Vehicle Control Unit Research Articles

A Learning-Based Vehicle-Cloud Collaboration Approach for Joint Estimation of State-of-Energy and State-of-Health

The state-of-energy (SOE) and state-of-health (SOH) are two crucial quotas in the battery management systems, whose accurate estimation is facing challenges by electric vehicles' (EVs) complexity and changeable external environment. Although the machine learning algorithm can significantly improve the accuracy of battery estimation, it cannot be performed on the vehicle control unit as it requires a large amount of data and computing power. This paper proposes a joint SOE and SOH prediction algorithm, which combines long short-term memory (LSTM), Bi-directional LSTM (Bi-LSTM), and convolutional neural networks (CNNs) for EVs based on vehicle-cloud collaboration. Firstly, the indicator of battery performance degradation is extracted for SOH prediction according to the historical data; the Bayesian optimization approach is applied to the SOH prediction combined with Bi-LSTM. Then, the CNN-LSTM is implemented to provide direct and nonlinear mapping models for SOE. These direct mapping models avoid parameter identification and updating, which are applicable in cases with complex operating conditions. Finally, the SOH correction in SOE estimation achieves the joint estimation with different time scales. With the validation of the National Aeronautics and Space Administration battery data set, as well as the established battery platform, the error of the proposed method is kept within 3%. The proposed vehicle-cloud approach performs high-precision joint estimation of battery SOE and SOH. It can not only use the battery historical data of the cloud platform to predict the SOH but also correct the SOE according to the predicted value of the SOH. The feasibility of vehicle-cloud collaboration is promising in future battery management systems.

Domain Adversarial Neural Network-Based Intrusion Detection System for In-Vehicle Network Variant Attacks

A controller area network (CAN) bus that controls real-time communication and data transmission of electronic control units in vehicles lacks security mechanisms and is highly vulnerable to attacks. The detection effectiveness of existing In-Vehicle network intrusion detection systems (IDSs) is usually reliant on training samples of known attacks. Owing to the simple structure of the CAN bus protocol, attackers can easily create variants based on known attacks. In this study, we propose a CAN bus IDS based on a domain adversarial neural network, which can achieve high detection performance on unlearned variant attack data. In addition, we used feature fusion techniques to synthetically extract the features of the attack data to improve detection capability. The experimental results demonstrate that our proposed model has high performance and generalization ability in attack detection.

A Model Predictive Control Approach With Slip Ratio Estimation for Electric Motor Antilock Braking of Battery Electric Vehicle

In this article, based on the estimation of optimal slip ratio, a model predictive control approach is proposed for the antilock braking system with motor of a battery electric vehicle. A wheel dynamics model with a virtual control quantity is established. A method for estimating the optimal slip ratio of different roads is introduced, which consists of a sliding mode observer for longitudinal braking force and a recognizer of slip ratio. A ground slip map that describes the relationship between slip ratio and ground adhesion coefficient is proposed, which is utilized to identify the optimal slip ratio. Based on the model predictive control algorithm, a braking torque controller is designed to track the optimal slip ratio for good performance of braking stability and security. The vehicle control unit embedded with the estimator and model predictive control law is tested in a battery electric vehicle on the dry and wet roads. The experimental results indicate that the proposed approach has a good performance for the estimation and control of slip ratio during the antilock braking with electric motor, especially suitable for the low adhesion road conditions.

Acceleration slip regulation by electric motor torque of battery electric vehicle with nonlinear model predictive control approach

Step increase of electric motor torque results in wheel slip during the acceleration of battery electric vehicle (BEV), making vehicle body instability paramount. This is particularly challenging for regulating the torque of electric motor to keep stability in the process of acceleration, for which regulating the wheel slip to an ideal set-point is difficult for the central driving powertrain that adopts one electric motor to drive both left and right wheels by a differential. While much of the research on acceleration slip regulation has focused on solving the wheel anti-slip of internal combustion powertrain by electronic stability programme, comparatively little is known about the wheel slip control by electric motor torque of the central driving powertrain that is the most common style of driveline in the market of BEV currently. Here we discuss a series of studies on the acceleration slip regulation by electric motor torque that, collectively, design an approach of how the nonlinear model predictive controller based on a novel driving dynamics model regulates the wheel slip to an ideal set-point from the ideal slip ratio curve. Testing and validating this approach embedded into vehicle control unit are carried out in a BEV to fully realise the wheel slip control by electric motor torque during the acceleration.

