Electronic Components and Systems for Automotive Applications

Every second year the French Society of Automotive Engineers organizes in Paris the SIA-CESA conference on Electric Components and Systems for Automotive Applications. It addresses actual questions in the domain of all electronics components and systems with the aim to give a market insight and open the floor for business discussions

Car electrification, autonomous driving and connectivity are shaping the future of cars in a new mobility. Renault is facing this challenge with a complete set of electric vehicles, various concept cars and in particular initiatives in the shared mobility domain.

The French Automotive Organization PFA, in order to appreciate the evolution of CO₂ emissions in the world by 2035, have built with the BDO-BIPE, a projection model of the parks, sales and technological mix of light vehicles and trucks. The results of the model, is produced annually in order to integrate recent changes in the different markets and to take into account the most up-to-date and plausible regulatory and technological developments over the time horizon.

Digitalization is proving to be a game changer in bridging the gap between the heterogeneous skills and markets. It increases productivity through optimisation over the entire supply chain and lets new services emerge through the convergence of applications domains. In this paper, we are providing a review of the main automotive trends and are highlighting how digitalization (especially by information communication technologies—ICT) is supporting, even pushing innovation. We are especially mapping to the IOT4CPS and SCOTT projects to present key results related to Internet of Things supporting the digital transition in the automotive domain.

The world’s largest car market with currently about 24 million new registrations (total vehicle market 26 million vehicles) per year relies more and more on electric mobility. Thus, in principle, a (pre-)decision for the automotive industry worldwide regarding new drive concepts has been made. Also, by the end of 2017, more than 80% of the buses in China’s megacities had already been converted to electric operation. By 2020, there will be more than 1.5 million electric cars and plug-in hybrids sold in China, with almost 54,500 electric cars sold in Germany in 2017, 117% more than the year before, yet Germany and Europe lag far behind. These are all irreversible signs that China is consistently advancing towards (E) automotive market leadership on the road to electrification of transport. Including topics such as disposal of lithium-ion cells, construction of charging infrastructure (target is one pillar per NEV per km in a few years), enough battery production capacity, stringent fuel consumption regulations and quotas for electric cars.

Nanomakers is a French start-up that manufactures at industrial scale four types of nanosilicon for high capacities Li-ion batteries for electric vehicles (EV): Those ultra fine particles are produced with narrow particle size distribution by laser pyrolysis. This process is reproducible, robust and the particles have low oxygen contents. The existing Li-ion technology uses for the anodes graphite with 350 mAh/g capacities. Nanomakers particles enable the manufacturing of high capacities Li-ion batteries anodes: 800 mAh/g along with a good capacity retention along cycling (>100 cycles). For silicon anodes, various formulations are studied but the most promising uses pre-composite in which the silicon is dispersed in a carbon matrix which is then partly decomposed by a thermal treatment. It is processed at a much lower temperature than graphitization processes. This micronic pre-composite can then be used as a high capacity raw material for anodes without changing anode preparation existing processes. The use of carbon-coated particles improves the anode performances during cycling, especially the capacity stability. This presentation compares the results obtained with various preparations and types of particles. The carbon coating has a positive impact on the batteries performances.

Batteries (mostly Li-ion) for electric and hybrid vehicles are integrated in a metal structure (battery compartment) associated with the chassis. A dedicated electronic (called BMS, Battery Management System), controls and balances the voltage between the different elements of the battery. However it appears this monitoring does not cover all the failures mode of the battery or at least it is possible to better anticipate their occurrence. Glycol water mixture coming from the cooling system, abnormal concentration of methane, overheating, shocks the battery compartment would have absorbed and presence of smokes are all relevant of a battery failure. As a supplier of metal envelops for the battery, DURA has developed a complete system to detect those threats, in particular the liquid and the smoke detection systems. The set of sensors is placed in the battery compartment, while the ECU which manages them and integrates the various strategies and also communicates the resulting information to the vehicle is located outside this compartment.

