Vehicle Operation Research Articles

Selection of energy storage systems for a special purpose rail vehicle based on simulation analysis

The issue of power supply to electric rail vehicles leads to a separation of the rail network into electrified and unelectrified portions, where the sections lacking electrification exclude the operation of electric rail vehicles powered from the overhead lines. The potential solution to this problem was found in adding energy storage systems to electric rail vehicles, to allow them some range of travel beyond the electrified lines. A simulation analysis of a special purpose rail vehicle travelling across a non-electrified section of railway line was conducted to assess the energy consumption rate and the necessary energy storage capacity. Three energy storage solutions were simulated, showing the travel range they can provide, with the aim of finding the lowest battery capacity solution that would still allow the vehicle to safely complete the simulated drive. The final selection of energy storage system capacity was done based on the assumed expected range outside electrified railway weighed against the mass and cost of the extra energy storage system added to the vehicle. For a vehicle with a mass of 65 tons a battery system with a capacity of 600 Ah was found to be sufficient.

Reinforcement Learning Based Speed Control with Creep Rate Constraints for Autonomous Driving of Mining Electric Locomotives

The working environment of mining electric locomotives is wet and muddy coal mine roadway. Due to low friction between the wheel and rail and insufficient utilization of creep rate, there may be idling or slipping between the wheels and rails of mining electric locomotives. Therefore, it is necessary to control the creep rate within a reasonable range. In this paper, the autonomous control algorithm for mining electric locomotives based on improved ε-greedy is theoretically proven to be convergent and effective firstly. Secondly, after analyzing the contact state between the wheel and rail under wet and slippery road conditions, it is concluded that the value of creep rate is an important factor affecting the autonomous driving of mining electric locomotives. Therefore, the autonomous control method for mining electric locomotives based on creep control is proposed in this paper. Finally, the effectiveness of the proposed method is verified through simulation. The problem of wheel slipping and idling caused by insufficient friction of mining electric locomotives in coal mining environments is effectively suppressed. Autonomous operation of vehicles with optimal driving efficiency can be achieved through quantitative control and utilization of the creep rate between wheels and rails.

State of health estimation method based on real data of electric vehicles using federated learning

Accurately estimating the state of health (SOH) of lithium-ion batteries (LIBs) is of paramount importance for the development and operation of electric vehicles, as it directly impacts driving performance. Currently, there are numerous methods for estimating SOH, but only some are suitable for real-world data, most existing methods for estimating SOH use data of lithium-ion battery cells from laboratory operations to train and test. Firstly, obtaining data usage of battery from the real world takes work. Secondly, the model for estimating SOH must be representative and generalized because they are trained on separate data for each electric vehicle. Lastly, collecting data for centralized training may lead to huge data uploads This study goes beyond that by using battery data from real-world vehicle operations. This study is based on 10 in-service electric trucks over 3 years, adopting methods such as utilizing data acquisition technology from battery management systems (BMS) and other controllers, as well as vehicle networking data reporting technology, to propose a SOH estimation method utilizing a new machine learning approach based on artificial neural network (ANN) and federated learning (FL) to predict the SOH of lithium batteries in real-world scenarios. This study developed an ANN local model and compared it with the popular long short-term memory (LSTM) model, finding that the ANN model is relatively more suitable as a local model for electric vehicles. In addition, we apply the FL framework to train the model to reduce the amount of uploaded electric vehicles’ battery data. We verified the results of the model on experimental data. Meanwhile, we analyzed the model on actual data by comparing its mean absolute and relative errors. The results show that federated training method achieves better generalization in predicting SOH compared to centralized training method and another traditional machine learning models. This study provides an effective solution of electric vehicle batteries SOH prediction and offers new ideas and methods for the generalization of traditional machine learning models.

Active Vibration Control on a Tire-Wheel Assembly using Piezoelectric Spatial Modal Filter

Abstract Vibrations due to tire-road contact in wheeled vehicles induce acoustic discomfort especially beyond 35 km·h−1. This paper proposes an active control method to reduce the vibration transmission from the tire-road contact to the vehicle through piezoelectric transducers located directly on the wheel spokes. Our approach relies on a double spatial modal filter to physically focus the control energy on the wheel pumping mode while avoiding any spillover phenomena. In addition, a band pass controller ensures maximum damping on the targeted mode. The proposed control strategy is applied first to the clamped wheel in order to validate the static performance of the spatial controller. Then the wheel is excited through the tire and the efficiency of the controller is evaluated through the measured force passing by the wheel hub. Finally, the tire-wheel assembly (TWA) is placed on an experimental set-up recreating the vehicle operating conditions with wheel rotation at different velocities and road excitation. The experimental results confirm the efficiency of the proposed control method and its robustness to the dynamics evolution of the structure in function of the TWA angular velocity.

