Recently, various provinces have adjusted their time-of-use electricity pricing policies, making peak-valley arbitrage for industrial and commercial energy storage a hot topic. This profit model, which involves charging at low prices and discharging at high prices to earn the price difference, has attracted many to calculate potential returns. However, the actual returns often differ from theoretical calculations. What causes this discrepancy?
This profit model highly depends on the BMS’s accurate judgment of the battery’s state, especially the estimation accuracy of SOC (State of Charge).
1. What is SOC?
SOC refers to the percentage of the remaining capacity of the battery relative to its total capacity, which is a key parameter for industrial and commercial energy storage systems. If the estimation is inaccurate, it can lead to insufficient charging during off-peak periods and premature discharging during peak periods, affecting the returns from peak-valley arbitrage.
According to industry standards, the SOC estimation accuracy of the BMS must be controlled within ±5%. For example, in a 500kW/1MWh industrial and commercial energy storage system, using a two-charge two-discharge mode, operating for 330 days a year, with a charge-discharge efficiency of 92% and a depth of discharge of 90%. If the SOC accuracy is 5%, the actual operational system charge-discharge capacity may incur about 5% loss, significantly lowering the profit level.
2. How high should the SOC accuracy of BMS be for industrial and commercial peak-valley arbitrage?
SOC (State of Charge) is a core indicator of the BMS, directly reflecting the remaining battery capacity. In the context of peak-valley arbitrage, this value directly affects the cost of electricity purchases and sales revenue, making it a key to profitability.
(1) Low SOC accuracy, guaranteed losses in peak-valley arbitrage?
Charging phase:
SOC estimation deviation: During off-peak charging, if the BMS underestimates SOC, showing 10% when the actual remaining is 20%, it will terminate charging early, leading to a 10% undercharge; if it overestimates SOC, showing 90% when the actual is 80%, continuing to charge may lead to overcharging, causing battery damage or even thermal runaway.
Dynamic error impact: During charging, the battery temperature rise can cause the voltage to appear higher; for example, the voltage of a lithium iron phosphate battery at 35°C is 50mV higher than at 25°C. If the BMS does not perform temperature compensation, it may mistakenly interpret the elevated voltage as a full charge state and stop charging early. This will result in the battery being charged to only 85%, but the system mistakenly believes it is full, leading to a 15% reduction in electricity sold during peak discharging, affecting arbitrage returns.
Discharging phase:
During peak discharging, users expect to maximize electricity usage. However, if the BMS overestimates SOC, it will terminate discharging early, wasting sellable electricity; if it underestimates SOC, it will force discharge to the protection threshold, causing battery over-discharge and shortening its lifespan.
Cumulative error issue: Assuming a daily SOC error of ±2%, after a week, the error accumulates to ±14%, and after a month, it reaches ±60%. At this point, the displayed SOC by the BMS is severely inconsistent with the actual SOC, and the returns from peak-valley arbitrage will completely lose controllability, becoming a random event.
(2) Why do industry insiders consider ±1% SOC accuracy as the “passing line” and ±0.5% as the guarantee for profit?
Currently, most industrial and commercial energy storage manufacturers do not meet the SOC accuracy standards. Although the “Energy Storage Inverter Testing Technical Regulations” stipulate that the SOC estimation error of energy storage systems must be ≤±5%, this is merely the minimum safety standard, not a guarantee for profit.
When the SOC error is controlled within ±1%, the depth of battery charge and discharge can be maintained within a reasonable range, and the cycle life of lithium iron phosphate batteries can be extended by over 30%. Conversely, if the SOC error of the BMS is large, the battery capacity may degrade from its initial state to 80% within three years.
3. Technical Barriers to SOC Accuracy
1. BMU Sampling Accuracy
BMU, or Battery Management Unit, is a core component of the BMS, and its measurement accuracy of cell voltage directly affects SOC judgment. Experimental data shows that for every ±2mV deviation in single cell voltage measurement, the SOC estimation deviation can reach 1%. For example, with a 16-bit ADC chip, a ±5mV sampling error can lead to the SOC displaying 98% when the battery is fully charged, while it is actually overcharged by 5%.
