A branch current estimation and correction method for a parallel connected battery system based on dual BP neural networks

• Published in: Green Energy and Intelligent Transportation 2022-09, Vol.1 (2), p.100029, Article 100029
• Main Authors: Yu, Quanqing, Liu, Yukun, Long, Shengwen, Jin, Xin, Li, Junfu, Shen, Weixiang
• Format: Article
• Language: English
• Subjects: BP neural Network; Branch current estimation and correction; Electric vehicles; Lithium-ion battery pack
• ISSN: 2773-1537; 2773-1537
• DOI: 10.1016/j.geits.2022.100029

In the actual use of a parallel battery pack in electric vehicles (EVs), current distribution in each branch will be different due to inconsistence characteristics of each battery cell. If the branch current is approximately calculated by the total current of the battery pack divided by the number of the parallel branches, there will be a large error between the calculated branch current and the real branch current. Adding current sensors to measure each branch current is not practical because of the high cost. Accurate estimation of branch currents can give a safety warning in time when the parallel batteries of EVs are seriously inconsistent. This paper puts forward a method to estimate and correct branch currents based on dual back propagation (BP) neural networks. In the proposed method, one BP neural network is used to estimate branch currents, the other BP neural network is used to reduce the estimation error cause by current pulse excitations. Furthermore, this paper makes discussions on the selection of the best inputs for the dual BP neural networks and the adaptability of the method for different battery capacity and resistence differences. The effectiveness of the proposed method is verified by multiple dynamic conditions of two cells connected in parallel. [Display omitted] •A branch current estimation method for parallel battery packs is proposed.•A method to eliminate the current estimation error caused by dynamic excitation is proposed.•The influence of inputs to the dual neural networks on the estimated results is discussed in depth.•The effectiveness of the proposed method is validated by a battery pack with two parallel cells.