This paper reviews recent developments in the design optimization of electrical drive systems for electric vehicles (EVs) and proposes a pathway to develop next-generation AI design platforms that integrate system-level optimization methods and digital twins. First, a comprehensive review is presented to five design optimization models for EV motors, including multiphysics, multiobjective, multimode, robust, and topology optimization, as well as six efficient optimization strategies, such as multilevel optimization and AI-based approaches. Several recommendations on the practical application of these optimization strategies are also presented. Second, representative optimization methods for power converters and control systems of EV drives are summarized. Third, application-oriented and robust system-level design optimization strategies for EV drive systems are discussed. Finally, two proposals are presented and discussed for the design of next-generation EV drive systems and their integration with battery management systems. They are AI-powered automatic design optimization platforms that integrate large language models and a digital-twin-assisted system-level optimization framework. Two case studies on in-wheel motors and drive systems are also included to demonstrate the performance and effectiveness of various optimization methods.

This paper reviews recent work on dynamic virtual power plants (DVPPs) using an Energy–Information–Market framework. It addresses the important problem of how DVPPs can support low-inertia power system operation and feeder-level stability under high renewable penetration. First, system-level studies on low-inertia operation and frequency control are used to frame quantitative requirements on rate of change of frequency, nadir, and quasi-steady-state limits. Second, energy-layer models are surveyed, including participation-factor-based DVPP controllers, grid-forming architectures, model-free frequency regulation, and robust frequency-constrained scheduling for allocating virtual inertia and fast frequency response (FFR) across distributed energy resource fleets. Third, information-layer and market-layer models are reviewed, covering stochastic and robust bidding, distribution locational marginal price-based clearing, peer-to-peer and community markets, privacy-preserving coordination, and emerging governance and cybersecurity schemes for DVPP participation. Across these strands, much of the literature remains centred on steady-state active and reactive power dispatch, with dynamic security enforced as constraints rather than formulated as verifiable and tradable services. This review identifies gaps in dynamic metrics and benchmarks, forecasting of available inertia and FFR capacity, market-physics co-design, multi-aggregator interaction, and experimentally validated DVPP implementations. These findings suggest that DVPPs can “sell stability” at the feeder level only through co-designed control, information, and market mechanisms and outline a research roadmap for this purpose.