mechanical engineering - march 2013

A hybrid powertrain contains at least two power sources, typically an internal combustion engine (ICE) as the primary power source, and a secondary power source, such as an electric motor. Hybrids save fuel by exploiting the additional fl exibilities available in the design and operation of the hybrid powertrain, including load leveling, regenerative braking, engine shut-down, component right-sizing, and if available, manipulat-ing the electronic continuously variable transmission. The added fl exibility in the powertrain operation and design means model-based analysis, simulation and control play an even more prominent role in vehicle design and operation. In other words, having the best components (bat-tery, motor, etc.) is not enough to ensure the success of an electrifi ed vehicle. Top-notch performance is only achieved by the proper selection of powertrain confi gura-tion, proper sizing of all components, and optimal control, in addition to fi rst-rate powertrain components.

In this article, we will review past suc-cesses and future challenges of model-based approaches for the analysis, design and control of hybrid vehicles. The take-away message is that this is a fi eld that has signifi cant potential for future research and development.

HISTORICAL PERSPECTIVEThe origin of the hybrid electric vehicle (HEV) dates back to 1899, when Dr. Ferdinand Porsche, then a young engineer at Jacob Lohner & Co, built the fi rst hybrid vehicle

1. The Lohner-Porsche

gasoline-electric Mixte, used a gasoline engine rotating at a constant speed to drive a dynamo, which charged a bank of accumulators. These, in turn, fed current to electric motors contained within the hubs of the front wheels. In the United States, the fi rst reference to a hybrid-electrical vehicle may be found in a 1905 patent fi ling by H. Pieper

1, a German-born

Belgian. By the time the patent was issued in 1908, reciprocating ICEs had improved signifi cantly and hybrid vehicles, much like battery-electric vehicles (BEVs), had disappeared from the market.

Nearly a century later, in 1993, eight agencies of the U.S. government formed a partnership with the three major automo-tive manufacturers to advance vehicle technology, with the goal of producing highly fuel-effi cient vehicles. The Partner-ship for a New Generation of Vehicles (PNGV)

3, involved DaimlerChrysler, Ford,

and General Motors. It’s most widely pub-licized, but not only, goal was to develop production vehicles capable of achieving

80 miles per gallon (approximately 3 liters per 100 km) by 2003. The program ended in 2001, with the automakers having demonstrated (but not launched production of) the GM Precept, the Ford Prodigy and the Chrysler ESX. All of these vehicles were characterized by the use of lightweight materials, hybrid pow-ertrains, and other technological innova-tions. PNGV provided the opportunity for substantial research to be carried out in collaborations among the automotive companies, their suppliers, national labs, and universities.

HYBRID VEHICLESHybrid vehicles are generally classifi ed ac-cording to their powertrain architecture as shown in Figure 1. A series hybrid uses a large electric motor to propel the vehicle while using the ICE and a second electric machine to generate electricity to directly provide propulsion, or to charge a battery. The Diesel-Electric propulsion system used in locomotives would be an example of such architecture, and a similar concept can also be implemented in a hydraulic hybrid using hydraulic pumps and mo-tors, and accumulators. A parallel hybrid can blend mechanical power from the ICE and the electric motor(s) through appro-priate mechanical coupling and transmis-sion elements to deliver mechanical power to the road or to recharge the battery. The Honda Civic Hybrid is an example, and such an architecture can also be realized in hydraulic hybrids using hydrostatic transmissions. A third confi guration, the one that is most commonly found among production hybrid passenger vehicles today, is the power-split hybrid, in which the properties of both a series and parallel hybrids are achieved, frequently by using one or more planetary gear sets to couple two electric machines, to the ICE on one side, and to the driveline on the other. The

Hybrid and electrified vehicles have demonstrated significant fuel economy improvement, especially for city driving, and are gaining market acceptance. Hybrid and plug-in hybrid vehicles have captured about 3.3% of the U.S. market 1, with an annual sales growth this year of 65%, and they also account for more than 20% of the Japanese market 2. In

Japan, the high market penetration is due to government incentives and high fuel prices (~ $8/gallon). The success of hybrid vehicles in Japan demonstrates the potential for hybrid vehicles in other urban markets with high fuel prices, such as large cities in Europe and Asia.

