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With the continuous development of the industrial and energy industries, the development of new energy vehicles is entering a period of rapid development and is one of the hot research directions today. Due to the needs of different working environments, the demand for mobile power sources in automobiles is increasing, which means that battery design and battery system management (BMS) determine their work efficiency. How to enable users to accurately and in real-time understand the usage status of their electric vehicle batteries is a very important thing, and it is also an important challenge faced in the development process of electric vehicles. This article proposes a battery state-of-charge (SOC) estimation method based on the extended Kalman filter algorithm (EKF) for one of the core areas of the BMS–battery state-of-charge (SOC). According to the guidance and direction of Industry 4.0 in Germany, we hope to address some of the aforementioned challenges for users of automotive and robotics products while developing our industry. Therefore, we made some innovative explorations in this direction. In this study, it was found that the algorithm can adjust parameters in real-time to achieve better convergence. The final estimation results indicate that the algorithm had high accuracy and robustness and can meet the current needs of battery estimation for new energy vehicles, providing an important means for the safety control of automotive BMS. In the long run, this will change the current situation of battery monitoring using mobile power sources. At the same time, it provided an effective and practical implementation method and template for current production estimation, which has a certain heuristic effect on the future process of Industry 4.0 and production estimation.
Li et al. (Thu,) studied this question.