Accurate lithium-ion battery modeling with inverse repeat binary sequence for electric vehicle applications

Highlights: • A novel broadband excitation signal for battery modeling is proposed and developed. • The drift and even-order nonlinear effects for parameter estimation are eliminated. • The performances of the proposed method are compared with three common tests. • The robustness of the proposed method is validated at different temperatures. • The proposed method can be easily applied to other types of batteries. -- Abstract: The attribute of battery current excitation signal significantly influences the battery model parameter identification accuracy. However, currently the studies mainly focus on the selection of battery models and the improvement of algorithm, and overlook the influence of excitation signal. More importantly, the conventional excitation signals, which are unsuited to the processes that subjected to the nonlinear effects, can lead to poor estimation accuracy of model parameters. Therefore, this paper proposes a novel excitation signal design method called inverse repeat binary sequence (IRBS). The theoretical analysis shows that the antisymmetric characteristic of IRBS can overcome the adverse effects of the direct current component and even-order nonlinearities for parameter estimation. Then, the design parameters of the excitation signal are determined for real application by analysing the single-sided amplitude spectrum of the typical battery test loading profiles of electric vehicles, and model parameters are estimated by means of particle swarm optimization algorithm. Finally, the experimental results of different temperatures based on the LiNiMnCoO₂ lithium-ion battery validate that IRBS is feasible, and has the higher accuracy than three commonly used excitation signal design methods.