Yifan Cui

Bio: Yifan Cui is an academic researcher from University of Shanghai for Science and Technology. The author has contributed to research in topics: Extended Kalman filter & Battery (electricity). The author has an hindex of 2, co-authored 7 publications receiving 33 citations.

A fast capacity estimation method based on open circuit voltage estimation for LiNixCoyMn1-x-y battery assessing in electric vehicles

Abstract: With the widespread popularity of electric vehicles (EV), effective assessment for retired EVs has become increasingly critical. Unlike traditional internal combustion vehicles, for EV, batteries account for a large proportion of the entire vehicle cost. Therefore, a fast battery capacity estimation method based on open-circuit voltage (OCV) estimation is forthwith proposed. The method calculates capacity using the ratio of the change in electric quantity to the corresponding change in state-of-charge (SOC), and the SOC is estimated via a fast OCV estimation method proposed in this paper. The fast test procedure includes a charging/discharging test and a short rest, which take less than 30 minutes in total and provide the data for the battery capacity estimation. For estimation, a weighted voltage relaxation model, containing two parallel resistor–capacitor (RC) components, is established. Its parameters are then optimized using the beetle antenna search algorithm with the approximate OCV range obtained in the test and an early voltage relaxation curve. The results of the experiments show that the proposed model and algorithm can accurately estimate the OCV, and the capacity estimation can be quickly realized in half an hour while limiting inaccuracy to less than 3%.

Sequent extended Kalman filter capacity estimation method for lithium-ion batteries based on discrete battery aging model and support vector machine

Abstract: Precise battery capacity estimation plays an important role in the future intelligent battery management system. In this paper, a fusion estimation method based on support vector machine and discrete battery aging model is put forward to enhance the online capacity estimation accuracy of lithium-ion batteries under variable temperature conditions. During the constant current charging process, the support vector machine is developed to estimate the battery capacity, which first trains a single 18650 battery offline and then tests the accuracy of the model using two other batteries of the same type intermittently. Subsequently, the discrete aging model of the battery is proposed to continuously estimate the capacity of the battery. However, unmodelled dynamics between battery aging model and real physical battery is easily occur in the process of modeling, which affects the accuracy and robustness of the model. Therefore, a sequent extended Kalman filter algorithm is deployed for solving the problem. The first Kalman filter takes the identified value of support vector machine as observation value to update the model parameters of discrete battery aging model. The second Kalman filter fuses the identified value of support vector machine and the discrete battery aging model after updating model parameters to improve the precision of online battery capacity estimation. The experimental results indicate that the proposed discrete battery aging model and support vector machine have good applicability, and the algorithm used can online modify the parameters of the model. When the model parameters are modified four times, the fusion estimation error is less than 2%.

Sorting, regrouping, and echelon utilization of the large-scale retired lithium batteries: A critical review

Abstract: With the rapid development of electric vehicles, the safe and environmentally friendly disposal of retired lithium batteries (LIBs) is becoming a serious issue. Echelon utilization of the retired LIBs is a promising scheme because of its considerable potential for generating economic and environmental value. The most outstanding technical challenge of echelon utilization is how to sort and regroup the large-scale retired LIBs efficiently and accurately. In this paper, the status and challenges of echelon utilization for the retired LIBs are reviewed. First, the criteria, policies, regulations, markets, costs, and values of echelon utilization are summarized comprehensively to illustrate its potential and expose existing problems and pain points. Second, the key technologies related to large-scale echelon utilization of LIBs are detailed; valuable opinions and technical routes are presented for the selection and rapid estimation of sorting indices, the classification and regrouping algorithm, evaluation of the sorting results, and other aspects. In particular, a multilevel and multidimensional fast sorting method is proposed for large-scale echelon utilization of retired LIBs that considers different scenario constraints. Valuable solutions to the key technical problems are given, such as predicting the characteristics of retired LIBs with in-service data and building a fast sorting model from a small number of samples to sort large quantities of LIBs. Finally, the technological prospects of echelon utilization are discussed. Big data and artificial intelligence can be used to promote further development and application of echelon utilization, which may eventually be applied to managing the whole life cycle of LIBs.