Parameter identification of permanent magnet synchronous machines (PMSMs) represents a well-established research area. However, parameter estimation of multiple running machines in large-scale applications has not yet been investigated. In this context, a flexible and automated approach is required to minimize complexity, costs, and human interventions without requiring machine information. This paper proposes a novel identification strategy for surface PMSMs (SPMSMs), highly suitable for large-scale systems. A novel multistep approach using measurement data at different operating conditions of the SPMSM is proposed to perform the parameter identification without requiring signal injection, extra sensors, machine information, and human interventions. Thus, the proposed method overcomes numerous issues of the existing parameter identification schemes. An IoT/cloud architecture is designed to implement the proposed multistep procedure and massively perform SPMSM parameter identifications. Finally, hardware-in-the-loop results show the effectiveness of the proposed approach.

Digital Twins (DTs) are increasingly deployed in Industry 4.0 to enable real-time monitoring, analysis, and control, yet the transition from isolated DT instances to plant-wide ecosystems across cloud and edge infrastructures introduces fragmentation and coordination challenges among heterogeneous assets, data sources, and services. This paper addresses this gap by proposing a cloud-native Digital Twin Computing Layer (DTCL) that provides a unified control and orchestration plane for composing and operating DT applications in Smart Manufacturing. The DTCL is designed as a three-tier architecture comprising a developer-facing user interface, a Deploy Engine for automated deployment and lifecycle management, and a Service Catalog of reusable, independently deployable microservices. Standardized interaction is supported through semantic DT models and API- and message-based communication mechanisms. A governance workflow, based on service discovery and validation, is introduced to support non-redundant integration and controlled evolution of services. The approach is demonstrated through a Smart Manufacturing predictive maintenance case study and further extended with a Smart Mobility scenario for urban public transport planning, highlighting the flexibility of the DTCL across different application domains. Overall, the DTCL supports modular composition, interoperability, and lifecycle governance across heterogeneous Digital Twin applications, providing a scalable foundation for both industrial and urban data-driven scenarios.