Batteries development has changed through the past years with the emergence of electric vehicles. The sustainability of the battery must be ensured along with its safety as they deliver high voltage and the technology used is more difficult to control. Moreover, these batteries may be used in full autonomous vehicles, with no direct human control. In this paper we will show how to set up a complete workflow from the safety requirements down to the software implementation while fulfilling the ISO 26262 objectives. We also show the benefits of using a model-based approach and the gains that can be obtained on some testing or reviews activities. Finally, we complete the flow with the integration within a full-virtual system, which allows for implementing the hardware design, as well as the integration testing.

Silicon Carbide (SiC) components have been used for a number of years in industrial applications and now are progressively entering the automotive segment. The first devices of this wide-bandgap material to be used in vehicle applications were SiC diodes in on-board charger (OBC) applications. The excellent SiC diode switching performance is ideal for the freewheeling path in the power factor correction stages. The market for automotive grade SiC components is showing impressive growth aligned to the rise in electric vehicles with SiC MOSFETs being used in traction inverters as well as OBC or DC-DC converter applications. This paper explains the significant advantages that wide-bandgap technologies have compared to well established Si technologies. This applies especially for the total cost of ownership approach, not considering the single device only but the complete application or interaction between different components. When a new semiconductor material is introduced into the automotive sector, the stringent qualification requirements mean that a special focus is needed on the manufacturing strategy. This paper also describes a manufacturing strategy designed to serve the automotive market, the key reliability factors and how improvements have been achieved.

During the funding project EmPower a new embedding concept was developed for integration of power components. The embedding technology shows promising potential for automotive applications regarding high miniaturization potential as well as good electrical and thermal performance. Additionally the concept provides good reflow stability and thermal shock behavior. This could be realized with the demonstrators of the EmPower project and single chip test vehicle for deeper investigation of the potentials of this embedding technology according AEC-Q101 requirements. High robustness on power cycling was an outstanding exploration result.

A new approach for quantifying “good enough” for the behaviour of autonomous vehicles (AVs) is presented here. It is deducted from general behaviour guidelines for (human) behaviour in traffic, and especially the wording of §1 of the German Traffic Code (“Straßenverkehrsordnung, StVO”) is taken into account. The German traffic code generally requires constant caution and mutual consideration; in a second paragraph, it more specifically requires, that “no one is harmed, endangered or unnecessarily hindered or bothered.” The here presented approach proposes a way how to specify and finally quantify those key words. These quantifications can be derived from normal (i.e. safe) traffic practice, which has ever since been established based on the human capabilities to cope with traffic situations. First, this quantification defines the guidelines for a safe own behaviour of an AV, for normal (avoid hindering or bothering others, but never endanger them) and extraordinary (never harming others) traffic situations. It establishes behaviour rules and thus testable performance guidelines for a single vehicle. Second, based on the fact, that everybody should comply with §1, certain behaviour of traffic partners can be trustfully expected in any interactive traffic situation (even an AV should not be harmed or endangered by any other traffic participant), but a safe and robust reaction on (rare) expectable situations (collision free, if only being hindered or bothered by other participants) needs to be ensured. The quantifications can help to define the functional design space for AV behaviour in normal traffic scenarios, with the goal of “not endangering” traffic partners. This leads to more robust behaviour than the “no collision” goal for extraordinary situations. The concept allows to deduct testable performance goals in reference situations, with reasonable passing criteria. The quantification of the key words is mainly based on the reaction times for safety-relevant actions in traffic scenarios, in combination with manageable reaction patterns. Human reactions are generally limited by necessary perception, interpretation, reasoning and action times. Thus, the requirement for AVs must ensure equal or better total performance. This cannot (and is not required to) be tested in every conceivable situation; but typical traffic reference situations must be agreed on, which define borderline cases. Some outer conditions must be summarized by testing for the extreme case. Any testing procedure should include verification of the ability for autonomous stopping; for the worst case of total sensor loss under extreme weather conditions, the “Blind stopping procedure” is proposed. It must be accepted that there exist conceivable situations, in which a single traffic participant will not be able to avoid an accident (loss of own controllability), once a situation has evolved to a certain criticality. The quantifications help to draw a limit line between “avoidable” and “unavoidable” (force majeure, natural disaster) accidents. In order to stay away as much as possible from getting into such uncontrollable situations, an AV needs to demonstrate a perception of “level of danger” and a sense for its own capabilities (“self-awareness”). Testing for these skills should be part of release and certification procedures.

