Hybrid Pulse Power Characterization Test Research Articles

Application-specific electrical characterization of high power batteries with lithium titanate anodes for electric vehicles

This study shows results of extensive experimental measurements performed on high power lithium titanate based batteries. Characterization tests are performed over a wide temperature range (−20 °C – +40 °C) by employing electrochemical impedance spectroscopy and modified hybrid pulse power characterization tests. Furthermore, the behavior of battery impedance parameters over the battery lifetime with regard to temperature, State-of-Charge and their influence on available battery power in an example of electric vehicles is discussed. Based on extracted parameters, a reduced order equivalent circuit model considering the nonlinearity of the charge transfer resistance is parametrized. The obtained results indicate that ohmic resistance increases with decreasing State-of-Charge while the shape of the curve remains almost constant over the battery lifetime. The total impedance determined at 1 mHz shows almost no dependence on State-of-Charge and remains constant over the whole State-of-Charge range. The necessity of considering the impact of the current dependence of the direct current resistance at least at low temperatures (i.e., below 0 °C) is confirmed. Moreover, by investigating the Butler-Volmer equation the behavior of exchange current density and symmetry factor is analyzed for various temperatures and State-of-Charges over the battery lifetime.

Temperature dependent power capability estimation of lithium-ion batteries for hybrid electric vehicles

The power capability of lithium-ion batteries affects the safety and reliability of hybrid electric vehicles and the estimate of power by battery management systems provides operating information for drivers. In this paper, lithium ion manganese oxide batteries are studied to illustrate the temperature dependency of power capability and an operating map of power capability is presented. Both parametric and non-parametric models are established in conditions of temperature, state of charge, and cell resistance to estimate the power capability. Six cells were tested and used for model development, training, and validation. Three samples underwent hybrid pulse power characterization tests at varied temperatures and were used for model parameter identification and model training. The other three were used for model validation. By comparison, the mean absolute error of the parametric model is about 29 W, and that of the non-parametric model is around 20 W. The mean relative errors of two models are 0.076 and 0.397, respectively. The parametric model has a higher accuracy in low temperature and state of charge conditions, while the non-parametric model has better estimation result in high temperature and state of charge conditions. Thus, two models can be utilized together to achieve a higher accuracy of power capability estimation.

Online state of charge estimation of lithium-ion batteries: A moving horizon estimation approach

Online state of charge (SOC) estimation of lithium-ion batteries (LIBs) relies not only on accurate battery model but also on effective state estimation method. In this study, a nonlinear battery state-space model based moving horizon estimation (MHE) approach is proposed to estimate SOC within the full range. The relationship between SOC and circuit parameters in battery model is captured by polynomial functions. The essential arrival cost in the MHE problem formulation is approximated by the filtering scheme and its covariance matrix is updated by extended Kalman filter (EKF) method. Hybrid pulse power characterization test is first used to guide battery model construction and tuning parameters determination in MHE. The constant current discharge test and dynamic stress test are then used to validate the applicability of the MHE and investigate the performance comparisons between MHE and EKF. The results demonstrate that compared to the EKF, the MHE is less sensitive to the poor initial SOC guesses and has faster convergence to the true SOC. The results thus validate that the MHE provides a potential promising approach to perform accurate, reliable and robust SOC estimation of LIBs.

High Energy Density Battery Electrodes with Low and Anisotropic Tortuosity

The high inactive materials content (e.g., separators, current collectors, conductive additives, binder and packaging) of current nonaqueous battery designs contributes directly to high battery cost and reduces specific energy and energy density. Area specific capacity, which is perhaps the most objective measure of design performance, cannot be increased arbitrarily by increasing electrode thickness and/or density, due to kinetic limitations. In the limit of high density and large thickness, salt depletion within the electrolyte-filled porosity typically becomes rate-limiting. In order to reach higher usable energy at practical C-rates from thick, dense electrodes, it is essential to tailor the topology of the pore structure to reduce tortuosity anisotropy. Here we report on the fabrication, characterization of tortuosity, and electrochemical testing of thick, dense electrodes made by the freeze-casting of aqueous suspensions followed by drying/sintering. This shaping technique allows design of oriented and interconnected structures that have tortuosity anisotropy, low absolute tortuosity, and high ionic transport. We report on the characterization of tortuosity of directionally fabricated electrodes with similar porosity while varying the solidification rate that resulted in different microstructural features. The electrochemical performance of the fabricated electrodes in half cells exhibits high areal capacity. Hybrid pulse power characterization (HPPC) and dynamic stress tests (DST) were also performed.

