Search Results

The software installed in Electronic Control Units (ECUs) has witnessed a significant scale expansion as the functionality of Intelligent Connected Vehicles (ICVs) has become more sophisticated. To seek convenient long-term functional maintenance, stakeholders want to access ECUs data or update software from anywhere via diagnostic. Accordingly, as one of the external interfaces, Diagnostics over Internet Protocol (DoIP) is inevitably prone to malicious attacks. It is essential to note that cybersecurity threats not only arise from inherent protocol defects but also consider software implementation vulnerabilities. When implementing a specification, developers have considerable freedom to decide how to proceed. Differences between protocol specifications and implementations are often unavoidable, which can result in security vulnerabilities and potential attacks exploiting them.

Fuel cells’ soft output characteristics and mismatched voltage levels with subordinate electrical devices necessitate the use of DC/DC converters, which are an important part of the power electronic subsystem of the fuel cell system. The staggered parallel Boost topology is commonly employed in fuel cell DC/DC converters. This paper focuses on the control characteristics of the two-phase interleaved parallel Boost topology in the context of a fuel cell system. Specifically, we derive the small-signal model and output-control transfer function of the topology, and design a controller based on frequency characteristic analysis. Our proposed controller uses a cascaded double-ring structure and supports both constant current and constant voltage switching modes. To evaluate the effectiveness of our proposed control strategy, we conduct simulation and prototype testing.

Multiple object detection and tracking are central aspects of modeling the environment of autonomous vehicles. Lidar is a necessary component in the autonomous driving system. Without Lidar sensors, we will most probably not see fully self-driving cars become a reality. Lidar sensing gives us high-resolution data by sending out thousands of laser signals. In advanced driver assistance systems or automated driving systems, 3-D point clouds from lidar scans are typically used to measure physical surfaces. Lidar is a powerful sensor that you can use in challenging environments where other sensors might prove inadequate. Lidar can provide a complete 360-degree view of a scene. This paper designs Lidar based multi-target detection and tracking system based on the traditional point cloud processing method including down-sampling, denoising, segmentation, and clustering objects.

Multi-Target tracking is a central aspect of modeling the surrounding environment of autonomous vehicles. Automotive millimeter-wave radar is a necessary component in the autonomous driving system. One of the biggest advantages of radar is it measures the velocity directly. Another big advantage is that the radar is less influenced by environmental conditions. It can work day and night, in rainy or snowy conditions. In the expressway scenario, the forward-looking radar can generate multiple objects, to properly track the leading vehicle or neighbor-lane vehicle, a multi-target tracking algorithm is required. How to associate the track and the measurement or data association is an important question in a multi-target tracking system. This paper applies the nearest-neighbor method to solve the data association problem and uses an extended Kalman filter to update the state of the track.

Aiming at the problems of ineffective collision avoidance and vehicle instability in the process of vehicle emergency braking in road conditions with low adhesion and sudden change in adhesion coefficient, a stability-coordinated emergency braking and collision avoidance control system SEBCACS) is proposed. First, according to the motion of the ego vehicle and the target vehicle as well as the road adhesion conditions, a collision time model is proposed for evaluating the vehicle collision risk, and the expected deceleration required to avoid the collision is calculated. Then, the MPC method is used to calculate the yaw moment generated by the four-wheel braking force required to maintain vehicle stability according to the actual and reference yaw rate and side slip angle deviation. Then it is decided whether to implement additional yaw moment control according to the body stability evaluation results.

The parameter setting has a great influence on the noise reduction performance of the road noise active control (RNC) system. This paper analyzes and optimizes the parameters of the RNC system. Firstly, the model of the RNC system is established based on the FxLMS algorithm. Based on this model, taking the maximum noise reduction as the evaluation index, the sensitivity analysis of convergence coefficient, filter order, and reference signal gain was carried out using the Sobol method with the data measured by a real vehicle on asphalt pavement at 40km/h. The results show that there is no significant interaction between the three parameters. Then, using the idea of orthogonal experiment, the simulation results of the control model are analyzed by taking the maximum noise reduction as the evaluation index. It is found that the convergence coefficient has the greatest effect on the maximum noise reduction, followed by the filter order, and the reference signal gain has the least effect.

Aiming at the test safety problems in the early stage of self-driving cars development, firstly the virtual vehicle on-board CAN data acquisition module of the present project was designed based on virtual LabVIEW. Then a wireless remote control system for the self-driving car was constructed, which integrated the built virtual vehicle on-board CAN data acquisition system, the remote real-time image monitoring module and the remote upper computer control module based on ZigBee wireless transmission. It can execute the environmental awareness training and continuous and complex motion manipulation testing of the vehicle without relying on the driver, which can solve the safety problems in the tests of initial development of self-driving cars. Finally, the four-wheel independent steering electric vehicle was used as the self-driving test vehicle, and the wireless remote control system was tested on the double lane change type path and S-type path.

