open-source transport simulation (MATSim) for both the diesel and the electric waste collection vehicles. To compare the life cycle costs, we analyze the data using total cost of ownership (TCO). Environmental impacts are evaluated based on a Well-to-Wheel approach. We present a comparison of the two propulsion types for the exemplary use case of Berlin. And we are able to generate a suitable planning to handle Berlin’s waste collection demand using battery electric vehicles only. The TCO calculation reveals that the electrification raises the total operator cost by 16-30 %, depending on the scenario and the battery size with conservative assumptions. Furthermore, the greenhouse gas emissions (GHG) can be reduced by 60-99%, depending on the carbon footprint of electric power generation.DFG, 398051144, Analyse von Strategien zur vollständigen Dekarbonisierung des urbanen Verkehr

Social Sustainability in the Development of Service Robots

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.EFRE, 1247-B5-O, MURMEL: Mobiler Urbaner Roboter zur MüllEimer LeerungDFG, 398051144, Analyse von Strategien zur vollständigen Dekarbonisierung des urbanen Verkehr

Fuel cell drive for urban freight transport in comparison to diesel and battery electric drives: a case study of the food retailing industry in Berlin

The option of decarbonizing urban freight transport using battery electric vehicle (BEV) seems promising. However, there is currently a strong debate whether fuel cell electric vehicle (FCEV) might be the better solution. The question arises as to how a fleet of FCEV influences the operating cost, the greenhouse gas (GHG) emissions and primary energy demand in comparison to BEVs and to Internal Combustion Engine Vehicle (ICEV). To investigate this, we simulate the urban food retailing as a representative share of urban freight transport using a multi-agent transport simulation software. Synthetic routes as well as fleet size and composition are determined by solving a vehicle routing problem. We compute the operating costs using a total cost of ownership analysis and the use phase emissions as well as primary energy demand using the well to wheel approach. While a change to BEV results in 17–23% higher costs compared to ICEV, using FCEVs leads to 22–57% higher costs. Assuming today’s electricity mix, we show a GHG emission reduction of 25% compared to the ICEV base case when using BEV. Current hydrogen production leads to a GHG reduction of 33% when using FCEV which however cannot be scaled to larger fleets. Using current electricity in electrolysis will increase GHG emission by 60% compared to the base case. Assuming 100% renewable electricity for charging and hydrogen production, the reduction from FCEVs rises to 73% and from BEV to 92%. The primary energy requirement for BEV is in all cases lower and for higher compared to the base case. We conclude that while FCEV have a slightly higher GHG savings potential with current hydrogen, BEV are the favored technology for urban freight transport from an economic and ecological point of view, considering the increasing shares of renewable energies in the grid mix.TU Berlin, Open-Access-Mittel - 2022DFG, 398051144, Analyse von Strategien zur vollständigen Dekarbonisierung des urbanen Verkehr

A fast model predictive control allocation of distributed drive electric vehicles for tire slip energy saving with stability constraints

Abstract 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

Economic Assessment of Different Air-conditioning and Heating Systems for Electric City Buses Based on Comprehensive Energetic Simulations

The air-conditioning and heating of the passenger cabin in an electric bus leads to a significant increase of the auxiliaries’ energy consumption. Due to limited battery capacity, the daily operating range of electric buses depends considerably on the ambient climate. In particular, heating is an energy-intensive process since no waste heat from the IC engine is available. Energetic simulations show drastic range reductions when the interior is heated by an electric resistance heater. In contrast, the range reduction can be limited noticeably if a heat pump is utilised. Therefore, the selection of heating and air-conditioning (HVAC) systems has a significant impact not only on the range but also on the operating costs of an electric vehicle. The aim of this study is to conduct a cost analysis for different HVAC systems of an electric city bus. The economic assessment is based on comprehensive energy consumption simulation and a life-cycle costing approach considering all expenses of the operation period. The examination reveals that the heat pump systems feature significant energy savings compared to conventional HVAC systems. However, over a life span of twelve years, the current high acquisition cost of a heat pump system is not compensated when only considering direct cost