Simultaneous model selection and parameter estimation for lithium‐ion batteries: A sequential MINLP solution approach

Equivalent circuit model (ECM) is a practical and commonly used tool not only in state of charge (SOC) estimation but also in state of health (SOH) monitoring for lithium‐ion batteries (LIBs). The functional forms of circuit parameters with respect to SOC in ECM are usually empirical determined, which cannot guarantee to obtain a compact and simple model. A systematical solution framework for simultaneous functional form selection and parameter estimation is proposed. A bi‐objective mixed‐integer nonlinear programming (MINLP) model is first constructed. Two solution approaches, namely the explicit and implicit methods, are then developed to balance model accuracy and model complexity. The former explicitly treats the model complexity as a constraint and the latter implicitly embeds the model complexity into the objective as a penalty. Both approaches require sequential solution of the transformed MINLP model and an ideal and nadir ideal solutions‐based criterion is utilized to terminate the solution procedure for determining the optimal functional forms, in which ideal solution and nadir ideal solution represent the best and worst of each objective, respectively. Both explicit and implicit approaches are thoroughly evaluated and compared through experimental pulse current discharge test and hybrid pulse power characterization test of a commercial LIB. The fitting and prediction results illustrate that the proposed methods can effectively construct an optimal ECM with minimum complexity and prescribed precision requirement. It is thus indicated that the proposed MINLP‐based solution framework, which could automatically guide the optimal ECM construction procedure, can be greatly helpful to both SOC estimation and SOH monitoring for LIBs. © 2015 American Institute of Chemical Engineers AIChE J, 62: 78–89, 2016

Effect of energy-regenerative braking on electric vehicle battery thermal management and control method based on simulation investigation

Battery thermal management is important for the safety and reliability of electric vehicle. Based on the parameters obtained from battery hybrid pulse power characterization test, a two-degree-of-freedom lumped thermal model is established. The battery model is then integrated with vehicle driving model to simulate real-time battery thermal responses. An active control method is proposed to reduce heat generation due to regenerative braking. The proposed control method not only subjects to the braking safety regulation, but also adjusts the regenerative braking ratio through a fuzzy controller. By comparing with other regenerative braking scenarios, the effectiveness of the proposed strategy has been validated. According to the results, the proposed control strategy suppresses battery temperature rise by modifying the charge current due to regenerative braking. The overlarge components of current are filtered out whereas the small ones are magnified. Therefore, with smaller battery temperature rise, more energy is recovered. Compared to the traditional passive heat dissipating, the proposed active methodology is feasible and provides a novel solution for electric vehicle battery thermal management.

Lithium-ion capacitor – Characterization and development of new electrical model

Due to the wide applications of lithium-ion capacitors (LiCs) in various fields and due to the lack of comprehensive study regarding LiC modeling, there is a need for an accurate electrical model, which can predict the LiC's behavior at different load conditions. Characterization helps better understanding of investigated energy storage system and reveals its functioning range, linear and nonlinear variations and also its limits. As the electrochemical based energy storages are non-linear, the presented model should be linearized in the range of the specific application. Therefore the identification procedures should be performed under various conditions as needed. Results from hybrid pulse power characterization (HPPC) test have been used for the system identification of LiC in the time domain. Since system identification in the time domain cannot provide a complete map of variation of model parameters, a wide range frequency impedance spectroscopy (10 kHz–20 mHz) and at various conditions such as current, state of charge (SoC) levels and temperatures has been performed in order to identify the parameters of assumed model in the frequency domain, which is an unique analysis and never has been performed for lithium ion capacitors. The used model has been chosen based on probable chemical reactions, which occur within a specific frequency range. Frequency domain analysis can identify model variation, which is not possible in time domain identification. In this paper a new identification approach has been introduced based on combination of time and frequency domains. Based on proposed method, identification process in frequency domain helps to identify parameters which are not variable in function of current and SoC variations. Therefore the mentioned parameters can be withdrawn from the cost function in the time domain and it improves the performance of system identification procedure. The proposed method improves the model accuracy around 2% at time domain and around 5% at frequency domain.

