Reduction of particulate emissions in diesel hybrid electric vehicles with a PMP-based control strategy

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Energy Procedia 00 (2018) 000–000 Energy Procedia (2018) 000–000 Energy Procedia 148 (2018) 994–1001 Energy Procedia 00 00 (2017) 000–000

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73rd Conference of the Italian Thermal Machines Engineering Association (ATI 2018), 12-14 73rd Conference of the Italian Thermal Machines September 2018, Engineering Pisa, Italy Association (ATI 2018), 12-14 September 2018, Pisa, Italy

Reduction particulate emissions in diesel hybrid electric vehicles Reduction of of particulate emissions in diesel hybrid electric The 15th International Symposium on District Heating and Cooling vehicles with with aa PMP-based PMP-based control control strategy strategy a,∗ a,b Assessing the feasibility of using heat demand-outdoor Laura Tribioli , Ginothe Bella Laura Tribiolia,∗, Gino Bellaa,b University of Roma “Niccol`o Cusano”, Via Don C. Gnocchi, 3, Roma, 00166, Italy temperature function for a long-term district heat demand forecast University of Roma “Niccol` Cusano”,Via Viadel Don C. Gnocchi, 3, Roma, 00166, Italy University of Roma “Tor oVergata”, Politecnico, 1, Roma, 00133, Italy a a

b b University

of Roma “Tor Vergata”, Via del Politecnico, 1, Roma, 00133, Italy

I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc a Abstract IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Abstract Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France Over the last cDépartement decades the Systèmes pollutantÉnergétiques emissions etofEnvironnement passenger-car- diesel engines have been significantly reduced by the emission IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Over the last the that pollutant of passenger-car diesel engines haveexceed been significantly reduced standards, but decades it is known in realemissions driving conditions these vehicles significantly the regulation limits. by Forthe thisemission reason, standards, but number it is known that inand realmunicipalities driving conditions these significantly the diesel regulation thislocal reason, an increasing of cities already arevehicles or are thinking aboutexceed banning cars limits. to copeFor with air an increasing of cities with and municipalities already7are are thinking about are banning diesel copecars without local air pollution. Thisnumber aspect together the upcoming EURO air or pollution regulations expected to cars pushtodiesel of the pollution. This aspect together with the upcoming EURO 7 air pollution regulations are expected to push diesel cars out of the market, because of a too-much demanding requirement on the reduction in particulate and carbon dioxide emissions. The only way Abstract market, of akeep too-much requirement on the reductiontheir in particulate and carbon dioxide emissions. only waya to makebecause these cars a sharedemanding in the automotive market is probably hybridization. The aim of these paper isThe to propose to makestrategy these cars keep a share in in thediesel-hybrid automotive market is aimed probably their hybridization. The aim of these paper is tothe propose a control for the power split vehicles, at minimizing particulate emissions and controlling battery District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the control strategy for the power split in diesel-hybrid vehicles, aimed at minimizing particulate emissions and controlling the battery state of charge aim, a sector. supervisory based high on Pontryagins designed greenhouse gassimultaneously. emissions fromTothethis building Thesecontroller, systems require investmentsMinimum which arePrinciple, returned is through the for heat state of energy charge management simultaneously. To this aim, controller, Electric based onVehicle Pontryagins Principle, isThe designed for on-line of aa supervisory Diesel withdemand aMinimum parallel proposed sales. Due to the changed optimization climate conditions and Plug-in buildingHybrid renovation policies, heat in architecture. the future could decrease, on-line energy management optimization of a emissions Diesel Plug-in Hybrid Electric parallel architecture. The proposed strategy focuses on the reduction particulate that represents a majorVehicle issue towith meetaemissions standards in such vehicles. prolonging the investment returnofperiod. strategy focuses on the reduction of particulate emissions thatsimulations represents aismajor issue toorder meettoemissions standards in such vehicles. InThe thismain study a methodology based on software in the loop applied in properly tune the proposed scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat energy demand In this study astrategy methodology based on software in the loop simulations is applied inwork order to properly the tunepotential the proposed energy management and to understand the overall powertrain performance. This demonstrates of using forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of the 665 management strategy and to understand the overall powertrain performance. This cycles. work demonstrates the potential of using the hybrid architecture to limit particulate emissions even under real-world-like driving buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district hybrid architecture to limit particulate emissions even under real-world-like driving cycles. renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were ccompared  2018 The Thewith Authors. Published by Elsevier Elsevier Ltd. © 2018 Authors. Published by results from a dynamic heatLtd. demand model, previously developed and validated by the authors. c 2018  The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) The results showed that when only weather change is license considered, the margin of error could be acceptable for some applications This is anand openpeer-review access article under the CC BY-NC-ND licensecommittee (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of thefor scientific committee of the the 73rd 73rd Conference Conference ofthe theItalian Italian ThermalMachines Machines Selection under responsibility of the scientific of of Thermal (the error in annual demand was lower than 20% all weather scenarios However, introducing renovation Selection and peer-review under responsibility of the scientific committee of theconsidered). 73rd Conference of theafter Italian Thermal Machines Engineering Association (ATI 2018). scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). Engineering Association (ATI 2018). The value Pollutant of slopeEmissions; coefficient increased on average within the rangeEnergy of 3.8% up to 8% per decade, that corresponds to the Keywords: Emissions Reduction; Particulate; Diesel-HEV; Management; decrease inPollutant the number of heating hours of 22-139h duringDiesel-HEV; the heating season (depending on the combination of weather and Emissions; Emissions Reduction; Particulate; Energy Management; Keywords: renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. Nomenclature P Power (W)

