Kandler Smith

Model-Based Electrochemical Estimation and Constraint Management for Pulse Operation of Lithium Ion Batteries

High-power lithium ion batteries are often rated with multiple current and voltage limits depending on the duration of the pulse event. These variable control limits, however, are difficult to realize in practice. In this paper, a linear Kalman filter based on a reduced order electrochemical model is designed to estimate internal battery potentials, concentration gradients, and state-of-charge (SOC) from external current and voltage measurements. A reference current governor predicts the operating margin with respect to electrode side reactions and surface depletion/saturation conditions responsible for damage and sudden loss of power. The estimates are compared with results from an experimentally validated, 1-D, nonlinear finite volume model of a 6 Ah hybrid electric vehicle battery. The linear filter provides, to within ~ 2%, performance in the 30%-70% SOC range except in the case of severe current pulses that draw electrode surface concentrations to near saturation and depletion, although the estimates recover as concentration gradients relax. With 4 to 7 states, the filter has low-order comparable to empirical equivalent circuit models commonly employed and described in the literature. Accurate estimation of the batterys internal electrochemical state enables an expanded range of pulse operation.

Electric vehicle charge optimization including effects of lithium-ion battery degradation

This paper presents a method for minimizing the cost of electric vehicle (EV) charging given variable electricity costs while also accounting for estimated costs of battery degradation using a simplified lithium-ion battery lifetime model. The simple battery lifetime model, also developed and presented here, estimates both energy capacity fade and power fade due to temperature, state of charge profile, and daily depth of discharge. This model has been validated by comparison with a detailed model [6], which in turn has been validated through comparison to experimental data. The simple model runs quickly in a MATLAB script, allowing for iterative numerical minimization of charge cost. EV charge profiles optimized as described here show a compromise among four trends: charging during low-electricity cost intervals, charging slowly, charging towards the end of the available charge time, and suppression of vehicle-to-grid power exportation. Finally, simulations predict that batteries charged using optimized charging last longer than those charged using typical charging methods, potentially allowing smaller, cheaper batteries to meet vehicle lifetime requirements.

Model Order Reduction of 1D Diffusion Systems Via Residue Grouping

A model order reduction method is developed and applied to 1D diffusion systems with negative real eigenvalues. Spatially distributed residues are found either analytically (from a transcendental transfer function) or numerically (from a finite element or finite difference state space model), and residues with similar eigenvalues are grouped together to reduce the model order. Two examples are presented from a model of a lithium ion electrochemical cell. Reduced order grouped models are compared to full order models and models of the same order in which optimal eigenvalues and residues are found numerically. The grouped models give near-optimal performance with roughly 1/20 the computation time of the full order models and require 1000―5000 times less CPU time for numerical identification compared to the optimization procedure.

Comparison of Plug-In Hybrid Electric Vehicle Battery Life Across Geographies and Drive Cycles

In a laboratory environment, it is cost prohibitive to run automotive battery aging experiments across a wide range of possible ambient environment, drive cycle and charging scenarios. Since worst-case scenarios drive the conservative sizing of electric-drive vehicle batteries, it is useful to understand how and why those scenarios arise and what design or control actions might be taken to mitigate them. In an effort to explore this problem, this paper applies a semi-empirical life model of the graphite/nickel-cobalt-aluminum lithium-ion chemistry to investigate impacts of geographic environments under storage and simplified cycling conditions. The model is then applied to analyze complex cycling conditions, using battery charge/discharge profiles generated from simulations of PHEV10 and PHEV40 vehicles across 782 single-day driving cycles taken from Texas travel survey data.