Automising the CAN Communication System Using Raspberry-Pi

Abstract: This Project is set an objective of developing and improvement of communication system of Electrical parameters control in EV system withaccuracy the use of latest controller Raspberry-pi and CAN module, today’straction control unit TCU has CAN communication platform, which communicates with all accessories e.g., Cluster, Vehicle control unit, Battery management system BMS, CAN plays vital role in vehicle network management for program flashing. The focus of this project to check effectiveness CAN protocol in TCU withrespect to e-motor functions to have commandcontrol and atomize its functions andacknowledge the status and error from the TCU, an addition to that managing the all data received from the system in a cloud management is to be performed here with example of electric motor and illuminating devices. Cloud data storage will be help full forindustries and owner of object in securities. Adding CAN bus with Raspberry-pi for many applications is the aim of this project by addingnecessary futures.

A Fuzzy-based Sliding Mode Control Approach for Acceleration Slip Regulation of Battery Electric Vehicle

Due to quick response and large quantity of electric motor torque, the traction wheels of battery electric vehicle are easy to slip during the initial phase of starting. In this paper, a sliding mode control approach of acceleration slip regulation is designed to prevent the slip of the traction wheels. The wheel slip ratio is used as the state variable for the formulation of system dynamics model. The fuzzy algorithm is utilized to adjust the switch function of sliding mode controller. After stability and robustness analysis, the sliding mode control law is transferred into C code and downloaded into vehicle control unit, which is validated under wet and dry road conditions. The experimental results with a small overshoot and a quick response during starting indicate that the sliding mode controller has good control effect on the slip ratio regulation. This article proposes an acceleration slip regulation method that improves the safety during acceleration for battery electric vehicle.

A Fuzzy Logic Approach on the Evaluation of Driving Styles and Investigation of Drivability Calibration Effects

Increased customer expectations lead the automobile manufacturers to develop innovative solutions, such as mode selection functions that provide different performance and comfort settings for the drivers. Almost all brands have different types of driving modes installed on their vehicles, such as sport mode, economy mode, off-road mode, etc. In the current technology, the mode selection is manually done by the driver. Thus, no effort is taken to match the driver style with available driving modes. However, driving mode selection should be done through an intelligent system such as vehicle control unit, in order to optimize customer expectations related to vehicle performance, driving comfort, and fuel consumption. This can be achieved by the analysis of all drivability maneuvers during any driving cycle. Based on the results of these analyses, drivability calibration settings of the vehicle can be adjusted depending on driver behaviors. In addition, fuel consumption can be improved using suitable calibration for each driver type. In this study, an experimental investigation is carried out in which vehicle data is collected for eleven different drivers at three different drivability calibrations. Furthermore, fuzzy logic algorithms are utilized in order to distinguish the driver characteristics. First, data from nine drivers are used in order to train the fuzzy logic approach. Then, the trained fuzzy logic scheme is used to assess the characteristics of two other drivers, who were left out in the training data set. Hence, it is aimed to obtain an intelligent prediction procedure that can estimate the characteristics of a driver based on their driving styles.

Real-time data-driven fault diagnosis of proton exchange membrane fuel cell system based on binary encoding convolutional neural network

The performance of proton exchange Membrane fuel cell (PEMFC) fault diagnosis system plays an important role in normal operation of PEMFC. Therefore, a new fault diagnosis algorithm based on binary matrix encoding neural network called BinE-CNN is proposed. In BinE-CNN, high-dimensional features are extracted through binary encoding, and the feature maps are transferred to a convolutional neural network (CNN) to realize seven-category fault classification. For development of BinE-CNN, a PEMFC model is modeled to generate simulative datasets. Simulative test precision and Frames per second (FPS) of BinE-CNN have reached respectively 0.973 and 999.8 (better than support vector machines (SVM), long short-term memory neural network (LSTM), etc.). In experimental verification section, fault datasets are collected during bench test. After that, BinE-CNN is deployed on vehicle control unit (VCU) to verify its engineering value (real-time and precision). The result meet both requirements, with time cost of 96.15 ms and precision of 0.931 . • Binary encoding (BinE) is used as data preprocessing to extract abstract details. • A multi-classification multi-level fault diagnosis algorithm: BinE-CNN is designed. • AVL CRUISE M is used to generate simulation fault datasets. • BinE-CNN is translated into C language and deployed on a VCU (Infineon TC275). • An over-120kW FCS to used verify the real-time and precision of BinE-CNN.

