Model-Based Diagnosis of an Automotive Electric Power Generation and Storage System Annalisa Scacchioli, Member, IEEE, Giorgio Rizzoni, Fellow, IEEE, Mutasim A

This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS 1 Model-based Diagnosis of an Automotive Electric Power Generation and Storage System Annalisa Scacchioli, Member, IEEE, Giorgio Rizzoni, Fellow, IEEE, Mutasim A. Salman, Senior Member, IEEE, Weiwu Li, Simona Onori, Member, IEEE, and Xiaodong Zhang, Member, IEEE

Abstract—This paper presents mathematical models, design and Ef Inducted electromotive forces of the excitation experimental validation, and calibration of a model-based diagnos- field. tic algorithm for an electric-power generation and storage automo- H ,H Hypothesis of no fault and faulty. tive system, including a battery and an alternator with a rectifier 0 1 and a voltage regulator. Mathematical models of these subsystems I0 Current flow of the battery into resistance R0 . are derived, based on the physics of processes involved as charac- IB Battery current. terized by time-varying nonlinear ordinary differential equations. IL Load current. The diagnostic problem focuses on detection and isolation of a spe- Ia ,Ib ,Ic Current of phases a, b, c. cific set of alternator faults, including belt slipping, rectifier fault, Idc Alternator current. and voltage regulator fault. The proposed diagnostic approach is eq based on the generation of residuals obtained using system models Idc Alternator current output from the equivalent and comparing predicted and measured value of selected variables, alternator model. nom including alternator output current, field voltage, and battery volt- Idc Nominal value of alternator current output. age. An equivalent input–output alternator model, which is used If Alternator field current. in the diagnostic scheme, is also formulated and parameterized. L ,L ,L Self-inductances of the stator for phases a, b, c. The test bench used for calibration of thresholds of the diagnostic a b c algorithm and overall validation process are discussed. The effec- Lab,Lba Stator–stator mutual inductances. tiveness of the fault diagnosis algorithm and threshold selection is Lac,Lca Stator–stator mutual inductances. experimentally demonstrated. Lbc,Lcb Stator–stator mutual inductances. Index Terms—Automotive, electric power generator, electric Laf ,Lfa Stator–rotor mutual inductances. power storage system, electrical systems, model-based diagnosis. Lbf ,Lfb Stator–rotor mutual inductances. Lcf ,Lfc Stator–rotor mutual inductances. Lf Field winding self-inductance. L Self-inductance of the rotor. NOMENCLATURE r Ls Self-inductance of the stator. Γ Voltage controller. Lss Stator–stator mutual inductance. ΔVref Voltage regulator fault. M Peak stator–rotor mutual inductance. Δωe Belt slipping fault. PD ,PI ,PM Probability of detection, false alarm, and misde- Φ Phase angle between stator windings. tection. A Battery’s surface area. R Internal ohmic resistance of the battery. Ahnominal Battery nominal capacity. R0 Overvoltage resistance of the battery model. C0 Capacitance related to the dynamic phenomena Ra ,Rb ,Rc Resistances of stator windings. of the battery. Rf Field winding resistance. E0 Open-circuit voltage of the battery. Rr Rotor winding resistance. Ea ,Eb ,Ec Inducted electromotive forces on windings a, b, c. Rs Stator winding resistance. SoC Battery state of charge. SoC0 Initial battery state of charge. Manuscript received May 27, 2011; revised August 27, 2012; accepted T Battery temperature. October 24, 2012. This paper was recommended by Associate Editor K. Hall. VB Battery voltage. A. Scacchioli is with the Faculty of the Department of Mechanical Engineer- V ,V ,V Voltage of the stator at terminals a, b, c. ing, School of Engineering, New York University, New York City, NY 11201 a b c USA (e-mail: [email protected]; [email protected]). Vc0 Voltage across the capacitor in the battery model. G. Rizzoni and S. Onori are with the Center for Automotive Research, The Vdc Alternator voltage. Ohio State University, Columbus, OH 43210 USA (e-mail: [email protected]; V eq Alternator voltage output from the equivalent al- [email protected]). dc M. A. Salman is with the General Motors Research and Development Center, ternator model. Warren, MI 48088 USA (e-mail: [email protected]). Vf Alternator field voltage. W. Li was with the Center for Automotive Research, The Ohio State Univer- V eq Field voltage output from the equivalent alterna- sity, 930 Kinnear Road, Columbus, OH 43210 USA. He is now with Cummins f Inc., Columbus, IN 47202 USA (e-mail: [email protected]). tor model. X. Zhang is with the Faculty of the Department of Electrical Engineer- Vref Alternator reference voltage. ing, Wright State University, Dayton, OH 45435 USA (e-mail: xiaodong. V f Alternator reference voltage with voltage regula- [email protected]). ref Digital Object Identifier 10.1109/TSMCC.2012.2235951 tor fault.