Nomenclature

kP

Power (W) coefficient Calibration

T T

S oC S oC

State of Charge State of Charge

kω Calibration coefficient The Authors. Published by Elsevier Ltd. t © 2017 Time (s) Angular Speed (rad/s) under responsibility of the Scientific Committee of The 15th International Symposium tτPeer-review Time (s) ω Angular Speed Sample Time (s) θ Temperature (K)(rad/s)on District Heating and τCooling.Sample Time (s) θ Temperature (K) Torque (Nm) Torque (Nm)

Keywords: Heat demand; Forecast; Climate change ∗ Corresponding ∗ Corresponding

author. E-mail address:author. [email protected] E-mail address: [email protected] c©2018 1876-6102 2017The TheAuthors. Authors.Published Publishedby byElsevier ElsevierLtd. Ltd. 1876-6102 1876-6102 © 2018 The TheAuthors. Authors. Published by Elsevier copen 1876-6102  2018 Published by Elsevier Ltd. Ltd. Peer-review under responsibility of thethe Scientific Committee of The 15th International Symposium on District Heating and Cooling. This is an access article under CC BY-NC-ND license This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the 73rd Conference of the Italian Thermal Machines (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and Association peer-review under of the scientific comEngineering (ATI responsibility 2018). Selection and73rd peer-review under of theMachines scientific Engicommittee of the Conference of responsibility the Italian Thermal 10.1016/j.egypro.2018.08.062 mittee ofAssociation the 73rd Conference of the Italian Thermal Machines Engineering (ATI 2018). neering Association (ATI 2018).

