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The lifetime and efficiency of diesel particulate filters (DPFs) strongly depend on the proper and periodic cleaning and servicing. Unfortunately, in some cases, inappropriate methods are applied to clean the DPFs, e.g., using air compressors without proper disposal procedures which can have negative impacts on human health, the environment, and DPF's efficiency. However, there is no information available about the properties of this kind of PM. This research is therefore presented to explore the physicochemical and toxicity properties of aged PM trapped in a DPF (using compressed air for PM sampling) employing STEM, SEM, EDS, Organic Carbon Analyzer, TGA/DSC, and Raman Spectrometer for investigating the physicochemical properties, and assays of cell viability, cellular reactive oxygen species (ROS), interleukin-6, and tumor necrosis factor-alpha (TNF-α) for investigating the toxicity properties. Also, analyses from fresh PM samples from the diesel vehicle at two engine speeds are presented. It is found that at a certain/fixed PM number/mass for all three samples tested, the PM from DPF compared with the fresh PM can have both positive (particularly having the lowest water-soluble total carbon ratio) and negative impacts on human health (particularly having the highest cell death rate of 13.4%, ROS, and TNF-α) and the environment.

Despite the improvement in technologies for the production of alternative fuels (AFs), and the needs for using more AFs for motor vehicles for the reductions in air pollution and greenhouse gases, the number of alternative fuel vehicles (AFVs) in the global transportation sector has not been increasing significantly (there are even small drops for adapting some AFs through the projections) in recent years and even in the near future with projections to 2050. And gasoline and diesel fuels will remain as the main energy sources for motor vehicles. After reviewing the latest advantages and disadvantages of AFVs, including flexible-fuel, gas, electric, hybrid electric, and fuel cell electric vehicles, it is found that the higher price of AFVs, compared to that of gasoline and diesel vehicles, might be one of the main barriers for their wider adoption. But on the other hand, there is the “chicken and egg” problem. Because people mostly do not select AFVs due to their higher price and sometimes their less infrastructure availability compared to those of gasoline and diesel vehicles, however, governments and AFVs manufacturers claim that the insignificant demand volume and less interest by people to purchase them, is one of the main reasons for a higher price and less infrastructure availability of AFVs. So, what should we do for adopting AFVs? This review shows that there are two very important and fundamental points that might cause a rise in the demand and usage of AFVs, rather than waiting for the reduction in AFVs prices. Those points are car salespeople's and people's knowledge about AFVs and the environmental issues, and their encouragement to accept and use AFVs. Although the AFVs are available on the market for many years, many people around the world have no/less/old/wrong knowledge about the current AFVs. Thus, most of these people reject these vehicles for usage, even when their important parameters such as purchase price, operating cost, driving range, and fuel availability be the same (or close) as those of gasoline or diesel vehicles. Detailed information, examples, and recommendations to the increases in people's knowledge and encouragement are presented in this review., Highlights • Latest advantages and drawbacks of alternative fuel vehicles (AFVs) are presented. • AFVs include flexible-fuel, gas, electric, hybrid, and fuel cell electric vehicles. • People's knowledge and encouragement on AFVs are important for wider AFVs adoption. • Several technical recommendations for increase in AFVs adoption rate are presented. • This review is useful for the researchers, governments, and policymakers on AFVs., Alternative fuel and vehicle policy; Alternative fuel vehicles; Clean fuels; Consumer purchase intention; Environmental concern; Gasoline and diesel vehicles.

