IN Brief:
- Jungheinrich is working with Monolith to speed battery evaluation for future electric material handling equipment.
- The collaboration uses machine learning models trained on real test data to predict battery performance earlier in development.
- Faster battery qualification is becoming central to electric forklift development as product ranges expand and duty requirements increase.
Jungheinrich is working with Monolith to accelerate battery evaluation for its next generation of electric material handling equipment, using AI models trained on real-world test data to predict battery performance earlier in the development cycle.
The collaboration is designed to help Jungheinrich engineers assess how battery technologies will behave as they are integrated into new vehicle platforms, reducing the need for lengthy and repetitive physical test campaigns. Monolith’s software will ingest data generated from Jungheinrich’s battery testing activity, train predictive machine learning models, validate the outputs, and help identify the next experiments most likely to improve qualification and product decisions.
Battery assessment has become one of the most complex parts of industrial truck development. Performance is no longer judged simply by runtime or charge speed in isolation. Engineers are balancing thermal behaviour, lifecycle durability, power delivery under different duty cycles, charging patterns, packaging, safety, and compatibility across a widening range of trucks. As manufacturers push further into electrification, battery qualification starts to determine how quickly new models can move from concept to commercial release.
That makes the Monolith partnership a useful signal of where engineering practice is heading. AI in industrial development is not replacing physical testing, but it is increasingly being used to narrow the test space and get to useful answers sooner. When test data can be turned into predictive models early, engineering teams can make faster decisions about chemistry, pack behaviour, system integration, and design trade-offs without waiting for every question to be resolved through full physical cycles.
Jungheinrich already has a broad electric materials handling portfolio and has been pushing harder into lithium-ion and higher-capacity electric truck development. The company’s newer electric counterbalance ranges and heavy-duty concepts reflect a wider shift in the sector, where electrification is moving well beyond lighter warehouse duty and into applications that once defaulted almost automatically to internal combustion. That raises the stakes for battery development. The better the modelling, the faster manufacturers can close the gap between customer expectations and real-world truck performance.
The commercial context is just as important as the technical one. Product development timelines are under pressure, while customers expect more capable electric fleets, higher uptime, and clearer total-cost-of-ownership arguments. Slower battery qualification can hold back entire product families, particularly when manufacturers are trying to scale across multiple truck classes at once. Reducing prototype demand and focusing test resources more intelligently can therefore affect far more than the engineering department. It shapes how quickly new equipment reaches warehouses, factories, and distribution operations.
That is why this collaboration deserves attention. It reflects a wider move across manufacturing toward data-driven engineering, but it also highlights a very specific constraint in materials handling. The race to electrify forklifts and industrial trucks will not be won by ambition alone. It will be won by how quickly manufacturers can validate batteries, integrate them confidently, and turn development insight into trucks that perform consistently in the field.