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˙ S oC State of Charge Time Derivative m ˙ Mass flow rate (g/s) H Hamiltonian Function (kg/s) λ Co-state (kg) Subscripts and Superscripts r

Operating region

i f f uel ICE BP PM PM, s PM, d

995

Initial Value Final Value Fuel Internal Combustion Engine Battery Pack Particulate Matter Static Particulate Matter Dynamic Particulate Matter

1. Introduction Legislations limits on the particulate emissions of diesel engines are becoming so stringent that the an increasing number of cities and municipalities already are or are thinking about banning diesel cars to cope with local air pollution.A solutions to avoid the premature end of diesel engines is certainly the hybridization of the powertrain. To date, power management of hybrid vehicles and, in particular, of diesel hybrid electric vehicles (D-HEV) have been particularly devoted to the increase of the fuel economy, while reducing quasi-static nitrogen oxide (NO x ) emissions, demonstrating the ability to achieve a satisfactory trade-off [1], [2]. Yet, [3] has shown that, when the energy management strategy is aggressively optimized for best efficiency, even being able to save a considerable amount of fossil fuel, the hybrid vehicle emits more particulate than the conventional one. This is mainly due to the transient effects, which are hard to catch with model-based controllers, that do not take into account the intake manifold pressure build-up delay caused by turbocharger and manifold filling inertias [4]. These aspects have the most influence on the particulate matter (PM) emissions which may be significantly high at the very beginning of a steep load change. Phenomenological approaches may be able to give more accurate results, but with a calculation time not suitable for real-time control-oriented applications. A proper model of transient emissions is thus needed to take into account these aspects, even if a control-oriented model should be fast enough to allow implementing the energy management strategy in real time. Among the strategies proposed by [5] for a parallel HEV, the heuristic energy management strategy which reduces the torque change rate and limits its maximum torque at a fixed value to avoid high transients has been shown to have lower transient PM emissions. [6] proposes to modify the cost function in order to adapt the optimization criterion and take into account NO x emissions. The optimization problem is solved by means of the Pontryagin’s Minimum Principle (PMP), where the calibration parameter is tuned to set a trade-off between fuel consumption and NO x emissions. In [7] an optimal energy management strategy based on dynamic programming, steady-state engine maps and a validated transient PM emission model is derived in order to balance fuel consumption, raw PM emissions and raw NO x emissions for a D-HEV. For the transient PM emissions a modified version of the semi-empirical approach proposed by [8] has been used, which allows the turbocharger lag to be somehow taken into account. Nevertheless, a control strategy based on dynamic programming is not implementable online, since it has the curse of running backward in time, which is impractical in real time [9]. In this paper a PMP-based control strategy is used to minimize the fuel consumption of a parallel diesel PHEV (D-PHEV), including raw PM emissions considerations. A semi-empirical model is included for transiemnt raw PM emissions, based on [8], validated by means of available experimental data. The cost function is not extended to include the particulate matter emissions as in [7], since this would require the calibration of two optimization parameters, increasing the complexity of the control problem and reducing the possibility of obtaining a near-to-the-optimum online solution [10]. Nevertheless, it is shown that the optimal calibration factor for the fuel consumption aslo minimizes static PM emissions. Since an optimal model-based control strategy is sensitive to the engine maps and can result in frequent and steep changes of the operating conditions of the engine, which are the major causes of high transient PM emissions, some heuristic rules have been included, similarly to the approach proposed by [5], to mitigate this behaviour. Finally, a charge depleting/charge sustaining (CD/CS) strategy based on PMP is tested and compared to the optimal solution of the PMP, demonstrating that significant PM emission reductions can be obtained with an acceptable increase of the fuel consumption (and thus of CO2 emissions) with respect to the optimal solution.

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2. Powertrain architecture and vehicle modeling In this study, a quasi-static forward-looking model-based simulator developed in Simulink/Matlab environment has been employed. The driver model is based on a PI controller, which senses the actual vehicle speed and actuates the torque request in order to follow a desired speed profile. The actual vehicle speed is obtained by solving the vehicle longitudinal dynamics. The baseline vehicle considered for the analysis is a Mercedes 1.7L Diesel Engine. To make a fair comparison, in the D-PHEV, the same internal combustion engine (ICE) has been considered, which has been linked to the transmission shaft by a clutch and coupled by a belt to a permanent magnet electric machine (EM). The main specifications of both ICE and EM are listed in Table 1. Both the engine and the motor have been simulated by means of their performance maps, i.e. brake specific fuel consumption (BSFC), efficiency, emissions maps and torque-speed, power-speed curves. Table 1. Components Specifications. Internal Combustion Engine Displacement No. Cyclinders Configuration Maximum Torque Maximum Power

