The core function of BMS is actually a hierarchical closed-loop control system. The ultimate goal of our architecture design is always to maximize the cycle life of lithium iron phosphate (LiFePO4) or ternary lithium battery clusters, and to ensure absolute safety.
At first, the battery management unit (BMU) was responsible for “capturing” real-time analog data through a high-precision analog front end (AFE)—specifically single cell voltage and temperature. At the same time, the main controller monitors the total current of the battery pack through a high-precision sensor. We will use ampere-hour integration combined with Kalman filtering and other algorithms to accurately estimate SOC (state of charge) and SOH (state of health). Based on these calculations, the EMS makes global scheduling decisions, the PCS executes the power conversion, and the BMS performs key interventions to ensure safety. BMS will initiate cell equalization (active or passive) to level the voltage difference, or control MOSFETs and high-voltage contactors to prevent overcharge/overdischarge. All this data is also synchronized among the ‘3S’ via industrial protocols such as CAN Bus or Modbus, ensuring that the entire storage system operates strictly within a safe operating area (SOA).
The following is a detailed disassembly of this process:
Phase 1: Analog Front End (AFE) Based Data Capture
The working principle of any BMS is based on the accurate collection of physical data. In the fixed energy storage scenario, a battery cluster often contains hundreds of cells. BMS adopts a hierarchical architecture, allowing the slave control unit (BMU) to act as the “nerve endings” of the entire system. The key here is the high-precision analog front end (AFE). The work of these stages is essentially to “translate” the physical state of the battery into digital signals, ensuring that the subsequent processing stages get accurate, real-time data.
After the data is digitized, it is the “brain” of the master Controller to take over. Just looking at the raw data of voltage and current, you can’t directly judge how much energy is left in the battery (SOC), or how long it will last (SOH). To solve this problem, the BMS must introduce complex algorithms. Although the ampere-hour integration method can track the current through time integration, it must produce cumulative drift error after long-term operation. Therefore, mature BMS architectures combine it with Kalman Filtering. This is the closed-loop algorithm that can recursively update estimates and correct errors in real time. Only by calculating the SOC and SOH accurately can the system avoid pushing the battery beyond its limits, which is directly related to the return on assets that customers are most concerned about—that is, maximizing cycle life.
This is the final step of the closed loop. Based on the estimated state, the BMS must actively intervene to keep the battery in a safe working area (SOA).
The voltage inconsistency between cells is the main culprit for the “short board effect” of ESS capacity. The BMS initiates an equalization function to eliminate these differences.
Anyone who engages in energy storage knows that a stationary battery system is not an island. It relies on the robust communication network connecting the 3S architecture. The workflow operates as follows:
By integrating the perception of BMS, the execution of PCS, and the decision-making of EMS, we truly close the control loop and ensure the long-term and reliable operation of this energy asset.
Author: Kevin
I am a Senior Engineer at Gerchamp’s BMS R&D Department with over 12 years of industry experience. I specialize in leading the architecture design and core algorithm development for our advanced Battery Management Systems.
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