+86 13332949210
info@xihobattery.com
Xiho
Jul 04 2025
Battery "Energy Dashboard": In-depth analysis of how SOE reshapes the lithium battery management landscape
SOC tells you "how much power is left", SOE reveals "how much power can be used" - in the era of efficient energy storage, energy state (SOE) is becoming a core indicator for lithium-ion battery management.
1. What is SEO？
In the application field of lithium-ion batteries, especially in large-scale energy storage systems, it is far from enough to know the remaining power (SOC). State of Energy (SOE) is literally translated as "energy state", which is defined as:
SOE = (current available energy / total available energy) × 100%
For example: a storage battery with a rated energy of 10kWh (such as LiFePO4 battery), when the current available energy is 6.5kWh, SOE = 65%. This means that the system still has 65% of effective energy available for dispatch.
The essential difference between SOE and SOC:
SOC: Focus on the "remaining charge ratio" of rechargeable batteries, similar to the "liquid level height" of fuel tanks;
SOE: Reflects the "actually available energy" of lithium-ion batteries under real working conditions, and requires comprehensive calculation of internal resistance loss, temperature attenuation, aging, output power limit and other factors.
SOE = (current available energy / total available energy) × 100%
For example: a storage battery with a rated energy of 10kWh (such as LiFePO4 battery), when the current available energy is 6.5kWh, SOE = 65%. This means that the system still has 65% of effective energy available for dispatch.
The essential difference between SOE and SOC:
SOC: Focus on the "remaining charge ratio" of rechargeable batteries, similar to the "liquid level height" of fuel tanks;
SOE: Reflects the "actually available energy" of lithium-ion batteries under real working conditions, and requires comprehensive calculation of internal resistance loss, temperature attenuation, aging, output power limit and other factors.
2. How is SEO calculated?
(1) Voltage integration method: simple but error accumulation
Principle: Based on the voltage-energy relationship, the available energy is calculated by power integration.
Advantages: easy to implement and widely used in basic energy storage battery management units (BMS).
Weaknesses: Measurement noise leads to error accumulation and requires regular calibration, especially in the flat voltage platform area of LiFePO4 battery, where the accuracy drops sharply.
(2) Fixed-point integration method: dynamic tracking of energy flow
Principle: Real-time collection of voltage and current data of lithium-ion batteries, combined with time integration to calculate the actual released energy.
Advantages: Intuitively reflects dynamic working conditions and is suitable for short-term scheduling of energy storage systems.
Weaknesses: Relying on high-precision sensors, the integration error is significant when the current fluctuates greatly.
(3) Hybrid algorithm: Kalman filter + equivalent circuit model (ECM)
Principle: Fusion of battery model (such as Thevenin ECM) and adaptive filtering algorithm (such as AUKF), real-time fusion of voltage, current, and temperature data to dynamically correct SOE.
Breakthrough application: The Yunnan Power Grid research team uses the AUKF algorithm, combined with the first-order RC model of lithium-ion batteries, to achieve online estimation of SOE of wearable devices with an error rate of <3%; Joyson New Energy proposed a quasi-sliding mode control algorithm, dynamically switching the AH integral/EKF/Sigma point filter weights to optimize the SOE convergence speed of energy storage batteries.
Challenges: High computational complexity, requiring high-performance embedded chip support.
Principle: Based on the voltage-energy relationship, the available energy is calculated by power integration.
Advantages: easy to implement and widely used in basic energy storage battery management units (BMS).
Weaknesses: Measurement noise leads to error accumulation and requires regular calibration, especially in the flat voltage platform area of LiFePO4 battery, where the accuracy drops sharply.
(2) Fixed-point integration method: dynamic tracking of energy flow
Principle: Real-time collection of voltage and current data of lithium-ion batteries, combined with time integration to calculate the actual released energy.
Advantages: Intuitively reflects dynamic working conditions and is suitable for short-term scheduling of energy storage systems.
Weaknesses: Relying on high-precision sensors, the integration error is significant when the current fluctuates greatly.
(3) Hybrid algorithm: Kalman filter + equivalent circuit model (ECM)
Principle: Fusion of battery model (such as Thevenin ECM) and adaptive filtering algorithm (such as AUKF), real-time fusion of voltage, current, and temperature data to dynamically correct SOE.
Breakthrough application: The Yunnan Power Grid research team uses the AUKF algorithm, combined with the first-order RC model of lithium-ion batteries, to achieve online estimation of SOE of wearable devices with an error rate of <3%; Joyson New Energy proposed a quasi-sliding mode control algorithm, dynamically switching the AH integral/EKF/Sigma point filter weights to optimize the SOE convergence speed of energy storage batteries.
Challenges: High computational complexity, requiring high-performance embedded chip support.
3.What is the role of SEO?
(1) Accurate energy scheduling
Multi-battery group coordination: In the cascade utilization energy storage system, SOE accurately quantifies the actual output capacity of lithium-ion battery groups with different aging degrees to avoid over-discharge risks;
Dynamic strategy switching: Based on the SOE value, the energy storage system automatically switches between "peak-valley arbitrage", "black start" and "photovoltaic consumption" modes to improve economic efficiency.
(2) Safety warning
Deep discharge prevention: When the SOE of the lithium-ion battery is <10%, the BMS is triggered to force power limitation or load shedding;
Aging adaptation: Combined with SOH (health status), the SOE warning threshold is dynamically adjusted to ensure the safety of the LiFePO4 battery system throughout its life cycle.
(3) Endurance prediction
Electric vehicles: SOE integrates road conditions, slope, and air conditioning load, and the mileage prediction accuracy is 40% higher than that of SOC;
Off-grid microgrid: Based on the SOE value of the energy storage battery, the diesel generator is automatically started or non-critical loads are shelved.
From "capacity indicator" SOC to "energy dashboard" SOE, it marks the transition of lithium-ion battery management from "measurement" to "cognition". In the new energy storage era dominated by LiFePO4 battery, SOE is not only data, but also a key bridge connecting the physical properties of rechargeable batteries and the value of the system - it keeps every joule of energy under control.
Multi-battery group coordination: In the cascade utilization energy storage system, SOE accurately quantifies the actual output capacity of lithium-ion battery groups with different aging degrees to avoid over-discharge risks;
Dynamic strategy switching: Based on the SOE value, the energy storage system automatically switches between "peak-valley arbitrage", "black start" and "photovoltaic consumption" modes to improve economic efficiency.
(2) Safety warning
Deep discharge prevention: When the SOE of the lithium-ion battery is <10%, the BMS is triggered to force power limitation or load shedding;
Aging adaptation: Combined with SOH (health status), the SOE warning threshold is dynamically adjusted to ensure the safety of the LiFePO4 battery system throughout its life cycle.
(3) Endurance prediction
Electric vehicles: SOE integrates road conditions, slope, and air conditioning load, and the mileage prediction accuracy is 40% higher than that of SOC;
Off-grid microgrid: Based on the SOE value of the energy storage battery, the diesel generator is automatically started or non-critical loads are shelved.
From "capacity indicator" SOC to "energy dashboard" SOE, it marks the transition of lithium-ion battery management from "measurement" to "cognition". In the new energy storage era dominated by LiFePO4 battery, SOE is not only data, but also a key bridge connecting the physical properties of rechargeable batteries and the value of the system - it keeps every joule of energy under control.