Hybridandvehicles:

THE ROLE OF DYNAMICS AND CONTROL

BY Giorgio Rizzoni and Huei Peng

10 MARCH 2013

0313MEM_DSC_RizzoniNew.indd 10 1/31/13 3:12 PM

F o c u s o n D y n a m i c S y s t e m s & C o n t r o l

Toyota Prius is a commercially successful example of this architecture.

An HEV is considered charge sustain-ing if the electric energy storage system is recharged only by power supplied by the ICE or by regenerative braking. If, on the other hand, the vehicle is designed to deplete stored energy in the battery during the course of a trip, ending the trip with a lower state of charge than at the start and requiring re-charging from the electrical grid, the vehicle is called charge depleting, or a plug-in hybrid (PHEV). The Chevrolet Volt is the fi rst commercially produced ex-ample of such an architecture, although this concept was proposed and demonstrated in the early 1990s

4.

The charge-depleting vs. charge-sustain-ing distinction is not of practical use in hy-draulic or mechanical hybrids, as very little energy can be stored in an accumulator or a fl ywheel, although during the PNGV years there was signifi cant effort made to develop commercial fl ywheel batteries that would be capable of storing signifi cant energy.

GROUND VEHICLE MODELING AND ENERGY ANALYSISTo understand the potential bene� ts of any hybrid vehicle architecture we fi rst need to understand vehicle energy and power re-quirements, which are imposed by its duty cycle. Duty cycles vary dramatically de-pending on the intended use of the vehicle, and range from the driving habits of the average consumer to requirements imposed by commercial or military vehicles.

Forces acting on a vehicleThe power required to motor a vehicle is

known as the road load, and can be broken into several components: rolling resistance, vehicle internal frictional losses, aerody-namic drag, grade (if any), and inertial loads (accelerating the vehicle). A small portion of the power is used for accessory loads (e.g., alternator, air-conditioning compressor, power steering pump). In trucks or utility ve-hicles, a signifi cant portion of the power may be used to drive equipment (e.g., hydraulic pumps) in addition to the accessory loads. Here we present a basic analysis of the forces acting on a vehicle, considering the vehicle as a lumped mass, and only accounting for longitudinal motion

5-6. With reference to

Fig. 2, consider a vehicle of mass M, moving at a speed V on a grade of angle α. The total road load is defi ned as the force impeding

FIGURE 1 Three general types of hybrid powertrain configurations.

vehicles:

vehicle motion:Ftotal(t) = Fa (t) + Fg (t) + Fr(t) + Facc (t) 1

While the total power associated with this load is:

Ptotal (t) = Ftotal (t) •V(t) 2

The aerodynamic drag is modeled by:

Fa = Fa (V) = ½ ρa Af Cd Veff 2 3

where Cd is the drag coeffi cient, Af is the projected vehicle frontal area, ρa is air density, Veff = V - Vwind is the effective air/vehicle relative velocity, where Vwind is the resultant vector of the head wind force.

A wide range of factors affect the total rolling resistance of the vehicle, including tire temperature, infl ation pressure, and age. The rolling resistance force is often approximated by: Fr(V, α ) = MgcosαCr(V) 4

The rolling resistance coeffi cient, Cr depends on the type of tire, the tire pres-sure, temperature, the vehicle mass and the road surface. Typically Cr = 0.013 – 0.02 for smooth pavement

9.

The force due to the road grade can be modeled as Fg = Mg sin α where α is the angle of the road with the horizontal (see Fig. 2). Thus, the total aerodynamic drag, rolling resistance and grade force resisting the motion of the vehicle is:

Ftotal = ½ ρa Cd Af V 2eff + CrMgcosα + Mg sin α

To overcome the road load, Ftotal(t), the pow-ertrain (conventional or hybrid) must supply a motive force at the wheel, FMW (t), which can be related to the wheel torque TMW (t), using the effective wheel radius, RW, by:

FMW (t) = RW TMW (t) 6

The balance of these forces determines the net acceleration (or deceleration) of the vehicle, according to Newton’s second law of motion:

Meff dV = FMW (t) - Ftotal (t) 7

where Meff is the effective mass of the vehicle referenced to the wheels, that is the sum of the vehicle mass plus the equivalent mass result-ing from the moments of inertia of all rotating components, including engine and driveline. The latter is dependent on the transmission speed ratio if the vehicle is equipped with a multi-speed transmission.

