Jungheinrich uses predictive AI models from software company Monolith to improve the batteries of its next generation of electric forklifts. This allows the manufacturer to accurately estimate battery performance at an early stage and accelerate development processes.
The approach relies on analyzing early battery test data. Using machine learning models, critical performance indicators are predicted before extensive physical testing campaigns are required. This allows engineers to make technical decisions faster and reduce the number of costly and time-consuming tests.
According to Jungheinrich, battery development is becoming increasingly complex due to the rapid evolution of technologies and integration into new truck platforms. Monolith’s AI software helps make this complexity manageable by converting large amounts of test data into actionable insights. At the same time, the platform provides a central environment in which engineering teams can securely share data, models and recommendations.
The use of AI in engineering is rapidly gaining importance. Research from McKinsey shows that data-driven AI methods can accelerate R&D processes by 20% to 80%. For Jungheinrich, this not only means faster innovation, but also lower development costs and a more efficient path to more sustainable electric vehicles.
