Active equalization control strategy of
            <scp>Li‐ion</scp>
            battery based on state of charge estimation of an electrochemical‐thermal coupling model

Summary State of health (SOH) prediction is always a research hotspot in the field of lithium‐ion batteries (LIBs). Machine learning (ML) methods have received widespread attention for their high prediction accuracy. However, the existing studies only focus on extracting features from simple constant current charge‐discharge curves or using features that require pre‐processing, while the actual discharge current is random and can affect battery aging. Besides, the online sequential extreme learning machine (OSELM) currently used in ML lacks a more efficient online learning and update mechanism in terms of prediction. Therefore, this paper firstly extracts effective features from the random discharge data and conducts a mechanism analysis to verify its rationality. Then, we propose a drift detection based on the Bernstein inequality (BI‐DD) algorithm and use it to guide the OSELM to save learning time. The experimental results show the OSELM based on the BI‐DD can perform good learning for SOH prediction in a shorter time. The learning time can be reduced by up to 88.87% and the mean absolute error (MAE) does not exceed 1%, which is a promising SOH prediction method. Highlights Extract aging features from random discharge data and the rationality of the extracted features is analyzed according to the mechanism. A drift detection algorithm based on Bernstein inequality (BI‐DD) is proposed. An OSELM based on concept drift detection is proposed and for SOH online learning and prediction.

“…According to classical circuit analysis, the battery terminal voltage and open circuit voltage satisfy the following equation 32 :

C_{po} \frac{{italicdU}_{po}}{dt} + \frac{U_{po}}{R_{po}} = I,

U = U_{ocv} - {IR}_{oh} - U_{po},

Section: Statistical Learning Approachmentioning

confidence: 99%

“…The circuit diagram is shown in Figure 3. According to classical circuit analysis, the battery terminal voltage and open circuit voltage satisfy the following equation 32 :…”

Section: The Voltage Upward Appreciation After Dischargementioning

confidence: 99%

State of health prediction for lithium‐ion batteries with a novel online sequential extreme learning machine method

Tian

Qin

2020

“…The cell imbalances are detected and controlled by the balancing algorithms and are classified as state of charge (SOC), terminal voltage and open circuit voltage (OCV) based 6 . Using OCV or SOC provides better estimation of SOC, but the outcomes depend on accuracy of the SOC estimation algorithm 7 . It is not an efficient method for batteries which have flat OCV versus SOC curves (eg, LiFePO 4 ).…”

Section: Introductionmentioning

confidence: 99%

Adaptive passive balancing in battery management system for e‐mobility

Duraisamy

Deepa

2021

Summary Balancing the lithium‐ion battery pack is essential to enhance the energy usage and life cycle of the battery. This paper analyses passive cell balancing method of Li‐ion battery for e‐mobility application based on the energy loss and cost estimation through simulation and hardware implementation. As a part of the simulation, an electrical equivalent circuit battery model is developed and model parameters are obtained using electrochemical impedance spectroscopy test. The experimented passive balancing topologies include a switched shunting resistor circuit with appropriate control logic. Final voltage‐based balancing algorithm is implemented to balance the cells at high state of charge. Experimental results are compared against the theoretical investigation. Based on the outcome of this experiment, the most significant characteristics of the passive balancing system can be developed by considering the impact on the battery performance, energy loss, and cost.

“…[18][19][20] The model-based method is a combination of battery model, parameter identification algorithm and filter algorithm. Electrochemical model 21,22 and equivalent circuit model (ECM) 23 are two kinds of battery models widely used for describing the characteristics of LIBs. For online SOC estimation, ECM is widely used because of a good balance between computation complexity and estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

State of charge estimation for lithium‐ion battery based on an intelligent adaptive unscented Kalman filter

Sun

Zhang

et al. 2020

Adaptive unscented Kalman filter (AUKF) has been widely used for state of charge (SOC) estimation of lithium-ion battery. The noise covariance of the conventional AUKF method is updated based on the innovation covariance matrix (ICM), which is estimated using the error innovation sequence (EIS). However, the distribution of EIS changes due to the time-varying noise, load current dynamics and modelling error, which will lead to inaccurate ICM estimation. Therefore, an intelligent adaptive unscented Kalman filter (IAUKF) method is proposed to detect the distribution change of EIS. Then, the ICM is estimated based on the EIS after the distribution change. Results show that the IAUKF method can improve SOC estimation accuracy significantly. Compared with that of the AUKF method, the root mean squared error and the mean absolute error of SOC based on the IAUKF method decrease by 43.70% and 72.37% under random walk discharge condition, respectively. In addition, the computation time of the IAUKF method slightly increases by 6.27% compared with that of AUKF method. Finally, the effect of initial parameters on the SOC estimation accuracy was analysed. The results indicate that proper algorithm tuning, such as initial window length of EIS for ICM update and the threshold value, can further improve the SOC accuracy based on the proposed IAUKF method. The proposed IAUKF method also shows high robustness against initial measurement noise covariance.