lead-acid battery state detection for automotive electrical energy management

130 Pages·2015·7.64 MB·English

LEAD-ACID BATTERY STATE DETECTION FOR
AUTOMOTIVE ELECTRICAL ENERGY MANAGEMENT
PhD thesis
Ga´bor Ba´ra´ny
Supervisor: Dr. P´eter G´asp´ar
BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS
FACULTY OF TRANSPORTATION ENGINEERING AND VEHICLE ENGINEERING
March 18, 2015
Alul´ırott, B´ar´any G´abor kijelentem, hogy ezt a doktori ´ertekez´est magam k´esz´ıtettem ´es
abban csak a megadott forr´asokat haszn´altam fel. Minden olyan r´eszt, amelyet sz´o szerint,
vagy azonos tartalomban, de ´atfogalmazva m´as forr´asb´ol ´atvettem, egy´ertelmu˝en, a forr´as
megad´as´aval megjel¨oltem.
A dolgozat b´ır´alatai ´es a v´ed´esr˝ol k´eszu¨lt jegyz˝ok¨onyv a k´es˝obbiekben a Budapesti
Mu˝szaki ´es Gazdas´agtudom´anyi Egyetem d´ek´ani hivatal´aban lesz el´erhet˝o.
Budapest, March 18, 2015
..............................
Ba´ra´ny Ga´bor
Magyar nyelvu˝ o¨sszefoglalo´
Az ´olomsavas akkumul´atorok k¨ozponti helyet t¨oltenek be a modern g´epj´armu˝vek elektro-
mos energia ell´at´o h´al´ozat´aban, f˝ok´epp az aut´oipari felhaszn´al´as szempontj´ab´ol vonz´o tu-
lajdons´agaik ´es kedvez˝o ´aruknak k¨osz¨onhet˝oen. Az egyre szigorod´o t¨orv´enyi szab´alyoz´asok
arrak´esztetik azaut´ogy´art´okat,hogyfejlettu¨zemanyag´esk´arosanyagkibocs´ajt´ast cs¨okkent˝o
technol´ogi´akat vezessenek be, mint pl. a Start/Stop, f´ekenergia visszat´apl´al´as ´es a vitorl´az´as
funkcionalit´as. Egy energia menedzsment rendszer keru¨lt bevezet´esre hogy biztos´ıtsa az aut´o
elektromos rendszer´enek megb´ızhat´osa´g´at, meghosszabb´ıtsa az akkumul´ator ´elettartam´at ´es
t´amogassa a szigorod´o CO kibocs´at´ast korl´atoz´o t¨orv´enyi hat´ar´ert´ekek betart´as´at mind
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hagyom´anyos mind hibrid j´armu˝vekben. Az elektromos energia menedzsment rendszer pon-
tos inform´aci´okat ig´enyel az akkumul´ator ´allapot´ar´ol. Ezt ´altal´aban egy olyan elektromos
akkumul´atorszenzor biztos´ıtja, amirendelkezik ´allapotdetekt´al´as´espredikt´ıvfunkci´okkalis.
Az akkumul´ator t¨olt¨otts´egi ´allapot´anak (SOC)´es ¨oregedetts´egi ´allapot´anak (SOH)´eszlel´esre
alapvet˝o ahhoz, hogy az akkumul´ator bet¨olthesse kulcsszerep´et a funkcionalit´as ´es biztons´ag
garant´al´as´anak ´erdek´eben.
Agyakorlatbanazakkumul´atortulajdons´agaitk¨ozvetlenu¨lnemlehetm´ernik¨olts´eghat´ekony
m´odon, ez´ert ez az akkumul´ator feszu¨lts´eg´enek, ´aram´anak ´es h˝om´ers´eklet´enek m´er´es´eb˝ol
keru¨lkik¨ovetkeztet´esre. Ezent´ezisazakkumul´ator´allapotfelismer´esk´etalapvet˝okih´ıv´as´aval,
azSOCu´jrakalibr´al´as´aval´esazakkumul´atorbels˝oellen´all´as´anakmeghat´aroz´as´avalfoglalkozik.
