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Supporting Information

Title: Bridging the gap in a resource and climate-constrained world with advanced gasoline compression-ignition hybrids

Amir F.N. Abdul-Manana,*, Hyun-Woo Wonb, Yang Lic, S. Mani Sarathyc, Xiaomin Xied, Amer A. Amere

a Strategic Transport Analysis Team, Beijing Research Center, Aramco Asia, Beijing, China.b Aramco Fuel Research Center, Aramco Overseas Company B.V. Paris, France.c King Abdullah University of Science and Technology (KAUST), Clean Combustion Research Center, Physical Sciences and Engineering Division, Thuwal, Saudi Arabia.d Key Laboratory for Power Machinery & Engineering M.O.E., Shanghai Jiao Tong University, Shanghai, China.e Fuel Technology R&D Division, Saudi Aramco Research & Development Center (R&DC), Dhahran, Saudi Arabia.* Corresponding Author: [email protected]

S1. Methodology

Figure S1 provides a simplified outline of the approach that was adopted to assess the fuel consumption and tailpipe CO2 emissions from the vehicles that were simulated in this study.

Figure S1 An overview of the simulation approach adopted in this study.

S1.1. Vehicle Simulation

MATLAB® and Simulink® were used to develop a simulation tool to assess the efficiency improvement and emissions reduction opportunities that can be realized by adopting different hybrid architectures in a novel fuel and powertrain combination (i.e. an advanced gasoline compression ignition engine, GCI, running on a new high-reactivity fuel formulation).

The Supervisor module optimizes the operational mode of the vehicle depending on various requirements and conditions. 9 operation modes were available as options within the simulation, those were:

I. IDLE mode describes the behavior of the vehicle when it is idling, during which the entire system is at rest with no generation of power.

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II. IDLE (GEN) mode is when the combustion engine acts as a generator to replenish the battery’s SOC if it is depleted during vehicle idling.

III. EV mode describes a vehicle running exclusively on electric power from the battery. IV. Braking (GEN) mode is when the electric motor of the vehicle acts as a generator to recover

the energy losses during vehicle deceleration and braking. V. ICE (only) mode describes a parallel hybrid running exclusively on the combustion engine.

VI. ICE + GEN mode is when the combustion engine and the electric motor are both capable of providing power to the wheels, as in the case of a parallel hybrid, with the excess engine power generated being used to recharge the battery

VII. ICE + EV mode is when the combustion engine and the electric motor are both capable of providing power to the wheels, as in the case of a parallel hybrid, with the extra power needed supplied by the battery through the electric motor.

VIII. ICE (GEN) mode describes a series hybrid, in which only the electric motor can provide power to the wheels while the engine functions only as a generator. During this mode, the engine supplies the power to the electric motor to run the vehicle, in which any excess power generated is being used to recharge the battery.

IX. ICE (GEN) + EV mode describes a series hybrid, in which only the electric motor can provide power to the wheels while the engine functions as a generator. During this mode, the battery provides the additional power needed through the electric motor to complement the power derived from the engine generator.

The optimization algorithm aims to maximize efficiency, whilst minimizing emissions, by varying the hybrid operation mode. The energy management control logic for the 3 hybrid architectures are described in more details below. S1.1.1. Energy management control logic – Parallel HEV

To formulate the control strategy, 3 different parameters were considered as input. The torque demand from the ICE is the main control parameter since the ICE is directly connected to the wheels. The other 2 control parameters are battery SOC and vehicle speed. All 3 parameters affect the mode of hybrid operation (Figure S2). Increasing the torque demand on the ICE would result in lower utilization of the EV mode to avoid high battery discharge rate; at really high torque demand, the battery would supply the additional power required through the electric motor only if the battery’s SOC is high. If the battery’s SOC depletes to lower levels, any excess power generated by the ICE would be used to recharge the battery.