The literature shows that information about the physical, chemical, and cell toxicity properties of particulate matter (PM) from diesel vehicles is not rich as the existence of a remarkable number of studies about the combustion, performance, and emissions of diesel vehicles using renewable liquid fuels, particularly biodiesels and alcohols. Also, the PM analyses from combustion of spent coffee ground biodiesel have not been comprehensively explored. Therefore, this research is presented. Pure diesel, 90% diesel + 10% biodiesel, and 90% diesel + 9% ethanol + 1% biodiesel, volume bases, were tested under a fast idle condition. STEM, SEM, EDS, Organic Carbon Analyzer, TGA/DSC, and Raman Spectrometer were employed for investigating the PM physical and chemical properties, and assays of cell viability, cellular reactive oxygen species, interleukin-6, and tumor necrosis factor-alpha were examined for investigating the PM cell toxicity properties. It is found that the application of both biodiesel and ethanol has the potential to change the PM properties, while the impact of ethanol is more than biodiesel on the changes. Regarding the important aspects, biodiesel can be effective for better human health (due to a decrease in cell death (-60.8%)) as well as good diesel particulate filter efficiency (due to lower activation energy (-7.6%) and frequency factor (-83.2%)). However, despite a higher impact of ethanol on the reductions in activation energy (-24.8%) and frequency factor (-99.0%), this fuel causes an increase in cell death (84.1%). Therefore, biodiesel can be an appropriate fuel to have a positive impact on human health, the environment, and emissions catalysts performance, simultaneously.

Precision Medicine in Oncology requires tailoring of therapeutic strategies to individual cancer patients. Due to the limited quantity of tumor samples, this proves to be difficult, especially for early stage cancer patients whose tumors are small. In this study, we exploited a 2.4 × 2.4 centimeters polydimethylsiloxane (PDMS) based microfluidic chip which employed droplet microfluidics to conduct drug screens against suspended and adherent cancer cell lines, as well as cells dissociated from primary tumor of human patients. Single cells were dispersed in aqueous droplets and imaged within 24 hours of drug treatment to assess cell viability by ethidium homodimer 1 staining. Our results showed that 5 conditions could be screened for every 80,000 cells in one channel on our chip under current circumstances. Additionally, screening conditions have been adapted to both suspended and adherent cancer cells, giving versatility to potentially all types of cancers. Hence, this study provides a powerful tool for rapid, low-input drug screening of primary cancers within 24 hours after tumor resection from cancer patients. This paves the way for further technological advancement to cutting down sample size and increasing drug screening throughput in advent to personalized cancer therapy.

Precision Medicine in Oncology requires tailoring of therapeutic strategies to individual cancer patients. Due to the limited quantity of tumor samples, this proves to be difficult, especially for early stage cancer patients whose tumors are small. In this study, we exploited a 2.4 × 2.4 centimeters polydimethylsiloxane (PDMS) based microfluidic chip which employed droplet microfluidics to conduct drug screens against suspended and adherent cancer cell lines, as well as cells dissociated from primary tumor of human patients. Single cells were dispersed in aqueous droplets and imaged within 24 hours of drug treatment to assess cell viability by ethidium homodimer 1 staining. Our results showed that 5 conditions could be screened for every 80,000 cells in one channel on our chip under current circumstances. Additionally, screening conditions have been adapted to both suspended and adherent cancer cells, giving versatility to potentially all types of cancers. Hence, this study provides a powerful tool for rapid, low-input drug screening of primary cancers within 24 hours after tumor resection from cancer patients. This paves the way for further technological advancement to cutting down sample size and increasing drug screening throughput in advent to personalized cancer therapy.

A novel online wavelet least-squares support machine fuzzy predictive control for engine lambda regulation is presented in this article. The prediction model of the proposed online wavelet least-squares support machine fuzzy predictive control is built and updated with a newly proposed modelling algorithm, namely, online wavelet least-squares support machines. The proposed online wavelet least-squares support machine adopts wavelet function that can inherit the local analysis ability and feature extraction from the wavelet transformation, as well as a novel online incremental and decremental updating procedure that can maintain the built prediction model to be accurate, sparse and updated without losing the generalization by continually adding the latest useful data and pruning out the outdated data. Besides, an advanced fuzzy optimizer is proposed to determine the optimal control signal for the online wavelet least-squares support machine fuzzy predictive control, which is faster than the traditional optimizers. The proposed online wavelet least-squares support machine fuzzy predictive control was implemented on a real performance test car and compared with the latest lambda control techniques based on various modelling algorithms, optimizers, updating procedure and support vector kernel for evaluating the effectiveness. The experimental results show that the proposed online wavelet least-squares support machine fuzzy predictive control is a promising scheme for lambda regulation.

