Vehicles

This paper attempts to develop a Dynamic Mode Decomposition (DMD)-based Reduced Order Model (ROMs) that can quickly but accurately predict the forces and moments experienced by a road vehicle such that they be used by an on-board controller to determine the vehicle’s trajectory. DMD can linearize a large dataset of high-dimensional measurements by decomposing them into low-dimensional coherent structures and associated time dynamics. This ROM can then also be applied to predict the future state of the fluid flow. Existing literature on DMD is limited to low Reynolds number applications. This paper presents DMD analyses of the flow around an idealized road vehicle, called the Ahmed body, at a Reynolds number of $2.7\times {10}^6$. The high-dimensional dataset used in this paper was collected from a computational fluid dynamics (CFD) simulation performed using the Menter’s Shear Stress Transport (SST) turbulence model within the context of Improved Delayed Detached Eddy Simulations (IDDES). The DMD algorithm, as available in the literature, was found to suffer nonphysical dampening of the medium-to-high frequency modes. Enhancements to the existing algorithm were explored, and a modified DMD approach is presented in this paper, which includes: (a) a requirement of higher sampling rate to obtain a higher resolution of data, and (b) a custom filtration process to remove spurious modes. The modified DMD algorithm thus developed was applied to the high-Reynolds-number, separation-dominated flow past the idealized ground vehicle. The effectiveness of the modified algorithm was tested by comparing future predictions of force and moment coefficients as predicted by the DMD-based ROM to the reference CFD simulation data, and they were found to offer significant improvement. Full article

Nowadays, cars are an essential part of daily life, and failures, especially of the engine, need to be avoided. Here, we used the determination of the battery voltage as a reference measurement to determine possible malfunctions. Thereby, we compared the use of a digital oscilloscope with the direct measurement of the battery voltage via the electronic control unit. The two devices were evaluated based on criteria such as price, sampling rate, parallel measurements, simplicity, and technical understanding required. Results showed that the oscilloscope (Picoscope 3204D MSO) is more suitable for complex measurements due to its higher sampling rate, accuracy, and versatility. The on-board diagnostics (VCDS HEX-V2) is more accessible to non-professionals, but it is limited in its capabilities. We found that the use of an oscilloscope, specifically the Picoscope, is preferable to measure battery voltage during the engine start-up process, as it provides more accurate and reliable results. However, further investigation is required to analyse numerous influences on the cranking process and the final decision for the appropriate measurement device is case specific. Full article

The Stanley controller is a proven approach for path tracking control in automated vehicles. If time delays occur, for example, in signal processing and steering angle control, precision and stability decrease. In this article, enhancements for the Stanley controller are proposed to achieve stable behavior with improved tracking accuracy. The approach uses the curvature of the path as feedforward, whereby the reference point for the feedforward input differs from that of the controller setpoints. By choosing a point further along the path, the negative effects of system delay are reduced. First, the parameters of the Stanley controller are calibrated using a straight line and circle maneuver. Then, the newly introduced feedforward parameter is optimized on a dynamic circuit. The approach was evaluated in simulation and validated on a demonstrator vehicle. The validation tests with the demonstrator vehicle on the dynamic circuit revealed a reduction of the root-mean-square cross-track error from 0.11 m to 0.03 m compared to the Stanley controller. We proved that the proposed approach optimizes the Stanley controller in terms of compensating for the negative effects of system delay. This allows it to be used in a wider range of applications that would otherwise require a more complex control approach. Full article

In this paper, we present a feasibility study on predicting the torque signal of a passenger car with the help of a neural network. In addition, we analyze the possibility of using the proposed model structure for temperature prediction. This was carried out with a neural network, specifically a three-layer long short-term memory (LSTM) network. The data used were real road load data from a Jaguar Land Rover Evoque with a Twinster gearbox from GKN. The torque prediction generated good results with an accuracy of 55% and a root mean squared error ($RMSE$) of 49 Nm, considering that the data were not generated under laboratory conditions. However, the performance of predicting the temperature signal was not satisfying with a coefficient of determination ($R^2$) score of −1.396 and an $RMSE$ score of $69.4$ °C. The prediction of the torque signal with the three-layer LSTM network was successful but the transferability of the network to another signal (temperature) was not proven. The knowledge gained from this investigation can be of importance for the development of virtual sensor technology. Full article

Transient car emission tests generate huge amount of test data, but their results are usually evaluated only using their “accumulated” cycle values according to the homologation limits. In this work, two machine learning models were developed and applied to a truck RDE test and two light-duty vehicle chassis emission tests. Different from the conventional approach, the engine parameters and fuel consumption were acquired from the Engine Control Unit, not from the test measurement equipment. Instantaneous engine values were used as input in machine-learning-based digital twins. This novel approach allows for much less costly vehicle tests and optimizations. The paper’s novel approach and developed digital twins model were able to predict both instantaneous and accumulated fuel consumption with good accuracy, and also for tests cycles different to the one used to train the model. Full article

