Research Of Online Parameter Identification Algorithms For Lithium-ion Batteries In Electric Vehicles

Lithium-ion Battery Model’s Parameter Identification is the main purpose of this thesis.Recursive Least Square(RLS)algorithms are considered as a kind of efficient on-line method.The accuracy,stability,robustness and fast tracking ability of the RLS algorithm are significantly influenced by a parameter named forgetting factor.The performance of the RLS algorithm that has a fixed forgetting factor is quite different under different battery working conditions.In order to improve the adaptability of RLS algorithms,a variable forgetting factor method is put forward.The novel algorithm is named as VFF-RLS algorithm.In this thesis,following works are done:Firstly,a kind of ternary Lithium-ion battery is employed in this thesis.Its capacities and SOC-OCV relationships under different temperatures are obtained by a series of battery experiments.The experiment data is also used to verify the performance of the VFF-RLS algorithm.Secondly,the mathematical equations of Thevenin battery model and DP battery model are introduced.The discretization of these equations is derived to adapt the demands of online automatic control.Then,the principle of the RLS algorithm is introduced.The effects of the forgetting factor on parameter identification and SOC estimation are analyzed.A novel variable forgetting factor method is put forward.A model parameter and SOC co-estimation method is designed to verify this new method.A simulation platform is established in Matlab/Simulink software.The accuracy,robustness,fast tracking ability and adaptability of the VFF-RLS algorithm are verified by comparing with RLS algorithm.It can be concluded that the forgetting factor in the VFFRLS algorithm could be optimized through different battery working conditions and algorithm prediction errors.The influences of temperature could be eliminated by the VFFRLS algorithm that proves this algorithm has good adaptability.