Software algorithms and control strategies for lithium battery BMS

At the moment when the new energy industry is booming, lithium batteries are widely used in electric vehicles, energy storage systems and other fields due to their advantages such as high energy density and long cycle life. As the core component of the lithium battery system, its software algorithms and control strategies are directly related to the performance, safety and service life of the lithium battery. This article will conduct in-depth discussions on the software algorithms and control strategies of lithium battery BMS, and focus on introducing advanced technologies and application cases in the industry.

1. The core functions and software architecture of lithium battery BMS

Core Functions

• Battery status monitoring: Real-time collection of key parameters such as voltage, current, temperature and other key parameters of the battery, providing data basis for subsequent state estimation and control strategies.  
• Battery state estimation: Accurately estimating the state of charge (SOC), state of health (SOH), and state of power (SOP) of the battery is the key to intelligent battery management by the BMS.  
• Battery balancing management: Through active or passive balancing, ensure the consistency of each single cell in the battery pack and prolong the service life of the battery pack.  
• Charge and discharge control: According to the state and working condition requirements of the battery, the charging and discharging process is reasonably controlled to prevent the occurrence of abnormal conditions such as overcharge and overdischarge.  
• Thermal Management Control: Monitor the battery temperature and take appropriate measures, such as turning on the fan cooling or heating film, to ensure that the battery is operating within the appropriate temperature range, improving battery performance and safety.  
• Fault diagnosis and protection: real-time monitoring of the operating status of the battery system, timely detection and diagnosis of faults, and taking protective measures, such as cutting off the circuit, alarm, etc., to prevent the expansion of faults and ensure system safety.

Software Architecture 

• Real-time Operating System (RTOS) or bare-metal programs: Responsible for timing control and task scheduling to ensure that the functions of the BMS can be executed in real time and efficiently.  
• Application layer software: Implementing core functions such as battery status estimation, charge and discharge control, and fault diagnosis is a key part of BMS intelligently managing batteries.  
• User interface: Provides data visualization, system parameter configuration and diagnostic information to facilitate users to monitor and operate the BMS system.

2. Battery status estimation algorithm

SOC Estimation

• Amphibious integration method: calculate the charge and discharge amount of the battery by integrating current, thereby obtaining the SOC value. This method is simple and easy to use, but it is easily affected by factors such as the accumulation of current sensor errors and battery self-discharge during long-term use, resulting in an increase in estimation error.
• 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, the open circuit voltage is measured and compared with the pre-established open circuit voltage-SOC curve to obtain the current SOC value. This method has high accuracy, but due to factors such as battery temperature and aging, the open circuit voltage-SOC curve will change and compensation is required.
• Kalman filtering method: 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 estimates in real time, and suppress measurement noise and model errors. It has high estimation accuracy and strong anti-interference ability. It is one of the most advanced SOC estimation methods at present, but the calculation volume is relatively large and has high requirements for processor performance. For example, when processing nonlinear systems, the extended Kalman filtering (EKF) algorithm estimates the SOC of the battery by linearizing approximation, which can control the estimation error to less than 5%.

SOH Evaluation

• 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 nominal capacity. This method has 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. 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. SOH can be estimated by measuring the changes in the internal resistance of the battery. However, when used alone, this method is susceptible to factors such as temperature and SOC, and comprehensive evaluation is required in combination with other methods.  
• 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.

3. Battery balance control strategy

Passive equalization

• Principle: By connecting resistors in the battery pack, the excess electrical energy of a single cell with a higher voltage is consumed in the form of thermal energy, so that the voltages of each single cell tend to be consistent.  
• Advantages: Simple circuit, low cost, mature technology, and high reliability.  
• Disadvantages: Low energy utilization rate, only suitable for charging process, slow equalization speed, not suitable for large-capacity battery packs.