Effective air-ratio control is desirable to maintain the best engine performance. However, traditional air-ratio control assumes the lambda sensor located at the tail pipe works properly and relies strongly on the air-ratio feedback signal measured by the lambda sensor. When the sensor is warming up during cold start or under failure, the traditional air-ratio control no longer works. To address this issue, this paper utilizes an advanced modelling technique, kernel extreme learning machine (ELM), to build a backup air-ratio model. With the prediction from the model, a limited air-ratio control performance can be maintained even when the lambda sensor does not work. Such strategy is realized as fault tolerance control. In order to verify the effectiveness of the proposed fault tolerance air-ratio control strategy, a model predictive control scheme is constructed based on the kernel ELM backup air-ratio model and implemented on a real engine. Experimental results show that the proposed controller can regulate the air-ratio to specific target values within a satisfactory tolerance under external disturbance and the absence of air-ratio feedback signal from the lambda sensor. This implies that the proposed fault tolerance air-ratio control is a promising scheme to maintain air-ratio control performance when the lambda sensor is under failure or warming up.

Air-ratio is an important engine parameter that relates closely to engine emissions, power, and brake-specific fuel consumption. Model predictive controller (MPC) is a well-known technique for air-ratio control. This paper utilizes an advanced modelling technique, called online sequential extreme learning machine (OSELM), to develop an online sequential extreme learning machine MPC (OEMPC) for air-ratio regulation according to various engine loads. The proposed OEMPC was implemented on a real engine to verify its effectiveness. Its control performance is also compared with the latest MPC for engine air-ratio control, namely diagonal recurrent neural network MPC, and conventional proportional---integral---derivative (PID) controller. Experimental results show the superiority of the proposed OEMPC over the other two controllers, which can more effectively regulate the air-ratio to specific target values under external disturbance. Therefore, the proposed OEMPC is a promising scheme to replace conventional PID controller for engine air-ratio control.

This paper studies a new active vehicle suspension controlled by Fuzzy-PID controller with wheel base preview. By this new algorithm, the fuzzy controller controls the parameters of the PID in time .Then the wheelbase preview is integrated to ensure the future road information is combined with the current state of the vehicle effectively. A sensor is placed on the front suspension collects and feeds forward the preview information as an input to the rear suspension system . MATLAB simulations show that using such control strategy can obtain a low noise and better robustness performance than the traditional PID control algorithm.

Hysteresis nonlinearity degrades the positioning accuracy of a piezostage and requires a suppression for precision micro-/nanopositioning applications. This paper proposes two new approaches to modeling and compensating the rate-dependent hysteresis of a piezostage driven by piezoelectric stack actuators. By formulating the hysteresis modeling as an online nonlinear regression problem, online least squares support vector machine (SVM) (LS-SVM) and online relevance vector machine (RVM) models are proposed to capture the hysteretic behavior. Both hysteresis models are capable of updating continually with subsequent samples. After a comparative study on modeling performances, an inverse model-based feedforward combined with proportional-integral-derivative feedback control is presented to alleviate the hysteresis effect. Experimental results show that the LS-SVM model-based control scheme is over 86% more accurate than the RVM model-based one in the motion tracking task, whereas the latter is 14 times faster than the former in terms of updating time. Moreover, both LS-SVM and RVM model-based control schemes can suppress the rate-dependent hysteresis to a negligible level, which validates the feasibility and effectiveness of the proposed approaches.

Engine power, brake-specific fuel consumption, and emissions relate closely to air ratio (i.e., lambda) among all the engine variables. An accurate and adaptive model for lambda prediction is essential to effective lambda control for long term. This paper utilizes an emerging technique, relevance vector machine (RVM), to build a reliable time-dependent lambda model which can be continually updated whenever a sample is added to, or removed from, the estimated lambda model. The paper also presents a new model predictive control (MPC) algorithm for air-ratio regulation based on RVM. This study shows that the accuracy, training, and updating time of the RVM model are superior to the latest modelling methods, such as diagonal recurrent neural network (DRNN) and decremental least-squares support vector machine (DLSSVM). Moreover, the control algorithm has been implemented on a real car to test. Experimental results reveal that the control performance of the proposed relevance vector machine model predictive controller (RVMMPC) is also superior to DRNNMPC, support vector machine-based MPC, and conventional proportional-integral (PI) controller in production cars. Therefore, the proposed RVMMPC is a promising scheme to replace conventional PI controller for engine air-ratio control.