Well‐to‐wheels analysis of greenhouse gas emissions for passenger vehicles in Middle East and North Africa

AbstractBattery electric vehicles (BEVs) are widely considered a pathway to achieve low carbon mobility. BEVs emit zero emissions from the tailpipe, but their life cycle carbon reduction compared to gasoline vehicles varies based on primary energy sources, electricity generation, and use efficiency. The Middle East and North Africa (MENA) region is an area rich in fossil fuels, meriting a detailed comparison between the emissions from BEV and other powertrains. We developed a MENA‐specific life cycle model that estimates well‐to‐wheel (WTW) greenhouse gas (GHG) emissions from passenger transport with internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), plug‐in hybrid electric vehicles, and BEVs. MENA's average WTW GHG emissions for all supply chain steps including combustion emissions from vehicle operation are 767 g/kWh and 84 g CO2eq/MJ for electricity and gasoline, respectively, but are highly variable due to heterogeneity in upstream supply chains. The use of hybrid gasoline ICEVs provides the largest emission reduction opportunity for existing vehicle fleets in 9 of the 16 MENA countries. For these nine countries, replacing gasoline ICEVs with HEVs could, on average, reduce country‐level life cycle GHG emissions by 47%. There is a similar emission reduction opportunity for 14 of the 16 MENA countries when normalizing vehicle efficiencies irrespective of the powertrain shares and other trends in existing vehicle fleets. Future scenario analysis shows that BEVs would have the lowest WTW GHG emissions among all powertrains in most MENA countries only if significantly reduced electricity transmission losses and cleaner grid mix are realized, although a high cost of infrastructure developments is expected.

Estimation of battery temperature during drive cycle operation by the time evolution of voltage and current

During the operation of electric vehicles, accurate temperature monitoring of lithium-ion batteries by battery management system is crucial to their safety. However, temperature sensors possess limitations as they necessitate regular calibration to avoid inaccuracies, which can be challenging to accomplish in certain scenarios. As an alternative, a general sensorless temperature estimation framework is developed in this study to either serve as a complementary approach alongside sensors or function independently to ensure reliable temperature measurement. The proposed framework relies solely on data regarding battery voltage and current measurements, and this information is employed to determine the parameters of the equivalent circuit model via the vector-type recursive least squares algorithm in the first step. In the second step, these parameters and the voltage/current data serve as the input to the deep neural network (DNN) to estimate the instantaneous battery surface temperature during dynamic driving cycles under various ambient temperatures and degradation scenarios. To reflect the effect of past battery operation on the current temperature, a newly defined current integral function is proposed, by which the input time length of the DNN model is suggested. Four combinations of the model input are studied, and the best input option yields an average estimation error of the battery temperature that falls below 0.5 °C.

Modeling the economic cost of congestion in Addis Ababa City, Ethiopia

Road traffic which results in significant time lags, increased fuel consumption, and financial losses, remains a noteworthy challenge in developed and developing countries. As a result, the Ethiopian Government and the City Administration of Addis Ababa have built extensive road networks and imposed restrictions on driving, vehicle acquisition and parking. However, despite all these efforts, drivers and passengers waste significant time on long traffic queues, resulting in unpredictable and delayed travel. The current study investigated the cost of travel time delay, vehicle operating costs, time reliability, and the factors influencing these variables. The study used questionnaires, measurements, and traffic counting techniques to collect data from nine road segments. The sample comprised 3240 participants. The cost functions of both drivers and passengers were examined using a multiple linear regression model, with estimation performed using ordinary least squares. According to the findings, the economic costs of congestion depend on the number of lanes, the length of the road segment, the volume of traffic, and the respondents’ income level. The study also revealed that travel, vehicle operation, and unreliability costs account for 74%, 6%, and 20%, respectively, of the total congestion costs.