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nom Vref Nominal alternator reference voltage. KI ,KP Gains of the voltage regulator. + + λ Vector of flux linkages. IL E Vector of inducted electromotive forces on the - - stator’s windings. Alternator Battery Current Sink I Vector of phase currents. L Matrix of inductances of the alternator. Fig. 1. Simplified EPGS automotive system diagram. Lf (θe ) Vector of mutual inductances stator field. R Matrix of stator resistances. V Vector of terminal voltages of windings of stator. others address comfort and convenience of the driving experi- f Parameters’ vector of the three-phase passive ence. Among automotive electrical systems, the electrical power rectifier. generation and storage system, or EPGS system, is of critical α, β, γ, η, Parameters of the equivalent alternator model. importance. κ, λ Parameters of the equivalent alternator model. The EPGS system is designed to efficiently satisfy electrical λa , λb , λc Flux linkage of phases a, b, c. power requirements of various subsystems, such as the engine ωe Electrical speed. electronic control unit (ECU), starter, headlights, radio, air con- f ωe Electrical frequency with belt slipping fault. ditioning, and steer-by-wire system. In this case, the EPGS sys- ωm Engine speed. tem is actually the “engine” of all other electrical or electronic θe Electrical angle. subsystems. θm Engine phase angle. Fig. 1 shows a conceptual representation of the EPGS system c Battery’s lumped specific heat. in which the major subsystems—alternator and battery—are fa ,fb ,fc Parameters of the three-phase passive rectifier. connected to a single-lumped load representing all other elec- ga ,gb ,gc Conduction state of the diode in the branches trical/electronic subsystems. The alternator serves the purpose a, b, c. of satisfying all electrical load requirements in the steady state hc Convective heat transfer coefficient. (when the engine is ON), while the battery satisfies transient h Selected threshold. and engine-off power requirements. In addition, the alterna- hA Heat transfer rate. tor also charges the battery as needed. As vehicles have become h1 Optimal threshold related to belt slipping fault. equipped with more complex and interdependent electronic sys- h2 , Optimal threshold related to rectifier diode fault. tems, the number of electronic controls and software related to h3,up, Optimal threshold related to regulator fault. warranty problems has increased dramatically. In particular, due h3,down Optimal threshold related to regulator fault. to the distributed nature of a vehicle electrical system, it is of- m Battery mass. ten very difficult to accurately identify the location of faults mc Battery’s convective heat losses. in general, and of intermittent faults in particular. This necessi- p Number of poles of the alternator. tates powerful diagnostics that automatically detect, isolate, and pdf Probability density function. potentially compensate for faults. Such diagnostic capabilities p0 ,p1 Probability density function for H0 and H1 . can minimize down time, improve resource management via r Random variable related to a residual. condition-based maintenance, and minimize warranty costs for r1 ,r2 ,r3 Residuals. the automotive manufacturer. r sigr1 Fault signature of residual 1 . In published literature, one can find several studies on mod- r sigr2 Fault signature of residual 2 . eling of the electrical system, with particular attention given to r sigr3 Fault signature of residual 3 . the generator and battery [1]–[8]. Surprisingly, none of these k Stator–rotor magnetic coupling. focuses specifically on addressing the problem from a diagnostic perspective. There has been a lot of research on diag- I. INTRODUCTION nosis or health monitoring of the energy storage system (or battery) [9]–[13], but very little work on diagnostics of the HIS introductory section provides motivation and back- automotive power generation system (or alternator) has been ground about the diagnostics of electrical automotive sys- T reported in past years [14], [15]. Among results reported, even tems. The state-of-the-art of this research field and a description fewer are based on a systematic model-based approach [16], of objectives and main contributions of the paper are also pro- [17]. This paper focuses on the development of a model-based vided at the end of the section. fault diagnostic method for the EPGS system. It provides a summary of a research project conducted by The Ohio State Univer- A. Motivation sity Center for Automotive Research and the General Motors The past several decades have witnessed an exponential in- R&D; some of the results have been reported in [18]–[20]. Thus, crease in the number and sophistication of electronic systems this study presents a complete model-based fault diagnosis ap- and subsystems in vehicles to meet various regulations and proach for the EPGS alternator subsystem, including its experi- customer demands. Some of these subsystems are critical to mental validation. In this paper, we describe the EPGS system by measure vehicle performance and safety; on the other hand, time-varying nonlinear ordinary differential equations derived This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

SCACCHIOLI et al.: MODEL-BASED DIAGNOSIS OF AN AUTOMOTIVE ELECTRIC POWER GENERATION AND STORAGE SYSTEM 3

regulator fault belt fault diode fault

alternator electrical mechanical belt ratio speed speed load current reference + field field AC phase diode alternator - battery voltage excitation synchronous bridge battery voltage - regulator voltage field current generator current rectifier current + current alternator voltage

Fig. 2. Automotive EPGS system, including battery, alternator, and its subsystems with regulator, belt, and diode fault. from the physics of processes involved. The alternator model is 3) Design, implementation, and experimental validation of a based on a model of an AC synchronous generator [1] derived robust diagnostics for belt and diode faults of EPGS. using the principle of magnetic induction, a model of the diode 4) Application of an optimal threshold selection and cal- bridge rectifier [4], and a proportional-integral (PI) control de- ibration method based on statistical analysis of ap- scribing the voltage regulator. Battery dynamic equations utilize propriate experimental tests (design of experiment a second-order equivalent circuit to characterize electrical be- methodology). havior and a first-order model to describe thermal behavior. The This paper is organized as follows. Section II describes the load is modeled by providing a prescribed load current profile, complete EPGS mathematical system and its optimized simula- which is obtained from vehicle experimental data and known in- tion model with its experimental validation. Section III presents formation about typical load currents associated with common the EPGS diagnostic algorithm, including threshold selection accessories. In order to obtain a robust diagnostic algorithm, and and calibration. Section IV describes the experimental test bench in light of its implementation in the vehicle, our approach uti- used for the calibration and validation process of EPGS diag- lizes an equivalent mathematical linear time-invariant alternator nostics, including a complete experimental validation example model, where the equivalence of the two models is described in of EPGS diagnostics and a discussion of the results. Section V terms of input–output behavior. presents our concluding remarks and a short description of the future.

B. Objectives and Contributions of This Paper II. ELECTRIC POWER GENERATION The diagnostic objectives of this study are summarized in the AND STORAGE SYSTEM MODEL following with reference to Fig. 2. This section describes mathematical models of EPGS sub- 1) Belt fault: an input fault that occurs when the alternator systems (alternator and battery) based on the physics of pro- belt does not have proper tension to keep the alternator cesses involved. They are extracted from existing literature. In pulley rotating synchronously with the engine shaft; it particular, for the alternator, the AC synchronous generator is may be caused by aging of the serpentine belt or incorrect extracted from [1] and [3], and the three-phase diode bridge rec- installation, and its effect is a decrease in alternator output tifier from [4], while the battery model is extracted from [21]. voltage, which in some cases can be compensated by the However, this paper presents two novel contributions in terms of voltage regulator. system model: development and experimental validation of the 2) Diode fault: a failure of one of the bridge rectifier diodes, accurate full EPGS model and development, identification, and causing one branch of the circuit to be open; characteris- experimental validation of an equivalent linear physical-based tics of this fault include a large ripple in output voltage model of the alternator and, thus, of the EPGS system. and current, while its effect is a reduced output alternator current, caused by the lack of rectification in one of the phases. A. Electric Power Generation and Storage System 3) Regulator fault: incorrect reference voltage caused, for Mathematical Model example, by a software error in the ECU that leads to incorrect regulation. 1) Mathematical Model of the Alternator: The common au- In achieving these objectives, using a model-based diagnostic tomotive claw-pole alternator (see Fig. 3) is composed of the approach and adopting experimental validations, in this paper, following subsystems: 1) three-phase AC synchronous genera- we present the following contributions. tor; 2) three-phase diode bridge rectifier; and 3) voltage regu- 1) Development and experimental validation of an accurate lator (consisting of a switching control mechanism with a fixed model of the EPGS system, including analysis of uncer- reference voltage). tainty of the model. AC voltage generated by the alternator is rectified to provide 2) Development and experimental validation of an equivalent the dc voltage required for supplying various electrical loads linear model for the alternator and then for the complete and for charging the battery. The three-phase AC synchronous EPGS system. generator model [1] is based on the following coupled circuit This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