Road hazards are one of the significant sources of fatalities in road accidents. The accurate estimation of road hazards can ensure safety and enhance the driving experience. Existing methods of road condition monitoring are time-consuming, expensive, inefficient, require much human effort, and need to be regularly updated. There is a need for a flexible, cost-effective, and efficient process to detect road conditions, especially road hazards. This work presents a new method to deal with road hazards using smartphones. Since most of the population drives cars with smartphones on board, we aim to leverage this to detect road hazards more flexibly, cost-effectively, and efficiently. This paper proposes a cloud-based deep-learning road hazard detection model based on a long short-term memory (LSTM) network to detect different types of road hazards from the motion data. To address the issue of large data requests for deep learning, this paper proposes to leverage both simulation data and experimental data for the learning process. To address the issue of misdetections from an individual smartphone, we propose a cloud-based fusion approach to further improve detection accuracy. The proposed approaches are validated by experimental tests, and the results demonstrate the effectiveness of road hazard detection. Full article

The main objective of this research paper was to develop two intelligent state estimators using shallow neural network (SNN) and NARX architectures from a large class of deep learning models. This research developed a new modelling design approach, namely, an improved hybrid adaptive neural fuzzy inference system (ANFIS) battery model, which is simple, accurate, practical, and well suited for real-time implementations in HEV/EV applications, with this being one of the main contributions of this research. On the basis of this model, we built four state of charge (SOC) estimators of high accuracy, assessed by a percentage error of less than 0.5% in a steady state compared to the 2% reported in the literature in the field. Moreover, these estimators excelled by their robustness to changes in the model parameters values and the initial “guess value” of SOC from 80–90% to 30–40%, performing in the harsh and aggressive realistic conditions of the real world, simulated by three famous driving cycle procedure tests, namely, two European standards, WLTP and NEDC, and an EPA American standard, FTP-75. Furthermore, a mean square error (MSE) of 7.97 × 10⁻¹¹ for the SOC estimation of the NARX SNN SOC estimator and 5.43 × 10⁻⁶ for voltage prediction outperformed the traditional SOC estimators. Their effectiveness was proven by the performance comparison with a traditional extended Kalman filter (EKF) and adaptive nonlinear observer (ANOE) state estimators through extensive MATLAB simulations that reveal a slight superiority of the supervised learning algorithms by accuracy, online real-time implementation capability, in order to solve an extensive palette of HEV/EV applications. Full article

The yaw acceleration required for circuit driving is determined by the time variation of the yaw rate due to two factors: corner radius and velocity at the center of gravity. Torque vectoring systems have the advantage where the yaw moment can be changed only by the longitudinal force without changing the lateral force of the tires, which greatly affects lateral acceleration. This is expected to improve the both the spinning performance and the orbital performance, which are usually in a trade-off relationship. In this study, we proposed a yaw moment control technology that actively utilized a power unit with a brake system, which was easy to implement in a system, and compared the performance of vehicles equipped with and without the proposed system using the Milliken Research Associates moment method for quasi-steady-state analysis. The performances of lateral acceleration and yaw moment were verified using the same method, and a variable corner radius simulation for circuit driving was used to compare time and performance. The results showed the effectiveness of the proposed system. Full article

The evolution toward electric vehicle nowadays appears to be the main stream in the automotive and transportation industry. In this paper, our attention is focused on the architectural modifications that should be introduced into the car body to give a proper location to the battery pack. The required battery pack is a big, heavy, and expensive component to be located, managed, climatized, maintained, and protected. This paper develops some engineering analyses and shows sketches of some possible solutions that could be adopted. The possible consequences on the position of the vehicle center of gravity, which in turn could affect the vehicle drivability, lead to locate the battery housing below the passenger compartment floor. This solution is also one of the most interesting from the point of view of the battery pack protection in case of a lateral impact and for easy serviceability and maintenance. The integration of the battery pack’s housing structure and the vehicle floor leads to a sort of sandwich structure that could have beneficial effects on the body’s stiffness (both torsional and bending). This paper also proposes some considerations that are related to the impact protection of the battery pack, with particular reference to the side impacts against a fixed obstacle, such as a pole, which are demonstrated to be the most critical. By means of some FE simulation results, the relevance of the interplay among the different parts of the vehicle side structure and battery case structure is pointed out. Full article