Empirical Modeling of Lithium-ion Batteries Based on Electrochemical Impedance Spectroscopy Tests

An empirical model for commercial lithium-ion batteries is developed based on electrochemical impedance spectroscopy (EIS) tests. An equivalent circuit is established according to EIS test observations at various battery states of charge and temperatures. A Laplace transfer time based model is developed based on the circuit which can predict the battery operating output potential difference in battery electric and plug-in hybrid vehicles at various operating conditions. This model demonstrates up to 6% improvement compared to simple resistance and Thevenin models and is suitable for modeling and on-board controller purposes. Results also show that this model can be used to predict the battery internal resistance obtained from hybrid pulse power characterization (HPPC) tests to within 20 percent, making it suitable for low to medium fidelity powertrain design purposes. In total, this simple battery model can be employed as a real-time model in electrified vehicle battery management systems.

A comparative study of equivalent circuit models of ultracapacitors for electric vehicles

This paper comparatively examines three types of equivalent circuit models for ultracapacitors. They are the classic model, the multi-stage ladder model and the dynamic model. These models are consciously selected from the state-of-the-art lumped models reported in the literature. A test rig is developed and used to load the ultracapacitor and to collect the test data. The genetic algorithm (GA) is employed to extract the optimal model parameters based on the Hybrid Pulse Power Characterization (HPPC) test. The performance of these models is evaluated and compared by measuring the model complexity, accuracy, and robustness against “unseen” data collected in the Dynamic Stress Test (DST) and a self-designed pulse test (SDP). The validation results show that the dynamic model has the best overall performance.

Online Parameter Identification of Ultracapacitor Models Using the Extended Kalman Filter

Ultracapacitors (UCs) are the focus of increasing attention in electric vehicle and renewable energy system applications due to their excellent performance in terms of power density, efficiency, and lifespan. Modeling and parameterization of UCs play an important role in model-based regulation and management for a reliable and safe operation. In this paper, an equivalent circuit model template composed of a bulk capacitor, a second-order capacitance-resistance network, and a series resistance, is employed to represent the dynamics of UCs. The extended Kalman Filter is then used to recursively estimate the model parameters in the Dynamic Stress Test (DST) on a specially established test rig. The DST loading profile is able to emulate the practical power sinking and sourcing of UCs in electric vehicles. In order to examine the accuracy of the identified model, a Hybrid Pulse Power Characterization test is carried out. The validation result demonstrates that the recursively calibrated model can precisely delineate the dynamic voltage behavior of UCs under the discrepant loading condition, and the online identification approach is thus capable of extracting the model parameters in a credible and robust manner.

Calculation and Characteristics Analysis of Lithium Ion Batteries’ Internal Resistance Using HPPC Test

Internal resistance of battery can really reflect its own characteristics, which including health status of the battery, inconsistency, state of charge, thermal runaway. Method to calculate the internal resistance of the battery using HPPC (Hybrid Pulse Power Characterization) test which proposed by the FreedomCAR is introduced. Ohmic resistance, polarization resistance, discharge resistance and regen resistance are calculated based on MATLAB programming using HPPC test. The differences between the four kinds of internal resistance of lithium ion batteries are compared. Characteristics of the four kinds of internal resistance in charged-state and discharged-state are analyzed.

Long cycle life lithium ion battery with lithium nickel cobalt manganese oxide (NCM) cathode

Lithium ion batteries with lithium nickel cobalt manganese oxide (NCM) cathode were characterized by extensive cycling (>2000 cycles), discharge rate test, hybrid pulse power characterization test (HPPC), and electrochemical impedance spectroscopy (EIS). The crystal structure, morphology and particle size of cathode materials were characterized by X-ray diffraction and scanning electron microscopy (SEM). It was demonstrated that the rate performance and cycle life of battery are closely related to the cathode material composition and electrode design. With proper selection of cathode composition and electrode design, the lithium ion battery cell achieved close to 3500 cycles with 85% capacity retention at 1C current.