4 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS

where p is the number of poles of the alternator. Assuming a (ﬁeld) balanced machine, the balance equation for three-phase current voltage regulator Idc is

I f Lf Rf Ia + Ib + Ic =0. (6)

Laf Ra I The three-phase diode bridge rectiﬁer model [4] associates a a L bf Rb Ib b switching function (ga , gb , gc ) with each of the bridge branches, c Vdc Ea representing the conduction state of the diode present in the Eb Lcf Rc Ic Va Vb Vc branch. The switching function is equal to 1 if the diode is active Ec or 0 if it is not. Its mathematical representation is described by

Va = fa Vdc,Vb = fb Vdc,Vc = fc Vdc (7) AC synchronous generator diode bridge rectifier 2ga − gb − gc 2gb − gc − ga fa = ,fb = Fig. 3. Automotive alternator structure with its electrical subsystems: excita- 3 3 tion field, three-phase AC synchronous generator, and diode bridge rectifier. 2g − g − g f = c a b (8) c 3 of the electrical dynamics of the stator and rotor: Idc = ga Ia + gb Ib + gc Ic . (9)

Ea = Ra Ia + Va (1a) The voltage regulator maintains alternator output voltage at a predetermined level across the engine’s complete speed range, E = R I + V (1b) b b b b independent of load and engine speed. The specific set point Ec = Rc Ic + Vc (1c) for the regulator may vary as a function of operating conditions and is typically a lookup table. Without loss of generality, we E = R I + V (1d) f f f f consider the following PI controller voltage: where Va , Vb , and Vc are the applied terminal voltages; Vf is t − − the excitation field voltage; Ra , Rb , and Rc are the resistance of Vf = KP (Vref Vdc)+KI (Vref Vdc)dt (10) t0 the stator winding; and Rf is the resistance of the field winding. Ea , Eb , and Ec are induced electromotive forces (EMF) of where field voltage Vf is saturated at Vdc, while KP and KI are individual phases, and Ef is the inducted EMF of the excitation appropriate gains. field, which are given by Given the following matrix-vector notation: ⎛ ⎞ ⎛ ⎞ ⎛ ⎞ dλa dλb dλc dλf Ia Va Ea Ea = ,Eb = ,Ec = ,Ef = − (2) ⎝ ⎠ ⎝ ⎠ ⎝ ⎠ dt dt dt dt I = Ib , V = Vb , E = Eb I V E where λa , λb , λc , and λf are flux linkages of individual phases, c c c ⎛ ⎞ ⎛ ⎞ ⎛ ⎞ which are defined as f λ Laf (θe ) a a ⎜ ⎟ ⎝ ⎠ λ ⎝ ⎠ ⎝ ⎠ λa = −La Ia − LabIb − LacIc + Laf (θe )If (3a) f = fb , = λb , Lf (θe )= Laf (θe ) f λ λ = −L I − L I − L I + L (θ )I (3b) c c Laf (θe ) b ba a b b bc c bf e f ⎛ ⎞ ⎛ ⎞ λ − − − Rs 00 L L L c = LcaIa LcbIb Lc Ic + Lcf (θe )If (3c) ⎜ ⎟ s ss ss ⎝ ⎠ ⎝ ⎠ R = 0 Rs 0 , L = Lss Ls Lss λf = Lf If − Lfa(θe )Ia − Lfb(θe )Ib − Lfc(θe )Ic (3d) 00Rs Lss Lss Ls where L , L , and L are the self-inductance of the stator related a b c and after assuming a balanced three-phase circuit to the three phases, and Lab, Lba, Lac, Lca, Lbc, and Lcb are the stator–stator mutual inductance, while the stator–rotor mutual Ra = Rb = Rc = Rs (11a) inductances of the machine are described by La = Lb = Lc = Ls (11b) Laf (θe )=Lfa(θe )=M cos(θe ) (4a) Lab = Lba = Lac = Lca = Lbc = Lcb = Lss (11c) Lbf (θe )=Lfb(θe )=M cos(θe +Φ) (4b) simpler and more compact mathematical expressions of the al- Lcf (θe )=Lfc(θe )=M cos(θe − Φ) (4c) ternator and of the excitation field can be presented as follows: with M being the peak stator–rotor mutual inductance, Φ is the E = RI + V (12a) angle between stator windings, and θe is the phase angle of the d E = λ (12b) alternator. The alternator’s relation with the mechanical angular dt displacement θm (degrees) is described by T λ = −LI + Lf (θe )If (12c) p θ = θ (5) e 2 m V = fVdc (12d) This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

SCACCHIOLI et al.: MODEL-BASED DIAGNOSIS OF AN AUTOMOTIVE ELECTRIC POWER GENERATION AND STORAGE SYSTEM 5

ωm ωe belt ratio IL Ia Vref + Vf excitation f AC diode - controller I synchronous Ib bridge Idc IB battery Vdc field generator rectifier (16)-(19) - (10) (15) (14) Ic (9) + Vdc Va Vb Vc diode bridge rectifier (7)

belt fault diode fault Δωe IL + regulator fault ωm ωe ωe +Δωe belt ratio ΔVref +

Ea Idc Vref + Vref +ΔVref+ excitation controller Vf If ﬁeld Eb ﬁeld induced batteryload Vdc + - (10) (15) (12)-(13) Ec Vdc SimPower Subsystems

Fig. 4. EPGS model. (Top) Functional block diagram of the automotive EPGS mathematical model, including alternator and battery. (Bottom) Schematicsofthe automotive EPGS simulation model with fault injection.

