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    CO2 reduction in a common rail direct injection engine using the combined effect of low carbon biofuels, hydrogen and a post combustion carbon capture system
    (Taylor & Francis, 2021) Varuvel, Edwin Geo; Thiyagarajan, S.; Sonthalia, Ankit; Prakash, T.; Awad, Sary; Aloui, Fethi; Pugazhendhi, Arivalagan
    The transportation sector is a major emitter of carbon dioxide emissions. It is a known fact that carbon dioxide is the cause of global warming which has resulted in extreme weather conditions as well as climate change. In this study a combination of different methods of expediting the CO2 emission from a single cylinder common rail direct injection (CRDI) engine has been explored. The methods include use of low carbon content biofuels (lemon peel oil (LPO) and camphor oil (CMO), inducing hydrogen in the intake manifold and zeolite based after-treatment system. Initial engine operation with the low carbon content biofuel blends resulted in reduced smoke and CO2 emissions. Substitution of the blends with hydrogen further assisted in decrease in emission and improvement in engine efficiency. Later on in the exhaust pipe an after-treatment system containing zeolite was placed. The emissions were found to reduce even further and at full load condition the lowest CO2 (39.7% reduction) and smoke (49% reduction) emissions were observed with LPO blend and hydrogen induction. The NO emission with hydrogen induction increases for both the blends, however, it was seen that the zeolite based treatment system was effective in reducing the emission as well. As compared to baseline diesel, the maximum reduction in NO emission was 23% at full load with LPO blend, hydrogen induction and after-treatment system.
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    Early prediction of the remaining useful life of lithium-ion cells using ensemble and non-ensemble algorithms
    (John wiley and sons inc, 2025) Bai, Femilda Josephin Joseph Shobana; Sonthalia, Ankit; Subramanian, Thiyagarajan; Aloui, Fethi; Bhatt, Dhowmya; Varuvel, Edwin Geo
    Lithium-ion cells have become an important part of our daily lives. They are used to power mobile phones, laptops and more recently electric vehicles (both two- and four-wheelers). The chemical behavior of the cells is rather complex and non-linear. For reliable and sustainable use of the cells for practical applications, it is imperative to predict the precise pace at which their capacity will degrade. More importantly, the lifetime of the cells must be predicted at an early stage, which would accelerate development and design optimization of the cells. However, most of the existing methods cannot predict the lifetime at an early stage, since there is a weak correlation between the cell capacity and lifetime. In this study for accurate forecasting of the battery lifetime, the patterns of the parameters such as cell current, voltage, temperature, charging time, internal resistance, and capacity were examined during charging and discharging cycle of the cell. Twelve manually crafted features were prepared from these parameters. The dataset for the features was created using the raw data of the first 100 cycles of 124 cells. Six ensemble and non-ensemble machine learning algorithms, namely, multiple linear regression (MLR), decision tree, support vector machine (SVM), gradient boosting machine (GBM), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost), were trained with the features for predicting the life-cycle of the cells. The R2 and root mean squared error (RMSE) values of MLR, decision tree, SVM, GBM, LGBM, and XGBoost were found to be 0.72 and 201, 0.83 and 155, 0.85 and 146, 0.92 and 100, 0.9 and 112, and 0.94 and 95, respectively. The prediction accuracy of lithium-ion cell life-time was found to be the best with the XGBoost algorithm. This shows that only first 100 cycles are required foraccurately predicting the number of cycles the lithium-ion cell can work for. Lastly, the results of the study were compared with the available studies in the literature. Three studies were chosen, and the RMSE of the method proposed in this study was found to be higher than the three studies by 43, 17, and 20. Therefore, the proposed method is a suitable option for predicting the lifetime of lithium-ion cells during the early stages of its development.
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    Evaluating the Effectiveness of Boosting and Bagging Ensemble techniques in forecasting lithium-ion battery useful life
    (Wiley, 2025) Sonthalia, Ankit; Bai, Femilda Josephin Joseph Shobana; Aloui, Fethi; Varuvel, Edwin Geo
    It is essential to forecast the exact rate at which the cell's capacity would decline for practical uses, to comprehend the intricate and non-linear behavior of the cell. Furthermore, the majority of studies provided subpar prediction criteria, making early cell lifetime prediction difficult. Applying reliable and accurate aging models to the dynamic on-road conditions presents additional challenges. In this work, the battery lifetime during its earliest phases of use was accurately predicted using machine learning models. After analyzing the patterns of the parameters, 12 hand-crafted features were selected and the raw data of the first 100 cycles of 126 cells was used for creating the dataset for the features. The dataset was then used to train five machine learning models namely random forest, gradient boosting machine (GBM), light gradient boosting machine (LGBM), extreme gradient boosting machine (XGBoost), and gradient boost with categorical features (CATBoost). The statistical analysis reveals that XGBoost algorithm present the best result with a R2 value of 0.95 and root-mean-square-error (RMSE) of 97 cycles. Lastly, in comparison to existing studies, the RMSE significantly reduced from a maximum of 138 to 97 cycles.
