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There is a rapidly growing interest in the use of cloud computing for automotive vehicles to facilitate computation and data intensive tasks. Efficient utilization of on-demand cloud resources holds a significant potential to improve future vehicle safety, comfort, and fuel economy. In the meanwhile, issues like cyber security and resource allocation pose great challenges. In this paper, we treat the resource allocation problem for cloud-based automotive systems. Both private and public cloud paradigms are considered where a private cloud provides an internal, company-owned internet service dedicated to its own vehicles while a public cloud serves all subscribed vehicles. This paper establishes comprehensive models of cloud resource provisioning for both private and public cloud-based automotive systems. Complications such as stochastic communication delays and task deadlines are explicitly considered. In particular, a centralized resource provisioning model is developed for private cloud and chance constrained optimization is exploited to utilize the cloud resources for best Quality of Services. On the other hand, a decentralized auction-based model is developed for public cloud and reinforcement learning is employed to obtain an optimal bidding policy for a “selfish” agent. Numerical examples are presented to illustrate the effectiveness of the developed techniques. [ABSTRACT FROM AUTHOR]

In order to fulfill the energy and power demand of battery electric vehicles, a hybrid battery system with a high-energy and a high-power battery pack can be implemented as the energy source. This paper explores a cloud-based multi-objective energy management strategy for the hybrid architecture with a deep deterministic policy gradient, which increases the electrical and thermal safety, and meanwhile minimizes the system's energy loss and aging cost. In order to simulate the electro-thermal dynamics and aging behaviors of the batteries, models are built for both high-energy and high-power cells based on the characterization and aging tests. A cloud-based training approach is proposed for energy management with real-world vehicle data collected from various road conditions. Results show the improvement of electrical and thermal safety, as well as the reduction of energy loss and aging cost of the whole system with the proposed strategy based on the collected real-world driving data. Furthermore, processor-in-the-loop tests verify that the proposed strategy can achieve a much higher convergence rate and a better performance in terms of the minimization of both energy loss and aging cost compared with state-of-the-art learning-based strategies. [Display omitted] • Cloud-based health-conscious energy management for hybrid battery systems. • Power-split considering electro-thermal safety, energy efficiency and aging cost. • DDPG-based strategy trained and validated with real-world vehicle data. • Processor-in-the-loop tests with a machine learning-capable embedded device. • Outperforming other approaches in training efficiency and system performance. [ABSTRACT FROM AUTHOR]