PurposeNowadays, automotive engines are controlled by electronic control units (ECUs), and the engine idle speed performance is significantly affected by the setup of control parameters in the ECU. The engine ECU tune‐up is done empirically through tests on a dynamometer (dyno). In this way, a lot of time, fuel and human resources are consumed, while the optimal control parameters may not be obtained. The purpose of this paper is to propose a novel ECU setup optimization approach for engine idle speed control.Design/methodology/approachIn the first phase of the approach, Latin hypercube sampling (LHS) and a multi‐input/output least squares support vector machine (LS‐SVM) is proposed to build up an engine idle speed model based on dyno test data, and then a genetic algorithm (GA) is applied to obtain optimal ECU setting automatically subject to various user‐defined constraints.FindingsThe study shows that the predicted results using the estimated model from LS‐SVM are in good agreement with the actual test results. Moreover, the optimization results show a significant improvement on idle speed performance in a test engine.Practical implicationsAs the methodology is generic it can be applied to different vehicle control optimization problems.Originality/valueThe research is the first attempt to integrate a couple of paradigms (LHS, multi‐input/output LS‐SVM and GA) into a general framework for constrained multivariable optimization problems under insufficient system information. The proposed multi‐input/output LS‐SVM for modelling of multi‐input/output systems is original, because the traditional LS‐SVM modelling approach is suitable for multi‐input, but single output systems. Finally, this is the first use of the novel integrated framework for automotive engine idle‐speed control optimization.

Air-ratio is an important engine parameter which relates closely to engine emissions, power, and brake-specific fuel consumption. Model predictive controller (MPC) is a well-known technique for air-ratio control. This paper utilizes two advanced techniques, discrete wavelet transformation (DWT) and relevance vector machine (RVM), to develop wavelet relevance vector machine model predictive controller (W-MPC) for air-ratio regulation. To compensate for the modelling error of W-MPC and system disturbances, the W-MPC is proposed to connect in parallel with a proportional-integral (PI) controller so as to form a new hybrid model predictive controller (H-MPC). The proposed H-MPC is implemented on a real engine to evaluate its effectiveness. Its control performance is also compared with the W-MPC without PI controller and the latest MPC for engine air-ratio control in the literature. Experimental results show the superiority of the proposed H-MPC over the other two controllers, which can more effectively regulate the air-ratio to target values under external disturbance. Therefore, the proposed H-MPC is a promising scheme for engine air-ratio control.

