Wanli Chang

System architecture and software design for electric vehicles

This paper gives an overview of the system architecture and software design challenges for Electric Vehicles (EVs). First, we introduce the EV-specific components and their control, considering the battery, electric motor, and electric powertrain. Moreover, technologies that will help to advance safety and energy efficiency of EVs such as drive-by-wire and information systems are discussed. Regarding the system architecture, we present challenges in the domain of communication and computation platforms. A paradigm shift towards time-triggered in-vehicle communication systems becomes inevitable for the sake of determinism, making the introduction of new bus systems and protocols necessary. At the same time, novel computational devices promise high processing power at low cost which will make a reduction in the number of Electronic Control Units (ECUs) possible. As a result, the software design has to be performed in a holistic manner, considering the controlled component while transparently abstracting the underlying hardware architecture. For this purpose, we show how middleware and verification techniques can help to reduce the design and test complexity. At the same time, with the growing connectivity of EVs, security has to become a major design objective, considering possible threats and a security-aware design as discussed in this paper.

Automotive Cyber–Physical Systems: A Tutorial Introduction

This tutorial gives an introduction to novices in CPS and particularly highlights the basics of control theory with respect to automotive applications. The authors furthermore describe the “semantic gap” between control models and their implementation and conclude that a new CPS-oriented design approach is required.

Dimensioning and configuration of EES systems for electric vehicles with boundary-conditioned adaptive scalarization

Electric vehicles (EVs) are widely considered as a solution for efficient, sustainable and intelligent transportation. An electrical energy storage (EES) system is the most important component in an EV in terms of performances and cost. This work proposes an approach for optimal dimensioning and configuration of EES systems in EVs. It is challenging to find optimal design points in the parameter space, which expands exponentially with the number of battery types available and the number of cells that can be implemented for each type. A multi-objective optimization problem is formulated with the driving range, rated power output, installation space and cost as design targets. We report a novel boundary-conditioned adaptive scalarization technique to solve both convex and concave problems. It provides a Pareto surface of evenly distributed Pareto points, presents the group of Pareto points according to different specific requirements from automotive manufacturers and also takes the fact in EES system design into account that the importance of an objective could be nonlinear to its value. Numerical and practical experiments prove that our proposed approach is effective for industry use and produces optimal solutions.

Multi-Objective Co-Optimization of FlexRay-Based Distributed Control Systems

Recently, research on control and architecture co- design has been drawing increasingly more attention. This is because these techniques integrate the design of the controllers and the architecture and explore the characteristics on both sides to achieve more efficient design of embedded control systems. However, there still exist several challenges like the large design space and inadequate trade-off opportunities for different objectives like control performance and resource utilization. In this paper, we propose a co-optimization approach for FlexRay-based distributed control systems, that synthesizes both the controllers and the task and communication schedules. This approach exploits some FlexRay protocol specific characteristics to reduce the complexity of the whole optimization problem. This is done by employing a customized control design and a nested two-layered optimization technique. Therefore, compared to existing methods, the proposed approach is more scalable. It also allows multi-objective optimization taking into account both the overall control performance and the bus resource utilization. This approach generates a Pareto front representing the trade-offs between these two, which allows the engineers to make suitable design choices.

Cyber-Physical Systems Design for Electric Vehicles

Electric vehicles are emerging as a solution to environmental changes and transportation challenges in growing mega-cities. Compared to combustion engine vehicles, electric vehicles bring along new challenges in the CPS design. This paper gives an overview of several of these challenges and presents initial and potential solutions for the design of the electric powertrain and E/E architectures for electric vehicles. The powertrain consists of multiple complex CPS such as the battery, the electric motor, and a distributed energy management system. These components require a complex monitoring and control in order to guarantee safety and maintain a high efficiency. For this purpose, novel E/E architectures become necessary that facilitate a predictable distributed computation and communication, requiring a paradigm shift towards fully time-triggered systems. These E/E architectures will also enable novel CPS such as innovative driver assistance systems, x-by-wire control to further increase the safety and energy-efficiency of electric vehicles, and a pervasive interaction of the vehicle and the grid. Instead of focusing on the specific applications, this paper describes the prerequisite architectural changes that are necessary to implement these novel functions.

Battery- and Aging-Aware Embedded Control Systems for Electric Vehicles

In this paper, for the first time, we propose a battery- and aging-aware optimization framework for embedded control systems design in electric vehicles (EVs). Performance and reliability of an EV are influenced by feedback control loops implemented into in-vehicle electrical/electronic (E/E) architecture. In this context, we consider the following design aspects of an EV: (i) battery usage, (ii) processor aging of the in-vehicle embedded platform. In this work, we propose a design optimization framework for embedded controllers with gradient-based and stochastic methods taking into account quality of control (QoC), battery usage and processor aging. First, we obtain a Pareto front between QoC and battery usage utilizing the optimization framework. Well-distributed non-dominated solutions are achieved by solving a constrained bi-objective optimization problem. In general, QoC of a control loop highly depends on the sampling period. When the processor ages, on-chip monitors could be used to measure the delay of the critical path, based on which, the processor operating frequency is reduced to ensure correct functioning. As a result, the sampling period gets longer opening up the possibility of QoC deterioration, which is highly undesirable for safety-critical applications in EVs. Utilizing the proposed framework, we take into account the effect of processor aging by re-optimizing the controller design with the prolonged sampling period resulting from processor aging. We illustrate the approach considering electric motor control in EVs. Our experimental results show that the effect of processor aging on QoC deterioration can be mitigated by controller re-optimization with a slight compromise on battery usage.