Accounting for Lithium-Ion Battery Degradation in Electric Vehicle Charging Optimization

This paper presents a method for minimizing the cost of vehicle battery charging given variable electricity costs while also accounting for estimated costs of battery degradation using a simplified lithium-ion battery lifetime model. The simple battery lifetime model, also developed and presented here, estimates both energy capacity fade and power fade and includes effects due to temperature, state of charge profile, and daily depth of discharge. This model has been validated by comparison with a detailed model developed at National Renewable Energy Laboratory, which in turn has been validated through comparison with experimental data. The simple model runs quickly, allowing for iterative numerical minimization of charge cost, implemented on the charger controller. Resulting electric vehicle (EV) charge profiles show a compromise among four trends: 1) charging during low-electricity cost intervals; 2) charging slowly; 3) charging toward the end of the available charge time; and 4) suppression of vehicle-to-grid power exportation. Simulations based on experimental Prius plug-in hybrid EV usage data predict that batteries charged using optimized charging last significantly longer than those charged using typical charging methods, potentially allowing smaller batteries to meet vehicle lifetime requirements. These trends are shown to hold across a wide range of battery sizes and hence are applicable to both EVs and plug-in hybrid EVs.

Model-based electrochemical estimation of lithium-ion batteries

A linear Kalman filter based on a reduced order electrochemical model is designed to estimate internal battery potentials, concentration gradients, and state of charge (SOC) from external current and voltage measurements. The estimates are compared with results from an experimentally validated one-dimensional nonlinear finite volume model of a 6 Ah hybrid electric vehicle battery. The linear filter gives, to within ~2%, performance in the 30%-70% SOC range, except in the case of severe current pulses that draw electrode surface concentrations to near saturation and depletion; however, the estimates recover as concentration gradients relax. With 4 to 7 states, the filter has low order comparable to empirical equivalent circuit models but provides estimates of the batterypsilas internal electrochemical state.

Simulation and field-testing of hybrid ultra-capacitor/battery energy storage systems for electric and hybrid-electric transit vehicles

This paper investigates the use of ultra-capacitors combined with batteries as an improved energy storage system for electric, hybrid electric, and hybrid fuel cell transit vehicles. A demonstrator hybrid electric vehicle with an ultra-capacitor system was constructed and used to validate simulations. Results suggest a significant reduction in peak currents experienced by the battery pack in drive cycles with a high number of starts and stops.

Modular approach for continuous cell-level balancing to improve performance of large battery packs

Energy storage systems require battery cell balancing circuits to avoid divergence of cell state of charge (SOC). A modular approach based on distributed continuous cell-level control is presented that extends the balancing function to higher level pack performance objectives such as improving power capability and increasing pack lifetime. This is achieved by adding DC-DC converters in parallel with cells and using state estimation and control to autonomously bias individual cell SOC and SOC range, forcing healthier cells to be cycled deeper than weaker cells. The result is a pack with improved degradation characteristics and extended lifetime. The modular architecture and control concepts are developed and hardware results are demonstrated for a 91.2 Wh battery pack consisting of four series li-ion battery cells and four dual active bridge (DAB) bypass DC-DC converters.

Improving Petroleum Displacement Potential of PHEVs Using Enhanced Charging Scenarios

Describes NRELs RD vehicles charged during the day would save about 5% more fuel than those charged at night.

Degradation mechanisms and lifetime prediction for lithium-ion batteries — A control perspective

Predictive models of Li-ion battery lifetime must consider a multiplicity of electrochemical, thermal, and mechanical degradation modes experienced by batteries in application environments. To complicate matters, Li-ion batteries can experience different degradation trajectories that depend on storage and cycling history of the application environment. Rates of degradation are controlled by factors such as temperature history, electrochemical operating window, and charge/discharge rate. We present a generalized battery life prognostic model framework for battery systems design and control. The model framework consists of trial functions that are statistically regressed to Li-ion cell life datasets wherein the cells have been aged under different levels of stress. Degradation mechanisms and rate laws dependent on temperature, storage, and cycling condition are regressed to the data, with multiple model hypotheses evaluated and the best model down-selected based on statistics. The resulting life prognostic model, implemented in state variable form, is extensible to arbitrary real-world scenarios. The model is applicable in real-time control algorithms to maximize battery life and performance. We discuss efforts to reduce lifetime prediction error and accommodate its inevitable impact in controller design.