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Figure S2 Energy management logic for parallel HEV model

S1.1.2. Energy management control logic – Series HEV

Since the EM is the only means of power to the wheels, power demand from the EM is considered as the primary control parameter. The other 2 control parameters are the battery’s SOC and the vehicle speed. OP1, OP2 and OP3 refers to 3 different operating points of the ICE that have been selected for the series hybrid. OP2 is the highest efficiency point in the ICE map and OP1 is relatively high efficiency point with low torque and speed. OP3 is the operating point for maximum power from the ICE. Figure S3 illustrates the control strategy with multiple operating points for the engine. As demand for power on the EM increases, the series hybrid will be less dependent on the EV mode to avoid excessively draining the battery’s SOC. The ICE plays a bigger role with increasing power demand however, the selection of ICE operating point also depends on the battery’s SOC. At low battery SOC, the higher power demand would necessitate that the ICE shifts its operation from a high efficiency operating point (OP1 or OP2) to the operating point for maximum power (OP3).

Figure S3 Energy management logic for series HEV model

S1.1.3. Energy management control logic – Parallel/Series HEV

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The control strategy parameter for the 2-mode hybrid in Figure S4 is similar to the series hybrid. The only difference is in the operational mode change. Here, the vehicle acts as a series hybrid at low speed and as a parallel hybrid at highway cruising. In ICE+GEN mode, the ICE is directly powering the wheels and the remaining power is used to recharge the battery. In ICE (GEN) + EV mode, the vehicle operates like a series hybrid with ICE operating in three different operating zones.

Figure S4 Energy management logic for parallel/series HEV model

S1.2. Carbon intensity of electric power

This study provides a comparative assessment of the lifecycle performances of GCI hybrid against a comparable EV powered by electricity derived from different power generation mixes. It has a wide geographical scope, which includes China, EU and the USA, and at the same time, it provides depth as it is a detailed assessment down to provincial and state levels for the 3 geographical regions. It is important to note that the analysis for China is limited to mainland China and it excludes Tibet and Taiwan given the lack of reliable data.

Most widely used data on electric power generation are aggregated on a country basis. Furthermore, differences in methodology, scope and assumptions have resulted in significant variations between different sources of data in the literature. Accessibility to credible data at such granular level (i.e. broken down by provinces and states) poses significant challenge. However, the data used in this analysis were based on methodology, scope and assumptions that were made consistent as much as possible and is described further in the succeeding paragraphs.

For the U.S, the carbon intensity for electric power generated by the individual states were obtained from Shivley et al. [1]. Emissions due to transmission and distribution losses were incorporated using country average-data as reported by the World Bank [2], and cross-checked against the World Energy Council (WEC) [3]. Table S1 provides the emissions profile for the 50 states in the USA.

Table S1 Summary of CO2 emissions intensity for electric power generation by the 50 states in USA [1]

StatesCarbon Intensity

(gCO2eq/kWh)

Total CO2 Emissions(kg CO2)

Electricity generation

(MWh)

After including T&D Losses –

(gCO2eq/kWh)Wyoming WY 919 43045067697 46830260 976West Virginia WV 891 65420835222 73417807 946Kentucky KY 862 62166290874 72142031 915Indiana IN 810 81458456229 100525909 861

