With the increasing popularity of electric vehicles, the performance of power batteries as their core energy directly determines the mileage and safety of the whole vehicle. The design of the battery management system is the key to ensure the efficient and safe operation of the power battery. The core algorithm of the battery management system is like the "brain" of the battery, which is responsible for monitoring the status of the battery and the surrounding environment in real time, carrying out accurate power calculation and safety management, as well as the life and performance of the battery, as well as the energy consumption of the vehicle.
Battery state-of-charge algorithm
The battery state of charge (SOC) is a key parameter to describe the remaining charge of the battery, which is of great significance for the range and battery safety of electric vehicles. The core of the SOC algorithm is to accurately estimate the remaining charge of the battery. Commonly used SoC estimation methods include ampere-hour integration method, open-circuit voltage method, internal resistance measurement method, etc. Ampere-hour integration is an easy way to estimate SOC based on the integration of current, but it is susceptible to temperature and battery aging. The open-circuit voltage method estimates SOC by measuring the open-circuit voltage of the battery in a static state, which has high accuracy but requires a long time to stand. The internal resistance measurement method estimates the SOC by measuring the internal resistance of the battery, which is easy to operate, but requires high accuracy of the measurement equipment. In practical applications, a combination of multiple methods is often used to achieve high-precision estimation of SoC.
Battery state of health algorithm
The state of battery health (SOH) is used to evaluate the degree of degradation of the battery's performance, which is an important parameter to ensure the safety and longevity of the battery. The SOH algorithm mainly focuses on the changes in key indicators such as the capacity, internal resistance, and self-discharge rate of the battery. Commonly used SOH estimation methods include electrochemical model method, capacity comparison method, neural network method, etc. The electrochemical model method is based on the electrochemical model of the battery for SOH estimation, which has high accuracy, but has poor adaptability to the calibration of model parameters and temperature changes. The capacity comparison method estimates SOH by comparing the capacity of old and new batteries, which is simple to operate, but requires high accuracy in initial capacity measurement. The neural network method uses a large amount of historical data to train the model, and the SOH is the best for the model, which has good generalization ability, but the data demand is large and the training process is time-consuming.
Battery balancing control algorithm
The power battery is composed of multiple single cells in series, and the performance of each single battery is different due to the different manufacturing process and aging degree. The cell balancing control algorithm is used to reduce this difference and ensure the performance and safety of the entire group of cells. Common equalization control algorithms include passive equalization and active equalization. Passive balancing relies on resistors, capacitors and other components to consume excess power to achieve balancing, with simple structure but low energy conversion efficiency. Active equalization transfers power through energy converters such as DC DC converters to achieve balance, and the energy conversion efficiency is high, but the structure is complex and the cost is high. In practice, the balancing strategy needs to be considered according to the specific needs and costs.
Charging control algorithms
The charging control algorithm is used to guide the charging process of the battery, ensuring safe, fast and efficient charging. The charging control algorithm needs to consider multiple factors such as charging mode (fast charging, slow charging), charging power, charging state, etc. The charging current is large and the charging speed is fast in the fast charging mode, but it has a certain impact on the battery life; The charging current is small and the charging time is long, but it has little impact on the battery life. The selection of charging power should minimize energy consumption on the premise of ensuring charging speed and safety. The monitoring of the charging state can use a variety of sensors to detect the temperature, voltage and other parameters of the battery in real time to ensure the safety and controllability of the charging process.
Book recommendations
Core Algorithm of Power Battery Management System
The core algorithm of power battery management system combines the author's research and practice for more than ten years, expounds the characteristics and technical problems of power battery management system, and elaborates on the experimental design, dynamic modeling, state of charge estimation, state of health estimation, peak power, remaining life, low temperature rapid heating and optimal charging, and the engineering application and practical problems of the corresponding core algorithm for the application of new energy vehicles, and is equipped with detailed algorithm practice steps and development process. It can be used as a reference book for technicians in related fields, and can also be used as a professional course textbook for senior undergraduate and graduate students majoring in automobiles.
Click here for the link **Power battery management system core algorithm" pdf electronic version.