Browsing by Author "Knap, Vaclav"

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    Concurrent real-time estimation of state of health and maximum available power in lithium-sulfur batteries
    (MDPI, 2018-08-16) Knap, Vaclav; Auger, Daniel J.; Propp, Karsten; Fotouhi, Abbas; Stroe, Daniel-Ioan
    Lithium-sulfur (Li-S) batteries are an emerging energy storage technology with higher performance than lithium-ion batteries in terms of specific capacity and energy density. However, several scientific and technological gaps need to be filled before Li-S batteries will penetrate the market at a large scale. One such gap, which is tackled in this paper, is represented by the estimation of state-of-health (SOH). Li-S batteries exhibit a complex behaviour due to their inherent mechanisms, which requires a special tailoring of the already literature-available state-of-charge (SOC) and SOH estimation algorithms. In this work, a model of SOH based on capacity fade and power fade has been proposed and incorporated in a state estimator using dual extended Kalman filters has been used to simultaneously estimate Li-S SOC and SOH. The dual extended Kalman filter’s internal estimates of equivalent circuit network parameters have also been used to the estimate maximum available power of the battery at any specified instant. The proposed estimators have been successfully applied to both fresh and aged Li-S pouch cells, showing that they can accurately track accurately the battery SOC, SOH, and power, providing that initial conditions are suitable. However, the estimation of the Li-S battery cells’ capacity fade is shown to be more complex, because the practical available capacity varies highly with the applied current rates and the dynamics of the mission profile.
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    Design, build and validation of a low-cost programmable battery cycler
    (The Electrochemical Society, 2016-12-07) Propp, Karsten; Fotouhi, Abbas; Knap, Vaclav; Auger, Daniel J.
    The availability of laboratory grade equipment for battery tests is usually limited due to high costs of the hardware. Especially for lithium-sulfur (Li-S) batteries these experiments can be time intensive since the cells need to be precycled and are usually cycled with relatively low loads. To improve the availability of test hardware, this paper conducts a study to design and test a low cost solution for cycling and testing batteries for tasks that do not necessarily need the high precision of professional hardware. While the described solution is in principle independent of the cell chemistry, here it is specifically optimized to fit to Li-S batteries. To evaluate the accuracy of the presented battery cycler, the hardware is tested and compared with a professional Kepco bipolar power source. The results indicate the usefulness for application oriented battery tests with real life cycles, although inaccuracies occur in the current measurements.
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    Improved state of charge estimation for lithium-sulfur batteries
    (Elsevier, 2019-10-23) Propp, Karsten; Auger, Daniel J.; Fotouhi, Abbas; Marinescu, Monica; Knap, Vaclav; Longo, Stefano
    Good state of charge estimation in lithium-sulfur batteries (Li-S) is vital, as the simplest convention methods commonly used in lithium-ion batteries – open-circuit voltage measurement and ‘coulomb counting’–are often ineffective for Li-S. Since Li-S is an ewbattery chemistry, there are few published techniques. Existing techniques based on the extended Kalman filter and the unscented Kalman filter have shown some promise, existing work has explored only one of many possible estimator architectures: a single filter based on a pre-calibrated behavioural reparameterization of an equivalent circuit network whose parameters vary as a function of state of charge and temperature. Such filters have been shown to be reasonably effective in practical cases, but they can converge slowly if initial conditions are unknown, and they can become inaccurate with changes in current density. It is desirable to understand whether other possible estimator architectures offer improved performance. One such alternative architecture is the ‘dual extended Kalman filter’, which uses voltage and current measurements to estimate into a short-term dynamic circuit parameters then uses the outputs of this in a slower acting state-of-charge estimator. This paper develops a ‘behavioural’ form of the dual extended Kalman filter, and applies this to a lithium-sulfur battery. The estimator is adapted with a term to model circuit current dependence, and demonstrated using pulse-discharge tests and scaled automotive driving cycles including some with initially partially discharged batteries. Compared to the published state-of-the-art, the new estimators were are found to be between 16.4% and 28.2% more accurate for batteries that are initially partially discharged to a 60% SoC level; the new estimators also converge faster. The resulting estimators have the potential to be extended to state-of-health measures, and the ‘behavioural’ circuit reparameterization is likely to be of use for other battery chemistries beside lithium-sulfur.
