Electronic Control Unit Software Research Articles

Virtual Development of Advanced Thermal Management Functions Using Model-in-the-Loop Applications

Development challenges in the automotive industry are constantly increasing due to the high number of vehicle variants, the growing complexity of powertrains, and future legal requirements. In order to reduce development times while maintaining a high level of product quality and financial feasibility, the application of new model-based methods for virtual powertrain calibration is a particularly suitable approach. In this context, TME and FEV combine advanced thermal management models with electronic control unit (ECU) models for model-in-the-loop applications. This paper presents a development process for ECU and on-board diagnostics (OBD) functions of thermal management systems in hybrid electric vehicles. Thanks to the highly accurate 1D/3D-models, optimal control strategies for electrically actuated components can be developed in early development phases. Virtual sensors for local temperatures are developed for the ECU software to enable a cost-effective use of dedicated control functions. Furthermore, an application for OBD cooling system leakage detection is shown. Finally, the transferability of the methodology to a battery cooling system is demonstrated.

AI-based classification of CAN measurements for network and ECU identification

Due to the constantly increasing number of functions offered by a modern vehicle, the complexity of vehicle development is also increasing as a result. A first indication of this connection is provided by the number of ECUs (electronic control units) used in current development vehicles. Furthermore, each ECU also performs more functions and is not only electrically networked with the other ECUs, but also logically and functionally. On this basis, new cooperative functions are being developed, which are used for example for autonomous driving. In vehicle development, more and more test sequences (diagnostic scripts) are established for function testing of individual components, systems and cross-functional methods. Due to decentralization and the modular approach, modern development vehicles consist of different numbers of ECUs. The high number of ECUs in purpose and number poses a challenge for test creation and updating. The ECU software is also developed in cycles within the vehicle cycle. This results in a very high software variance. This variance leads to the fact that in the vehicle development with global test conditions works. Global test conditions at this point mean that more ECUs are included in the measurement procedure than are installed in the vehicle. The vehicle structure (control unit and its software version) is not known to the person performing the measurement. He relies on the fact that his ECUs are inside in the global measurement task. This means that the vehicle network architecture is uncertain, which can lead to errors during test execution. Since the ECUs that are actually installed in the vehicle are first determined during test execution, this results in a longer script runtime than would be necessary. To support the development engineer and prevent avoidable errors, the diagnostic system should configure itself as far as possible. This means that individually customized measurements for each vehicle should be calculated in the cloud and not the global measurement tasks. For a diagnostic system to be able to configure itself independently, the vehicle network structure must be determined in a first step. This can be done by a simple CAN measurement (measurementXY.asc). An AI is able to analyze this measurement and classify the occurring ECUs as well as CAN networks. For larger measuring devices with more than one CAN interface, the user who analyzes the measurement is interested in which CAN was connected. Here, the AI is suitable to determine the name of the network and the communicating ECUs based on the communication that runs over the bus. For this purpose, the AI classifies the number of communicating ECUs based on the time intervals at which messages are sent. In addition, the AI can be supported by a special diagnostic script (global.pattern) to determine the vehicle structure at the OBD (on-board diagnostics) interface with maximum accuracy. Three AI approaches are presented, all connected in series and passing results to each other (pipeline mode). First comes the AI that separates vehicle communication from diagnostic communication. Based on the vehicle communication, the network name can be determined. Based on the diagnostic messages, the ECUs can be determined.

Method of Differential-Based Remote Upgrade for ECUs on Vehicles*

In view of the problem that the current update flashing of the traditional automotive electronic control unit (ECU) software highly relies on professional maintenance personnel and external equipment, a method for ECU remote data flashing is proposed to achieve remote and efficient upgrade of ECU software. Furthermore, through the introduction of backup partitions and differential upgrade methods, the problems of lack of fault-tolerant processing, high bandwidth requirements and low efficiency in traditional ECU software update flashing are also solved. In this article, a remote update system is built, including the server-side and the vehicle, and the interface and communication protocols are defined based on HTTPS. The above methods have been verified on NXP IMX 6 platform. Associated testing of the simulation of the ECU by the STM32 and ROCKCHIP demo board has been made based on the CAN and Ethernet simulating the vehicle network architecture. By the real data catching from Ethernet and the CAN, the details of the log file of the programs’ event tracking information are analyzed to obtain the experimental results. The results indicated that the incremental remote data updating system built in this article can remotely update the CAN software accurately and efficiently in all simulation tests, which proved the practicability, accuracy, and efficiency of this method.

