Challenges for Qualitative Electrical Reasoningin Automotive Circuit Simulation

Neal Snooke and Chris PriceDepartment of Computer Science,University of Wales, Aberystwyth,UK

nns{cjp}Caber.ac .uk

Abstract

Qualitative reasoning about electricalsystems has reached a level of achievementwhich allows it to be used for applicationson realistic automotive circuits . The type ofcircuits for which it is most effective can becharacterised as circuits with a single steadystate for each combination of inputs . Manyautomotive circuits with more complexoverall behaviour can be approximatedusing this type of modelling by representingthe behaviour of more complex componentsonly at a functional level, or by judicioususe of simplifying assumptions .

This paper will consider examples ofcircuitry in modern cars where suchapproximations of behaviour areunsatisfactory, and will examine themodelling issues that are thrown up by thesecases, in order to identify challenges forqualitative electrical reasoning against whichfuture advances in the field can be assessed .

Introduction

Electrical circuits were among the earliest devicesto be modelled by Al researchers, givingqualitative reasoning about electrical systems alegacy of some two decades of research . Thislengthy development is beginning to paydividends in industrial applications .

Over the past six years, we have developed theFLAME system to perform failure mode andeffects analysis [Price 96, Price and Pugh 96] andsneak circuit analysis [Price, Snooke and Landry96] . Within the last two years, automotiveengineers working in consultation with researchershave applied the FLAME system to the differentcircuits in a modern car. This has provided abreadth of modelling experimentation that existsfor very few other qualitative reasoning systems.

This paper takes stock of what has been donein modelling electrical circuits, and look at whatfurther work needs to be done, based on theexperience of the FLAME system . Thisexperience is confirmed by the similar work beingdone by Mauss and Neumann [Mauss and'

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Neumann 95, Mauss and 96] in modellingautomotive electrical circuits . The paper considerswhat has been achieved in modelling car electricalsystems, the areas where improvements areneeded, and the challenge of developments in theway in which car subsystems are implemented .

Qualitative electrical reasoning -state of the art

Basic Strategy

In order to persuade automotive engineers to usequalitative reasoning, it must be painless for themto build qualitative models of the circuits as theyare being designed . This means that the qualitativemodels must use the same types of componentsthat the engineers are used to dealing with, andthat component descriptions must be reusable .

The underlying algorithms performingreasoning about electrical flow only useconnectivity information, and the circuit topologyused these algorithms will change as the state ofcomponents change . This has led to two levels ofreasoning being employed .

Higher level reasoning: From the descriptionof each component in the circuit, andknowledge of that component's state . generatea static network of connected resistancesrepresenting the electrical connectivity of thecircuit at this point in time .

Lower level reasoning: Evaluate the staticnetwork, and assign new states to eachcomponent .

These two levels are linked by a simulationcontroller which applies the changes in activityfrom the lower level to the circuit description atthe higher level and generates a new topology forevaluation at the lower level if necessary .

In our own work on the FLAME system, thelower level reasoning is based on [Lee andOrmsby 93], and is described in [Price and Pugh96] . The higher level reasoning employscomponent descriptions that describe the internaltopology of a component in different states and

under different failure modes . Examples ofcomponent descriptions are given in [Pugh andSnooke 96] . A component description consists of :

Terminals . Terminals are the inputs andoutputs for the component . So, for example,an open relay has four terminals, two to thecoil, and two to the relay switch . They are thepoints where other components can connect tothis component .

Internal topology of component . Thefunctionality of the component is determinedin terms of links between terminals . Theselinks can contain resistances which can changeaccording to the state of other parts of thecomponent .

Dependencies. Dependencies define how theinternal resistances of a component change asthe state of the other parts of the componentchange . If the open relay has two resistances,one for its coil and one for its switch, then onedependency would be that when the state ofthe coil was "Active" (current is flowingthrough it) then the value of the switchresistance is zero (the switch is closed) .

External states . As will be seen in the nextsection, there are cases where the function of acomponent cannot be completelyencapsulated, and the "no function instructure" rule has to be violated to obtain thedesired behaviour . External states makeinformation about the component's internalstate available to other components . Engineersare warned to use this feature only undercertain conditions in order to ensure that theirresults are meaningful .

