BMS battery status estimation: SOC, SOH and SOP key insights

In today's new energy era, the application of battery technology is everywhere, from electric vehicles to renewable energy systems, to all kinds of consumer electronic products. As the core component of the battery system, one of its key responsibilities is to accurately estimate the state of the battery, including state of charge (SOC), state of health (SOH) and power state (SOP). Accurate estimation of these state parameters is crucial for efficient, safe and reliable operation of the battery.

SOC: Accurately control the remaining battery power

SOC (State of Charge) is the state of charge of the battery. It reflects the proportional relationship between the remaining battery power and the total capacity, and it intuitively displays the battery's "capacity margin" just like a car's fuel gauge. The following are several common SOC estimation methods and their characteristics:

• Amphibious integration method: calculate the charge and discharge amount of the battery by integrating current to obtain the SOC value. This method is simple and easy to use, but during long-term use, due to the accumulation of errors of the current sensor and the self-discharge of the battery, the SOC estimation error may increase. Therefore, it is often necessary to regularly fully charge the battery to improve estimation accuracy.
• Open circuit voltage method: Estimate based on the correspondence between the open circuit voltage of the battery and the SOC. After the battery is left to stand for a period of time, measure its open circuit voltage and compare it with the pre-established open circuit voltage-SOC curve to obtain the current SOC value. The advantage of this method is that it has high accuracy and is not affected by the battery's self-discharge, but it requires the battery to be in a static state, and the open circuit voltage-SOC curve will change due to factors such as the temperature and aging of the battery, so these factors need to be compensated.
• Kalman filtering method: This is a recursive algorithm based on state space model, which can fuse multiple source information such as battery voltage, current, temperature, etc., update the SOC estimate in real time, and suppress measurement noise and model error. It has high estimation accuracy and strong anti-interference ability, and is one of the most advanced SOC estimation methods at present. However, the calculation amount of this method is relatively large and requires high performance of the processor.

SOH: Insight into the health of the battery

SOH (State of Health) represents the health status of the battery, which reflects the degree of performance degradation of the battery relative to the new battery, and is an important indicator for evaluating battery life and reliability. Here are several commonly used SOH estimation methods:

• Capacity testing method: SOH is determined by performing a complete charge and discharge cycle of the battery and measuring the ratio of its actual capacity to the nominal capacity. This method can directly reflect the capacity attenuation of the battery, with high accuracy, but requires deep charging and discharging of the battery, which takes a long time and will have a certain aging effect on the battery. Therefore, it is usually used for offline testing and evaluation of the battery.
• Internal resistance testing method: The internal resistance of a battery increases with the increase of aging, so SOH can be estimated by measuring the changes in the internal resistance of the battery. This method is simple and easy to implement and can reflect the aging trend of the battery to a certain extent. However, relying solely on internal resistance changes to evaluate SOH has certain limitations, because internal resistance will also be affected by factors such as temperature and SOC.
• Data pattern recognition method: Use machine learning algorithms, such as artificial neural networks, support vector machines, etc. to learn and analyze the battery's historical data and real-time running data, establish a battery's health status model, and predict SOH based on the input feature data. This method can mine complex nonlinear relationships in battery data, with high estimation accuracy and adaptability, but requires a large amount of training data and professional data processing and analysis capabilities.

SOP: Accurately evaluate battery power capabilities

SOP (State of Power) refers to the maximum power that a battery can safely output or absorb at a certain moment. It is particularly important for high-power applications such as electric vehicles. The following are several SOP estimation methods and their characteristics:

• Estimation method based on battery model: By establishing an equivalent circuit model or thermodynamic model of the battery, combining the state information of the battery, such as SOC, temperature, current, etc., the parameters such as the battery's internal resistance, polarization voltage, etc. are calculated, and the SOP is obtained. This method can accurately reflect the power characteristics of the battery, but the model establishment and parameter identification are relatively complex, and the model accuracy and computing capabilities of the battery are required.
• Machine learning method: Use machine learning algorithms to learn and train the battery's historical power data and related state characteristics, and establish SOP prediction models, such as neural networks, decision trees, etc. This method can automatically learn the power characteristics of the battery based on a large amount of historical data, and has strong adaptability and anti-interference ability, but a large amount of accurate data is required during the model training process, and the model's interpretability is relatively poor.

Application scenarios for battery status estimation

• Electric Vehicles: Accurate SOC estimation can provide reliable range information for electric vehicle drivers to avoid driving interruptions caused by insufficient power; SOH evaluation helps predict the service life of the battery and promptly reminds users to maintain or replace the battery; SOP estimation can ensure that the vehicle can operate normally under high-power conditions such as acceleration and climbing, while avoiding battery overload and damage, improving the safety and reliability of the vehicle.
• Renewable energy system: In renewable energy power generation systems such as solar and wind energy, BMS's accurate estimation of the battery status can ensure efficient utilization and stable operation of the energy storage system. By reasonably managing the charging and discharging process of the battery, optimizing the distribution and scheduling of energy according to SOC and SOP, improving the utilization rate of renewable energy and power supply reliability, extending the service life of the battery, and reducing the maintenance cost of the system.

Development trends

With the continuous development of battery technology and the increasing application demand, BMS battery status estimation technology is also constantly innovating and improving. In the future, battery status estimates will develop in the following directions:

• Higher accuracy and reliability: With more advanced sensor technology, signal processing algorithms and data fusion methods, the accuracy and reliability of SOC, SOH and SOP estimation are further improved, estimation errors and uncertainties are reduced, and more powerful support for the refined management and safe operation of batteries.
• More intelligent algorithms: Artificial intelligence technologies such as deep learning and reinforcement learning will be widely used in battery state estimation, allowing BMS to automatically learn the complex characteristics of the battery