Vehicles, Volume 4, Issue 4 (December 2022) – 27 articles

The driving behaviour of Connected and Automated Vehicles (CAVs) may influence the final acceptance of this technology. Developing a driving style suitable for most people implies the evaluation of alternatives that must be validated. Intelligent Virtual Drivers (IVDs), whose behaviour is controlled by a program, can test different driving styles along a specific route. However, multiple combinations of IVD settings may lead to similar outcomes due to their high variability. The paper proposes a method to identify the IVD settings that can be used as a reference for a given route. The method is based on the cluster analysis of vehicular data produced by a group of IVDs with different settings driving along a virtual road scenario. Vehicular data are clustered to find IVDs representing a driving style to classify human drivers who previously drove on the same route with a driving simulator. The classification is based on the distances between the different vehicular signals calculated for the IVD and recorded for human drivers. The paper includes a case study showing the practical use of the method applied on an actual road circuit. The case study demonstrated that the proposed method allowed identifying three IVDs, among 29 simulated, which have been subsequently used as a reference to cluster 26 human driving styles. These representative IVDs, which ideally replicate the driving style of human drivers, can be used to support the development of CAVs control logic that better fits human expectations. A closing discussion about the flexibility of the method in terms of the different natures of data collection, allowed for depicting future applications and perspectives. Full article

The objective of this study is to determine the ability of a two-point steering control model to account for the influence of a haptic guidance system in different visibility conditions. For this purpose, the lateral control of the vehicle was characterized in terms of driving performance as well as through the identification of anticipation and compensation parameters of the driver model. The hypothesis is that if the structure of the model is valid in the considered conditions, the value of the parameters will change in coherence with the observed behavior. The results of an experiment conducted on a driving simulator demonstrate that the identified model can account for the cumulative influence of the haptic guidance system and degraded visibility. The anticipatory gain is sensitive to changes in driving conditions that have a direct influence on the produced trajectory, and the compensatory gain is sensitive to a decrease in the variability of the lateral position. However, a model with only the steering wheel angle as output is not able to determine whether the change in lateral position variability is due to the driver’s lack of anticipation or to the assistance provided by the haptic guidance system. Full article

With increasing urban population, there are more and more vehicles, causing traffic congestion. In order to solve this problem, the development of an efficient and fair intersection management system is an important issue. With the development of intelligent transportation systems, the computing efficiency of vehicles and vehicle-to-vehicle communications are becoming more advanced, which can be used to good advantage in developing smarter systems. As such, Autonomous Intersection Management (AIM) proposals have been widely discussed. This research proposes an intersection management system based on Advantage Actor-Critic (A2C) which is a type of reinforcement learning. This method can lead to a fair and efficient intersection resource allocation strategy being learned. In our proposed approach, we design a reward function and then use this reward function to encourage a fair allocation of intersection resources. The proposed approach uses a brake-safe control to ensure that autonomous moving vehicles travel safely. An experiment is performed using the SUMO simulator to simulate traffic at an isolated intersection, and the experimental performance is compared with Fast First Service (FFS) and GAMEOPT in terms of throughput, fairness, and maximum waiting time. The proposed approach increases fairness by 20% to 40%, and the maximum waiting time is reduced by 20% to 36% in high traffic flow. The inflow rates are increased, average waiting time is reduced, and throughput is increased. Full article

In this work, a machine learning-based energy management system is developed using a long short-term memory (LSTM) network for fuel cell hybrid buses. The neural network implicitly learns the complex relationship between various factors and the optimal power control from massive data. The selection of the neural network inputs is inspired by the adaptive Pontryagin’s minimum principle (APMP) strategy. Since an estimated value of the global average fuel cell power is required in the machine learning-based energy management strategy (EMS), some global features of driving cycles are extracted and then applied in a feedforward neural network to predict the average fuel cell power appropriately. The effectiveness of the machine learning-based energy management, with the integration of the mechanism of estimating the average fuel cell power based on the forward neural network, is tested under two different driving cycles from the training environment, with comparisons to a commercially used rule-based strategy. Based on the simulation results, the learning-based strategy outperforms the rule-based strategy regarding the charge-sustaining mode conditions and fuel economy. Moreover, compared to the best offline hydrogen consumption, the machine learning-based strategy consumed 0.58% and 0.36% more than the best offline results for both driving cycles. In contrast, the rule-based strategy consumed 1.80% and 0.96% more than optimal offline results for the two driving cycles, respectively. Finally, simulations under battery and fuel cell aging conditions show that the fuel economy of the machine learning-based strategy experiences no performance degradation under components aging compared to offline strategies. Full article