and IB R R0

C Ef = Rf If + Vf (13a) + 0

E0 VB d VC E = − λ (13b) f dt f - T λf = −Lf (θe )I + Lf If (13c) Fig. 5. Electrical battery model based on the equivalent Thevenin´ circuit. Vf =Γ(Vdc) (13d) where Γ denotes PI controller voltage. Finally, rotor–stator dy- where I is the battery current (positive during charging and namic is given by B negative during discharging), VB is the battery voltage, R is the

battery internal ohmic resistance, R0 is the overvoltage resis- ˙ − −1 −1 d ˙ I = L (RI + V)+L Lf (θe )ωe If + Lf (θe )If tance, C0 is the capacitance, and E0 is the open-circuit voltage. dθe (14) The SoC is described by after substituting values of (12a) and (12b) into the derivative 1 t of (12c). Similarly, from (13), one gets the following dynamic SoC = SoC − I dt (17) 0 Ah B of the ﬁeld circuit: nominal t0

where IB is the battery current during charging or discharging, ˙ −Rf − 1 1 T ˙ If = If Vf + (I + Lf (θe )I). (15) t is the related time, Ahnominal is the nominal battery capacity, Lf Lf Lf and SoC0 is the initial SoC at t0 . Note that this model does not lend itself to the development of The battery thermal model is described by a lumped first- simple diagnostic algorithms, for example, based on observers, order model due to its nonlinear and hybrid nature. hc A R 2 R0 2 2) Mathematical Model of the Battery: For the aim of this T˙ = − (T − T∞)+ I + I (18) mc mc B mc 0 study, the battery system model is given by 1) an electric model, 2) SoC model, and 3) a thermal model. The battery electric where m is the mass of the battery, c is the battery’s lumped model [21], which is based on an equivalent Thevenin´ circuit specific heat, hc is the convective heat transfer coefficient, A (see Fig. 5), is described by is the surface area, T is battery temperature, assumed uniform, and I0 is the current flow into resistance R0 . Introducing, T1 = − − − VB = E0 RIB VC 0 (16a) T T∞, (18) becomes V ˙ − C 0 ˙ −hc A R 2 R0 2 VC 0 = (16b) T1 = T1 + IB + I0 . (19) R0 C0 mc mc mc This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

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trated in the bottom of Fig. 4. Identified parameters of the alternator are given in Table I. The EPGS system model was validated using experimental data gathered from the test bench, which is described in Sec- tion IV-A. Engine speed and load profiles (shown in Fig. 8) were designed to test the alternator’s response to load changes and acceleration at low, medium, and high engine speeds. Ex- perimental data are filtered (in MATLAB) using a fourth-order low-pass digital Butterworth filter with a cutoff frequency, of 2.5 Hz. With such a cutoff frequency, dominant dynamics of the system in response to load transients are preserved. Fig. 7 shows experimental and simulation results for alternator current, alternator voltage, and field voltage, suggesting that the implemented Fig. 6. Battery internal ohmic resistance R for charging and discharging (blue automotive EPGS simulator is an acceptable representation of shape) phases. described automotive EPGS system dynamics.

The parameters, E0 , C0 , R, and R0 of (16)–(19), are func- III. ELECTRIC POWER GENERATION AND STORAGE DIAGNOSIS tions of battery state of charge SoC, battery temperature T , and PROBLEM AND PROPOSED APPROACH battery current IB . A typical shape of the battery internal ohmic resistance R during charging and discharging is shown in Fig. 6. In this section, we present an equivalent but simplified EPGS Details are in [21]. While this study is not concerned with di- model that is used for the development of a model-based diagnostics battery faults, it is important for the accuracy and agnostic algorithm based on a parity equation approach. An robustness of the EPGS diagnostics system to include a realistic optimal threshold selection and calibration approach are also battery model in the overall system model. proposed in this section. Fault detection and isolation (FDI) aims to recognize faulty behavior of components based on measured signals. A typical B. Electric Power Generation and Storage System Simulation approach of FDI is analytical redundancy: constructing residual Model With Faults generators based on models of the system (for example, through the design of state observers or parity equations). Unfortunately, The highly nonlinear and complex EPGS mathematical model the complexity of the EPGS system is significant. For the al- described through (2)–(19) and shown in the block diagram ternator system, the combination of nonlinear dynamics of the on the top of Fig. 4 has been implemented in Simulink. The three-phase generator with the switched, state-dependent behav- Simulink model was partly validated using a Saber simulator, ior of the diode bridge rectifier and with varying-time parameters EPSSIM, made available by GM R&D [18]. A second EPGS of the battery makes the design of such residual generators very simulation model (see the bottom of Fig. 4) with a modified challenging. For example, linearization is impossible in the pres- alternator model was also developed by using the SimPower- ence of hard nonlinearities. A direct nonlinear parity equation Systems library of Simulink together with Simulink itself. or observer design for such a complex nonlinear switch system The SimPowerSystems is a module-based power system sim- is also extremely difficult. In [22], the authors present several ulation tool available in MATLAB. It can be combined seam- nonlinear sliding model observer-based methods for alternator lessly with analytical equations to realize simulation of com- EMF estimation. However, those methods are dependent on the plex systems. A big advantage of utilizing SimPowerSystems availability of alternator phase current, which is not available in in the EPGS simulation model is the possibility of simulating our case. the diode bridge rectifier subsystem, which provides extreme flexibility and convenience for simulation of the diode fault. In the final automotive EPGS system simulator (see the bot- A. Need of an Equivalent Alternator Model tom of Fig. 4), the alternator stator, rectifier, battery, and load model are implemented and embedded in SimPowerSystems, In light of its implementation in a vehicle, the approach pro- while the controller, excitation field, and the induced electro- posed in this study utilizes an equivalent mathematical linear magnetic field (EMF) are implemented in Simulink. The ser- time-invariant alternator model. The equivalence of the two pentine belt slip fault, rectifier diode open fault, and voltage models is in terms of input–output behavior. Equivalent rep- regulator fault are considered as automotive EPGS system faults. resentation is made possible by replacement of the AC syn- These faults can easily be implemented by changing only one chronous generator and diode bridge rectifier with an equiva- parameter or open-circuiting one diode. lent DC generator. Input–output behavior of the subsystem in The belt slipping fault and voltage regulator fault are de- the big black box in Fig. 11 is functionally similar to that of a f f scribed as additive faults through ωe = ωe +Δωe and Vref = DC machine. Vref +ΔVref , respectively. The rectifier diode open fault is di- An equivalent excitation field (20a) and a first-order linearized rectly implemented in SimPower. Fault injection is also illus- model based on a DC generator equation (20b) describe the This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

SCACCHIOLI et al.: MODEL-BASED DIAGNOSIS OF AN AUTOMOTIVE ELECTRIC POWER GENERATION AND STORAGE SYSTEM 7

Fig. 7. Automotive EPGS full model validation: (left) ﬁeld voltage, (middle) alternator current, and (right) alternator voltage. input–output behavior of this subsystem TABLE I PARAMETERS OF THE ALTERNATOR

