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Whitepaper

Enhancing EV battery safety and reliability with predictive analytics

AI-enabled analytics let you analyze, monitor, and forecast battery performance across vehicles or fleets, adding an extra layer of safety by identifying issues other systems miss and providing centralized access to all relevant battery insights.

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Key challenges addressed:

  • Reliability issues: Downtime and unexpected breakdowns leading to increased maintenance costs and operational disruptions.
  • Safety concerns: Risks associated with battery fires and thermal propagation.
  • System integration: Lack of interconnectedness among battery management, quality management, and fleet management systems.
  • Battery aging: Issues like dendrite growth, lithium plating, and temperature/voltage spreads affecting battery performance over time.

How battery analytics solves these problems:

Predictive maintenance:

  • Anticipate and address issues before they lead to failures.
  • Plan maintenance based on real-time conditions rather than predefined schedules, saving costs.

‍Enhanced monitoring:‍

  • Continuous oversight of safety and reliability trends across entire fleets.
  • Detailed alerts and notifications for timely action.‍

Data-driven insights:‍

  • Utilize historical and real-time data to build comprehensive profiles of battery health.
  • Identify and rectify underlying issues not detectable by conventional BMS.‍

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