• Energy Research

Reliability and performance of cyber-physical systems (CPS) in electric vehicles (EVs) are influenced by three design aspects: (i) controller design, (ii) battery usage, i.e., Battery rate capacity and aging effects, (iii) processor aging of the in-vehicle embedded platform. In this paper, we present a two-phase design optimization framework involving control algorithm development, battery modeling and processor aging analysis. First, before processor aging, a Pareto front between quality of control (QoC) and battery usage is obtained. Second, as the processor ages, the controller is re-optimized considering the prolonged sampling period to avoid QoC deterioration of safety-critical applications with a compromise on battery usage and comfort-related applications. An outlook following this line of research on reliable CPS design in EVs is discussed, that we believe, will be pursued by researchers in the CPS community.

Abstract This paper presents a new approach to heat exchanger network (HEN) design making extensive use of randomization techniques. It is exceedingly simple to implement and gives new insight into the hardness and the cost landscape underlying a given problem. At the same time, the results from our algorithm may be used as good initial solutions required by most non-linear optimization problem formulations of HEN design. Practical networks involve trade-off between a number of factors, all of which are difficult to incorporate in a single design methodology. Our approach is blind to any design heuristic and generates a sufficiently large number of networks that can be further evaluated to pick up the most suitable network depending on specific design requirements. However, the current version of the algorithm is limited to HEN synthesis problems that can be solved without stream splitting. We have experimented with the three standard literature problems and obtained results that compare well with the previously published results, which justify further research in this direction.

Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.