### Browsing by Author "Munisamy, Srinivasan"

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Item Open Access Data supporting: 'State of Charge Estimation of Lithium Sulfur Batteries using Sliding Mode Observer'(Cranfield University, 2022-08-31 12:46) Munisamy, Srinivasan; Wu, WenxuanShow more Data set used for paper titled 'State of Charge Estimation of Lithium Sulfur Batteries using Sliding Mode Observer'.Show more Item Open Access How accurate is state of charge as a predictor of remaining useful work? (ICLSB 2019)(Cranfield University, 2021-05-13 09:21) Auger, Daniel; Munisamy, Srinivasan; Fotouhi, AbbasShow more Presentation given at ICLSB 2019. Abstract follows: It is well known that lithium sulfur cells have a distinctive open-circuit voltage profile: at high states of charge there is a €˜high plateau€™, starting at around 2.35 V, and at low states of charge there is a flatter €˜low plateau€™ at near constant voltage. This presentation will discuss the implications this profile might have for the prediction of the work that cell is capable of doing before it is fully discharged, which is vital for real-world applications. This presentation will introduce a family of techniques that has been developed for the creation of low-complexity dynamic models [1,2] and their application in state estimation algorithms embedded within real-life battery management systems using extended Kalman filters, unscented Kalman filters and particle filters [3,4] or adaptive neuro-fuzzy inference systems (ANFIS) [5]. So far, algorithms for management of lithium-sulfur have all been based on state of charge, rather than €˜remaining energy€™. In a practical application, the real information the end user needs is an answer to the question €˜how much work can I still do?€™ The work done by a cell is the product of the terminal voltage and the current delivered to the load, and a Coulomb-based metric, dependent on current alone, is only a proxy for this. In this presentation, we explore the question €˜how accurate is state of charge as a predictor of remaining useful work?€™ In this presentation, the title question is addressed, both using theoretical and simulation studies comparing a lithium-ion cell [6] from the literature with a model of development-grade industrial lithium sulfur cell [1] and through the analysis of experimental data collected from the same lithium-sulfur cell. In the theoretical studies, it is observed that with low currents, state of charge is a good predictor of remaining useful work in both the lithium-ion cell and the lithium-sulfur one, but that where the remaining useful work predictions for the lithium-ion cell are least accurate at the mid-discharge point, the remaining useful work predictions for the lithium-sulfur cell were least accurate near to the transition between the high and low plateau. The results of the theoretical analysis were supported by an analysis of the experimental data. From these results, no significant motivation was been identified for refactoring estimation algorithms in terms of state of energy. At the time this abstract is prepared, work to consider the accuracy of prediction of remaining useful work at higher loads is underway, and the results of this will also be presented at the conference. [1] Propp K, Marinescu M, Auger DJ, O'Neill L, Fotouhi A, Somasundaram K, Offer GJ, Minton G, Longo S, Wild M & Knap V (2016) Multi-temperature state-dependent equivalent circuit discharge model for lithium-sulfur batteries, Journal of Power Sources, 328 (October) 289-299. Dataset/s: 10.17862/cranfield.rd.c.3292031 [2] Fotouhi A, Auger DJ, Propp K, Longo S, Purkayastha R, O'Neill L & Walus S (2017) Lithium-Sulfur cell equivalent circuit network model parameterization and sensitivity analysis, IEEE Transactions on Vehicular Technology, 66 (9) 7711-7721. [3] Propp K, Auger DJ, Fotouhi A, Longo S & Knap V (2017) Kalman-variant estimators for state of charge in lithium-sulfur batteries, Journal of Power Sources, 343 (March) 254-267. Dataset/s: 10.17862/cranfield.rd.3834057 [4] Knap V, Auger DJ, Propp K, Fotouhi A & Stroe D-I (2018) Concurrent real-time estimation of state of health and maximum available power in lithium-sulfur batteries, Energies, 11 (2133) 1-23. [5] Fotouhi A, Auger D, Propp K & Longo S (2018) Lithium-sulfur battery state-of-charge observability analysis and estimation, IEEE Transactions on Power Electronics, 33 (7) 5847-5859. [6] Antaloae C, Marco J & Assadian F (2012) A novel method for the parameterization of a Li-ion cell model for EV/HEV control applications. IEEE Transactions on Vehicular Technology, 61(9), 3881€“3892. https://doi.org/10.1109/TVT.2012.2212474Show more Item Open Access A likelihood-partitioned Bayesian framework for lithium