Fuel consumption, subsequent emissions and safe operation of class 8 vehicles are of prime importance in recent days. It is imperative that a vehicle operates in its true optimal operating region, given a variety of constraints such as road grade, load, gear shifts, battery state of charge (for hybrid vehicles), etc. In this paper, a research study is conducted to evaluate the fuel economy and subsequent emission benefits when applying predictive control to a mild hybrid line-haul truck. The problem is solved using a combination of dynamic programming with backtracking and model predictive control. The specific fuel-saving features that are studied in this work are dynamic cruise control, gear shifts, vehicle coasting and torque management. These features are evaluated predictively as compared to a reactive behavior. The predictive behavior of these features is a function of road grade. The result and analysis show significant improvement in fuel savings along with NOx benefits. Out of the control features, dynamic cruise (predictive) control and dynamic coasting showed the most benefits, while predictive gear shifts and torque management (by power splitting between battery and engine) for this architecture did not show fuel benefits but provided other benefits in terms of powertrain efficiency. Full article

The vast, open, and interconnected characteristics of the transportation system make it a prime target for terrorists and hackers. However, there are no standard measures of transport system vulnerability to physical or cyberattacks. The separation of governance over different modes of transport increases the difficulty of coordination in developing and enforcing a common security index. This paper contributes a perspective and roadmap toward developing multimodal security indices that can leverage a variety of existing and emerging connected vehicle, sensing, and computing technologies. The proposed technologies include positive train control (PTC), vehicle-to-everything (V2X), weight-in-motion (WIM), advanced air mobility (AAM), remote sensing, and machine learning with cloud intelligence. Full article

Driving style classification does not only depend on objective measures such as vehicle speed or acceleration, but is also highly subjective as drivers come with their own definition. From our perspective, the successful implementation of driving style classification in real-world applications requires an adaptive approach that is tuned to each driver individually. Within this work, we propose a transfer learning framework for driving style classification in which we use a previously developed rule-based algorithm for the initialization of the neural network weights and train on limited data. Therefore, we applied various state-of-the-art machine learning methods to ensure robust training. First, we performed heuristic-based feature engineering to enhance generalized feature building in the first layer. We then calibrated our network to be able to use its output as a probabilistic metric and to only give predictions above a predefined neural network confidence. To increase the robustness of the transfer learning in early increments, we used a synthetic oversampling technique. We then performed a holistic hyperparameter optimization in the form of a random grid search, which incorporated the entire learning framework from pretraining to incremental adaption. The final algorithm was then evaluated based on the data of predefined synthetic drivers. Our results showed that, by integrating these various methods, high system-level performance and robustness were met with as little as three new training and validation data samples in each increment. Full article

Road safety has always been a crucial priority for municipalities, as vehicle accidents claim lives every day. Recent rapid improvements in video collection and processing technologies enable traffic researchers to identify and alleviate potentially dangerous situations. This paper illustrates cutting-edge methods by which conflict hotspots can be detected in various situations and conditions. Both pedestrian–vehicle and vehicle–vehicle conflict hotspots can be discovered, and we present an original technique for including more information in the graphs with shapes. Conflict hotspot detection, volume hotspot detection, and intersection-service evaluation allow us to understand the safety and performance issues and test countermeasures comprehensively. The selection of appropriate countermeasures is demonstrated by extensive analysis and discussion of two intersections in Gainesville, Florida, USA. Just as important is the evaluation of the efficacy of countermeasures. This paper advocates for selection from a menu of countermeasures at the municipal level, with safety as the top priority. Performance is also considered, and we present a novel concept of a performance–safety trade-off at intersections. Full article

