Reduced order model (ROM) of a pouch type lithium polymer battery based on electrochemical thermal principles for real time applications

Accurate and fast estimation of state of charge and health during operations plays pivotal roles in prevention of overcharge or undercharge and accurately monitor the state of cells degradation, which requires a model that can be embedded in the battery management system. Currently, models used are based on either empirical equations or electric equivalent circuit components with voltage sources or a combination of the two. The models are relatively simple, but limited to represent a wide range of operating behaviors that includes the effects of temperature, state of charge (SOC) and degradation, to name a few. On the other hand, full order models (FOM) are multi-dimensional or multi-scale models based on electrochemical and thermal principles capable of representing the details of the cell behavior, but inadequate for real time applications, simply because of the high computational time. Therefore, there are needs for the development of a model with an intermediate performance and real time capability, which is accomplished by reduction of the FOM that is called a reduced order model (ROM). The battery used for development of the ROM is a pouch type of lithium ion polymer battery (LiPB) made of LiMn2O4 (LMO)/carbon. The reduction of the model was carried out for ion concentrations, potentials and kinetics, the ion concentration in the electrode using the polynomial approach and the ion concentration in the electrolyte using the state space method, and potentials and electrochemical kinetics by linearization. In addition, the energy equation is used to calculate the cell temperature, on which the diffusion coefficient and the solid electrolyte interphase (SEI) resistance are dependent. The computational time step is determined based on the total computational time and errors at a given SOC range and different C-rates. ROM responses are compared with those of the FOM and experimental data at a single cycle and multiple cycles under different operating conditions. The results show that calculation time of the ROM is reduced to one fifteenth of the FOM, approximately, while the accuracy can be maintained. (C) 2013 Elsevier Ltd. All rights reserved.