Modeling and Simulation Research on Lithium-Ion Battery in Electric Vehicles Based on Genetic Algorithm

Based on the genetic algorithm (GA), a novel type of parameters identification method on battery model was proposed. The battery model parameters were optimized by genetic optimization algorithm and the other parameters were identified through the hybrid pulse power characterization (HPPC) test. Accuracy and efficiency of the battery model were validated with the dynamic stress test (DST). Simulation and experiment results shows that the proposed model of the lithium-ion battery with identified parameters was accurate enough to meet the requirements of the state of charge (SoC) estimation and battery management system.

A non-conventional fluorinated separator in high-voltage graphite/LiNi0.4Mn1.6O4 cells

Graphite/LiNi0.4Mn1.6O4 cells assembled with a new reinforced polyvinylidene fluoride (pVDF)–nano crystalline cellulose (NCC) separator and EC–DMC 1 M LiFAP electrolyte with additives were tested by deep charge/discharge cycles at different C-rates and by the FreedomCAR DOE protocol to simulate the dynamic functioning of the batteries in power-assist full hybrid electric vehicles (HEVs). The results of this study evidence the beneficial impact of the pVDF–NCC macroporous membrane with respect to the polypropylene monolayer Celgard®2400 separator on the high C-rate cell performance. The deep charge/discharge of the cell with pVDF–NCC at C/1 effective rate provided 101 W h kg−1 to be compared with 85 W h kg−1 of the cell with Celgard®2400 (the cell weight was considered twice the composite electrode weight of both electrodes). Also hybrid pulse power characterization tests based on the FreedomCAR protocol at 5 C and 10 C demonstrated the superior performance of the cells with pVDF–NCC with respect to that of the cells with Celgard®2400 even if both cells exceed the FreedomCAR goals of power and energy for minimum and maximum power-assist HEV.

Enhanced thermal safety and high power performance of carbon-coated LiFePO4 olivine cathode for Li-ion batteries

The carbon-coated LiFePO4 Li-ion oxide cathode was studied for its electrochemical, thermal, and safety performance. This electrode exhibited a reversible capacity corresponding to more than 89% of the theoretical capacity when cycled between 2.5 and 4.0 V. Cylindrical 18,650 cells with carbon-coated LiFePO4 also showed good capacity retention at higher discharge rates up to 5C rate with 99.3% coulombic efficiency, implying that the carbon coating improves the electronic conductivity. Hybrid Pulse Power Characterization (HPPC) test performed on LiFePO4 18,650 cell indicated the suitability of this carbon-coated LiFePO4 for high power HEV applications. The heat generation during charge and discharge at 0.5C rate, studied using an Isothermal Microcalorimeter (IMC), indicated cell temperature is maintained in near ambient conditions in the absence of external cooling. Thermal studies were also investigated by Differential Scanning Calorimeter (DSC) and Accelerating Rate Calorimeter (ARC), which showed that LiFePO4 is safer, upon thermal and electrochemical abuse, than the commonly used lithium metal oxide cathodes with layered and spinel structures. Safety tests, such as nail penetration and crush test, were performed on LiFePO4 and LiCoO2 cathode based cells, to investigate on the safety hazards of the cells upon severe physical abuse and damage.

Evaluating Real-Life Performance of Lithium-Ion Battery Packs in Electric Vehicles

In regard to the increasing market launch of plug-in hybrid electric vehicles (PHEVs), understanding battery pack performance under electric vehicle (EV) operating conditions is essential. As lifetime still remains an issue for battery packs, it is a necessity to monitor the battery pack's state-of-health (SOH) on-board. Standard performance tests for health evaluation do not apply since operation interruptions and additional testing equipment are beyond question during ordinary EV usage. We suggest a novel methodology of performance estimation from real-life battery data. On the basis of battery pack data collected during PHEV operation, a support vector machine model is constructed that serves as source for performance evaluation figures. The SOH indicator "10 s discharge resistance" as known from hybrid pulse power characterization (HPPC) tests is chosen to exemplify how performance degradation can be followed over a year.

