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This paper presents the design of an Eco-Driving Assistant System (EDAS) in which the main goal is to minimize the energy use of battery electric vehicles, in particular, vehicles utilized for public transportation. The system optimizes the speed profile of a real route schedule while satisfying the constraints imposed on speed and time. It includes a driver feedback and a driver scoring GUI which allows the driver improving his/her driving skills and comparing him/herself to a “theoretical perfect driver”. The system also includes a backward simulator that generates information related to the vehicle operation under the particular route to be optimized. The output information from the simulator is used as an input to the optimization algorithm. The simulator was validated using real data from a battery electric vehicle. The EDAS system was tested for three different driving profiles and energy consumption reductions of up to 30.33% were achieved.

In the next 20 years fully autonomous vehicles are expected to be in the market. The advance on their development is creating paradigm shifts on different automotive related research areas. Vehicle interiors design and human vehicle interaction are evolving to enable interaction flexibility inside the cars. However, most of today’s vehicle manufacturers’ autonomous car concepts maintain the steering wheel as a control element. While this approach allows the driver to take over the vehicle route if needed, it causes a constraint in the previously mentioned interaction flexibility. Other approaches, such as the one proposed by Google, enable interaction flexibility by removing the steering wheel and accelerator and brake pedals. However, this prevents the users to take control over the vehicle route if needed, not allowing them to make on-route spontaneous decisions, such as stopping at a specific point of interest.

This paper presents the design, implementation and validation of a forward simulator for a battery electric bus, developed in MATLAB/Simulink. This simulator allows performing energy consumption analyses for different bus routes. In addition, a user can modify some parameters that affect the powertrain operation to understand their influence in the energy consumption of the bus. These analyses allow the electric bus manufacturers to adapt their powertrain designs and control strategies for different transit agencies with different routes and energy requirements. The simulator was validated using real data from a battery electric bus. The results showed a good correlation between the real and the simulated data. In particular, the absolute error between the real and the simulated State of Charge (SOC), which is one of the most important parameters for this kind of vehicles, was 3.24%.

The current business model of the automotive industry is based on individual car ownership, yet new ridesharing companies such as Uber and Lyft are well capitalized to invest in large, commercially operated, on-demand mobility service vehicle fleets. Car manufacturers like Tesla want to incorporate personal car owners into part-time fleet operation by utilizing the company’s fleet service. These robotaxi fleets can be operated profitably when the technology works in a reliable manner and regulators allow driverless operation. Although Mobility-as-a-Service (MaaS) models of private and commercial vehicle fleets can complement public transportation models, they may contribute to lower public transportation ridership and thus higher subsidies per ride. This can lead to inefficiencies in the utilization of existing public transportation infrastructure.

Quantum computing is considered the “next big thing” when it comes to solving computational problems impossible to tackle using conventional computers. However, a major concern is that quantum computers could be used to crack current cryptographic schemes designed to withstand traditional cyberattacks. This threat also impacts future automated vehicles as they become embedded in a vehicle-to-everything (V2X) ecosystem. In this scenario, encrypted data is transmitted between a complex network of cloud-based data servers, vehicle-based data servers, and vehicle sensors and controllers. While the vehicle hardware ages, the software enabling V2X interactions will be updated multiple times. It is essential to make the V2X ecosystem quantum-safe through use of “post-quantum cryptography” as well other applicable quantum technologies.