AI-augmented battery management with cell-level predictive diagnostics, real-time SOH/SOC estimation, and cloud fleet analytics — reducing warranty costs and extending battery lifespan across electric vehicle fleets.
Electric vehicle fleet operators — buses, delivery vans, logistics vehicles — face a compounding cost problem. Battery packs represent 30-40% of vehicle cost, and warranty claims from premature degradation consume 15-20% of fleet operating budgets. A single bus battery pack costs $80-150K to replace, and fleet operators typically manage 50-500 vehicles.
Current BMS implementations provide basic charge/discharge management and cell balancing, but lack predictive diagnostics. SOH (State of Health) estimation in most production BMS units has ±10-15% accuracy — meaning a battery reported at 80% SOH could actually be at 65% or 95%. This uncertainty forces conservative replacement schedules, wasting batteries with remaining useful life, or delayed replacement, risking in-service failures.
Fleet managers have no cross-vehicle analytics. Each vehicle's BMS operates independently, with no ability to compare degradation patterns across the fleet, identify vehicles with unusually fast degradation (indicating manufacturing defects or operational abuse), or optimize charging strategies based on fleet-wide data. The result: higher warranty costs, shorter effective battery life, and no data foundation for second-life battery decisions.
Promwad delivers a smart BMS architecture that augments standard battery management with AI-driven predictive diagnostics at the cell level and fleet-wide analytics in the cloud. The system operates as both a vehicle-level controller and a fleet intelligence platform.
The key innovation is cell-level digital twin technology. Each cell in the pack has a continuously updated model predicting its remaining useful life based on charge cycles, temperature history, impedance trends, and comparison with fleet-wide degradation curves. This enables SOH estimation with ±2-3% accuracy — a 5x improvement over standard BMS, translating directly into optimized replacement timing and warranty cost reduction.
The full BMS replacement requires vehicle integration during manufacturing or major service. However, the telematics gateway + cloud analytics component can be retrofitted non-invasively — connecting to the existing BMS via CAN bus to extract cell data without modifying the production BMS firmware. This provides fleet analytics and predictive diagnostics even with legacy BMS hardware.
The BMS platform supports LFP (lithium iron phosphate), NMC (nickel manganese cobalt), and NCA (nickel cobalt aluminum) chemistries. The AI SOH models are trained per chemistry type, with transfer learning enabling rapid adaptation to new cell suppliers within the same chemistry family.
The EU Battery Regulation (2023/1542) requires Battery Passports with state-of-health data, carbon footprint declarations, and safe decommissioning documentation. The smart BMS with cell-level digital twins provides the data infrastructure required for Battery Passport compliance — and the second-life readiness scoring directly supports the regulation's circular economy requirements.
Promwad follows ASPICE v4.0 CL2 aligned processes for automotive software development. For BMS projects targeting OEM integration, full ASPICE CL2 audit preparation is included in the Scale phase. The V-model development approach with HIL/SIL validation is maintained throughout all phases.