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Öğe A deep learning multi-feature based fusion model for predicting the state of health of lithium-ion batteries(Elsevier ltd, 2025) Sonthalia, Ankit; Bai, Femilda Josephin Joseph Shobana; Varuvel, Edwin Geo; Chinnathambi, Arunachalam; Subramanian, Thiyagarajan; Kiani, FarzadLithium-ion batteries have become the preferred energy storage method with applications ranging from consumer electronics to electric vehicles. Utilization of the battery will eventually lead to degradation and capacity fade. Accurately predicting the state of health (SOH) of the cells holds significant importance in terms of reliability and safety of the cell during its operation. The battery degradation mechanism is strongly non-linear and the physics-based model have their inherent disadvantages. The machine learning method has become popular for estimating SOH due to its superior non-linear mapping, adaptive, and self-learning capabilities, made possible by advances in deep learning technologies. In this study parallel hybrid neural network is formulated for predicting the state of health of lithium-ion cell. Firstly, the factors that have an effect on the cell state were analysed. These factors are cell voltage, charging & discharging time and incremental capacity curve. The features were then processed for use as input to the model. Spearman correlation coefficient analysis shows that all the factors had a positive correlation with SOH. While charging time has a negative correlation with the other features. Next the deep learning models namely convolution neural network (CNN), temporal convolution network (TCN), long-short-term memory (LSTM) and bi-directional LSTM were used to make fusion models. The number of layers in CNN and TCN were also varied. The hyperparameters used in the models were optimized using Bayesian optimization algorithm. The models were validated through comparative experiments on the University of Maryland battery degradation dataset. The prediction accuracy with CNN 3-layer LSTM was found to be the best for the training and the test dataset. The overall R2 value, root mean squared error (RMSE) and mean absolute percentage error (MAPE) with the model was found to be 0.999646, 0.003807 and 0.3, respectively. The impact of the features on the model was also analysed by removing one feature and retraining the model with the other features. The effect of discharging time and the peak of the discharge incremental capacity curve was maximum. The analysis also reveals that either charging voltage or discharging voltage can be used. Further, the proposed model was also compared with the other studies. The comparison shows that the R2, RMSE and MAPE values of the proposed model was better.Öğe Application of machine learning algorithms for predicting the engine characteristics of a wheat germ oil–hydrogen fuelled dual fuel engine(Elsevier, 2022) Joseph Shobana Bai, Femilda Josephin; Shanmugaiah, Kaliraj; Sonthalia, Ankit; Devarajan, Yuvarajan; Varuvel, Edwin GeoIn this research work, performance and emission parameters of wheat germ oil (WGO) -hydrogen dual fuel was investigated experimentally and these parameters were predicted using different machine learning algorithms. Initially, hydrogen injection with 5%, 10% and 15% energy share were used as the dual fuel strategy with WGO. For WGO +15% hydrogen energy share the NO emission is 1089 ppm, which is nearly 33% higher than WGO at full load. As hydrogen has higher flame speed and calorific value and wider flammability limit which increases the combustion temperature. Thus, the reaction between nitrogen and oxygen increases thereby forming more NO. Smoke emission for WGO +15% hydrogen energy share is 66%, which is 15% lower compared to WGO, since the heat released in the pre-mixed phase of combustion is increased to a maximum with higher hydrogen energy share compared to WGO. Different applications including internal combustion engines have used machine learning approaches for predictions and classifications. In the second phase various machine learning techniques namely Decision Tree (DT), Random Forest (RF), Multiple Linear Regression (MLR), and Support Vector Machines (SVM)) were used to predict the emission characteristics of the engine operating in dual fuel mode. The machine learning models were trained and tested using the experimental data. The most effective model was identified using performance metrics like R-Squared (R2) value, Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The result shows that the prediction by MLR model was closest to the experimental results. © 2022 Hydrogen Energy Publications LLCÖğe 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, ArivalaganThe 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.