Enhanced High-Definition Video Transmission for Unmanned Driving in Mining Environments

In the development of intelligent mines, unmanned driving transportation has emerged as a key technology to reduce human involvement and enable unmanned operations. The operation of unmanned vehicles in mining environments relies on remote operation, which necessitates the low-latency transmission of high-definition video data across multiple channels for comprehensive monitoring and precise remote control. To address the challenges associated with unmanned driving in mines, we propose a comprehensive scheme that leverages the capabilities of 5G super uplink, edge collaborative computing, and advanced video transmission strategies. This approach utilizes dual-frequency bands, specifically 3.5 GHz and 2.1 GHz, within the 5G super uplink framework to establish an infrastructure designed for high-bandwidth and low-latency information transmission, crucial for real-time autonomous operations. To overcome limitations due to computational resources at terminal devices, our scheme incorporates task offloading and edge computing methodologies to effectively reduce latency and enhance decision-making speed for real-time autonomous activities. Additionally, to consolidate the benefits of low latency, we implement several video transmission strategies, such as optimized network usage, service-specific wireless channel identification, and dynamic frame allocation. An experimental evaluation demonstrates that our approach achieves an uplink peak rate of 418.5 Mbps with an average latency of 18.3 ms during the parallel transmission of seven channels of 4K video, meeting the stringent requirements for remote control of unmanned mining vehicles.

Research on SOC estimation of lithium-ion batteries based on robust full order proportional integral observer

Accurate state of charge (SOC) estimation of lithium-ion batteries is vital for the reliable and safe operation of electric vehicles. This paper introduces a novel SOC estimation method, the robust full-order proportional integral observer (RF-PIO), designed to overcome SOC estimation misalignment challenges in electric vehicles during high load shocks and temperature fluctuations. These challenges arise from the degradation of proportional integral observer stability. The RF-PIO method incorporates a novel proportional link into the disturbance estimation, significantly improving the observer's robustness without increasing computational complexity. This enhancement allows for more accurate tracking of model parameters under dynamic conditions. Extensive comparison experiments, in conjunction with a variety of driving data, validate the RF-PIO method. The results indicate that the SOC estimation error remains below 2.11%, even amidst unknown model parameters and high-frequency noise interference.

3D dynamic contact analysis of tyre internal deformation using 2D image sensor

Due to the increasing weight of electric vehicles, advanced tyre technology is urgently needed. In particular, there is a need for dynamic contact analysis to determine the energy interaction between tyres and road surfaces. However, the methods developed to date are limited in that they require either numerous contact patches inside the tyre or sensor devices on the ground. In this study, we propose a new method for dynamic tyre-ground contact analysis that overcomes these limitations. By installing a two-dimensional image sensor inside the tyre, deformations can be observed and quantified three-dimensionally during vehicle operation. This method advances tyre engineering through innovative analysis of tyre deformation during operation.

Marketing Events Mediating BEV Features and Operating Costs: Impact on Purchase Intentions

This research aims to find out the lack of influence of Batery Electric Vehicle (BEV) features and vehicle operating perspective costs on the interest in purchasing electric cars with marketing events as a mediation and review from an Islamic. This devotional population is a society over 21 years old that is domiciled in Jabodetabek. The sample in this study was made up of people who drove more than three times in one week, for a total of 100 respondents. The sampling technique uses a non-probability method with purposive sampling techniques through the distribution of questionnaires with Google forms. The analytical method used is the Partial Least squares Structural Equation Modeling method (PLS-SEM). The results of the study show that Feature influences Purchase Intention, Feature influences Marketing Event, Vehicle Operating Cost influences Purchase Intention, Marketing Event influences Purchase Intentions, Marketing Event mediates the influence of Feature on Purchase Intentions, Features, Vehicle Operating Costs, Marketing Events, and Purchase Intention BEV cars among the people in Jabodetabek. The implications of the results of this study present that purchase intention towards BEVs encourages producer management to optimally utilize all the variables analyzed in this study proportionally.

Study on Fault Diagnosis Technology for Efficient Swarm Control Operation of Unmanned Surface Vehicles

The purpose of this study is to design a Swarm Control algorithm for the effective mission performance of multiple unmanned surface vehicles (USVs) used for marine research purposes at sea. For this purpose, external force information was utilized for the control of multiple USV swarms using a lead–follow-formation technique. At this time, to efficiently control multiple USVs, the LSTM algorithm was used to learn ocean currents. Then, the predicted ocean currents were used to control USVs, and a study was conducted on behavioral-based control to manage USV formation. In this study, a control system model for several USVs, each equipped with two rear thrusters and a front lateral thruster, was designed. The LSTM algorithm was trained using historical ocean current data to predict the velocity of subsequent ocean currents. These predictions were subsequently utilized as system disturbances to adjust the controller’s thrust. To measure ocean currents at sea as each USV moves, velocity, azimuth, and position data (latitude, longitude) from the GPS units mounted on the USVs were utilized to determine the speed and direction of the hull’s movement. Furthermore, the flow rate was measured using a flow rate sensor on a small USV. The movement and position of the USV were regulated using an Artificial Neural Network-PID (ANN-PID) controller. Subsequently, this study involved a comparative analysis between the results obtained from the designed USV model and those simulated, encompassing the behavioral control rules of the USV swarm and the path traced by the actual USV swarm at sea. The effectiveness of the USV mathematical model and behavior control rules were verified. Through a comparison of the movement paths of the swarm USV with and without the disturbance learning algorithm and the ANN-PID control algorithm applied to the designed simulator, we analyzed the position error and maintenance performance of the swarm formation. Subsequently, we compared the application results.