Automotive engine air-ratio plays an important role of emissions and fuel consumption reduction while maintains satisfactory engine power among all of the engine control variables. In order to effectively control the air-ratio, this paper presents a model predictive fuzzy control algorithm based on online least-squares support vector machines prediction model and fuzzy logic optimizer. The proposed control algorithm was also implemented on a real car for testing and the results are highly satisfactory. Experimental results show that the proposed control algorithm can regulate the engine air-ratio to the stoichiometric value, 1.0, under external disturbance with less than 5% tolerance., {"references":["S. Ji, S., C. R. Cherry, M. Bechle, Y. Wu and J. D. Marshall, \"Electric\nVehicles in China: Emissions and Health Impacts,\" Environmental\nScience & Technology, vol. 46, no. 4, pp. 2018-2024, 2011.","S. H. L. Yim and S. R. H. Barrett, \"Public Health Impacts of Combustion\nEmissions in the United Kingdom,\" Environmental Science &\nTechnology, vol. 46, no. 8, pp. 4291-4296, 2012.","W. H. Crouse, Automotive Mechanics, 10th ed. McGraw-Hill, 1993.","Z. Zmudka and S. Postrzednik, \"Inverse aspects of the three-way catalytic\nconverter operation in the spark ignition engine,\" Journal of KONES\nPowertrain and Transport, vol. 18, no. 2, pp. 509-516, 2011.","P. K. Wong, L. M. Tam, K. Li and C. M. Vong, \"Engine Idle-speed\nSystem Modelling and Control Optimization Using Artificial\nIntelligence,\" Proceeding of IMechE - Part D, Journal of Automobile\nEngineering, vol. 224, no. D1, pp. 55-72, 2010.","J. Lauber, T. M. Guerra and M. Dambrine, \"Air-fuel Ratio Control in a\nGasoline Engine,\" International Journal of System Science, vol. 42, no. 2,\npp. 277-286, 2010.","P. K. Wong, H. C. Wong, C. M. Vong and W. F. Ip, \"Predictive Air-ratio\nController for Automotive Engines Based on Online Neural Network:\nDesign and Experimental Evaluation,\" in Proc. 9th IEEE/ACIS\nInternational Conference on Computer and Information Science,\nYamagata, 2010, pp. 277-286.","M. Canakci, A. N. Ozsezen, E. Arcaklioglu and A. Erdil, \"Predication of\nPerformance and Exhaust Emission of a Diesel Engine Fueled with\nBiodiesel Produced from Waste Frying Palm Oil,\" Expert Systems with\nApplications, vol. 36, no. 5, pp. 9268-9280, 2009.","P. K. Wong, H. C. Wong and C. M. Vong, \"Online Time-sequence\nIncremental and Decremental Least Squares Support Vector Machines for\nEngine Air Ratio Prediction,\" International Journal of Engine Research,\nvol. 13, no. 1, pp. 28-40, 2012.\n[10] P. K. Wong, Q. Xu, C. M. Vong and H. C. Wong, \"Rate-dependent\nHysteresis Modeling and Control of a Piezostage Using Online Support\nVector Machine and Relevance Vector Machine,\" IEEE Transactions on\nIndustrial Electronics, vol. 59, no. 4, pp. 1988-2001, 2012.\n[11] J. Suykens, J. De Brabanter, L. Lukas and J. Vandewalle, Lease Squares\nSupport Vector Machines, 1st ed. World Scientific Press, 2002.\n[12] J. Suykens, J. De Brabanter, L. Lukas and J. Vandewalle, \"Weighted\nLeast Squares Support Vector Machines: Robustness and Sparse\nApproximation,\" Neurocomputing, vol. 48, no. 1-4, pp. 85-105, 2002.\n[13] C. C. Lee, \"Fuzzy Logic Control System: Fuzzy Logic Controller - Part\nI,\" IEEE Transactions on System Man, and Cybernetics, vol. 20, no. 2, pp.\n404-418, 1990."]}

Semi-active air suspension is increasingly used on heavy-duty vehicles due to its capabilities of consuming less power and low cost and providing better ride quality. In this study, a new low cost but effective approach, fuzzy-wheelbase preview controller with wavelet denoising filter (FPW), is developed for semi-active air suspension system. A semi-active suspension system with a rolling lobe air spring is firstly modeled and a novel front axle vertical acceleration-based road prediction model is constructed. By adopting a sensor on the front axle, the road prediction model can predict more reliable road information for the rear wheel. After filtering useless signal noise, the proposed FPW can generate a noise-insensitive control damping force. Simulation results show that the ride quality, the road holding, the handling capability, the road friendliness, and the comprehensive performance of the semi-active air suspension with FPW outperform those with the traditional active suspension with PID-wheelbase preview controller (APP). It can also be seen that, with the addition of the wavelet filter, the impact of sensor noise on the suspension performance can be minimized.

