Modern methods in engine knock signal detection

In this paper, a review is given of some of the modern methods in the detection of knock in internal-combustion engines and some comparisons are made between these methods and the effectiveness of each one of them is indicated through a statement of the advantages and disadvantages of each method. In this way it will be possible to clarify how to deal with the original signal and the associated signal noise through some of the modern algorithms in the field of soft computing such as an Artificial Neural Network (ANN), Genetic Algorithms (GA), Wavelet Transform (WT), Fuzzy logic, Supported Vector Machine (SVM) and some statistical methods

Fuel consumption mathematical models for road vehicle – a review

Since the invention of the automobile, engineers and researchers alike have worked towards improving the automobile in various ways from safety, handling and performance to efficiency and durability. As technology in the IT and computing sector evolves into a very helpful tool for detailed calculations, an advantage and possibility for detailed models is there to assist with very detailed assessment on fuel and energy consumption on today’s vehicles. This review is meant to explore in detail what has been achieved by years of joint research through advanced modelling and the following factors such as emissions software and how these models play an important role in sustainable road transport for the masses. The mathematical models also display varying characteristics where models are created striking a balance between complexity, accuracy, and the number of variables to be included

Simulation of fuel economy for Malaysian urban driving

By understanding the implications of real-world driving conditions, improved fuel economy via a strategy of key technologies can be implemented to assist fuel economy validation during development programs. Vehicles in real-world driving conditions regularly travel at idle, low and medium speeds, particularly for urban driving, and this has a crucial weight in overall vehicle fuel economy, given the residencies at the lower engine speed and load region. This paper presents the validation of the derived engine conditions representing Malaysian actual urban driving in an attempt to formulate representative fuel economy data. The measurements were conducted through on-road urban driving within Kuala Lumpur to establish representative driving conditions. The effectiveness of the proposed conditions was then validated in terms of fuel economy using a simulation. The discrepancy between the fuel economy in the proposed conditions and the real-world measurements has improved, falling to 11.9% compared to 43.1% reported by the NEDC

The effect of 48V mild hybrid technology on fuel consumption of a passenger car by using simulation cycle

The ASEAN's legislation has become more regulatory towards electric vehicles for automotive manufacturers to ensure the environment is preserved better for future generations. The ASEAN roadmap 2025 requirement in optimizing a conventional vehicle's fuel consumption is implemented with hybrid technology in targeting the automotive industry worldwide to achieve energy-efficient vehicles. This research aims to develop a vehicle model via 1D simulation cycle and implement the 48V mild hybrid to lower vehicle fuel consumption considering perspective in drive cycles data. The vehicle model used in this research is a D-segment vehicle powered by a 1.8L TGDI engine. The base model will be created using a GT Suite software where data is compared and analyzed with actual vehicle measurement. There will be two models produced; with and without Belt-Alternator-Starter (BAS) system. They will be further investigated for their functions based on NEDC and RDC drive cycles for fuel consumption. However, implementing the add-on technology from this simulation improved overall vehicle fuel consumption by 7.7% in NEDC and 1.7% in RDC. The results obtained for the optimization of the vehicle have shown difference by the results of each engine characteristics such as engine fuel flow rate, speed, torque, the BAS functions, and state of charge. The research proposes its findings to understand the practical usage of 48V mild hybrid system in fuel reduction and provide reliable proof to use as a reference for initiative studies

Review of driving behavior towards fuel consumption and road safety

One of the main concerns for automobile researchers is establish a driving method that is efficient to the engine and ensures road safety. Most studies categorized driver’s behavior based on aggressiveness while driving. It was found that aggressive drivers tend to provoke fast start and quick acceleration, driving at high engine revolution, and causing sudden speed change that are prone to road accidents. On the other hand, eco-driving which consists of gentle acceleration, coast-down deceleration, maintaining a steady speed and avoidance of high speed is much safer than the aggressive driving. At the same time smooth, experienced pattern in the eco-driving consumes lesser fuel in a statistically significant way. Driver’s aid system in modern cars have been invented to assist the driver into eco-driving were claimed to be effective. However, in the absence or failure of such system, drivers are suggested to drive by maintaining steady speed, avoiding sudden stop and harsh acceleration. Nevertheless, the suggestions varied between one another because there are no certain standards that could be referred to. This paper gathers the findings from available studies and highlights the recommended driving behavior in a passenger car that could be adapted in the effort to simultaneously improve the fuel economy and road safety

Application of Elman and Cascade neural network (ENN and CNN) in comparison with adaptive neuro fuzzy inference system (ANFIS) to predict key fuel properties of ABE-diesel blends

Today, a growing interest to use Acetone-Butanol-Ethanol (ABE) as a biofuel has emerged. Fuel properties play important roles to determine engine’s performance, combustion, and emission behaviors. Yet, the determination of fuel properties is expensive and time-consuming. Previous studies on ABE did not provide information on how to predict its fuel properties. This study developed an Elman and Cascade neural networks (ENN and CNN) and compared their results with adaptive neuro inference system (ANFIS) to predict ABE’s key fuel properties. Three properties, i.e., calorific value, density, and kinematic viscosity were used as the target outputs, while ABE, acetone, butanol, and ethanol ratio were selected as the input parameters. The ENN and CNN models were trained using 10 different training algorithms, while the ANFIS model was examined using eight unique membership functions. To evaluate the prediction accuracy of each model, six different parameters were employed. Results showed that, compared to ENN and CNN, the ANFIS model gave the best performance accuracy with the least errors to predict the key fuel properties of ABE-diesel blends. For calorific value, density, and kinematic viscosity prediction, the best results of the ANFIS model were given by triangular, Pi curve, and trapezoidal membership functions, respectively. Therefore, ANFIS gave the best model of all the investigated models in this study