sulfur battery state discharging of charge estimation(IEEE, 2022-04-14) Munisamy, SrinivasanShow more Lithium sulfur (Li-S) batteries are promising energy storage devices and alternative to lithium-Ion (Li-Ion) batteries in electric grid and vehicle applications. However, compared to Li-Ion, the discharge voltage of Li-S is much complex and nonlinear. This results a challenging state of charge (SoC) estimation problem while Li-S is discharging. For such a problem, the traditional extended Kalman filter fails to provide accurate SoC. Therefore, this paper proposes a novel likelihood partitioned Bayesian filtering (LPBF) framework and its linearized version for SoC estimation of discharging Li-S battery cell. Though both traditional EKF and linearized LPBF use a prediction error minimization based equivalent circuit network (ECN) parameterization, the LPBF uses a partitioned ECN parameterization. The portioned models result two likelihoods, whereas the EKF uses a single state-space model throughout discharge from 100 percent SoC to zero SoC. With experiment data obtained at two different temperature conditions, numerical simulation results compare both EKF and linearized LPBF based SoC estimators. Simulation results show that the LPBF's accuracy is impressive, about 97 percent, for considered dynamic load current, operating temperature and uncertain initial SoC conditions.Show more Item Open Access State of charge estimation of lithium sulfur batteries using sliding mode observer(IEEE, 2022-07-21) Munisamy, Srinivasan; Wu, WenxuanShow more The lithium-sulfur (Li-S) batteries are high energy storage systems that can be used for electric grid and solar power air vehicle applications. Such applications require an accurate state of charge (SOC) estimator to control and optimize battery performance. Modelling and estimation of discharging Li-S are highly challenging than other batteries as the discharge voltage of Li-S batteries has highly nonlinear and typical characteristics than the Lithium-Ion batteries. For Li-S battery SOC estimation, literature has proposed filters and machine learning techniques, but no literature on sliding mode observer (SMO). This paper presents the SMO for discharging Li-S SOC estimation and compares it to the extended Kalman filter (EKF). Both estimators use a first-order equivalent circuit network (ECN) model of Li-S cell parameters given in the literature. The performance of such ECN model based SOC estimators influenced by the Q- uncertainty, which is a perturbation in the form of process noise state-space model. Therefore, this work studies an optimal trade-off characteristic of SMO and EKF over the Q-uncertainty. With constant and mixed-amplitude pulse load current sequences, numerical simulation has performed. Simulation results illustrate that the SMO is optimal, converges to the true SOC than the EKF when the perturbation increased.Show more Item Open Access State of energy estimation in electric propulsion systems with lithium-sulfur batteries(Institution of Engineering and Technology (IET), 2020-12-03) Munisamy, Srinivasan; Auger, Daniel J.; Fotouhi, Abbas; Hawkes, B.; Kappos, E.Show more Lithium-Sulfur (Li-S) batteries are an emerging and appealing electrical energy storage technology. The literature on the Stateof- charge (SoC) estimation of Li-S is readily available. In real-world, battery operated vehicles and equipment need to monitor the electrical energy. This paper focuses on State-of-Eneergy (SoE) estimation of Li-S battery based electric propulsion system. This paper bridges literature gap of the SoE estimation of Li-S battery. While comparing mathematically, the definition of the SoC and SoE batteries are different. Reviewing the SoC estimation, this paper compares the SoC and SoE estimation for same data set. The challenges in Li-S SoC and SoE estimation include battery modelling and time-varying parameters and nonlinear voltage measurement, which has deeply skewed high-plateau and flatted low-plateau characteristics. Modelling Li-S battery as a Thevenin’s equivalent circuit network (ECN), the battery parameters are estimated using Predict Error Minimization (PEM) approach. For estimate SoC and SoE, the extended Kalman filter (EKF) is used. Since the parameters are high sensitive to battery current, the estimators use parameters obtained by polynomial fitting model. A simple switching logic based on SoCmeasurement voltage is used to join the high- and low-plateau. The degree of observability analysis is used to investigate the performance of SoE estimation by the EKF. Using experiment test data, simulation results demonstrate the performance of both SoC and SoE estimators. Results show that the SoE estimation is as close to the SoC estimationShow more