Electric Motor

1.7 L 4 straight-4 pistons 187.1 Nm@2200 rpm 60 kW@3300 rpm

Rated Power Max/Min Peak Torque Max/Min Rated Torque

Battery Pack

75 kW 270 Nm@3000-4200 rpm 130 Nm@0-5500 rpm

Energy Capacity Operating Voltage Max Charge Current Min Discharge Current SoC Range

13 kWh 340 V 180 A -60 A 0.95-0.25

The transmission is an automated manual transmission supervised by a logical controller. This control shifts the gears by acting on the signal of the accelerator pedal and the angular speed of the ICE and manages the clutch position during the shifting. The transmission gear ratios are those of the baseline vehicle. A schematic of the vehicle powertrain is provided in Figure 1:

DIFFERENTIAL

ICE FUEL TANK

GEARBOX

EM

BATTERY

Fig. 1. Vehicle powertrain schematic

The electric storage system is a 105S 2P battery pack of Li-Ion cells. The pack main characteristics are given in Table 1. The battery pack electrical dynamics are modelled by means of a zero-th order equivalent electric circuit, which models the battery cell as an electric circuit [11]. 3. Control problem formulation The optimization problem, which lies in minimizing the fuel consumption over the driving cycle, has been tackled by solving the Pontryagin’s Minimum Principle, which requires to define the Hamiltonian function as: ˙ ˙ f uel (PBP ) + λ(t)S oC(S oC, PBP ) H(S oC, PBP , λ(t)) = m

(1)

where S oC is the state variable and λ is the co-state, which varies with time. Nonetheless, this variation is negligible when the battery efficiency is almost constant, as in this application. As unique value of λ assures that the S oC trajectory satisfies the boundary conditions - i.e. S oC(t = ti ) = S oCi and S oC(t = t f ) = S oC f , if no variation of the co-state is taking place over vehicle operation, determining the initial value of the optimization parameter λ becomes crucial. More details about the application of the control problem solution with the PMP are provided in [11].

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3.1. PM emissions model Based on [7], the transient correction model presented in this paper uses a correction which is related to the engine operating point and to the torque rate of change as per Eq.(2):   T (t) − T (t − τ) ICE ICE m ˙ PM,d (t) = kr ωICE (t − τ), T ICE (t − τ) · with r = 1, .., 5 (2) τ

and the total raw PM emissions are simply evaluated as the sum of the static and dynamic emissions: m ˙ PM = m ˙ PM,s + m ˙ PM,d

(3)

The correction parameter kr depends on the previous engine operating point and takes into account the increase PM transient emissions due to the load increase, where the load is defined as the ratio between the current engine torque at time step t and the maximum engine torque at the rotational speed at same time t. The main cause of transient soot formation is the ignition delay caused by the lack of air in the combustion chamber due to the turbo lag. This effect is particularly evident for low loads and low speeds, where the turbocharger operates at almost zero boost pressure, and for medium speeds as load increases. High speeds and high loads, instead, require the lowest correction. For the other operating regions (i.e. low speed and medium/high loads and high speed and low/medium loads), the transient operation produces a similar effect on the emissions and the same correction coefficient can be used. Therefore, five regions can be identified and five coefficients require to be evaluated. A visual map of kr is provided in Figure 2, with the magnitude increasing from r = 1 to r = 5.

