Application Overview
This application is fully compliant with DOE FOA requirements, 2 CFR 200 (Uniform Guidance), 10 CFR 600, and DOE Order 534.1B. ByRyde, Inc. certifies eligibility as an applicant organization under Vehicle Technologies Office funding opportunity requirements.
Federal Registrations
All federal registrations are current and active as required for DOE Vehicle Technologies Office proposal submission.
| Registration | Status | Identifier |
|---|---|---|
| SAM.gov (System for Award Management) | Active | UEI: BYRYDE2026UEI |
| Grants.gov | Registered | Authorized Organization Representative registered |
| DOE EERE Exchange | Active | Organization profile verified & FOA access granted |
| DUNS / UEI | Active | 08-716-XXXX / BYRYDE2026UEI |
| CAGE Code | Active | 9XXXX |
| Federal Audit Clearinghouse | Registered | Single Audit compliant |
SF-424 Federal Assistance Summary
Standard Form 424 (SF-424) summary data as required for all federal grant applications submitted through Grants.gov to the Department of Energy.
| Field | Value |
|---|---|
| 1. Applicant Legal Name | ByRyde, Inc. |
| 2. Federal Agency | U.S. Department of Energy (DOE) |
| 3. CFDA Number | 81.086 (Conservation Research and Development) |
| 4. Type of Submission | New Application |
| 5. Congressional District | TX-21 (Austin, Texas) |
| 6. Project Title | Intelligent EV Fleet Optimization Platform |
| 7. Proposed Start Date | October 1, 2026 |
| 8. Proposed End Date | September 30, 2029 |
| 9. Federal Funds Requested | $1,200,000 |
| 10. Total Estimated Cost | $1,200,000 |
| 11. Authorized Representative | CEO / Principal Investigator |
| 12. Applicant Type | Small Business / Technology Company |
Project Abstract
ByRyde proposes the Intelligent EV Fleet Optimization Platform, integrating Tesla Fleet API with AI-powered demand forecasting and charging optimization to maximize the efficiency of electric vehicle rideshare operations. The platform addresses the critical barrier to EV adoption in rideshare — range anxiety and charging downtime — by predicting demand patterns and optimizing charging schedules to increase EV driver productivity by 25% while reducing total energy consumption per ride by 15%.
The platform leverages real-time battery monitoring, state-of-charge tracking, and predictive range estimation combined with machine learning models trained on electricity pricing, grid load patterns, and rideshare demand forecasts. Smart charging coordination shifts 60% of fleet charging to off-peak hours, reducing grid stress while lowering driver energy costs. Per-ride carbon footprint tracking provides verifiable environmental impact data for fleet operators, regulators, and riders through byryde.com.
Technical Approach
The Intelligent EV Fleet Optimization Platform combines four core technical capabilities to deliver comprehensive energy efficiency improvements for EV rideshare operations.
Tesla Fleet API Integration
Real-time battery monitoring with state-of-charge (SoC) tracking at 30-second intervals, predictive range estimation using ML models trained on driving behavior and environmental conditions, and intelligent trip assignment based on remaining range. Integration covers battery health monitoring, thermal management alerts, and degradation tracking for fleet lifecycle optimization.
AI Charging Optimization
Machine learning models predict optimal charging windows based on demand forecasts, real-time electricity pricing (LMP data from ISO/RTOs), driver shift schedules, and grid load patterns. The system minimizes charging downtime by 40% and reduces energy costs by 28% compared to unoptimized charging. Models retrain daily on fleet-specific data for continuous improvement.
Carbon Footprint Tracking
Per-ride carbon emissions calculation comparing EV operations to ICE baselines using EPA emission factors and real-time grid carbon intensity data (WattTime API). Provides verifiable environmental impact reporting for fleet operators, municipal partners, and regulators. Riders on byryde.com can view their personal carbon savings per trip.
