Ted Miller

A capacity and power fade study of Li-ion cells during life cycle testing

We tested three lithium-ion cells to evaluate capacity and power fade during cycle life testing of a hybrid electric vehicle (HEV) cell with varying state of charge (ΔSOC). Test results showed that the cells had sufficient power and energy capability to meet the Partnership for a New Generation of Vehicles (PNGV), now called FreedomCAR, goals for Power Assist at the beginning of life and after 120,000 life cycles using 48 cells. The initial static capacity tests showed that the capacity of the cells stabilized after three discharges at an average of 14.67 Ah. Capacity faded as expected over the course of 120,000 life cycles. However, capacity fade did not vary with ΔSOC. The hybrid pulse power characterization (HPPC) tests indicated that the cells were able to meet the power and energy goals at the beginning of testing and after 120,000 life cycles. The rate of power fade of the lithium-ion cells during cycle life testing increased with increasing ΔSOC. Capacity fade is believed to be due to lithium corrosion at the anode, and power fade suggested a buildup of the SEI layer or a decrepitation of the active material.

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.

Workshop on engineering models for advanced batteries vehicle OEM panel session model outputs—industry perspectives

The vehicle level requirements for a battery model have been defined. An introduction providing vehicle manufacturer experience in developing battery models to date and describing the ideal battery model characteristics has been provided. Battery performance and thermal model requirements are defined in terms of minimum and desired outputs. Model verification is discussed and recommended variables offered. Other complementary data needs are also included.

Automaker Energy Storage Needs for Electric Vehicles

The success of electric vehicles (EVs) is strongly tied to their performance and ability to meet customer expectations. A comparison of EV battery performance against the requisite targets created by the international community is presented. The performance attributes of greatest interest are energy, power and life. It is shown that only power has achieved the level of performance required by the automotive community for mass commercialization.