Lithium battery state estimation refers to the estimation of important parameters such as the remaining capacity, health status, and usable life of lithium batteries through the monitoring and analysis of the internal state of lithium batteries. The accuracy of state estimation is critical for the safe and efficient use of lithium batteries, and here are some key technologies:
1.Voltage and current monitoring: By monitoring the voltage and current of lithium batteries, information such as the amount of electricity, state of charge (SOC), and state of health (SOH) of lithium batteries can be obtained. This information is an important basis for the state estimation of lithium batteries.
2.Data analysis and modeling: Through the analysis and modeling of historical data, a mathematical model of the lithium battery can be established for the remaining capacity, health status, usable life and other parameters of the lithium battery. Commonly used modeling methods include neural network-based methods, Kalman filter-based methods, etc.
3.Data fusion and optimization: Through the fusion and optimization of data from multiple sensors, the accuracy and reliability of lithium battery state estimation can be improved. Commonly used data fusion and optimization methods include weighted average, Kalman filtering, etc.
4.Artificial intelligence technology: Artificial intelligence technology, such as machine learning and deep learning, can be used for modeling and ** lithium battery state estimation. These techniques can automatically learn and extract useful features and patterns from historical data to improve the accuracy and reliability of lithium battery state estimation.
5.Hardware implementation of lithium battery state estimation: The hardware implementation of state estimation includes sensors, data acquisition circuits, processors, and memories. Hardware design needs to consider factors such as accuracy, speed, and power consumption of data acquisition to ensure the accuracy and reliability of state estimation.
Specific case description:
Let's say we have a lithium-ion battery and we want its residual charge (SOC). We can measure the terminal voltage (V), current (I), temperature (T) and time (T) of the battery. We can then use data processing and analysis techniques to extract useful features and information. The following is a specific implementation of the state estimation of a lithium battery:
1.Data acquisition: Use ammeter and voltmeter to measure the discharge current and discharge voltage of lithium batteries in real time, and record time and temperature at the same time.
2.Data processing: The collected data is filtered, denoised, etc., and the data is fitted with time series and curves to obtain the variation law of lithium battery discharge voltage and discharge current.
3.Model establishment: Based on the physical characteristics of lithium batteries and the state estimation algorithm of lithium batteries, a mathematical model of the remaining power of lithium batteries and variables such as discharge voltage, discharge current, time and temperature is established.
4.Model training: Use a large amount of lithium battery data for training, optimize model parameters, and improve the accuracy of the model.
5.State estimation: The trained model is used to estimate the remaining power of the lithium battery, and the error analysis of the estimation results is performed.
6.Result output: Output the remaining power estimation result of the lithium battery, and provide the error analysis result for the user's reference.
The above is an example of lithium battery state estimation, which may require more complex algorithms and more data acquisition and processing steps.
Specific to the algorithm, we can use machine learning algorithms, such as support vector machine (SVM), decision tree, regression tree (RT), etc., to perform the remaining power, and the training model is to use multiple sets of data for training. We then feed the new data into the trained model, which will ** the SoC of that battery.
We can also use physical models to retain power, such as impedance models, neural network models, etc. We can build a model of a lithium-ion battery based on these models, and then use the collected data to train and tune it. Finally, we can *** the SoC of the battery.