Wireless power transfer (WPT) is a power transfer technique widely used in many industrial applications, medical applications, and electric vehicles (EVs). This paper deals with the dynamic modeling of the resonant inverter employed in the WPT systems for EVs. To this end, the Generalized State-Space Averaging and the Laplace Phasor Transform techniques have been the flagship methods employed so far. In this paper, the modeling of the resonant inverter is accomplished by using the novel Modulated Variable Laplace Transform (MVLT) method. Firstly, the MVLT technique is discussed in detail, and then it is applied to model a study-case resonant inverter. Finally, a study-case resonant inverter is developed and utilized to validate the theoretical results with MATLAB/Simulink. Full article

This paper describes the characteristics of worldwide scientific contributions to the field of electric vehicles (EVs) from 1955 to 2021. For this purpose, a search within the Scopus database was conducted using “Electric Vehicle” as the keyword. As a result, 50,195 documents were obtained through analytical and bibliometric techniques and classified into six communities according to the subject studied and the collaborative relationships between the authors. The most relevant publications within each group, i.e., those related to the most publications, were analyzed. The result shows 104,344 authors researching on EVs in 149 different countries with 225,445 relations among them. Furthermore, the most frequent language in which these publications were written as well as the h-index values of their authors were analyzed. This paper also highlights the wide variety of areas involved in EV development. Finally, the paper raises numerous issues to consider in order to broaden knowledge about EVs, their efficiency, and their applications in the near future for the development of sustainable cities and societies. Full article

Achieving sustainable freight transport and citizens’ mobility operations in modern cities are becoming critical issues for many governments. By analyzing big data streams generated through IoT devices, city planners now have the possibility to optimize traffic and mobility patterns. IoT combined with innovative transport concepts as well as emerging mobility modes (e.g., ridesharing and carsharing) constitute a new paradigm in sustainable and optimized traffic operations in smart cities. Still, these are highly dynamic scenarios, which are also subject to a high uncertainty degree. Hence, factors such as real-time optimization and re-optimization of routes, stochastic travel times, and evolving customers’ requirements and traffic status also have to be considered. This paper discusses the main challenges associated with Internet of Vehicles (IoV) and vehicle networking scenarios, identifies the underlying optimization problems that need to be solved in real time, and proposes an approach to combine the use of IoV with parallelization approaches. To this aim, agile optimization and distributed machine learning are envisaged as the best candidate algorithms to develop efficient transport and mobility systems. Full article

With the electrification of transport (BEVs) and the growing benefits of smart vehicles, there is a need for a simple solution to perform real-time monitoring of the BEV and its battery for diagnostics and coordinated charging. The On-Board Diagnostics (OBD) system, originally designed for internal combustion engine cars (ICE), can be used to extract the necessary BEV data. This paper presents a developed framework for a low-cost solution to online monitoring of BEVs. A Raspberry Pi Zero W, along with other auxiliary components, was installed in two Hyundai Ioniq Battery Electric cars to communicate with the vehicles via the OBD-II port. A python script was developed to periodically request the vehicle data by sending various Parameter IDs to the vehicles and storing the raw response data. A web server was created to process the hexadecimal encoded data and visualize the data on a dashboard. The key parameters, such as the battery state of health (SOH), state of charge (SOC), battery temperature, cell voltages and cumulative energy consumption, were successfully captured and recorded, which can now facilitate trending for battery diagnostics and future integration with smart chargers for coordinated charging. Full article