Failure modes. Topology and dependenciescan be redefined for different failure modes,so that the component acts appropriately whenfailures are being simulated .

In Mauss and Neumann's work, the lower levelreasoning is based around rebuilding the staticnetwork of resistances as a series-parallel-star(SPS) tree . This allows easy evaluation of circuitactivity, and enables the use of quantitativeinformation if it is available . The higher levelreasoning does not seem as well developed as thecomponent descriptions outlined above, butcertainly could be extended to provide similarcoverage.

One area of uncertainty about the lower levelreasoning is whether Mauss and Neumann'schoice of representation is more efficient as thetopology of the network changes . There arecertainly cases where changes to circuit topologywill only mean changing a small part of the starnetwork . However, the complexity of the mapping

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between the original circuit and the SPS treemeans that it is not always possible to tell whatchanges to the SPS tree should be made for achange in circuit state, and so it may be necessaryto rebuild the SPS tree completely after eachchange .

Coping with complex behaviour

The FLAME system has been used to simulate theelectrical systems of modern automobiles, and isable to produce reasonable results for SS% of thecircuitry . For many of the circuits, somemodelling compromises are necessary, and thissection gives examples of the kinds of modellingstrategies that have been employed in order toproduce useful results .

Encapsulate complex behaviour in acomponent. This is the most common way ofdealing with complex components . Forexample, where modern cars use ECUs(computerised control) to switch circuits, it isnot necessary, or in many cases even desirable,to model the software that is performing theswitching . The relevant behaviour of the ECUcan be modelled as connections between ECUterminals with resistances that switch betweenzero and infinity . This strategy fits in well withthe two levels of reasoning outlined in theprevious section . It can also work for non-electrical components with switchingbehaviour .

Distribute complex behaviour among severalcomponents . For example, because of thequalitative nature of the simulation, effectswhich depend on quantitative values cannot bedirectly modelled . For instance, this happens insome windscreen wipe systems, where a multi-way switch changes the value of a resistance,and an ECU reads an analogue value on a lineto decide on the speed of the windscreenwiper. This can be achieved by a mild violationof the "no function in structure" principle,where you have two linked components, theswitch and the ECU . The switch sets anexternal state giving its analogue value, and theECU reads that analogue value from theswitch . It only does this if the wire on itsterminal coming from the switch is ACTIVE.This allows it to deal correctly with failureconditions such as the wire failing open.

Simplify the complex behaviour so that it ismanageable . Electronic cruise controlsystems contain fairly complex algorithms fordeciding what to do, but for examiningelectrical failures, they can be simplified tothree conditions : above desired speed, below

desired speed and at desired speed . Thisallows the basic behaviour of the cruise controlto be exercised, without consideration of muchunnecessary detail .

Ignore the complex behaviour altogether .When performing failure mode and effectsanalysis (FMEA) on the electrical circuitry of acar, it is possible to ignore some phenomenaaltogether, and still yproduce a reasonableFMEA report . Take an indicator light forexample . The light will flash on and off whenthe circuit is working correctly . However. theoverall behaviour of the circuit can beobtained by treating the lamp as a "normal"lamp rather than a flashing one .

This section has considered the compromisesneeded in order to be able to model automotivecircuits with the FLAME svstem . The next sectionwill look at how modelling can be extended tomake some of those compromises unnecessary .

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Improving Qualitative Reasoningof Electrical Circuits

The number of compromises needed in order tosuccessfully model automotive electrical circuitscan be reduced by improving the range ofphenomena that can be modelled . This sectionwill consider the areas in which qualitativeelectrical reasoning needs to be extended in orderto improve modelling capabilities and to be ableto model some of the other 15% of circuits thatcannot be usefully modelled now .

Recent automotive design

For many years most automotive circuits followedthe same basic pattern of the battery providingpower to devices such as lamps and motors via setsof switches . The circuits of some high powerdevices had indirect control via relays to lowerswitch ratings and the required amount of highlyrated cable under the dashboard (as illustrated infigure 1) .