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Missouri MO 784 65475415112 83561290 832Utah UT 738 27300800371 36972437 784North Dakota ND 705 28853070508 40906093 749New Mexico NM 705 23633245159 33519593 749Wisconsin WI 660 43105664361 65281612 701Ohio OH 652 77638012509 119056859 693Colorado CO 650 35676177788 54845275 691Hawaii HI 642 6925274386 10785126 682Nebraska NE 610 21923088378 35919731 648Montana MT 556 15704474599 28269965 590Arkansas AR 524 32536361178 62111392 556Michigan MI 502 56496291402 112445472 534Texas TX 486 220000000000 452888021 516Minnesota MN 463 27638200350 59750455 491Iowa IA 462 26073459553 56471986 490Alaska AK 457 2472444134 5406591 486Florida FL 447 106000000000 238118711 474Rhode Island RI 434 2100612902 4836608 461Arizona AZ 431 46652364274 108217319 458Kansas KS 430 22072102994 51379886 456Oklahoma OK 425 32547404271 76535749 452Georgia GA 424 54667741293 128958519 450Mississippi MS 420 25421650745 60565629 446Delaware DE 414 3188489390 7707909 439Tennessee TN 412 32234991019 78162645 438Louisiana LA 400 38894252408 97179096 425Alabama AL 396 55040142439 139152831 420North Carolina NC 365 47824744176 130893704 388Illinois IL 363 66080743830 181957431 386Pennsylvania PA 363 72715868047 200489640 385Maryland MD 353 12302755825 34869084 375Massachusetts MA 343 11255420757 32821906 364Nevada NV 332 12785850472 38533730 352Virginia VA 319 29828280714 93566635 339South Carolina SC 261 24434505201 93584847 277South Dakota SD 237 2468839047 10435059 251New Jersey NJ 223 16953405194 76157505 236Connecticut CT 213 7335510872 34416101 226New York NY 190 24519693092 129060324 202California CA 162 35440177718 219103848 172Maine ME 125 1277878046 10209419 133Oregon OR 113 6619173602 58694693 120New Hampshire NH 109 1911954236 17552634 116Washington WA 87 10077809755 115586200 93Idaho ID 72 1141205441 15910288 76Vermont VT 3 7301142 2101856 4

For the EU, the carbon intensity for electric power generated by the 28 member states were obtained from the European Environmental Agency (EEA) [4]. Emissions due to transmission and distribution

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losses were incorporated using country average-data as reported by the World Bank [2], and cross-checked against the World Energy Council (WEC) [3]. Table S2 provides the emissions profile for the 28 member states of the EU.

Table S2 Summary of CO2 emissions intensity for electric power generation by the 28 member states in the EU [4].

EU Member StatesCarbon Intensity

(gCO2eq/kWh)

After including T&D Losses –

(gCO2eq/kWh)Austria 85.1 90Belgium 169.6 179Bulgaria 470.2 510Croatia 210 238Cyprus 676.9 704Czech Republic 512.7 536Denmark 166.1 176Estonia 818.9 874Finland 112.8 117France 58.5 62Germany 440.8 458Greece 623 674Hungary 260.4 293Ireland 424.9 459Italy 256.2 274Latvia 104.9 114Lithuania 18 22Luxembourg 219.3 233Malta 648 678Netherlands 505.2 529Poland 773.3 823Portugal 324.7 357Romania 306 339Slovakia 132.3 136Slovenia 254.1 266Spain 265.4 291Sweden 13.3 14United Kingdom 281.1 305

For China, the carbon intensity for electric power generated by the 30 provinces were obtained from Li et al. [5]. Given the lack of data, this study focuses only on mainland China, excluding Tibet and Taiwan. The lifecycle emissions for electricity generation as reported by Li et al. had included emissions due to the manufacturing of infrastructures, such as the construction of the power plant itself. This is not within the scope of this study, and to be consistent with data obtained for other regions (i.e. EU and USA), the emissions for the provinces in mainland China were recalculated to exclude indirect emissions due to infrastructure construction.

Li et al. had calculated the lifecycle emissions for the individual provinces by using the IPCC’s generic emissions factors for the different power plants. By referring to the same IPCC table (Table A.III.2 in ref [6]), emissions factors for indirect emissions were omitted in this study. As a result, the

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recalculated carbon intensity values for the provinces in mainland China were on average about 10% lower than the values reported by Li et al. However, emissions due to transmission and distribution losses were incorporated using country average-data as reported by the World Bank [2], and cross-checked against the World Energy Council (WEC) [3]. Table S3 provides the emissions profile for the 30 provinces in mainland China.

Table S3 Summary of CO2 emissions intensity for electric power generation for the 30 provinces in mainland China [5].