A new method to evaluate the energy consumption and driving range of electric vehicles running on intercity routes is proposed. This method consists of a hybridization of a predictive method and the application of online information during the driving run. The method uses specific algorithms for dynamic conditions based on real driving conditions, adapting the calculation method to the characteristics of the route and the driving style; electric vehicle characteristics are also taken into consideration for the driving range calculation. Real data were obtained from driving tests in a real electric vehicle under specific driving conditions and compared with the results generated by a simulation process specifically developed for the new method run under the same operating conditions as the real tests. The comparison showed very good agreement, higher than 99%. This method can be customized according to the electric vehicle characteristics, the type of route and the driving style; therefore, it shows an improvement in the determination of the real driving range for an electric vehicle since it applies real driving conditions instead of protocol statistical data. Full article

This work represents the development of a Fuel Cell Hybrid Electric Vehicle (FCHEV) model, its validation, and the comparison of different control strategies based on the Toyota Mirai (1st generation) vehicle and its subsystems. The main investigated parameters are hydrogen consumption, and the variation of the state of charge, current, and voltage of the battery. The FCHEV model, which is made up of multiple subsystems, is developed and simulated in MATLAB^® Simulink environment using a rule-based control strategy derived from the real system. The results of the model were validated using the experimental data obtained from the open-source Argonne National Laboratory (ANL) database. In the second part, the equivalent consumption minimization strategy is implemented into the controller logic to optimize the existing control strategy and investigate the difference in hydrogen consumption. It was found that the ECMS control strategy outperforms the rule-based one in all drive cycles by 0.4–15.6%. On the other hand, when compared to the real controller, ECMS performs worse for certain considered driving cycles and outperforms others. Full article

Battery Electric Vehicles (BEVs) have an increasingly large share of the vehicle market. To ensure a safe and long operation of the mostly large underfloor-mounted traction batteries, they must be developed and tested in advance under realistic conditions. Current standards often do not provide sufficiently realistic requirements for environmental and lifetime testing, as these are mostly based on data measured on cars with an Internal Combustion Engine (ICE). Prior to this work, vibration measurements were performed on two battery-powered electric vehicles and a battery-powered commercial mini truck over various road surfaces and other influences. The measurement data are statistically evaluated so that a statement can be made about the influence of various parameters on the vibrations measured at the battery pack housing and the scatter of the influencing parameters. By creating a load profile based on the existing measurement data, current standards can be questioned and new insights gained in the development of a vibration profile for the realistic testing of battery packs for BEVs. Full article

Road transport is shifting towards electrified vehicle solutions to achieve the Conference of the Parties of the United Nations Framework Convention on Climate Change (COP27) carbon neutrality target. According to life cycle assessment analyses, battery production and disposal phases suffer a not-negligible environmental impact to be mitigated with new recycling processes, battery technology, and life-extending techniques. The foundation of this study consists of combining the assessment of vehicle efficiency and battery ageing by applying supercapacitor technology with different topologies to more conventional battery modules. The method employed here consists of analysing different hybrid energy storage system (HESS) topologies for light-duty vehicle applications over a wide range of operating conditions, including real driving cycles. A battery electric vehicle (BEV) has been modelled and validated for this aim, and the reference energy storage system was hybridised with a supercapacitor. Two HESSs with passive and semi-active topologies have been analysed and compared, and an empirical ageing model has been implemented. A rule-based control strategy has been used for the semi-active topology to manage the power split between the battery and supercapacitor. The results demonstrate that the HESS reduced the battery pack root mean square current by up to 45%, slightly improving the battery ageing. The semi-active topology performed sensibly better than the passive one, especially for small supercapacitor sizes, at the expense of more complex control strategies. Full article

The topics of climate change and pollutant emission reduction are dominating societal discussions in many areas. In automotive development, with the introduction of real driving emissions (RDE) testing and the upcoming EU7 legislation, there are endless boundary conditions and potential scenarios that need to be evaluated. In terms of vehicle calibration, this is leading to a strong focus on alternative approaches such as virtual calibration. Due to the flexibility of virtual test environments and the variety of RDE scenarios, the amount of data collected is rapidly increasing. Supporting the calibration engineers in using the available data and identifying relevant information and test scenarios requires efficient approaches to data analysis. This paper therefore discusses the potential of data clustering to support this process. Using a previously developed approach for event detection in emission calibration, a methodology for the automatic categorization of events is presented. Approaches to clustering algorithms (hierarchical, partitioning, and density-based) are discussed and applied to data of interest. Their suitability for different signals is investigated exemplarily, and the relevant inputs are analyzed for their usability in calibration procedures. It is shown which clustering approaches have the potential to be implemented in the vehicle calibration process to provide added value to data evaluation by calibration engineers. Full article