As the key assembly of new energy vehicles, the noise and vibration, and harshness (NVH) performance of integrated electric drive system directly affects the driving quality of new energy vehicles. In this paper, the vibration noise characteristic test of 3in1 electric drive system is carried out in the semi-muffler chamber. In order to compare and analyze the difference between 2in1 and 3in1 electric drive system NVH performance, the power electronics unit (PEU) in the 3in1 system was removed and placed on the ground away from the platform, and vibration noise test was carried out. In order to analyze the difference of NVH performance between 2in1 status and 3in1 status, the PEU in the 3in1 system was removed and placed on the ground far away from the bench, and the NVH test was carried out. The microphone signal at 1m position and the vibration acceleration signal of the key structural surface of the system are measured experimentally.

With the strict requirements of harmful emission regulations, carbon peaking and neutralization goal, the internal combustion engine (ICE) industry is facing great challenges. Compared with pure ICE powertrain, hybrid powertrain has the advantages on fuel consumption and harmful emissions, which is more suitable for the market today. In series hybrid powertrain, because of the direct mechanical connection between ICE and motor, the motor can be used as an assistant in optimizing the performance of ICE. In order to realize the cycle-based or crank angle-based control of ICE, a high-frequency motor control system need to be built. Field Programmable Gate Array (FPGA) has the characteristics of high calculation frequency and high reliability to meet the demand. At the same time, the ICE control based on LabVIEW and FPGA has been realized.

Proton Exchange Membrane Fuel Cell (PEMFC) which has advantages of starting fast, high energy density, high efficiency, lower operating temperature and little pollution is widely regarded as one of the most promising energy sources. The PEMFC system includes several subsystems such as air supply subsystem, hydrogen supply subsystem, thermal management subsystem, water management subsystem, energy management subsystem and so on. The Air supply subsystem has great influence on the performance and life of PEMFC stack. Whether oxygen supply in air supply subsystem is sufficient or not will affects reaction rate of fuel, the operating temperature and degradation of PEMFC stack and so on. To solve the issue of oxygen starvation in PEMFC stack, the control strategies for improving dynamic response and preventing air shortage of the PEMFC air supply subsystem are reviewed.

Proton exchange membrane fuel cells (PEMFC) is considered the most promising alternative vehicle power in the future owing to its highly power density and zero carbon emission. However, Voltage inconsistency of PEMFC is an essential issue that influences the performance of a PEMFC. It is affected by flow-rate and relative humidity of the inlet air. It’s necessary to establish a control strategy to ensure air supplied timely. A PEMFC system bench with 30 cells (the cells are numbered 1-30 in the direction from near to far from the air intake port) assembled in series was established to investigate the control strategy of air supply system. According to fuel cell’s position of the lowest voltage and the corresponding air flow rate, there are three different operations as follows. When it appears not in the low numbered area and the air flow rate is high, it indicates that humidity of the cell is insufficient and it needs to reduce power of the blower or close the bypass-valve.

With the increasing concern on environmental pollution and CO2 emission all over the world, range-extended electrical vehicle (REEV) has gradually got more attention because it could avoid the mileage anxiety of the battery electrical vehicles (BEV) and get high energy efficiency. Nevertheless, NVH performance of internal combustion engine range extender (ICRE) is a critical problem that affects the driving experiences for REEV. In this paper, a two-cylinder PFI gasoline engine and a permanent magnet synchronous motor (PMSM) are coaxially mounted to run as an ICRE. The ICRE control system was established based on Compact RIO hardware and LabVIEW, who has the functions of the intake throttle PID closed-loop control, autonomous ICRE operation control, and speed PID closed-loop control. In this paper, the gasoline engine was first driven to the idle condition by PMSM in speed-control mode.

The backlash between engaging components in a driveline is unavoidable, especially when the gear runs freely and collides with the backlash, the impact torque generated increases the vibration amplitude. The power-split hybrid electric vehicle generates output torque only from the traction motor during the launching process. The nonlinear backlash can greatly influence the driveability of the driveline due to the rapid response of the traction motor and the lack of the traditional clutches and torsional shock absorbers in the powertrain. This paper focuses on the launch vibration of the power-split hybrid electric vehicle, establishes a nonlinear driveline model considering gear backlash, including an engine, two motors, a Ravigneaux planetary gear set, a reducer, a differential, a backlash assembly, half shafts, and wheels.