Evaluating Real-Life Performance of Lithium-Ion Battery Packs in Electric Vehicles

In regard to the increasing market launch of plug-in hybrid electric vehicles (PHEVs), understanding battery pack performance under electric vehicle (EV) operating conditions is essential. As lifetime still remains an issue for battery packs, it is a necessity to monitor the battery pack's state-of-health (SOH) on-board. Standard laboratory performance tests for health evaluation do not apply since operation interruptions and additional testing equipment are out of the question during ordinary EV usage. We suggest a novel methodology of performance estimation from real-life battery data. On the basis of battery pack data collected during PHEV operation, a support vector machine model capturing battery behavior characteristics is constructed. By virtually testing this battery model, access to standard performance evaluation figures can be gained. The SOH indicator “10 s discharge resistance” as known from hybrid pulse power characterization (HPPC) tests is chosen to exemplify how performance can be followed over a year.

System Identification and Estimation Framework for Pivotal Automotive Battery Management System Characteristics

The battery management system (BMS) is an integral part of an automobile. It protects the battery from damage, predicts battery life, and maintains the battery in an operational condition. The BMS performs these tasks by integrating one or more of the functions, such as protecting the cell, thermal management, controlling the charge-discharge, determining the state of charge (SOC), state of health (SOH), and remaining useful life (RUL) of the battery, cell balancing, data acquisition, communication with on-board and off-board modules, as well as monitoring and storing historical data. In this paper, we propose a BMS that estimates the critical characteristics of the battery (such as SOC, SOH, and RUL) using a data-driven approach. Our estimation procedure is based on a modified Randles circuit model consisting of resistors, a capacitor, the Warburg impedance for electrochemical impedance spectroscopy test data, and a lumped parameter model for hybrid pulse power characterization test data. The resistors in a Randles circuit model usually characterize the self-discharge and internal resistance of the battery, the capacitor generally represents the charge stored in the battery, and the Warburg impedance represents the diffusion phenomenon. The Randles circuit parameters are estimated using a frequency-selective nonlinear least squares estimation technique, while the lumped parameter model parameters are estimated by the prediction error minimization method. We investigate the use of support vector machines (SVMs) to predict the capacity fade and power fade, which characterize the SOH of a battery, as well as estimate the SOC of the battery. An alternate procedure for estimating the power fade and energy fade from low-current Hybrid Pulse Power characterization (L-HPPC) test data using the lumped parameter battery model has been proposed. Predictions of RUL of the battery are obtained by support vector regression of the power fade and capacity fade estimates. Survival function estimates for reliability analysis of the battery are obtained using a hidden Markov model (HMM) trained using time-dependent estimates of capacity fade and power fade as observations. The proposed framework provides a systematic way for estimating relevant battery characteristics with a high-degree of accuracy.

SOC Estimation of Ni-MH Battery Pack Based on Approved HPPC Test and EKF Algorithm for HEV

In this paper, Methods of SOC estimation of Extended Kalman Filter (EKF) is studied based on the characteristics of Nickel-Metal Hydride (Ni-MH) battery pack with 120 cells in series and 8Ah capacity for HEV. In the study of EKF-based SOC estimation, the improved Thevenin circuit model is adopted, and a new hybrid pulse power characterization （HPPC） test is designed to identify the model parameters by using piecewise linear regression method. In this way, the precision of the circuit model is improved. In addition, The Kalman gain matrix is optimized for EKF iterative algorithm by two ways: a constant gain is increased taking into account the entire process; a dynamic gain which increases at the beginning of abrupt change and decreases rapidly after abrupt change is set up. The improvement achieves a good tracing prediction.

Electrochemical Performance of Carbon-Coated HQ-LiFePO4 Cathode

The carbon-coated LiFePO4 Li-ion cathode material was studied for its electrochemical performance. This electrode exhibited a reversible capacity corresponding to more than 86% of the theoretical capacity, when cycled between 2.5V and 4.0V. The positive electrode material displayed excellent capacity retention for prolonged cycling periods and also showed high rate capability upto 5C discharge rates. High power obtained at high discharge rates were attributed to the effects of electronic conductivity improvement enabled by carbon-coating of LiFePO4. Carbon-coating of the electrode was observed as capacitance component in the electrochemical impedance spectra (EIS). Hybrid pulse power characterization (HPPC) tests were also performed on commercial 18650 size cells, using carbon-coated LiFePO4 electrode, to observe the suitability of the positive electrode to HEV applications.