Using the total load we arrive at the total required power:

in which Pacc is the power required for the accessories.

DRIVING CYCLESTo evaluate fuel economy, emissions and performance of a vehicle, it is customary to use standard driving cycles, e.g. the Envi-ronmental Protection Agency (EPA) driving cycles, which are typically implemented on a

5

Ptotal =[ ½ ρa Cd Af V 2eff + CrMgcosα + Mg sin α ]V + Pacc 8

MARCH 2013 11


chassis dynamometer in the case of light-duty vehicles

7. These cycles are represented

as traces of vehicle speed versus time. For example, the federal test procedure (FTP) schedule, commonly used to assess a light-duty vehicle’s fuel economy and emissions covers a distance of 11.04 mi (17.77 km), for a duration of 1,874 s at an average speed of 21.2 mi/h (34.1 km/h). Prior to executing the FTP, the vehicle is kept in cold soak (i.e., a stabilized ambient thermal state) over-night. At the beginning of the test, the engine is fi red and operated during the warm-up phase. At the end of the fi rst portion of the cycle (i.e., the Federal Urban Driving Schedule, FUDS, shown in Fig. 3), the vehicle is stopped for a rest period of 10 minutes (hot soak). Then the beginning of the second FUDS cycle is executed. This overall procedure is designed to provide a representative sample of vehicles during their warm-up phase and operating at nominal operating temperatures.

Using the road load model, the force at the wheel is given by:

FMW = ½ ρa Cd Af V 2eff + Mg cos α Cr + Mg sin α + (M + Meq) dV 9

Assuming no grade (α = 0) and no wind, the power at the wheel is given by:

PMW = ½ ρa Cd Af V 3 + Cr Mg V + MeffV dV

The energy to be provided at the vehicle wheels over the cycle is then given by:

EMW= PMWdt = ½ ρa Cd Af V 3 dt + Cr Mg V dt + Meff V dV

In 11, the integral terms are vehicle independent, but are cycle dependent, while their coeffi cients are vehicle dependent.

One method for improving fuel economy is to recover and store as much of the vehicle’s energy as possible during decelerations. The process of storing the energy is commonly known as regen-erative braking. Typically, the recovery of brake energy is only a fraction of the available energy. Analyzing a vehicle driving cycle to understand the potential availability of kinetic energy recovery

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is very important in selecting the appropriate hybrid vehicle architecture, and in choosing the best means for kinetic energy recovery

8-9. As an example, consider the FUDS cycle, shown in

Figure 3, a relatively gentle driving cycle, chosen to represent urban driving, it would theoretically be possible to recover 3.6 MJ of energy if all of the kinetic energy during deceleration can be recovered. However, for safety reasons, regenerative braking systems cannot completely replace friction brakes.

In addition to kinetic energy recovery, hybrid powertrain architectures also permit engine downsizing, as well as the ability to implement idle reduction strategies using engine start-stop technologies. Further, the electric drivetrain makes it possible to operate, in a torque-speed engine map, in regions with lower brake-specifi c fuel consumption.

ENERGY STORAGE SYSTEMSEnergy storage is the lynchpin of a hybrid vehicle. In HEVs such storage comes from electrochemistry, but it is also possible to store energy using fl uid power and mechanical systems. In the present section we examine the most common energy storage system technologies. Figure 4 is a Ragone plot depicting the relationship between specifi c power and specifi c energy of vari-ous energy storage devices, including for comparison also energy conversion devices such as the internal combustion engine and the fuel cell. Clearly some energy storage devices are better suited for providing power, while others are more effective at providing longer-term energy. Even within one specifi c technol-

FIGURE 2 Forces acting on a vehicle

FIGURE 3 FUDS driving cycle and related parameters, including power available for regeneration, shown in red.

FUDS -‐ Federal Urban Driving Schedule

Cycle parameters

Duration = 1372 s

Distance = 12.368 km

Mean speed = 9.01 m/s

Statistics:

Mean positive acceleration = 0.497 m/s2

Mean negative acceleration = -‐0.345 m/s2

Mean aerodynamic power = 0.83 kW

Mean aerodynamic energy = 1.14 MJ

Mean positive inertial power = 6.43 kW

Mean negative inertial power = -‐4.46 kW

Inertial energy expenditure = 3.6 MJ

Regeneration potential = -‐3.6 MJ

Figure 3 -‐ FUDS driving cycle and related parameters, including power available for regeneration, shown in red.

