Battery Assets Are Your Biggest Risk and Opportunity

Current Reality

Hidden Degradation

  • Batteries fail without warning
  • Warranty claims eat profits
  • Safety incidents damage reputation

Operational Blindness

  • No visibility into cell-level health
  • Can’t optimize charge/discharge
  • Missing revenue opportunities

Data Overload

  • Thousands of data points
  • No actionable insights
  • Reactive, not predictive

BattOps Solution

Predictive Intelligence

  • 90-day advance failure warning
  • Proactive maintenance scheduling
  • Risk mitigation before incidents

Optimization Engine

  • Cell-level digital twins
  • Revenue-optimized cycling
  • 20% more usable capacity

Actionable Insights

  • Clear recommendations
  • Prioritized alerts
  • ROI-driven decisions

The SOC Accuracy Advantage

Precision SOC: The Hidden Profit Multiplier

The Economics of Accuracy: Every 1% improvement in SOC accuracy = $300K annual value per 100MWh

BESS Operations

Traditional BMS (±5-10% error)

  • Operating Window: 20-80% SOC
  • Usable Capacity: 60MWh
  • Daily Cycles: 1.5
  • Annual Revenue: $4.2M
  • ROI: 12%

Physics-Informed (±1.3% error)

  • Operating Window: 10-90% SOC
  • Usable Capacity: 80MWh
  • Daily Cycles: 2
  • Annual Revenue: $5.6M
  • ROI: 18%

Why Physics Models Achieve 98.7% Accuracy

We Track What Others Can't See:

Real-World Validation

Know exactly how much energy is available, not just voltage readings

Test Conditions

  • New cells
  • After 500 cycles
  • Temperature extremes (-20°C)
  • High C-rate (2C)
  • Mixed chemistry packs

BattGenie

  • ±1.3%
  • ±1.5%
  • ±2.0%
  • ±1.8%
  • ±2.1%

Industry Standard

  • ±5%
  • ±8%
  • ±12%
  • ±10%
  • ±15%

The Profit Cascade

10M+

More usable capacity

98.7%

SOC Accuracy

33%

More cycles per year

$1.4M

Additional revenue per 100MWh

2 Years

Faster payback

Electric Vehicles

Electric Fleets: Every Battery, Every Journey, Optimized

Fleet Management Capabilities

Per Vehicle Tracking

  • Individual SOH
  • Charging History
  • Degradation Rate
  • Range prediction

Optimization Actions

  • Route assignment by health
  • Optimal charging schedules
  • Preventive maintenance
  • Battery warranty management

Fleet Metrics

  • Health distribution
  • Usage patterns
  • Charging efficiency
  • Failure probability

Business Impact

  • Residual value tracking
  • TCO optimization
  • Infrastructure planning
  • Fleet rotation strategy

Vehicle ID

  • EV-001
  • EV-002
  • EV-003

Current SOH

  • 94%
  • 87%
  • 91%

Best Route

  • Long haul
  • City
  • Regional

Charge Plan

  • 80% max
  • 70% max
  • 85% max

How BattOps Works? Powered by BattStudio's Physics-Informed Intelligence

The steps involved in using BattOps:

01

BMS streams (CAN/Modbus)

02

Environmental data

03

Operational history

04

Market signals

01

Physics models
(P2D equations)

02

Self-calibration

03

Degradation tracking

04

Predictive algorithms

01

Fleet aggregation

02

Pattern recognition

03

Optimization engine

04

Risk assessment

01

Dashboards

02

API endpoints

03

Alerts & notifications

04

Reports & recommendations

Flexible deployment options designed for your operational requirements

Cloud Deployment

  • Fully managed SaaS
  • Auto-scaling
  • 99.9% uptime SLA
  • SOC 2 compliant
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Hybrid Deployment

  • Edge processing
  • Cloud analytics
  • Reduced latency
  • Data sovereignty
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On-Premise

  • Complete control
  • Air-gapped option
  • Custom integration
  • Enterprise support
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From Prototype to Production in 8 Weeks

Week 1-2: Data Integration & Setup

Week 3-4: Model Calibration

Week 5-6: Dashboard Configuration

Week 7-8: Training & Go-Live

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For any inquiries or assistance related to BattOps

Venkat Subramanian

CTO, Chief Scientific Advisor, and Co-Founder

Prof. Venkat Subramanian is currently the Ernest Dashiell Cockrell II Professor of Mechanical & Material Science Engineering at the University of Texas, Austin.

