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1. 4x4 Hybrid Electric Vehicle vs. Fully Electric Vehicle Mobility in Drastically Changing Terrain Conditions

   Jesse Paldan, Vladimir Vantsevich (July 2020, Proceedings of the 3rd International Conference of IFToMM Italy)

   null (Ed.)

   Active driveline technologies allow vehicles to dynamically control power distribution among driving wheels to improve vehicle operational parameters that impact terrain mobility. Two 4x4 electrified drivelines are compared which provide a variable power split: a hybrid electric vehicle with a controllable power transmitting unit and a fully electric vehicle (FEV) with individual electric wheel drives. The individual e-drives have the potential to improve mobility when the left and right wheel terrain conditions are drastically different. 

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2. Autonomous Mobility Improvements of Hybrid Electric 4x4 with Controllable Power Transmitting Unit

   Vantsevich, Vladimir; Paldan, Jesse (July 2020, Proceedings of the 2020 USCToMM Symposium on Mechanical Systems and Robotics)

   null (Ed.)

   Bringing vehicle autonomy to the level of its driveline system means that the autonomous vehicle has the capability to autonomously control the distribution of power between its driving wheels. A vehicle can therefore improve mobility by autonomously redistributing wheel power. For this implementation, vehicle mobility must first be quantified by suitable mobility indices, derived from vehicle dynamics, to numerically show a wheel or vehicle is close to immobilization as well as evaluate the effect of mobility improvements on the vehicle velocity. A velocity-based mobility index combines wheel traction with velocity to maximize effectiveness of movement. Computer simulations demonstrate the potential to improve velocity by optimizing vehicle mobility of a 4x4 vehicle with a hybrid electric power transmitting unit. 

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3. Energy-Aware Resource Management in Vehicular Edge Computing Systems

   https://doi.org/10.1109/IC2E48712.2020.00012  

   Bahreini, Tayebeh; Brocanelli, Marco; Grosu, Daniel (April 2020, Proc. of the IEEE International Conference on Cloud Engineering (IC2E 2020))

   The low-latency requirements of connected electric vehicles and their increasing computing needs have led to the necessity to move computational nodes from the cloud data centers to edge nodes such as road-side units (RSU). However, offloading the workload of all the vehicles to RSUs may not scale well to an increasing number of vehicles and workloads. To solve this problem, computing nodes can be installed directly on the smart vehicles, so that each vehicle can execute the heavy workload locally, thus forming a vehicular edge computing system. On the other hand, these computational nodes may drain a considerable amount of energy in electric vehicles. It is therefore important to manage the resources of connected electric vehicles to minimize their energy consumption. In this paper, we propose an algorithm that manages the computing nodes of connected electric vehicles for minimized energy consumption. The algorithm achieves energy savings for connected electric vehicles by exploiting the discrete settings of computational power for various performance levels. We evaluate the proposed algorithm and show that it considerably reduces the vehicles' computational energy consumption compared to state-of-the-art baselines. Specifically, our algorithm achieves 15-85% energy savings compared to a baseline that executes workload locally and an average of 51% energy savings compared to a baseline that offloads vehicles' workloads only to RSUs. 

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4. A Vehicle Coordination and Charge Scheduling Algorithm for Electric Autonomous Mobility-on-Demand Systems

   https://doi.org/10.23919/ACC45564.2020.9147734  

   Boewing, Felix; Schiffer, Maximilian; Salazar, Mauro; Pavone, Marco (July 2020, 2020 American Control Conference (ACC))

   null (Ed.)

   This paper presents an algorithmic framework to optimize the operation of an Autonomous Mobility-on-Demand system whereby a centrally controlled fleet of electric self-driving vehicles provides on-demand mobility. In particular, we first present a mixed-integer linear program that captures the joint vehicle coordination and charge scheduling problem, accounting for the battery level of the single vehicles and the energy availability in the power grid. Second, we devise a heuristic algorithm to compute near-optimal solutions in polynomial time. Finally, we apply our algorithm to realistic case studies for Newport Beach, CA. Our results validate the near optimality of our method with respect to the global optimum, whilst suggesting that through vehicle-to-grid operation we can enable a 100% penetration of renewable energy sources and still provide a high-quality mobility service. 

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5. A Vehicle Coordination and Charge Scheduling Algorithm for Electric Autonomous Mobility-on-Demand Systems

   Boewing, Felix; Schiffer, Maximilian; Salazar, Mauro; Pavone, Marco (March 2020, Proceedings of the American Control Conference)

   null (Ed.)

   This paper presents an algorithmic framework to optimize the operation of an Autonomous Mobility-on-Demand system whereby a centrally controlled fleet of electric self-driving vehicles provides on-demand mobility. In particular, we first present a mixed-integer linear program that captures the joint vehicle coordination and charge scheduling problem, accounting for the battery level of the single vehicles and the energy availability in the power grid. Second, we devise a heuristic algorithm to compute near-optimal solutions in polynomial time. Finally, we apply our algorithm to realistic case studies for Newport Beach, CA. Our results validate the near optimality of our method with respect to the global optimum, whilst suggesting that through vehicle-to-grid operation we can enable a 100% penetration of renewable energy sources and still provide a high-quality mobility service. 

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