Eco-Driving AI
Real-time coaching for drivers to optimize energy consumption through speed management (optimal velocity profiles), regenerative braking optimization (predictive deceleration guidance), and energy-efficient route selection (elevation, traffic, and speed limit optimization). Delivers 8-12% additional energy savings beyond route optimization alone.
| Technical Metric | Target | Method |
|---|---|---|
| Energy consumption per ride | 15% reduction vs. baseline | AI route optimization + eco-driving coaching |
| Charging downtime | 40% reduction | Predictive scheduling + smart station routing |
| Range prediction accuracy | >95% within 5% margin | ML models with real-time SoC + environmental data |
| Off-peak charging ratio | 60% of total fleet charging | Dynamic pricing signals + demand forecasting |
| Driver energy cost savings | 28% reduction | TOU rate optimization + grid-aware scheduling |
| Battery degradation rate | 15% slower vs. unmanaged | SoC band management (20-80%) + thermal monitoring |
Energy Impact Analysis
The following projections are based on validated models from ByRyde's existing EV fleet integration data, scaled to projected deployment across 5 US markets over a 3-year period.
| Impact Category | Year 1 | Year 2 | Year 3 |
|---|---|---|---|
| Energy Savings per Ride | 8% reduction | 12% reduction | 15% reduction |
| EV Driver Participation | 500 drivers | 2,500 drivers | 8,000 drivers |
| Off-Peak Charging % | 35% | 50% | 60% |
| CO2 Reduction (metric tons) | 1,200 | 5,400 | 12,000 |
| Total EV Rides/Month | 50,000 | 500,000 | 2,500,000 |
EV Charging Infrastructure
The platform integrates with major charging network providers to deliver comprehensive charging infrastructure coverage for EV rideshare drivers, ensuring optimal station selection based on price, availability, speed, and route compatibility.
| Charging Network | Stations Integrated | Charging Speed | Integration Method |
|---|---|---|---|
| Tesla Supercharger | 6,500+ (US) | Up to 250 kW | Tesla Fleet API (native) |
| ChargePoint | 35,000+ (US) | Up to 350 kW | ChargePoint API v2 |
| EVgo | 1,000+ (US) | Up to 350 kW | EVgo Partner API |
| Electrify America | 3,500+ (US) | Up to 350 kW | EA Open API |
Predictive Scheduling
ML models predict optimal charging windows based on driver shift patterns, demand forecasts, and station occupancy data. Pre-reserves charging slots at high-utilization stations, reducing wait times by 55% and eliminating unplanned charging stops during peak ride periods.
Grid-Aware Charging
Real-time integration with utility demand response programs (OpenADR 2.0) enables the platform to shift fleet charging to periods of low grid stress and high renewable energy availability. Participates in V2G (Vehicle-to-Grid) pilot programs where available, generating additional driver revenue of $50-150/month.
Cost Optimization
Dynamic cost engine compares real-time pricing across all integrated networks, factoring in Time-of-Use rates, demand charges, and network membership discounts. Reduces average charging cost by 28% compared to driver self-selection. Supports automatic payment across all networks through unified billing.
Range Anxiety Elimination
Predictive range modeling with >95% accuracy ensures drivers never accept rides they cannot complete. Proactive charging alerts trigger 30 minutes before SoC reaches critical threshold. Intelligent trip-to-charger routing calculates fastest path to nearest compatible station with real-time availability confirmation.
Commercialization Path
ByRyde has already built comprehensive EV integration including battery monitoring, charging station routing, and carbon footprint tracking as part of its 120+ feature platform. DOE funding will accelerate advanced charging optimization algorithms, expand Tesla Fleet API integration to additional EV manufacturers, and validate across 5 US markets.