His research interests include energy systems engineering, electrochemical engineering, computationally efficient algorithms for state-of-charge (SOC) and state-of-health (SOH) estimation of lithium-ion batteries, multiscale simulation, and design of energetic materials, kinetic Monte Carlo methods, model-based battery management system for electric transportation, and renewable microgrids and nonlinear model predictive control. Prof. Subramanian was awarded the Dean’s award for excellence in graduate study in 2001 for his doctoral research.

He is a Fellow of the Electrochemical Society and a past Technical Editor of the Journal of the Electrochemical Society. He was also the chair of the IEEE Division of the Electrochemical Society. His codes for Lithium-ion batteries are the fastest reported in the literature and his algorithm for solving index 1 nonlinear DAEs is the most robust compared to any other algorithm reported as of today.

Prof. Subramanian received his B.Tech. degree in Chemical and Electrochemical Engineering from the Central Electrochemical Research Institute (CECRI), Karaikudi, India, in 1997 and the Ph.D. degree in Chemical Engineering from the University of South Carolina, Columbia, SC, USA, in 2001.

Manan Pathak

CEO and Co-Founder

Dr. Manan Pathak is the Chief Executive Officer and co-founder of BattGenie.

He earned his PhD at the University of Washington, where he obtained his graduate thesis on model-based Battery Management Systems. He has 7+ peer-reviewed publications with over 300 citations, and extensive experience with physics-based battery models, numerical methods and derivation of optimal charging profiles.

Chintan Pathak

CPO and Co-Founder

Dr. Chintan Pathak is the Chief Product Officer and co-founder of BattGenie.

He earned his PhD from the University of Washington and he obtained his graduate thesis on optimal locations of battery charging stations in the state of Washington. He has over 13 years of experience in software engineering and embedded systems.

Akshay Subramaniam

Battery Modeling Scientist

Akshay Subramaniam leads electrochemical model development and identification tasks at BattGenie. He also contributes towards BMS algorithm development and validation, and helps maintain our models, databases, and testing pipelines. He received his Ph.D. from the University of Washington during which he gained extensive experience in the development of control-oriented electrochemical models. He has 10+ peer-reviewed publications and is proficient in several aspects of battery systems engineering including numerical simulation techniques, optimization for design and fast charging, parameter estimation, and battery data analysis.

Taejin Jang

Battery Simulation Scientist

Dr. Taejin Jang is a Battery Simulation Scientist at BattGenie. Dr. Jang received his Ph.D in Materials Science from University of Texas at Austin and an MS in Chemical Engineering from UW. He also has BS and MS degrees in Materials Science & Chemical Engineering from Yokohama National University in Japan. He spent three years in the automotive devices industry at Samsung Electronics. He has 7+ years’ experience in battery modeling and simulation, encompassing Li-ion and next-generation batteries.

Bing Syuan Wang

Senior Battery Software and Data Engineer

Bing Syuan Wang is the Senior Battery Software and Data Engineer at BattGenie.
He earned his Masters in Electrical Engineering from the University of Washington. He has over 6 years’ experience in software engineering and in working with battery data.

Aditya Parsai

Fullstack Software Engineer

Aditya Parsai is a Fullstack Software Engineer at BattGenie. He graduated in Civil Engineering from IIT(BHU). With 8 years’ experience, he contributes to helping businesses succeed in the digital space by staying attuned to the evolving tech landscape. His work spans from front-end development to back-end system engineering, ensuring smooth integration and functionality. He recognizes the importance of storytelling and is adept in translating complex ideas into user-friendly interfaces to enhance user experiences.