This study proposes geometric path plans composed of two stages to automatically perform perpendicular and parallel parking with a reverse path in a narrow space. In perpendicular parking, the minimum width of the available parking spot is computed before the start of the parking operation, and the availability of parking and the optimal stage of the path plan are determined by considering the surrounding space together. In a similar concept, even in parallel parking, it is possible to determine whether to park by calculating the minimum length of the available parking spot and the number of repetitions of motion before starting the parking operation. The theoretical results for the parking spot and surrounding space related to the geometric path plans of perpendicular and parallel parking were confirmed through model car tests. Efficient automatic parking will be enabled by selecting and establishing an appropriate path plan along with the availability of parking in consideration of the parking spot and the surrounding space before starting the parking operation. Full article

A real-time, metadata-driven electric vehicle routing optimization to reduce on-road energy requirements is proposed in this work. The proposed strategy employs the state–action–reward–state–action (SARSA) algorithm to learn the EV’s maximum travel policy as an agent. As a function of the received reward signal, the policy model evaluates the optimal behavior of the agent. Markov chain models (MCMs) are used to estimate the agent’s energy requirements on the road, in which a single Markov step represents the average energy consumption based on practical driving conditions, including driving patterns, road conditions, and restrictions that may apply. A real-time simulation in Python with TensorFlow, NumPy, and Pandas library requirements was run, considering real-life driving data for two EVs trips retrieved from Google’s API. The two trips started at 4.30 p.m. on 11 October 2021, in Los Angeles, California, and Miami, Florida, to reach EV charging stations six miles away from the starting locations. According to simulation results, the proposed AI-based energy minimization framework reduces the energy requirement by 11.04% and 5.72%, respectively. The results yield lower energy consumption compared with Google’s suggested routes and previous work reported in the literature using the DDQN algorithm. Full article

This paper aims to analyze the recent development of taxi services in Slovakia on two levels. The first is the area of technological change, which includes the use of digital platforms for the closing of the contract between the passenger and the taxi operator. The second level of perspective is the legislative change. Commonly used taxi digital platforms (applications) have started to require a taxi concession from self-employed drivers, and many other requirements. We will also analyze the issue of value-added tax. This article processes quantitative data on the number of valid taxi concessions. The data were obtained from the unified information system in road transport and supplemented with other statistical inputs. The article describes the distribution of more than 6819 issued concessions in individual regions and analyzes 12,477 taxi vehicles registered in these licenses for operating a taxi service. This article also includes the numbers of performed technical and emission inspections of taxi vehicles. With these data, it is possible to prove a sharp increase in interest in the concession due to the introduction of digital applications. A significant change in business conditions in this area can lead to an increase in the number of businesses by 70% in larger cities, while the issue of sustainability is questionable. In the last part, the article also deals with the issue of electromobility, and environmental aspects connected with taxi legislation changes. Full article

The study is dedicated to elaborating and analyzing a technique for autonomous vehicle (AV) motion planning based on sequential trajectory and kinematics optimization. The proposed approach combines the finite element method (FEM) basics and nonlinear optimization with nonlinear constraints. There were five main innovative aspects introduced in the study. First, a 7-degree polynomial was used to improve the continuity of piecewise functions representing the motion curves, providing 4 degrees of freedom (DOF) in a node. This approach allows using the irregular grid for roadway segments, increasing spans where the curvature changes slightly, and reducing steps in the vicinity of the significant inflections of motion boundaries. Therefore, the segment length depends on such factors as static and moving obstacles, average road section curvature, camera sight distance, and road conditions (adhesion). Second, since the method implies splitting the optimization stages, a strategy for bypassing the moving obstacles out of direct time dependency was developed. Thus, the permissible area for maneuvering was determined using criteria of safety distance between vehicles and physical limitation of tire–road adhesion. Third, the nodal inequality constraints were replaced by the nonlinear integral equality constraints. In contrast to the generally distributed approach of restricting the planning parameters in nodes, the technique of integral equality constraints ensures the disposition of motion parameters’ curves strictly within the preset boundaries, which is especially important for quite long segments. In this way, the reliability and stability of predicted parameters are improved. Fourth, the seamless continuity of both the sought parameters and their derivatives is ensured in transitional nodes between the planning phases and adjacent global coordinate systems. Finally, the problem of optimization rapidity to match real-time operation requirements was addressed. For this, the quadrature integration approach was implemented to represent and keep all the parameters in numerical form. The study considered cost functions, limitations stipulated by the vehicle kinematics and dynamics, as well as initial and transient conditions between the planning stages. Simulation examples of the predicted trajectories and curves of kinematic parameters are demonstrated. The advantages and limitations of the proposed approach are highlighted. Full article

