A Novel Methodology to Characterize the Constitutive Behaviour of Polyethylene terephthalate for the Stretch Blow Moulding process

The stretch blow moulding (SBM) process is the main method for the mass production of PET containers. And understanding the constitutive behaviour of PET during this process is critical for designing the optimum product and process. However due to its nonlinear viscoelastic behaviour, the behaviour of PET is highly sensitive to its thermomechanical history making the task of modelling its constitutive behaviour complex. This means that the constitutive model will be useful only if it is known to be valid under the actual conditions of interest to the SBM process. The aim of this work was to develop a new material characterization method providing new data for the deformation behaviour of PET relevant to the SBM process. In order to achieve this goal, a reliable and robust characterization method was developed based on an instrumented stretch rod and a digital image correlation system to determine the stress-strain relationship of material in deforming preforms during free stretch-blow tests. The effect of preform temperature and air mass flow rate on the deformation behaviour of PET was also investigated.

Improved Real-time State-of-Charge Estimation of LiFePO4 Battery Based on a Novel Thermoelectric Model

Li-ion batteries have been widely used in electric vehicles, and battery internal state estimation plays an important role in the battery management system. However, it is technically challenging, in particular, for the estimation of the battery internal temperature and state-ofcharge (SOC), which are two key state variables affecting the battery performance. In this paper, a novel method is proposed for realtime simultaneous estimation of these two internal states, thus leading to a significantly improved battery model for realtime SOC estimation. To achieve this, a simplified battery thermoelectric model is firstly built, which couples a thermal submodel and an electrical submodel. The interactions between the battery thermal and electrical behaviours are captured, thus offering a comprehensive description of the battery thermal and electrical behaviour. To achieve more accurate internal state estimations, the model is trained by the simulation error minimization method, and model parameters are optimized by a hybrid optimization method combining a meta-heuristic algorithm and the least square approach. Further, timevarying model parameters under different heat dissipation conditions are considered, and a joint extended Kalman filter is used to simultaneously estimate both the battery internal states and time-varying model parameters in realtime. Experimental results based on the testing data of LiFePO4 batteries confirm the efficacy of the proposed method.