Active Equalization

• Principle: The energy of a single battery with a higher energy in the battery pack is actively transferred to a single battery with a lower energy through specific circuits (such as bidirectional DC-DC converters, transformers, etc.) to a single battery with a lower energy to achieve energy reallocation and equalization.  
• Advantages: High energy utilization rate, fast balance speed, bidirectional adjustment, suitable for large-capacity, high string battery packs, can effectively improve the overall performance and service life of the battery pack.  
• Disadvantages: The circuit is complex, the cost is high, and the control accuracy is high.

Equilibrium strategy optimization 

• Based on the fuzzy control algorithm: dynamically adjust the equalization threshold and equalization current according to the real-time state of the battery pack, such as the difference in single voltage and temperature, and give priority to single batteries with large voltage differences to improve equalization efficiency and reduce energy loss.  
• Genetic algorithm based: by simulating biological evolutionary processes, optimizing equilibrium paths and parameters, and finding the optimal equilibrium control strategy to achieve better equilibrium effect and higher energy utilization.

4. Charge and discharge control strategy

Charging control strategy

• Constant current and constant voltage charging method: This is the most commonly used lithium battery charging method at present. In the early stage of charging, the battery is charged with a constant current. When the battery voltage reaches a certain value, it switches to a constant voltage charging until the charging is over. This method can effectively improve charging efficiency, reduce charging time, and avoid overcharging to the battery.  
• Multi-stage charging method: divide the charging process into multiple stages, such as pre-charging, constant current charging, constant voltage charging, floating charging, etc. Depending on the status and requirements of the battery, different charging currents and voltages are used at different stages to further improve charging efficiency and battery performance and extend battery life.  
• Intelligent charging strategy: Dynamically adjust charging current and voltage based on battery status estimation and real-time monitoring data. For example, based on the battery's SOC, SOH, temperature and other parameters, the charging curve is optimized, personalized charging is achieved, and charging safety and efficiency are improved.

Discharge control strategy

• Overdischarge protection: Monitor the battery voltage in real time. When the voltage of the single battery is lower than the set overdischarge threshold, cut off the discharge circuit in time to prevent the battery from being discharged deeply and avoid irreversible damage to the battery. For example, the over-discharge threshold of lithium iron phosphate batteries is usually around 2.5V, and the over-discharge threshold of ternary lithium batteries is about 2.8V.  
• Power limit and dynamic adjustment: limit discharge power according to the battery status and working conditions requirements to avoid battery overload. In applications such as electric vehicles, the discharge power can be dynamically adjusted according to factors such as the driving status of the vehicle, the SOC and temperature of the battery to ensure the safe operation of the battery, and at the same time improve the vehicle's power performance and range.  
• Discharge equalization control: During the discharge process, combined with battery equalization management, appropriate equalization adjustments are performed on single cells with low voltages, so that the battery pack maintains good consistency during the discharge process, and improve the overall discharge performance and service life of the battery pack.

5. Thermal management control strategy

Temperature monitoring and early warning

• Multi-point monitoring: Arrange multiple temperature sensors at key locations of the battery pack to monitor the temperature distribution of the battery in real time. By collecting temperature data at different locations, the thermal state of the battery pack can be more accurately understood, providing a basis for thermal management and control.  
• Temperature warning: Set a temperature warning threshold. When the battery temperature exceeds the warning range, an alarm signal will be issued in time to remind the system to take corresponding measures. For example, when the battery temperature reaches 45℃, a high temperature warning is issued; when the temperature drops below 0℃, a low temperature warning is issued

Heat dissipation control strategy 

• Air-cooled heat dissipation: Use fans and other equipment to accelerate the flow of air around the battery pack, taking away the heat generated by the battery. By controlling the fan speed, dynamically adjusting the heat dissipation intensity according to factors such as battery temperature and discharge power to ensure that the battery temperature is within a reasonable range. For example, when an electric vehicle is driving at high speed or when a battery is discharged at high power, the fan speed is increased and the heat dissipation effect is enhanced.  
• Liquid-cooled heat dissipation: For high-power and large-capacity battery systems, liquid-cooled heat dissipation is adopted. By circulating the coolant, the heat generated by the battery is quickly transmitted and emitted. Liquid-cooled heat dissipation has the advantages of high heat dissipation efficiency and high temperature control accuracy, which can effectively reduce the temperature gradient of the battery pack and improve the performance and life of the battery.