Self-driving features rely upon autonomous control of vehicle kinetics, and this manuscript compares several disparate approaches to control predominant kinetics. Classical control using feedback of state position and velocities, open-loop optimal control, real-time optimal control, double-integrator patching filters with and without gain-tuning, and control law inversion patching filters accompanying velocity control are assessed in Simulink, and their performances are compared. Optimal controls are found via Pontryagin’s method of optimization utilizing three necessary conditions: Hamiltonian minimization, adjoint equations, and terminal transversality of the endpoint Lagrangian. It is found that real-time optimal control and control-law patching filter with velocity control incorporating optimization are the two best methods overall as judged in Monte Carlo analysis by means and standard deviations of position and rate errors and cost. Full article

The Highway Safety Manual (HSM) initial version provides several safety performances functions (SPFs) that can be used to predict collisions on a roadway network. The calibration of the HSM SPFs for Fatal and Injury (FI), Property Damage Only (PDO), and Total crashes for Urban Four-lane Divided Roadway Segments (U4D) in Muscat, Sultanate of Oman, and the development of new SPFs were investigated in this paper. The HSM SPFs were calibrated first with the HSM methodology, and then new forms of specific SPFs were evaluated for Muscat urban roads to determine the best model using the Poisson-Gamma regression technique. The results of this study show that the HSM calibrated SPFs provide the best fit of the data used in this study and would be the best SPFs for predicting collisions in the City of Muscat. The developed collision model describes the mean crash frequency as a function of the natural logarithm of the annual average daily traffic, segment length, and speed limit. Overall, this study provides an important foundation for the implementation of HSM methods in Muscat city, and it may aid in making SPFs established in more developed countries adaptable for use in less developed countries. Full article

This paper aims at providing an overview of the most used injury criteria (IC) and injury metrics for the study of the passive safety of vehicles. In particular, the work is focused on the injury criteria that can be adopted when finite element simulations and Human Body Models (HBMs) are used. The HBMs will result in a fundamental instrument for studying the occupant’s safety in Autonomous Vehicles (AVs) since they allow the analysis of a larger variety of configurations compared to the limitations related to the traditional experimental dummies. In this work, the most relevant IC are reported and classified based on the body segments. In particular, the head, the torso, the spine, the internal organs, and the lower limbs are here considered. The applicability of the injury metrics to the analyses carried out with the HBMs is also discussed. The paper offers a global overview of the injury assessment useful to choose the injury criteria for the study of vehicle passive safety. To this aim, tables of the presented criteria are also reported to provide the available metrics for the considered body damage. Full article

Transportation electrification is a pivotal factor in accelerating the transition to sustainable energy. Electric vehicles (EVs) can operate either as loads or distributed power resources in vehicle-to-grid (V2G) or vehicle-to-vehicle (V2V) linkage. This paper reviews the status quo and the implications of transportation electrification in regard to environmental benefits, consumer side impacts, battery technologies, sustainability of batteries, technology trends, utility side impacts, self-driving technologies, and socio-economic benefits. These are crucial subject matters that have not received appropriate research focus in the relevant literature and this review paper aims to explore them. Our findings suggest that transitioning toward cleaner sources of electricity generation should be considered along with transportation electrification. In addition, the lower cost of EV ownership is correlated with higher EV adoption and increased social justice. It is also found that EVs suffer from a higher mile-per-hour charging rate than conventional vehicles, which is an open technological challenge. Literature indicates that electric vehicle penetration will not affect the power grid in short term but charging management is required for higher vehicle penetration in the long-term scenario. The bi-directional power flow in a V2G linkage enhances the efficiency, security, reliability, scalability, and sustainability of the electricity grid. Vehicle-to-Vehicle (V2V) charging/discharging has also been found to be effective to offload the distribution system in presence of high EV loads. Full article

