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Recent developments in emissions regulations are pushing Non-Road Mobile Machineries manufacturers towards the adoption of more efficient solutions to reduce the amount of pollutants per unit of work performed. Electrification can be a reasonable alternative to traditional powertrain to achieve this goal. The higher complexity of working machines architectures requires, now more than ever, better design and testing methodologies to better integrate electric systems into mechanical and hydraulic layouts. In this work, the attention focused on the use of a Hardware in the Loop (HIL) approach to test performance of an energy management strategy (called load observer) developed specifically for an orchard tractor starting from field characterization. The HIL bench was designed to replicate a scaled architecture of a parallel hybrid electric tractor at mechanical and electrical level. The vehicle behavior was simulated with a personal computer connected on the CAN BUS network designed for the HIL system. Several tasks were simulated starting from data gathered during field measurements of a daily use of the machine. Results showed good performance in terms of load split between the two power sources and stability of the speed control although the variability of the applied load.

An in-vehicle controller area network (CAN) bus is vulnerable because of increased sharing among modern autonomous vehicles and the weak protocol design principle. Spoofing attacks on a CAN bus can be difficult to detect and have the potential to enable devastating attacks. To effectively identify spoofing attacks, we propose the authentication of sender identities using a recurrent neural network with long short-term memory units (RNN-LSTM) based on the features of a fingerprint signal. We also present a way to generate the analog fingerprint signals of electronic control units (ECUs) to train the proposed RNN-LSTM classifier. The proposed RNN-LSTM model is accelerated on embedded Field-Programmable Gate Arrays (FPGA) to allow for real-time detection despite high computational complexity. A comparison of experimental results with the latest studies demonstrates the capability of the proposed RNN-LSTM model and its potential as a solution to in-vehicle CAN bus security.

In-vehicle electronic control unit (ECU) communications generally count on private protocols (defined by the manufacturers) under controller area network (CAN) specifications. Parsing the private protocols for a particular vehicle model would be of great significance in testing the vehicle’s resistance to various attacks, as well as in designing efficient intrusion detection and prevention systems (IDPS) for the vehicle. This paper proposes a suite of methods for parsing ECU private protocols on in-vehicle CAN network. These methods include an algorithm for parsing discrete variables (encoded in a discrete manner, e.g., gear state), an algorithm for parsing continuous variables (encoded in a continuous manner, e.g., vehicle speed), and a parsing method based on upper-layer protocols (e.g., OBD and UDS). Extensive verifications have been performed on five different brands of automobiles (including an electric vehicle) to demonstrate the universality and the correctness of these parsing algorithms. Some parsing tips and experiences are also presented. Our continuous-variables parsing algorithm could run in a semi-automatic manner and the parsing algorithm from upper-layer protocols could execute in a completely automatic manner. One might view the results obtained by our parsing algorithms as an important indicator of penetration testing on in-vehicle CAN network.

Nowadays, automotive wire harnesses have become very complex systems as the number of electronic components and Electronic Control Units (ECUs) inside a vehicle have increased dramatically. To manage this complexity, and to support the automotive systems of tomorrow, new communication techniques need to be investigated. In this work, a direct modulated Power-Line Communication (PLC) method is proposed to drastically reduce the number of interconnections and give advantages in cost, complexity, and weight. Two transmitter topologies based on a Switching Capacitor approach are presented and implemented in a Test Chip fabricated in a 180 nm HV-CMOS SOI technology. The proposal is validated by communication tests connecting the designed chip and a discrete component-based demonstrator receiver through an unshielded twisted pair cable. Compared to commercially available solutions, the proposed approach can reach a data rate of 2 Mbps, making it able to implement high-speed event-driven networks, such as the Controller Area Network (CAN).

This paper focuses on the interdisciplinary research on the design of a smart gateway for managing the dynamic error code testing collected and generated by the Electronic Control Unit (ECU) from the automotive industry. The techniques used to exchange information between the ECU code errors and knowledge bases, based on data fusion methods, allowed us to consolidate and ensure data reliability, and then to optimize processed data in our distributed electronic systems, as the basic state for Industry 4.0 standards. At the same time, they offered optimized data packets when the gateway was tested as a service integrator for ECU maintenance. The embedded programming solutions offered us safe, reliable, and flexible data packet management results on both communication systems (Transmission Control Protocol/Internet Provider (TCP/IP) and Controller Area Network (CAN) Bus) on the Electronic Control Unit (ECU) tested for diesel, high-pressure common rail engines. The main goal of this paper is to provide a solution for a smart, hardware–software, Industry-4.0-ready gateway applicable in the automotive industry.