Integrated Velocity Prediction Method and Application in Vehicle-Environment Cooperative Control Based on Internet of Vehicles

Rapid progress has been gained in the field of advanced communication technologies, which also promote parallel developments in the Internet of Vehicles (IoVs). In this context, vehicle-environment cooperative control can be integrated into next-generation vehicles to further improve the vehicle's performance, in particular energy efficiency. Accurate prediction of future velocity profiles on basis of IoVs can be a critical breakthrough, which can contribute much to vehicle operation efficiency promotion. In this paper, an integrated velocity prediction (IVP) method fully taking advantage of IoVs is proposed and demonstrated through a case study. In the IVP method, both the macroscopically and microscopically predicted velocity profiles are considered. The macroscopic velocity profiles are predicted via traffic flow analysis (TFA) in multi-access edge computing units (MECUs) which are situated alongside the route. Microscopic velocity profiles are forecasted through Mondrian forest (MF) algorithm in the on-board vehicle control unit (VCU). Final velocity prediction is generated through combination of the macroscopic and microscopic profiles in frequency domain in on-board VCU through fast Fourier transform (FFT) and inverse FFT. A case study validates the distinguished performance of IVP method and demonstrates its significant contribution to vehicle performance improvement.

Modeling and thermal management of proton exchange membrane fuel cell for fuel cell/battery hybrid automotive vehicle

The proton exchange membrane fuel cell (PEMFC) stack is a key component in the fuel cell/battery hybrid vehicle. Thermal management and optimized control of the PEMFC under real driving cycle remains a challenging issue. This paper presents a new hybrid vehicle model, including simulations of diver behavior, vehicle dynamic, vehicle control unit, energy control unit, PEMFC stack, cooling system, battery, DC/DC converter, and motor. The stack model had been validated against experimental results. The aim is to model and analyze the characteristics of the 30 kW PEMFC stack regulated by its cooling system under actual driving conditions. Under actual driving cycles (0–65 kW/h), 33%–50% of the total energy becomes stack heat; the heat dissipation requirements of the PEMFC stack are high and increase at high speed and acceleration. A PID control is proposed; the cooling water flow rate is adjusted; the control succeeded in stabilizing the stack temperature at 350 K at actual driving conditions. Constant and relative lower inlet cooling water temperature (340 K) improves the regulation ability of the PID control. The hybrid vehicle model can provide a theoretical basis for the thermal management of the PEMFC stack in complex vehicle driving conditions.

Implementation of an Autonomous Overtaking System Based on Time to Lane Crossing Estimation and Model Predictive Control

According to statistics, the majority of accidents are attributed to driver negligence, especially when a driver intends to lane change or to overtake another vehicle, which is most likely to cause accidents. In addition, overtaking is one of the most difficult and complex functions for the development of autonomous driving technologies because of the dynamic and complicated task involved in the control strategy and electronic control systems, such as steering, throttle, and brake control. This paper proposes a safe overtaking maneuver procedure for an autonomous vehicle based on time to lane crossing (TLC) estimation and the model predictive control scheme. As overtaking is one of the most complex maneuvers that require both lane keeping and lane changing, a vision-based lane-detection system is used to estimate TLC to make a timely and accurate decision about whether to overtake or remain within the lane. Next, to maintain the minimal safe distance and to choose the best timing to overtake, the successive linearization-based model predictive control is employed to derive an optimal vehicle controller, such as throttle, brake, and steering angle control. Simultaneously, it can make certain that the longitudinal acceleration and steering velocity are maintained under constraints to maintain driving safety. Finally, the proposed system is validated by real-world experiments performed on a prototype electric golf cart and executed in real-time on the automotive embedded hardware with limited computational power. In addition, communication between the sensors and actuators as well as the vehicle control unit (VCU) are based on the controller area network (CAN) bus to realize vehicle control and data collection. The experiments demonstrate the ability of the proposed overtaking decision and control strategy to handle a variety of driving scenarios, including a lane-following function when a relative yaw angle exists and an overtaking function when the approaching vehicle has a different lateral velocity.

A Software-in-the-Loop Simulation of Vehicle Control Unit Algorithms for a Driverless Railway Vehicle

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.