The proton exchange membrane fuel cell (PEMFC) system has emerged as the state-of-art power source for the electric vehicle, but the widespread commercial application of fuel cell vehicle is restricted by its short service life. An enabling high accuracy model holds the key for better understanding, simulation, analysis, subsystem control of the fuel cell system to extract full power and prolong the lifespan. In this paper, a quasi-dynamic lumped parameters model for a 3kW stack is introduced, which includes filling-and-emptying volume sub-models for the relationships between periphery signals and internal states, static water transferring sub-model for the membrane, and empirical electrochemical sub-model for the voltage response. Several dynamic experiments are carried out to identify unknown parameters of the model.

Engine start while driving is one of the most typical and frequent work conditions for hybrid vehicles. Engine start has very significant impact on the driving comfort. Engine start, especially a dynamical engine start, have high control requirements regarding control time, torque output and riding comfort. In some hybrid transmissions such as P2, engine is cranked and synchronized through wet clutch slipping. Because clutch pressure control has time-varying delay and estimation precision of engine torque by ECU (Engine Control Unit) is poor, conventional PID controller is unable to meet the high requirements of control quality. A new control algorithm is proposed in this paper to cope with all these challenges. The new control algorithm is based on LADRC (Linear Active Disturbance Rejection Controller) and is improved through combination with Smith predictor and Adaline network. LADRC is adopted to reduce negative effects of poor precision of engine torque.

Proton exchange membrane fuel cell (PEMFC) system is expected to be the next generation vehicle powertrain. However, water management is still the main problem which directly affects the performance, reliability and durability in PEMFC system. To ensure the accurate water content estimation, High Frequency Resistance (HFR) is the most representative indicator in laboratory. The HFR is calculated by detecting the Alternating Current (AC) voltage response of fuel cell under the excitation of 1k Hz AC current. The voltage level of the AC excitation affects the measurement of HFR. Generally, 5mV AC excitation is used to measure HFR for the fuel cell with an output voltage between 0.6-1V. So, online HFR monitor is a big challenge for vehicle application, due to its low Signal/Noise Ratio (S/N), poor Electromagnetic Compatibility (EMC) environment, high common mode voltage and several hundred cells. In this work, an online fuel cell HFR monitor controller is developed.

It’s significant to analyze the Experimental performance of active control system for road noise. In this paper, a 2-channel active control system of vehicle road noise based on FXLMS algorithm is established. The complexity of Filtered-x Least Mean Square algorithm (FXLMS) is analyzed. The bench test and road test are carried out to test and analyze the performance of the control system. Firstly, the general mathematic model of the multi-channel active control system based on FXLMS algorithm is established. The computational complexity of the algorithm is analyzed. Secondly, a hardware-in-the-loop (HIL) test bench based on multi-channel FXLMS algorithm and a measurement system based on DASP are set up, to measure the noise reduction performance of active noise control system under various working conditions. Finally, the bench test and the road test are carried out and the results are analyzed.

Based on the vector space decomposition (VSD) transformation, the phase currents of the asymmetric dual three phase permanent magnet synchronous motor (ADT-PMSM) can be mapped into three orthogonal subspaces, i.e., α-β subspace, x-y subspace and O1-O2 subspace. The mechanical energy conversion takes place in the α-β subspace, while in the x-y and O1-O2 subspaces only losses are produced. With neutral points being isolated, O1-O2 subspace can be omitted. So the vector control algorithm can control the α-β and x-y subspaces separately to realize the four dimensional current control. In the α-β subspace, deviation decoupling control method is employed to realize the mechanical energy conversion, which is robust to the motor parameters.

It is considered that slow convergence speed and large calculation amount of commonly used adaptive algorithm in the active control system for vehicle interior noise yield noise reduction performance and hardware requirements problems. In this paper, a 4-channel active control of vehicle interior noise based on adaptive notch filter is established, and road test is carried out to test and analyze the performance of the control system. Firstly, the general mathematic model of the multi-channel active control system based on adaptive notch filter is established. The computational complexity of the algorithm is analyzed and compared with that of the FXLMS algorithm. Secondly, a hardware-in-the-loop test bench based on multi-channel adaptive notch filter is set up, to measure the noise reduction performance of ANC system under various working conditions.

On the dual-motor electric vehicle, which is driven by two electric motors mounted on the front and rear axles respectively, longitudinal dynamic control and electro-dynamic braking can be achieved by controlling the torque of front and rear axle motors respectively. Suspension displacement is related to the wheel torque, thus the pitch of vehicle body can be influenced by changing the torque distribution ratio. The pitch of the body has a great influence on the vehicle comfort, which occurs mainly during acceleration and braking progress. Traditionally active suspension is adopted to control the pitch of body. Instead, in this paper an ideal torque distribution strategy is developed to limit the pitch during acceleration and braking progress. This paper first explores the relationship between the torque distribution and the body pitch through the real vehicle test, which reveals the feasibility of the vehicle comfort promotion by optimizing the torque distribution coefficient.