Li-ion Battery Fractional Order Modeling And Charge Strategy Research

Electric vehicles have been an integral part of modern automobile industry, due to its advantages on much better fuel economy and lower emissions. As the principal part of electric vehicles, the performance of Li-ion battery influences the vehicle dynamics and fuel economy greatly. An effective battery management system can ensure the battery safety and efficiency and is the precondition for promotion and application on electric vehicles. Due to the difficulties of observing the states of battery and the high cost of application, Li-ion battery dynamic behaviors and charging strategy have been the key technology of battery management system.This dissertation paper presents a state-space model with non-integer order derivatives for lithium ion battery and the proposed fractional-order model is used for research on battery charging control. Concrete research content including following several aspects:(1) Based on the electrochemical impedence spectroscopy and the dynamic behaviors of battery system, a fractional-order model was proposed to describe battery dynamics. The derived model with non-integer derivative took the form of equivalent circuit models. The Oustaloup recursive approximation was introduced to model the fractional differentiation operator in integer-order state-space form.(2) A particle-swarm optimization algorithm was used to identify model parameters by using time-domain test data. The model accuracy and robustness were validated by using datasets from different driving cycles, aging levels and cells of the same chemistry. Then, the proposed model was compared with the 1-RC model and the other five equivalent circuit models to prove its superiority. The result indicated that the derived FOM had better dynamic performance and gave more accurate terminal voltage estimation.(3) Based on the proposed fractional-order model, a dual-objective optimal charging strategy for Li-ion batteries was developed, which considers the conflict objectives of charging time and charging loss. The pseudo-spectral method was adopted to discretize the control problem into a nonlinear programming problem. The influences of the charging voltage threshold, temperature, and health status on the charging results were analyzed.