Parameter Description Value Unit I˙f = −αIf + βVf (20a) Rs stator winding resistance 30 mΩ Rr rotor winding resistance 3.44 Ω I˙dc = −γIdc + ηωe + κIf − λVdc. (20b) Ls self inductance of the stator 198 µH Lr self inductance of the rotor 500 mH Lss stator-stator mutual inductance 90 µH In (20), input signals are engine speed ωe , field voltage Vf , and M peak stator-rotor mutual inductance 230 µH p 12 alternator voltage Vdc, while battery current IB is the output—all number of poles these variables are available through specific sensors in the vehi- k stator-rotor magnetic coupling 0.99 Φ phase angle between stator windings 120 0 cle, while the six parameters α, β, γ, η, κ, and λ are determined using MATLAB/Simulink identification methods, through a parameters’ estimation procedure based on four overlapping regions (see Fig. 9). The input dataset is subdivided into four regions, each of them is uniquely associated with a parameter set, and a lookup table with these parameters is selected dynamically by using engine speed as the scheduling variable. The average engine speed in each region is mapped to the parameter value calculated in that region using a 700 r/min separation between data points. Lin- ear interpolation and extrapolation are utilized to approximate parameter values between table entries or beyond table bounds. The parameter interpolation approach allowed the equivalent linear model to adequately reproduce behavior of the nonlinear model under a wide range of operating conditions. During the EPGS equivalent model validation procedure, we compared response of the EPGS system based on the nonlinear model of the alternator with response of the EPGS system based on the equivalent linear alternator model with an input dataset different from that used during optimization. Load current and engine speed profiles simulate power accessories turned ON and OFF during Fig. 8. Automotive EPGS full model and EPGS equivalent model validation: two common steady-state engine speeds at idle (850 r/min) and load current and engine speed input profiles used for validation of both the full and the equivalent EPGS model. at cruising (2100 r/min), as depicted in Fig. 8. Validation results of Fig. 10 show that the equivalent model works reasonably well in each range.

about faults. The residuals used for FDI are deﬁned as B. Fault Diagnosis Algorithm Design − eq nom The designed fault diagnosis algorithm proposed in this paper r1 =(Idc Idc )/Idc (21a) uses a parity equation approach based on an equivalent model r =(V − V eq)/V nom (21b) and compares behavior of the alternator with the behavior of the 2 f f ref − eq nom equivalent model to produce residuals that contain information r3 =(Vdc Vdc )/Vref (21c) This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

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Fig. 9. Automotive EPGS equivalent model identiﬁcation: (left) Parameters’ estimation procedure based on four overlapping regions, (right) Identiﬁed parameters for the equivalent alternator model: α, β, η, γ, κ, λ.

Fig. 10. Automotive EPGS equivalent model validation: (left) ﬁeld voltage, (middle) alternator current, and (right) alternator voltage.

nom where Idc is the nominal alternator current, chosen as 120 [A], Equivalent alternator model nom ωm ωe and Vref is the nominal reference voltage, chosen as 14.46 [V]. belt ratio The fault diagnosis process is illustrated in Fig. 12. IL Fault diagnosis process is based on the following assump- equivalent Vref+ Vf excitation f dc - B dc controller I DC generator I I battery V tions: 1) alternator output current Idc is known; 2) the ﬁeld ﬁeld - (10) (15) (20) + (16)-(19) Vdc voltage Vf , regulator output, is known; and 3) alternator output voltage Vdc is measured. It is important to notice that the equivalent model is used as an open-loop estimator for the full Fig. 11. Need of an equivalent alternator model: Block diagram of the auto- model without fault. The behavior of the equivalent model is motive EPGS system with an equivalent alternator model (DC machine in the not affected by faults. Under the assumption of a single fault orange box). occurrence, fault signature and fault isolation strategy can be described by the fault signatures TABLE II FAULT ISOLATION LOGIC 1, if std(r1 ) >h1 = sigr1 (22) 0, if std(r1 )

h2 0 x 1 Voltage regulator (increased ref ) = 0 0 -1 Voltage regulator (drop Vref ) sigr2 (23) 0, if mean(r2 )