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    Experimental assessment on the performance, emission and combustion characteristics of a safflower oil fueled CI engine with hydrogen gas enrichment
    (Elsevier Ltd, 2022) Praveena, V.; Joseph Shobana Bai, Femilda Josephin; Balasubramanian, Dhinesh; Devarajan, Yuvarajan; Aloui, Fethi; Varuvel, Edwin Geo
    Inducting hydrogen with biodiesel in a compression ignition (CI) engine contributes to improvising the performance characteristics of the engine and minimize long-term issues. Combustion of hydrogen along with intake air impacts positively in air quality by preventing the formation of toxic emissions like hydrocarbons (HC) and carbon monoxide (CO). The benefits of hydrogen such as good diffusion rate, lesser ignition energy and fast flame propagation rate promotes a more homogenously mixed air fuel ratio. This experimental work focuses on enhancement of the performance and combustion characteristics of a direct injection compression ignition (DICI) engine by enriching the biodiesel with various levels of hydrogen gas supplement at the intake manifold. The brake thermal efficiency of the engine with safflower oil biodiesel is 31.15 % which is far inferior to that of diesel with 34 %. As an effort to improve the performance characteristics of the CI engine, hydrogen gas is inducted at 4 %, 8 % and 12 % energy share. HC, CO and smoke emission decreases by 15.09 %, 34.6 % and 18 % respectively compared to neat biodiesel at full load of 5.2 kW. An opposite trend is observed in NOx emissions which are raised from 1650 ppm to 1852 ppm. A 12.2 % increase in NOx emissions are realized due to homogenous flammable mixture that combusts closer to Top dead center (TDC). The hydrogen enrichment with safflower oil biodiesel influences the combustion characteristics in a positive vein except for the NOx emissions, which could be minimized through the use of retrofit devices like selective catalytic reducer, diesel oxidation catalyst etc.
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    A systematic review on biofuel production and utilization from algae and waste feedstocks- a circular economy approach
    (Pergamon-Elsevier Science Ltd, 2024) Praveena, V.; Martin, Leenus Jesu; Matijosius, Jonas; Aloui, Fethi; Pugazhendhi, Arivalagan; Varuvel, Edwin Geo
    Energy demand on a global measure grows continuously due to increased population, industrialization and economic growth. Fossil fuel resources that are currently available are definitely not sufficient to meet the growing demand. In addition, the continuous emissions from automobiles and industrial sectors should be attended to so that a complete remedial and sustainable alternative for fossil fuels is obtained. The appropriate replacement for fossil fuel is biofuel, as they are renewable and eco-friendly. First generation and second generation biodiesel derived from various sources are extensively researched and experimented practically by the past researchers. This article summarizes a continuous and comprehensive assessment of different feedstocks needed for third and fourth generations of biodiesel. Various sources of feedstock, steps for biodiesel production, yield of biodiesel obtained through different methods, properties of biodiesel like fatty acid profile, density, viscosity, cetane number, flash point, cloud point, economic feasibility and considerations are also discussed. Third generation biodiesel like microalgae can be widely used in CI engines. It is observed that their performance and combustion analysis in a CI engine is determined by the physico chemical properties of obtained biodiesel and nature of feedstock. Species selection and cultivation methods of microalgae, future perspectives of cultivating techniques and lipid production are summarized in detail. Fourth generation biodiesel like solar fuels and synthetic biomass production are covered, though their application in various energy fields is still not revealed. The type of transesterification that best suits the free fatty acid profile of fuel is selected and other reaction parameters like reaction time, reaction temperature, catalyst quantity and oil methanol molar ratio are explained individually for third generation feedstocks. Proper adoption of suitable methods would help in yielding the maximum biodiesel. Future energy demand can be dealt with by the combination of various third and fourth generation oil feedstocks.