Öğe Comparative analysis to reduce greenhouse gas (GHG) emission in CI engine fuelled with sweet almond oil using ammonia/after treatment system(Elsevier, 2024) Sonthalia, Ankit; Varuvel, Edwin Geo; Subramanian, Thiyagarajan; Josephin, Femilda J. S.; Alahmadi, Tahani Awad; Pugazhendhi, ArivalagaThe present study analyses the various techniques to reduce CO 2 emission, a major contributor to GHG emissions. Diesel was replaced with prunus amygdalus dulcis (sweet almond oil) -fuelled single -cylinder compression ignition (CI) engine. Due to the high viscosity of sweet almond oil, a transesterification procedure was used to convert it to biodiesel. In this experiment, the diesel fuel was entirely substituted with biodiesel (B100) in order to evaluate the emissions, combustion characteristics, and performance of the CI engine operating at a consistent 1500 revolutions per minute under varying loads. In comparison to diesel, tailpipe CO 2 emissions were greater when biodiesel was utilized due to its higher carbon content in the molecular structure. However, plantations absorbs CO 2 emissions from atmosphere causing 'net negative CO 2 emission '. No carbon fuel ammonia was introduced into the intake air using sweet almond oil biodiesel as the base fuel in order to reduce exhaust CO 2 emissions. Under various load conditions, ammonia was introduced at varying flow rates ranging from 10 to 30 LPM. It is observed that increase in ammonia flow rate led to reduction in CO 2 emission. CO 2 emission was reduced from 11.2 % for biodiesel to 6.9 % with 30 LPM ammonia. An after -treatment system was designed with calcite/ activated carbon and retrofitted in exhaust pipe and tested with B100 as fuel. The results indicate that calcite reduces CO 2 more effectively than CO 2 capture systems based on activated carbon. CO 2 emission with calcite is 9.6 % and with activated carbon it is 10.2 % at maximum load condition. The utilization of a calcite -based CO 2 capture system in conjunction with biofuel is believed to effectively mitigate the adverse effects of global warming by generating a net negative CO 2 effect and reducing engine out emissions. Based on the experimental results, compared to after treatment system, ammonia addition with biodiesel is more effective in reducing CO 2 emission without much affecting the other parameters.Öğe 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 GeoLithium-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.Öğe Effect of hydrogen on compression-ignition (CI) engine fueled with vegetable oil/biodiesel from various feedstocks: A review(Elsevier Ltd., 2022) Thiyagarajan, S.; Varuvel, EdwinGeo; Karthickeyan, V.; Sonthalia, Ankit; Kumar, Gopalakrishnan; Saravanan, C.G.; Dhinesh, B.; Pugazhendhi, ArivalaganCompression ignition (CI) engines used in the transportation sector operates on fossil diesel and is one of the biggest causes of air pollution. Numerous studies were carried out over last two decades to substitute the fossil diesel with biofuels so that the net carbon dioxide (CO2) emission can be minimized. However, the engine performance with these fuel was sub-standard and there were many long-term issues. Therefore, many researchers inducted hydrogen along with the biofuels. The present study gives an outlook on the effect of hydrogen addition with biodiesel/vegetable oil from various sources in CI engine. Engine parameters (brake thermal efficiency, brake specific fuel consumption), combustion parameters (in-cylinder pressure and heat release rate) and emission parameters (unburned hydrocarbon (HC), carbon monoxide (CO), oxides of nitrogen (NOx) and smoke emissions) were evaluated in detail. The results show that hydrogen induction in general improves the engine performance as compared to biodiesel/vegetable oil but it is similar/lower than diesel. Except NOx emissions all other emissions showed a decreasing trend with hydrogen addition. To counter this effect numerous after-treatment systems like selective catalytic reduction (SCR), exhaust gas recirculation (EGR), selective non-catalytic reduction system (SNCR) and non-selective catalytic reduction system (NSCR) were proposed by researchers which were also studied in this review.