α Mg

Fg

Facc

FaV

Fr

FaAerodynamicresistance force

FrRolling resistance force

FgGrade resistance force

FaccAccessories and driveline losses

V Vehicle speed

FUDS: FEDERAL URBAN DRIVING SCHEDULE

CYCLE PARAMETERS ...Duration = 1372 sDistance = 12.368 kmMean speed = 9.01 m/s

STATISTICS ...Mean positive acceleration = 0.497 m/s2

Mean negative acceleration = -0.345 m/s2

Mean aerodynamic power = 0.83 kWMean aerodynamic energy = 1.14 MJMean positive inertial power = 6.43 kWMean negative inertial power = -4.46 kWInertial energy expenditure = 3.6 MJRegeneration potential = -3.6 MJ

dt

dtcycle cycle cycle

10

11dt

dt

12 MARCH 2013


Figure 4 -‐ Specific Power versus Specific Energy for Various Short-‐Term Energy Storage Systems [10].


ogy class (e.g. batteries or fl ywheels), designs can be optimized for energy or power applications. For example, in a plug-in hybrid-electric vehicle or in a pure electric vehicle, energy capacity is a dominant consideration, while in a commer-cial vehicle that operates according to a duty cycle with frequent starts and stops, for example a delivery van or a refuse hauling vehicle, a technology capable of recovering signifi cant power may be preferred over one that has signifi cant energy capacity.

ELECTROCHEMICAL ENERGY STORAGEBatteries, modeling and state of charge and state of health estimation

Thomas Edison tried to produce practical battery-electric vehicles about a century ago. Low battery energy density and reliability prevented success. Batteries continue to be the performance and cost bottleneck for all electrifi ed vehicles today. Because batteries are expensive and subject to failure if not treated properly, vehicle manufacturers are faced with a tradeoff between a larger battery pack with higher cost allowing for less stressful demand, and a right-sized battery pack that causes batteries to be operated near their practical limits, with accelerated aging and reliability concerns. As an example, the NiMH battery pack in the Toyota Prius is only used in a region of SOC that corresponds to at most 25% of the battery capacity. Oversizing the battery pack has resulted in very reli-able operation, with vehicles (and battery packs) currently on the road approaching ten years of service. To ensure safe, reliable and effi cient operations of the traction batteries, an effective battery management sys-tem (BMS) must be developed. A BMS serves many functions that pri-marily rely on sensors, including temperature monitoring, over-charging and over-discharging prevention, and cell-to-cell imbalance detection and mitigation. Two of the most notable examples are the estimation of battery SOC and State-of-Health (SOH)

11-12. For these two BMS functions, ac-

curate battery models that can be implemented in real-time are necessary.Both electrochemical models

13-15 and equivalent circuit models

16-18 have been

applied to battery SOC and SOH estimation (see Fig. 5). The electrochemical models describe distributed electrochemistry behaviors in the electrodes and electrolyte and typically deploy partial differential equations with a large number of unknown parameters. The complexity often leads to signifi cant computer memory and computation requirement and the models are usually over-parame-terized and robust.

Equivalent circuit battery models use standard electric circuit elements as the building blocks to describe the battery voltage-current relationship. Despite the simple model structure, when proper complexity is included, battery behavior can be reconstructed with high accuracy. As an example, given the battery cur-rent profi le, the voltage can be predicted with a standard deviation error as low as 6 mV

16, which is as accurate as the best validated results from electrochemi-

cal models, and more than adequate for practical BMS functions. Surprisingly, a simple RC circuit plus a resistance is adequate and robust as a model for SOC estimation

16.

SOH has a different meaning for different applications. In battery packs used in HEVs, ohmic loss or impedance is important because it affects the maximum power that can be delivered by the battery. For PHEVs or BEVs the key perfor-mance bottleneck is battery capacity. Battery impedance is easy to calculate using the voltage-current information. The battery capacity can be measured when the battery is fully discharged and then charged in a laboratory. The fi eld of SOH estimation is a relatively immature fi eld and further development is still needed.