Continuous Automotive Software Updates through Container Image Layers

The vehicle-embedded system also known as the electronic control unit (ECU) has transformed the humble motorcar, making it more efficient, environmentally friendly, and safer, but has led to a system which is highly dependent on software. As new technologies and features are included with each new vehicle model, the increased reliance on software will no doubt continue. It is an undeniable fact that all software contains bugs, errors, and potential vulnerabilities, which when discovered must be addressed in a timely manner, primarily through patching and updates, to preserve vehicle and occupant safety and integrity. However, current automotive software updating practices are ad hoc at best and often follow the same inefficient fix mechanisms associated with a physical component failure of return or recall. Increasing vehicle connectivity heralds the potential for over the air (OtA) software updates, but rigid ECU hardware design does not often facilitate or enable OtA updating. To address the associated issues regarding automotive ECU-based software updates, a new approach in how automotive software is deployed to the ECU is required. This paper presents how lightweight virtualisation technologies known as containers can promote efficient automotive ECU software updates. ECU functional software can be deployed to a container built from an associated image. Container images promote efficiency in download size and times through layer sharing, similar to ECU difference or delta flashing. Through containers, connectivity and OtA future software updates can be completed without inconveniences to the consumer or incurring expense to the manufacturer.

ASFIT: AUTOSAR-Based Software Fault Injection Test for Vehicles

With recent increases in the amount of software installed in vehicles, the probability of automotive software faults that lead to accidents has also increased. Because automotive software faults can lead to serious accidents or even mortalities, vehicle software design and testing must consider safety a top priority. ISO 26262 recommends fault injection testing as a measure to verify the functional safety of vehicles. However, the standard does not clearly specify when and where faults should be injected, and the tools to support fault injection testing for automotive software are also insufficient. In the present study, we define faults that may occur in Automotive Open System Architecture (AUTOSAR)-based automotive software and propose a fault injection method to be applied during the software development process. The proposed method can inject different types of faults that may occur in AUTOSAR-based automotive software, such as access, asymmetric, and timing errors, while minimizing performance degradation due to fault injection, and without using any separate hardware devices. The superior performance of the proposed method is demonstrated through empirical studies applied to fault injection testing of a range of vehicle electronic control unit software.

Automated Scheduling for Optimal Parallelization to Reduce the Duration of Vehicle Software Updates

The needs-oriented expansion and maintenance of vehicle functions through software updates will increase drastically due to current megatrends such as autonomous driving or connected car services. Both, shorter innovation cycles and increasing software volumes resulting from these megatrends, increase update frequency and duration equally. Consequently, a reduction of update duration will become even more important in the future. Popular literature has shown that in a vehicle-internal network of up to 100 electronic control units (ECUs), the parallelization of individual ECU updates has great potential to reduce the total update duration. However, the methods presented so far do not show an optimal parallel scheduling algorithm that uses the full potential of the vehicle-internal network. In addition, some of the approaches are also limited to certain bus systems or do not have an automated parallelization strategy based on ECU requirements. The approach introduced here uses a high abstraction model for optimal parallel ECU software update scheduling. For this purpose, we developed two scheduling algorithms, one as a mixed-integer linear program and the other as a hybrid algorithm. We verified our approach in a real testing environment with up to 20 ECUs regarding update duration and computation time. Evaluation shows that the reprogramming time can be reduced by up to 77% compared to sequential update processing through optimal parallelization. Furthermore, the model is very easy to adapt to different and new vehicle generations due to its high abstraction.

Implementing an Iot Vehicular Diagnostics System Under an Rtos Environment Over Ethernet IP

Due to ever more complex electronic systems and the frequently growing volume of data in vehicles, vehicle access for diagnostics and Electronic Control Unit (ECU) programming has to be correspondingly efficient. For this purpose, communication-based on Ethernet technology has been precise with Diagnostics over Internet Protocol (DoIP). In recent times, Ethernet-based DoIP is useful to automotive systems, and in-vehicle gateways have been initiated to assimilate Ethernet with usual in-vehicle networks, such as the Local Interconnect Network (LIN), Controller Area Network (CAN) and FlexRay. The preface of in-vehicle gateways and Ethernet based diagnostic protocols not only decreases the difficulty of the networks but also reduces the renew time for ECU software reprogramming while permitting the use of a range of services, together with remote diagnostics. In this work, a remote diagnostic vehicular gateway is implemented under a Real Time Operating System (RTOS) environment for an automotive system is presented.

