Browsing by Author "Propp, Karsten"
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Item Open Access Accuracy versus simplicity in online battery model identification(IEEE, 2016-09-22) Fotouhi, Abbas; Auger, Daniel J.; Propp, Karsten; Longo, StefanoThis paper presents a framework for battery modeling in online, real-time applications where accuracy is important but speed is the key. The framework allows users to select model structures with the smallest number of parameters that is consistent with the accuracy requirements of the target application. The tradeoff between accuracy and speed in a battery model identification process is explored using different model structures and parameter-fitting algorithms. Pareto optimal sets are obtained, allowing a designer to select an appropriate compromise between accuracy and speed. In order to get a clearer understanding of the battery model identification problem, “identification surfaces” are presented. As an outcome of the battery identification surfaces, a new analytical solution is derived for battery model identification using a closed-form formula to obtain a battery’s ohmic resistance and open circuit voltage from measurement data. This analytical solution is used as a benchmark for comparison of other fitting algorithms and it is also used in its own right in a practical scenario for state-of-charge estimation. A simulation study is performed to demonstrate the effectiveness of the proposed framework and the simulation results are verified by conducting experimental tests on a small NiMH battery pack.Item Open Access 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-IoanLithium-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.Item Open Access Data for "Electric Vehicle Battery Parameter Identification and SOC Observability Analysis: NiMH and Li-S Case Studies"(Cranfield University, 2017-11-21 11:49) Fotouhi, Abbas; Auger, Daniel; Propp, Karsten; Longo, StefanoIn this study, battery model identification is performed to be applied in electric vehicle battery management systems. Two case studies are investigated: nickel-metal hydride (NiMH), which is a mature battery technology, and lithium-sulfur (Li-S), a promising next-generation technology. Equivalent circuit battery model parameterization is performed in both cases using the Prediction-Error Minimization (PEM) algorithm applied to experimental data. Performance of a Li-S cell is also tested based on urban dynamometer driving schedule (UDDS) and the proposed parameter identification framework is applied in this case as well. The identification results are then validated against the exact values of the battery parameters. The use of identified parameters for battery state-of-charge (SOC) estimation is also discussed. It is shown that the set of parameters needed can change with a different battery chemistry. In the case of NiMH, the battery open circuit voltage (OCV) is adequate for SOC estimation whereas Li-S battery SOC estimation is more challenging due to its unique features such as flat OCV-SOC curve. An observability analysis shows that Li-S battery SOC is not fully observable and the existing methods in the literature might not be applicable for a Li-S cell. Finally, the effect of temperature on the identification results and the observability are discussed by repeating the UDDS test at 5, 10, 20, 30, 40 and 50 degree Celsius. File created in MATLAB 2015a.Item Open Access Data for "Lithium-Sulfur Battery State-of-Charge Observability Analysis and Estimation"(Cranfield University, 2017-11-21 11:49) Fotouhi, Abbas; Auger, Daniel; Propp, Karsten; Longo, Stefano3.4 Ah Li-S cell pulse discharge test data at 30 degree. File created in MATLAB 2015a.Item Open Access 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.Item Open Access Deterministic observability calculations for zero-dimensional models of lithium–sulfur batteries(Elsevier, 2024-03-29) Rodriguez, Veronica M.; Shateri, Neda; Fotouhi, Abbas; Propp, Karsten; Auger, Daniel J.Among the various energy storage technologies under development, the lithium‑sulfur (Li–S) battery has considerable promise due to its higher theoretical energy density, small environmental footprint, and low projected costs. One of the main challenges posed by Li–S is the need for a battery management system (BMS) that can accommodate the system's complex multi-step redox behaviours; conventional approaches for lithium-ion batteries do not transfer. Most existing approaches rely on equivalent circuit network models, but there is growing interest in ‘zero-dimensional’ electrochemical models which can potentially give insights into the relative polysulfide species concentrations present at any given time. To be useful for state estimation, a model must be ‘observable’: it must be possible to uniquely determine the internal state through observation of the system's behaviour over time. Previous studies have assessed observability using numerical methods, which is an approximation. This study derives an analytic expression for the observability criterion, which allows greater confidence in the results. The analytic observability criterion is then validated against a numerical comparator. A zero-dimensional model from the literature is