This paper presents the first results of the publicly funded ECSEL project DENSE (Adverse weather environmental sensing system). DENSE seeks to eliminate one of the most pressing problems of automated driving: the inability of current systems to sense their surroundings under all weather conditions. The task in DENSE is to develop a sensor suite for automatic driving, by means of which the vehicle environment can be reliably detected 24/7 under these bad weather conditions. In this paper, the state of the art of environmental sensor technology is first examined and evaluated in the CEREMA weather chamber. Then, the architecture of the DENSE Sensor Suite is presented and the development results of the most important system components are described. The results show that the realization of a 24/7 all-weather sensor suite is absolutely feasible with these components .

The LIDAR is seen as a key enabling technology for future autonomous transportation. The specification and performance of LIDAR devices are crucial to enable safe perception algorithms using a combination of several sensing technologies. Nevertheless, do existing LIDAR devices fulfill automotive expectations? We have tested a set of LIDARs covering the most representative technologies available on the market. This text reviews some relevant results showing limitations and advantages of this technology in the context of autonomous vehicles.

How will machines and plants be maintained in the future? Opinions differ widely. Some are propagating industry 4.0 as a key technology, others are very skeptical about the huge data collection needed for this approach. However, a lot of machine data is already available today—it only needs to be used intelligently. Thanks to the latest boost in the field of neural networks empowered by the availability of computing power at low cost, there are great opportunities for condition monitoring and predictive maintenance.

In this paper we present a novel approach for a future automotive embedded high-performance computing (eHPC) platform. The platform is based on an European Processor Initiative (EPI), Common Platform (CP). This paper also gives an overview of the automotive industry’s challenges and the general architectural aspects of EPI CP.

Over the last two decades, various proprietary modifications have been applied to Ethernet technology to enable deterministic communication required for time-critical systems. Time-Sensitive Networking (TSN) standards are under development by the Institute of Electrical and Electronic Engineers (IEEE) and address hard timing requirements of Ethernet-based distributed applications. The main objective of these standards is to offer an open and vendor-independent networking infrastructure for mixed-critical applications. The support of mixed-criticality reduces heterogeneity of networking technologies and simplifies not only the development but also the integration of distributed applications. However, the scheduling and verification of these networks require advanced expertise and are time-consuming. In this paper, a graphical modeling framework is introduced which facilitates scheduling synthesis and configuration of TSN networks. The created graphical models are automatically translated into a network knowledge base, which is the core of the framework. It is used to process queries regarding infrastructural verification and selective information extraction in order to build scheduling constraints.

This paper looks at the relation between IOT (Internet of Things) assisted automated driving and the challenge of air pollution causing mobility access restrictions for European cities. After a brief discussion of the EC regulatory framework, the need for additional pollution data will be described suggesting to shift existing single-source air quality models to 2D and even 3D measurement platforms for improved air quality monitoring. Finally, the role of telecommunication and ICT industry for automated driving solutions will be elaborated, following by the description of pilot services contracted in the Horizon 2020 EU funded Autopilot project, currently under project execution in several European large-scale pilot sites.