Heating Control Strategies

• Low-temperature preheating: In a low-temperature environment, when the battery temperature drops below a certain value (e.g., 0°C), activate a heating device, such as a heating film or PTC heater, to preheat the battery pack and raise its temperature to a suitable operating range. During the preheating process, the heating power and heating time should be controlled to avoid damage to the battery caused by excessive heating. 
• Temperature equalization control: During the heating process, the temperature of each cell in the battery pack rises evenly through a reasonable control strategy to avoid local overheating or excessive temperature difference. For example, zonal heating control is used to adjust the heating power according to the temperature of each area to achieve uniform distribution of battery pack temperature.

6. Fault diagnosis and protection strategies

Fault diagnosis algorithm 

• Rule-based diagnosis: formulate a series of diagnostic rules based on abnormal characteristics of the battery's voltage, current, temperature and other parameters. When the monitored parameters exceed the preset safety range or there are mutations, the corresponding diagnostic rules will be triggered to determine the type and location of the fault. For example, when the battery voltage suddenly drops to zero, it is judged that there may be a short circuit fault.  
• Statistical method: Use historical data and statistical models to analyze the changing trends and correlations of battery parameters. By analyzing the statistical characteristics of battery parameters, such as mean, variance, correlation coefficient, etc., the battery performance degradation and potential faults are discovered in a timely manner. For example, when the internal resistance of the battery gradually increases and exceeds a certain threshold, it is predicted that the battery may experience an aging failure.  
• Machine learning methods: train machine learning models, such as support vector machines, random forests, neural networks, etc. to identify the normal and abnormal behavior patterns of the battery. By inputting a large amount of battery operation data, the model can learn the characteristics and behavior patterns of the battery, thereby achieving automatic diagnosis and early warning of faults. Machine learning methods have high diagnostic accuracy and adaptability, but require a large amount of training data and professional model training technology.

Failure protection measures

• Cut off circuit: When serious faults are diagnosed, such as short circuit, overcharging, overdischarge, etc., cut off the battery charge and discharge circuit in time to prevent the fault from expanding and protect the safety of the battery and system. For example, quickly cut off the circuit by controlling the on and off of the MOSFET or relay.  
• Fault alarm and indication: In the event of a fault, an audible and light alarm signal is issued to remind the user or system administrator to pay attention. At the same time, the fault type and related information are displayed through the fault indicator light or display screen, which facilitates troubleshooting and handling.  
• Fault isolation: In large battery systems, such as energy storage systems, when a battery module or cluster fails, the faulty part is isolated from the entire system through DC circuit breakers, fuses and other equipment to prevent the spread of the fault and ensure the normal operation of the system.

7. Communication management strategy

Communication protocol selection

• CAN bus protocol: has the advantages of high-speed communication capabilities, low bit error rate, and support for multi-node connections. It is widely used in electric vehicles, energy storage systems and other fields. The CAN bus can realize efficient communication between BMS and vehicle controllers, chargers, inverters and other devices, ensuring the accuracy and reliability of data transmission.  
• RS-485 protocol: suitable for long-distance communication, has the characteristics of strong anti-interference ability and many connected nodes, and is often used for monitoring and management of large-scale energy storage systems. Through the RS-485 bus, multiple BMS slave units can be connected to the master units to achieve centralized monitoring and management.  
• Wireless communication protocol: such as Bluetooth, Wi-Fi, ZigBee, etc., which can be used for wireless communication between BMS and mobile devices, host computers, etc. The wireless communication method has the advantages of easy installation and high flexibility, which facilitates users to monitor the battery status and configure parameters in real time.

