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Qnovo's battery management software leads the industry in safety, accuracy and performance for electric vehicles and fleets.

• All-weather, all-temperature fast charging in just 16 minutes and 2x faster in cold temperatures as compared to traditional charging for 800V systems (10-80% state of charge)
• Adaptive fast charging solution allows for the safe tradeoff of available cycle life for faster charging on-demand
• Improvement in battery safety by 1000x which is essential when charging faster
• Zero safety incidents supported by millions of kilometers of on-road tested passenger and fleet vehicles and across 200+ million consumer device deployments worldwide
• 98.7% accuracy in identifying batteries with health issues
• Battery cost reduction by 10-30% through vendor selection, warranty/recall risk management and the reduction of battery pack sizes

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Savings Potential

According to ABI Research, software like Qnovo’s will enable automakers to save US $18 billion in potential savings annually in 2030, equivalent to US $76 billion cumulatively from 2024.

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Emissions Reduction Potential

Our greenhouse gas emissions modeling shows that Qnovo’s software has the potential to save 30 million tonnes of CO2 annually by 2030.

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Proven Performance Across Leading Manufacturers

Qnovo works with major automotive OEMs worldwide to enhance the safety and reliability of their electric vehicles:

• Integrated testing across all major cell vendors, including CATL, LG Energy Solutions, Samsung SDI, SK On and others
• Compatibility with diverse battery chemistries, including NCM (622, 811) and LFP
• Road-tested reliability with electric vehicles
• Advanced safety monitoring proven effective in commercial fleet applications

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Our Technology Advantage

• Software-only solution requires no additional hardware or computing resources
• Seamless integration with AutoSAR or equivalent vehicle operating systems
• Compatible with both 400V and 800V architectures supporting DC fast charging 
• Supports transition to 48-V auxiliary batteries in EVs and autonomous vehicles
• Real-time diagnostics and predictive analytics enable safer and longer-lasting battery performance‍
• Modular implementation across diverse vehicle platforms with minimal engineering overhead

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_{*The above numbers are based on Qnovo’s models and data, subject to change given constraints and changes to data sets over time.}

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By optimizing battery performance in demanding commercial applications, Qnovo is significantly reducing the total cost of ownership while enhancing overall operational reliability and safety.

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Qnovo software extends battery lifespan up to 40% for commercial applications including heavy-duty trucks, buses, and electrified railway systems.

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Qnovo’s Commercial Vehicle services integrates with fleet management solutions to improve operations.

• TCO reduction by eliminating at least one replacement of battery pack
• On-vehicle implementation, optimizing and supporting multi-pack architecture
• Safety monitoring with both real-time and predictive battery management, especially in cabin-occupied scenarios while opportunity charging‍
• Fleet-wide monitoring, analytics, and optimization‍
• 20% TCO improvement in Class 8 electric trucks over 15-years of operation

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Implementation Advantages

• No additional hardware required for integration with existing BMS systems
• Seamless software implementation for single and multi-pack systems with well-defined APIs
• Compatible with all major battery vendors including CATL, LG Energy Solutions, Samsung SDI, SK On and others
• Support for diverse chemistries including LTO, LFP and NCM
• Mitigating the impact of vehicle-to-grid (V2G) on trucks, trains and bus batteries

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_{*The above numbers are based on Qnovo’s models and data, subject to change given constraints and changes to data sets over time.}

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Qnovo's industrial battery solutions provide comprehensive health and safety management for equipment operating in harsh conditions, from construction sites to mining operations.

Our software identifies faulty batteries, either from manufacturing defects or packs with damage incurred in the field.

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Key Performance Benefits

• Extended operational time with optimized battery management reducing downtime
• Uptime and asset life (health) drive total cost of ownership (TCO) reduction
• Predictive maintenance alerts preventing unexpected equipment failures
• Remote battery performance and diagnostics via near field communication or through connected cloud services
• Enhanced safety protocols for high-stress industrial environments
• 98.7% safety accuracy in identifying battery health issues and potential defects

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Industrial-Specific Advantages

• Rugged environment compatibility designed for extreme temperatures and conditions
• Equipment optimization through real-time battery health monitoring
• Maintenance cost reduction with predictive analytics and early warning systems
•  Integration flexibility with existing industrial equipment battery management systems (BMS)
•  Multi-voltage support for diverse industrial applications from 12V to 800V systems

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_{*The above numbers are based on Qnovo’s models and data, subject to change given constraints and changes to data sets over time.}

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With a prevention over suppression approach to battery incidents in BESSs, our battery management software focuses on safety.