h3,up sig = − (24) r3 ⎩ 1, if mean(r3 )
SCACCHIOLI et al.: MODEL-BASED DIAGNOSIS OF AN AUTOMOTIVE ELECTRIC POWER GENERATION AND STORAGE SYSTEM 9
(regulator fault) (belt fault) (diode fault) ΔVref Δωe
ωm ωe + Idc belt ratio Idc + excitation field + IB AC genearator battery Vf + - Vref + Vf bridge rectifier regulator Vdc Vdc EPGS full model IL residual fault generator signature EPGS equivalent model eq (21a)-(21c) (22)-(24) Idc
eq eq - IB Vf equivalent battery eq alternator model + Vf eq eq regulator Idc Vdc eq eq Vdc Vdc
Fig. 12. Model-based fault diagnostic scheme for an automotive alternator.
C. Threshold Selection and Calibration It can be defined by Residual processing is a very important part of the FDI ∞ scheme. In fact, because of model inaccuracy, disturbance, or PF = p0 (x)dx (26) h measurement noise, conditions for perfectly robust residual gen- eration cannot be met in practice. Thus, it is important to be where h is the selected threshold, and p0 is the pdf of the able to systematically design detection thresholds to make deci- random variable r under hypothesis H0 . sions from residuals. Optimal threshold selection that is based 3) Probability of a misdetection (miss) (PM ): the probability on statistical hypothesis testing is a commonly used threshold that we choose hypothesis H0 when H1 is the correct selection method [23]. hypothesis. It can be defined by 1) Optimal Threshold Selection Method: The optimal h threshold selection method is based on statistical hypothesis PM = p1 (x)dx (27) −∞ testing concepts [24] where residuals are viewed as a sequence of independent random variables. Residuals corresponding to where h is the selected threshold, and p1 is the pdf of the normal operation are assumed to be randomly distributed under random variable r under hypothesis H1 . the hypothesis H0 (no fault), while residuals corresponding to a Optimal threshold selection should result in PD as high as faulty condition are assumed to be randomly distributed under possible and PF and PM as low as possible. However, these hypothesis H1 (faulty). Thus, the binary hypothesis test is made. objectives are usually conflicting in real applications. Thus, the Let r be the random variable corresponding to a residual. threshold selection problem is always a tradeoff between miss The rule adopted most commonly is that whenever a sample detection and false alarms. A practical but effective way to of random variable r is above threshold value, hypothesis H1 obtain the statistical optimal threshold is by minimizing total is chosen, whereas H0 is selected if the sample is below the probability of error (PF + PM ). threshold. If the pdf associated with r under each hypothesis is known, we can compute various probabilities that are relevant IV. VALIDATION OF DIAGNOSIS FOR THE ELECTRIC POWER in the context of fault diagnosis. GENERATION AND STORAGE SYSTEM We can define the following. In this section, we illustrate the experimental setup that is 1) Probability of detection (P ): the probability that we D used for the validation of the EPGS system and its diagnostics choose hypothesis H when H is indeed the correct hy- 1 1 algorithms. In addition, experimental results of threshold selec- pothesis. It can be defined by tion and calibration and diagnostic algorithms are shown and discussed. ∞ PD = p1 (x)dx (25) h A. Experimental Setup and Electric Power Generation and Storage Faults Simulation
where h is the selected threshold, and p1 is the pdf of the The test bench setup provides a reliable validation platform random variable r under hypothesis H1 . for a real electrical power generation system and allows simula- 2) Probability of a false alarm (PF ): the probability that we tion of component faults and validation of the EPGS model and choose hypothesis H1 when H0 is the correct hypothesis. the EPGS fault diagnosis algorithms. A schematics of an EPGS This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
10 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS
Fig. 13. Experimental setup and EPGS faults simulation by the test bench: (left) Part of the EPGS test bench setup at The Ohio State University Center of Automotive Research, (middle) belt fault, and (right) diode fault.
encountered in real vehicles in order to validate designed diag- nostic algorithms. The belt fault, rectifier diode open fault, and voltage regulator fault are considered as EPGS system faults in this study. The simulation of two kinds of EPGS faults has been made possible on the test bench: belt slip fault and diode fault, as shown in the middle and right of Fig. 13. For alternator belt slip fault simulation, a manually tunable idler is used between the alternator and motor pulley. By adjusting the position of the idler, the tightness of the belt can be controlled precisely. A common fault with the alternator rectifier is a diode fault. The diode may be damaged by high voltage or other causes. With Wall power the test bench, this kind of fault can be realized by cutting off the connection wire of one diode. For convenience of operation, a manual switch can be added to the diode connection wire. signal Therefore, by turning the switch ON or OFF, we can alternate Sensor signals conditioning box easily between no fault and diode fault condition. SC-2345
Fig. 14. Schematics of the EPGS test bench, including an electric motor, a vehicle alternator, a programmable power load, and a 12-V lead-acid battery. B. Threshold Selection Experimental Results Threshold selection is crucial to the accuracy and robustness test bench is provided in Fig. 14. The automotive EPGS experi- of any diagnostic system. For this reason, we developed a reli- mental test bench, used in this study, is located at The Ohio State able and accurate model of the EPGS, and then we spent time University Center for Automotive Research, as depicted on the quantifying uncertainty in the model. There was clear separa- left of Fig. 13, and it is mainly composed of an electric motor, a tion between residual distribution under normal conditions and vehicle alternator, a programmable power load, and a 12-V lead- under faulty ones. In the experimental validation, many tests us- acid battery. In the test bench used in this study, electric motor ing design of experiment (DOE) methodology were conducted. speed can be controlled by serial communication; therefore, it However, because of article size limitation, only one example is used to simulate engine rotation performance. Programmable of these tests is shown and discussed. electric load is used to simulate vehicle load demands by run- From the signature equations (22)–(24), four thresholds ning a predefined load current profile. The electric load can also needed to be calibrated: h1 , which is related to the belt slip be remotely controlled by serial communication. The battery fault, h2 , which is related to rectifier diode fault, and h3,up and and alternator work together to provide current demanded by h3,down, which are related to regulator fault. With the EPGS test the load. The test bench data acquisition (DAQ) system was bench, we are able to simulate the fault in the belt and in the developed on MATLAB DAQ toolbox; it provides a seamless rectifier. In this paper, we only calibrate thresholds h1 and h2 , linkage between DAQ and data analysis in the MATLAB en- while assuming for residual thresholds h3,up and h3,down values vironment. The developed DAQ system collects all selected of (0.04) and (−0.04), respectively. signals: alternator voltage, alternator current, battery voltage, To apply the threshold selection methods introduced previ- battery current, load current, motor RPM, alternator RPM, and ously, an extensive number of experimental tests were conducted alternator field voltage. The DAQ system is also used to control to generate the estimation of a residual pdf. Thresholds h1 and motor speed and electric load current level as programmed. An h2 are selected by the statistical optimal threshold method, and important function of the test bench is to simulate EPGS faults their final values are 0.053 and 0.13, respectively (see Fig. 15). This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
