Batteries are gaining entry into every home and office for they are widely used because of their variant benefits. However, these batteries are prone to failure caused by charge imbalance in the batteries connected in either series or parallel, which can sometimes be catastrophic and hence they require to be properly monitored in a real-time manner. There exist many battery balancing schemes which are broadly grouped into either passive or active schemes. All these schemes have their own advantages and disadvantages, and hence it is upon the user to decide on which scheme will best work for them. However, research has proven that the hybrid scheme will be the best as it couples the benefits of all schemes. This study will review the various battery cell balancing methodologies and evaluate their relationship with battery performance. At present there are a few studies tackling the mechanical vibration of battery balancing performance. This study shows that battery balancing performance during long-term should be evaluated from various temperature and vibration frequencies.

As the secondary widely used battery, lithium-ion batteries (LIBs) have become the core component of the energy supply for most devices. Accurately predicting the current cycle time of LIBs is of great importance to ensure the reliability and safety of the equipment. In this paper, considering the nonlinear and non-Gaussian capacity degradation characteristics of LIBs, a remaining useful life (RUL) prediction method based on the exponential model and the particle filter is proposed. The cycle life test data of LIBs published by prognostics center of excellence in national aeronautics and space administration were exponentially experiencing the rule of degradation. And then the extrapolation method was used to get the quantitative expression of the uncertainty of life expectancy of LIBs, i.e. the prediction mean and the probability distribution histogram. The prognostic horizon index and the new specific accuracy index were applied to evaluate the prediction performance. Moreover, the prediction error under different prediction starting points is given. Compared with other methods such as the auto-regressive integrated moving average model, the fusion nonlinear degradation autoregressive model and the regularized particle filter algorithm, the proposed algorithm has a better prediction performance. According to the accuracy index, the proposed prediction method has better prediction accuracy and convergence. The RUL prediction for LIBs can provide a better decision support for the maintenance and support systems to optimize maintenance strategies, and reduce maintenance costs.

Equivalent circuit models are a hot research topic in the field of lithium-ion batteries for electric vehicles, and scholars have proposed a variety of equivalent circuit models, from simple to complex. On one hand, a simple model cannot simulate the dynamic characteristics of batteries; on the other hand, it is difficult to apply a complex model to a real-time system. At present, there are few systematic comparative studies on equivalent circuit models of lithium-ion batteries. The representative first-order resistor-capacitor (RC) model and second-order RC model commonly used in the literature are studied comparatively in this paper. Firstly, the parameters of the two models are identified experimentally; secondly, the simulation model is built in Matlab/Simulink environment, and finally the output precision of these two models is verified by the actual data. The results show that in the constant current condition, the maximum error of the first-order RC model is 1.65% and the maximum error for the second-order RC model is 1.22%. In urban dynamometer driving schedule (UDDS) condition, the maximum error of the first-order RC model is 1.88%, and for the second-order RC model the maximum error is 1.69%. This is of great instructional significance to the application in practical battery management systems for the equivalent circuit model of lithium-ion batteries of electric vehicles.

The deterioration of the health state of lithium-ion batteries will lead to the degradation of the battery performance, the reduction of the maximum available capacity, the continuous shortening of the service life, the reduction of the driving range of electric vehicles, and even the occurrence of safety accidents in electric vehicles driving. To solve the problem that the traditional battery management system is difficult to accurately manage and predict its health condition, this paper proposes the mechanism and influencing factors of battery degradation. The battery capacity is selected as the characterization of the state of health (SOH), and the long short-term memory (LSTM) model of battery capacity is constructed. The intrinsic pattern of capacity degradation is detected and extracted from the perspective of time series. Experimental results from NASA and CALCE battery life datasets show that the prediction approach based on the LSTM model can accurately predict the available capacity and the remaining useful life (RUL) of the lithium-ion battery.