Impact of Oil Viscosity on Emissions and Fuel Efficiency at High Altitudes: An RSM Analysis

1. Introduction

The exponential growth of the vehicle fleet in recent decades has generated a significant increase in polluting emissions, becoming one of the leading environmental challenges at a global level [1]. Road vehicle growth in developed and developing countries is projected to increase by 45% by 2025, affecting traffic, traffic density, and emissions [2]. In 2023, the EU approved a series of Commission suggestions to align the EU's climate, energy, transport, and taxation policies to reduce net greenhouse gas emissions by at least 55% by 2030, compared to 1990 levels. This initiative aims to make the EU the first climate-neutral continent by 2050 [3]. Therefore, reducing environmental pollutant emissions from internal combustion engines (ICEs) requires the development of more efficient engines in terms of fuel consumption, emission generation, and power density [4]. Harmful components of engine exhaust gases include nitrous oxides (NOx), carbon dioxide (CO₂), carbon monoxide (CO), hydrocarbons (HC), and particulate matter (PM) [5] which have a direct impact on air quality and are a significant risk factor for human health, contributing to global warming and acid rains [6].

ICEs are complex systems involving various components: lubrication, friction, charge cycles, supercharging, mixture formation, ignition, combustion systems, electronics and mechanics for engine management, transmission shift control, powertrain, sensors, actuators, cooling, exhaust emissions, operating fluids, filtration, etc., providing alternatives to optimize its performance [7]. One of these alternatives to reduce fuel consumption and, therefore, minimize the emission of polluting gases into the environment is based on lowering mechanical losses and increasing engine efficiency [8]. In this regard, strategies have been developed to reduce these losses in the ICEs [9,10,11,12,13].

Hybrid surface modification techniques, such as coatings, textures, and nanoparticles, can improve the tribological performance of engine components [14]. Improving surface coatings through micro-reliefs on the inner surface of cylinder liners can reduce mechanical losses in internal combustion engines by an average of 10.8% and increase mechanical efficiency by 4.0% [10,15]. Hazar et al. propose coating engine components with MgO-ZrO₂ and ZrO₂ which provides a thermal barrier, increasing engine power and reducing fuel consumption while improving pollutant emissions [16].

On the other hand, downsizing internal combustion engines can improve fuel utilization, reduce emissions, and increase efficiency by reducing the weight of moving parts such as pistons and crankshafts [17,18] which can reduce CO₂ emissions by about 18% in warm engine conditions for mid-class vehicles [19]. Podrigalo et al. conclude that a rational reduction in effective engine capacity can lead to a 9.5% reduction in fuel consumption while maintaining the specified maximum speed and dynamic properties of cars [20]. Likewise, reducing the gap between compression rings and increasing the twist angle can help reduce leakage flows by 37% and contribute to minimizing global emissions [21].

Similarly, the use of low-viscosity oils (LVO) is adopted to reduce mechanical losses in ICEs due to the ease of implementation costs versus the advantages of reducing pollutant emissions and fuel consumption [12,22,23,24]. These oils reduce frictional power loss and wear load on compression ring surfaces, leading to maximum fuel economy in internal combustion engines [25]. LVO can reduce fuel consumption by around 2% in light-duty diesel engines [22], and 5% in urban transport buses [26] depending on the test conditions, offering a cost-effective way to increase engine efficiency and reduce CO₂ emissions. Hawley et al. determine up to 3.5% fuel economy improvement in engines using lower-viscosity lubricants, compared to current production lubricants [27]. In the same way, Ishizaki et al. conclude that ultra-low viscosity engine oils can reduce CO₂ emissions by 0.6% in 1.5-1.8 L gasoline engines in New European Driving Cycles (NEDC) mode and improve fuel efficiency in passenger vehicles, but their cost-effectiveness depends on both viscosity reduction and oil drain interval extension [28]. However, the use of low-viscosity oils in ICE results in magnified wear due to thinner oil films and requires additional wear protection additives for effective performance [29,30].

Another factor that significantly affects fuel consumption and pollutant emissions is altitude. These altitude changes have a direct impact on the performance, fuel consumption, and emissions of ICEs [31]. Diesel vehicles, in particular, have higher CO₂, CO, and NOx emission factors than gasoline vehicles. These emissions increase with altitude because there is lower atmospheric pressure, temperature, and oxygen concentration, resulting in reduced combustion efficiency in automotive engines with atmospheric pressure being the primary environmental factor affecting emissions [32] by lengthening the ignition delay, increasing energy release, and prolonging the late combustion period, leading to reduced thermal and combustion efficiency [33]. Wan et al. declare that as altitude increases from 0 to 2000 meters, engine torque drops by 2.9%, BSFC increases by 2.6%, NOx emissions reduce by 11.8%, and opacity smoke increases by 26.2% [34], while He et al. state that high altitude increases diesel engine emissions of HC, CO, and smoke, with average increases of 30%, 34%, and 35% at 1000 meters [35]. NOx emissions vary with engine types and working conditions [36].