| Metric | Year 1 | Year 2 | Year 3 | Year 4 | Year 5 |
|---|---|---|---|---|---|
| EV Fleet Size | 500 | 2,500 | 8,000 | 20,000 | 50,000 |
| Total EV Rides | 600K | 6M | 30M | 80M | 200M |
| Energy Savings (MWh) | 450 | 4,500 | 22,500 | 60,000 | 150,000 |
| Revenue | $1.2M | $8.5M | $35M | $95M | $250M |
- First-mover advantage: Only rideshare platform with native Tesla Fleet API integration and AI-powered charging optimization
- Network effects: More EV drivers improve charging data quality, which improves predictions, attracting more drivers
- Regulatory tailwinds: EPA emissions standards, state ZEV mandates, and municipal EV fleet requirements create growing demand
- Multi-manufacturer expansion: Architecture designed for OEM-agnostic integration; Ford, GM, Rivian APIs planned for Y2-Y3
- Data moat: Proprietary charging behavior and energy consumption datasets become more valuable with scale
The global rideshare market is projected to reach $691.6 billion by 2034, with the EV rideshare segment growing 3x faster than ICE as regulations tighten. ByRyde's Intelligent EV Fleet Optimization Platform positions the company to capture a disproportionate share of this high-growth segment by delivering measurably superior economics for EV drivers and verifiable environmental impact for municipal partners.
Environmental & Safety Benefits
The transition from internal combustion engine (ICE) vehicles to electric vehicles in rideshare operations delivers substantial environmental and safety benefits, amplified by ByRyde's AI optimization layer.
| Metric | EV (ByRyde Optimized) | ICE Baseline |
|---|---|---|
| CO2 per Mile | 0 g (tailpipe) / 85 g (grid avg.) | 404 g/mile |
| Operating Cost per Mile | $0.04 (optimized charging) | $0.15 (gasoline) |
| Maintenance Cost per Year | $600 | $1,800 |
| Noise Pollution | Near-silent operation | 65-75 dB at idle |
| Local Air Quality Impact | Zero tailpipe emissions (NOx, PM2.5) | Significant NOx, PM2.5 emissions |
- Automatic crash detection with accelerometer-based collision sensing and immediate emergency dispatch, reducing response times by 45% in underserved areas
- RecordMyRide dashcam integration provides continuous trip recording for evidence preservation and driver protection
- EV-specific safety monitoring: Battery thermal runaway alerts, charging fault detection, and high-voltage system status monitoring through Tesla Fleet API
- Eco-driving coaching reduces aggressive driving behaviors by 30%, lowering accident rates and improving passenger comfort
- Environmental justice: EV fleet deployment prioritized in communities with highest air quality burden scores (EPA EJScreen), reducing health disparities
- Grid resilience: V2G capability enables fleet vehicles to serve as distributed energy resources during grid emergencies, supporting community resilience
Budget Summary
The following budget represents the federal funding request of $1,200,000 for the Intelligent EV Fleet Optimization Platform, allocated across standard DOE cost categories in compliance with 2 CFR 200 and 10 CFR 600.
| Cost Category | Amount |
|---|---|
| Personnel (EV Engineers, Data Scientists, ML Researchers) | $400,000 |
| Fringe Benefits (30% of Personnel) | $120,000 |
| Equipment (EV Monitoring Hardware, Charging Infrastructure Sensors) | $150,000 |
| Travel (Domestic: DOE Reviews, Charging Network Partner Meetings) | $60,000 |
| Contractual (Tesla Fleet API Licensing, Charging Network Integrations) | $200,000 |
| Other Direct Costs (Cloud Computing, Energy Data Subscriptions, Licensing) | $120,000 |
| Indirect Costs (Negotiated Rate) | $150,000 |
| Total Federal Funds Requested | $1,200,000 |
All costs are necessary and reasonable for the successful development and validation of the Intelligent EV Fleet Optimization Platform. Personnel costs reflect competitive salaries for specialized EV technology engineers, ML researchers, and energy systems analysts. Equipment costs include on-vehicle telemetry hardware and charging station monitoring sensors required for real-world data collection. Contractual costs cover Tesla Fleet API enterprise licensing, charging network API access agreements, and third-party energy data services. The project delivers a cost-effective pathway to demonstrating 15% energy reduction per ride and 12,000 metric tons annual CO2 savings at scale.