Because lithium batteries have non-linear operating characteristics, the evaluation of their performance is a very complex issue. Generally, manufacturers perform aging tests on lithium batteries, that is, evaluate the performance of lithium batteries based on measuring capacity, internal resistance, voltage, and other internal and external parameters during charge and discharge cycles. But this requires precise equipment which is not realistic for users, so it is necessary to evaluate the status of health (SOH) for lithium-ion batteries from the side. The cycle life test data for lithium-ion batteries reveals that discharge time is highly correlated with health status, especially the data during the discharge/charge platform period is the easiest to obtain and the most reliable. Besides, the time health indicator is more effective than the voltage indicator, and it is easier to obtain than an internal resistance indicator and capacity indicator. In this paper, three indicators of optimal discharge, optimal charge, and hybrid health indicators are constructed and analyzed in detail. Then use simple Bayesian Monte Carlo theory to evaluate the degradation status of batteries to verify the effects of these indicators and compare them with capacity indicators. The experimental results of four batteries show that the scheme is simple and convenient, and can effectively evaluate the SOH of lithium-ion batteries.

Generally, the State-of-Health (SOH) monitoring and Remaining Useful Life (RUL) prediction and assessment of lithium-ion (Li-ion) batteries need to use sensors to obtain the degradation test data of the same type of batteries and establish the degradation model for reference. However, when the battery type is unknown, a usable reference model cannot be obtained, so its prediction and evaluation may be relatively inconvenient. In this paper, the State of-Health prediction for lithium-ion batteries based on a novel hybrid scheme is proposed. Firstly, historical charge/discharge time series and capacity series are extracted to analyze and construct Health Indicators, then using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose the Health Indicator series into the trend and non-trend terms. Among them, the relatively smooth trend item data series uses the Autoregressive Integrated Moving Average model (ARIMA) for prediction; when dealing with the data series of non-trend items which are obviously non-smooth and seemingly random, the residuals predicted by ARIMA and the non-trend items obtained by CEEMDAN decomposition are combined into new non-trend items; then the least square support vector machine (LSSVM) is introduced to build a nonlinear prediction model and make predictions. Finally, combining the prediction results of the trend item data series and the non-trend item data series as a reference for the assessment of the state of health and remaining useful life. The 13 experimental results of 3 batteries verify the effectiveness of the scheme.

The accuracy of predicting the remaining useful life of lithium batteries directly affects the safe and reliable use of the supplied equipment. Since the degradation of lithium batteries can easily be influenced by different operating conditions and the regeneration and fluctuation of battery capacity during the use of lithium batteries, it is difficult to construct an accurate prediction model of lithium batteries. Therefore, research into high-precision methods of predicting the remaining useful life has been a popular topic for the whole-life management system of lithium batteries. In this paper, a new hybrid optimization method for predicting the remaining useful life of lithium batteries is proposed. The proposed method incorporates two different swarm intelligence optimization algorithms. Firstly, the whale optimization algorithm is used to optimize the variational mode decomposition (WOAVMD), which can decompose the historical life data into several trend components and non-trend components. Then, the sparrow search algorithm is applied to optimize the long short-term memory neural network (SSALSTM) to predict the non-trend component and the autoregressive integrated moving average model (ARIMA) is used to predict trend components. Finally, the prediction results of each component are integrated to evaluate the remaining useful life of lithium batteries. Results show that better prediction accuracy is obtained in the prediction experiments for several types of batteries in both the NASA and CALCE battery datasets. The generalization ability of the algorithm has also been effectively improved owing to the optimization of parameters of the variational mode decomposition (VMD) and the long short-term memory neural network (LSTM).

Generally, most approaches using methods such as cropping, rotating, and flipping achieve more data to train models for improving the accuracy of detection and segmentation. However, due to the difficulties of labeling such data especially semantic segmentation data, those traditional data augmentation methodologies cannot help a lot when the training set is really limited. In this paper, a model named OFA-Net (One For All Network) is proposed to combine object detection and semantic segmentation tasks. Meanwhile, using a strategy called “1-N Alternation” to train the OFA-Net model, which can make a fusion of features from detection and segmentation data. The results show that object detection data can be recruited to better the segmentation accuracy performance, and furthermore, segmentation data assist a lot to enhance the confidence of predictions for object detection. Finally, the OFA-Net model is trained without traditional data augmentation methodologies and tested on the KITTI test server. The model works well on the KITTI Road Segmentation challenge and can do a good job on the object detection task.