2. Materials and Methods

2.1. Description of the Experimental Setup

For the present study, a data acquisition protocol is established through an experimental design using Response Surface Methodology (RSM), which will allow visual analysis of the average result for a particular area of the levels of the input factors or variables such as lubricant viscosity, engine speed, and applied load, thus evaluating the sensitivity of the output variables (emissions and fuel consumption) to such changes in operating conditions.

In this study, the experiments were performed on a Chevrolet Corsa Evolution 1.5 SOHC, four cylinders, four-stroke, and SI (spark ignition) gasoline engine in the city of Cuenca, Ecuador, which is located 2,560 meters above sea level. The engine specification is given in Table 1.

This vehicle is mounted on a dynamometer MAHA LPS 3000, which is composed of eddy current dynamometer brakes, which, in addition to measuring traction and power at the same time, can also generate loads with revolutions within a range of 0 – 10,000 rpm, speed from 0 to 260 km/h and constant tractive force from 0 - 6 KN, as shown in Figure 1. The dynamometer is also equipped with an AIC 5008 fuel flow meter capable of measuring volumetric flow rate from 0 to 120 l/h with a sensitivity of 0.01.

Exhaust gases were measured using a Brain Bee AGS-688 analyzer, which can determine the different concentrations of HC, CO, CO₂, O_2, and NOx emitted in the experimental unit vehicle, as shown in Table 2.

The levels of the input variable, oil viscosity, are characterized by the kinematic viscosity measured in cSt @ 100 °C of three lubricating oils with similar compounds, whose technical specifications are shown in Table 3.

2.2. Response Surface Methodology

The Response Surface Methodology (RSM) is based on several mathematical and statistical methodologies that are used to develop a suitable functional relationship between a factor of interest $\left(y\right)$, and certain control (input) variables denoted by $x_1,x_2,\dots .,x_k$. This relationship is not commonly known, but can be approximated by a polynomial model of lower degree as expressed in Equation (1):

$$y=f\text{'}\left(x\right)\beta +\epsilon$$

(1)

where ${x=(x}_1,x_2,\dots .,x_k)\text{'}$, $f\left(x\right)$ represents a p-element vector function consisting of powers and cross products of powers of $x_1,x_2,\dots .,x_k$ until reaching a certain degree denoted by $d(>1)$. $\beta$ is a vector of p coefficients which are constant, and unknown being denoted as parameters, and $\epsilon$ is the random experimental error, assumed to have zero mean. This is conditional on the idea that the model in Equation (1) provides an adequate representation of the response. In this case, the quantity $f\text{'}\left(x\right)\beta$ represents the mean response, i.e., the expected value of y, and is denoted by $\mu \left(x\right)$. Typically, the response surface methodology uses two important models. These are special cases of the model presented in Equation (1). Equation (2) presents the first-degree polynomial model $(d=1)$:

$$y={\beta }_0+\sum _{i=1}^k{\beta }_i{X}_i+\epsilon$$

(2)

Equation (3), states the second-degree model $(d=2)$:

$$y={\beta }_0+\sum _{i=1}^k{\beta }_i{x}_i+\sum _{i=1}^k\sum _{j\ge 1}^k{\beta }_{ij}{x}_i{x}_j+\sum _{j=1}^k{\beta }_{ii}{x_i}^2+\epsilon$$

(3)

where $k$ is the number of variables, for this study $(k=3)$; $x_i$, $x_j$ y ${x_i}^2$ represent these variables. ${\beta }_0$ are the constant terms, ${\beta }_i$ the coefficients of the linear terms $x_i$, ${\beta }_{ii}$ the coefficients of quadratic terms ${x_i}^2$ and ${\beta }_{ij}$ the coefficients of the interaction of the terms $x_{ij}$.Finally $\epsilon$ is the residual associated with the experiment. Table 4 shows the input variables and levels.

Figure 2 shows a schematic illustration of the experimental setup. The test vehicle is mounted on the dynamometer (a), on which the speed and load are monitored (b). Inference variables such as coolant temperature, wheels, and lubricant are monitored through the dynamometer's sensor console (c). During data acquisition, fuel flow (d) and pollutant emissions (e) are recorded to finally analyze the data obtained in the experiment through specific software (f).