Jason B. Siegel

Online Parameterization of Lumped Thermal Dynamics in Cylindrical Lithium Ion Batteries for Core Temperature Estimation and Health Monitoring

Lithium ion batteries should always be prevented from overheating and, hence, thermal monitoring is indispensable. Since only the surface temperature of the battery can be measured, a thermal model is needed to estimate the core temperature of the battery, which can be higher and more critical. In this paper, an online parameter identification scheme is designed for a cylindrical lithium ion battery. An adaptive observer of the core temperature is then designed based on the online parameterization methodology and the surface temperature measurement. A battery thermal model with constant internal resistance is explored first. The identification algorithm and the adaptive observer is validated with experiments on a 2.3Ah 26650 lithium iron phosphate/graphite battery. The methodology is later extended to address temperature-dependent internal resistance with nonuniform forgetting factors. The ability of the methodology to track the long-term variation of the internal resistance is beneficial for battery health monitoring.

Neutron Imaging of Lithium Concentration in LFP Pouch Cell Battery

This paper shows how neutron radiography can be used for in situ quantification of the lithium concentration across battery electrodes, a critical physical system state. The change in lithium concentration between the charged and discharged states of the battery causes a change in number of detected neutrons after passing through the battery. Electrode swelling is also observed during battery charging. The experimental setup and the observations from testing a pouch cell with LFP cathode and graphite anode are reported here. The bulk Li concentration across the electrode and folds of the pouch cell is quantified at various states of charge. To interpret the measurements, the optics of the neutron beam (geometric unsharpness) and detector resolution are considered in order to quantify the lithium concentration from the images due to the thinness of the electrode layers. The experimental methodology provides a basis for comprehensive in situ metrology of bulk lithium concentration.

The estimation of temperature distribution in cylindrical battery cells under unknown cooling conditions

The estimation of temperature inside a battery cell requires accurate information about the cooling conditions even when the battery surface temperature is measured. This paper presents a model-based approach for estimating temperature distribution inside a cylindrical battery under unknown convective cooling conditions. A reduced-order thermal model using a polynomial approximation of the temperature profile inside the battery is used. A dual Kalman filter (DKF), a combination of a Kalman filter and an extended Kalman filter, is then applied for the identification of the convection coefficient and the estimation of the battery core temperature. The thermal properties are modeled by volume averaged lumped-values under the assumption of a homogeneous and isotropic volume. The model is parameterized and validated using experimental data from a 2.3 Ah 26650 lithium-iron-phosphate battery cell with a forced-air convective cooling during hybrid electric vehicle drive cycles. Experimental results show that the proposed DKF-based estimation method can provide an accurate prediction of the core temperature under unknown cooling conditions by measuring battery current and voltage along with surface and ambient temperatures.

Parameterization and Validation of an Integrated Electro-Thermal Cylindrical LFP Battery Model

In this paper, for the first time, an equivalent circuit electrical model is integrated with a two-state thermal model to form an electro-thermal model for cylindrical lithium ion batteries. The parameterization of such model for an A123 26650 LiFePO4 cylindrical battery is presented. The resistances and capacitances of the equivalent circuit model are identified at different temperatures and states of charge (SOC), for charging and discharging. Functions are chosen to characterize the fitted parameters. A two-state thermal model is used to approximate the core and surface temperatures of the battery. The electrical model is coupled with the thermal model through heat generation and the thermal states are in turn feeding a radially averaged cell temperature affecting the parameters of the electrical model. Parameters of the thermal model are identified using a least squares algorithm. The electro-thermal model is then validated against voltage and surface temperature measurements from a realistic drive cycle experiment.Copyright

Supercapacitor Electrical and Thermal Modeling, Identification, and Validation for a Wide Range of Temperature and Power Applications

Supercapacitors benefit from unique features including high power density, long cycle life, wide temperature operation range, durability in harsh environments, efficient cycling, and low maintenance cost. This paper presents a validated lumped and computationally efficient electrical and thermal model for a cylindrical supercapacitor cell. The electrical model is a two-state equivalent electric circuit model with three parameters that are identified using temporal experiments. The dependence of the parameters on the state of charge, current direction and magnitude (20-200 A), and temperatures ranging from -40 °C to 60 °C is incorporated in the model. The thermal model is a linear 1-D model with two states. The reversible heat generation which is significant in double-layer capacitors is included in the thermal model. The coupling of the two models enables tuning of the temperature-dependent parameters of the electrical model in real time. The coupled electrothermal model is validated using real-world duty cycles at subzero and room temperatures with root-mean-square errors of (82 mV-87 mV) and (0.17 °C-0.21 °C) for terminal voltage and temperature, respectively. This accurate model is implementable in real-time power applications and also thermal management studies of supercapacitor packs.

