Abstract

In recent times, there has been a drastic increase in the number of road accidents and their severity. These accidents mostly happen due to human error. Some common human errors like distracted driving, drowsy driving, driving at high speeds and delayed reaction times of the drivers result in fatal accidents that cause loss of life and damage to property. These factors can be eliminated by means of self-driving cars. A driverless car (or self-driving car or autonomous car) is a vehicle that can travel from one point to another by self-navigating without any human intervention by using sensors, cameras, radar, GPS, and AI (Artificial Intelligence). We will be implementing a Driverless Car using Deep Learning, OpenCV, DonkeyCar, TensorFlow and C++/Python.

Introduction

There has been a large spike in the number of automobile accidents these days. According to the U.S. Department of Transportation (USDOT), it has been estimated that about 20,160 people have died in road accidents in the first six months of 2021 in the U.S. alone. In India, data from the National Crime Records Bureau (NCRB) shows that in 2020, an average of 37 people died per 100 car crashes which results in a total of about 1.3 lakh deaths. Also, we need to note that these numbers include the period of lockdown due to the Covid-19 pandemic, during which there was minimal traffic movement.The major causes of these catastrophic accidents include distracted driving, drowsy and drunk driving. These factors lead to increased human error, which in turn increases the risk of having an accident. We can overcome the dangerous consequences of human error using autonomous vehicles or driverless cars. Most leading automobile manufacturers across the globe have started research and development of semi-autonomous and fully autonomous cars.

Conclusion & Future Scope

Driver errors while driving are the most prominent cause of traffic accidents. Driverless cars have the following benefits related to road transport: reduce stress due to driving, reduce driver cost, provide mobility of non-drivers, increased safety, and decrease in traffic congestion. Even a person with disabilities can make use of autonomous vehicles the same as any other individual and can even customize the vehicles for more convenience. A self driving car not only increases safety, but it also improves efficiency and productivity of the people who spend most of their time stuck in traffic and trying to reach their work places. Donkey is a self-driving car platform which uses a high-level Python library to attain autonomous driving. We have trained the Donkeycar to drive on its own based on driving style and path. This uses a supervised learning technique often referred to as behavioral cloning. Next, we carry out steering and throttle calibration. The reason for calibrating the car is to make it drive consistently. Later, the car is driven using a web controller/ physical joystick controller. As the driving car becomes reliable, we used Keras to train a neural network to drive like a human.

Abstract

     Artificial Intelligence (AI) and Machine learning (ML) represents an important evolution in computer science and data processing systems which can be used in order to enhance almost every technology-enabled service, products, and industrial applications. A subfield of artificial intelligence and computer science is named machine learning which focuses on using data and algorithms to simulate learning process of machines and enhance the accuracy of the systems. Machine learning systems can be applied to the cutting forces and cutting tool wear prediction in CNC machine tools in order to increase cutting tool life during machining operations. Optimized machining parameters of CNC machining operations can be obtained by using the advanced machine learning systems in order to increase efficiency during part manufacturing processes. Moreover, surface quality of machined components can be predicted and improved using advanced machine learning systems to improve the quality of machined parts. In order to analyze and minimize power usage during CNC machining operations, machine learning is applied to prediction techniques of energy consumption of CNC machine tools. In this paper, applications of machine learning and artificial intelligence systems in CNC machine tools is reviewed and future research works are also recommended to present an overview of current research on machine learning and artificial intelligence approaches in CNC machining processes. As a result, the research filed can be moved forward by reviewing and analysing recent achievements in published papers to offer innovative concepts and approaches in applications of artificial Intelligence and machine learning in CNC machine tools.

Introduction

     CNC machining operation is one of the most important part-production methodologies, and it is often referred to as the engine of modern manufacturing processes. The automotive and medical sectors, aerospace, gas and oil, and warehousing services, are using the CNC machining operations to create parts in different applications [1]. CNC machining is generally used in manufacture of every machine, molded part, or finished product as one of the most important manufacturing processes. CNC machinery has paved the way in manufacturing and machining, allowing businesses to achieve their goals and targets in a variety of ways. However, because manufacturing methodologies is always evolving and new technologies are being introduced, it is critical to consider future of CNC machining operations [2, 3]. Machine learning (ML) is the study of computer algorithms that gives computers the capacity to automatically learn from data and prior experiences in order to find patterns and make predictions without human involvement. The ML and applications in different areas of study are considered to be a component of artificial intelligence [4], [5], [6].