Fuel efficiency and pollution reduction relate closely to air-ratio (i.e. lambda) among all of the automotive engine control variables. Accurate lambda prediction is essential for effective lambda control. This paper presents an online sequential algorithm for relevance vector machine (RVM) to build a time-dependent RVM lambda function which can be continually updated whenever a sample is added to, or removed from, the training dataset. In order to evaluate the effectiveness of the online sequential algorithm, three lambda time series obtained from experiments under different engine operating conditions were employed. The prediction results under the online sequential algorithm over unseen cases were compared with those under decremental least-squares support vector machine. From the experiments, the online sequential RVM shows promising results and is superior to the typical online algorithm.

This work proposes a coupled finite element model for actively controlled constrained layer damped (CLD) rotating plate with self-sensing technique and frequency-dependent material property in both the time and frequency domain analyses. Constrained layer damping with viscoelastic material can effectively reduce the vibration in rotating structures. However, most existing research models use complex modulus approach to model the viscoelastic material, but it limits to frequency domain analysis and the frequency dependency of the viscoelastic material is not well-included as well. It is meaningful use of the anelastic displacement fields (ADFs) that is done in order to include the frequency dependency of the material for both the time and frequency domains. Also, unlike previous ones, all types of damping are taken into account by this finite element model. Thus, in this work, a single layer finite element is adopted to model a three-layer active constrained layer damped rotating plate in which the constraining layer is made of piezoelectric material to work as both the self-sensing sensor and actuator. This newly proposed finite element model is validated, and then, as shown in numerical studies, this proposed approach can achieve effective vibration reduction in both the frequency and time domains.

Vehicle engine emissions closely relate to air-ratio called lambda (λ). The current practice of examination of gasoline engine emissions is partially based on λ reading at engine idle speed. For the λ value over a specific vehicle examination standard, it indicates that a significant amount of emissions is produced. The concept of telemetry with traffic lights is proposed that allows lambda data collected from the vehicle on road to be reported in real time at a traffic light. The data can be sent via a radio transmitter in the vehicle to a radio receiver mounted on the traffic light which is connected to a PC. So, the authority can monitor the engine emission data via computer networks while the vehicles stop at red traffic lights and enforce the vehicle owners taking immediate action to fix their vehicle emission problems if necessary.

The paper proposes a new active vehicle suspension with wheelbase preview information for a half-car model. A sensor placed on the front suspension collects and feeds forward the preview information as an input to the rear suspension system. Then a fuzzy controller is used to process the obtained preview information and the feedback signal of vehicle state. The objective of application of the fuzzy controller is to improve the suspension robustness against sensor noise. Numerical simulations show that the fuzzy controller provides higher robust performance than a conventional PID controller in the presence of sensor noise. Moreover, the preview information significantly decreases the rear wheel unsprung mass acceleration so that the riding comfort can be improved.

Modern automotive spark‐ignition (SI) power performance usually refers to output power and torque, and they are significantly affected by the setup of control parameters in the engine management system (EMS). EMS calibration is done empirically through tests on the dynamometer (dyno) because no exact mathematical engine model is yet available. With an emerging nonlinear function estimation technique of Least squares support vector machines (LS‐SVM), the approximate power performance model of a SI engine can be determined by training the sample data acquired from the dyno. A novel incremental algorithm based on typical LS‐SVM is also proposed in this paper, so the power performance models built from the incremental LS‐SVM can be updated whenever new training data arrives. With updating the models, the model accuracies can be continuously increased. The predicted results using the estimated models from the incremental LS‐SVM are good agreement with the actual test results and with the almost same average accuracy of retraining the models from scratch, but the incremental algorithm can significantly shorten the model construction time when new training data arrives.

The current practice of gasoline engine emission examination is partially based on λ reading. For the λ value of a car over a specific standard, it indicates the car failed the examination. Although modern automobiles have built-in λ sensors to monitor the efficiency of the catalytic converter and can warn the driver when the converter breaks down, mandatory vehicle examination cannot be taken every day for each car. The concept of telemetry with traffic lights is proposed that allows λ data collected from the vehicle on road to be reported immediately at a traffic light. The data can be sent via a radio transmitter in the vehicle to a radio receiver mounted on the traffic light which is connected to a server. So, the authority can monitor the engine emissions via computer networks while the vehicles stop at red traffic lights. Experimental results show that the idea is feasible.