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    Kalman-variant estimators for state of charge in lithium-sulfur batteries
    (Cranfield University, 2022-05-01 01:10) Propp, Karsten; Auger, Daniel; Fotouhi, Abbas; Longo, Stefano; Knap, Vaclav
    This fileset is a set of MATLAB/Simulink R2016a models implementing state-of-charge estimators for lithium-sulfur batteries as described in the associated publications. The associated experimental data is also included. Instructions are included in a 'readme.txt' file in the root directory.
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    Kalman-variant estimators for state of charge in lithium-sulfur batteries
    (Elsevier, 2017-01-20) Propp, Karsten; Auger, Daniel J.; Fotouhi, Abbas; Longo, Stefano; Knap, Vaclav
    Lithium-sulfur batteries are now commercially available, offering high specific energy density, low production costs and high safety. However, there is no commercially-available battery management system for them, and there are no published methods for determining state of charge in situ. This paper describes a study to address this gap. The properties and behaviours of lithium-sulfur are briefly introduced, and the applicability of ‘standard’ lithium-ion state-of-charge estimation methods is explored. Open-circuit voltage methods and ‘Coulomb counting’ are found to have a poor fit for lithium-sulfur, and model-based methods, particularly recursive Bayesian filters, are identified as showing strong promise. Three recursive Bayesian filters are implemented: an extended Kalman filter (EKF), an unscented Kalman filter (UKF) and a particle filter (PF). These estimators are tested through practical experimentation, considering both a pulse-discharge test and a test based on the New European Driving Cycle (NEDC). Experimentation is carried out at a constant temperature, mirroring the environment expected in the authors' target automotive application. It is shown that the estimators, which are based on a relatively simple equivalent-circuit–network model, can deliver useful results. If the three estimators implemented, the unscented Kalman filter gives the most robust and accurate performance, with an acceptable computational effort.
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    MATLAB and Simulink Models for 'Improved State of Charge Estimation for Lithium-Sulfur Batteries'
    (Cranfield University, 2022-05-01 01:10) Auger, Daniel; Propp, Karsten; Fotouhi, Abbas; Marinescu, Monica; Knap, Vaclav; Longo, Stefano
    This fileset consists of Simulink models of a state estimator for lithium-sulfur batteries, as described in a research paper that has been submitted for publication.
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    Methodology for assessing the lithium-sulfur battery degradation for practical applications
    (Electrochemical Society, 2018-05-26) Knap, Vaclav; Stroe, Daniel Ioan; Purkayastha, Rajlakshmi; Walus, Sylwia; Auger, Daniel J.; Fotouhi, Abbas; Propp, Karsten
    Lithium-Sulfur (Li-S) battery is an emerging battery technology receiving growing amount of attention due to its potential high contributions of gravimetric energy density, safety and low production cost. However, there are still some obstacles preventing their swift commercialization. Li-S batteries are driven by different electrochemical processes than commonly used Lithium-ion batteries, which often results in their very different behavior. Therefore, the modelling and testing have to be adjusted to reflect this unique behavior to prevent possible biases. A methodology for a reference performance test for the Li-S batteries is proposed in this study to point out the Li-S battery features and provide guidance to users how to deal with them and possible results into standardization.
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    Multi-temperature state-dependent equivalent circuit discharge model for lithium-sulfur batteries
    (Elsevier, 2016-08-12) Propp, Karsten; Marinescu, Monica; Auger, Daniel J.; O'Neill, Laura; Fotouhi, Abbas; Somasundaram, Karthik; Offer, Gregory J.; Minton, Geraint; Longo, Stefano; Wild, Mark; Knap, Vaclav
    Lithium-sulfur (Li-S) batteries are described extensively in the literature, but existing computational models aimed at scientific understanding are too complex for use in applications such as battery management. Computationally simple models are vital for exploitation. This paper proposes a non-linear state-of-charge dependent Li-S equivalent circuit network (ECN) model for a Li-S cell under discharge. Li-S batteries are fundamentally different to Li-ion batteries, and require chemistry-specific models. A new Li-S model is obtained using a ‘behavioural’ interpretation of the ECN model; as Li-S exhibits a ‘steep’ open-circuit voltage (OCV) profile at high states-of-charge, identification methods are designed to take into account OCV changes during current pulses. The prediction-error minimization technique is used. The model is parameterized from laboratory experiments using a mixed-size current pulse profile at four temperatures from 10 °C to 50 °C, giving linearized ECN parameters for a range of states-of-charge, currents and temperatures. These are used to create a nonlinear polynomial-based battery model suitable for use in a battery management system. When the model is used to predict the behaviour of a validation data set representing an automotive NEDC driving cycle, the terminal voltage predictions are judged accurate with a root mean square error of 32 mV.