Data management and transmission optimization

• Data acquisition and processing: Reasonably design the data acquisition frequency and accuracy, and collect key parameter data according to the status and application requirements of the battery. The collected data is filtered, calibrated, fusion and other processing to improve the accuracy and reliability of the data and provide high-quality data support for subsequent state estimation and control strategies.
• Data transmission optimization: adopts data compression and packaging technologies to reduce data transmission volume and improve transmission efficiency. At the same time, optimize the communication data frame structure to ensure the integrity and real-timeness of data transmission. For example, in CAN bus communication, the ID and length of the data frame are allocated reasonably to avoid data conflicts and transmission delays.

8. Practical application cases and industry trends

Practical Application Cases

• Electric Vehicle: In an electric vehicle project, an SOC estimation method based on the extended Kalman filtering algorithm is adopted, combined with multi-stage charging control strategy and passive equalization management, to achieve high-precision state estimation and effective management of the battery. The BMS system can dynamically adjust the charging current and voltage according to the battery status and vehicle driving needs, optimize the charging and discharging process of the battery, and improve the vehicle's cruising range and battery life. At the same time, through communication with the CAN bus of the vehicle controller, battery status information is transmitted in real time to ensure the safe operation of the vehicle.  
• Energy storage system: In a large energy storage power station, a distributed BMS architecture is adopted, combined with active equalization technology and thermal management strategies based on fuzzy control algorithms, to achieve efficient management and control of large-scale lithium battery packs. The BMS system ensures the temperature uniformity and safety of the battery pack during charging and discharging through multi-point temperature monitoring and intelligent heat dissipation control. At the same time, using wireless communication technology, data transmission and remote monitoring of the energy storage system and remote monitoring center are realized, which facilitates real-time monitoring and management of the operating status of the energy storage system, and improves the reliability and maintainability of the energy storage system.

Industry Trends

• Intelligent and adaptive control: The future lithium battery BMS will be more intelligent and have adaptive control capabilities. By introducing technologies such as artificial intelligence and machine learning, BMS can learn the battery's characteristics and working conditions in real time, automatically adjust control strategies and algorithm parameters, realize more accurate state estimation and more optimized management control, and improve the performance and life of the battery system.  
• High precision and high reliability: As the application scale of lithium batteries in electric vehicles, energy storage and other fields continues to expand, the accuracy and reliability requirements for BMS are also increasing. BMS will adopt more advanced sensor technology, signal processing algorithms and fault diagnosis methods to improve the accuracy of battery status monitoring and estimation, while strengthening the reliability design and redundant design of the system to ensure the stable operation of the BMS under various harsh operating conditions.  
• Integration and Modularity: In order to reduce costs and improve system scalability and maintainability, lithium battery BMS will move towards integration and modularity. The hardware and software functions of the BMS are modularly designed to facilitate flexible combination and expansion according to different application scenarios and battery configurations. At the same time, the BMS is deeply integrated with battery packs, inverters, chargers and other equipment to form a more compact and efficient energy management system.  
• Integration with other technologies: Lithium battery BMS will be deeply integrated with technologies such as the Internet of Things, big data, and cloud computing to realize remote monitoring, intelligent management and data analysis of battery systems. Through IoT technology, BMS can upload real-time data of the battery to the cloud platform, realizing remote monitoring and fault warning of the battery system. Using big data and cloud computing technology, a large amount of battery operation data is analyzed and mined, providing data support for battery health management, performance optimization and life prediction, and promoting the continuous development and progress of lithium battery technology.

To sum up, the software algorithms and control strategies of lithium battery BMS are the key to ensuring the safe and efficient operation of lithium batteries. By continuously optimizing battery status estimation algorithms, balanced control strategies, charge and discharge control strategies, thermal management control strategies, fault diagnosis and protection strategies, and communication management strategies, the performance, life and reliability of lithium batteries can be improved and the new energy industry's growing demand for lithium battery systems can be met. In the future, with the continuous innovation and progress of technology, lithium battery BMS will make greater breakthroughs in intelligence, high precision, high reliability, integration, etc., provide stronger support for the development of the lithium battery industry, promote the sustainable development of the new energy industry, and help the global energy transformation and sustainable development process.

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