Vehicle technology development drives economic development but also causes severe mobile pollution sources. Eco-driving is an effective driving strategy for solving air pollution and achieving driving safety. The on-board diagnostics II (OBD-II) module is a common monitoring tool used to acquire sensing data from in-vehicle electronic control units. However, different vehicle models use different controller area network (CAN) standards, resulting in communication difficulties, however, relevant literature has not discussed compatibility problems. The present study researched and developed the universal OBD-II module, adopted deep learning methods to evaluate fuel consumption, and proposed an intuitive driving graphic user interface design. In addition to using the universal module to obtain data on different CAN standards, this study used deep learning methods to analyze the fuel consumption of three vehicles of different brands on various road conditions. The accuracy was over 96%, thus validating the practicability of the developed system. This system will greatly benefit future applications that employ OBD-II to collect various types of driving data from different car models. For example, it can be implemented for achieving eco-driving in bus driver training. The developed system outperforms those proposed by previous research regarding its completeness and universality.

Time-synchronization technology can provide a common notion of time among the participating nodes on a network. This is essential not only for protocol operation for time-critical services but also for the time stamp for information included in the message. Precise time information can be very crucial for such things as autonomous driving because there are various sensor measurements from multiple cameras, and radio detection and ranging (radar) and light detection and ranging (LiDAR) are used for perceiving the current situation via sensor fusion. A well-known synchronization method, IEEE 1588, denoted as the precision time protocol (PTP), can be used for various applications. For in-vehicle networks of autonomous cars, we have to consider that the network may be comprised of subnetworks based on different protocols such as controller area network (CAN) and Ethernet. However, implementing PTPs on such heterogeneous vehicle networks causes several problems. First, the PTP procedure must be modified to be implement on a CAN network. Second, to calculate the delay and offset for PTP, the processing delay that occurs during message conversion must be considered. In this paper, we propose a synchronization method for CAN&ndash, Ethernet networks to solve these problems. The performance of the proposed synchronization method is evaluated by experiments on a real CAN&ndash, Ethernet network.

With the introduction of the more electric aircraft, there is growing emphasis on improving overall efficiency and thus gravimetric and volumetric power density, as well as smart functionalities and safety of an aircraft. In future on-board power distribution networks, so-called high voltage DC (HVDC, typically +/−270VDC) supplies will be introduced to facilitate distribution and reduce the associated mass and volume, including harness. Future aircraft power distribution systems will also very likely include energy storage devices (probably, batteries) for emergency back up and engine starting. Correspondingly, novel DC-DC conversion solutions are required, which can interface the traditional low voltage (28 V) DC bus with the new 270 V one. Such solutions presently need to cater for a significant degree of flexibility in their power ratings, power transfer capability and number of inputs/outputs. Specifically, multi-port power-scalable bi-directional converters are required. This paper presents the design and testing of such a solution, addressing the use of leading edge wide-band-gap (WBG) solid state technology, especially silicon carbide (SiC), for use as high-frequency switches within the bi-directional converter on the high-voltage side.

Advances in vehicle technology have resulted in the development of vehicles equipped with sensors to acquire standardized information such as engine speed and vehicle speed from the in-vehicle controller area network (CAN) system. However, there are challenges in acquiring proprietary information from CAN frames, such as the brake pedal and steering wheel operation, which are essential for driver behavior analysis. Such information extraction requires electronic control unit identifier analysis and accompanying data interpretation. In this paper, we present a system for the automatic extraction of proprietary in-vehicle information using sensor data correlated with the desired information. First, the proposed system estimates the vehicle&rsquo, s driving status through threshold-, random forest-, and long short-term memory-based techniques using inertial measurement unit and global positioning system values. Then, the system segments in-vehicle CAN frames using the estimation and evaluates each segment with our scoring method to select suitable candidates by examining the similarity between each candidate and its estimation through the suggested distance matching technique. We conduct comprehensive experiments of the proposed system using real vehicles in an urban environment. Performance evaluation shows that the estimation accuracy of the driving condition is 84.20%, and the extraction accuracy of the in-vehicle information is 82.31%, which implies that the presented approaches are quite feasible for automatic extraction of proprietary in-vehicle information.