Securing the Communication between Automotive Grade Processors

The project proposes about securing communication between Electronic Control Units (ECUs) in vehicles. ECUs are the microprocessors which are used in the vehicles/automobiles and these processors are also termed as automotive grade processors. Few of the automotive grade processors are “Renesas Processors”, “Raspberry Pi Processors”, “Freescale Processors”, etc. The goal of the project is to incorporate secure data encryption and decryption measures and its impact on the system hardware along with the increase in communication latency. A danger that has been insulant tended to in existing vehicle security assaults, in which the enemy compromises the program of ECUs. In most cases, the assailants overseen to remotely abuse vulnerabilities found on web empowered ECUs such as the media transmission unit or the infotainment framework and from there along the side move through the in vehicle organize and take over security basic components such as the brake, steering wheel and the motor control ECUs. Due to the criticality of such occurrences and the expanding level of security dangers against vehicles, there's a critical got to address the security concerns of inner and outside vehicular communication to ensure the security of travelers. Hence it is vital to maintain safe and secured transmission of data in mobiles.

Design Requirements of Solar Powered Plug In Hybrid Electric Vehicles

The Paper is focused to give the design requirements of SPPHEV (Solar Powered Plug in Hybrid Electric vehicle), which is one of the solution to reduce the air pollution. As transportation in India is mainly dependent on Fossil fuels to drive the vehicle. Installation of Solar panels on the roof top of Electric vehicle is proposed in this paper which helps to adopt the full range electric vehicles in near future. The proposed model is solar powered PHEV (SPPHEV) in which the vehicle battery gets charged with multiple energy sources specifically Power from Photo voltaic (PV), Grid power, Regenerative power and Engine power. Vehicle Control Unit is designed to standardize the flow of power from the energy sources available and also to monitor SOC (State of Charge) of the battery.

Real-Time Energy-Efficient Driver Advisory System for High-Speed Trains

An on-board energy-efficient driving advisory system is proposed for a high-speed train, which runs on a vehicle control unit. A data interaction framework is presented to monitor train real-time state and temporary speed limitations. A bilevel optimal control method is designed to calculate the energy-optimal speed profile in real time. Specifically, the upper level is to calculate the min-time speed profile based on Pontryagin’s maximum principle, and the lower level is to determine coast profiles according to the Lagrange multiplier technique. The driving advice is extracted from the optimized trajectories. If the deviation of the train trajectory is detected, the speed profile is updated online. To highlight the achievable improvements of the proposed optimal control method, a pseudospectral method and a quadratic programming method are introduced as performance comparison. Simulation results show that the proposed method yields an energy-optimal solution within a control cycle of 256 ms. The driver-in-the-loop tests prove that the driving advice can be exactly followed by drivers. The proposed driver advisory system can improve the performance of punctuality and energy efficiency.

A Torque Demand Model Predictive Control Approach for Driving Energy Optimization of Battery Electric Vehicle

In this paper, based on model predictive control algorithm, a torque demand control approach is proposed to optimize driving energy consumption of battery electric vehicle, which consists of demand control approach and model predictive controller. The demand control approach is developed to compute the driving mode and the desired vehicle speed. For the design of control law, a novel driving dynamics model of battery electric vehicle is formulated into a set of differential equations by vehicle speed, the front and rear wheel speed. A model predictive control law is designed to compute the optimized torque of electric motor. The torque demand model predictive control algorithm is downloaded into vehicle control unit, which is equipped on the battery electric vehicle for experimental validation. The New European Driving Cycle is utilized to test the control law on the real road. The experimental results indicate that the proposed model predictive controller has a preferable performance in reducing energy consumption, which can improve 1.81% over the original control strategy in the urban road cycle and 1.67% in the city highway condition. It can be considered that the torque demand model predictive control approach is a good candidate for driving energy optimization of battery electric vehicle.

