Xiao Guang Yang

Electrochemical Model-Based State of Charge and Capacity Estimation for a Composite Electrode Lithium-Ion Battery

Increased demand for hybrid and electric vehicles has motivated research to improve onboard state of charge (SOC) and state of health estimation (SOH). In particular, batteries with composite electrodes have become popular for automotive applications due to their ability to balance energy density, power density, and cost by adjusting the amount of each material within the electrode. SOH algorithms that do not use electrochemical-based models may have more difficulty maintaining an accurate battery model as the cell ages under varying degradation modes, such as lithium consumption at the solid-electrolyte interface or active material dissolution. Furthermore, efforts to validate electrochemical model-based state estimation algorithms with experimental aging data are limited, particularly for composite electrode cells. In this paper, we first present a reduced-order electrochemical model for a composite LiMn2O4-LiNi1/3Mn1/3Co1/3O2 electrode battery that predicts the surface and bulk lithium concentration of each material in the composite electrode, as well as the current split between each material. The model is then used in dual-nonlinear observers to estimate the cell SOC and loss of cyclable lithium over time. Three different observer types are compared: 1) the extended Kalman filter; 2) fixed interval Kalman smoother; and 3) particle filter. Finally, an experimental aging campaign is used to compare the estimated capacities for five different cells with the measured capacities over time.

Model-based state of charge estimation and observability analysis of a composite electrode lithium-ion battery

Composite electrode lithium-ion batteries can offer improved energy and power density, as well as increased cycle life compared to batteries with a single active material electrode. Both available power and cell life are functions of the local current allocated to each composite material, however there are no examples in literature of electrochemical-based models of composite electrode cells that are suitable for estimation and control. We present a reduced order, electrochemical model of a composite LiMn2O4 - LiNi1/3Mn1/3Co1/3O2 cell that predicts bulk and surface concentrations of each composite material, as well as the local current allocated to each material. Observability properties are analyzed by approximating the system as linear over certain operating conditions. A solution method is developed to use the model in an extended Kalman filter for online state of charge estimation, which is validated with experimental data.

Robustness evaluation for state-of-charge and state-of-health estimation considering electrochemical parameter uncertainties

Electrified automotive powertrains benefit from precise knowledge of the battery state-of-charge and state-of-health to aggressively utilize the battery for fuel economy and range improvements while ensuring overall system safety and reliability. Uncertainties associated with the electrochemical parameters that govern the concentration, potential, and reaction rate dynamics within Li-ion cells can lead to state estimation errors and non-optimal battery usage. In this paper, results are presented towards quantifying the effect of parametric uncertainty in an automotive-oriented battery state estimation algorithm. Extensive simulations are conducted via a design of experiments approach to quantify closed-loop robustness and identify electrochemical parameters whose uncertainties create disproportionately large estimation errors. The results indicate that the effects of parametric uncertainty can be minimized by applying closed-loop estimation to the states that exhibit the largest overpotential within the cell.