Load [%]

100

k1

80

k2 k3

60

k4 k5

40 20 0 500

1000

1500

Speed [rpm]

2000

2500

3000

Fig. 2. Look-up table for the kr correction coefficient

Regarding the need of including the torque rate of change, it mainly depends on the fact that transient emission overshoot is higher the greater is the load change [8]. This is because a high-pressure fuel jet is suddenly injected into an air environment, practically unchanged from the previous steady-state conditions. This results in a highly heterogeneity of the mixture to be burned, which obviously worsen the combustion process. 3.2. Vehicle energy management To take into account both the fuel consumption and the PM emissions in the energy management of the vehicle, a CD/CS strategy is proposed where the torque maximum change has been limited to 10 Nm for each sample time (0.1 ∆T ICE s), to mitigate the effect of the term in Eq. (2). The reason of choosing a CD/CS strategy instead of the optimal τ one - i.e. the solution that minimizes the fuel consumption, which, for a PHEV, is a blended strategy [12] - has three main explanations: 1. a vehicle running under this strategy firstly operates in all electric mode and then uses the engine to sustain the battery state of charge while contributing to traction. This has the effect of increasing the load of the engine, thus reducing the dynamic emissions by acting on the coefficient kr in Eq. (2); 2. the main issue in the design of a blended strategy (i.e. optimal solution for PHEVs) implementable online lies in the need of an a-priori knowledge of a number of information (i.e. total travelled distance, speed and grade

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3.8 1.93

2.11

1.94

1.95

1.96

1.97

1.98

1.99

2

2.1

64

2.095

63

2.09

62

0.715

0.72

0.725

λ (kg)

0.73

0.735

0.74

FUDS-FHDS

5.285 5.28

66 65

2.085 0.71

196 2.01

67

FC PM

2.105

Fuel (L)

198

PM (mg)

Fuel (L)

3.85

Artemis Urban

2.115

5

366

5.275

364

5.27

362

5.265

360

5.26

358

5.255

356 354

5.25

61 0.745

5.245 2.28

2.285

2.29

2.295

2.3

λ (kg)

(a)

2.305

2.31

2.315

2.32

352

λ (kg)

(b) Aachen

3.31

368

FC PM

PM (mg)

200 FC PM

Fuel (L)

NEDC

3.9

PM (mg)

998

(c)

218

US06

5.2

FC PM

400

FC PM

3.305

3.3

PM (mg)

Fuel (L)

Fuel (L)

PM (mg)

217.5

217

3.295 1.51

1.515

1.52

1.525

1.53

1.535

1.54

1.545

1.55

216.5

5 2.41

2.42

2.43

2.44

2.45

2.46

2.47

2.48

350 2.49

λ (kg)

λ (kg)

(d)

(e)

Fig. 3. (a) NEDC FC and PM vs λ (b) Artemis Urban FC and PM vs λ (c) FUDS-FHDS FC and PM vs λ (d) Aachen FC and PM vs λ (e) US06 FC and PM vs λ

traces [11]) to optimally tune the co-state λ cycle by cycle, in order to achieve the desired S oC(t = t f ). On the other hand, the optimal value of the co-state for the charge sustaining phase, in this particular application, has resulted in being rather independent on the driving cycle features. Therefore this strategy can be implemented more efficiently; 3. the worsening in the fuel consumption with respect to the blended solution has resulted in being more restrained than the improvement in the PM emissions. A blended strategy tends to a CD/CS strategy if the co-state is underestimated: as shown in Fig. 3, underestimating the optimization parameter λ is better than over-estimating it, both in terms of PM emissions and fuel consumption. In the US06 and FUDS-FHDS cycles, underestimating λ even enhances the PM emissions. 4. Results A set of driving cycles has been performed to investigate the behaviour of the powertrain under the application of the optimal control framework, namely: US06, NEDC, Artemis Urban, a concatenation of FUDS and FHDS (hereafter referred to as FUDS-FHDS), and a real-world driving cycle obtained in the city of Aachen from GPS direct measurements. This driving cycle can be classified as extra urban driving cycle, having an average speed of 42 km/h. Table 2. Optimal results Driving Cycle

FC (conv) [l/100km]

FC (Opt) [% wrt conv]