     Machine learning and artificial intelligence in particular raise plenty of concerns about the future of CNC machining operations and how these concepts will evolve future works of manufacturing companies [7]. The way a machine learns, adapts, and optimizes output can also be influenced by real-time data, analytics, and deep learning. Data sets are essential for operators to understand how a machine works and, eventually, how a whole floor of machines works together [8, 9]. Due to the development of affordable, reliable, and resilient sensors and acquisition and communication systems, novel implementations of machine learning approaches for tool condition monitoring can be presented [10]. Machine learning systems are capable of completely examine data and identify various types of areas which should be modified. Machine tools are increasingly being equipped with edge computing options to record internal drive signals at high frequency in order to supply the necessary vast quantity of data for the use of machine learning techniques in manufacturing [11]. Productivity and efficiency are two areas where artificial intelligence can modify CNC machine tools operations in order to enhance accuracy of CNC machining operations[12]. Machines can generate and analyze production data and provide real-time findings to human operators are effective devices for increasing productivity in part production processes. As a result, shop owners can quickly adjust the way a machine operates using the modified data generated by advanced machine learning algorithms in order to enhance productivity of part manufacturing [13]. Having more knowledge and making better decisions in process planning strategies means less downtime on the work floor during process of part production. Production and maintenance process of part manufacturing using CNC machine tools can be developed using the machine learning and the artificial intelligence in order to enhance efficiency in part manufacturing operations [14].

Conclusion

     Machine learning and artificial intelligence are applied to various industrial applications in order to improve performances of industrial process. To increase accuracy as well as efficiency during CNC machining operations, different applications of the machine learning and artificial intelligence systems are studied in different research works. Reducing machine downtime, optimization of CNC machine tools, cutting tool wear prediction, cutting force model, CNC machine tool maintenance, monitoring of machining operations, surface quality prediction and energy prediction systems are some examples of machine leniting applications in the development of CNC machining operations. ML techniques are recently applied to energy consumption prediction models in order to decrease the energy consumption during CNC machining operations. Accuracy and radiality of energy consumption models are significantly enhanced using the ML methodologies in comparison to the tradition’s methods of energy usage predictions during CNC machining operations.

     The applications of machine learning and artificial intelligence systems in CNC machine tools are examined in this study by analyzing recent achievements from published papers. The main aim of this study is to provide an overview of current studies on machine learning and artificial intelligence techniques in CNC machining operations in order to provide a useful study for the researchers in the interesting field. Network of sensors and cloud data sources can connect CNC machines together can be employed to provide smart CNC machine tools. The machining industry’s efficiency can be increased as it transitions to smart machining techniques, allowing it to achieve self-optimization and adaption to uncontrolled circumstances. However, developing the applications of advanced machine learning systems in CNC machining operations as the combination of physical, computers, and networking process created challenges and difficulties regarding the safety and security of the web of data. In order to provide secure and advanced connections between the different CNC machine tool, the security of networks should be enhanced.

Abstract

Keywords

References

Abstract

Connected vehicles can transmit real-time information to traffic control management systems. Despite the recent technical advances of telecommunication networks and mobile computing there have been no real-time adaptive traffic signal control experiments with connected vehicles. Most of the research in this field has been carried out only with simulations. In this work we present the computer system that was adopted to regulate traffic signals in real-time with “smartphone-connected” vehicles as the only source of information. We introduce the description of the computer system architecture that was deployed in an experiment of a Floating Car Data (FCD)-based adaptive traffic signal in which a traffic signal has been regulated in real-time with 100% “smartphone-connected” vehicles. The description of the system based on commonly-used technologies could help others to develop and deploy new traffic signal management systems in new “connected” intersections.

