Battery management is the key technical link for electric vehicles. A good battery management system can realize the balanced charge and discharge of batteries, reducing the capacity degradation and the loss of health caused by battery overcharge and discharge, which all depend on the real-time and accurate estimation of the battery’s state of charge (SOC). However, the battery’s SOC has highly complex nonlinear time-varying characteristics related to the complex chemical and physical state and dynamic environmental conditions, which are difficult to measure directly, and this has become a difficulty in design and research. According to the characteristics of ternary lithium-ion batteries of electric vehicles, a battery SOC dual estimation algorithm based on the Variable Forgetting Factor Recursive Least Square (VFFRLS) and Multi-Innovation Unscented Kalman Filter (MIUKF) is proposed in this paper. The VFFRLS algorithm is used to estimate battery model parameters, and the MIUKF algorithm is used to estimate the battery’s SOC in real time. The two algorithms are coupled to update battery model parameters and estimate the SOC. The experiment results show that the algorithm has high accuracy and stability.

Recently, the extraction efficiency and quality of depth data have become the major concern in stereo vision. This work presents stereo depth estimation method based on Bidirectional Matching Method (BM ${}^2$) aiming at achieving a good speed-accuracy trade-off. In this study, a novel bidirectional matching process is proposed for matching cost computation. Additionally, a robust cost function is designed for computing color, gradient and local binary pattern similarity between potential matching points. Next, an efficient adaptive spatially smooth edge-preserving filter is applied to minimize matching ambiguities. Following this, a Knock-Out Disparity Selection Technique (KO-DST) that iteratively solves a sequence of discrete binary optimization problem is used to generate disparity map. Finally, a discontinuity preserving disparity refinement is applied to correct disparity errors and refine the resulting disparity map. Rigorous experiments and analysis on Middlebury datasets validated the effectiveness of the presented idea and its superiority over some state-of-the-art algorithms. The proposed method runs approximately two times faster than the standard bidirectional matching.