Greases for electric vehicle motors: Bearing friction torque under driving cycle conditions and the thickener effect on oil release

Performance of Lithium Complex (LiX), Polyurea (PU), and Polypropylene (PP) greases in SKF6208 bearings subjected to driving cycle conditions for 28 days (equivalent to 23,000 km of electric vehicle operation) was studied by continuously measuring bearing friction torque and temperature. The energy dissipation was correlated to the differences in oil bleeding and rheology for the three greases. Evolution of the friction torque, friction torque hysteresis, and changes in grease rheology were dominated by the oil release property. The latter was determined by the thickener system and its particular response to the conditions imposed by the driving cycle. A quantitative estimate of the carbon footprint from using these greases to lubricate bearings under driving cycle conditions is also presented.

Real-time estimation of vehicle inertia parameters based on Kalman-bucy filter

Vehicle inertial parameters such as mass and moments of inertia are required for most vehicle dynamic control systems. Due to the wide range variation of these parameters during vehicle operation, accurate estimation of their values in real-time plays an important role in improving the efficiency of vehicle control systems. In this article, the vehicle sprung mass and moments of inertia are estimated in real-time based on a Kalman-Bucy filter algorithm designed for a spatial vibration model of a two-axle truck. This proposed method requires measuring only the vertical, roll, and pitch velocity of the sprung mass and, therefore can reduce the sensor cost significantly. The simulation results for a random roughness road profile according to ISO 8608 class C with step variations in sprung mass and moments of inertia showed that the designed estimator rejected the process and measurement noises and tracked the real vehicle parameters effectively with acceptable errors

Improved deep learning based state of charge estimation of lithium ion battery for electrified transportation

The increasing interests and recent advancements in artificial intelligence and machine learning have significantly accelerated the development of novel techniques for the state estimation of batteries in the battery management systems (BMS) of electrified vehicles. To ensure the safe and reliable operation of an electric vehicle, it is essential to determine the state of charge (SOC) among the battery's numerous states. Deep learning models have been effective for SOC estimation due to the nonlinear and time-varying characteristics of a battery management system. This article seeks to describe an SOC estimator design method using deep neural network (DNN) models, particularly non-recurrent and recurrent neural networks, owing to their excellent computational intelligence and capacity for self-learning. A thorough investigation has been conducted at various temperatures, and the model is trained on data received from the cell during discharging processes like the urban dynamometer driving schedule (UDDS). Finally, the accuracy of the models is compared based on various performance indices. From the results, it has been concluded that the DNN models acquire a Mean Absolute Error (MAE) of less than 1 % and a Root Mean Square Error (RMSE) of close to 1 % when validated over various datasets at temperatures of 10 °C, 25 °C, and 40 °C.

Forecasting Hydrogen Vehicle Refuelling for Sustainable Transportation: A Light Gradient-Boosting Machine Model

Forecasting Hydrogen Vehicle Refuelling for Sustainable Transportation: A Light Gradient-Boosting Machine Model

End-to-End Target Liveness Detection via mmWave Radar and Vision Fusion for Autonomous Vehicles

The successful operation of autonomous vehicles hinges on their ability to accurately identify objects in their vicinity, particularly living targets such as bikers and pedestrians. However, visual interference inherent in real-world environments, such as omnipresent billboards, poses substantial challenges to extant vision-based detection technologies. These visual interference exhibit similar visual attributes to living targets, leading to erroneous identification. We address this problem by harnessing the capabilities of mmWave radar, a vital sensor in autonomous vehicles, in combination with vision technology, thereby contributing a unique solution for liveness target detection. We propose a methodology that extracts features from the mmWave radar signal to achieve end-to-end liveness target detection by integrating the mmWave radar and vision technology. This proposed methodology is implemented and evaluated on the commodity mmWave radar IWR6843ISK-ODS and vision sensor Logitech camera. Our extensive evaluation reveals that the proposed method accomplishes liveness target detection with a mean average precision of 98.1%, surpassing the performance of existing studies.