Provinces in mainland China

Electricity generation (MWh)

Total CO2 Emissions - recalculated by authors(kg CO2)

Original Carbon Intensity from Li et al. (gCO2eq/kWh)

Re-calculated Carbon Intensity (gCO2eq/kWh)

Recalculated carbon intensity after including T&D Losses – (gCO2eq/kWh)

Anhui 277978123 196081 771 705 749Beijing 47584049 16419 462 345 366

Chongqing 87342285 48132 601 551 585Fujian 247612555 90692 407 366 389Gansu 158657052 63264 442 399 423

Guangdong 494026628 229159 516 464 493Guangxi 170647610 64015 420 375 398Guizhou 230195178 117161 562 509 541Hainan 37913053 14674 437 387 411Hebei 308052357 187496 668 609 646

Heilongjiang 95165538 61217 682 643 683Henan 321894369 222469 752 691 734Hubei 278248223 93545 378 336 357Hunan 156986729 68184 492 434 461

Inner Mongolia 515997492 306391 645 594 631Jiangsu 529021323 335148 696 634 673Jiangxi 130534183 79717 674 611 649

Jilin 84217126 43590 575 518 550Liaoning 216878672 106426 539 491 521Ningxia 168328966 103768 670 616 655Qinghai 77840597 15664 240 201 214Shaanxi 211097539 137850 714 653 694

Shandong 547522226 367378 758 671 713Shanghai 82806585 52487 705 634 673

Shanxi 366057020 232568 695 635 675Sichuan 396140849 36686 122 93 98Tianjin 73880427 49166 734 665 707

Xinjiang 327875821 200372 665 611 649Yunnan 332280603 29867 117 90 95Zhejiang 322357611 151647 520 470 500

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S2. ResultsS2.1. Sensitivity Analysis 1 – effects of battery size on vehicle efficiency

GCI hybrids offer significant efficiency improvement potentials. To a large extent the degree of efficiency improvement depends on the size of the battery installed, and therefore the total electric distance achievable. Figure S5 plots the percent improvements in vehicle efficiency for the different GCI hybrid configurations and battery sizes. It can be seen in the case of the parallel hybrid that an increase in battery size from 1.5kWh to 5 kWh could lead to about 2-4 percentage points improvements in vehicle efficiency. As a result, a parallel hybrid and a 2-mode hybrid, with similar battery capacity, would offer comparable efficiency improvements relative to an unhybridized GCI. This would suggest that the installed battery capacity has a larger influence on the improvement in vehicle efficiency and tailpipe emissions than the choice of hybrid architecture when everything else remain equal.

Figure S5 Improvements in vehicle efficiency for GCI hybrids adopting different architectures and equipped with varying battery sizes.

S2.2. Sensitivity Analysis 2 – effects of carbon intensity of energy sources & vehicle efficiency

Figure S6 depicts the sensitivity of the WTW GHG emissions results to the carbon intensity of the energy sources used to power the vehicles and the energy efficiency of the vehicles modelled in this study. The sensitivity analysis was performed by varying the input parameters by +/- 10%. All powertrains are highly sensitive to vehicle efficiency, but only full electric vehicles are sensitive to the differences in energy sources used to power the vehicle. Varying the fuels carbon intensity for the GCI engines by 10% resulted in less than 2% changes to the overall WTW GHG emissions. This is expected given that 80% of the WTW emissions are due to fuels combustion from TTW, and only 20% are attributable to fuels production.

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Figure S6 Effects of varying the carbon intensity of the energy sources used to power the vehicle, and vehicle efficiency on the WTW GHG emissions of all the powertrains evaluated in this study.

S2.3. Uncertainty Analysis

Figure S7 evaluates the uncertainty in WTW GHG emissions for the emerging powertrains by varying the efficiency of fuels production, power generation mix and vehicle drive cycle. Data for refinery efficiencies were obtained from Argonne National Laboratory’s (ANL) GREET 2018 model [7] by adopting the various fuels production scenario based on differences in crude API (Crude API range between 20-34), Sulphur contents (between 1% - 3% Sulphur) and product slates (heavy product yields range from 9% - 12% on energy basis). These are some parameters that are known to affect the efficiency and emissions from a refinery. The power generation mixes were varied to reflect regional differences, while

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the two drive cycles (NEDC and WLTP) represent the effects of vehicle speed and load on the overall efficiency. Drive cycle variation was modelled in this study using similar methodological approaches.

