Hardware and Software Composition and Solutions for Energy Coordination Controllers

The hardware and software composition of an energy storage coordination controller is as follows:

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I. Hardware Composition

1. Core Processor Unit

   • Multi-core Heterogeneous Architecture: Utilizes a combination of ARM (for strategic computation) + FPGA (for real-time signal processing) + DSP/ADC (for high-precision sampling), supporting millisecond-level response and parallel computing‌12.
   • Domestic Solution: Supports fully domestic chips (e.g., ARM Cortex-A55 multi-core processors) to ensure supply chain security‌12.

2. Data Acquisition Module

   • High-Precision Sampling Circuit: 16-bit AD acquisition chip (sampling rate ≥1MSPS), voltage/current measurement error <0.5%, supporting 24 AC channels and 16 DC channels‌12.
   • Isolation and Protection Design: Key signal channels use optocoupler isolation (interference immunity up to 4kV common mode / 2kV differential mode) to ensure reliability in strong electromagnetic environments‌12.

3. Communication and Expansion Interfaces

   • Redundant Communication Interfaces: Dual Gigabit Ethernet ports, multiple RS485/CAN buses, supporting protocols such as Modbus, EtherCAT, and IEC 61850 (including GOOSE)‌12.
   • Low-Latency Transmission: Integrates TSN (Time-Sensitive Networking) technology, with end-to-end communication latency <10ms‌1.

4. Protection and Power Supply Unit

   • Power Adaptation: Wide voltage input (43~160VDC), dual outputs (5V/24V), and overcurrent/overvoltage protection mechanisms‌24.
   • Fault Recorder Module: Built-in fault recording function, logging abnormal event data (≥256 entries)‌3.

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II. Software Composition

1. Layered Control Strategy

   Layer

   Function

   Technical Implementation

   Bottom Layer Execution

   Basic operations such as charge/discharge control, inverter switching

   Real-time operating system (e.g., Linux/domestic Kylin OS) driving hardware operations‌23

   Middle Layer Coordination

   Multi-device power allocation, responding to grid dispatch commands

   Model Predictive Control (MPC) algorithm, minute-level strategy adjustment‌12

   Top Layer Decision-Making

   Generating long-term dispatch plans based on weather/load forecasts

   Deep learning optimization algorithms, supporting peak shaving, valley filling, frequency regulation, and voltage regulation‌15

2. Communication and Protocol Stack

   • Multi-Protocol Conversion Engine: Built-in protocol stack supports Modbus to MQTT, EtherCAT to OPC UA conversion, adapting traditional devices for cloud platform interconnection‌1.
   • Security Verification Mechanism: Dual redundant checksum (CRC + parity check), data error rate <10⁻⁹‌1.

3. Intelligent Function Modules

   • Graphical Configuration Tool: Supports visual programming of interface signals, control logic, and alarm rules, enabling flexible function customization‌23.
   • State of Health (SOH) Management: Battery life degradation prediction and maintenance early warning‌26.
   • Fault Recording Analysis: Records grid abnormal events to assist in fault source localization‌3.

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III. Software and Hardware Collaborative Features

• Real-time Performance Assurance: FPGA processes high-frequency sampling signals (≤10ms cycle), while ARM performs strategic computations, meeting the fast response requirements for grid frequency/voltage regulation‌15.
• Reliability Design: Fanless wide-temperature structure (-40℃~+70℃), IP67 protection rating, adapting to harsh outdoor environments‌35.
• Scalability: Modular hardware interfaces support communication management for 128 PCS units, and the software supports 300+ concurrent TCP/IP connections‌3.

Note: Hardware configurations are adjusted according to application scenarios (e.g., microgrid controllers adopt a compact design), and software algorithms require continuous optimization of generalization capabilities to adapt to multi-energy coordination scenarios‌57.