NEDC Artemis Urban FUDS-FHDS Aachen US06

6.42 9.42 5.19 6.27 6.16

-39.9 -53.2 -29.9 -42.0 -29.3

PM (conv) [mg/km] 31.9 84.99 32.02 41.96 24.45

PM (Opt) [% wrt conv] -56.1 -76.1 -50.0 -52.6 -26.8

Some results obtained with the optimizer are reported in Table 2, where the fuel consumption (FC) and the particulate emissions (PM) are provided for the conventional vehicle (conv) and compared to the optimal solution (Opt) of the D-PHEV. The effect of coupling the engine to an electric machine has obviously a great impact on both the fuel consumption and the total particulate emissions, which are first of all related to the amount of fuel burned in

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the engine. Therefore, a reduction of the fuel consumption has an undoubted positive effect on the overall particulate emissions. Nonetheless, Table 2 shows that the transient emission share in the D-PHEV highly increases with respect to the conventional vehicle. In order to understand if the effect is due to the operating region of the engine or to the instantaneous torque changes, as an index of the aggressiveness of the driving cycle, the occurrences of torque change ∆T ICE rates ( ) greater than 100 Nm/s have been computed as a percentage of the total driving cycle duration, while the τ PM transient emissions have been compared to the total estimated PM emissions. These data are provided in Table 3. Table 3. Optimal results Driving Cycle

Occurrences (conv) [%]

Occurrences (Opt) [%]

NEDC Artemis Urban FUDS-FHDS Aachen US06

12.52 21.6 23.36 27.83 30.2

9.04 3.13 12.6 12.45 19.9

This index helps to catch the opposite behaviour that the two multiplying terms in Eq. (2) may have. Considering the Artemis Urban cycle, it is clear that a urban driving pattern, which is performed strongly in electric mode, presents a very low percentage of occurrences of torque rate of change grater than 100 Nm/s. At the same time, for a conventional vehicle, where the electric motor is not available, a urban pattern is usually characterized by frequent changes in ∆T ICE torque and speed and this term is definitely higher. This has a great effect on the in Eq. (2). Moreover, in the τ conventional vehicle, the engine runs the most of the time at low loads and low speeds, where the turbo lag has a great impact on transient PM emissions, with a higher value of kr associated, explaining why the transient emissions of the D-PHEV are significantly lower than the conventional one. With regard to the US06 cycle, this is an extraurban driving cycle characterized by frequent steep changes in the load and an average operation at medium/high speeds and high loads. This behaviour is only partially mitigated by the presence of the electric motor and the occurrences of a torque change greater than 100 Nm/s is high and the transient emissions are reduced less than in the other driving cycles. The implementation of a CD/CS strategy with a limitation on the maximum rate of change for the engine torque can thus mitigate these aspects.

Velocity (km/h)

100 50 0 -50 83

83.2

83.4

83.6

83.8

84

84.2

84.4

84.6

84.8

85

Torque (Nm)

200 conv Opt CDCS Opt CDCS Lim

150 100 50 0 83

83.2

83.4

83.6

83.8

84

84.2

84.4

84.6

84.8

85

SoC [%]

33 conv CDCS Opt CDCS Lim

32 31 30 29 83

83.2

83.4

83.6

83.8

84

84.2

84.4

84.6

84.8

85

Distance (km)

Fig. 4. Aachen driving cycle: speed, torques and SoC profiles for the conventional vehicle (conv) and the optimal (Opt), CD/CS optimal (CDCS Opt) and CD/CS limited (CDCS Lim) strategies

In Fig. 4, a picture of the final part of the Aachen driving cycle is provided, in terms of speed traces, engine torques with the different controllers and battery states of charge for the D-PHEV. One can immediately note how the spikes in the engine torques are smoothed by the CD/CS Limited strategy, which practically implies that the engine is rather used as a charger for the battery than directly involved in traction. This can be in fact seen in the bottom plot of Fig. 4