With the fast-ongoing evolution towards C.A.R.E. vehicles (Connected, Automated, Ride-Sharing, Electrified) interior arrangements are deeply transformed. Both seats and surfaces as part of the interior are becoming populated with actuators and sensors in order to make them truly adaptive and predictive to the various situations encountered while driving. This evolution leads to the perception of the full interior as an extensive “Human Machine Interaction (HMI)” system providing customized comfort, safety, smart and enjoyable life-on-board in all situations. The main benefit for the car users is utilizing the driving time for productive activities in continuity with their occupations outside of the car. This paper explains the end-to-end architecture (“Cockpit Intelligence Platform”—CIP) views of Faurecia for such holistic interior HMI solutions. Faurecia is a worldwide leader in car interiors which has created an extensive partnership network in the last years in order to meet these new challenges. Faurecia demonstrated its concrete solutions and innovations through prototypes and demonstrators at the Frankfurt Motor Show 2017, Las Vegas CES 2018 and recently at the Paris Motor Show, where the company attracted a considerable attention from the automotive community.

Today, the automotive sector is under a huge transformation in the use cases and services due to two main vectors: automated driving and connectivity. Cars enter into the world of the Internet of Things to offer new experiences. The connected car has become an enhancer for some already existing use cases, an enabler for use cases that today have technical limitations and an opportunity for others where the automotive sector was not participating. There are huge expectations on 5G to bring up new opportunities thanks to the vertical approach taken this time, completely different from the previous mobile generation networks. The automotive is considered as one of the key verticals. The new paradigm for the Intelligent & Connected Vehicle is based on: Some of the key technology enablers, which can modify the use cases and the business models, will be exposed.

Future Mobility enabled by Autonomous driven vehicles, ITS (intelligent transportation system), 5G and V2X will bring more comfort, convenience while travelling, but in first place Safety. Most significant is accident avoidance with 96%. A lot of saved lives and material damages will be the benefit for the whole society. Huawei strives to contribute in many segments of future mobility. As a traditionally communication vendor Huawei is market leader in 5G and V2X, but with recent release of AI computing portfolio including processing brain for autonomous driving called MDC (mobile data center) has opened a new chapter by contributing directly in Automotive industry.

Symbol of the 20th century economy, the automobile is one of the mass consumer products that has impacted society as a whole. Commonly associated with the notion of freedom, cars are often considered as more than just a mean of transportation. Indeed, they represent a private area in which people can enjoy a form of autonomy of decision, without encountering any external interferences. Today, as connected vehicles move into the mainstream, such a vision no longer corresponds to the reality. In-vehicle connectivity is rapidly expanding from luxury models and premium brands to high-volume midmarket models, and vehicles are now massive data hubs. Not only vehicles, but drivers and passengers are also becoming more and more connected. As a matter of fact, many models launched over the past few years on the market integrate sensors and connected on board equipment, which may collect and record, among other things, the engine performance, the driving habits, the locations visited, and potentially even the driver’s eye movements, or other biometric data for authentication purposes. Such data processing is taking place in a complex ecosystem, which is not limited to the traditional players of the automotive industry, but is also shaped by the emergence of new players belonging to the digital economy. Aware of the issues at stake for the protection of motorists’ privacy in this ecosystem, the Commission Nationale de l’Informatique et des Libertés (CNIL)—the French data protection authority—developed a reference framework enabling professionals to comply with the General Data Protection Regulation (GDPR), applicable as from 25 May 2018, thus making the compliance simpler and ensuring that users enjoy transparency and control in relation to their data.

The General Data Protection Regulation (GDPR) is in force since May 25 2018. This has an important impact on the design and development of new technologies based on exploitation of data, in particular private data. Not complying with the requirements can have high financial consequences up to 4% of the worldwide turnover of a company as a fine. At first glance, GDPR seems to be a constraint. However the, this regulation offers also the opportunity to develop new services based on big data processing and become competitive in a domain, which is considered being the petroleum of the 21st century. The paper describes the impact and opportunities of the GDPR on the Internet of things (IoT) and connected cars by highlighting two examples:

