Monolith, artificial intelligence (AI) software provider to the world’s most innovative engineering teams, today announces the launch of its newly commissioned Forrester Consulting 2024 study titled AI for EV Battery Validation.
The study reveals that nearly two thirds of automotive leaders expect the potential impact of AI to be extremely or very significant with over half indicating that Engineering AI (EngAI) – a sensible form of AI that learns from masses of engineering data to help test teams understand otherwise intractable problems – to be crucial to staying competitive in electric-vehicle (EV) battery development.
The new study surveyed 165 senior decision-makers in automotive engineering in North America and major European automotive markets, exploring their views on the application of EngAI in the development of EV batteries. In an industry increasingly dominated by balancing the seemingly conflicting goals of faster time to market and maintaining high product quality, the study reveals first-hand insights into the pressures that automotive engineering players are facing in the race to develop industry-leading vehicles, and where intelligent technologies such as AI can address these urgent challenges to accelerate innovation.
Dr. Richard Ahlfeld, CEO and Founder of Monolith, said: “EV and particularly battery development is highly competitive, and with that comes a lot of pressure to move faster. Engineering AI can learn to solve problems much faster than any human, and that’s what automotive leaders are starting to understand.
“Of course there’s uncertainty and misunderstanding around AI, but if you have to squeeze what previously took five years into three, engineers need to make the most of the new tools available to them. AI built specifically for engineering offers an intelligent, cost-effective solution for leaders in the automotive industry to gain a competitive edge, faster.”
Reflecting the industry’s emphasis on introducing competitive, sustainable products to market in the quickest time possible, the study spotlights how 64% of automotive engineering leaders stress the requirement to reduce the time and effort spent on EV battery validation. In the same vein, two out of three believe it’s imperative to reduce dependency on physical tests, while still ensuring compliance with safety and quality standards.
In spite of this urgent need, 66% of senior decision-makers agree that it is imperative to reduce reliance on physical test while still ensuring compliance with safety and standards, with 62% agreeing that their current virtual validation tools, including physical simulation, do not fully ensure that battery designs meet all validation criteria.
The influence that EngAI is increasingly having in the automotive industry has driven more focus on the technology among automotive engineering leaders. While 44% of respondents express serious concern about the potential effect that the technology could have on their business’ staff-count, over half (58%) have declared AI to be critical in ensuring they stay competitive in EV battery development.
The automotive industry has seen unpredictable levels of demand for EVs in recent times, compounded by broader macro-economic circumstances. Commercial pressures felt in these conditions lead senior engineering decision-makers to seek smart solutions for reducing costs and development time – and EngAI is expected to make waves in this respect.
Respondents expect EngAI to cut years, quarters or months in development cycles – including in cell characterisation testing (61%), module and pack testing (56%) regulatory testing (53%) and charging optimisation testing (48%). Meanwhile, they anticipate AI will help them achieve cost savings from $10 million to over $100 million in ageing and lifetime battery testing (37%), repeating tests due to failures (39%), thermal runaway testing (36%), and regulatory testing (32%).
Monolith is democratising AI for engineering with its bespoke SaaS platform that uses no-code, machine-learning software, giving domain experts the power to leverage existing, valuable testing datasets for their product development. The platform analyses and learns from this information, using it to generate accurate, reliable predictions that enable engineering teams to reduce costly, time-intensive prototype testing programmes. Integrating highly effective innovations such as a ‘Next Test Recommender’ tool and the industry’s first AI-powered ‘Anomaly Detector’ functionality, Monolith provides engineers with intelligent solutions to develop higher-quality products in half the time.
SOURCE: Monolith
