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The aim of this work is to schedule the charging of electric vehicles (EVs) at a single charging station such that the temporal availability of each EV as well as the maximum available power at the station are considered. The total costs for charging the vehicles should be minimized w.r.t. time-dependent electricity costs. A particular challenge investigated in this work is that the maximum power at which a vehicle can be charged is dependent on the current state of charge (SOC) of the vehicle. Such a consideration is particularly relevant in the case of fast charging. Considering this aspect for a discretized time horizon is not trivial, as the maximum charging power of an EV may also change in between time steps. To deal with this issue, we instead consider the energy by which an EV can be charged within a time step. For this purpose, we show how to derive the maximum charging energy in an exact as well as an approximate way. Moreover, we propose two methods for solving the scheduling problem. The first is a cutting plane method utilizing a convex hull of the, in general, nonconcave SOC–power curves. The second method is based on a piecewise linearization of the SOC–energy curve and is effectively solved by branch-and-cut. The proposed approaches are evaluated on benchmark instances, which are partly based on real-world data. To deal with EVs arriving at different times as well as charging costs changing over time, a model-based predictive control strategy is usually applied in such cases. Hence, we also experimentally evaluate the performance of our approaches for such a strategy. The results show that optimally solving problems with general piecewise linear maximum power functions requires high computation times. However, problems with concave, piecewise linear maximum charging power functions can efficiently be dealt with by means of linear programming. Approximating an EV’s maximum charging power with a concave function may result in practically infeasible solutions, due to vehicles potentially not reaching their specified target SOC. However, our results show that this error is negligible in practice.

As electric vehicles are gaining increasing worldwide interest, advances in driving range and safety become critical. Modern automotive battery management systems (BMS) compete with challenging performance and safety requirements and need to monitor a large amount of battery parameters. In this paper, we propose power line communications (PLC) for high voltage (HV) traction batteries to reduce the BMS wiring effort. By modeling a small-scale battery pack for frequencies up to 300 MHz, we predict the PLC channel transfer characteristics and validate the results using a PLC hardware demonstrator employing a narrowband single-carrier modulation. The results demonstrate that battery PLC is a demanding task due to low access impedances and cell coupling effects, yet transfer characteristics can be improved by optimal impedance matching. PLC for HV BMS not only saves weight and cost, but also improves flexibility in BMS design. PLC enables single-cell monitoring techniques such as online electrochemical impedance spectroscopy (EIS) without additional wiring. Online EIS can be used for in-situ state and temperature estimation saving extra sensors. This work unveils possible coexistence issues between PLC and battery monitoring. In particular, we demonstrate that certain PLC data or packet rates have to be avoided not to interfere with EIS measurements.

To reduce CO2 emissions, it is necessary to cover the increasing energy demand of e-mobility with renewable energy sources. Therefore, the influence of increasing e-mobility and synergy effects between e-mobility and renewable energy sources need to be investigated. The case study presented here shows results from the analysis of grid-side and energetic synergy effects between e-mobility charged only at work and photovoltaic (PV) potentials. The basis of the grid study is a simplified cell-based grid model. Following the determination of synthetic charging profiles for e-mobility, PV potential profiles, load and production profiles, we perform load flow calculations for different scenarios and a simulation period of one year using the grid model. After the grid study, the energy analyses are carried out using four key performance indicators. The grid study shows that line overloads caused by PV production are only reduced and not avoided by increasing e-mobility and vice versa. The increase in the power peak of e-mobility, by shifting the charging processes into the peak of PV potentials, leads to a reduction of the production surplus in summer, while in winter the line utilisation increases. By modelling PV potentials on real irradiation and temperature data, the investigation of key performance indicators can identify not only seasonal fluctuations but also daily fluctuations.

One important goal of the climate commitment in the European Union (EU) is to reduce primary energy demand in the transport sector and increase the use of renewables, since around 33% of primary energy is consumed in this sector. Therefore, the EU ordered its member states to raise the number of electric vehicles (EVs) within Europe. Consequently, the energy demand for electricity will rise as a function of the number of EVs. To avoid local grid overload and guarantee a higher percentage of clean energy, EV charging stations can be supported by a combined system of grid-connected photovoltaic modules and battery storage. In this paper, the focus lies on the feasibility and economic aspects of such systems. To provide an overview of the different e-charging station combinations, a support tool was modelled and developed, making it possible to size and manage EVs charging stations with only a few input parameters. Thanks to its easy handling, the tool suits a wide spectrum of users. Due to enhanced optional settings, this tool is suitable for detailed input parameters for professionals as well. Input categories are basically divided into the photovoltaic (PV) system, battery storage, the charging station itself, and investment analysis. The tool supports decisions for solar charging stations designed for different parking locations like offices, schools, and public and private places.

