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Abstract:For model-based state of charge (SOC) estimation methods, the battery model parameters change with temperature, SOC, and so forth, causing the estimation error to increase. Constantly updating model parameters during battery operation, also known as online parameter identification, can effectively solve this problem. In this paper, a lithium-ion battery is modeled using the Thevenin model. A variable forgetting factor (VFF) strategy is introduced to improve forgetting factor recursive least squares (FFRLS) to variable forgetting factor recursive least squares (VFF-RLS). A novel method based on VFF-RLS for the online identification of the Thevenin model is proposed. Experiments verified that VFF-RLS gives more stable online parameter identification results than FFRLS. Combined with an unscented Kalman filter (UKF) algorithm, a joint algorithm named VFF-RLS-UKF is proposed for SOC estimation. In a variable-temperature environment, a battery SOC estimation experiment was performed using the joint algorithm. The average error of the SOC estimation was as low as 0.595% in some experiments. Experiments showed that VFF-RLS can effectively track the changes in model parameters. The joint algorithm improved the SOC estimation accuracy compared to the method with the fixed forgetting factor.

This paper proposes a variational model with barriers for Retinex, borrowing the ideas of barrier methods. We first present an energy functional and then deduce a new energy functional from it by adding two barriers. The proposed model is defined as a constrained optimization problem associated with the deduced energy functional. Next, an alternating minimization scheme is used to solve the proposed model. Some theoretic analyses are given for the proposed model and algorithm. Finally, numerical examples are presented to show the effectiveness of the proposed model with its algorithm.

Abstract-In this paper, we present a fast and effective method for solving the Poisson-modified total variation model proposed in [9]. The existence and uniqueness of the model are again proved using different method. A semi-implicit difference scheme is designed to discretize the derived gradient descent flow with a large time step and can guarantee the restored image to be strictly positive in the image domain. Experimental results show the efficiency and effectiveness of our method.

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