Peaxy Lifecycle Intelligence for batteries is a highly scalable data analytics platform that securely captures, parameterizes and stores the entire data value chain of your serialized battery assets. This central source of truth in turn feeds AI algorithms that drive cost savings, performance optimizations and faster R&D cycles.
The scalable cloud infrastructure of PLI for batteries supports any number of R&D, manufacturing and field operations around the world.
By collecting ambient data from field deployments, PLI for batteries gains predictive precision that allows entirely new data monetization streams for manufacturers, systems integrators and operators alike.
PLI for batteries allows any accredited user across the enterprise to make data-empowered decisions — from the CEO needing top-line cost trends, to the chemical engineer tweaking an electrolyte recipe, to the field technician seeking to minimize downtime.
PLI for batteries serves a wide range of industry use cases:
PLI for batteries lets your R&D department design sophisticated experiments that test new materials, processes and operating regimes, with AI tools that identify factors for success and failure. When manufacturing, easily schedule batch production runs and assign them to customers. Digital travelers capture and parameterize the entire assembly journey, securing the provenance of every BoM component.
Full part traceability lets AI quickly isolate the root causes of underperforming batteries in the field. By matching hyperlocal ambient operating conditions to the granular capture of usage patterns, PLI for batteries allows opportunities to offer extended lifecycle warranties, while monitoring compliance to contractual bounds. Further monetization opportunities include the selling of incremental capacity upgrades or energy storage as a service.
PLI for batteries provides robust data monitoring, visualization and reporting tools, with additional views suitable for remote operation centers. A unique degradation curve for each battery is calculated from usage patterns and ambient conditions, creating a digital twin of each battery for predictive analytics and preventative maintenance. A service manager schedules maintenance events to minimize system disruption.
Deploy digital twins and machine learning algorithms to predict and model battery behavior. Use predefined (and your own) algorithms trained against real-time operational data to derive performance indicators, identify patterns of inefficiency, detect anomalies and make decisions with minimal user intervention.
Create and dynamically update degradation curves against individually deployed batteries to provide a precise residual value. Track degradation in real time for every deployed asset, giving you a high confidence interval on the health of the battery within a leasing program.
With a degradation curve unique to each deployed battery, know precisely when to replace batteries before they impact system performance. The Service Manager can schedule maintenance events to minimize system disruption.
Systems integrators and operators can struggle to extract succinct performance indicators from batteries across a lifecycle counted in thousands of cycles. PLI for batteries calculates composite scores from raw datasets to drive design improvements, manufacturing quality and efficiency, and warranty compliance in the field.
With complete insight into historical usage patterns and operating conditions, identify opportunities to optimize the performance of deployed batteries, and sell this incremental capacity to operators. Or transition to a leasing arrangement, and sell energy storage as a service.
Asset Creator for Manufacturing lets you commission batteries and allocate the necessary resources across production lines as customer orders arrive. Easily integrate with existing ERP and CRM solutions.
Digital Traveler for Manufacturing lets you prescribe and capture complex workflows for each serialized asset in the build process, across automated, semi-automated and manual stations. Scan vendor lots as they enter the workflow for complete traceability.
Given an energy storage need and a location, automatically fetch climate data and any potential tax credits to instantly calculate the size and cost of the required battery deployment. If integrating with renewables, simulate before-and-after scenarios to estimate lifetime net benefits.
Digital Traveler for R&D eliminates paper entirely from the battery build process to instantly parameterize data at each stage of assembly, enabling root-cause analysis of performance variations during testing.
Data pipeline integrations for multiple cycler brands (Neware, Arbin, Maccor, Bio-Logic) generate validated time series and cyclic statistics. Battery test data can be grouped, analyzed and visualized across any common attribute. AI-powered anomaly detection quickly identifies those attributes linked to success and to failure.
Experiment Creator for R&D lets you define experiments with varied battery populations to test new materials, manufacturing processes or operating regimes.