SCACCHIOLI et al.: MODEL-BASED DIAGNOSIS OF AN AUTOMOTIVE ELECTRIC POWER GENERATION AND STORAGE SYSTEM 11
Idc Vf load current test bench motor speed Vdc input profiles measured ωm fault Ieq residuals signals I dc isolation L eq Vf equivalent EPGS model eq Vdc
Fig. 16. Experimental validation: Block diagram of the EPGS diagnostics validation process.
Fig. 15. Statistical optimal thresholds: h1 (top) and h2 (bottom).
C. Electric Power Generation and Storage Diagnostics Experimental Results In this section, we present experimental results of the diag- nosis of two components of the alternator (belt slip and diode bridge rectifier). Experimental evaluation results were obtained for diagnosis of the three faults. However, because of article size limitation, only diode and belt faults have been discussed. Reg- ulator fault and corresponding threshold calibration are more straightforward than the other two faults. Fig. 17. Experimental validation of the EPGS diagnosis: Load current input After threshold calibration is performed, effectiveness of the profile (top) and motor speed input profile (bottom). EPGS diagnosis strategy and the selected threshold needs to be experimentally validated. The experimental validation of the fault diagnosis algorithm is carried out offline and following the For the first 60 s, no fault happens; during 60–120 s, a short scheme in Fig. 16. belt slip fault is introduced from 80 to 88 s; from 120 to 180 s, In the validation phase, it is important to check diagnostic sys- the diode fault is injected on the whole period. The generated tem performance under all possible operating conditions both fault residual and corresponding fault signature are shown in normal and faulty. A DOE methodology is adopted to validate Fig. 18. Checking with fault isolation logic given in Table II, performance of the system and selected threshold values. In this we can conclude that for the first 60 s, no fault is detected; from paper, because of size limitation, only one example of valida- 80 to 90 s, a belt slip fault is detected; and from 120 to 180 s, tion was shown and discussed. In particular, a combined 180 s a diode fault is detected. The conclusions match exactly with experimental data are used as a diagnosis validation example. the experimental setup. Correct FDI of this validation shows Input profiles, which are shown in Fig. 17, are composed of effectiveness of the fault diagnosis algorithm and threshold se- three identical sections. lection methods. This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
12 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS
Fig. 18. Experimental validation of the EPGS diagnosis: residuals (top) and fault signatures (bottom).
V. C ONCLUSION AND FUTURE WORK ACKNOWLEDGMENT The authors would like to thank General Motors for the finan- This conclusive section presents a summary of the main ac- cial and technical support and the people of the laboratory and complishments presented in this paper, and a description of fur- facilities at the Center for Automotive Research, The Ohio State ther investigations of this research. Main contributions of this University, Columbus, for their assistance with the experiments. paper are as follows: In particular, they thank C. Suozzo. They would also like to thank 1) development and experimental validation of an accurate Prof. P. Pisu of Clemson University, Clemson, SC, for his valu- EPGS system model; able discussions and support during the first phase of this study, 2) development and experimental validation of an equivalent and Prof. D. J. Perreult of the Massachusetts Institute of Technol- simplified model of the alternator and, then, of the EPGS ogy, Cambridge, for his kind help during bibliographic review. system; 3) design, implementation, and experimental validation of a REFERENCES robust diagnostics for belt and diode faults; [1] V. Caliskan, “Modeling and simulation of a claw-pole alternator: Detailed 4) application of an optimal threshold selection and calibra- and average models,” Laboratory for Electromagnetic and Electronic Sys- tion method based on statistical analysis of experimental tems, Massachusetts Institute of Technology, Cambridge, Tech. Rep. 00- 009, Oct. 12, 2000. tests. [2] H. Bai, S. Pekarek, J. Tichenor, W. Eversman, D. Buening, G. Holbrook, Experimental results showed that the diagnosis strategy is M. Hull, R. Krefta, and S. Shields, “Analytical derivation of a coupled- effective in detecting and isolating the belt slipping fault and the circuit model of a claw-pole alternator with concentrated stator windings,” IEEE Trans. Energ. Convers., vol. 17, no. 1, pp. 32–38, Mar. 2002. diode bridge rectifier fault. [3] V.Caliskan, D. J. Perreault, T. M. Jahns, and J. G. Kassakian, “Analysis of Main future work that should be conducted in this research three-phase rectifiers with constant-voltage loads,” IEEE Trans. Circuits includes the following: Syst. I, Fundam. Theory Appl., vol. 50, no. 9, pp. 1220–1227, Sep. 2003. [4] G. D. Marques, “A simple and accurate system simulation of three-phase 1) expansion of the diagnostic system to include other alter- diode rectifiers,” in Proc. 24th Annu. Conf. IEEE Ind. Electron. Soc., Aug. nator faults, such as winding and bearing faults; 31–Sep. 4, 1998, pp. 416–421. 2) design a hierarchical diagnosis algorithm that takes into [5] G. Henneberger and S. Kuppers, “Field calculation and dynamic simula- tion of a claw-pole alternator,” in Proc. 7th Int. Conf. Electr. Mach. Drives, account sensor and battery faults; Durham, U.K., Sep. 11–13, 1995, pp. 286–290. 3) integration of alternator diagnosis with other diagnostics, [6] S. C. Tang, T. A. Keim, and D. J. Perreault, “A thermal modeling of Lun- including battery and sensors; dell alternators,” IEEE Trans. Energ. Convers., vol. 20, no. 1, pp. 25–36, Mar. 2005. 4) development of a supervisory diagnostics system for the [7] Z. M. Salameh, M. A. Casacca, and W. A. Lynch, “A mathematical model vehicle power system; for lead-acid batteries,” IEEE Trans. Energ. Convers., vol. 7, no. 1, pp. 93– 5) validation of the diagnostics algorithm onboard in a real 98, Mar. 1992. [8] A. Ferreira, J. Pomilio, G. Spiazzi, and L. de Araujo Silva, “Energy vehicle environment. management fuzzy logic supervisory for electric vehicle power supplies This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
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system,” IEEE Trans. Power Electron., vol. 23, no. 1, pp. 107–115, Jan. Giorgio Rizzoni (F’04) received the B.S., M.S., and 2008. Ph.D. degrees from the University of Michigan, Ann [9] H. Blanke, O. Bohlen, S. Buller, R. W. De Doncker, B. Fricke, Arbor, in 1980, 1982 and 1986, respectively, all in A. Hammouche, D. Linzen, M. Thele, and D. U. Sauer, “Impedance mea- electrical and computer engineering. surements on lead-acid batteries for state-of-charge, state-of-health and He is the Ford Motor Company Chair in electrome- cranking capability prognosis in electric and hybrid electric vehicles,” J. chanical systems, and a Professor with the Depart- Power Sources, vol. 144, no. 2, pp. 418–425, 2005. ments of Mechanical and Aerospace and Electrical [10] F. Huet, “A review of impedance measurements for determination of and Computer Engineering, The Ohio State Univer- the state-of-charge or state-of-health of secondary batteries,” J. Power sity, Columbus, where since 1999, he has been the Sources, vol. 70, no. 1, pp. 59–69, 1998. Director of the Center for Automotive Research, an [11] M. Urquidi-Macdonald and N. A. Bomberger, “Predicting failure of sec- interdisciplinary university research center with the ondary batteries,” J. Power Sources, vol. 74, no. 1, pp. 87–98, 1998. Ohio State University College of Engineering. His research interests include [12] J. Yan, W. Li, and Q. Zhan, “Failure mechanism of valve-regulated lead- future ground vehicle propulsion systems, including advanced engines, electric acid batteries under high-power cycling,” J. Power Sources, vol. 133, no. 1, and hybrid-electric drivetrains, advanced batteries, and fuel cell systems. pp. 135–140, 2004. Dr. Rizzoni became