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which shows that the state of charge related to the CD/CS Limited strategy is higher than the CD/CS optimal one. This strategy also prevents the engine to work at low loads, which instead frequently happens for the CD/CS optimal strategy. In Table 4, fuel consumptions are reported for the proposed control strategy, compared to the conventional vehicle, the CD/CS Optimal and the Optimal ones. As one can immediately note, the fuel consumption is definitely lower than the conventional counterpart, while worsening if compared to the optimal solution. Nonetheless, the increase in fuel consumption seems to be tolerable when looking at Table 5, where the dynamic PM emissions for the proposed control strategy, compared to the conventional vehicle, the CD/CS Optimal and the Optimal ones, are provided. Table 4. CD/CS Limited (CDCS Lim) fuel consumption (FC) w.r.t. Optimal (Opt), CD/CS Optimal (CDCS Opt) and ICE-only driven vehicle (conv) Driving Cycle

CDCS Lim FC [% wrt conv]

CDCS Lim FC [% wrt Opt]

NEDC Artemis Urban FUDS-FHDS Aachen US06

-38.8 -46.9 -10.9 -25.8 -10.7

+8.10 +4.94 +18.3 +24.5 +21.9

CDCS Lim FC [% wrt CDCS Opt] +2.21 +2.10 +7.23 +8.65 +10.2

In Table 5, it is possible to see that the proposed control strategy is able to dramatically reduce the particulate emissions associated to the dynamic behaviour of the engine. Table 5. CD/CS Limited (CDCS Lim) PMd emissions w.r.t. Optimal (Opt), CD/CS Optimal (CDCS Opt) and ICE-only driven vehicle (conv) Driving Cycle

CDCS Lim PMd [% wrt conv]

CDCS Lim PMd [% wrt Opt]

NEDC Artemis Urban FUDS-FHDS Aachen US06

-28.1 -75.3 -26.2 -34.5 -26.6

-46.1 -59.7 -42.3 -48.5 -62.7

CDCS Lim PMd [% wrt CDCS Opt] -43.7 -55.1 -52.3 -54.8 -46.1

Finally, Table 6 shows the overall PM emissions for the proposed control strategy, compared to the conventional vehicle, the CD/CS Optimal and the Optimal ones. Table 6. CD/CS Limited (CDCS Lim) PM emissions w.r.t. Optimal (Opt), CD/CS Optimal (CDCS Opt) and ICE-only driven vehicle (conv) Driving Cycle

CDCS Lim PM [% wrt conv]

CDCS Lim PM [% wrt Opt]

NEDC Artemis Urban FUDS-FHDS Aachen US06

-83.2 -90.6 -66.4 -70.6 -65.0

-37.6 -54.6 -32.8 -37.9 -48.3

CDCS Lim PM [% wrt CDCS Opt] -35.7 -50.2 -42.3 -45.5 -46.1

Obviously these percentages are slightly lower because of the increasing of the static emissions, which are directly related to the fuel consumption. The presented control strategy is characterized by higher fuel consumptions which are in any case associated to a higher static PM emission, at least if considering the optimal solution. The driving cycle with the highest static emissions is Aachen, which is a real-world-like driving cycle. 5. Conclusions A real time control strategy for the energy management of diesel plug-in HEVs has been presented, which takes into account also particulate matter emissions by coupling a map-based approach for the static emissions with a semi-