Real-time simulation of a turbo-shaft engine's electronic control unit

Hardware-in-the-loop (HIL) simulation is a comprehensive and repeatable manner for system-level testing of any control system. One of the first steps in an HIL simulation is to execute a preliminary real-time test where all parts of the control system are modeled numerically on separate personal computers (PC). In this paper, a real-time simulation of such preliminary real-time test has been conducted for an electronic control unit (ECU) of a gas turbine engine. The plant of the control system is a gas turbine model for a two-shaft turbo-shaft engine, loaded on an industrial personal computer. The turbine's controller is actually another computer on which ECU's software model is generated via software as well. ECU acts as a controller for the gas turbine and it ensures the operational reliability by limiting the angular speed, angular acceleration of the engine's shafts and other parameters within their allowable working range. Signal interactions between control system parts are created via data acquisition cards. Different gas turbine load functions are fed as inputs to the engine model and results are compared to those of a same control system, modeled completely on an individual PC in real time. The latter is otherwise known as software-in-the-loop (SIL) simulation. The results show the acceptable functionality of the test setup.

Fault Localization Method by Partitioning Memory Using Memory Map and the Stack for Automotive ECU Software Testing

Recently, the usage of the automotive Electronic Control Unit (ECU) and its software in cars is increasing. Therefore, as the functional complexity of such software increases, so does the likelihood of software-related faults. Therefore, it is important to ensure the reliability of ECU software in order to ensure automobile safety. For this reason, systematic testing methods are required that can guarantee software quality. However, it is difficult to locate a fault during testing with the current ECU development system because a tester performs the black-box testing using a Hardware-in-the-Loop (HiL) simulator. Consequently, developers consume a large amount of money and time for debugging because they perform debugging without any information about the location of the fault. In this paper, we propose a method for localizing the fault utilizing memory information during black-box testing. This is likely to be of use to developers who debug automotive software. In order to observe whether symbols stored in the memory have been updated, the memory is partitioned by a memory map and the stack, thus the fault candidate region is reduced. A memory map method has the advantage of being able to finely partition the memory, and the stack method can partition the memory without a memory map. We validated these methods by applying these to HiL testing of the ECU for a body control system. The preliminary results indicate that a memory map and the stack reduce the possible fault locations to 22% and 19% of the updated memory, respectively.

The Design of Model based ECU Software for Retarder System in Commercial Vehicle

This paper describes the design of model based retarder ECU (Electronic Control Unit) software which is first developed in Korea in order to apply the commercial vehicle. The ECU of retarder system has a specific control function regarding manual braking mode and automatic braking mode to optimize the braking performance of retarder system. Manual braking mode is separated into mode 1, mode 2, and mode 3 in relation to the maximum braking torque by retarder lever selection of driver. Automatic braking mode is operated by driver’s speed limitation set and the vehicle speed is kept by automatic braking control in downhill. According to the result of braking performance test through SILS (Software In the Loop Simulation) and vehicle test, it was evaluated that the ECU software was optimized for retarder system and there was no issue on the design method of model based software. This study was performed under supporting by Ministry of Trade, Industry and Energy and KIAT in Korea.

Using Schur complement for realtime automotive applications

AbstractRealtime simulation of stiff physical models of mechatronic systems within electronic control unit software functions poses amongst others the challenge to solve linear systems of equations with minimal comutational effort. For this, one key approach is exploiting the model structure. In this paper, Schur complement based reformulations are examined for a tailored exploitation of the sparsity of a pipe model on the level of linear equations. (© 2015 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Model-based optimization of injection strategies for SI engine gas injectors