Electrochemical, or double-layer, or super capacitorsElectrochemical capacitors are a special class of capacitors with extraor-

dinarily large energy densities compared to traditional capacitors, as well as

signifi cantly greater power densities in com-parison to batteries. While supercapacitors are not appropriate replacements for batteries in most hybrid applications, they are well suited to certain duty cycles that require high-power capabilities and long life. An example of such an application is the refuse-hauling truck developed by Oshkosh Truck Corporation

19, in

which a supercapacitor energy storage system was used to exploit the frequent start-stop duty

cycle of a residential refuse hauling truck.

Hydraulic energy storage systemsAll hybrid vehicles available on the pas-

senger car market today are HEVs. However, for heavy-duty applications hybrid hydraulic vehicles (HHVs) are excellent alternatives because they are more cost effective and have much higher power density and reliability. In addition, hydraulic actuators typically do not require a dedicated cooling system, which is advantageous for cost, reliability and packag-ing reasons. A major challenge for hydraulic actuators is the fact that hydraulic accumulators have much lower energy density, which means they are excellent “power boost” devices but do not have adequate energy for extended pure hy-draulic driving range. The short burst of power means they are only suitable for urban opera-tions and not highway driving. The size of the hydraulic accumulators and reservoirs also limit the applicability of hydraulic hybrid technolo-gies to passenger cars.

Hydraulic hybrids are already available on the heavy vehicle market. Eaton Corp. has a parallel hybrid architecture (i.e., the Eaton Hydraulic Launch Assist), which reduces fuel consumption by about 20-30%, and a serial hydraulic hybrid system which achieves a 50% fuel saving in urban cycles. Parker Hannifi n

FIGURE 4 Specific Power versus Specific Energy for Various Short-Term Energy Storage Systems 10.

MARCH 2013 13


launched a serial hydraulic system in 2010 with similar fuel saving. Several other companies, including Bosch Rexroth, Caterpillar, and Komatsu are also developing hydraulic trucks or construction machines such as excavators, mostly based on parallel or power split confi gurations. We expect hydraulic hybrids to gain signifi cant market share in the near future.

Mechanical (kinetic) energy storage systemsIn addition to electrochemical and fl uid power energy stor-

age, energy can also be stored mechanically using a rotating disc (i.e., fl ywheel). Investigation into the concept of storing energy in a rotating disc for passenger cars was prompted by rising gasoline prices in the 1970’s. Andrew Frank at the University of Wisconsin investigated the idea of using a large fl ywheel to store energy and allow engine-off operations

20.

This concept was implemented in a prototype vehicle using a hydrostatic continuously variable transmission (CVT) and a large steel fl ywheel, which showed fuel economy improve-ments of up to 33%.

The introduction of kinetic energy recovery in race cars, starting with the 2009 Formula 1 racing season, prompted the development of regenerative braking systems with high power density, resulting in a high speed fl ywheel system with a full toroidal CVT with fl ywheel speeds exceeding 60,000 rev/min and 60 kW of power with a total system weight of only 25 kg

21. Flybrid Systems, under the license from toroidal CVT de-

signer Torotrak, has been working with car manufacturers to bring the technology to production vehicles and have reported that up to 21% of the energy needed to propel a vehicle can be recovered from braking

21.

MODELING AND CONTROL OF ELECTRIFIED VEHICLESModeling: hierarchy of models

Control strategies for hybrid electric vehicles are aimed at meeting several simultaneous objectives. The primary one is fuel consumption, but minimizing engine emissions and

Figure 5 -‐ Electrochemical models and equivalent circuit models have both been used to describe battery dynamic behavior and for battery management system functions. Figures from [14] and [18].

FIGURE 5 Electrochemical models and equivalent circuit models have both been used to describe battery dynamic behavior and for battery management system functions. Figures from 14 and 18.

Load IR2

R1V0

Source

C (=1/∫c2R2)

maintaining or enhancing drivability are also important. Regardless of the powertrain topology, the essence of the HEV control problem is the instan-taneous management of the power fl ows from energy converters to achieve the control objectives. One important characteristic of this general problem is that the control objectives are mostly integral in nature (fuel consump-tion and emission per mile of travel), or semi-local in time, such as driv-ability, while the control actions are local in time. Furthermore, the control objectives are often subject to integral constraints, such as maintaining the battery SOC within a prescribed range. Much can be learned from global optimization exercises over a priori known driving cycles. However, these solutions do not directly lend themselves to practical implementations.