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empirical approach for the dynamic ones. In particular, the methodology is based on the CD/CS strategy, where the charge sustenance is optimized with PMP. This strategy has been chosen, in spite of its non-optimal results, because of its simplicity and, in particular, because it maximizes electricity usage in urban paths, where soot formation might be significant, thus drastically reducing the particulate emissions. A limit on the maximum instantaneous torque variation has been included in the strategy, in order to mitigate the PM emissions connected to the frequent and steep changes in the engine torque, which may derive from the optimization. The proposed strategy has been then demonstrated to achieve a fuel consumption definitely lower than the conventional counterpart and not very far from the optimal solution. In spite of this slight worsening of the fuel consumption, a significant reduction on PM emissions can be achieved, especially acting on the soot formation associated to the transient operation on the diesel engine. Results have been validated by simulating several driving cycles with different characteristics, in terms of total length and velocity profiles. The strategy has been shown to be able to reduce of more than 50%, on average, the dynamic PM emissions in almost all the driving conditions, with respect to the optimal solution. Nevertheless, the overall PM emissions reduction decreases to around the 35% with respect to the optimal solution, because of the slight worsening of the fuel consumption, which causes an increase of the static PM emissions. Future developments may be devoted to the realization of an adaptive sub-optimal control strategy which increases the fuel economy, thus reducing also the soot formation directly correlated to the fuel consumption. References [1] Johnson, V., Wipke, K., Rausen, D.. HEV control strategy for real-time optimization of fuel economy and emissions. SAE Journal of Engines 2000;2000-01-1543. [2] Thibault, L., Sciarretta, A., Degeilh, P.. Reduction of pollutant emissions of diesel mild hybrid vehicles with an innovative Energy Management Strategy. In: IEEE Intelligent Vehicles Symposium (IV). 2017,. [3] Hagena, J.R., Filipi, Z.S., Assanis, D.N.. Transient diesel emissions: Analysis of engine operation duringa tip-in. SAE Technical Paper 2006;2006-01-1151. [4] Ericson, C., Westerberg, B., Egnell, R.. Transient emission predictions with quasi stationary models. SAE Technical Paper 2005;2005-013852. [5] Lindenkamp, N., Stber-Schmidt, C.P., Eilts, P.. Strategies for reducing NOx- and particulate matter emissions in diesel hybrid electric vehicles. SAE Technical Paper 2009;2009-01-1305. [6] Serrao, L., Sciarretta, A., Grondin, O., Chasse, A., Creff, Y., Domenico, D.D., et al. Open issues in supervisory control of hybrid electric vehicles: A unified approach using optimal control methods. Oil & Gas Science and Technology - Revue d`IFP Energies nouvelles, Institut Franais du Ptrole 2013;68(1):23–33. [7] Nuesch, T., Wang, M., Isenegger, P., Onder, C., Steiner, R., Macri-Lassus, P., et al. Optimal energy management for a diesel hybrid electric vehicle considering transient PM and quasi-static NOx emissions. Control Engineering Practice 2014;29:266–276. [8] Giakoumis, E.G., Lioutas, S.C.. Diesel-engined vehicle nitric oxide and soot emissions during the European light-duty driving cycle using a transient mapping approach. Transportation Research Part D 2010;15:134143. [9] Bianchi, D., Rolando, L., Serrao, L., Onori, S., Rizzoni, G., Al-Khayat, N., et al. A rule-based strategy for a series/parallel hybrid electric vehicle: an approach based on dynamic programming. 2010 ASME Dynamic Systems and Control Conference, Cambridge, MA, Sept 13-15 2010;. [10] Tribioli, L., Cozzolino, R., Chiappini, D., Iora, P.. Energy management of a plug-in fuel cell/battery hybrid vehicle with on-board fuel processing. Applied Energy 2016;184:140 – 154. URL: http://www.sciencedirect.com/science/article/pii/S0306261916314398. doi:http://doi.org/10.1016/j.apenergy.2016.10.015. [11] Onori, S., Tribioli, L.. Adaptive Pontryagin’s Minimum Principle Supervisory Controller Design for the Plug-in Hybrid GM Chevrolet Volt. Applied Energy 2015;147:224–234. [12] Kim, N., Cha, S., Peng, H.. Optimal control of hybrid electric vehicles based on Pontryagin’s Minimum Principle. Control Systems Technology, IEEE Transactions on 2011;19(5):1279–1287.