When compared to the GCI vehicles (hybridized and non-hybridized), the WTW GHG emissions for a full BEV has larger uncertainties given the more diverse energy sources associated with the power generation mix. Moreover, in cases of BEVs, emissions due to power generation and supply accounts for most of the WTW emissions, whereas in the case of traditional GCI and hybrid GCI vehicles, production of energy supply represents only about 20-30% of the total WTW GHG emissions. However, the vehicle drive cycle can have a significant impact on the overall WTW GHG emissions for both, a full BEV and GCI (hybridized and non-hybridized) vehicles.

Figure S7 WTW GHG emissions ranges for the different emerging powertrain options.

Figure S8 shows the effectiveness of batteries in reducing the WTW GHG emissions for the different powertrains. For each powertrain, the ranges were derived by taking into account the uncertainty in the WTW GHG emissions as depicted in Figure S7.

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Figure S8 The range in GHG emission benefits per kWh of battery deployed.S2.4. Effectiveness of battery in reducing WTW GHG emissions

This study has shown that the size of the installed battery can offer large WTW GHG emissions reduction. However, given that the supply of critical minerals required for battery manufacturing are concentrated in limited geographical regions, and the potentially large sustainability drawbacks associated with rising demand for these minerals, there have been growing calls for better allocation of our limited resources for addressing changes in the global climate. Figure S9 show the effectiveness of every kWh of battery in reducing the absolute WTW GHG emissions of the vehicle measured relative to conventional SI engine and an unhybridized GCI. Here it can be seen that there is diminishing benefit for every additional kWh of battery size increase.

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Figure S9 The effectiveness of every kWh of battery in reducing the WTW GHG emissions of a passenger car.

References

[1] G. Shivley, I. Azevedo and C. Samaras, "Assessing the evolution of power sector carbon intensity in the United States," Environmental Research Letters, vol. 13, no. 064018, 2018.

[2] World Bank, "Electric power transmission and distribution losses (% of output)," The World Bank Group, [Online]. Available: https://data.worldbank.org/indicator/EG.ELC.LOSS.ZS. [Accessed 13 6 2019].

[3] WEC, "Rate of electricity T&D losses," Enerdata. World Energy Council (WEC), [Online]. Available: https://wec-indicators.enerdata.net/world-rate-of-electricity-T-D-losses.html. [Accessed 18 6 2019].

[4] EEA, "Overview of electricity production and use in Europe," European Environment Agency (EEA), 4 2019. [Online]. Available: https://www.eea.europa.eu/data-and-maps/indicators/overview-of-the-electricity-production-2/assessment-4. [Accessed 18 6 2019].

[5] X. Li, K. J. Chalvatzis and D. Pappas, "Life cycle greenhouse gas emissions from power generation in China’s provinces in 2020," Applied Energy, vol. 223, pp. 93-102, 2018.

[6] IPCC, "Annex III: Technology-specific cost and performance parameters. In: Climate change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change," Cambridge University Press, 2014.

[7] GREET, "The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation

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(GREET) Model," Argonne National Laboratory (ANL), 2018.[8] C. Yi, B. I. Epureanu, S.-K. Hong, T. Ge and X. G. Yang, "Modeling, control, and

performance of a novel architecture of hybrid electric powertrain system architecture of hybrid electric powertrain system," Applied Energy, vol. 178, pp. 454-467, 2016.

[9] G. Wu, X. Zhang and Z. Dong, "Powertrain architectures of electrified vehicles: Review, classification and comparison," Journal of the Franklin Institute, vol. 352, pp. 425-448, 2015.

[10] M. Sabri, K. Danapalasingam and M. Rahmat, "A review on hybrid electric vehicles architecture and energy management strategies," Renewable and Sustainable Energy Reviews, vol. 53, pp. 1433-1442, 2016.

[11] NRC, "COST, EFFECTIVENESS, AND DEPLOYMENT OF FUEL ECONOMY TECHNOLOGIES FOR LIGHT-DUTY VEHICLES," The National Academies Press, Washington DC, 2015.

[12] EIA, "Electricity. U.S. Energy Information Administration (EIA)," U.S. Energy Information Administration, 2019. [Online]. Available: https://www.eia.gov/electricity/. [Accessed 18 6 2019].

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