International audience; Power management of a one-converter parallel structure with battery and supercapacitor is addressed in this paper. The controller is implemented on a DSP from a Microchip and uses a Controller Area Network (CAN) bus communication for data exchange. However, the low data transmission rate of the CAN bus data impacts the performances of regular power management strategies. This paper details an initial strategy with a charge sustaining mode for an application coupling a battery with supercapacitors, in which low performances have been witnessed due to the high sampling time of the CAN bus data. Therefore, a new strategy is proposed to tackle the sample time issue based on a depleting mode. Simulation and experimental results with a dsPIC33EP512MU810 DSP based on a 10 kW hybrid system proves the feasibility of the proposed approach.

The Internet of Things (IoT) is an industry-recognized next intelligent life solution that increases the level of comfort, efficiency, and automation for citizens through numerous sensors, smart devices, and cloud stations connected physically. As an important application scenario of IoT, the Internet of Vehicles (IoV) plays an extremely critical role in the intelligent transportation field. In fact, the In-Vehicle Network of smart vehicles that are recognized as the core roles in intelligent transportation is currently the Controller Area Network (CAN). However, the In-Vehicle CAN bus protocol has several vulnerabilities without any encryption, authentication, or integrity checking, which severely threatens the safety of drivers and passengers. Once malicious attackers hack the vehicular gateway and obtain the access right of the CAN, they may control the vehicle based on the vulnerabilities of the CAN bus protocol. Given the severe security risk of CAN, we proposed the CANsec, a practical In-Vehicle CAN security evaluation tool that simulates malicious attacks according to major attack models to evaluate the security risk of the In-Vehicle CAN. We also show a usage case of the CANsec without knowing any information from the vehicle manufacturer.

In this study, we designed and implemented a smart junction box (SJB) that was optimized for supplying power to low-voltage headlights (13.5 V) in electric vehicles. The design incorporated a number of automotive semiconductor devices, and components were placed in a high-density arrangement to reduce the overall size of the final design. The heat generated by the SJB was efficiently managed to mount an Intelligent Power Switch (IPS), which was used to power the headlights onto the printed circuit board (PCB) to minimize the impact on other components. The SJB was designed to provide power to the headlights via pulse width modulation to extend their lifetime. In addition, overload protection and fail/safe functions were implemented in the software to improve the stability of the system, and a controller area network (CAN) bus was provided for communications with various components in the SJB as well as with external controllers. The performance of the SJB was validated via a load operation test to assess the short circuit and overload protection functions, and the output duty cycle was evaluated across a range of input voltages to ensure proper operation. Based on our results, the power supplied to the headlights was found to be uniform and stable.

With a deep connection to the internet, the controller area network (CAN) bus of intelligent connected vehicles (ICVs) has suffered many network attacks. A deep situation awareness method is urgently needed to judge whether network attacks will occur in the future. However, traditional shallow methods cannot extract deep features from CAN data with noise to accurately detect attacks. To solve these problems, we developed a SDAE+Bi-LSTM based situation awareness algorithm for the CAN bus of ICVs, simply called SDBL. Firstly, the stacked denoising auto-encoder (SDAE) model was used to compress the CAN data with noise and extract the deep spatial features at a certain time, to reduce the impact of noise. Secondly, a bidirectional long short-term memory (Bi-LSTM) model was further built to capture the periodic features from two directions to enhance the accuracy of the future situation prediction. Finally, a threat assessment model was constructed to evaluate the risk level of the CAN bus. Extensive experiments also verified the improved performance of our SDBL algorithm.