1. Introduction

Both connected vehicles and connected traffic signals are poisoned to become an important part of the forthcoming Internet of Things (IoT) and of Intelligent Transportation Systems (ITS). Connected vehicles may help to manage and control road traffic in many new ways. Classic road traffic engineering practice was founded on efforts to allocate demand on transit systems [1] and on better road traffic control by adopting tools such as: traffic simulation [2–7] dynamic network loading equilibrium and dynamic models [8–11] and the implementation of efforts to influence user route choice [12–16]. There have been no practical advances in traffic signal regulation in the last years and traffic signals are very often controlled with prefixed signal settings that are not always appropriate for the always-changing traffic flows. This can cause traffic congestion that is a serious problem in cities and also a great cause of air pollution. The use of inductive loops for real-time traffic signal control is the current standard and its limitation lies in the expensive infrastructure and maintenance resources that are necessary to keep them working. Existing technologies such as smartphones can contribute to creating cheap “smartphone-connected” vehicles that can act as floating probe vehicles [17,18] (Floating Car Data: FCD), or the use of dedicated local wireless networks can create a dedicated vehicle to infrastructure (V2I) communication systems. By combining smart-phones with existing wireless mobile phone data networks and Satellite localization systems, ITS can be deployed at a very low cost compared with ad hoc developed wireless systems that would bring higher costs. The first research efforts on smart-phone-based FCD did not benefit from satellite positioning systems, nowadays, mobile phones can obtain position data and transmit this information; thus it is possible to evaluate travel times and speeds [19,20]. An assessment of some safety issues is also possible [21]. with assistance for a safer driving [22]. The estimation of fuel consumption [23–26], the measurement of road pavement problems [27,28], and route choice behavior-based research are also possible with smart-phones.

The high-redundancy actuator (HRA) is a linear displacement actuation system, which consists of a large number of small electromechanical actuation elements arranged in a series and parallel configuration. The performance of a HRA with 9 direct-driven linear electromechanical actuators was analyzed under different fault conditions with the aid of the MATLAB simulation model. The results showed that the HRA has the capability to provide inherent fault-tolerance. Hence, the sudden failure of the system in the presence of fault was avoided. However, the capability of the actuator was found to be reduced gradually depending upon the number of faults.

Introduction

Faulty elements in any engineering system can be a huge hassle. Sometimes, they might cause irrevocable damage to the industries. Particularly, a fault in the safety-critical system can lead to disasters of unimaginable magnitude. So, designing modern engineering systems with a high level of reliability is of utmost importance to avoid any unexpected failures [1]. One important way this can be prevented is by having a fault-tolerance design that can tolerate any malfunctions of the individual components of the system. In general, fault-tolerance can be achieved by adopting a redundancy technique which includes the addition of resources, time or information beyond the normal requirements of the system being operated. In recent years, fault-tolerance has become one of the greatest tools for providing safety to any mechatronic system. The aircraft actuation and nuclear power systems are some of the most important systems that need redundancy to ensure reliability [2]. More recently, the aerospace field significantly increased the use of multi redundant electrical actuation systems to avoid unexpected failures [3,4]. However, in the case of nuclear power systems, hardware redundancy is introduced to confirm a high level of reliability of the control system [5]. Traditionally, the fault-tolerance for the actuation system is attained by connecting two or more identical actuators in parallel and the task to be completed by this group of actuators in parallel, is carried out by any one of the individual actuators from the same group. The fact that each of these actuators has the capability to perform the required task alone enables them to override any faulty actuators [6]. However, these actuators in parallel increases the weight and cost of the system and eventually, reduces the efficiency of the system. Moreover, the use of pure parallel configuration holds no value in the event of jamming/lock-up of an actuator within it.

1- INTRODUCTION

2- SYSTEM LAYOUT

3- CONTROL SCHEME

4- CONCLUSION

REFERENCES

Abstract

In this paper, we are proposing currentcontrolled hill-climbing strategy used in Maximum power point tracking (MPPT) of the solar photovoltaic array to Charge OFF Board Electric vehicle lithium-ion batteries within the safety limits and supported rating of batteries so that EV battery does not get damaged and can also increase the life of the battery. Here, a current sensor and potential difference sensor-based electric vehicle off-board battery charging by hill-climbing strategy is tested on MATLAB simulation. The satisfactory steady-state battery is charging within the rated charging limit, under variable solar irradiance.