translated into an ordinary differential equation (ODE) form to define the state variables matrix A, the output matrix C, and subsequently the observability matrix O. These are compared to simulated numerical equivalents. In addition, the sensitivity of the numerical process has been demonstrated. The results have the potential to offer greater confidence in conclusions around observability, which in turn gives greater confidence in the effects of any algorithms based on them.Item Open Access Electric vehicle battery management algorithm development using a HIL simulator incorporating three-phase machines and power electronics(2016-09-09) Fotouhi, Abbas; Propp, Karsten; Samaranayake, LilanthaThis paper describes a hardware-in-the-loop (HIL) test rig for the test and development of electric vehicle battery management and state-estimation algorithms in the presence of realistic real-world duty cycles. The rig includes two back-to-back connected brushless DC motors, the respective power electronic controllers, a target battery pack, a dSPACE real-time simulator, a thermal chamber and a PC for human-machine interface. The traction motor is commanded to track a reference velocity based on a drive cycle and the target battery pack provides the required power. Except the battery pack and the electric machine which are real, other parts of a vehicle powertrain system are modelled and used in the real-time simulator. A generic framework has been developed for real-time battery measurement, model identification and state estimation. Measurements of current and battery terminal voltage are used by an identification unit to extract parameters of an equivalent circuit network (ECN) model in real-time. Outputs of the identification unit are then used by an estimation unit which uses an artificial intelligent model trained to find the relationship between the battery parameters and state-of-charge (SOC). The results demonstrate that even with a high noise level in measured data, the proposed identification and estimation algorithms are able to work well in real-time.Item Open Access Electric vehicle battery model identification and state of charge estimation in real world driving cycles(Institute of Electrical and Electronics Engineers, 2015-09-24) Fotouhi, Abbas; Propp, Karsten; Auger, Daniel J.This paper describes a study demonstrating a new method of state-of-charge (SoC) estimation for batteries in real-world electric vehicle applications. This method combines realtime model identification with an adaptive neuro-fuzzy inference system (ANFIS). In the study, investigations were carried down on a small-scale battery pack. An equivalent circuit network model of the pack was developed and validated using pulse-discharge experiments. The pack was then subjected to demands representing realistic WLTP and UDDS driving cycles obtained from a model of a representative electric vehicle, scaled match the size of the battery pack. A fast system identification technique was then used to estimate battery parameter values. One of these, open circuit voltage, was selected as suitable for SoC estimation, and this was used as the input to an ANFIS system which estimated the SoC. The results were verified by comparison to a theoretical Coulomb-counting method, and the new method was judged to be effective. The case study used a small 7.2 V NiMH battery pack, but the method described is applicable to packs of any size or chemistry.Item Open Access Electric vehicle battery parameter identification and SOC observability analysis: NiMH and Li-S case studies(The Institution of Engineering and Technology, 2017-07-06) Fotouhi, Abbas; Auger, Daniel J.; Propp, Karsten; Longo, StefanoIn this study, battery model identification is performed to be applied in electric vehicle battery management systems. Two case studies are investigated: nickel-metal hydride (NiMH), which is a mature battery technology, and lithium-sulphur (Li-S), a promising next-generation technology. Equivalent circuit battery model parameterisation is performed in both cases using the prediction-error minimisation algorithm applied to experimental data. Performance of a Li-S cell is also tested based on urban dynamometer driving schedule (UDDS) and the proposed parameter identification framework is applied in this case as well. The identification results are then validated against the exact values of the battery parameters. The use of identified parameters for battery state-of-charge (SOC) estimation is also discussed. It is shown that the set of parameters needed can change with a different battery chemistry. In the case of NiMH, the battery open circuit voltage (OCV) is adequate for SOC estimation whereas Li-S battery SOC estimation is more challenging due to its unique features such as flat OCV–SOC curve. An observability analysis shows that Li-S battery SOC is not fully observable and the existing methods in the literature might not be applicable for a Li-S cell. Finally, the effect of temperature on the identification results and the observability is discussed by repeating the UDDS test at 5, 10, 20, 30, 40 and 50°C.Item Open Access Electric vehicle battery parameter identification and SOC observability analysis: NiMH and Li-S case studies(IET, 