Robust lane-following algorithms are one of the main challenges in developing effective automated vehicles. In this work, a team of four undergraduate students designed and evaluated several automated lane-following algorithms using computer vision as part of a Research Experience for Undergraduates program funded by the National Science Foundation. The developed algorithms use the Robot Operating System (ROS) and the OpenCV library in Python to detect lanes and implement the lane-following logic on the road. The algorithms were tested on a real-world test course using a street-legal vehicle with a high-definition camera as input and a drive-by-wire system for output. Driving data were recorded to compare the performance of human driving to that of the self-driving algorithms on the basis of three criteria: lap completion time, lane positioning infractions, and speed limit infractions. The evaluation of the data showed that the human drivers successfully completed every lap with zero infractions at a 100% success rate in varied weather conditions, whereas, our most reliable algorithms had a success rate of at least 70% with some lane positioning infractions and at lower speeds. Full article

In recent years, the wireless sniffing technique (WST) has become an emerging technique for collecting real-time traffic information. The spatiotemporal variations in wireless signal collection from vehicles provide various types of traffic information, such as travel time, speed, traveling path, and vehicle turning proportion at an intersection, which can be widely used for traffic management applications. However, three problems challenge the applicability of the WST to traffic information collection: the transportation mode classification problem (TMP), lane identification problem (LIP), and multiple devices problem (MDP). In this paper, a WST-based intelligent traffic beacon (ITB) with machine learning methods, including SVM, KNN, and AP, is designed to solve these problems. Several field experiments are conducted to validate the proposed system: three sensor topologies (X-type, rectangle-type, and diamond-type topologies) with two wireless sniffing schemes (Bluetooth and Wi-Fi). Experiment results show that X-type has the best performance among all topologies. For sniffing schemes, Bluetooth outperforms Wi-Fi. With the proposed ITB solution, traffic information can be collected in a more cost-effective way. Full article

In the wake of previous works, the authors propose a new approach for the identification and evolution of the flame front in an optical SI engine. Currently, it is an essential prerogative to characterize the capability of innovative igniters to guarantee earlier flame development in critical operating conditions, such as ultra-lean mixture, towards which automotive research is moving to deal with the ever more stringent regulations on pollutant emissions. The core of the new approach lies in the R-CNN Mask method. The latter consists of a conceptually simple and general framework for object instance segmentation. It can efficiently detect objects contained in an image while simultaneously generating a high-quality segmentation mask for each instance. In particular, the aim this work is to develop an automatized algorithm for detecting, as objectively as possible, the flame front evolution of lean/ultra-lean mixtures ignited by low-temperature plasma-based ignition systems. The capability of the Mask R-CNN algorithm to automatically estimate the binarized area, without setting a defined binarized threshold, allows us to perform an analysis of the flame front evolution completely independent from the user interpretation. Mask R-CNN can detect the kernel in advance and can identify events as regular combustions instead of misfires or anomalies if compared to other traditional approaches. These features make the proposed method the most suitable option to analysis the real behavior of the innovative ignition systems at critical operating conditions. Full article