In addition to minimizing vehicle energy use, vehicle designers must also meet a number of other practical constraints that present tradeoffs. Exhaust gas emissions, which are tightly regulated, can be signifi cantly affected in a hybrid propulsion system. For example, engine starting and stopping cycles may have an impact on the thermal and chemical environment of the catalysts used in the exhaust aftertreatment system. Drivability is also strongly infl uenced by the architecture and control of a hybrid powertrain, as is vehicle noise-vibration-harshness (NVH). Miller22

presents an excellent overview of all of the challenges presented by hybrid propulsion systems, from an industrial perspective.

Each of these control problems, fuel economy and CO2 emissions mini-

mization, meeting regulated emissions constraints, achieving acceptable performance and drivability, requires the use of models appropriate for the task, and much work has been done to develop such models.

Energy modeling and instantaneous optimization as a surrogate for global optimization The optimal energy management problem in a hybrid electric vehicle

consists of fi nding the control u(t) that leads to the minimization of the performance index J, where m

f is the mass fl ow rate of fuel used:

subject to constraints that are related to: i) physical limitations in the actuators and in the stored energy; and ii) to the requirement to maintain the battery SOC within prescribed limits. Since the minimization needs to be performed over an unknown driving cycle, a realizable solution to this problem is very diffi cult, if not impossible, to achieve. Three approaches

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14 MARCH 2013


to solve this problem have been proposed over the years, namely, rule-based con-trols, local optimization solutions as surrogates of the global solution, and formal optimal control solutions, including Pontryagin’s minimum principle and dynamic programming.

A local optimization method that has demonstrated considerable success is the Equivalent fuel Consumption Minimization Strategy (ECMS), originally proposed by Paganelli

23. The method is based on accounting for the use of stored electrical

energy, in units of chemical fuel use (g/s), such that one can define an “equivalent fuel consumption” taking into account the cost of electricity:

In 13, Ebatt

is the energy capacity of the battery, Qlhv

is the lower heating value of the chemical fuel, and s(t) is the equivalent factor that assigns a cost to the use of electric-ity. Then, the global minimization problem is converted into a local minimization problem:

where the virtual fuel consumption of the electric propulsion system is approximated by the use of an equivalent cost of the electricity, representing future fuel use required to replenish the stored electrical energy used in the present. The equivalence factor is cycle dependent, and can be adapted as a function of driving, geographical and traf-fic conditions

24-25. This approach has been shown to closely approximate the global

optimal solution.Formal optimal control methods have also been applied to this horizon optimiza-

tion problem, using deterministic dynamic programming26-27

, stochastic dynamic pro-gramming

28, model predictive control, or Pontryagin’s minimum principle

29-30. Serrao

et al. in31

present a comparison of deterministic dynamic programming, ECMS and a Pontryagin solution, and show that ECMS can in fact be interpreted as a Pontryagin minimum principle solution, in which the equivalent fuel consumption is the co-state in the Hamiltonian function that is minimized to achieve the minimum

31.

Drivability anD nvHDrivability is a comprehensive term that encompasses vehicle responsiveness, operating smoothness and driving comfort

36. In general, the discomfort caused by vibrations and

accelerations depends on the vibration frequency and direction, the point of contact with the body and the duration of vibration exposure. Vibrations between 0.5 and 80 Hz are significant in exciting human body response. The most effective excitation fre-quency for horizontal vibrations lies between 1 and 2 Hz and that for vertical vibrations is from 4 to 8 Hz. Vibrations ranging from 2.5 to 30 Hz generate strong resonance with amplified magnitude of up to 200~350%, which may cause permanent damage on human organs and body parts. Drivability can be quantitatively evaluated by vehicle interior noise level, jerk amplitude and acceleration characteristics. Moreover, engine start/stop frequency is important in HEVs. Several quantitative measures used to as-sess drivability and noise, vibration and harshness (NVH) problems are discussed in

32.

