Driver’s Personality and Behavior for Boosting Automobile Security and Sensing Health Problems Through Fuzzy Signal Detection Case Study: Mexico City

Automobile security became an essential theme over the last years, and some automakers invested much money for collision avoidance systems, but personalization of their driving systems based on the user’s behavior was not explored in detail. Furthermore, efficiency gains could be had with tailored systems. In Mexico, 80% of automobile accidents are caused by human beings; the remaining 20% are related to other issues such as mechanical problems. Thus, 80% represents a significant opportunity to improve safety and explore driving efficiency gains. Moreover, when driving aggressively, it could be connected with mental health as a post-traumatic stress disorder. This paper proposes a Tailored Collision Mitigation Braking System, which evaluates the driver’s personality driving treats through signal detection theory to create a cognitive map that understands the driving personality of the driver. In this way, aggressive driving can be detected; the system is then trained to recognize the personality trait of the driver and select the appropriate stimuli to achieve the optimal driving output. As a result, when aggressive driving is detected continuously, an automatic alert could be sent to the health specialists regarding particular risky behavior linked with mental problems or drug consumption. Thus, the driving profile test could also be used as a detector for health problems.

Actual-time modeling of a subway vehicle and Optimal driving management with GA and ABC algorithms

The optimization of operations of subway systems has critical importance in terms of energy efficiency and costs. Therefore, driving management of subway vehicles has been gaining more importance day by day. Optimal Driving Management (ODM) is the optimization of the velocity trajectory of a subway vehicle by considering operating conditions and travel time. In this study, the driving of a subway vehicle has been modeled dynamically with all parameters that affect driving. So, a realistic model has been prepared. Then, a new objective function has been proposed to reduce energy consumption by using the subway vehicle’s acceleration and braking forces parameters for ODM. The Artificial Bee Colony algorithm (ABC) and Genetic algorithm (GA) have been used on the prepared model to determine the driving dynamics of the subway vehicle. The performance of the algorithms has been evaluated in the real line network, which has multiple stations with different characteristics. The energy consumption has been reduced by 10.47% in GA and 8.92% in ABC compared to the actual driving values. Moreover, the results of the study has been analyzed in terms of passenger comfort, cost, and emission values.

Simulation of Optimal Driving for Minimization of Fuel Consumption or NOx Emissions in a Diesel Vehicle

Significant reduction in fuel consumption and NOx emissions can be achieved just by changing the driving along the road. In this paper, dynamic programming is employed to find two different driving profiles optimized for fuel consumption and NOx creation minimization in a diesel vehicle. Results, show that the fuel reduction driving cycle leads to fuel savings of 4% compared with the average consumption with arbitrary driving. The NOx reduction driving profile improves the emissions of arbitrary driving by a 34.5%. NOx oriented driving profile improves the emissions of the fuel-oriented cycle by a 38% at the expense of a fuel consumption penalty of 10%. This result points out the difficulty of a simultaneous NOx and fuel consumption reduction, stressing the efforts to be done in this field during the following years. Strategies followed and conclusions drawn from this paper are relevant concerning vehicle autonomy integration.

EXPERIMENTAL VALIDATION OF COMBINED TRIP ESTIMATOR FOR SMALL POWER ELECTRIC TRACTOR

The combined use of the electric tractor in high-speed travel and high-torque towing must involve a trip range estimation and an optimal driving behavior of the vehicle. The paper proposes an estimation method based on the measured usable energy reserve and on prediction of the power consumption for the two selected operating modes: rolling and towing. As driver’s interface will be used an interactive graphical display which can be used for the initial settings and further adjustments of some of the working parameters. The demonstrations are sustained by trip recordings used for calibration process and for error mitigation.

A Systematic Review of Technologies, Control Methods, and Optimization for Extended-Range Electric Vehicles

For smart cities using clean energy, optimal energy management has made the development of electric vehicles more popular. However, the fear of range anxiety—that a vehicle has insufficient range to reach its destination—is slowing down the adoption of EVs. The integration of an auxiliary power unit (APU) can extend the range of a vehicle, making them more attractive to consumers. The increased interest in optimizing electric vehicles is generating research around range extenders. These days, many systems and configurations of extended-range electric vehicles (EREVs) have been proposed to recover energy. However, it is necessary to summarize all those efforts made by researchers and industry to find the optimal solution regarding range extenders. This paper analyzes the most relevant technologies that recover energy, the current topologies and configurations of EREVs, and the state-of-the-art in control methods used to manage energy. The analysis presented mainly focuses on finding maximum fuel economy, reducing emissions, minimizing the system’s costs, and providing optimal driving performance. Our summary and evaluation of range extenders for electric vehicles seeks to guide researchers and automakers to generate new topologies and configurations for EVs with optimized range, improved functionality, and low emissions.

The Economic Aspect of Using Different Plug-in Hybrid Driving Techniques in Urban Conditions

Plug-in hybrids (PHEV) have become popular due to zero-emission driving, e.g., in urban areas, and using an internal combustion engine on longer distances. Energy consumption by the PHEV depends on many factors which can be either dependent or independent of the driver. The article examines how the driver can use the vehicle’s capabilities to influence its wear. Determining the optimal driving technique, due to the adopted nature of the timetable, is the basic variable that determines the profitability of using a given drive system. Four driving techniques have been selected to determine which one can offer the largest advantages. A vehicle-dedicated application has recorded the drivetrain performance on a predetermined route through an urban area. The analysis of results has demonstrated which of the driving techniques provides measurable effects in terms of reduced energy consumption and the shortest travelling time. The study shows longitudinal acceleration and torque generated by the electric drive. The information included in the study can help any PHEV user reduce the operating cost by applying an appropriate driving technique. The proposed research introduces the possibilities of assessing the influence of the driving style on energy consumption. The innovative side of this research is the observation of stochastic phenomena that are difficult to detect when using approximation modelling.

The Influence of Operating Strategies regarding an Energy Optimized Driving Style for Electrically Driven Railway Vehicles

The aim of this paper is the optimization of velocity trajectories for electrical railway vehicles with the focus on total energy consumption. On the basis of four fundamental operating modes—acceleration, cruising, coasting, and braking—energy-optimal trajectories are determined by optimizing the sequence of the operating modes as well as the corresponding switching points. The optimization approach is carried out in two consecutive steps. The first step ensures compliance with the given timetable, regarding both time and position constraints. In the second step, the influence of different operating strategies, such as load distribution and the switch-off of traction components during low loads, are analyzed to investigate the characteristics of the energy-optimal velocity trajectory. A detailed simulation model has been developed to carry out the analysis, including an assessment of its capabilities and advantages. The results suggest that the application of load-distribution techniques, either by a switch-off of parallel traction units or by a load-distribution between active units, can affect the energy-optimal driving style.