INTRODUCTION

The transportation sector has seen a major shift towards electric vehicles as it not only stops our dependence on fossil fuels and stop pollution from these vehicles that vehicles release. Hence, we are moving towards cleaner sources of energy. Also, photovoltaic resources are increasing in demand as they are the source of clean energy and don’t emit carbon dioxide

Two types of charging method are there ON Board charging and OFF Board charging. ON Board charging is when Battery is kept in the Electric vehicle and charging circuit is not on the vehicle itself but near the charging station. And the battery is charged directly through the DC supply.

1- Introduction

2- Experimental rotor/AMB system

3- Strain measurement system

4- Strain measurement calibration

5- Dynamic results

6- Conclusions

References

Abstract

Active magnetic bearing systems provide contact-free rotor levitation, which allows near frictionless rotation and high rotational speeds when they operate normally. Under certain operational conditions, a transient fault, overload condition, or disturbance may occur. Touchdown bearings or bushings are therefore implemented in such systems to prevent contact between rotor and stator laminations. If the rotor makes contact with a touchdown bearing, the rotor dynamics may become transient or persistent in contact. Appropriate control strategies through the magnetic bearings to restore contact-free rotor operation may extend the life of touchdown bearings, and minimise operational downtime. To achieve this, an understanding of the contact dynamics is required, together with the relationship between contact and magnetic bearing forces. In this paper, rotor/touchdown bearing contact conditions are investigated experimentally using an active magnetic bearing system with a flexible rotor. Design methodology is presented for a measurement system capable of providing rotor/touchdown bearing contact related data, based on strain measurement. Strain induced contact signals are calibrated against applied magnetic bearing forces. The frequency dependent behaviour of the active magnetic bearing system is considered using evaluated force and phase measurements. The measurement data represent system identification for the potential of active magnetic bearing force-based contact control.

Introduction

The ability of Active Magnetic Bearings (AMBs) to provide contactless rotor levitation with vibration control has a number of benefits, including high-speed rotor operation, the elimination of the need for lubrication, long life and reliability, and energy efficiency due to the low friction losses. However, passive bearings are also used in AMB systems as Touchdown Bearings (TDBs) to prevent potentially damaging rotor/magnetic bearing stator contact from occurring. TDBs support the rotor while the AMB system is not operating, but also limit excursions of the rotor during operation, such as in cases of power loss, component failure, rotor mass loss, or other significant external disturbances. Such cases may cause rotor/TDB contact. Since TDBs are functional components of AMB systems, it is crucial to understand their influence on rotor dynamic behaviour in contact cases, in order to minimise potential damage. This is particularly important as TDB life and condition impacts directly on the reliability of AMB systems.

1- Introduction

2- System overview

3- Methodology

4- Experiments

5- Transfer learning

6- Conclusion

References

Abstract

Machine vision technologies have been widely used for automating the product quality control, but the defect inspection for codes on complex backgrounds is still a challenging task in the plastic container industry. In this work, an efficient and accurate inspection solution based on deep learning was proposed aiming at the detection of codes on complex backgrounds for the plastic container such as beverage packages. Firstly, image processing algorithms such as the region translation method, morphological processing, and image matching technology based on SIFT (Scale Invariant Feature Transform) features were implemented to generate synthetic defective samples, which moderated the class-imbalance problem. Data augmentation strategies were used to increase the amount of training data. Secondly, the ShuffleNet V2 framework was adapted to inspect inkjet codes on complex backgrounds. Additionally, the transfer learning was used to transfer the trained model to other inspection tasks for different kinds of packages. Finally, the proposed approach was built onto an in-line code inspection apparatus for the plastic container industry, and an accuracy of 0.9988 was achieved. The in-line testing results of false detection and omission detection rates demonstrated that the proposed solution can fully meet the production requirements. To the best of our knowledge, this report describes the first time that deep learning has been applied to the industrial defect inspection for the plastic container industry.

