To accurately predict quality loss of high reliability and long‐life product in service, a dynamic quality characteristics model is established based on linear degeneration and random error. According to this model, the expectation and variance of dynamic quality characteristics can be figured out. Moreover, assuming that the quality characteristics follow the normal distribution at the initial stage, the expectation of quality loss and the life distribution of dynamic quality characteristics follow three types are derived, such as L‐type (Larger is better), S‐type (Smaller is better), and N‐type (Nominal is better). According to the discount theory of quality loss, the present value model of dynamic quality loss based on the life is derived by combining life and the present value of dynamic quality loss. Then, this model is used to evaluate the quality loss of a GaAs laser and the rationality of the model is analyzed.

In order to achieve accurate state of charge (SOC) estimation of Lithium-Ion Battery, A method that dual Extended Kalman filters (DEKF) optimized by PSO-based Gray Wolf optimizer (MGWO) is proposed. A second-order equivalent circuit model with two resistor-capacitor branches is applied. The battery parameters are determined by battery test. Dual Extended Kalman filters are divided into state filter and parameter filter. Parameter filter is applied to adjust battery parameters online, state filter is applied to SOC estimation. Meanwhile, MGWO is applied to optimize the noise covariance matrix to improve the state estimation accuracy of SOC which reduces the linearization error from EKF. The results shows that the accuracy of algorithm is improved by adding online parameter identification and the optimization of the noise covariance matrix, meanwhile, the proposed method can adapt to the initial error well.

Estimation of battery state and parameters play an important role in electric vehicle battery management system (BMS). Second-order RC model is applied, the initial parameters of battery model are determined by experiments. Data points of open circuit voltage and state of charge (OCV-SOC) are determined by experiment. Different function forms are used to fit the OCV-SOC discrete points, and the function form with great fitting effect is selected as the OCV-SOC fitting form. Dual extended Kalman filter which is divided into Parameter filter and state filter is applied. Battery state in state filter is a fast-time-varying parameter, The battery model parameters in parameter filter are divided into two parts. the battery model parameters are classified according to the influence of each parameter on the terminal voltage. A longer sampling time is applied to the parameters that have a strong impact on the terminal voltage, and a longest sampling time is applied to the parameters that have a weak impact on the terminal voltage. The time-scale classification method is validated both quantitatively and qualitatively. Compared with the previous methods, the three-time-scale classification method can reduce the number of parameter updates.

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If lithium-ion batteries are used under high temperature conditions for a long time, it will accelerate the aging of the battery, and the excessive temperature difference will also affect the consistency of the battery. The cooling system of the battery can make the battery work more safely, and it is important to optimize the heat dissipation of the Lithium-ion battery. It is difficult for batteries to improve the overall performance by optimizing only a single factor. Based on orthogonal analysis and response surface, the thermal performance of the cell was analyzed with five factors, such as runner thickness, runner width, inlet temperature, inlet flow rate, and ambient temperature. A particle swarm algorithm was used to optimize the parameters of the variables, and the best cooling performance can be obtained with the optimized parameters. Using this optimization method, the overall temperature of the cell can be reduced and the uniformity of temperature distribution can be improved. The strategy can be widely applied to improve the structure of the cell and reduce the analysis time.