The recovery of kinetic energy (KER) in electric vehicles was analyzed and characterized. Two main systems were studied: the use of regenerative brakes, and the conversion of potential energy. The paper shows that potential energy is a potential source of kinetic energy recovery with higher efficiency than the traditional system of regenerative brakes. The study compared the rate of KER in both cases for a BMWi3 electric vehicle operating under specific driving conditions; the results of the analysis showed that potential energy conversion can recover up to 88.2%, while the maximum efficiency attained with the regenerative brake system was 60.1%. The study concluded that in driving situations with sudden and frequent changes of vehicle speed due to traffic conditions, such as in urban routes, the use of regenerative brakes was shown to be the best option for KER; however, in intercity routes, driving conditions favored the use of potential energy as a priority system for KER. Full article

Energy moderation of the road transportation sector is required to limit climate change and to preserve resources. This work is focused on the moderation of vehicle consumption by optimizing the speed policy along an itinerary while taking into account vehicle dynamics, driver visibility and the road’s longitudinal profile. First, a criterion is proposed in order to detect speed policies that are impeding drivers’ eco-driving ability. Then, an energy evaluation is carried out and an optimization is proposed. A numerical application is performed on a speed limiting point with 20 usage cases and 5 longitudinal slope values. In the hypothesis of a longitudinal slope of zero, energy savings of 27.7 liter per day could be realized by a speed sign displacement of only 153.6 m. Potential energy savings can increase to up to 308.4 L per day for a $-4\%$ slope case, or up to 70.5 L per day for an ordinary $-2\%$ slope, with a sign displacement of only 391.5 m. This results in a total of 771,975 L of fuel savings over a 30 year infrastructure life cycle period. Therefore a methodology has been developed to help road managers optimize their speed policies with the aim of moderating vehicle consumption. Full article

Heavy vehicles exiting (or entering) a tunnel at high speed under a strong crosswind is a particularly critical condition since the aerodynamic load changes drastically, greatly affecting the lateral stability of the vehicle. Often, active control systems (active suspensions, active front steering, etc.) and infrastructure elements (e.g., wind fences) are proposed to reduce the induced risks. To help the design of these devices, the present paper investigates the response of the vehicle–driver system in the case of a high-sided lorry exiting a tunnel under crosswind, by using Driver-In-the-Loop simulations. The study was performed using the dynamic driving simulator of Politecnico di Milano and 28 test drivers. Vehicle and aerodynamic models have been developed to reproduce the phenomenon in a highly immersive environment. During the tests, several combinations of vehicle and wind speed were considered. The effect of vehicle loading condition (Empty and Laden) was also investigated. The performed tests allowed us to gain information about the sequence of the driver’s actions and associated delays, which may induce lane deviation or, in the worst case, rollover. It was found that lane invasion may happen for ratios of lateral aerodynamic force over vehicle weight force bigger than 0.1, while rollover could happen for ratios bigger than 0.3. Moreover, it was found that the driver’s response typically happens with a delay of ∼0.25 s with respect to the onset of the crosswind stimulus. Full article

In the vehicle-following scenario of autonomous driving, the change of driving style in the front vehicle will directly affect the decision on the rear vehicle. In this paper, a strategy based on a probabilistic neural network (PNN) for front vehicle intention recognition is proposed, which enables the rear vehicle to obtain the driving intention of the front vehicle without communication between the two vehicles. First, real vehicle data with different intents are collected and time—frequency domain variables are extracted. Secondly, Principal Component Analysis (PCA) is performed on the variables in order to obtain comprehensive features. Meanwhile, two cases are classified according to whether the front vehicle can transmit data to the rear vehicle. Finally, two recognition models are trained separately according to a PNN algorithm, and the two models obtained from the training are verified separately. When the front vehicle can communicate with the rear vehicle, the recognition accuracy of the corresponding PNN model reaches 96.39% (simulation validation) and 95.08% (real vehicle validation). If it cannot, the recognition accuracy of the corresponding PNN model reaches 78.18% (simulation validation) and 73.74% (real vehicle validation). Full article

In this paper, we pay significant attention to the most vulnerable road users (i.e., people with disabilities) when interacting with cyclists. The special needs of these groups are studied by distributing an online questionnaire about their perception and interaction with cyclists besides conducting an on-road experiment to test the possibility of sharing cycling infrastructure with wheelchair users. In an authentic case study, 2 cyclists and 5 wheelchair users were asked to ride their vehicles on a cycling lane in Madrid, in order to evaluate wheelchair users’ interaction with cyclists and reaction to the infrastructure by applying objective and subjective measures. The participants were provided with GPS, a speed sensor, and a head-mounted camera to record the experiment. The results show that people with disabilities feel threatened by cyclists who share the sidewalk with them; the respondents to the questionnaire suggested making the sidewalk free of cyclists to avoid conflict and improve safety. Moreover, the outputs of the experiment show positive feedback from wheelchair users when sharing cycling infrastructure regarding the improvement of speed and safety feeling. However, it is recommended to increase the number of wheelchair users to obtain more reliable and generalizable results. Full article