Öğe Effect of intake port design modifications on diesel engine characteristics fuelled by pine oil-diesel blends(TAYLOR & FRANCIS INC, 2022) Malaiperumal, Vikneswaran; Saravanan, Chidambaram Ganapathy; Raman, Vallinayagam; Kirubagaran, Raj Kiran; Pandiarajan, Premkumar; Sonthalia, Ankit; Varuvel, Edwin GeoThe effect of the modified intake port with various inclined nozzle angles such as 30 degrees, 60 degrees, and 90 degrees on the diesel engine characteristics when operated with pine oil-diesel blends is investigated. Prior to the engine experimental study, a computational analysis was performed to investigate the impact produced on the flow field parameters of an engine due to modified intake port design. The numerical study revealed increased swirl velocity and turbulence for intake port with a 60 degrees single-pass configuration compared to other design configurations. With evidence of improved swirl velocity and the proposed modified intake port design from the numerical study, an experimental investigation was performed using pine oil blends in the diesel engine with modified intake port configurations. The preliminary engine test findings with standard intake port design indicated that P50 (50% pine oil + 50% diesel) has higher peak engine cylinder pressure and heat release rates than P10 (10% pine oil + 90% diesel). Additionally, the 60 degrees single-pass configuration showed further increase in peak pressure and peak heat release followed by standard and other intake port design configurations. At high load, the P50 blend showed a 12.3% increase in BTE for 60 degrees intake port design configuration in comparison to the standard design configuration. While for the same blend, the engine out emissions like hydrocarbon (HC) and smoke were reduced by about 6.6% and 17.6%, respectively, and nitrogen oxide (NOX) emission was increased by 29% for the 60 degrees single-pass configuration when compared to the standard design configuration. Overall, the intended intake port design modification strategy increased the swirl velocity and turbulence, which improved the air/fuel mixing and combustion. This study identifies 60 degrees single-pass configuration as an optimum design on account of the aforementioned improved engine combustion, performance, and emissions.Öğe Enhancing the performance of renewable biogas powered engine employing oxyhydrogen: Optimization with desirability and D-optimal design(Elsevier Sci Ltd, 2023) Sharma, Prabhakar; Balasubramanian, Dhinesh; Khai, Chu Thanh; Venugopal, Inbanaathan Papla; Alruqi, Mansoor; Josephin, J. S. Femilda; Sonthalia, AnkitThe performance and exhaust characteristics of a dual-fuel compression ignition engine were explored, with biogas as the primary fuel, diesel as the pilot-injected fuel, and oxyhydrogen as the fortifying agent. The trials were carried out with the use of an RSM-based D-optimal design. ANOVA was used to create the relationship functions between input and output. Except for nitrogen oxide emissions, oxyhydrogen fortification increased biogas-diesel engine combustion and decreased carbon-based pollutants. For each result, RSM-ANOVA was utilized to generate mathematical formulations (models). The output of the models was predicted and compared to the observed findings. The prediction models showed robust prediction efficiency (R2 greater than 99.21%). The optimal engine operating parameters were discovered by desirability approach-based optimization to be 24 degrees crank angles before the top dead center, 10.88 kg engine loading, and 1.1 lpm oxyhydrogen flow rate. All outcomes were within 3.75% of the model's predicted output when the optimized parameters were tested experimentally. The current research has the potential to be widely used in compression ignition engine-based transportation systems.Öğe 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 GeoIt 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.