Rising EV and AI Loads Bring Connectors Into Early Design Decisions

· Source: Big Data & AI News - EE Times · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Robotics & Autonomous Systems · Depth: Intermediate, medium

Summary

Amphenol executives Suraj Shah and Vinesh Kaladharan report a significant shift in product design, with OEMs now considering connector systems in the early stages of vehicle and electronics architecture development due to rising demand for advanced interconnect solutions. This change is driven by software-driven systems, increased current requirements, and decentralized architectures in EVs, industrial systems, and hyperscale data centers. Amphenol, which reported $23 billion in revenue last year and $7.6 billion in Q1 2026 sales, is expanding its global and Indian operations, including R&D and manufacturing facilities. The company is developing connectors for higher power densities, such as 5,000 amperes for AI data centers, and addressing thermal management and increased mating cycles (up to 10,000) for durability in demanding applications like commercial EVs.

Key takeaway

For CTOs and VPs of Engineering designing next-generation EV or AI infrastructure, prioritize connector system integration early in the design process. The shift to higher power densities (400V-800V), decentralized architectures, and increased durability requirements means that late-stage connector decisions will introduce significant design constraints, thermal challenges, and potential reliability issues. Ensure your teams are evaluating advanced interconnect solutions capable of handling 5,000+ ampere loads and 10,000 mating cycles.

Key insights

Early integration of advanced connector design is critical for managing rising power, thermal, and durability demands in EVs and AI infrastructure.

Principles

Method

Amphenol's approach involves designing connectors for higher current loads (up to 5,000A), improved thermal management, and increased mating cycles (50 to 10,000) to meet evolving EV and AI infrastructure demands.

In practice

Topics

Best for: CTO, VP of Engineering/Data, AI Hardware Engineer, AI Architect, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Big Data & AI News - EE Times.