Active Equalization of Lithium Battery Pack with Adaptive Control based on DC Energy Conversion Circuit

Background:: How to solve the inconsistency of battery pack is a key point to ensure reliable operation of electric vehicles. Battery equalization is an effective measure to address the inconsistency. Passive equalization method has poor efficiency and thermal management problems. Average voltage equalization method is only suitable for situations where there is a significant voltage difference between batteries. The SOC-based equalization method is relatively difficult and may inevitably lead to the accumulation of errors during the process. Objective:: In order to avoid the disadvantages of traditional control methods, a new control method is proposed to improve the accuracy and self-adaptation of active equalization, which is easy to be realized without online calculation. Methods:: Cascaded bidirectional Buck-boost circuit is adopted as the novel equalization topology. Based on fuzzy PID theory, an adaptive digital-analog hybrid control strategy based on fuzzy PID is proposed in this paper. Parameter design of the fuzzy PID controller is carried out. A battery equalization system based on cascaded bidirectional Buck-boost circuit is designed and developed. Experimental verification is conducted on relevant hardware platforms. Results:: An adaptive digital-analog hybrid control strategy based on fuzzy PID is proposed. Compared to passive equalization, this proposed method provides high efficiency. Regarding traditional voltage control, the method improves control reliability and flexibility. Compared to the average voltage equalization method, the approach needs less convergence time. Moreover, the control method is much easier to realize than the SOC-based equalization method. Conclusion:: By using the presented adaptive control based on DC energy conversion circuit, the degree of self-adaptation of the equalization process has been obtained as higher and the inconsistency as smaller.

Multi-Strategical Thermal Management Approach for Lithium-Ion Batteries: Combining Forced Convection, Mist Cooling, Air Flow Improvisers and Additives

Maintaining the peak temperature of a battery within limits is a mandate for the safer operation of electric vehicles. In two-wheeler electric vehicles, the options available for the battery thermal management system are minuscule due to the restrictions imposed by factors like weight, cost, availability, performance, and load. In this study, a multi-strategical cooling approach of forced convection and mist cooling over a single-cell 21,700 lithium-ion battery working under the condition of 4C is proposed. The chosen levels for air velocities (10, 15, 20 and 25 m/s) imitate real-world riding conditions, and for mist cooling implementation, injection pressure with three levels (3, 7 and 14 bar) is considered. The ANSYS fluent simulation is carried out using the volume of fluid in the discrete phase modelling transition using water mist as a working fluid. Initial breakup is considered for more accurate calculations. The battery’s state of health (SOH) is determined using PYTHON by adopting the Newton–Raphson estimation. The maximum temperature reduction potential by employing an airflow improviser (AFI) and additives (Tween 80, 1-heptanol, APG0810, Tween 20 and FS3100) is also explored. The simulation results revealed that an additional reduction of about 11% was possible by incorporating additives and AFI in the multi-strategical approach. The corresponding SOH improvement was about 2%. When the electric two-wheeler operated under 4C, the optimal condition (Max. SOH and Min. peak cell temp.) was achieved at an air velocity of 25 m/s, injection pressure of 7 bar with AFI and 3% (by wt.) Tween 80 and a 0.1% deformer.

Estimating regional CO2 and NOx emissions from road transport using real-world data-based emission factors in Korea

The average-speed emission model (Speed-based model), a widely used and simple method of calculating road vehicle emissions, offers easy accessibility by expressing emissions as a function of average speed. However, there are limitations in expressing emissions generated through complex mechanisms simply as a function of speed. Real-world driving tests using a portable emission measurement system can incorporate the impact of vehicle driving load on emissions. In this study, we analyzed real-world emissions data from 94 light-duty vehicles and developed time-based emission factors depending on vehicle speed and vehicle-specific power (VSP). We also propose a speed-VSP based model to estimate regional CO2 and NOx emissions by combining time-based emission factors and vehicle operating times. The speed-based model and Speed-VSP based model exhibit a 44% difference in NOx emissions and a 29% difference in CO2 emission. In a comparison of the two models against RDE test results, the speed-VSP based model achieved high accuracy in predicting NOx and CO2 emissions with a lower root mean square error (RMSE). Specifically, for NOx emissions predictions, the speed-VSP based model achieved an RMSE of 122–270 mg/km, while the speed-based model showed a much higher RMSE of 435–476 mg/km. For CO2 emissions predictions, the speed-VSP based model achieved an RMSE of 34–56 mg/km, while the speed-based model showed a much higher RMSE of 36–72 mg/km. The results of this study present an opportunity to reassess and improve conventional method of measuring and evaluating emissions from road transport.