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All this has changed recently, with many circuitsfollowing the pattern of a network of ECUs, eachattached to a number of sensors (including buttonpanels, keypads, and potentiometer for user input)and actuators . The sensor signals are thusprocessed and shared via a digital bus to othermodules all of which ultimately control someactuators performing a wide range of tasksranging from electric windows to fuel injectors .This type of system combines a number of theproblems highlighted in this section, and theseproblems will now be itemized in the context ofthe column-mirror example in figure 2 .

There are many different types of signal fromthe sensors representing quantities in differentways . Typical methods include analog, pulsedamplitude modulated, pulse width modulated,frequency modulated etc .

The digital bus connecting the modules hascomplex error detection and correction itself .Individual modules know the status of networkconnections and other modules at any point intime and adjust behaviour accordingly .

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Fail safe and/or default modes are incorporatedin many modules to manage failure situations,and many modules have power down or sleepmodes to save power.

Feedback loops exist both as indirect loops(actuators affect sensors via other physicalquantities) and direct feedback (ECU devicesdetect their own outputs or outputs of others) .

Many ECU's have complex internalempirically defined lookup tables to controlbehaviour (especially engine/gearboxmanagement) .

Many actuators have a lot of built-in drivercircuitry to provide complex control . Thiscircuitry is built into the actuator by thecomponent supplier, and has to be treated as ablack box by the circuit designer .

Figure 2 : Sensor/ ECU/ actuator based column mirror circuit

Dynamic aspects

Circuits which contain continuously changingvalues - electrical feedback loops, oscillators,power on reset circuits - cannot be directlysimulated because there is no representation ofenergy storage, or of the explicit rate of change ofthe energy variables .

Where the behaviour of components can bemade into a discrete set of states, simulation canbe achieved by using the simulator in a multi passmode, with changes being made to the topologyof the resistance network after each pass .

In this way a capacitor is modeled in either acharged or a discharged state, with either a infiniteor zero resistance, and the change of state is madebased on the existence of current flow through it .This enables the representation of the power-on-reset circuit's major change in state, along withthe correct behaviour of short or open circuits inthe circuitry surrounding the capacitor .

Other effects such as the long term effect ofdrawing charge from a battery are not currentlyconsidered, although it should be possible tomodel these . Also, if simulation was extended intoother domains such as hydraulic, the issue ofmodelling capacity would become more relevantthan it is in the electrical domain .

Functions which cannot be linked toqualitative values

Typically the qualitative values utilized in thesimulation (ACTIVE / INACTIVE, or FORWARD/ INACTIVE / REVERSE where current directionis significant) are adequate to determine thepresence or absence of function . Occasionallyhowever the qualitative value set cannot representthe required functionality . This problemcommonly occurs in situations where a resistorbank is used to multiplex a number of separatesignal functions to some control circuitry .

A related situation occurs when the `amount'of functionality is not related to power dissipation,and is not a monotonic function . Simplysimulating the boundary conditions does notallow any assumptions concerning intermediateoperation (some sensors may exhibit thesebehaviors) . In these situations the inclusion ofnew key values raises many simulation issuesperhaps indicating that a quantitative approachmay be required in these situations .

Finally in the category of functional artifactsrelated to the qualitative analysis, there aresituations where a small current indicates somecomponent is ACTIVE without taking intoaccount that the amount of current concerned istoo small to actually cause the indicatedfunctionality . Two examples of this haveoccurred in our experience of simulating circuits :

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1) An ECU leakage current caused a relay coilto activate . In practice, the size of the currentwould have been far too small to activate thecoil .

2) Failure of a ground stud caused a current toflow in a warning lamp in series with severallarge (normal) loads . In the simulation, thisresulted in lighting the lamp, although inpractice the voltage drop would be too small toproduce any light .

An improved analysis which considers thechanges in current paths and changing pathresistance as well as qualitative voltage drop mightalleviate some such problems .

Improved behavioral substitutions

Removing the non electrical behaviors from theelectrical model might help simplify thesimulation and clean up the abstraction . Theoperation of switches (mechanical), magneticeffects in relays and ECU (logical/information)might be best modeled as a more abstractbehaviour with well defined links to control thestructure .

The problems of ECU components controlledby internal empirical lookup tables can bereduced by including simple versions of these aspart of the controlling state machine . If it isrequired to model failure modes for these devicesthen additional behaviour can be included torepresent the effect of each new failure .