Synthesis of Autonomous Vehicle Guideline for Public Road-Testing Sustainability

Autonomous vehicles have the potential to reduce the risk of accidents as they eliminate the element of human error from driving. Lack of attention, poor judgement, or physical limitations may lead to road incidents. Thus, the development and deployment of autonomous vehicles should be a priority. However, before being publicly available, autonomous vehicles must be tested to ensure their viability and safety by conducting public road testing. Autonomous vehicles have been designed and tested since the early 1900s; however, deployment of fully autonomous vehicles on public roads only started in the 2000s. Numerous countries have developed guidelines for public road testing, but those rules are not uniform, and discrepancies occur between nations. Issues such as vehicular safety, registrations, authority, insurance, cybersecurity, and infrastructures weigh differently in each country. Synthesizing these diverse national regulations into global guidelines would promote the safety and sustainability of autonomous vehicle testing and benefit all parties interested in autonomous vehicles

Cetane index prediction of ABE-diesel blends using empirical and artificial neural network models

Recent developments in internal combustion engines have heightened the need for alternative biofuel. In the last five years, acetone-butanol-ethanol (ABE) has been extensively studied as a promising biofuel. However, the detailed investigation of its fuel properties has not been performed. One of the vital fuel properties is the cetane index. It is used to define the ignition quality of fuel, but its determination is painstaking and expensive. No previous study has utilized both empirical mathematical and ANN models to predict the cetane index of ABE-diesel blends. This study aims to predict ABE’s cetane index by comparing five empirical mathematical models with seven artificial neural networks (ANN) training algorithms. To the best of our knowledge, this is the first study to examine the cetane index of ABE-diesel blends using both empirical and ANN models. Results revealed that the feed-forward backpropagation network with 4 input, 10 hidden, and 1 output neurons that was trained with Levenberg-Marquardt algorithm (ANN-LM) showed the best performance with the highest values of R (0.9992) and R2 (0.9984). It also has the lowest values of MAD, MSE, RMSE and MAPE at 0.2572, 0.4456, 0.6675, and 0.5304, respectively. As compared to the best empirical mathematical model (the 3rd order polynomial), the ANN-LM had slightly better performance accuracy. Therefore, the 4–10-1 ANN structure trained with Levenberg-Marquardt was found to be the best predictor for cetane index of ABE-diesel blends at various blending ratios

Physico-chemical properties of Acetone-Butanol-Ethanol (ABE)-diesel blends: Blending strategies and mathematical correlations

Butanol offers more promising results compared to lower carbon alcohol. Yet, it has not been commercially produced as a biofuel due to its expensive recovery process from Acetone-Butanol-Ethanol (ABE) fermentation. If ABE is used directly as a biofuel, the process will be more straightforward, thus eliminating its energy and cost-intensive purification process. Study on ABE as a biofuel has become a growing field for the last five years. Several preliminary studies have reported convincing results of using ABE-diesel blends in diesel engines. However, many of the studies on ABE lacks clarity regarding its fuel properties. In fact, no previous study has investigated the fuel properties of ABE. Therefore, this study aims to quantify some critical physico-chemical properties of ABE-diesel blends. Several important fuel properties were investigated in this study; calorific value, density, kinematic viscosity, distillation characteristics and cetane index. In terms of blending strategy, results from this study indicate that ABE(3 6 1) can be added up to 42% to diesel fuel, while ABE(6 3 1) and ABE(1 3 6) can only be added up to 22% and 23%, respectively. Also, the mathematical correlations to estimate ABE's fuel properties are presented. The equations developed in this study gave have high coefficient of determination values. They can serve as prediction models for future studies. Considering its relatively low-cost production and satisfying physico-chemical properties, ABE has the potential to become a promising alternative biofuel

Synthesis of Autonomous Vehicle Guideline for Public Road-Testing Sustainability

Autonomous vehicles have the potential to reduce the risk of accidents as they eliminate the element of human error from driving. Lack of attention, poor judgement, or physical limitations may lead to road incidents. Thus, the development and deployment of autonomous vehicles should be a priority. However, before being publicly available, autonomous vehicles must be tested to ensure their viability and safety by conducting public road testing. Autonomous vehicles have been designed and tested since the early 1900s; however, deployment of fully autonomous vehicles on public roads only started in the 2000s. Numerous countries have developed guidelines for public road testing, but those rules are not uniform, and discrepancies occur between nations. Issues such as vehicular safety, registrations, authority, insurance, cybersecurity, and infrastructures weigh differently in each country. Synthesizing these diverse national regulations into global guidelines would promote the safety and sustainability of autonomous vehicle testing and benefit all parties interested in autonomous vehicles