A rendszeres, le´all´ıtott j´armu˝ben t¨ort´en˝o SOC u´jrakalibr´al´as alapvet˝o fontoss´agu´ az
´aramintegr´al´asb´ol sz´armaz´o SOC sodr´od´as korrig´al´as´ahoz. Szu¨ks´eges hozz´a a nyugalmi
feszu¨lts´eg gyors´es megb´ızhat´o meghat´aroz´asa. Egy nyugalmi feszu¨lts´egre optimaliz´alt akku-
mul´ator modell keru¨l megfogalmaz´asra, amihez k´et ku¨l¨onb¨oz˝o megfigyel˝o keru¨l javasol´asra:
egy t¨obbsz¨or¨os modellalapu´ adapt´ıv becsl˝o (MMAE)´esegy Unscented K´alm´anszu˝r˝o(UKF)
alapu´ megfigyel˝o. Mindk´et megk¨ozel´ıt´es k´epesanyugalmifeszu¨lts´eg robusztusmegbecsl´es´ere
alacsony sz´am´ıt´asi ig´eny mellett. A megfigyel˝ok teljes´ıtm´enye numerikusan sz´amszeru˝s´ıt´esre
keru¨l val´os aut´oban fell´ep˝o k¨oru¨lm´enyeket figyelembe vev˝o szimul´aci´ok ´altal.
Az ´olom akkumul´ator bels˝o ellen´all´asa kulcs szerepet j´atszik az aut´o ind´ıthat´os´aga ´es
az elektromos ´aram ell´at´o h´al´ozat stabilit´as´anak szempontj´ab´ol. Ebben a dolgozatban a
de-facto bels˝o ellen´all´as defin´ıci´ok keru¨lnek bemutat´asra ´es ¨osszehasonl´ıt´asra a frekven-
cia tartom´anyban, egy nagy frekvenci´as akkumul´ator modell ´es elektrok´emiai impedancia
spektroszk´opi´as m´er´esek seg´ıts´eg´evel. A t´ezis f˝o hozz´aadott ´ert´eke a t¨ortszeru˝ ellen´all´as fo-
galm´anak bevezet´ese a differenci´alis ellen´all´as fogalm´anak ´altal´anos´ıt´asak´ent. Ezt k¨ovetve
egy matematikai keretrendszer ´es egy alacsony sz´am´ıt´asi kapacit´ast ig´enyl˝o becsl˝o algorit-
mus koncepci´oja keru¨l levezet´esre. Tov´abb´a egy´eb bels˝o ellen´all´as sz´am´ıt´asi elj´ar´asok, mint
a differenci´alis elj´ar´as, line´aris regresszi´os illeszt´es, ARX rekurz´ıv identifik´aci´os elj´ar´as ´es a
szu˝r˝obankos RMS elj´ar´asok keru¨lnek ¨osszefoglal´asra ´es teljes´ıtm´eny ´ert´ekel´esre val´os aut´os
m´er´esek felhaszn´al´as´aval. Legv´egu¨l egy k¨ovezketet´es keru¨l levon´asra az algoritmusok gyako-
rlati alkalmazhat´os´ag´ar´ol.
Summary
Lead-acid batteries play a major role in modern vehicles’ power net due to their attractive
properties for automotive application and favorable price. The ever stricter legal regula-
tions motivate carmakers to develop advanced fuel consumption and emission reduction
technologies, like Start/Stop, recuperation and coasting. An energy management system
is introduced to assure the reliability of the vehicle’s electric system, prolong the battery
lifetime and support the fulfillment of more stringent CO emission legal regulations in both
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conventional and hybrid vehicles. The electrical energy management system requires a pre-
cise knowledge about the battery status. This is usually provided by a battery sensor with
state detection and predictive functions. The detection of State-of-Charge (SOC) and State-
of-Health (SOH) is essential to help the battery fulfill its role as a key element for vehicle
functionality and safety.
In the praxis the battery properties cannot be measured directly in a cost effective way,
they are deducted from the measurement of battery voltage, current and temperature. In
this thesis two fundamental challenges of the automotive battery state detection, the SOC
recalibration and the internal resistance determination are addressed.
A periodic SOC recalibration during vehicle key-off is essential to correct the SOC drift
due to the current integration during vehicle active mode. It requires a fast and reliable
determination of the quiescent voltage. A quiescent voltage battery model is formalized
and two different observers are proposed: a Multiple Model Adaptive Estimator (MMAE)
and an Unscented Kalman Filter (UKF). Both approaches are capable of robust quiescent
voltage estimation in a computationally inexpensive way. The performance of the observers
is numerically quantified by simulations within application relevant boundary conditions.