A computationally efficient thermal model of cylindrical battery cells for the estimation of radially distributed temperatures

This paper presents a computationally efficient thermal model of a cylindrical lithium ion battery for real-time applications. Such a model can be used for thermal management of the battery system in electrified vehicles. The thermal properties are modeled by volume averaged lumped values under the assumption of a homogeneous and isotropic volume. A polynomial approximation is then used to estimate the radial temperature distribution that arises from heat generation inside the cell during normal operation. Unlike previous control oriented models, which use discretization of the heat equation, this model formulation uses two states to represent the average value of temperature and its gradient. The model is parameterized using experimental data from a 2.3 Ah 26650 Lithium-Iron-Phosphate (LiFePO4 or LFP) battery cell. Finally, a Kalman filter is applied based on the reduced order thermal model using measurements of current, voltage and surface temperature of the cell and ambient temperature. The effectiveness of the proposed approach is validated against core temperature measurements.

Quadruple adaptive observer of the core temperature in cylindrical Li-ion batteries and their health monitoring

Temperature monitoring is a critical issue for lithium ion batteries. Since only the surface temperature of the battery can be measured, a thermal model is needed to estimate the core temperature, which can be higher and hence more critical. In this paper, an on-line parameter identification scheme is designed for a cylindrical lithium ion battery thermal model, by which the parameters of the thermal model can be identified automatically. An adaptive observer is designed based on the on-line parameterization methodology and the closed loop architecture. A linear battery thermal model is explored first, where the internal resistance is assumed to be constant. The methodology is later extended to address temperature dependent internal resistance with non-uniform forgetting factors. The capability of the methodology to track the long term variation of the internal resistance is beneficial for battery health monitoring.

Modeling and validation of fuel cell water dynamics using neutron imaging

Using neutron imaging, the mass of liquid water within the gas diffusion layer and flow channels of an operating polymer electrolyte membrane fuel cell (PEMFC) is measured under a range of operating conditions. Between anode purge events, it is demonstrated that liquid water accumulates and is periodically removed from the anode gas channels; this event is well correlated with the dynamic cell voltage response. The estimation of flooding and cell performance is achieved by a spatially distributed (through-membrane plane), temporally-resolved, and two-phase (liquid and vapor) water model. Neutron imaging techniques have never before been applied to characterize flooding with a dead-ended anode and elucidate important issues in water management as well as provide a means for calibrating and validating a dynamic lumped parameter fuel cell model.

Maximum Power Estimation of Lithium-Ion Batteries Accounting for Thermal and Electrical Constraints

Enforcement of constraints on the maximum deliverable power is essential to protect lithium-ion batteries from over-charge/discharge and overheating. This paper develops an algorithm to address the often overlooked temperature constraint in determining the power capability of battery systems. A prior knowledge of power capability provides dynamic constraints on currents and affords an additional control authority on the temperature of batteries. Power capability is estimated using a lumped electro-thermal model for cylindrical cells that has been validated over a wide range of operating conditions. The time scale separation between electrical and thermal systems is exploited in addressing the temperature constraint independent of voltage and state-of-charge (SOC) limits. Limiting currents and hence power capability are determined by a model-inversion technique, termed Algebraic Propagation (AP). Simulations are performed using realistic depleting currents to demonstrate the effectiveness of the proposed method.Copyright

An Iterative Learning Control Approach to Improving Fidelity in Internet-Distributed Hardware-in-the-Loop Simulation

Abstract : One of the main challenges of co-simulating hardware-in-the-loop systems in real-time over the Internet is the fidelity of the simulation. The dynamics of the Internet may significantly distort the dynamics of the network-integrated system. This paper presents the development of an iterative learning control based approach to improve fidelity of such networked system integration. Towards this end, a new metric for characterizing fidelity is proposed first, which, unlike some existing metrics, does not require knowledge about the reference dynamics (i.e., dynamics that would be observed, if the system was physically connected). Next, using this metric, the problem of improving fidelity is formulated as an iterative learning control problem. Finally, the proposed approach is illustrated on a purely simulation-based case study. The conclusion is that the proposed approach holds significant potential for achieving high fidelity levels.