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Our battery models continually learn from accumulated field data, enabling accurate predictions of battery health and safety while determining optimal charging rates to maximize battery life and lifespan.

• Isolate faulty cells from healthy ones in the system, protecting the overall safety of the assets
• Fire prevention capabilities include slowing or stopping charging of a compromised battery
• System-wide visibility can pinpoint the health of every battery cell within the system with precise health assessment ranking and monthly audits
• 98.7% safety accuracy in identifying battery health issues and potential defects
• Reducing total cost of ownership (TCO) by up to 20%
• Real-time diagnostics of battery electrochemistry and intrinsic material properties with 10-12 metrics per series element/cell

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BESS-Specific Benefits

• True ROI understanding for project owners and financiers with comprehensive remaining useful life (RUL) of the asset
• System-wide optimization extending the lifespan of the entire energy storage system by pinpoint which cells to replace proactively, reducing truck rolls
• Accurate depreciation calculation with remote monitoring data
• Second-life enablement for batteries from EV applications, reducing deployment roadblocks
• Longevity confidence with precise health assessment of every battery regardless of age

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Safety Focus

Safety is critical with $3.6+ billion in estimated losses from 72 BESS facilities reporting fires over a 6-year period. There is a potential $45+ billion in losses by 2030 as BESS capacity expands by 10x.

• Proactive detection of battery anomalies that may lead to fires before they occur
• Predictive maintenance indicators enabling scheduled service before failures
• Prevention over suppression approach to battery safety

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Implementation Advantages

• Flexible software deployments on-site as well as remote cloud
• No additional hardware required for integration with existing BMS systems
• Compatible with all major battery vendors including CATL, LG Energy Solutions, Samsung SDI, SK On and others
• Support for diverse chemistries including NCM, NCA, and LFP

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_{*The above numbers are based on Qnovo’s models and data, subject to change given constraints and changes to data sets over time.}

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Creating safe, cost-efficient and performance-driven fleet operations through advanced battery diagnostics solution. Identifying faulty batteries for health and safety visibility before costly operational interruptions and downtime.

• Total Cost of Ownership (TCO) transparency through comprehensive battery data
• Safety monitoring with both real-time and predictive battery management, especially in cabin-occupied scenarios while opportunity charging
• Predictive and real-time battery performance, maintenance and safety warnings preventing costly downtime
• Additional 20-50 hours of uptime per year / per vehicle with optimized battery management‍
• 20% TCO improvement in Class 8 electric trucks over 15-years of operation

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Fleet-Specific Benefits

• Asset optimization through fleet-wide battery State of Health (SOH) monitoring
• Battery defect detection with 98.7% accuracy rate for identifying potential issues
• Route and charging optimization based on real-time battery data
• Predictive maintenance indicators enabling scheduled service before failures occur
• Use any charger without limitations - L1, L2, or DC Fast chargers up to 350kW
• Comprehensive cloud APIs for integration with telematics and fleet management systems

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Flexible Implementation Options

Qnovo’s Fleet Services offer on-site or cloud services for fleet-wide monitoring, analytics, and optimization.

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_{*The above numbers are based on Qnovo’s models and data, subject to change given constraints and changes to data sets over time.}

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Qnovo's software understands and responds to each unique battery within your device with:

• 200+ million smartphones deployed worldwide with zero safety incidents
• Four-year battery warranty offered by customers who use Qnovo software‍
• Battery defect detection with 98.7% accuracy rate for identifying potential issues‍
• Proven reliability with 50+ billion battery cycles in the field

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Technical Advantages

• Supports batteries with silicon anodes or silicon content
• No additional hardware required for implementation, Qnovo is a software-only solution
• Seamless integration with existing android-based battery management systems
• Reduced battery hazard incidents to nearly zero parts per billion

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Implementation Excellence

• Major customer adoption including Lenovo / Fujitsu, Docomo and other operators in Japan
• Cloud-enabled data improving safety while meeting strict privacy requirements
• Compatibility with all major battery vendors and chemistries

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_{*The above numbers are based on Qnovo’s models and data, subject to change given constraints and changes to data sets over time.}

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