The internal resistance of lead-acid batteries plays a key-role in vehicle startability and
power-net stability. In this thesis the de-facto internal resistance definitions are described
and compared in the frequency domain using a high frequency battery model and electro-
chemical impedance spectroscopy measurements. The main contribution is the introduction
of the fractional resistance as a generalization of the differential resistance. Following this
definition a mathematical framework and the concept of a computationally inexpensive es-
timation algorithm is derived. Furthermore other internal resistance calculation methods
like the differential method, linear regression fit, ARX recursive identification method and
the RMS with filter bank method are summarized and benchmarked based on real vehicle
measurements. Finally a conclusion is drawn on the applicability of the algorithms.
Acknowledgement
The author wishes to thank Mr. Ju¨rgen Motz, Dr. Martin H. K¨onigsman, Mr. Clemens
Schmucker and Dr. P´eter G´asp´ar, my supervisor at the Hungarian Academy of Sciences in
the Institute for Computer Science and Control. Without their support, in various forms,
the challenges encountered during the process of writing this thesis might never have been
solved.
Thanks also to my parents, who provided the item of greatest worth - opportunity. And
last but not least, I’m thankful to Csilla, my fianc´ee for her patience and continuos support
over the last three years.
Budapest, March 18, 2015
Ga´bor Ba´ra´ny
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Contents
1 Introduction 1
1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Structure of thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Lead-acid batteries 5
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Reactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2.1 Main reaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.2 Oxygen reaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.3 Hydrogen reaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3 Double layer effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.4 Porous electrodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.5 Electrode utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.6 Mass transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.7 Current density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.8 Potentials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.9 Inductance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.10 Aging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.11 Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3 Energy Management 31
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2 Generator management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.3 Load management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.4 Advanced energy management functions . . . . . . . . . . . . . . . . . . . . 39
3.4.1 Start/Stop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.4.2 Start/Stop with coasting . . . . . . . . . . . . . . . . . . . . . . . . . 41
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3.4.3 Boost-recuperation system . . . . . . . . . . . . . . . . . . . . . . . . 42
3.5 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4 Model-based State-of-Charge recalibration 46
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.2 Quiescent voltage battery model . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.2.1 Electrochemical model . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.2.2 Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.2.3 State-space model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.3 Observer design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.3.1 Multiple Model Adaptive Estimator . . . . . . . . . . . . . . . . . . . 55
4.3.2 Unscented Kalman filter . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.4 Validation of the observers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.4.1 Synthetic data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.4.2 Battery measurements . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
5 Internal resistance 65
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.2 Factors of internal resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.3 Impedance measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
5.4 High frequency model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.4.1 Randles circuit with capacitor . . . . . . . . . . . . . . . . . . . . . . 73
5.4.2 Randles circuit with constant phase element . . . . . . . . . . . . . . 75
5.4.3 Data fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.5 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.6 Estimation algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.6.1 Differential method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
5.6.2 Linear regression fit . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
5.6.3 ARX recursive identification with forgetting factor . . . . . . . . . . . 88
5.6.4 RMS with filter bank . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
5.6.5 Fractional resistance method . . . . . . . . . . . . . . . . . . . . . . . 91
5.6.6 Spectral analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
5.7 Benchmarking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
5.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
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6 Summary of theses 108
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List of Tables
2.1 Electrochemical reactions in a lead-acid battery . . . . . . . . . . . . . . . . 10
2.2 Symbols of the Butler-Volmer equation . . . . . . . . . . . . . . . . . . . . . 11
2.3 Volume to charge constant of the main reaction . . . . . . . . . . . . . . . . 19
2.4 Dependency of active surface area from electrode utilization . . . . . . . . . 22
2.5 BCI standard for SOC estimation of a 12V flooded lead acid car battery . . 29
3.1 CO legal limit targets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
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3.2 Typical current consumption of the electrical loads . . . . . . . . . . . . . . 37
3.3 Summary of hybrid car types and functions . . . . . . . . . . . . . . . . . . 45
5.1 EIS test cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
5.2 Model with capacitor: Parameters of Exide 70Ah wet battery at 25◦C . . . . 78
5.3 Model with CPE: Parameters of Exide 70Ah wet battery at 25◦C . . . . . . 79
5.4 Comparison of internal resistance determination algorithms . . . . . . . . . . 99
5.5 Comparison of internal resistance calculation methods . . . . . . . . . . . . . 100
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