For OEMs developing electric vehicles and equipment, success depends on more than battery capacity or component specifications. It depends on accurately understanding how platforms are used in the real world. When duty cycles are misunderstood, equipment can fall short of performance expectations, battery life can suffer, and total cost of ownership (TCO) can increase.
As telematics systems generate increasing volumes of operational data, OEMs have new opportunities to better understand real-world duty cycles and make more informed engineering decisions. In this blog, we explore how duty cycle analysis and telematics data help establish design requirements, the challenges of managing and integrating operational data, and how AI-driven analytics can transform that data into actionable insights that optimize performance, reduce costs, and accelerate electrification.
OEMs cannot design optimal vehicles and equipment without understanding the real-world duty cycles and environments that operators will put those platforms into. Parameters include the length of operation, thermal regulation, water and dust hazards, idling, auxiliary systems, charge time requirements and many more, which affect every engineering decision.
Consider electric construction equipment expected to be used for a full, eight-hour day. If engineers don’t account for a duty cycle (that includes idling, auxiliary systems, and attachments that rely on power take-off), operators will likely find the battery fully depleted before their shift ends, and fleet managers will notice downtime accumulating. Further, if batteries repeatedly undergo deep discharges, their lifespan will shorten, and maintenance or replacement costs will rise.
While these types of situations can be common, developing the solutions to actually address them requires data.
Unfortunately, lab environments and initial engineering discussions don’t reflect the harsh usage and environments in the real world. The only way to fully understand the duty cycle for a given application is to collect and analyze data from field deployments. The best source for this is onboard telematics data, which provides real-time information about a vehicle in operation. With telematics data, OEMs and fleet managers can monitor metrics for charging practices, thermal regulation, idle periods, component stress over time, and more.
Electric vehicles (EVs) are uniquely suited to facilitating onboard data collection, with capabilities that surpass internal combustion engines (ICEs). Electrified platforms inherently leverage more sensors and offer more connectivity and integration opportunities.
Telematics data now provides OEMs and fleet managers with more information than ever before. However, challenges remain in leveraging this data to achieve outcomes such as predictive fleet maintenance.
Onboard data transfer heavily relies on integrations because the different systems and components need to share a communication protocol to send or receive information, such as CAN bus. These integrations become more complicated if OEMs source the components from different suppliers, as interconnectivity may be lacking.
Even after optimizing onboard integrations for data collection, communication, and transfer, an OEM or fleet manager’s broader tech environment may be insufficient to transform that data into actionable insights. Some operations now retain massive ‘data lakes’, but they don’t have the capabilities to sift through and analyze it all.
Furthermore, OEM partners may provide their own dashboards for viewing telematics data, but the lack of a unified dashboard only complicates management and interpretation. End users increasingly ask OEMs and other vendors whether their systems communicate with the dashboard of another OEM or partner.
OEMs and fleet managers will need to leverage advancing AI capabilities to mine data and surface actionable insights from their ‘data lakes’. AI transforms onboard telematics into real-time guidance and intervention.
For example, a battery management system (BMS) will monitor voltage, current, and temperature and may be programmed to take specific, conditional actions to prevent outcomes such as overheating. A BMS is capable of real-time safety interventions, self-balancing cells, or predictive fault detection. But if a BMS is integrated with AI-driven analytics in real time, fleets will be able to flag their own predictive maintenance and optimize charging schedules to reduce energy costs through peak shaving strategies.
AI-driven analysis of telematics data will inform fleet operations, such as optimizing equipment utilization and resource allocation. Fleet-wide benchmarking and similar reports become accessible to managers and operators, helping them monitor hardware degradation, determine ideal replacement cycles and perform other predictive maintenance tasks.
One strategy these capabilities would support is vehicle or equipment rotation if usage patterns differ across operations. High- and low-use areas can swap vehicles and equipment to extend lifespans. Once that equipment’s usage has balanced out across the groups, fleet managers can reconsider motive power use or evaluate second-life strategies—directly lowering the operations’ average TCO.
AI-driven analysis would also allow OEMs and fleet managers to investigate data discrepancies that can occur between different systems or components as operations progress. A battery and charger could register different charge amounts, and analyzing the data could help explain why and whether one or the other is faulty.
When OEMs determine which integrated systems and individual components to install on an electric platform, they sometimes make pricing decisions that only seem beneficial. Perhaps OEMs can reduce their own costs by choosing a cheaper battery; but by selecting a battery with better lifespan and reliability, OEMs could reduce fleet managers’ total cost of ownership and incentivize more or repeated purchases.
Similarly, OEMs should prioritize platforms purpose-built for their specific applications. Only choose systems and components that exhibit top-of-the-line specifications (and higher prices) if they lower TCO over the vehicle or equipment’s lifespan. Overspeccing might seem harmless, but complications can arise if manufacturing costs are too high, particularly at scale.
As economic factors increasingly motivate fleet managers to consider electrification, they’re more likely to commit to upfront investments if the result is lower TCO.
For OEMs to thoroughly understand the duty cycles of the platforms they’re engineering, they must leverage real-world data. However, data collection and analysis can prove challenging, depending on system integrations, data transfers, and whether AI-assisted capabilities can surface actionable insights. If OEMs implement or improve these capabilities, they’ll be able to more easily optimize design and manufacturing processes to deliver market-leading performance at the lowest possible TCO.
About the Author
Lucia Salcido is Senior Product Manager at Flux Power, where she leads product strategy for the company's LiFT Pack battery systems and SkyEMS energy management platform. This article was developed in connection with Zapi Group's Future of Electrification (FOE) panel series, which brings together OEMs, component suppliers, and industry engineers to address the technical and strategic challenges of vehicle and equipment electrification.