Artificial intelligence (AI) can transform health care practices with its increasing ability to translate the uncertainty and complexity in data into actionable—though imperfect—clinical decisions or suggestions. In the evolving relationship between humans and AI, trust is the one mechanism that shapes clinicians’ use and adoption of AI. Trust is a psychological mechanism to deal with the uncertainty between what is known and unknown. Several research studies have highlighted the need for improving AI-based systems and enhancing their capabilities to help clinicians. However, assessing the magnitude and impact of human trust on AI technology demands substantial attention. Will a clinician trust an AI-based system? What are the factors that influence human trust in AI? Can trust in AI be optimized to improve decision-making processes? In this paper, we focus on clinicians as the primary users of AI systems in health care and present factors shaping trust between clinicians and AI. We highlight critical challenges related to trust that should be considered during the development of any AI system for clinical use.

Several hybrid-electric vehicle architectures have been commercialized to serve different categories of vehicles and driving conditions. Such architectures can be optimally controlled by switching among driving modes, namely, the power distribution schemes in their planetary gear (PG) transmissions, in order to operate the vehicle in the most efficient regions of engine and motor maps. This paper proposes a systematic way to identify the optimal architecture for a given vehicle drive cycle, rather than parametrically optimizing one or more pre-selected architectures. An automatic generator of feasible driving modes for a given number of PGs is developed. For a powertrain consisting of one engine, two motors and two PGs, this generator results in 1116 modes. A heuristic search is then proposed to find a near-optimal pair of modes for a given driving cycle and vehicle specification. In a study this process identifies a dual-mode architecture with an 8% improvement in fuel economy compared to a commercially available architecture over a standard drive cycle.

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Effective electrification of automotive vehicles requires designing the powertrain's configuration along with sizing its components for a particular vehicle type. Employing planetary gear (PG) systems in hybrid electric vehicle (HEV) powertrain architectures allows various architecture alternatives to be explored, including single-mode architectures that are based on a fixed configuration and multimode architectures that allow switching power flow configuration during vehicle operation. Previous studies have addressed the configuration and sizing problems separately. However, the two problems are coupled and must be optimized together to achieve system optimality. An all-in-one (AIO) system solution approach to the combined problem is not viable due to the high complexity of the resulting optimization problem. This paper presents a partitioning and coordination strategy based on analytical target cascading (ATC) for simultaneous design of powertrain configuration and sizing for given vehicle applications. The capability of the proposed design framework is demonstrated by designing powertrains with one and two PGs for a midsize passenger vehicle.

Existing hybrid powertrain architectures, i.e., the connections from engine and motors to the vehicle output shaft, are designed for particular vehicle applications, e.g., passenger cars or city buses, to achieve good fuel economy. For effective electrification of new applications (e.g., heavy-duty trucks or racing cars), new architectures may need to be identified to accommodate the particular vehicle specifications and drive cycles. The exploration of feasible architectures is combinatorial in nature and is conventionally based on human intuition. We propose a mathematically rigorous algorithm to enumerate all feasible powertrain architectures, therefore enabling automated optimal powertrain design. The proposed method is general enough to account for single and multimode architectures as well as different number of planetary gears (PGs) and powertrain components. We demonstrate through case studies that our method can generate the complete sets of feasible designs, including the ones available in the market and in patents.