a Fellow of the Society of Automotive Engineers in [13] E. Meissner and G. Richter, “Battery monitoring and electrical energy 2005. He is the recipient of the 1991 National Science Foundation Presidential management: Precondition for future vehicle electric power systems,” J. Young Investigator Award, and of several other technical and teaching awards. Power Sources, vol. 116, no. 1–2, pp. 79–98, 2003. [14] X. Zhang, H. Uliyar, L. Farfan-Ramos, Y. Zhang, and M. Salman, “Fault diagnosis of automotive electric power generation and storage systems,” in Proc. Int. Conf. Control Appl., Yokohama, Japan, Sep. 8–10, 2010, pp. 719–724. [15] Y. Zhang, S. Rajagopalan, and M. Salman, “A practical approach for belt slip detection in automotive electric power generation and storage system,” presented at the Aerosp. Conf., Big Sky, MT, Mar. 6–13, 2010. [16] M. M. Hamdan, G. Holler, K. A. Grolle, J. M. Macnamara, and K. A. Thakkar, “System and method for detecting alternator condition,” U.S. Patent 6 862 504, 2005. [17] A. Moyes, G. M. Burt, J. McDonald, J. R. Capener, J. Dray, and R. Goodfellow, “The application of expert systems to fault diagnosis in alternators,” in Proc. 7th Int. Conf. Electr. Mach. Drives, 1995, pp. 171– Mutasim A. Salman (SM’08) received the Bache- 175. lor’s degree in electrical engineering from the Uni- [18] A. Scacchioli, G. Rizzoni, and P. Pisu, “Model-based fault diagnosis de- versity of Texas at Austin, Austin, and the M.S. and sign for an electrical automotive system,” in Proc. Int. Mech. Eng. Congr. Ph.D. degrees in electrical engineering with special- Expo. ASME Dynam. Syst. Control Div., Chicago, IL, Nov. 5–10, 2006, ization in systems and control from the University of pp. 315–324. Illinois at Urbana-Champaign, Urbana-Champaign. [19] A. Scacchioli, G. Rizzoni, and P. Pisu, “Hierarchical model based fault He also received the executive M.B.A. degree from diagnosis for an electrical power generation storage automotive system,” Michigan State University. in Proc. 26th IEEE Amer. Control Conf., New York City, NY, Jul. 11–13, In 1984, he joined the GM R&D staff, Warren, MI, 2007, pp. 2991–2996. where he is currently a Laboratory Group Manager [20] W. Li, C. Suozzo, S. Onori, G. Rizzoni, M. A. Salman, and F. Zhang, and a Technical Fellow with the Electrical and Con- “Experimental calibration and validation of fault diagnosis of automotive trols Integration Laboratory, General Motors (GM) Research and Development electric power generation system,” in Proc. ASME Dynam. Syst. Control Center. He is responsible for the development and validation of algorithms for Conf., Ann Arbor, MI, Oct. 20–22, 2008, pp. 1317–1324. state of health monitoring, diagnosis, prognosis, and fault tolerant control of ve- [21] K. B. S. HanSung, “Dynamic battery modeling in hybrid electric vehicles,” hicle critical systems. He has pioneered the work on integrated chassis control in M.S. Thesis, Dept. Mech. Eng., The Ohio State University, Columbus, the late 1980s that led to the production of GM “Industry First” Stabilitrak1 and 2002. then to Stabilitrak3. He has extensive experience in hybrid vehicle, modeling, [22] C. De-Shiou, “Sliding mode observers for automotive alternators,” Ph.D. control, and energy management strategies. He holds 36 patents of which seven dissertation, Dept. Electr. Eng., The Ohio State University, Columbus, of them have already been used in products. He has coauthored more than 56 1998. refereed technical publications and a book. [23] J. Chen and R. J. Patton, Robust Model-Based Fault Diagnosis for Dy- Dr. Salman has several GM awards that include four GM prestigious Boss namic Systems. Boston, MA: Kluwer, 1999. Kettering, three McCuen, and two President and Chairman awards. [24] H. L. Van Trees, Detection, Estimation, and Modulation Theory.New York: Wiley, 1958.
Annalisa Scacchioli (M’04) received the Laurea de- gree in electrical engineering and the Ph.D. degree in electrical engineering and computer sciences from the University of L’Aquila, L’Aquila, Italy, in 2000 and 2005, respectively. She is a Visiting Assistant Professor of mechan- ical engineering with New York University (NYU) School of Engineering. Before joining NYU, she was a Postdoctoral Researcher with the Center for Au- tomotive Research, The Ohio State University, from 2005 to 2006, with the Department of Civil and Envi- Weiwu Li received the B.S. degree in mechanical en- ronmental Engineering, University of California at Berkeley, from 2007 to 2008, gineering from Jilin University, Changchun, China, and with the Daniel Guggenheim School of Aerospace Engineering, Georgia in 1997, the M.S. degree in mechanical engineering Institute of Technology, from 2008 to 2010, and a Visiting Researcher with Ford and the Ph.D. degree in control engineering from Motor Company’s Research and Advanced Engineering, Dearborn, MI, from Zhejiang University, Hangzhou, China, in 2000 and 2008 to 2010. Her research interests include the broad and interdiscliplinary 2004, respectively, and the M.S. degree in mechan- area of modeling, identification, feedback, control, and diagnosis applied to ical engineering from The Ohio State University, complex-engineered systems, with focus on automotive, civil-structural, and Columbus, in 2008. air-transportation systems. He is currently a Research Engineer with Cum- She is a member of the International Federation of Automatic Control and mins, Columbus, IN, working on alternative fuel the American Society of Mechanical Engineers. engines. This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
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Simona Onori (M’11) received the Laurea degree Xiaodong Zhang (M’02) received the B.S. degree (summa cum laude) from the Department of Electri- from the Huazhong University of Science and Tech- cal and Computer Engineering, University of Rome nology, Wuhan, China, in 1994, the M.S. degree from “Tor Vergata,” Rome, Italy, the M.S. degree in electri- Shanghai Jiao Tong University, Shanghai, China, in cal engineering from the University of New Mexico, 1997, and the Ph.D. degree from the University of Albuquerque, and the Ph.D. degree in control engi- Cincinnati, Cincinnati, OH, in 2001, all in electrical neering from the University of Rome “Tor Vergata,” engineering. in 2003, 2004, and 2007, respectively. He is currently an Associate Professor with the She is a Research Scientist with the Center for Au- Department of Electrical Engineering, Wright State tomotive Research, The Ohio State University (OSU), University, Dayton, OH. Before joining Wright State where she is also a Lecturer with the Department of University, he had more than five years of industrial Mechanical and Aerospace Engineering. Her research interests include control experience. He was a Senior Researcher with General Motor’s Research and system theory and applications. Her research is focused on model-based con- Development Center, Warren, MI, and a Program Manager for control and diag- trol design for advanced propulsion systems, energy management control, and nostics with Intelligent Automation Inc., Rockville, MD. His research interests optimization in hybrid electric vehicles (HEVs) and plug-in HEVs, fault diagno- include intelligent control systems, fault diagnosis and prognosis, fault-tolerant sis, and prognosis for automotive system applications, aging, characterization, control, and cooperative and distributed control. modeling, and identification of advanced batteries. Dr. Zhang is an Associate Editor of the IEEE TRANSACTIONS ON CONTROL Dr. Onori serves as an Associate Editor for the American Society of Me- SYSTEMS TECHNOLOGY and a member of the International Federation of Auto- chanical Engineers’ Dynamic Systems and Control Conference and American matic Control SAFEPROCESS Technical Committee. Control Conference, the Guest Editor for the International Journal of Power- train, and an Associate Editor for the Society of Automotive Engineer’s Journal of HEVs. She is the recipient of the 2012 Lumley Interdisciplinary Research Award by the OSU College of Engineering and TechColumbus 2011 Outstand- ing Technology Team Award.