Two‐layer online state‐of‐charge estimation of lithium‐ion battery with current sensor bias correction

To enhance the estimation accuracy of battery's state of charge, it is imperative to estimate the battery model parameter. To reduce the calculation efforts, the number of the battery model parameter to be estimated should be less while ensuring the state of charge estimation accuracy. Especially in engineering applications, the calculating ability is usually limited. So, it needs to choose the critical battery model parameter to be estimated. This paper's contributions are as follows: The global sensitivity analysis of the battery model parameter is achieved by the Monte Carlo simulation method. The results show that the open circuit voltage and the ohmic resistance are the high sensitivity parameters. Guided by the results of parameter sensitivity analysis, a dual extended Kalman filters method is utilized to achieve online battery model parameter estimation. The experiments prove that the state of charge estimation accuracy is improved by the online parameter estimation. Estimating high sensitivity parameters can reduce running time. And the SOC estimation accuracy can be guaranteed.

Section: Introductionmentioning

confidence: 99%

The equivalent circuit battery model parameter sensitivity analysis for lithium‐ion batteries by Monte Carlo simulation

Guo

Zhou

et al. 2019

“…Different from the direct update of Q and R in the literature, the adaptive adjustment strategy of the covariance matrix proposed in this study does not update Q and R directly. The adjustments of Q and R are given only when calculating the Kalman filter gain and the error covariance matrix.…”

Section: Battery Pack Imbalance Evaluationmentioning

confidence: 99%

Active cell balancing of lithium‐ion battery pack based on average state of charge

Zhang

et al. 2020

Summary Differences in the environment and parameters of lithium‐ion battery (LiB) cells may lead the residual capacity between the battery cells to be inconsistent, and the battery cells may be damaged due to overcharging or overdischarging. In this study, an active balancing method for charging and discharging of LiB pack based on average state of charge (SOC) is proposed. Two different active balancing strategies are developed according to the different charging and discharging states of LiB pack. When the LiB pack is charging, charging balance strategy is performed, wherein the battery cells whose SOC is higher than the average SOC of the LiB pack are balanced to increase the charging capacity of the entire LiB pack. When the LiB pack is discharging or static standing, discharging balance strategy is performed, wherein the batter cells whose SOC is lower than the average SOC of the LiB pack are balanced to increase the discharging capacity of the entire LiB pack. The experimental results show that the proposed active balancing method can reduce the inconsistency of residual energy between the battery cells and improve the charging and discharging capacity of the LiB pack.

“…The common SOC estimation methods include current integration method, Kalman filtering algorithm, and neural network algorithm . The current integration method is simple and is the most widely used SOC estimation method.…”

Section: Introductionmentioning

confidence: 99%

Equalization of series connected lithium‐ion batteries based on back propagation neural network and fuzzy logic control

Wang

Qin²,

Zhao

et al. 2020

Summary In this article, a nondissipative equalization scheme is proposed to reduce the inconsistency of series connected lithium‐ion batteries. An improved Buck‐Boost equalization circuit is designed, in which the series connected batteries can form a circular energy loop, equalization speed is improved, and modularization is facilitated. This article use voltage and state of charge (SOC) together as equalization variables according to the characteristics of open‐circuit voltage (OCV)‐SOC curve of lithium‐ion battery. The second‐order RC equivalent circuit model and back propagation neural network are used to estimate the SOC of lithium‐ion battery. Fuzzy logic control (FLC) is used to adjust the equalization current dynamically to reduce equalization time and improve efficiency. Simulation results show that the traditional Buck‐Boost equalization circuit and the improved Buck‐Boost equalization circuit are compared, and the equalization time of the latter is reduced by 34%. Compared with mean‐difference algorithm, the equalization time of FLC is decreased by 49% and the energy efficiency is improved by 4.88% under static, charging and discharging conditions. In addition, the proposed equalization scheme reduces the maximum SOC deviation to 0.39%, effectively reducing the inconsistency of batteries.