Edge Computing Capabilities

Advanced Edge Intelligence

The Flash's dedicated edge computing processor enables real-time decision making, advanced analytics, and autonomous control capabilities directly at the vehicle level.

Battery Management

Safety Monitoring

Predictive Maintenance

Performance Optimization

Real-time Processing

Edge Analytics

Core Algorithms

vs Basic monitoring

Performance Score

Response Time

0ms

vs Cloud dependency

Performance Score

Edge Computing Workflow

Real-time data processing and decision making at the vehicle edge

Data Acquisition

Sensors collect real-time data from vehicle systems

Edge Processing

Dedicated processor analyzes data using advanced algorithms

Decision Making

ML algorithms determine appropriate actions based on analysis

Real-time Action

Immediate response without cloud dependency

Step 1: Data Acquisition Details

Temperature sensors

Voltage monitoring

Current measurement

CAN bus data

Edge Compute Algorithms

Battery Management

State of Charge (SoC) Estimation

Real-time battery charge level calculation using advanced Kalman filtering

Accuracy:95%

Processing Time:

< 10ms

Battery Management

State of Health (SoH) Estimation

Battery degradation monitoring and capacity fade prediction

Accuracy:92%

Processing Time:

< 50ms

Safety Systems

Thermal Runaway Detection

Early detection of dangerous thermal conditions in battery cells

Accuracy:99%

Processing Time:

< 5ms

Battery Management

Cell Imbalance Detection

Monitoring and correction of voltage imbalances between battery cells

Accuracy:97%

Processing Time:

< 20ms

Performance Optimization

Charge/Discharge Rate Monitoring

Real-time monitoring and optimization of charging and discharging rates

Accuracy:94%

Processing Time:

< 15ms

Safety Systems

Short Circuit Detection

Immediate detection and isolation of short circuit conditions

Accuracy:99.5%

Processing Time:

< 1ms

Offline Edge Control

Real-Time Actuation

3 control functions

User/Driver Alerts

3 control functions

Fail-Safe Modes

3 control functions

Most Commonly Used Use Cases

Real-world implementations of edge algorithms with detailed technical specifications and implementation frameworks

Predictive Battery Degradation Algorithm

Kalman Filter-based SOH Estimator

Dynamic Range Estimation

Adaptive Energy Consumption Predictor (AECP)

Theft Detection & Recovery

Behavioral Anomaly Detection using HMM

Load Optimization Algorithm

Motor Signature Load Estimator (MSLE)

Common Implementation Framework

Standardized technical specifications across all edge algorithms

ARM Cortex-M4

Edge Hardware Level

1-10s

Update Frequency

<50KB

Memory per Algorithm

<100mW

Additional Power

Edge Computing Architecture

Dedicated edge processor with optimized algorithms for real-time vehicle intelligence and autonomous decision-making capabilities.

Dual Processor

Main + dedicated edge compute

Real-Time

< 0ms response time

AI/ML Ready

Custom algorithm deployment

Offline Operation

0% autonomous capability

Advanced Edge Intelligence

Core Algorithms

Response Time

Data Acquisition

Edge Processing

Decision Making

Real-time Action

Step 1: Data Acquisition Details

Edge Compute Algorithms

Offline Edge Control

Motor Power Reductionhigh< 10ms

Charging Disablecritical< 5ms

Power Contactor Controlcritical< 1ms

LED/Buzzer Alertsmedium< 1ms

BLE Phone Alertslow< 100ms

Dashboard Notificationsmedium< 50ms

Limp Mode Activationhigh< 100ms

Tamper Responsehigh< 50ms

Emergency Protocolscritical< 10ms

Most Commonly Used Use Cases

Implementation Details

Implementation Details

Implementation Details

Implementation Details

Edge Computing Architecture

Dual Processor

Real-Time

AI/ML Ready

Offline Operation