This paper presents the initial developments of new hardware devices targeted for CAN (Controller Area Network) bus communications in forest machines. CAN bus is a widely used protocol for communications in the automobile area. It is also applied in industrial vehicles and machines due to its robustness, simplicity, and operating flexibility. It is ideal for forestry machinery producers who need to couple their equipment to a machine that allows the transportation industry to recognize the importance of standardizing communications between tools and machines. One of the problems that producers sometimes face is a lack of flexibility in commercialized hardware modules, for example, in interfaces for sensors and actuators that guarantee scalability depending on the new functionalities required. The hardware device presented in this work is designed to overcome these limitations and provide the flexibility to standardize communications while allowing scalability in the development of new products and features. The work is being developed within the scope of the research project “SMARTCUT—Remote Diagnosis, Maintenance and Simulators for Operation Training and Maintenance of Forest Machines”, to incorporate innovative technologies in forest machines produced by the CUTPLANT S.A. It consists of an experimental system based on the PIC18F26K83 microcontroller to form a CAN node to transmit and receive digital and analog messages via CAN bus, tested and validated by the communication between different nodes. The main contribution of the paper focuses on the presentation of the development of new CAN bus electronic control units designed to enable remote communication between sensors and actuators, and the main controller of forest machines.

The realization of the first prototype of a vehicle requires several tests of the algorithms implemented on the electronic control unit (ECU). This represents an important step for conventional vehicles, which becomes fundamental when dealing with unmanned vehicles. Since there is no human supervision, most critical tasks are handled by the control unit, which results in higher complexity for the control algorithms. In this work, a software-in-the-loop (SiL) test bench is used to validate the control algorithms of a vehicle control unit (VCU) for a driverless railway vehicle (DLRV). The VCU manages the control of the traction motors, pneumatic braking systems, and range extender, as well as control of the hybrid powertrain configuration to guarantee a high level of availability via the use of redundant systems. The SiL test bench has been developed in a Simulink real-time environment, where the vehicle model is simulated along with its fundamental subsystems. The model communicates with the VCU through a CAN bus protocol in the same way that it will operate with a real vehicle. The proposed method can be used to simulate many mission profiles for the DLRV, which may last several hours each. Moreover, this kind of test bench ensures a high time resolution, which allows one to find solutions for problems which occur with a time scale that is much smaller than the simulation time scale.

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security., Comment: 23 Pages, 14 Figures, 6 Tables with Appendix

In order to meet the requirement for the real-time of the hydraulic walking robot (WLBOT) and the stability of its movement, an embedded controller is proposed, which takes charge of multi-sensor information processing and signal output of the servo valve. The controller is capable of receiving control command and sending processed information while communicating with an embedded single board computer PCM-3365 via Control Area Network (CAN) bus at a 200 Hz frequency. In this paper, an appropriate interrupt cycle is selected and a 2 kHz high-speed control loop is run after we research the relationship between analog-to-digital converter direct memory access (ADC–DMA) interrupt cycle, data volume, and sampling rate. Significantly, the control strategy of WLBOT joint is introduced and a proportional-integral-derivative (PID) compound controller with velocity feedforward compensation (VFC) is realized. Meanwhile, the Chebyshev filtering algorithm is utilized to attenuate the vibration noise of joint signals. What’s more, an impedance controller is designed to gain better locomotion behavior and compliance in joint force control. Finally, the joint angle tracking and robot walking experiments are implemented, where the feasibility of the design and the validity of the control algorithm is verified. The results show that the PID velocity feedforward compensation controller can reduce the maximum tracking error by 39.13% and 71.31% in the knee and hip joint and the impedance control can reduce the standard deviation (SD) of the foot force by 36.06% and 72.79%.

Advanced driving-assistance systems (ADAS) are intended to automatize driver tasks, as well as improve driving and vehicle safety. This work proposes an intelligent neuro-fuzzy sensor for driving style (DS) recognition, suitable for ADAS enhancement. The development of the driving style intelligent sensor uses naturalistic driving data from the SHRP2 study, which includes data from a CAN bus, inertial measurement unit, and front radar. The system has been successfully implemented using a field-programmable gate array (FPGA) device of the Xilinx Zynq programmable system-on-chip (PSoC). It can mimic the typical timing parameters of a group of drivers as well as tune these typical parameters to model individual DSs. The neuro-fuzzy intelligent sensor provides high-speed real-time active ADAS implementation and is able to personalize its behavior into safe margins without driver intervention. In particular, the personalization procedure of the time headway (THW) parameter for an ACC in steady car following was developed, achieving a performance of 0.53 microseconds. This performance fulfilled the requirements of cutting-edge active ADAS specifications. This work was supported in part by the Spanish AEI and European FEDER funds under Grant TEC2016-77618-R (AEI/FEDER, UE).

