Lithium-Sulfur (Li-S) batteries are a promising next-generation technology providing high gravimetric energy density compared to existing lithium-ion (Li-ion) technologies in the market. The literature shows that in Li-S, estimation of state of charge (SoC) is a demanding task, in particular due to a large flat section in the voltage-SoC curve. This study proposes a new SoC estimator using an online parameter identification method in conjunction with a classification technique. This study investigates a new prototype Li-S cell. Experimental characterization tests are conducted under various conditions; the duty cycle - intended to represent a real-world application - is based on an electric city bus. The characterization results are then used to parameterize an equivalent-circuit-network (ECN) model, which is then used to relate real-time parameter estimates derived using a Recursive Least Squares (RLS) algorithm to state of charge using a Support Vector Machine (SVM) classifier to estimate an approximate SoC range. The estimate is used together with a conventional coulomb-counting technique to achieve continuous SoC estimation in real-time. It is shown that this method can provide an acceptable level of accuracy with less than 3% error under realistic driving conditions.
Lithium Sulfur (Li-S) battery is generally considered as a promising technology where high energy density is required at different applications. Over the past decade, there has been an ever increasing volume of Li-S academic research spanning materials development, fundamental understanding and modelling, and application-based control algorithm development. In this study, the Li-S battery technology, its advantages and limitations from the fundamental perspective are firstly discussed. In the second part of this study, state-of-the-art Li-S cell modelling and state estimation techniques are reviewed with a focus on practical applications. The existing studies on Li-S cell equivalent-circuit-network modelling and state estimation techniques are then discussed. A number of challenges in control of Li-S battery are also explained such as the flat open-circuit-voltage curve and high sensitivity of Li-S cell’s behavior to temperature variation. In the last part of this study, current and future applications of Li-S battery are mentioned.
Solid oxide fuel cells (SOFC) have not received enough attention as a power source in the transportation sector. However, with the development of the technology, its advantages over other types of fuel cells, such as fuel flexibility and high energy efficiency, have made SOFC an interesting option. The present study aims at simulation and experimentally validation of the performance of a hydrogen-powered SOFC in an automotive application. A 6 kW SOFC stack is tested, and its model is integrated into a series hybrid electric vehicle model. A fuzzy controller is designed to regulate the charging current between the battery and the SOFC in the vehicle model. Experimental tests are also conducted in a few cases on the SOFC based on the simulation results. The performance of the real SOFC stack is then analysed under dynamic loads to see how the desired current is provided in practice. The results demonstrate an excellent performance of the SOFC stack under variable load conditions.
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.
Abstract This paper describes the development of a car driving cycle for the city of Tehran and its suburbs using a new approach based on driving data clustering. In this study, driving data gathering is performed under real traffic conditions using Advanced Vehicle Location (AVL) devices installed on private cars. The recorded driving data is then analyzed, based on “micro-trip” definition. Two driving features including “average speed” and “idle time percentage” are calculated for all micro-trips. The micro-trips are then clustered into four groups in driving feature space using the k -means clustering method. For development of the driving cycle, the nearest micro-trips to the cluster centers are selected as representative micro-trips. The new method for driving cycle development needs less computation compared to the SAPM method. In addition, it benefits the capability of the k -means clustering method for traffic condition grouping. The developed driving cycle contains a 1533 s speed time series, with an average speed of 33.83 km/h and a distance of 14.41 km. Finally, the characteristics of the developed driving cycle are compared with some other light vehicle driving cycles used in other countries, including FTP-75, ECE, EUDC and J10-15 Mode.
Abstract This study aims at developing an optimization framework for electric vehicle charging by considering different trade-offs between battery degradation and charging time. For the first time, the application of practical limitations on charging and cooling power is considered along with more detailed health models. Lithium iron phosphate battery is used as a case study to demonstrate the effectiveness of the proposed optimization framework. A coupled electro-thermal equivalent circuit model is used along with two battery health models to mathematically obtain optimal charging current profiles by considering stress factors of state-of-charge, charging rate, temperature and time. The optimization results demonstrate an improvement over the benchmark constant current–constant voltage (CCCV) charging protocol when considering both the charging time and battery health. A main difference between the optimal and the CCCV charging protocols is found to be an additional ability to apply constraints and adapt to initial conditions in the proposed optimal charging protocol. In a case study, for example, the ‘optimal time’ charging is found to take 12 min while the ‘optimal health’ charging profile suggests around 100 min for charging the battery from 25 to 75% state-of-charge. Any other trade-off between those two extreme cases is achievable using the proposed charging protocol as well.
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