Resource-aware Automotive Control Systems Design: A Cyber-Physical Systems Approach

As the automotive industry is entering the smart era through advancesin sensing, computation, storage, communication, and actuation technologies,a larger number of more complex control applications withbetter performances are expected to be on board. This requires an implementationplatform with abundant resources, which is undesired inthe cost-sensitive automotive domain. The implementation platform,often embedded in an Electronic Control Unit ECU and shared bymultiple applications to save cost, is mainly comprised of a processorfor computation, memory for storing instructions and data, and busfor internal and external communication. Conventionally, automotivecontrol systems are designed using model-based approaches, where thedetails of the implementation platform are ignored. Techniques thatintegrate the characteristics of implementation resources into controlalgorithms design are largely missing. Such a separate design paradigmis too conservative in resources dimensioning and utilization for modernvehicles. This article presents recently developed approaches in automotivecontrol systems design that take implementation resources intoconsideration, aiming to improve the control performances for a givenamount of resources, or equivalently, realize the required control performanceswith fewer resources. While communication resources have beenextensively explored in the literature of networked embedded controlsystems, we will focus on memory and computation resources, whichhave started to receive attention from the academic community andindustry just recently. As Electric Vehicles EVs have become a newtrend in the automotive industry, energy resources of EVs, i.e., thebatteries, are also investigated. A number of real-world applicationsvalidate the resource-aware automotive systems design techniques presentedin this article.

Reliable CPS Design for Mitigating Semiconductor and Battery Aging in Electric Vehicles

Reliability and performance of cyber-physical systems (CPS) in electric vehicles (EVs) are influenced by three design aspects: (i) controller design, (ii) battery usage, i.e., Battery rate capacity and aging effects, (iii) processor aging of the in-vehicle embedded platform. In this paper, we present a two-phase design optimization framework involving control algorithm development, battery modeling and processor aging analysis. First, before processor aging, a Pareto front between quality of control (QoC) and battery usage is obtained. Second, as the processor ages, the controller is re-optimized considering the prolonged sampling period to avoid QoC deterioration of safety-critical applications with a compromise on battery usage and comfort-related applications. An outlook following this line of research on reliable CPS design in EVs is discussed, that we believe, will be pursued by researchers in the CPS community.

Effectively utilizing elastic resources in networked control systems

The rapid growth in the size and complexity of modern Cyber-Physical Systems (CPS) imposes increasing demand for the embedded resources, especially the communication resources. As a result, resource-efficient CPS design has become an important issue. Towards the design of networked embedded control systems, a major branch of CPS, reliable and deterministic communication is able to achieve satisfactory control performance. However, the amount of this type of resource that can be provided by the embedded platform is often limited. On the other hand, it is difficult to guarantee the control performance with non-deterministic communication resources, due to their unpredictable behavior. In this paper, we propose a novel control scheme to efficiently utilize elastic communication resources. In general, the non-deterministic communication resources are flexibly deployed on top of the deterministic communication resources to achieve stability and good control performance. In the rare worst-case, when non-deterministic communication is completely unavailable, the deterministic communication resources are used to guarantee stability and the control performance satisfying the design requirement. The experimental results show that the performance of the control application is ensured to satisfy the design requirement in the worst case and that better control performance is achieved when non-deterministic resources are available.

Memory-Aware Embedded Control Systems Design

Control applications are often implemented on highly cost-sensitive and resource-constrained embedded platforms, such as microcontrollers with a small on-chip memory. Typically, control algorithms are designed using model-based approaches, where the details of the implementation platform are completely ignored. As a result, optimizations that integrate platform-level characteristics into the control algorithms design are largely missing. With the emergence of cyber-physical systems (CPS)-oriented thinking, there has lately been a strong interest in co-design of control algorithms and their implementation platforms, leading to work on networked control systems and computation-aware control algorithms design. However, there has so far been no work on integrating the characteristics of a memory architecture into the design of control algorithms. In this paper we, for the first time, show that accounting for the impact of on-chip memory (or cache) reuse on the performance of control applications motivates new techniques for control algorithms design. This leads to significant improvement in quality of control for given resource availability, or more efficient implementations of embedded control applications. We believe that this paper opens up a variety of possibilities for memory-related optimizations of embedded control systems, that will be pursued by researchers working on computer-aided design for CPS.