Gear shifting is the most commonly studied class of drivability problem. Recent work on the control of automatic, (automated) manual, continuously variable and electrically variable transmissions is surveyed in

33. For vehicles equipped with (auto-

mated) manual transmissions, passive driveline damping is not sufficient to quickly dissipate the transient effects of abrupt torque changes in the absence of torque converters. Even in automatic and CVTs, pedal tip-in/tip-out may lead to noticeable disturbances when the torque converter by-pass clutch is locked. Similar issues also occur in BEVs and HEVs if the electric machines are rigidly coupled to the driveline. The oscillations that correspond to the fundamental driveline resonance frequency


are often referred to as shuffle vibrations34

. Another undesired response characteristic, generally referred to as shunt, arises upon initial pedal application due the high rate of change of driveline torque. Typical pedal tip-in/tip-out related drivability issues are dem-onstrated in Figure 6. Figure 6a depicts the shunt and shuffle observed in a test vehicle when operated in pure electric mode with no torque compensation. A typical response delay experienced during engine-only operation is shown in Figure 6b. This figure also illustrates the brake release bump phenomenon that commonly occurs in automatic transmission equipped vehicles.

Pedal feel problems are more likely to occur in HEVs since the driver's demand for power (or acceleration) can be met using different actuators through different paths

35. Additional

drivability considerations may arise in hybrid vehicles due to the presence of multiple (often redundant) actuators and as a result of the operating principles of supervisory control strategies

36. Due to the complex nature of

hybrid drivetrains, HEVs are usually operated in fully or partially mode-based control

37, that

is, mode selection and switching is an integral part of the supervisory control strategy. Such a mode-based control is architecture dependent (e.g., a PHEV could operate in electric-only mode or in hybrid mode, with obvious differ-ences in the control strategies implemented and in the actuators used). One common ele-ment that is present in virtually all hybrid ve-hicles is the engine start-stop function. Since the start-stop function is frequently used in hybrid vehicles, its seamless implementation is crucial for consumer acceptability

38. Mode

transitions that affect drivability in HEVs are not limited to the engine start and stop events. For example, a mode change from hybrid operation to regenerative braking may excite driveline vibrations when the gear backlash reverses direction.

Emissions optimizationThe main selling point of hybrid vehicles is their superior fuel economy. While improved fuel economy typically has a trickle-down effect on emissions, this is not always the case. A well-known example is the lab test results of retrofitted Toyota Prius vehicles with larger (~5 kWh) battery packs

39, designed to enable

plug-in operation. While the added battery en-ergy improves overall fuel economy, the longer time interval between engine starts and stops caused the catalytic converter to not heat up properly. Therefore, tailpipe emissions at least

13

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an order of magnitude worse than a produc-tion Prius were measured. In this particular case, the engine-out emission did not change much, but the catalytic converter light-off was seriously affected. Since the conversion effi ciency of a cold catalyst is very low, fast catalyst warm-up and sustainment are the keys to minimizing tailpipe emissions

40.

To include tailpipe emission, in addition to fuel economy, in the performance index, catalyst temperature needs to be added as a dynamic state in the hybrid powertrain model. The catalyst can be treated as a ther-mal mass with engine exhaust gas as a heat-ing source, and heat exchange with ambient air as the main heat loss. Then there are at least three ways to solve this combined fuel economy-emission problem. The fuel economy and emission can both be included in the cost function with tunable weights; the fuel economy optimization problem can be solved with emission as a constraint; or emission can take priority during cold start and then switch to fuel economy dominant after catalytic converter light-off.

Figure 6 -‐ Undesirable drivability issues resulting from pedal tip-‐in: experimental data from Ohio State University ChallengeX HEV.

FIGURE 6 Undesirable drivability issues resulting from pedal tip-in: experimental data from Ohi

State University ChallengeX HEV.

(a) Driveline shuffle and shunt

Figure 6 -‐ Undesirable drivability issues resulting from pedal tip-‐in: experimental data from Ohio State University ChallengeX HEV.

(b) Response delay

PREDICTIVE/ADAPTIVE ENERGY MANAGEMENT AND IMPLICATIONS OF ITSDriving cycles and velocity pro� les have great impact on the performance of hybrid ve-hicles in terms of overall energy consumption, fuel economy and emissions. It has been suggested that road type and traffi c condition, driving habits, and vehicle operation modes have various degrees of impact on vehicle fuel consumptions

41-42. In addition,

incorporating knowledge derived from intelligent transportation systems (ITS) about online driving pattern recognition and traffi c and geographical information in control strategies is another path towards the optimization of PHEV energy management

43-44.