A mathematical model for the prediction of the mass injected by a gaseous fuel solenoid injector for spark ignition (SI) engines has been realized and validated through experimental data by the authors in a recent work [1]. The gas injector has been studied with particular reference to the complex needle motion during the opening and closing phases. Such motion may significantly affect the amount of injected fuel. When the injector nozzle is fully open, the mass flow depends only on the upstream fluid pressure and temperature. This phenomenon creates a linear relationship between the injected fuel mass and the injection time (i.e. the duration of the injection pulse), thus enabling efficient control of the injected fuel mass by simply acting on the injection time. However, a part of the injector flow chart characterized by strong nonlinearities has been experimentally observed by the authors [1]. Such nonlinearities may seriously compromise the air-fuel mixture quality control and thus increase both fuel consumption and pollutant emissions (SI engine catalytic conversion systems have very low efficiency for non-stoichiometric mixtures). These nonlinearities arise by the injector outflow area variation caused by needle impacts and bounces during the transient phenomena, which occur in the opening and closing phases of the injector. In this work, the mathematical model previously developed by the authors has been employed to study and optimize two appropriate injection strategies to linearize the injector flow chart to the greatest extent. The first strategy relies on injection pulse interruption and has been originally developed by the authors, whereas the second strategy is known in the automotive engine industry as the peak and hold injection. Both injection strategies have been optimized through minimum injection energy considerations and have been compared in terms of linearization effectiveness. Efficient linearization of the injector flow chart has been achieved with both injection strategies, and a similar increase in injector operating range has been observed. The main advantage of the pulse interruption strategy lies on its ease of implementation on existing injection systems because it only requires a simple engine electronic control unit software update. Meanwhile, the peak and hold strategy reveals a substantial lack of robustness and requires expressly designed injectors and electronic components to perform the necessary voltage commutation.

Automotive ECU Software Design Based on AUTOSAR

Aimed at the defects and deficiencies of traditional automotive ECU (Electronic Control Unit) development methods, AUTOSAR as a new standard for automotive software development makes the software development process simplified greatly. The existing AUTOSAR development tools are studied and compared in this article to analyze of their respective characteristics, and an ECU software design scheme of the system is summarized according to the methodology. The research results prove that AUTOSAR can raise the development efficiency and software portability.

Defending Connected Vehicles Against Malware: Challenges and a Solution Framework

Vehicles face growing security threats as they become increasingly connected with the external world. Hackers, researchers, and car hobbyists have compromised security keys used by the electronic control units (ECUs) on vehicles, modified ECU software, and hacked wireless transmissions from vehicle key fobs and tire monitoring sensors, using low-cost commercially available tools. However, the most damaging security threats to vehicles are only emerging. One such threat is malware, which can infect vehicles in a variety of ways and cause severe consequences. Defending vehicles against malware attacks must address many unique challenges that have not been well addressed in other types of networks. This paper identifies those vehicle-specific challenges, discusses existing solutions and their limitations, and presents a cloud-assisted vehicle malware defense framework that can address these challenges.

Hardware-in-the-Loop System for Testing Automotive Ecu Diagnostic Software

The purpose of this paper is to present an approach to testing ECU diagnostic software using hardware-in-the-loop (HIL) systems at Jaguar Land Rover (JLR). The increasing complexity of embedded software for automotive electronic control units (ECUs) requires a convenient and flexible environment for system testing. On-board diagnostic software represents a significant proportion of ECU software and consequently, the effort, in terms of time and manpower required to determine the validity of ECU software is becoming excessive. The process for validating on-board diagnostic software is traditionally achieved using tests carried out with actual vehicles. Some tests are difficult and expensive to perform in vehicles. Automated testing offers an approach that can improve the validation process whilst providing the potential for cost saves. Since JLR utilises ECUs from different suppliers, the development of a generic approach to automated testing is vital.

Development of logical and technical architectures for automotive systems

This paper presents a modeling approach for the development of software for electronic control units in the automotive domain. The approach supports the development of two related architecture models in the overall development process: the logical architecture provides a graphical, quite abstract representation of a typically large set of automotive functions. On this abstraction level no design decisions are taken. The technical architecture provides a software and a hardware representation in separated views: the software architecture describes the software realization of functions as software components, whereas the hardware architecture models hardware ntities, on which the software components are deployed. Logical as well as technical architectures only model structural information, but no behavioural information. A tight integration of both architecture levels—on the conceptual and on the tool level—with related development phases such as requirements engineering, behaviour modeling, code generation as well as version and configuration management resulting in a seamless overall development process is presented. This architecture modeling approach has been developed within a safety-relevant project at BMW Group. Positive as well as negative experiences with the application of this approach are described.