Currently, the industry has generally upgraded to 24-bit ADC chips. For instance, the BMU developed by Hangzhou Gaote strictly controls the voltage measurement error within ±0.8mV, equivalent to controlling the error to within 1 cm on a 100-meter track, significantly improving SOC estimation accuracy.
Temperature monitoring is also crucial. Once the temperature difference between cells exceeds 2°C, the internal resistance differences become apparent. BYD innovatively adopts a distributed temperature measurement scheme, equipping each 20Ah cell with an independent NTC sensor, improving temperature detection accuracy to ±0.2°C, halving the error compared to traditional schemes, providing precise data support for safe battery operation.
2. Algorithm Models
Currently, competition among manufacturers regarding SOC algorithms is fierce, with varying technical routes and performance.
In terms of algorithm technology routes, CATL adopts a dual-track system of “ampere-hour integration + open-circuit voltage calibration,” automatically calibrating every 30 minutes to ensure the accuracy of SOC calculations; Sungrow integrates neural network algorithms into the BCU, enabling the system to have self-learning capabilities, continuously optimizing SOC estimation accuracy with use.
In terms of accuracy maintenance, the industry generally believes that controlling SOC error for new batteries is relatively easy, while maintaining accuracy after battery cycling is the real challenge. Leading manufacturers, with advanced adaptive algorithms, can keep SOC deviation within ±2% even after 4000 cycles; in contrast, smaller manufacturers’ solutions may see SOC deviation expand to ±5% after 2000 cycles.
3. Balancing Capability
Cell consistency also affects the SOC accuracy of industrial and commercial energy storage BMS. In a certain 4MWh energy storage project, due to a single cell voltage being 30mV lower than others, when the system displayed a remaining capacity of 20%, that cell was already fully discharged, triggering the entire battery pack’s protection shutdown.
Hangzhou Gaote’s 1500V active balancing technology can control the voltage difference between individual cells within 5mV, laying a solid hardware foundation for accurate SOC calculations. In contrast, passive balancing technology typically only activates when the voltage difference exceeds 50mV, leading to SOC accuracy being 3-4 percentage points lower than active balancing solutions.
4. Selection Recommendations
When selecting a BMS, enterprises should focus on the following key indicators:
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SOC Accuracy: It is recommended to choose a BMS with SOC accuracy of no less than 5% to ensure the precision of charge-discharge strategies and profit calculations.
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Sampling Frequency: Prioritize products with a sampling frequency of ≥10Hz to ensure real-time and accurate SOC estimation.
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Communication Compatibility: The BMS should support mainstream protocols such as Modbus, CAN, and Ethernet to achieve seamless communication with PCS, EMS, and other devices.
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Safety Protection: It should have multiple protection mechanisms against overcharging, over-discharging, short circuits, and overcurrent, and be certified by international standards such as UL and CE to ensure system safety and reliability.
5. How to Determine if the SOC Accuracy of BMS is Sufficient?
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Focus on Dynamic Performance: Request accuracy data from manufacturers at different charge-discharge rates such as 1C and 0.5C. Peak-valley arbitrage often involves high-rate operations, and high static accuracy does not guarantee good dynamic performance.
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Emphasize Degradation Compensation: High-quality BMS should have cycle count awareness capabilities, allowing for automatic calibration of the SOC curve. Actual project verification shows that after enabling this feature for 3 years, SOC accuracy remains within ±2.5%.
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On-site Quick Verification: Use a clamp meter to compare the BMS displayed current with the measured current when the battery capacity is around 50%. If the deviation exceeds 5%, be cautious of potential algorithm defects.
In the context of widening peak-valley price differences and stricter capacity subsidy assessments, the BMS for industrial and commercial energy storage is no longer just a technical device—SOC accuracy directly relates to profits, becoming a core economic indicator.
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