RK3568 + FPGA-Based Coordinated Controller Solution for Energy Storage

Model: RK3568

RAM: Onboard 4GB (MAX 8GB)

NPU: Up to 1T

Storage: EMMC: 128GB (MAX 128GB)

GPU: ARM G52 2EE

Operating System: Ubuntu 20.04. Debian 11.

1.2 Industrial-Grade Design

• Wide Operating Temperature Range: -40℃~85℃ (with active cooling)
• Electromagnetic Compatibility: Complies with IEC 61000-4-2/4/5 standards
• Protection Rating: IP40 metal enclosure design
• Power Input: Supports DC 9~36V wide voltage input

II. Data Acquisition and Monitoring

2.1 Multi-Protocol Sensor Access

• Supports industrial protocols such as Modbus RTU/TCP, IEC104, CANopen
• Provides 8-channel analog input (16-bit ADC, ±10V range)
• Integrated digital acquisition module (supports opto-isolation and debouncing)

2.2 Real-time Status Monitoring

• Achieves microsecond-level data acquisition
• Key parameter monitoring
• PV array IV curve tracking
• Energy storage battery SOC/SOH estimation
• Inverter efficiency monitoring
• Environmental parameters (temperature, humidity, irradiance)

III. Predictive Maintenance Algorithms

3.1 Digital Twin Modeling

• Builds equipment health status models based on LSTM neural networks
• Achieves battery degradation trend prediction (error <3%)
• Supports component-level fault localization (localization accuracy >95%)

3.2 Early Warning Mechanism

• Multi-level threshold setting (Normal/Warning/Fault)
• Fault mode identification
• Module shading detection
• Combiner box arc fault early warning
• Inverter over-temperature protection

IV. Energy Management Strategies

4.1 Multi-Time Scale Optimization

• Day-ahead planning: Power generation forecasting based on weather forecasts
• Real-time dispatch: Millisecond-level response to grid dispatch commands
• Adaptive control: Dynamically adjusts charge and discharge strategies

4.2 Algorithm Implementation

5.2 Visualization Platform

VI. Practical Application Results

6.1 Efficiency Improvement

6.2 Typical Cases

• Model Predictive Control (MPC) algorithm

• Particle Swarm Optimization (PSO) for power allocation

• Robust optimization to handle prediction errors

• V. System Deployment Solution

  5.1 Network Architecture

• Build edge computing nodes (deployed on the PV inverter side)

• Adopt 4G/5G networking solutions

• Supports cloud-edge collaboration architecture

• Develop a monitoring interface based on Grafana

• Achieve 3D visualization (integrating Three.js library)

• Provide a full equipment lifecycle management dashboard

• Increased power generation: 8%-12% improvement through MPPT optimization

• Reduced O&M costs: Fault response time shortened by 60%

• Extended equipment lifespan: Predictive maintenance reduces unplanned downtime by 35%

• A 20MW PV power station project in Northwest China

• Configured with a 5MW/10MWh energy storage system