Introduction

The plastic packaging technologies have been experiencing explosive growth in the beverage industry. In order to ensure the product traceability and quality control, batch number and data codes are marked on the curved plastic surfaces early in the production process. The inkjet printing process, as the most commonly used type of printer, has been widely adopted to spray a digital image of the expiration date and manufacturing date by propelling droplets of ink onto the container. The quality of characters is affected by the performance of code printer and other external factors, and Legible codes on the containers provide consumers with important information and confidence for the inside product. If containers or packages with defective codes such as missing, incorrect, or unreadable codes are not identified in time, the product quality and corporate reputation will be compromised. Therefore, the code inspection that verifies the presence, position, and formation of printed codes is a key checkpoint in the plastic container industry to ensure products meet specifications prior to release and shipment to customers. With the development of machine learning and image processing technologies [1], the Optical Character Recognition (OCR) technology has been widely used by companies in the field of automatic code inspection on product containers, which greatly improves the accuracy and efficiency of code detection, and reduces their production costs as well as increases their profits. The general code detection is a composite process that comprises several phases such as the preprocessing, segmentation, feature extraction, and classification. The principal purpose is to compare the recognized characters with the correct codes and judge whether they are qualified. The widely used pixel binarization method for the character segmentation is the threshold-based segmentation method, and characters are segmented by setting a fixed or dynamic threshold.

Introduction

Model development

Model validation

Parametric study

Conclusion

References

Abstract

Fuel cell vehicles offer significant sustainability benefits by eliminating tailpipe emissions, increasing powertrain efficiency, and utilizing hydrogen that can be supplied from various sources including renewables. A pressure regulator in the hydrogen storage system on a fuel cell vehicle is an important component to ensure that the hydrogen delivery to the fuel cell stack meets the pressure and temperature requirements. A validated model of the regulator can be used to support the product design and optimization of the operating strategy. In this work, a pressure regulator model has been developed to capture the hydrogen discharge behaviors from the compressed hydrogen tank to the fuel cell stack. The focus of the model is to develop the pressure and temperature relationship at the regulator outlet given the inlet conditions from the storage tank. Besides the ideal-gas based derivation for pressure response, the model has used a constant-enthalpy approach to capture the hydrogen temperature increase associated with the pressure drop due to the Joule–Thomson effect. The model was validated with various testing data including hysteresis and dynamic flow conditions, showing satisfactory agreement. The validated model was then used for parametric studies. The modeling results concluded that the regulator inlet temperature has the strongest influence on raising the outlet temperature, while the regulator inlet pressure is an important factor although secondary to the inlet temperature. The comprehensive regulator modeling developed in this work provides the foundation for assessing and optimizing a key dynamic component in the hydrogen storage system.

Introduction

A fuel cell vehicle is regarded as an important zero-emission alternative that features comparable driving range and refilling time with internal-combustion-engine vehicles [1]. There are many model-based studies to improve the fuel cell system and control in a vehicular application [2e6]. Bao et al. [2,3] developed a dynamic model of fuel cell system to control and optimize the transient behaviors of air supply and hydrogen recirculation considering the mixed effects of gas flow, pressure and humidity. Hatti and Tioursi [4] demonstrated an artificial intelligence technique to control a proton exchange membrane fuel cell system process using a dynamic neural network. The anode recirculation system has been studied with theoretical modeling by Dadvar and Afshari [5], focusing on the optimization of stack and ejector design parameters. The pressure control components play an important role for the desired system performance. Hong et al. [6] presented a control oriented dynamic model for the fuel delivery system with anode recirculation and anode bleeding. Based on the model, a multi-input-multi-output nonlinear state feedback controller along with an optimized output feedback controller is proposed to maintain adequate hydrogen supply and suitable anode hydrogen concentration. However, as a typical simplification, an ideal behavior of reaching desired pressure from hydrogen tank without fluctuation and delay was assumed in these studies. Realistically, the dynamic response of pressure regulators and valves upon load change could influence the performance of system components and stack. For example, the primary flow rate as well as the recirculation ratio of an anode ejector is dependent on the upstream pressure [7]. Insufficient hydrogen supply upon load change could also lead to anode reversal and durability concern such as carbon corrosion [8], besides impacting the stack performance [9] and the accuracy of the abovementioned control strategies. Some component optimization for anode pressuremanagement has been performed.