Accident risk assessment is a research field that has been started in the 60’s, in particular on the basis of «risk triangle» theory proposed by Frank E. Bird in 1969. This theory of risk uses the notion of «near misses» observation that leads to «safety rules» in a large number of application fields such as manufacturing, firemen, etc. Note that this approach is the opposite of what some automotive teams are currently starting (observing «everything» recorded on a big database): Most of the time nothing happens (no incident, no surprise). Risk experts do not do this. They are focused on «near misses» observation only, instead of observing the global process where at least 99.99% of data are not relevant for risk assessment. And observation of accidents is not relevant neither: accident is a rare combination of «near miss» situation and bad luck (stochastic process). Risk experts work on safety rules made to keep the situation as far as possible from near miss. In the domain of road safety, this approach has been developed mainly by road infrastructure experts in order to understand deep and deterministic reasons of near misses and propose ways of shaping roads and road signs in order to minimize it. This paper deals with presentation of results of a 15 years long collaborative research that extracted knowledge from road infrastructure experts and researchers, and put it into an on-board knowledge-based artificial intelligence in order to score driving risk dynamically and in real time. Indeed, there is no inherently bad driving behaviour and there is no inherently dangerous infrastructures, contrary to what many people think (black spots, harsh/brutal driving behaviour, etc.). It is when driving behaviour is inappropriate to driving context that driving risk appears. Driving context is described by infrastructure characteristics (geometry and functionality), by location of other users of the infrastructure (cars, trucks, bicycles, pedestrians, …), by weather conditions, etc. In this list, infrastructure takes 75% of the global weight, and then the artificial intelligence uses the digital map, and does pattern recognition on this digital map, in order to describe infrastructure context. ADAS sensors give additional inputs such as inter-distance, size of free space, time to collision, visibility measurement … Then driving risk depends dynamically on the context (infrastructure, traffic, visibility, …) and on the driving behaviour, through a sensor plus map fusion, made by the artificial intelligence. This artificial intelligence has been integrated inside a software API called SafetyNex that is already under deployment. This tool can alert human driver before dangerous situation, letting time to smoothly slow down and avoid potential bad surprises. Of course it can also alert the AD (Autonomous driving system) in order to automate a cautious driving behaviour (in urban areas for instance). The Artificial Intelligence estimates driving risk 20 times per second and this paper shows on real uses cases how this risk assessment anticipates potential danger. In addition, risk profiles are recorded on the cloud. We think that this risk profile recording (both for human driver and autonomous car) can be a start of comparison in terms of road safety, and a good communication vector from car manufacturers to car insurance that will need «proofs» if they ever propose a special cheap pricing. It is important as car insurers are expected to pay a part of the value brought by ADAS and autonomous vehicle because they act on risk. Of course, car insurer will accept if and only if ADAS and AD reduce accident rate and cost loss. So we conclude on the economic value of artificial intelligence in connected and intelligent cars using the cloud to exchange risk data.

This paper analyzes V2X and IoT autonomous vehicles cybersecurity risks. Security assessments are a demanding activity because the auditor must gain domain specific knowledge during the assessment itself. One important aspect of investigation is the potential for the system users to inadvertently compromise security measures or privacy restrictions on data access. The autonomous driving (AD) domain is one of the first examples of application in which safety critical decisions are taken collaboratively by system users and Artificial Intelligence algorithms. Thus while the AD system must help the user driving and the driver/passenger to be able to control the autonomous car overall behavior, the on board security system must avoid rogue users from compromising the travel safety and availability first while also guaranteeing the user privacy. In this context security and safety can mandate seemingly contradicting requirements and also usability can be impaired by security measures. AD projects are trying to evolve risk assessment methodologies in order to deal with such scenarios. Moreover the introduction of IoT technologies into the AD context (e.g. the Autopilot project) is an important vulnerability that has to be taken into account. This paper describes an example in which a standard framework, designed for Industrial Automation Control Systems to V2X and IoT autonomous driving, is employed while keeping into account the AD and IoT requirements.