Guideway inspection is of great significance to the operation safety and riding quality of a commercial high-speed maglev transportation system. When analyzing guideway inspection data, it is important to obtain the location information for each piece of raw data and convert it from the time domain to the spatial domain for the analysis afterward. Previous studies have used the method of adding additional hardware such as GPS (global positioning system) receivers, LRF (location reference flag) readers, or onboard CAN (controller area network) bus adaptors to obtain location information. This paper presents a novel method for indirectly obtaining the location information via the use of data from the levitation and guidance control sensors perpendicular to the longitudinal direction to extract the characteristic information from the track. The method can be used for a long stator linear motor-driven maglev system and similar contactless rail transit systems. The results showed that the method could accurately identify the required location information in each stator tooth during the entire operation simultaneously with the operating information such as train velocity, direction, and track ID, without additional hardware installation and vehicle network connection. Thus, it could improve the pertinence of the results of guideway inspection equipment, and at the same time, facilitate the miniaturization and independence of guideway inspection equipment.

Modern vehicles are no longer simply mechanical devices. Connectivity between the vehicular network and the outside world has widened the security holes that hackers can use to exploit a vehicular network. Controller Area Network (CAN), FlexRay, and automotive Ethernet are popular protocols for in-vehicle networks (IVNs) and will stay in the industry for many more years. However, these protocols were not designed with security in mind. They have several vulnerabilities, such as lack of message authentication, lack of message encryption, and an ID-based arbitration mechanism for contention resolution. Adversaries can use these vulnerabilities to launch sophisticated attacks that may lead to loss of life and damage to property. Thus, the security of the vehicles should be handled carefully. In this paper, we investigate the security vulnerabilities with in-vehicle network protocols such as CAN, automotive Ethernet, and FlexRay. A comprehensive survey on security attacks launched against in-vehicle networks is presented along with countermeasures adopted by various researchers. Various algorithms have been proposed in the past for intrusion detection in IVNs. However, those approaches have several limitations that need special attention from the research community. Blockchain is a good approach to solving the existing security issues in IVNs, and we suggest a way to improve IVN security based on a hybrid blockchain.

Road sweepers are ubiquitous machines that help preserve our cities cleanliness and health by collecting road garbage and sweeping out dirt from our streets and sidewalks. They are often very mechanical instruments, needing to operate in harsh conditions dealing with all sorts of abandoned trash and natural garbage. They are usually composed of rotating brushes, collector belts and bins, and sometimes water or air streams. All of these mechanical tools are usually high in power demand and strongly subject to wear and tear. Moreover, due to the simple working logic often implied by these cleaning machines, these tools work in an &ldquo, always on&rdquo, /&ldquo, max power&rdquo, state, and any further regulation is left to the pilot. Therefore, adding artificial intelligence able to correctly operate these tools in a semi-automatic way would be greatly beneficial. In this paper, we propose an automatic road garbage detection system, able to locate with great precision most types of road waste, and to correctly instruct a road sweeper in order to handle them. With this simple addition to an existing sweeper, we will be able to save more than 80% electrical power currently absorbed by the cleaning systems and reduce by the same amount brush weariness (prolonging their lifetime). This is done by choosing when to use the brushes and when not to, with how much strength, and where. The only hardware components needed by the system will be a camera and a PC board able to read the camera output (and communicate via CanBus). The software of the system will be mainly composed of a deep neural network for semantic segmentation of images, and a real-time software program to control the sweeper actuators with the appropriate timings. To prove the claimed results, we run extensive tests onboard of such a truck, as well as benchmark tests for accuracy, sensitivity, specificity and inference speed of the system.