Intelligent Transportation Systems (ITS) allow the vehicle to communicate with other vehicles and the infrastructure to collect information about surrounding and expected events in the future, e.g. traffi c condition, turns, road grade, rain, snow, temperature, etc. Such information can assist in designing algorithms such as sto-chastic dynamic programming, model predictive control, etc.

45. ITS information can

be utilized for long-term trip forecast as well as short-term velocity and power profi le prediction. Static and dynamic information including road grade and road surface conditions, speed limits, traffi c light locations and timing, and real-time traffi c fl ow speeds can be used to build a long term forecast of the overall trip to the destina-tion. At the same time, information about the immediate surroundings, such as lane changing and turning decisions of the host and surrounding vehicles, and estimation of waiting time for turning on red, left turns and stop sign queuing, is helpful for refi ning short term prediction of future driving profi le.

Advances in GPS, telecommunication, and portable computing devices will change many aspects of vehicle energy management. In the future, we will see fuel-effi cient, environment- and traffi c-aware vehicles that integrate ITS and telematic systems with electrifi ed propulsion technology to achieve optimal energy management.

FUTURE OUTLOOKAs the penetration of plug-in vehicles (PEVs) increases, their impact on the power grid cannot be neglected; thus, consideration of increased electric power demand and of the timing of vehicle charging must be included in the control/optimization process. In the future it will become necessary to analyze information in real time to quantify the effects of infrastructure, environment, and traffi c fl ow on vehicle fuel economy and emissions, and to permit the application of forecasting and optimization methods for the energy management of PEVs.

The electric grid and the transportation system are the two largest sectors that produce greenhouse gas emissions. When large numbers of vehicles are electrifi ed and draw power from the electric grid, it is important to aim for reduced overall green-house gas emissions, rather than just shifting emissions from tailpipes to power plant stacks. Alternatives to coal or natural gas to provide energy for electrifi ed vehicles are therefore attractive. Using electricity generated from wind power to charge vehicles is one such alternative: not only does electricity from wind have a lower carbon footprint, but plug-in vehicle charging is also an effective way to mitigate wind intermittency. Electricity generated from solar power has similar characteristics, except that it is generated during daytime and is better suited for at-work PEV charging rather than charging at home.

In 36 states and the District of Columbia there is now a renewable power portfolio mandate. Other countries have already set an example on how to integrate renew-ables in the power grid, e.g., Denmark primarily uses hydropower to smooth varia-tions in wind generation. Controlling the charging of plug-in vehicles to alleviate the impact to the grid has been studied, including the idea of using plug-in vehicles as ancillary services to the grid, possibly with signifi cant renewable power sources con-nected to the grid. Modeling and simulating this integrated system requires informa-tion on detailed grid load profi les, power generation pricing and carbon emissions, wind statistics, vehicle usage statistics. In addition, charging control must balance multiple factors: grid stability, fully-charging all vehicles, minimizing data collection and communication, and overall system carbon emission minimization.

In conclusion, the design, modeling and control of hybrid vehicles is a subject rich in research opportunities for the dynamic systems and control community. We hope to have conveyed in this article the extent to which this subject lends itself to advances in dynamic modeling and model-based control.

16 MARCH 2013


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Giorgio Rizzoni is the Ford Motor Co. Chair in Electromechanical Systems and professor of mechanical and aerospace engineering and director of the Center for Automotive Research at Ohio State University (OSU). He is a Fellow of IEEE and SAE. His research activities are related to advanced propulsion systems for ground vehicles, energy efficiency, alternative fuels, the interaction between vehicles and the electric power grid, vehicle safety and intelligence, and policy and economic analysis of alternative fuels and vehicle fuel economy.

Huei Peng is a professor at the Department of Mechanical Engineering at the University of Michigan. He partici-pated in the design of several military and civilian concept hybrid vehicles, including FTTS, FMTV, Eaton parallel electric hybrid, and Super-HUMMWV. He is currently the U.S. Director of the Department of Energy sponsored Clean Energy Research Center—Clean Vehicle Consortium, which supports 40 clean vehicle research projects.

ABOUT THE AUTHORS

AcknowledgementsWe would like to acknowledge the invaluable contributions of our colleagues and students at University of Michigan and Ohio State University. We are grateful for financial support from the automotive industry as well as from the U.S. National Science Foundation, Department of Energy, and Army.



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