With the advantages of accurate and elaborate, the application of the electrochemical models has become a potential trend for states estimation and performance evaluation of Li-ion batteries in an advanced battery management system (BMS). This thesis investigates an original approach for joint estimation of state-of-charge (SoC) and state-of-health (SoH) based on the pseudo-two-dimensional (P2D) electrochemical model. First, the standard P2D model is established and reformulated into a nonlinear state-space form with guaranteed observability. Next, the particle filter (PF) algorithm is employed to estimate the average lithium concentration in real-time for SoC calculation. In addition, the battery SoH is calibrated based on the prediction of the average lithium concentration at...[ Read more ]

With the advantages of accurate and elaborate, the application of the electrochemical models has become a potential trend for states estimation and performance evaluation of Li-ion batteries in an advanced battery management system (BMS). This thesis investigates an original approach for joint estimation of state-of-charge (SoC) and state-of-health (SoH) based on the pseudo-two-dimensional (P2D) electrochemical model. First, the standard P2D model is established and reformulated into a nonlinear state-space form with guaranteed observability. Next, the particle filter (PF) algorithm is employed to estimate the average lithium concentration in real-time for SoC calculation. In addition, the battery SoH is calibrated based on the prediction of the average lithium concentration at the cut-off voltages of battery charging and discharging processes, which, in turn, improves the accuracy of online SoC estimation for aged batteries. For validation purposes, fifteen batteries with the aging state from 100 % to 70 % of initial capacity are tested under dynamic current profiles. The results show that the maximum SoH estimation error can be limited to 2.8%, and the SoC estimation error is bounded by 2% for new and aged batteries with the calibrated SoH.

The trustworthy state estimation of the electrochemical model based method is dependent on the accuracy of the model parameters being used, which need to be determined for individual batteries under dynamic operating conditions. In this thesis, critical model parameters are investigated and a novel online parameter identification method for the electrochemical model is established. In the method, the transfer function of the P2D model output equation is simplified to an auto regressive exogenous (ARX) form and the recursive least square (RLS) algorithm is employed for online parameter identification. The effectiveness of the proposed method is validated and compared with the conventional offline particle swarm optimization (PSO) method. The simulated battery terminal voltage using the identified model parameters are compared with the experimental measurements under 0°C, 25°C and 45°C. The results show that the proposed method generates more accurate model output than the conventional method with a much lower computational cost. The design scheme of a BMS platform specifically developed for electric vehicle (EV) application is also briefly presented in this thesis, which can also be used for verifying the applicability of the proposed state estimation and parameter identification algorithms.