Öğe Experimental investigation and performance prediction of gasoline engine operating parameters fueled with diisopropyl ether-gasoline blends: Response surface methodology based optimization(Elsevier Ltd., 2022) Sathyanarayanan, Seetharaman; Suresh, Sivan; Saravanan, C. G; Vikneswaran, M.; Dhamodaran, Gopinath; Sonthalia, Ankit; Joseph Shobana Bai, Femilda Josephin; Varuvel, Edwin GeoIn this research, gasoline engine performance and emission characteristics were studied when powered by diisopropyl ether-gasoline blends. The main objective of this study is to determine the behavior of diisopropyl ether-gasoline blends at various engine speeds and compression ratios. Further, the engine parameters were optimized using the response surface methodology. Enriched oxygen, higher latent heat of vaporization, and the readily volatile nature of the fuel enhanced the brake thermal efficiency and lowered the hydrocarbons and carbon monoxide due to a better combustion rate. The developed model exhibited superior R2 values with a 0.957 desirability factor. The optimum parameters such as speed, compression ratio, and fuel-blend concentrations were found at 2250 rpm, 10:1, and D25 (75% gasoline and 25% diisopropyl ether), respectively. The responses for the optimal input parameters were brake thermal efficiency (31.53%), specific fuel consumption (0.2923 kg/kWh), carbon monoxide (0.14% by Vol.), hydrocarbons (31 ppm), and oxides of nitrogen (708 ppm). The predicted values for optimum engine parameters were validated with the experimental data, and their percentage of absolute error was found to be less than 5%. Thus, the study concludes that diisopropyl-ether gasoline blends can be used as an alternative fuel to enhance the brake thermal efficiency and reduce the pollution level, and the proposed numerical model can predict the responses with high accuracy.Öğe Experimental investigation of ammonia gas as hydrogen carrier in prunus amygdalus dulcis oil fueled compression ignition engine(Elsevier Ltd, 2024) Sonthalia, Ankit; Geo Varuvel, Edwin; Subramanian, Thiyagarajan; Josephin JS, Femilda; Almoallim, Hesham S.; Pugazhendhi, ArivalaganThe present study aims to utilize ammonia gas as a hydrogen carrier with prunus amygdalus dulcis (sweet almond oil)-fueled single-cylinder compression ignition (CI) engine. Due to the high viscosity of sweet almond oil, a transesterification procedure was used to convert it to biodiesel. The diesel fuel was completely replaced with biodiesel to assess the performance, emission, and combustion characteristics of the CI engine running at a constant speed of 1500 rpm under different load conditions. Poor performance and combustion were exhibited with biodiesel in comparison to diesel. Lower brake thermal efficiency with higher fuel consumption and lower nitrous oxides (NOx) emissions were observed with biodiesel in comparison to diesel. While hydrocarbon (HC), carbon monoxide (CO), and smoke emissions were higher with biodiesel, to further improve the performance, hydrogen gas was introduced at different flow rates (10–30 LPM). Hydrogen improved the brake thermal efficiency with reduced carbon emissions. At maximum load condition, with 30 LPM hydrogen brake thermal efficiency is improved by 15 %. However, NOx emissions were higher with hydrogen induction compared to base fuels at all load conditions. NOx emissions were increased from 1274 ppm with biodiesel to 1451 ppm with 30 LPM hydrogen addition at maximum load. Although hydrogen is one of the most promising techniques to improve the performance of biodiesel, its higher NOx emissions and safety aspects make its practical application questionable. Hence, ammonia gas was used as a hydrogen carrier, and tests were conducted in dual fuel mode with biodiesel at different flow rates. It is observed that performance parameters in ammonia dual fuel mode are on par with those of biodiesel with reduced carbon and NOx emissions. Hence, ammonia can be considered a viable option to replace hydrogen as its carrier to meet global energy demands and also for its safer use. © 2024 Elsevier LtdÖğe Influence of hydrogen injection timing and duration on the combustion and emission characteristics of a diesel engine operating on dual fuel mode using biodiesel of dairy scum oil and producer gas(Elsevier Ltd, 2023) Lalsangi, Sadashiva; Yaliwal V.S.; Banapurmath N.R.; Soudagar, Manzoore Elahi M.; Balasubramanian, Dhinesh; Sonthalia, Ankit; Varuvel, Edwin Geo; Wae-Hayee, MakatarThe main aim of the present work is to investigate the influence of hydrogen injection timing and injection duration on the combustion and emissions of a CI engine functioning on dual fuel (DF) mode by employing diesel/dairy scum oil methyl ester (DiSOME)/Waste frying oil methyl ester (WFOME) - producer gas (PG) combination. Hydrogen flow rate was maintained constant (8 lpm) and injected in air-producer gas (PG) mixture an inlet manifold using a gas injector. In this current work, injection timing was varied from TDC to 15 deg., aTDC in steps of 5. Similarly, injection duration was adopted from 30 deg., CA to 90 deg., CA and differed