1- Introduction

2- Literature review

3- STEP-NC enabled approach

4- Energy consumption calculation via STEP-NC

5- Proposed model by considering energy consumption

6- An improved ACO solution for the proposed model

7- Case study and discussions

8- Conclusions

References

Abstract

Sustainable production plays an important role in product lifecycle management by considering the social sustainability. Energy-efficient machining is an efficient approach for sustainable production in current manufacturing sectors. Although many related efforts have been achieved, a comprehensive energy optimization approach oriented to manufacturing parts is still a challenge. Therefore, this paper selects Standard for the Exchange of Product model data-Numerical Control (STEP-NC) as the enabling technology to achieve energy-efficient machining. An optimization model is proposed based on the energy calculation method using the workingstep in STEP-NC. An improved ant colony optimization (ACO) solution, consisting of encoding and decoding, initialization, machining scheme generation, idea of local multiple iteration, evaluation, pheromone evaporation and update, is presented. A part with typical manufacturing features is applied to verify the effectiveness of the proposed approach. The generated solution can provide a comprehensive machining scheme for low energy demandI by improving the efficiency with 25% for solving the optimization problem.

Introduction

Due to soaring energy prices and environmental pollution, research on sustainable product lifecycle management (SPLM) has been focused recently. Sustainable production is one important phase of SPLM. Reducing energy consumption during machining operations plays a critical role in achieving sustainable production, i.e., energy-efficient machining. Energy-efficient machining has attracted increasingly more efforts in recent years (CamposecoNegrete, 2013; Gong et al., 2016; Velchev et al., 2014; Yan and Li, 2013; Zhou et al., 2016). Deciding machining schemes (MSs) for a part to be machined from the perspective of energy efficiency is an effective way to perform energy-efficient machining. A MS consists of many key elements, e.g., machining resources, machining parameters, tool path, process route, etc. In general, there exist more than one reasonable MS, which could constitute a group of candidate machining schemes (CMSs), and the best machining scheme (BMS), i.e., energy-efficient machining scheme (EEMS), is generated from this group. It is obvious that this process refers to an optimization problem. To solve the optimization problem, two key procedures are necessary, i.e., optimization model and the corresponding solution method. An optimization model includes optimization objective, optimization variables and constraints. The objective may be single (i.e. only energy consumption) or multiple (e.g. time, energy, tool life, etc.). No matter single-objective or multipleobjectives optimization, an energy consumption model for indicating the energy calculation approach during machining processes is essential, which is used to formulating the optimization objective. Besides, the optimization variables are the contributing factors to the objective, and the constraints are determined in terms of the specific conditions. Regarding the solution method, a metaheuristic algorithm is a type of effective method of solving the optimization model, such as genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), tabu search (TS), simulated annealing (SA) and NSGA-II.

1- Introduction

2- Materials and methods

3- Results and discussions

4- Conclusions

References

Abstract

Advanced heat removal technologies are critical for high-performance automotive engines. The conventional fluids being used today are based on a mixture of distilled water (DW) and ethylene glycol (EG), which widens the operational temperature range but at the same time limits the heat removal. Therefore, the use of nanofluids for improving heat transfer performance has soared over the past few years. The problem is that most of the reports highlight the short-term heat transfer results which may not be true over time. In this paper, a suggested best practice for analyzing the usage of nanofluids in heat transfer applications is presented, specifically for an actual car radiator. This work investigates the use of aluminum oxide (Al2O3) and titanium dioxide (TiO2) nanoparticles dispersed in DW and EG at 50:50 volumetric proportions. The choice of these oxide-based nanofluids is motivated by their anti-corrosive properties that are usually not analyzed or discussed in most of the articles. Furthermore, the emphasis is given on the presentation of a comprehensive characterization of the nanofluids including thermophysical properties (size, density, viscosity, thermal conductivity, corrosive behavior) and long-term stability (zeta potential) which are essential for an end-user to have. The results showed a maximum enhancement of the thermal performance by 24.21% using Al2O3 at a volume fraction of 0.3%. Friction factor and performance evaluation criterion (PEC) for the radiator experiments are calculated in order to determine the penalty in the pressure drop and to evaluate it properly. Finally, it is found that the values of PEC lie in the range of 1.03–1.31 which indicates significant flow enhancement.