Alternative quantitative effort/flowmodels

Recently more circuits contain electrical structureswhich do not readily fall into the 'power electrical'category that our representation supports . Anexample of this is Control Area Network busutilized for information transfer between modulesin the automotive environment . In these situationswe are interested in the higher level effects of theencoded protocol, rather than the electrical values,which require lots of interpretation .

Digital logic signals are becoming morecommon and again have characteristics whichmake simulation as a resistance network difficultnot least due to the importance of voltage ratherthan current as the meaningful signal . We cancurrently utilize the upper level dependencyexpressions combined with a simplified electricalinterface to model combinatorial logic devicesalthough the expressions can get fairly complexeven for relatively simple devices . For sequentiallogic we have a real problem due to the difficulty

of ordering the expressions . The solution appearsto be to use the state machine representation ofsequential logic, since this approach has oftenused in the past in the design of such devices .With this approach there will be only a very muchsimplified electrical model present and in thefuture it may be necessary to move up a level ofabstraction and consider the information flow andsignal through the system rather than voltages andcurrents .

Aggregation

For some kinds of design analysis (e.g . acomplete sneak circuit analysis), it is necessary toperform simulation across the whole circuitry of acar at once. This involves simulating a circuitmuch larger than any separate subsystem circuit .Fortunately, the requirement for simulating verylarge networks has arisen at the same time as therestructuring of schematics into a hierarchicalformat . It may be possible to convert largesections of the hierarchy into a homogenizedrepresentation indicating only the equivalentresistance network . The analysis would then begreatly speeded up for each simulation, with therequirement that only parts of the hierarchycontaining failures need be simulated in detail .

ConclusionsThe automotive domain has already proven auseful testing ground for practical qualitativemodelling and reasoning about electricalcircuitry . It has provided promising results onindustrial systems, and has produced applicationsthat have been accepted by industry .

A number of aspects of the present state ofqualitative electrical simulation have beenconsidered together with examples of whereimprovement or a different approach is necessaryto deal with specific electrical constructions foundin today's automotive circuits and the challengesof tomorrow's automotive circuits .

As the complexity of modern cars increases,the automotive domain is providing challengesthat reach to the heart of the qualitative reasoningendeavour, highlighting issues ofcompositionality, modelling choices, temporalreasoning and the trade-offs that have to be madein order to apply qualitative reasoning. Weanticipate that solutions to these challenges maybe of use in other areas of` qualitative reasoning .

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AcknowledgementsThis work has been carried out on the UK EPSRCfunded projects "Flame" and "Aquavit", withthe cooperation of Jaguar Cars Ltd, Ford MotorCompany Ltd, the Motor Industry ResearchAssociation, Integral Solutions Ltd and ViewlogicLtd.

References[Lee and Ormsby 93] Lee, M. and Ormsby, A.1993 . Qualitative Modelling of the Effects ofElectn'cal Circuit Faults . Artificial Intelligence inEngineering vol . 8, 293-300 .

[Mauss 95] Mauss, J . and Neumann, B . 1995 .Diagnosis by Algebraic Modelling and Fault-TreeInduction . In Proceedings of DX-95, Goslar,Germany, 73-80 .

[Mauss 96] Mauss, J. and Neumann, B . 1996 .Qualitative reasoning about electrical circuitsusing series-parallel-star trees. In Proceedings of10th International Workshop on QualitativeReasoning, 147-153, California, May 1996 .

[Price 96] Price, C . J . 1996 . Effortless IncrementalDesign FMEA, Proc . Ann. Reliability andMaintainability Symp., 43-47, IEEE Press .

[Price and Pugh 96] Price, C . J . ; Pugh, D. R .1996 . Interpreting Simulation with FunctionalLabels, Proc . 10th Annual Qualitative ReasoningWorkshop, Stanford Sierra Camp, AAAI Press .

[Price, Snooke and Landry 96] Price, C. J . ;Snooke, N . ; Landry, J . 1996 . Automated SneakIdentification . Engineering Applications ofArtificial Intelligence, 9(4), 423-427 .

[Pugh and Snooke 96] Pugh, D . and Snooke, N .1996 . Dynamic Analysis of Qualitative Circuits .In Proceedings of Annual Reliability andMaintainability Symposium, 37-42, IEEE Press .