in steps of 30. From the outcome of work, it is noticed that the best possible injection timing and injection duration were found to be 10 deg., aTDC and 60 deg., CA respectively. Results showed that, at optimum injection parameters, diesel-PG combination with hydrogen resulted in augmented BTE by 6.7% and 12.4%, decreased smoke by 26.04% and 36.4%, decreased HC by 16.6% and 22.4%, decreased CO by 23.5% and 29.6% and increased NOx by 12.4% and 22.1%, compared to DiSOME and WFOME supported DF operation. Investigation with DiSOME-hydrogen enriched PG combustion showed satisfactory operation. © 2022 Hydrogen Energy Publications LLCÖğe Moving ahead from hydrogen to methanol economy: scope and challenges(Springer Science and Business Media Deutschland GmbH, 2021) Sonthalia, Ankit; Kumar, Naveen; Tomar, Mukul; Varuvel, Edwin Geo; Subramanian, Thiyagarajan; Pugazhendhi, ArivalaganAbstract: Energy is the driver in the economic development of any country. However, most of the developing countries do not have sufficient oil reserves to cater to their energy requirement and depend upon oil producing countries. The perturbations in the crude oil price and adverse environmental impacts from fossil fuel usage are the biggest concern. Therefore, developing countries have started investing heavily in solar and wind power and are considering hydrogen as a future energy resource. However, to tap the potential of hydrogen as a fuel, an entirely new infrastructure will be needed for transporting, storing and dispensing it safely, which would be expensive. In the transportation sector, a liquid alternate to fossil fuels will be highly desirable as the existing infrastructure can be used with minor modifications. Among the possible liquid fuels, methanol is very promising. Methanol is a single carbon atom compound and can be produced from wide variety of sources such as natural gas, coal and biomass. The properties of methanol are conducive for use in gasoline engines since it has high octane number and flame speed. Other possible uses of methanol are: as a cooking fuel in rural areas and as a fuel for running the fuel cells. The present study reviews the limitations in the hydrogen economy and why moving toward methanol economy is more beneficial. Graphic Abstract: [Figure not available: see fulltext.]Öğe Synergistic effect of hydrogen and waste lubricating oil on the performance and emissions of a compression ignition engine(Pergamon-Elsevier Science Ltd, 2023) Kiran, S.; Martin, M. Leenus Jesu; Sonthalia, Ankit; Varuvel, Edwin GeoThe demand for energy is increasing every year. For a long time, fossil fuels have been used to satiate this energy demand. However, using hydrocarbon-based fossil fuels has led to an enormous rise of carbon dioxide levels in the atmosphere resulting in global warming. It is therefore necessary to look for alternatives to fossil fuels. The research carried out till date have shown biomass and waste-derived fuels as plausible alternatives to fossil fuels. The biomass feedstock includes jatropha oil, Karanja oil, cottonseed oil, and hemp oil among others and wastes include used cooking oil, used engine oil, used tire and used plastics etc. In this study, the authors aim to explore waste lubrication oil as a fuel for the diesel engine. The used lubrication oil was pyrolyzed and diesel-like fuel with 80% conversion efficiency was obtained. A blend of the fuel and diesel in the ratio of 80:20 on volume basis was prepared. Engine experiments at various load conditions was carried out with the blend. As compared to diesel, a 2% increase in thermal efficiency, 6.3%, 16.1% and 13.6% decrease in smoke, CO and HC emissions & 3.2% and 1.8% increase in NOx and CO2 emission were observed at full load with the blend. With an aim to further improve the engine perfor-mance and reduce the overall emissions from the engine exhaust, a zero-carbon fuel namely gaseous hydrogen was inducted in the intake manifold. The flow rate of hydrogen was varied from 3 to 12 Litres per minute (LPM). As compared to diesel, at maximum hydrogen flow rate the thermal efficiency increased by 12.2%. HC, CO and smoke emissions decreased by 42.4%, 51.6% and 16.8%, whereas NOx emissions increased by 22%. The study shows that the combination of pyrolyzed waste lubricant and hydrogen were found to be suitable as a fuel for an unmodified diesel engine. Such fuel combination can be used for stationary applications such as power backups.& COPY; 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