Miniaturized propulsion systems can enable many future CubeSats missions. The advancement of the Technology Readiness Level of this technology passes through the integration in a CubeSat platform and the assessment of the impact and the interactions of the propulsion systems on the actual CubeSat technology and vice versa. The request of power, the thermal environmental, and the electromagnetic emissions generated inside the platform require careful analyses. This paper presents the upgraded design and the validation of a CubeSat test platform (CTP) that can interface a wide range of new miniaturized propulsion systems and gather unprecedented information for these analyses, which can be fused with the commonly used ground support equipment. The CTP features are reported, and the main achievements of the tests are shown, demonstrating the effective capabilities of the platform and how it allows for the investigation of the mutual interactions at system level between propulsion systems and the CubeSat technology.

This study focuses on driver-behavior identification and its application to finding embedded solutions in a connected car environment. We present a lightweight, end-to-end deep-learning framework for performing driver-behavior identification using in-vehicle controller area network (CAN-BUS) sensor data. The proposed method outperforms the state-of-the-art driver-behavior profiling models. Particularly, it exhibits significantly reduced computations (i.e., reduced numbers both of floating-point operations and parameters), more efficient memory usage (compact model size), and less inference time. The proposed architecture features depth-wise convolution, along with augmented recurrent neural networks (long short-term memory or gated recurrent unit), for time-series classification. The minimum time-step length (window size) required in the proposed method is significantly lower than that required by recent algorithms. We compared our results with compressed versions of existing models by applying efficient channel pruning on several layers of current models. Furthermore, our network can adapt to new classes using sparse-learning techniques, that is, by freezing relatively strong nodes at the fully connected layer for the existing classes and improving the weaker nodes by retraining them using data regarding the new classes. We successfully deploy the proposed method in a container environment using NVIDIA Docker in an embedded system (Xavier, TX2, and Nano) and comprehensively evaluate it with regard to numerous performance metrics.

The automobile industry no longer relies on pure mechanical systems; instead, it benefits from many smart features based on advanced embedded electronics. Although the rise in electronics and connectivity has improved comfort, functionality, and safe driving, it has also created new attack surfaces to penetrate the in-vehicle communication network, which was initially designed as a close loop system. For such applications, the Controller Area Network (CAN) is the most-widely used communication protocol, which still suffers from various security issues because of the lack of encryption and authentication. As a result, any malicious/hijacked node can cause catastrophic accidents and financial loss. This paper analyses the CAN bus comprehensively to provide an outlook on security concerns. It also presents the security vulnerabilities of the CAN and a state-of-the-art attack surface with cases of implemented attack scenarios and goes through different solutions that assist in attack prevention, mainly based on an intrusion detection system (IDS).

In this paper, we deal with a simple embedded electronic system for an industrial pneumatic–hydraulic system, based on a low-cost programmable logic controller (PLC) and industrial electronic parts with 24 V logic. The developed system is a hydraulic pulse system and generates a series of high-pressure hydraulic pulses with up to a max. 200 bar output pressure level and with up to a max. 2 Hz output hydraulic pulses frequency. In this paper we are describing requirements, the concept of the embedded control system in a diagram, security features and its industrial network connectivity (CAN bus, MODBUS). In description of the software solution we describe the implementation of the program threads approach in this low-cost PLC. The PLC programming with threads generate two layers of services—physical and application layer, and as a result, the threads create the main control state machine. In conclusion, we describe the calibration method of the system and the calibration curves. For further study we offer readers the full programming code written in sequential function charts to be used as PLC language. The cost of the described industrial networked control system with industry standard optoelectronic insulated interfaces and certified industrial safety relay does not exceed €1000 Euros.

A high-power charging station for electric vehicles usually adopts a parallel structure of multiple power modules. However, due to the parameter differences among power modules, a parallel system always has circulating current issues. This paper takes a non-isolated AC/DC charging module as the research object and proposes a current sharing control strategy for multiple power modules based on the consensus algorithm. By constructing a sparse communication network with the CAN (Controller Area Network) protocol and exchanging current information, accurate current sharing among power modules is realized. Firstly, the zero-sequence circulating current issue is analyzed through a parallel model of the three-phase rectifier, with an improved circulating current restraint strategy proposed based on the zero-sequence voltage compensation. Then, the principle of the consensus algorithm is explained, which is applied to the current sharing control of multiple power modules. Finally, the proposal is tested by the designed simulation and experimental cases. From the obtained results, it can be seen that the proposed control strategy can effectively realize accurate current sharing among multiple power modules and well restrain the zero-sequence circulating current at the input side.

