Cite
Robust Adaptive Sliding-Mode Observer Using RBF Neural Network for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles.
MLA
Chen, Xiaopeng, et al. “Robust Adaptive Sliding-Mode Observer Using RBF Neural Network for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles.” IEEE Transactions on Vehicular Technology, vol. 65, no. 4, Apr. 2016, pp. 1936–47. EBSCOhost, https://doi.org/10.1109/TVT.2015.2427659.
APA
Chen, X., Shen, W., Dai, M., Cao, Z., Jin, J., & Kapoor, A. (2016). Robust Adaptive Sliding-Mode Observer Using RBF Neural Network for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles. IEEE Transactions on Vehicular Technology, 65(4), 1936–1947. https://doi.org/10.1109/TVT.2015.2427659
Chicago
Chen, Xiaopeng, Weixiang Shen, Mingxiang Dai, Zhenwei Cao, Jiong Jin, and Ajay Kapoor. 2016. “Robust Adaptive Sliding-Mode Observer Using RBF Neural Network for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles.” IEEE Transactions on Vehicular Technology 65 (4): 1936–47. doi:10.1109/TVT.2015.2427659.