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    Reference performance test Methodology for degradation assessment of lithium-sulfur batteries
    (Electrochemical Society, 2018-08-25) Knap, Vaclav; Stroe, D-I.; Purkayastha, R.; Walus, S.; Auger, Daniel J.; Fotouhi, Abbas; Propp, Karsten
    Lithium-Sulfur (Li-S) is an emerging battery technology receiving a growing amount of attention due to its potentially high gravimetric energy density, safety, and low production cost. However, there are still some obstacles preventing its swift commercialization. Li-S batteries are driven by different electrochemical processes than commonly used Lithium-ion batteries, which often results in very different behavior. Therefore, the testing and modeling of these systems have to be adjusted to reflect their unique behavior and to prevent possible bias. A methodology for a Reference Performance Test (RPT) for the Li-S batteries is proposed in this study to point out Li-S battery features and provide guidance to users how to deal with them and possible results into standardization. The proposed test methodology is demonstrated for 3.4 Ah Li-S cells aged under different conditions.
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    Self-balancing feature of Lithium-Sulfur batteries
    (Elsevier, 2017-11-05) Knap, Vaclav; Stroe, Daniel-Ioan; Christensen, Andreas E.; Propp, Karsten; Fotouhi, Abbas; Auger, Daniel J.; Schaltz, Erik; Teodorescu, Remus
    The Li-S batteries are a prospective battery technology, which despite to its currently remaining drawbacks offers useable performance and interesting features. The polysulfide shuttle mechanism, a characteristic phenomenon for the Li-S batteries, causes a significant self-discharge at higher state-of-charge (SOC) levels, which leads to the energy dissipation of cells with higher charge. In an operation of series-connected Li-S cells, the shuttle mechanism results into a self-balancing effect which is studied here. A model for prediction of the self-balancing effect is proposed in this work and it is validated by experiments. Our results confirm the self-balancing feature of Li-S cells and illustrate their dependence on various conditions such as temperature, charging limits and idling time at high SOC.
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    A self-discharge model of Lithium-Sulfur batteries based on direct shuttle current measurement
    (Elsevier, 2016-10-29) Knap, Vaclav; Stroe, Daniel-Ioan; Swierczynski, Maciej; Purkayastha, Rajlakshmi; Propp, Karsten; Teodorescu, Remus; Schaltz, Erik
    In the group of post Lithium-ion batteries, Lithium-Sulfur (Li-S) batteries attract a high interest due to their high theoretical limits of the specific capacity of 1672 Ah kg−1 and specific energy of around 2600 Wh kg−1. However, they suffer from polysulfide shuttle, a specific phenomenon of this chemistry, which causes fast capacity fade, low coulombic efficiency, and high self-discharge. The high self-discharge of Li-S batteries is observed in the range of minutes to hours, especially at a high state of charge levels, and makes their use in practical applications and testing a challenging process. A simple but comprehensive mathematical model of the Li-S battery cell self-discharge based on the shuttle current was developed and is presented. The shuttle current values for the model parameterization were obtained from the direct shuttle current measurements. Furthermore, the battery cell depth-of-discharge values were recomputed in order to account for the influence of the self-discharge and provide a higher accuracy of the model. Finally, the derived model was successfully validated against laboratory experiments at various conditions.
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    Significance of the capacity recovery effect in pouch lithium-sulfur battery cells
    (The Electrochemical Society, 2016-12-07) Knap, Vaclav; Zhang, Teng; Stroe, Daniel-Ioan; Schaltz, Erik; Teodorescu, Remus; Propp, Karsten
    Lithium-Sulfur (Li-S) batteries are an emerging energy storage technology, which is technically-attractive due to its high theoretical limits; practically, it is expected that Li-S batteries will result into lighter energy storage devices with higher capacities than traditional Lithium-ion batteries. One of the actual disadvantages for this technology is the highly pronounced rate capacity effect, which reduces the available capacity to be discharged when high currents are used. This drawback might be addressed by the use of the capacity recovery effect, which by introducing relaxation periods between consecutive pulse discharges of the battery, increases the available discharge capacity of the cell. The capacity recovery effect of the Li-S cell is studied in this paper using the pulse discharge technique, considering its dependence on the applied current, discharge step length, temperature, and on the length of the relaxation period between the discharging pulses.