Li-ion battery health assessment has been widely used in electric vehicles, unmanned aerial vehicle and other fields. In this paper, a new linear prediction method is proposed. By weakening the sensitivity of the Rainflow algorithm to the peak data, it can be applied to the field of battery, and can accurately count the number of Li-ion battery cycles, and skip the cumbersome link of parameter identification. Then, a linear criterion is proposed based on the idea of proportion, which makes the life prediction of Li-ion battery linear. Under the verification of multiple sets of data, the prediction error of this method is kept within 2.53%. This method has the advantages of high operation efficiency and simple operation, which provides a new idea for battery life prediction in the field of electric vehicles and aerospace.

Summary To accurately evaluate the state of charge (SOC) and state of health (SOH) of Li‐ion battery, the second‐order RC equivalent‐circuit model is used to characterize the battery performance, a novel dual adaptive Kalman filtering algorithm is presented by adding double cycles and noise adaptive steps to realize the joint estimation of the SOC and internal resistance. The state variables can be corrected with each other as go through the cycle under three operating conditions. The accuracy of the SOC estimation method proposed in this paper is significantly improved compared with the extended Kalman filtering and the unscented Kalman filtering algorithm. Under three operating conditions, the average error and the maximum error decreased obviously. An equation for calculating the SOH in terms of internal resistance increase was built. The estimation result of the SOH effectively simulated the actual situation, compared with the actual result, the maximum error under the three operating conditions are within a lower level than the improved unscented Kalman filtering algorithm. The convergence effect of the algorithm has obvious advantages over that of the algorithm used for comparison, which could effectively track the state change of the battery.

The accurate estimation and prediction of lithium-ion battery state of health are one of the important core technologies of the battery management system, and are also the key to extending battery life. However, it is difficult to track state of health in real-time to predict and improve accuracy. This article selects the ternary lithium-ion battery as the research object. Based on the cycle method and data-driven idea, the improved rain flow counting algorithm is combined with the autoregressive integrated moving average model prediction model to propose a new prediction for the battery state of health method. Experiments are carried out with dynamic stress test and cycle conditions, and a confidence interval method is proposed to fit the error range. Compared with the actual value, the method proposed in this paper has a maximum error of 5.3160% under dynamic stress test conditions, a maximum error of 5.4517% when the state of charge of the cyclic conditions is used as a sample, and a maximum error of 0.7949% when the state of health under cyclic conditions is used as a sample.

He M, Cao W. Novel coestimation strategy based on forgetting factor dual particle filter algorithm for the state of charge and state of health of the lithium-ion battery. Int

Temperature and cell hysteretic effects are two major factors that influence the reliability and safety in long-term management of battery-integrated systems.In this paper, a hysteresis-compensated electrical characteristic model is established to track the terminal voltage of batteries with the uncertain hysteretic effect of the open-circuit voltage. Then, an autoregressive exogenous model with multi-feature coupling is employed for the identification of the parameters to make them adaptive to the uncertainties of the temperature and hysteretic effects. After that, a novel method for state-of-charge (SOC) estimation based on an adaptive moving window-square root unscented Kalman filter is constructed to avoid the filtering divergence problem caused by the negative error covariance matrix. Multiple constraints, such as Coulombic efficiency, varying ambient temperatures, and hysteresis voltage, are considered for the SOC estimation. Experimental results show that the root-mean-square error for SOC calculation can be limited to 0.0211 when the temperature varied up to 40 C and the root-mean-square error of the voltage measurement noise up to 61.9 mV. The proposed method provides an effective way for batteryintegrated management of electric vehicles. K E Y W O R D Sadaptive moving window-square root unscented Kalman filter, adaptive noise matching, hysteresis-compensated modeling, lithium-ion battery, state-of-charge

This paper aims to improve the stability and robustness of the state-of-charge estimation algorithm for lithium-ion batteries. A new internal resistance-polarization circuit model is constructed on the basis of the Thevenin equivalent circuit to characterize the difference in internal resistance between charge and discharge. The extended Kalman filter is improved through adding an adaptive noise tracking algorithm and the Kalman gain in the unscented Kalman filter algorithm is improved by introducing a dynamic equation. In addition, for benignization of outliers of the two above-mentioned algorithms, a new dual Kalman algorithm is proposed in this paper by adding a transfer function and through weighted mutation. The model and algorithm accuracy is verified through working condition experiments. The result shows that: the errors of the three algorithms are all maintained within 0.8% during the initial period and middle stages of the discharge; the maximum error of the improved extension of Kalman algorithm is over 1.5%, that of improved unscented Kalman increases to 5%, and the error of the new dual Kalman algorithm is still within 0.4% during the latter period of the discharge. This indicates that the accuracy and robustness of the new dual Kalman algorithm is better than those of traditional algorithm.

Summary The remaining useful life (RUL) is a core parameter of the battery management system. To realize accurately predict the RUL, the paper takes the National Aeronautics and Space Administration battery test data set as the research object, and a battery capacity degradation model based on an exponential growth model is built to characterize the battery aging process. A novel cuckoo search optimization particle filtering algorithm is proposed for the RUL prediction by transferring the particles in the prior distribution region of the particle filtering algorithm to the maximum likelihood region. The initial cycle numbers are set differently, the capacity decay process of the four groups of batteries can be predicted completely, and the RUL of the batteries can be obtained. Compared with the commonly used particle filtering and unscented particle filtering algorithms, the results show that the proposed method has obvious advantages in the relative error of prediction and resampling rate under the four datasets. For the B0005 dataset, the relative errors are 4.8%, 3.2%, and 0.8%, respectively under 30, 50, and 70 cycles, corresponding to the particle filtering algorithm errors are 10.3%, 7.9%, and 4.8%, and the unscented particle filter algorithm errors are 6.3%, 4.8%, and 2.4%, respectively. In addition, the method for RUL prediction present in the paper has a high confidence level, and low resampling rate, which plays an important role in promoting the further application of lithium‐ion batteries. Highlights A novel cuckoo search optimization particle filtering algorithm is proposed. Different battery aging cycle number is distinguished to predict. Multiple battery RUL prediction is realized. RUL prediction has high prediction accuracy and high confidence.

No abstract