Lithium batteries are widely used in energy storage power systems such as hydraulic, thermal, wind and solar power stations, as well as power tools, military equipment, aerospace and other fields. The traditional fusion prediction algorithm for the cycle life of energy storage in lithium batteries combines the correlation vector machine, particle filter and autoregressive model to predict the cycle life of lithium batteries, which are subjected to many uncertainties in the prediction process and to inaccurate prediction results. In this paper, a probabilistic prediction algorithm for the cycle life of energy storage in lithium batteries is proposed. The LS-SVR prediction model was trained by a Bayesian three-layer reasoning. In the iterative prediction phase, the Monte Carlo method was used to express and manage the uncertainty and its transitivity in a multistep prediction and to predict the future trend of a lithium battery's health status. Based on the given failure threshold, the probability distribution of the residual life was obtained by counting the number of particles passing through the threshold. The wavelet neural network was used to study the sample data of lithium batteries, and the mapping relationship between the probability distribution of the residual life of lithium batteries and the unknown values were established. According to this mapping relation and the probability distribution of the residual life of lithium batteries, the health data could be deduced and then iterated into the input of the wavelet neural network. In this way, the predicted degradation curve and the cycle life of lithium batteries could be obtained. The experimental results show that the proposed algorithm has good adaptability and high prediction efficiency and accuracy, with the mean error of 0.17 and only 1.38 seconds by average required for prediction.World Electric Vehicle Journal 2019, 10, 7 2 of 17 directly measured but needs to be estimated in advance to decide whether to replace the battery to avoid some unnecessary events. The performance of batteries can be divided into two categories: electrical performance and reliability. Battery life is one of the important indicators to measure the electrical performance of batteries. Charge-discharge cycles include a charge operation and a discharge operation. The number of charge-discharge cycles that a battery can carry out while maintaining a certain output capacity is called the cycle life (service life) of the battery. For energy storage batteries, it is generally believed that the life of the battery is terminated when the available capacity of the battery decreases to 70% of the initial level [4]. Battery life includes cycle life and calendar life, where the former refers to the number of cycles of the battery from a certain charging and discharging system to the end of life and the latter refers to the time required for the battery to be stored in a certain state until the end of life. There are many complex physical and chemical reactions in the charge and discha...
The ultrafast dissociation dynamics of NO2 molecules was investigated by femtosecond laser pump-probe mass spectra and ion images. The results show that the kinetic energy release of NO+ ions has two components, 0.05 eV and 0.25 eV, and the possible dissociation channels have been assigned. The channel resolved transient measurement of NO+ provides a method to disentangle the contribution of ultrafast dissociation pathways, and the transient curves of NO+ ions at different kinetic energy release are fitted by a biexponential function. The fast component with a decay time of 0.25 ps is generated from the evolution of Rydberg states. The slow component is generated from two competitive channels, one of the channel is absorbing one 400 nm photon to the excited state A2B2, which has a decay time of 30.0 ps, and the other slow channel is absorbing three 400 nm photons to valence type Rydberg states which have a decay time less than 7.2 ps. The channel and time resolved experiment present the potential of sorting out the complex ultrafast dissociation dynamics of molecules.
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