Research on Vehicle Control Strategy and Hardware in Loop for Pure Electric FSAE Vehicle

As the environment protection and energy shortage drawing more and more attention, the development of new energy vehicle, especially electric vehicle has become an important tendency in vehicle industry. Affected by this trend, electric racing events has been held in Formula SAE (FSAE) in China since 2013. To achieve the testing of vehicle control strategy before debugging, enhance develop-efficiency, reduce the risk of vehicle debugging, this paper exploited a cost-effective, perfectly functional and easily operational HIL test platform for vehicle control unit. Firstly, this paper designed a reliable electrical system and safety circuit of vehicle according to racing rules, selected type of drive motor and high voltage battery pack. The software Cruise was used to simulate and check the correctness. After then, the vehicle control unit (VCU) including bottom drive module and control algorithm was built by means of rapid prototyping software development platform. Among this, the dynamic control algorithms as energy regeneration, yaw stability control in switching, and acceleration slip regulation were came up with and employed in the VCU aiming at the characteristics of racing car during contest. At last, the economical and applicable HIL system was studied and developed according to the need of vehicle control strategy testing. The joint simulation of Carsim and Simulink was used to check dynamic control algorithm. The HIL further verified vehicle control algorithm through loading program to VCU. The open-loop check to basically logic algorithm of VCU was conducted by setting component parameters and operating signal of driver in upper machine. The closed-loop test to control strategy of VCU was carried out through loading Simulink model of vehicle to TI 28335 development board.

Parameterized Energy-Optimal Regenerative Braking Strategy for Connected and Autonomous Electrified Vehicles: A Real-Time Dynamic Programming Approach

This paper presents a vehicle speed planning system called the energy-optimal deceleration planning system (EDPS), which aims to maximize energy recuperation of regenerative braking of connected and autonomous electrified vehicles. A recuperation energy-optimal speed profile is computed based on the impending deceleration requirements for turning or stopping at an intersection. This is computed to maximize the regenerative braking energy while satisfying the physical limits of an electrified powertrain. In automated driving, the powertrain of an electrified vehicle can be directly controlled by the vehicle control unit such that it follows the computed optimal speed profile. To obtain smooth optimal deceleration speed profiles, optimal deceleration commands are determined by a parameterized polynomial-based deceleration model that is obtained by regression analyses with real vehicle driving test data. The parameters are dependent on preview information such as residual time and distance as well as target speed. The key design parameter is deceleration time, which determines the deceleration speed profile to satisfy the residual time and distance constraints as well as the target speed requirement. The bounds of deceleration commands corresponding to the physical limits of the powertrain are deduced from realistic deceleration test driving. The state constraints are dynamically updated by considering the anticipated road load and the deceleration preference. For validation and comparisons of the EDPS with different preview distances, driving simulation tests with a virtual road environment and vehicle-to-infrastructure connectivity are presented. It is shown that the longer preview distance in the EDPS, the more energy-recuperation. In comparison with driver-in-the-loop simulation tests, EDPS-based autonomous driving shows improvements in energy recuperation and reduction in trip time.

The Migration of Engine ECU Software From Single-Core to Multi-Core

As multiple functions have been added to single-core-based engine electronic control units (ECUs) in vehicles, automotive researchers and manufacturers have actively studied multi-core architecture for engine ECUs. Multi-core architecture can provide load balancing and parallelism that can meet the requirements of international organization standard (ISO) 26262. However, since real-world engine ECUs have the most complex automotive open system architecture (AUTOSAR)-based control logic and datasets among automotive ECUs, developing multi-core-based engine ECUs is a substantial amount of work. Thus, automotive researchers and manufacturers will need new methodologies for multi-core-based engine ECUs. In this paper, we focus on designing a multi-core migration methodology and applying it to a real-world AUTOSAR-based engine ECU from HYUNDAI. We verify its practicability and enhanced performance. In conclusion, through connection with other automotive domain ECUs, it is demonstrated that a multi-core engine ECU using our migration technology can be applied in real-world automotive vehicles, leading to a significant improvement in performance.

Development of mobile car black box with synchronous action of vehicle control unit and android mobile using berlinite material

A caution for motorist, a new technique is developed to prevent accidents. The accidents are mostly occurs due to three reasons such as: drunk and drive, no medical assistant as early as possible and speaking over the mobile phones. As taken this into account a smart helmet is designed to prevent accidents. The drunk and drive is prevented by using Alcohol sensor(MQ2).The fastest medical assistant can be given by using Piezoelectric plate made of Berlinite material and PIC Microcontroller 16F887. The major accidents occur only when motorist speak over the mobile phone. To avoid this mobile phone is connected to the PIC microcontroller via Bluetooth and Bluetooth and PIC Microcontroller is connected using UART. Initially the call will be cut two times and if it is important to caller, the caller will be directed for some information and it is indicated to the motorist using LED-BIGGY and a buzzer.