In this paper, we describe a comparative analysis of a bus route scheduling problem as part of timetable trips. We consider the current uptake of electric buses as a viable public transportation option that will eventually phase out the diesel-engine-based buses. We note that, with the increasing number of electric buses, the complexity related to the scheduling also increases, especially stemming from the charging requirement and the dedicated infrastructure behind it. The aim of our comparative study is to highlight the brevity with which a multi-agent-system-based scheduling method can be helpful as compared to the classical mixed-integer linear-programming-based approach. The multi-agent approach we design is centralized with asymmetric communication between the master agent, the bus agent, and the depot agent, which makes it possible to solve the multi-depot scheduling problem in almost real time as opposed to the classical optimizer, which sees a multi-depot problem as a combinatorial heuristic NP-hard problem, which, for large system cases, can be computationally inefficient to solve. We test the efficacy of the multi-agent algorithm and also compare the same with the MILP objective designed in harmony with the multi-agent system. We test the comparisons first on a small network and then extend the scheduling application to real data extracted from the public transport of the Maltese Islands.

Lateral support systems in vehicles have a high potential for reduction of lane departure crashes. To profit from their full potential, such systems should function properly in adverse conditions. Literature indicates that their accuracy varies between day and night-time. However, detailed quantifications of the systems’ performance in these conditions are rare. The aim of this study is to investigate the differences in detection quality and view range of Mobileye 630 in dry daytime and night-time conditions. On-road tests on four rural road sections in Croatia were conducted. Wilcoxon signed-rank test was used to test the difference between the number of quality rankings while absolute average, average difference and standard deviation were used to analyse the view range. Also, a paired samples t-test was used to test the difference between conditions for each line on each road. The overall results confirm that a significant difference in lane detection quality view range exists between tested conditions. “Medium” and “high” detection confidence (quality level 3 and 2), increased by 5% and 8% during night-time compared to daytime while level 0 (“nothing detected”) decreased by 12%. The view range increased (almost 16% for middle line) during daytime compared to night-time. The findings of this study expand the existing knowledge and are valuable for research and development of machine-vision systems but also for road authorities to optimize the markings’ quality performance.

Electric vehicle (EV) batteries, i.e., currently almost exclusively lithium-ion batteries, are removed from the vehicle once they no longer meet certain requirements. However, instead of being disposed of or recycled, the removed batteries can be used in another, less demanding application, giving them a “second life”. Research in the field of second-life batteries (SLBs) is still at an early stage and, to better understand the “second life” concept and the related challenges, potential second-life applications need to be identified first. Using a detailed study of the scientific literature and an interview with field experts, a list of potential second-life applications was drafted. Afterwards, a technical, economic, and legal evaluation was conducted to identify the most promising options. The findings of this research consisted of the identification of 65 different mobile, semi-stationary and stationary second-life applications; the applications selected as most promising are automated guided vehicles (AGVs) and industrial energy storage systems (ESSs) with renewable firming purposes. This research confirms the great potential of SLBs indicating that second-life applications are many and belong to a broad spectrum of different sectors. The applications identified as most promising are particularly attractive for the second-life use of batteries as they belong to fast-growing markets.

In recent years, verification and validation processes of automated driving systems have been increasingly moved to virtual simulation, as this allows for rapid prototyping and the use of a multitude of testing scenarios compared to on-road testing. However, in order to support future approval procedures for automated driving functions with virtual simulations, the models used for this purpose must be sufficiently accurate to be able to test the driving functions implemented in the complete vehicle model. In recent years, the modelling of environment sensor technology has gained particular interest, since it can be used to validate the object detection and fusion algorithms in Model-in-the-Loop testing. In this paper, a practical process is developed to enable a systematic evaluation for perception–sensor models on a low-level data basis. The validation framework includes, first, the execution of test drive runs on a closed highway; secondly, the re-simulation of these test drives in a precise digital twin; and thirdly, the comparison of measured and simulated perception sensor output with statistical metrics. To demonstrate the practical feasibility, a commercial radar-sensor model (the ray-tracing based RSI radar model from IPG) was validated using a real radar sensor (ARS-308 radar sensor from Continental). The simulation was set up in the simulation environment IPG CarMaker® 8.1.1, and the evaluation was then performed using the software package Mathworks MATLAB®. Real and virtual sensor output data on a low-level data basis were used, which thus enables the benchmark. We developed metrics for the evaluation, and these were quantified using statistical analysis.