Introduction

The improvement in automotive technologies has led to increased thermal loads, and therefore higher cooling rates are required [1]. The addition of fins, microchannel, and turbulators are the traditional approaches used to increase cooling rates of the radiator which are already extended to their limits [1,2]. In recent years, the automotive industry has been focused on the weight reduction of the vehicle as it improves the fuel economy and the associated running costs [3]. Weight reduction can be achieved in the engine cooling system. Furthermore, many conventional coolants (lubricants and water) have characteristically shown poor heat transfer properties due to their lower thermal conductivities (TCs) [1,4]. Engine performance, fuel efficiency, and emissions are specific parameters of an automobile which are profoundly affected by convective heat transfer [5,6]. Therefore, a new compact and innovative coolants and/or coolant systems are needed to fulfill the growing needs for heat removal in a clean and eco-friendly way [7].

Self-alignment of strapdown inertial navigation systems incorporating micro-electromechanical systems (MSINS) is a great challenge for marine applications. In this paper, a self-alignment method for a rotating MEMS strapdown compass is proposed with the aim of solving this problem. First, based on an analysis of biaxial rotation modulation and initial alignment of the strapdown compass, a selfalignment method is presented and verified. Second, by analyzing the effects of biaxial rotation modulation, the proposed method is improved by speeding up the rotation and reducing the stop time of the biaxial rotation mechanism to shorten the initial alignment time, which effectively suppresses the influence of MEMS noise on the initial alignment error angle. The influence of ship swinging on the initial alignment error angle is also analyzed. The efficiency of the method is verified by experiments on a swinging base. Finally, a parameter adjustment approach is presented that allows the proposed method to be used with different types of MEMS. This approach is validated by experiments. All the experimental results demonstrate the efficiency and precision of the proposed method.

Introduction

An MEMS strapdown inertial navigation system (MSINS) is a combination of a strapdown inertial navigation system (SINS) and micro-electro-mechanical systems (MEMS) technology. Such systems are at the forefront of navigation research and are already in use for moderate to low-precision navigation of ships, owing to their advantages of, among other things, high reliability, low cost, and capability of miniaturization. Obtaining the initial alignment that determines the initial attitude is a prerequisite for navigational computation in a SINS [1]. This initial self-alignment relies on the inertial measurement unit (IMU) itself to complete the initial alignment without the aid of external information. Normally, the MEMS initial alignment depends on the MEMS accelerometer and magnetometer to determine horizontal attitude and heading attitude [2]. However, this method can determine the attitude only on a static base and in the absence of magnetic interference. It is not suitable for marine applications, where there are swinging motion and strong magnetic interference [3]. To remove the limitation imposed by the use of a magnetometer, a single-antenna method is adopted in [4]. However, it is only used on a moving base. To allow both swinging and static bases, a double-antenna method is adopted in [5], although this method is not suitable for use in bad weather. In [6], transmission alignment methods are adopted to avoid interference with GPS signals.