This paper presents a rule-based energy management strategy for a plug-in hybrid school bus (PHSB). In order to verify the effectiveness and rationality of the proposed energy management strategy, the powertrain and control models were built with MATLAB/Simulink. The PHSB powertrain model includes an engine model, ISG (integrated started and generator) model, drive motor model, power battery packs model, driver model, and vehicle longitudinal dynamics model. To evaluate the controller area network (CAN) bus performance features such as the bus load, signal hysteresis, and to verify the reliability and real-time performance of the CAN bus multi-node control method, a co-simulation platform was built with CANoe and MATLAB/Simulink. The co-simulation results show that the control strategy can meet the requirements of the PHSB’s dynamic performance. Meanwhile, the charge-depleting mode (CD) and charge-sustaining mode (CS) can switch between each other and maintain a state-of-charge (SoC) of around 30%, indicating that the energy management strategy effectively extends the working period of the CD mode and improves the fuel economy further. The energy consumption per 100 km includes 13.7 L diesel and 10.5 kW·h electricity with an initial SoC of 75%. The CANoe simulation results show that the bus communication performs well without error frames.

In some application domains, using a download cable to update the system on a programmable chip (SoPC) is infeasible, which reduces the maintainability and flexibility of the system. Hence the remote system update (RSU) scheme is being studied. In this scheme, the serial configuration (EPCS) device involves a factory mode configuration image, which acts as the baseline, and an application mode configuration image, which is used for some specific functions. Specifically, a new application mode image is delivered through the controller area network (CAN) with the improved application layer protocol. Besides, the data flow and data check for transmitting a new image are constructed to combine the transmission reliability with efficiency. The boot sequence copying hardware configuration code and software configuration code is analyzed, and the advanced boot loader is carried out to specify boot address of the application mode image manually. Experiments have demonstrated the feasibility of updating and running a new application mode image, as well as rolling back into the factory mode image when no application mode image is available. This scheme applies a single CAN bus, which makes the system easy to construct and suitable for the field distributed control system.

This article presents a recursive solution to the power-transmission loss in DC-powered networks. In such a network, the load cannot be modeled as a fixed equivalent resistance value, since the switching regulator may draw more or less current based on the actual supply voltage to meet the power demand. Although the power-transmission loss itself is simply I2 RL, I, in turn, depends on the load’s supply voltage, which, in turn, depends on I, making it impossible to derive a closed-form solution by classical resistive network analysis in general. The proposed approach is to first derive a closed-form solution to I in the one-node topology using the quadratic formula. Next, we extend our solution to a locally daisy-chained (LDC) network, where the network is readily decomposable into stages, such that the solution combines the closed-form formula for the current stage with the recursive solution for the subsequent stages. We then generalize the LDC topology to trees. In practice, the solution converges quickly after a small number of iterations. It has been validated on real-life networks, such as power over controller area network (PoCAN).

Nowadays, large-scale wind power farms (WPFs) bring new challenges for both electric systems and communication networks. Communication networks are an essential part of WPFs because they provide real-time control and monitoring of wind turbines from a remote location (local control center). However, different wind turbine applications have different requirements in terms of data volume, latency, bandwidth, QoS, etc. This paper proposes a hierarchical communication network architecture that consist of a turbine area network (TAN), farm area network (FAN), and control area network (CAN) for offshore WPFs. The two types of offshore WPFs studied are small-scale WPFs close to the grid and medium-scale WPFs far from the grid. The wind turbines are modelled based on the logical nodes (LN) concepts of the IEC 61400-25 standard. To keep pace with current developments in wind turbine technology, the network design takes into account the extension of the LNs for both the wind turbine foundation and meteorological measurements. The proposed hierarchical communication network is based on Switched Ethernet. Servers at the control center are used to store and process the data received from the WPF. The network architecture is modelled and evaluated via OPNET. We investigated the end-to-end (ETE) delay for different WPF applications. The results are validated by comparing the amount of generated sensing data with that of received traffic at servers. The network performance is evaluated, analyzed and discussed in view of end-to-end (ETE) delay for different link bandwidths.