Mobility is undergoing changes. Increasingly strict legislation regarding pollutant emissions and the protection of the environment are more important than ever. The change to electric mobility is also presenting the mobile world with new challenges and opportunities. Vehicles are becoming more and more efficient with higher power densities and better performance. Application-adapted components are being developed and used as a matter of preference. New production technologies can help to realise the change in mobility reliably. Additive manufacturing is one way of producing functionally integrated and performance-optimised components. AM offers the possibility to produce application-specific performance parts. Electric vehicles often have a problem with the thermal load of the components during power output and charging. Additively manufactured components with optimised topology and integrated cooling strive to achieve higher power density, enhanced cooling performance, and improved mechanical properties. AM not only makes it possible to produce functionally integrated and application-adapted components but also to reduce CO₂ emissions and conserve resources. The potential of additive manufacturing for mobility is particularly interesting for the spare and performance parts sector. Components can be improved in performance and manufactured directly on-site. The higher power density and the elimination of transport routes can make an additional significant contribution to environmental protection. This paper presents an overview of the current state of additive manufacturing in the field of electromobility with regard to replacement and performance parts using 3D metal printing. Based on an extensive literature research, a market overview is given. This serves as the basis for the further procedure and, building on this, the advantages of additive manufacturing are demonstrated using the example of an electric motor. The selected electric motor is an example of a defective component in a vehicle that needs to be replaced and whose performance can be improved by additive manufacturing and which can be produced on-site in a quantity of one. The motor is verified by means of a FEM simulation in order to determine the selection of an optimal water jacket topology and to demonstrate further potential for the future. Full article

The real-time application of powertrain-based predictive energy management (PrEM) brings the prospect of additional energy savings for hybrid powertrains. Torque split optimal control methodologies have been a focus in the automotive industry and academia for many years. Their real-time application in modern vehicles is, however, still lagging behind. While conventional exact and non-exact optimal control techniques such as Dynamic Programming and Model Predictive Control have been demonstrated, they suffer from the curse of dimensionality and quickly display limitations with high system complexity and highly stochastic environment operation. This paper demonstrates that Neuroevolution associated drive cycle classification algorithms can infer optimal control strategies for any system complexity and environment, hence streamlining and speeding up the control development process. Neuroevolution also circumvents the integration of low fidelity online plant models, further avoiding prohibitive embedded computing requirements and fidelity loss. This brings the prospect of optimal control to complex multi-physics system applications. The methodology presented here covers the development of the drive cycles used to train and validate the neurocontrollers and classifiers, as well as the application of the Neuroevolution process. Full article

The measurement of NO_x emissions in vehicles has so far been exclusively carried out during the type-approval process. For this purpose, high-precision gas measurement laboratory equipment and Portable Emission Measurement Systems (PEMS) are used. Both types of equipment are costly in terms of price, maintenance, complexity, and time of use (calibration and maintenance requirements). Currently, NO_x emissions measurements in Periodic Technical Inspections (PTIs) are being considered, but PEMS or laboratory equipment is unsuitable for this function, and PTI-grade equipment has to be used. Although CO and O₂ are currently being reliably measured with this equipment, there is not enough information about its accuracy for NO_x measurements. Accordingly, in this paper, simultaneous measures have been performed over the same engine in a test cell, with a laboratory and a PTI gas analyser to assess the accuracy of the latter. When performing the test with the most similar conditions found in PTI, our results show that the PTI gas analyser shows an average deviation of 2.6 ppm and 9% rel. with respect to high-precision laboratory equipment for concentrations below 700 ppm NO_x, which can be considered acceptable for periodic technical inspections. Full article

Electromobility has grown rapidly, and especially in China, Europe, and the United States. Within Europe, Germany is the largest market. Our goal in this paper is to provide a data-driven overview of the key data, including the number of vehicles sold, place of registration, battery capacity, and charging power, in Germany. The results were generated by linking car-registration data with the technical details for each car model. We identified more than 84% of the battery electric vehicles in the fleet, but the uncertainty is larger for plug-in hybrid electric vehicles. The number of sold electric vehicles doubled annually over the last two years. Simultaneously, the battery capacity and charging power per vehicle are rising. Combined, the two effects cause the cumulative battery capacity and charging power of the fleet to grow at an even faster pace. The battery energy built into electric vehicles in Germany registered on 1 August 2022 was 50.5 GWh, of which 9.5 GWh belonged to plug-in hybrids. The combined charging system became the dominant charger type for fast charging in Germany, and only 2% of the vehicle fleet used the competing CHAdeMO standard. To allow fellow researchers to work with the data, we published them free of charge on our data platform mobility charts, and we update the data monthly. Full article