Q2. What is the splice kalman filtering algorithm?
The splice Kalman filtering algorithm is based on the ordinaryKalman filtering algorithm, which is a technique for online linearization that is used to linearize the estimated parameters forperforming the iterate calculation treatment, thereby achieving the accurate energy state estimation of the lithium ion battery packs.
Q3. What is the process of the hybrid pulse power characterization test?
In the hybrid pulse power characterization test process, the battery pack is charged with 1C charging current rate firstly, and then the cyclic hybrid pulse powered characterization tests can be performed on it.
Q4. What is the way to estimate the charged state?
When the large changes occur, the traditional open circuit voltage and Ampere-hour integral methods cannot be used for estimating the charged state value online because of its low accuracy disadvantages, so the improved state estimation methods are urgently needed to improve its prediction effect.
Q5. What is the purpose of the hybrid pulse power characterization test?
Taking the 7ICP series lithium cobalt oxide battery pack as the experimental object, the hybrid pulse power characterization test is performed to obtain its dynamic characteristics and then the battery model parameters are calculated.
Q6. Why is the symmetric sampling strategy selected for the unscented transformation process?
The symmetric sampling strategy is selected for the unscented transformation process mainly because of its advantages in adaptive calculation without tedious calculation.
Q7. What is the reason why the precious factor initialization should be conducted?
As the estimation results are always influenced by the initial value of the parameters, the mean and variance values should be initialized accordingly and this is also the reason why the precious factor initialization should be conducted as the origin charged state through the foundation of the model factors.
Q8. What is the effect of the splice Kalman filtering algorithm on the battery?
Based on the composite equivalent modeling and its correction treatment, the splice Kalman filtering algorithm is realized to estimate the charged state value by using the open circuit voltage characteristic towards different charged state levels combined with the Ampere-hour integration methods, and its prediction accuracy is improved dramatically on the lithium ion battery packs according to the iterate calculation process that represents the mathematical description of the inner electrical variations.
Q9. What is the optimum charge state estimation algorithm for lithium ion batteries?
The charged state estimation is investigated for the lithium ion batteries based on a novel reduced-order electrochemical model, which could have a major impact on the energy storage systems worldwide and an advanced machine learning algorithm is conducted for the power batteries of diversified drive cycles.
Q10. What is the b factor used to weaken the weight of noise data onto the filtering?
b is a forgetting factor that is used to weaken the weight of noise data onto the filtering window, which is conducted with a long-term time period of several hours or more.
Q11. Why does the algorithm ignore the higher order terms after the Taylor expansion treatment?
Because this algorithm ignores the higher order terms other than the second-order after the Taylor expansion treatment, its estimation error mainly depends on the correcting calculation process.
Q12. What is the effect of the splice Kalman filtering algorithm on the battery pack?
The analysis charts show that the proposed splice Kalman filtering algorithm applied to the voltage traction follows the voltage of the terminal voltage variation on the lithium ion battery packs with high precision under current varying conditions, and the coincidence degree with the terminal voltage curve in the actual state is higher than the traditional extended Kalman filtering and unscented Kalman filter methods, indicating that it is suitable to be applied to the charged state prediction of the battery pack based on the natural patterns of high adaptation.
Q13. What is the effect of the neural network model on the estimation error?
the estimation error will be large if the data is insufficient which should be escaped due to its dependence on the experimental data.
Q14. What is the voltage between the two electrodes of the lithium ion batteries?
L o pp p p pU E IR U The authorU R C dU dt (1)Wherein, UL(k) is the voltage value between the two electrodes of the lithium ion batteries.
Q15. What is the ohmic internal resistance of the hybrid pulse power characterization test process?
As can be known from Table 1, the ohmic internal resistance of the whole hybrid pulse power characterization test process remains almost unchanged when the charged state is greater than 0.3, and the external polarization internal resistance remains substantially unchanged.