A method of cutting permanent magnets with circular arcs of offset center was proposed on the basis of the principle of similarity under the condition of slip drive to obtain a permanent magnet transmission scheme for low permanent magnet torque fluctuations. The magnetic flux distribution of each region in the magnetic circuit was altered by changing the shape of the permanent magnet. Different magnetic torque curves were obtained by selecting silicon steel sheet with different thicknesses. The relationship between the magnetic flux distribution and the relative rotation angle in the silicon steel sheet was further analyzed, and the direct cause of the influence of the thickness of the silicon steel sheet on the magnetic torque was obtained. The parameters of different eccentric arcs were selected for simulation calculation, and a smooth magnetic torque curve was obtained after fixing the thickness of the silicon steel sheet. On the basis of the results of the former two, the parameters of the eccentric arc were obtained by the magnetic torque value volatility using an approximation method, and the magnetic torque value was measured by making the physical object. According to the results of the simulation and experiment, the permanent magnets with eccentric arcs can be used to obtain the smooth magnetic torque in the range of a 0◦–90◦ relative rotation angle, which has a certain application value.

Introduction

The magnetic transmission concepts dated back to the early 20th century [1]. Recently, many studies on permanent magnet transmission have emerged. The axial-flux modulated superconducting magnetic gear has currently been specifically studied using a superconducting bulk magnet replacing conventional permanent magnet [2]. The coaxial magnetic gear with an eccentric pole of the inner rotor and Halbach arrays of the outer rotor was proposed and studied to improve the torque density of a coaxial magnetic gear (CMG) [3]. Substantial studies on the CMG were performed in [4]–[6]. Magnetic gears that were well suited to execute high-torque direct-drive operations for oceanic wave energy harvesting applications were designed [7]. The shape of the flux modulators of the concentric magnetic gear affects the torque capability and ripples [8]. A new geometry was proposed for the magnetic gear to reduce the eddy-current loss of permanent magnets and improve the torque transfer efficiency [9]. The permanent magnet shape is one of the main factors affecting the performance of permanent magnet transmission. The CMG has a great advantage when a large radius and a small axial length are permissible. Hence, the CMG is suitable for the drivetrain of vehicles [10]. A novel topology of a planetary magnetic gear having only two rotating parts demonstrated a torque density of 187 kN · m/m3 [11]. The air-gap magnetic fields and eddy-current densities of a non-rotary mechanical flux adjuster (MFA) were investigated under the different shifting distances of a MFA, which reveals the fairly good torque regulation effects [12].

I- Introduction

II- Development of the Passivated Emitter and Rear Cell

III- Interdigitated Back Contact and Bifacial Solar Cells

IV- Commercial Passivated Emitter and Rear Cells

V- Conclusion

References

Abstract

This paper reviews the development of the passivated emitter and rear cell (PERC) silicon solar cell in the 1980s, which set several efficiency records, but was not taken up commercially at the time. Following extensive development of suitable fabrication processes, materials, and production tools, the PERC solar cell is now on track to become the dominant commercial solar cell. Since photovoltaics (PV) itself is on track to become the dominant energy generation technology, the PERC is having a global impact in both energy generation and greenhouse gas emission reduction. Assuming an average growth rate of annual PV installations of 25%, PV mitigation of greenhouse gases will reach about 5% in 2022, including 2% from PERCs, with much higher values expected later in the 2020s. This review focuses on the period of development of the PERC during the 1980s.

INTRODUCTION

IN 2017, solar photovoltaics (PV) constituted 40% of global net new electricity generation capacity additions with fossil, nuclear, wind, hydro, and other renewables constituting the balance (see Fig. 1). PV is growing much faster than the other generation technologies and may exceed half of global net new generation capacity annual additions by 2020. Continued rapid growth of PV (and wind energy) would have a major impact on global emissions of greenhouse gases by displacing coal and gas electricity generation. The average growth rate of PV in terms of annual new deployment has been about 30% per year over the past ten years [1]. This growth rate, if continued until 2025, would result in PV producing 10% of the world’s electricity. For decades, about 90% of global solar cell production has been of the aluminum back surface field (Al-BSF) design. This cell design offers moderate efficiency and simple and reliable fabrication, and utilizes vast experience in terms of manufacturing tools, materials, and procedures. The cell manufacturing cost (separate from polysilicon and wafering) represents about one-quarter of the module cost, while the module cost is about one-half of the system cost [8]. Thus, the cell fabrication process is about one-eighth of the completed system cost. Most module and system costs depend upon the area of cell deployed and, hence, depend inversely on cell efficiency.