2016-04-21) Fotouhi, Abbas; Auger, Daniel J.; Propp, Karsten; Longo, StefanoIn this study, a framework is proposed for battery model identification to be applied in electric vehicle energy storage systems. The main advantage of the proposed approach is having capability to handle different battery chemistries. Two case studies are investigated: nickel-metal hydride (NiMH), which is a mature battery technology, and Lithium-Sulphur (Li-S), a promising next-generation technology. Equivalent circuit battery model parametrisation is performed in both cases using the Prediction-Error Minimization (PEM) algorithm applied to experimental data. The use of identified parameters for battery state-of-charge (SOC) estimation is then discussed. It is demonstrated that the set of parameters needed can change with a different battery chemistry. In the case of NiMH, the battery’s open circuit voltage (OCV) is adequate for SOC estimation. However, Li-S battery SOC estimation can be challenging due to the chemistry’s unique features and the SOC cannot be estimated from the OCV-SOC curve alone because of its flat gradient. An observability analysis demonstrates that Li-S battery SOC is not observable using the common state-space representations in the literature. Finally, the problem’s solution is discussed using the proposed framework.Item Open Access A hardware-in-the-loop test rig for development of electric vehicle battery identification and state estimation algorithms(Inderscience, 2018-03-01) Fotouhi, Abbas; Propp, Karsten; Samaranayake, Lilantha; Auger, Daniel J.; Longo, StefanoThis paper describes a hardware-in-the-loop (HIL) test rig for the test and development of electric vehicle battery parameterisation and state-estimation algorithms in the presence of realistic real-world duty cycles. The rig includes two electric machines, a battery pack, a real-time simulator, a thermal chamber and a PC for human-machine interface. Other parts of a vehicle powertrain system are modelled and used in the real-time simulator. A generic framework has been developed for real-time battery measurement, model identification and state estimation. Measurements are used to extract parameters of an equivalent circuit network model. Outputs of the identification unit are then used by an estimation unit trained to find the relationship between the battery parameters and state-of-charge. The results demonstrate that even with a high noise level in measured data, the proposed identification and estimation algorithms are able to work well in real-time.Item Open Access Influence of battery capacity on performance of an electric vehicle fleet(IEEE, 2016-11-23) Fotouhi, Abbas; Auger, Daniel J.; Propp, Karsten; Longo, StefanoIn this study, the influence of electric vehicle (EV) range on overall performance of an EV fleet is analysed. Various case-studies are investigated in which the EV fleet is simulated to cover a number of target points in a typical delivery problem. A trip scheduling algorithm is proposed in order to get all target points while considering the EVs range. The critical role of EV range in performance improvement of the whole fleet is analysed and an optimum EV range is obtained with regard to the whole fleet mileage. The results demonstrate that 250 km is an optimum range for an EV fleet to work in an area of 100×100 km². The number of target points, called task density, doesn’t affect the optimum EV range very much and it can be determined only based on size of the service area. Finally, lithium-sulfur battery is discussed as a promising technology to extend EV range.Item Open Access Lithium-sulfur battery state-of-charge observability analysis and estimation(IEEE, 2017-09-28) Fotouhi, Abbas; Auger, Daniel J.; Propp, Karsten; Longo, StefanoLithium-Sulfur (Li-S) battery technology is considered for an application in an electric vehicle energy storage system in this study. A new type of Li-S cell is tested by applying load current and measuring cell's terminal voltage in order to parameterize an equivalent circuit network model. Having the cell's model, the possibility of state-of-charge (SOC) estimation is assessed by performing an observability analysis. The results demonstrate that the Li-S cell model is not fully observable because of the particular shape of cell's open-circuit voltage curve. This feature distinguishes Li-S batteries from many other types of battery, e.g. Li-ion and NiMH. As a consequence, a Li-S cell's SOC cannot be estimated using existing methods in the literature and special considerations are needed. To solve this problem, a new framework is proposed consisting of online battery parameter identification in conjunction with an estimator that is trained to use the identified parameters to predict SOC. The identification part is based on the well-known Prediction-Error Minimization (PEM) algorithm; and the SOC estimator part is an Adaptive Neuro-Fuzzy Inference System (ANFIS) in combination with coulomb counting. Using the proposed method, a Li-S cell's SOC is estimated with a mean error of 4% and maximum error of 7% in a realistic driving scenario.Item Open Access Lithium-sulfur cell equivalent circuit network model parameterization and sensitivity analysis(Institute of Electrical