Accommodating temperature change in the identification of a nonlinear model of the Li-ion battery cell

The success of the next generation systems, from consumer electronics, aerospace systems to the modern transportation systems rely on their energy consumption, efficiency and lifetime (both calendar life and cycling life). One of the most crucial (important) components and still one of the weakest links in such systems is the main energy storage component i.e. the battery or battery pack [1]. Lithium ion batteries LiB) have gained acceptance into the consumer electronic, power grid, automotive and aerospace worlds as compared to other types of batteries because their high energy and high power density render them an excellent option for energy storage, particularly in hybrid and electric vehicles as well as an ideal candidate for a wide variety of applications. In order to develop a complete dynamic model of a lithium ion battery that is suitable for virtual-prototyping of portable battery-powered systems, accurate estimation of the state of charge (SOC) and state of health (SOH) is required, which in-turn depends on the quality of the models which are used for the estimation of these quantities. Temperature is known to have a significant impact on the performance, safety, and cycle lifetime of LiB [2–4]. In order to correctly predict (or
simulate) the short-term electric response of a LiB cell , it is neccessary to accommodate the effect of temperature in the dynamic model of the LiB cell. In this paper, a data-driven polynomial nonlinear state-space (PNLSS) model structure is proposed, which can accommodate the effect of temperature change when the battery operates in the nonlinear regime of its electrical operation.