and Electronics Engineers, 2017-04-18) Fotouhi, Abbas; Auger, Daniel J.; Propp, Karsten; Longo, Stefano; Purkayastha, Rajlakshmi; O'Neill, Laura; Walus, SylwiaCompared to lithium-ion batteries, lithium-sulfur (Li-S) batteries potentially offer greater specific energy density, a wider temperature range of operation, and safety benefits, making them a promising technology for energy storage systems especially in automotive and aerospace applications. Unlike lithium-ion batteries, there is not a mature discipline of equivalent circuit network (ECN) modelling for Li-S. In this study, ECN modelling is addressed using formal ‘system identification’ techniques. A Li-S cell’s performance is studied in the presence of different charge/discharge rates and temperature levels using precise experimental test equipment. Various ECN model structures are explored, considering the trade-offs between accuracy and speed. It was concluded that a ‘2RC’ model is generally a good compromise, giving good accuracy and speed. Model parameterization is repeated at various state-of-charge (SOC) and temperature levels, and the effects of these variables on Li-S cell’s ohmic resistance and total capacity are demonstrated. The results demonstrate that Li-S cell’s ohmic resistance has a highly nonlinear relationship with SOC with a break-point around 75% SOC that distinguishes it from other types of battery. Finally, an ECN model is proposed which uses SOC and temperature as inputs. A sensitivity analysis is performed to investigate the effect of SOC estimation error on the model’s accuracy. In this analysis, the battery model’s accuracy is evaluated at various SOC and temperature levels. The results demonstrate that the Li-S cell model has the most sensitivity to SOC estimation error around the break-point (around 75% SOC) whereas in the middle SOC range, from 20% to 70%, it has the least sensitivity.Item Open Access Low-cost programmable battery dischargers and application in battery model identification(IInstitute of Electrical and Electronics Engineers, 2015-09-25) Propp, Karsten; Fotouhi, Abbas; Auger, Daniel J.This paper describes a study where a low-cost programmable battery discharger was built from basic electronic components, the popular MATLAB programming environment, and an low-cost Arduino microcontroller board. After its components and their function are explained in detail, a case study is performed to evaluate the discharger's performance. The setup is principally suitable for any type of battery cell or small packs. Here a 7.2 V NiMH battery pack including six cells is used. Consecutive discharge current pulses are applied and the terminal voltage is measured as the output. With the measured data, battery model identification is performed using a simple equivalent circuit model containing the open circuit voltage and the internal resistance. The identification results are then tested by repeating similar tests. Consistent results demonstrate accuracy of the identified battery parameters, which also confirms the quality of the measurement. Furthermore, it is demonstrated that the identification method is fast enough to be used in real-time applications.Item Open Access 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, StefanoThis 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.Item Open Access A MATLAB graphical user interface for battery design and simulation; from cell test data to real-world automotive simulation(IEEE, 2016-06-30) Fotouhi, Abbas; Shateri, Neda; Auger, Daniel J.; Longo, Stefano; Propp, Karsten; Purkayastha, Rajlakshmi; Wild, MarkThis paper describes a graphical user interface (GUI) tool designed to support cell design and development of manufacturing processes for an automotive battery application. The GUI is built using the MATLAB environment and is able to load and analyze raw test data as its input. After data processing, a cell model is fitted to the experimental data using system identification techniques. The cell model's parameters (such as open-circuit-voltage and ohmic resistance) are displayed to the user as functions of state of charge, providing a visual understanding of the cell's characteristics. The GUI is also able to simulate the performance of a full battery pack consisting of a specified number of single cells using standard driving cycles and a generic electric vehicle model. After a simulation, the battery designer is able to see how well the vehicle would be able to follow the driving cycle using the tested cells. Although the GUI is developed for an automotive application, it could be extended to other applications as well. The GUI has been designed to be easily used by non-simulation experts (i.e. battery designers or electrochemists) and it is fully automated, only requiring the user to supply the location of raw test data.Item Open Access 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, KarstenLithium-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.Item Open Access 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, VaclavLithium-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.Item Open Access 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, KarstenLithium-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.