Life Cycle Cost Optimization of Industrial Electric Motors a Comparative Analysis of Efficiency Classes for Large Scale Implementation

1. Introduction

Industrial electric motor systems constitute roughly 45% of global electricity consumption, rendering them the predominant end-use of electrical energy worldwide [1-2]. Industrial electric motors in the European Union consume more than 300 TWh annually, constituting a considerable operational expense for manufacturing facilities and is a major source of carbon emissions. Despite the extensive availability of high-efficiency motor technologies adhering to International Efficiency (IE) classes IE3 and IE4, a substantial segment of the industrial motor fleet persists in utilizing obsolete, inefficient motors. The primary economic dilemma in motor replacement decisions is the balance between capital investment and long-term operational savings. Conventional procurement methods frequently emphasize reducing initial expenditures, overlooking that electricity costs over a motor's average 15 to 20 years lifespan can surpass its acquisition cost by a factor of 5 to 10 [3-6]. This short-term optimization strategy leads to significant economic losses and forfeited opportunities for enhancing energy efficiency. Life-cycle cost (LCC) analysis offers a thorough framework for assessing motor replacement decisions by considering all ownership-related expenses such as initial investment, installation, energy consumption, maintenance, and disposal [4]. Recent research has illustrated the implementation of LCC principles in diverse electrical equipment, such as transformers and power supplies, indicating that optimization algorithms can ascertain cost-effective configurations that reconcile technical performance with economic factors [7-10].

A substantial gap remains in the conversion of these analytical frameworks into practical decision-support tools available to industrial facility managers. Although advanced optimization algorithms have been effectively utilized in power system components, their complexity restricts adoption by practitioners seeking simple, implementable methods [11-13]. Recent studies have 

demonstrated that improving the efficiency of industrial electric motors can significantly reduce energy consumption, operating costs, and greenhouse effect while enhancing overall industrial sustainability. Researchers have increasingly employed life-cycle cost (LCC) analysis, economic assessment models, and optimization techniques to support motor selection and replacement decisions. Recent developments have also integrated sensitivity analysis, environmental assessment, and digital decision-support tools to improve the accuracy and practicality of investment evaluations. These studies collectively highlight the growing importance of combining technical performance with economic and environmental considerations when planning energy-efficiency improvements in industrial motor systems [13–15]. Current research has demonstrated significant progress in industrial motor efficiency optimization through the application of life-cycle cost analysis, economic evaluation techniques, and digital decision-support tools. Studies published between 2022 and 2026 have highlighted the importance of integrating technical performance, operating conditions, maintenance requirements, and environmental considerations into motor replacement decisions [16-18]. Furthermore, researchers have increasingly incorporated sensitivity analysis, uncertainty assessment, and sustainability indicators to improve the reliability of investment decisions for energy-efficient motor systems. These developments demonstrate a growing emphasis on comprehensive assessment methodologies that support both economic and environmental objectives, thereby providing a strong foundation for the present study and highlighting the need for practical, MATLAB-based decision-support frameworks for industrial applications.

This study addresses this gap by developing and validating a practical life-cycle cost optimization framework for industrial electric motor replacement decisions. The specific objectives are:

1. To establish a comprehensive LCC model incorporating acquisition costs, energy consumption, maintenance requirements, and salvage value for three motor efficiency classes: standard efficiency (IE1), premium efficiency (IE3), and super-premium efficiency (IE4).
2. To quantify the economic break-even point where higher-efficiency motors become financially advantageous despite their increased initial cost.
3. To develop a MATLAB-based simulation Framework that enables industrial decision-makers to perform customized LCC analysis for their specific operating conditions.
4. To validate the framework through case studies representative of typical industrial applications across varying operating conditions.

The novelty of this study lies in three major contributions. One of the Major Contribution is a practical MATLAB-based life-cycle cost optimization framework which is developed specifically for industrial decision-makers, enabling them straightforward evaluation of motor replacement alternatives under different operating conditions. Second most Important Contribution is the development of framework which integrates economic, technical, and environmental performance indicators, including life-cycle cost, payback period, net present value, and CO₂ emission reductions, within a unified assessment methodology In the third Contribution, unlike many previous studies that focus only on individual motor evaluations, this research extends the analysis to large-scale industrial implementation scenarios involving facilities operating up to 500 motors. This approach provides a more realistic assessment of the financial and environmental benefits of motor efficiency upgrades and bridges the gap between academic research and industrial practice [7].

Compared with existing life-cycle cost (LCC) assessment tools reported in recent literature, the proposed MATLAB-based framework offers various practical advantages. Many previously published LCC models primarily focus on theoretical economic evaluations or require specialized optimization software, which may limit their practical application in industrial environments. In contrast, the proposed framework integrates life-cycle cost analysis, net present value, payback period, sensitivity analysis, and environmental assessment within a single MATLAB platform. Furthermore, the framework allows users to evaluate multiple motor efficiency classes under different operating scenarios using a transparent and reproducible methodology. These features make the proposed approach more accessible to industrial practitioners and support informed decision-making for motor replacement and energy-efficiency investments.

2. Materials and Methods

2.1 Life Cycle Cost Model Formulation

The life-cycle cost for an electric motor is calculated as the sum of all costs incurred throughout its operational life, discounted to present value. The general LCC equation is expressed as:

LCC = C_acq+C_inst+C _ener +C_maint+C_disc-S ---------(1)

Where:

The energy cost component represents the largest fraction of total LCC and is calculated using the present value of an annuity formula:

C _ener = P × L × C_e × H × (1/η) × (1 + i)ⁿ - 1 / i(1+i)ⁿ ----------2

Where:

P = rated power output of the electric motor (kW),

LF = load factor expressed as a fraction of the motor’s rated capacity.

C_e =unit cost of electrical energy (€/kWh), and

t = annual operating duration measured in hours.

n = motor efficiency at the specified operating condition

r = discount rate used in the economic evaluation.

N = anticipated service life of the motor, expressed in years.The net present value (NPV) of upgrading from a baseline motor (IE1) to a higher efficiency motor is represented by the equation:

NPV = LCC_IE1 - LCC _IE3/IE4  ---------3

Where

·    NPV = net present value (€)

·      LCC _IE1 = life-cycle cost of the IE1 motor (€)

·     LCC _IE3/IE4 = life-cycle cost of the IE3 or IE4 motor (€)

If the upgraded motor exhibits a lower total life‑cycle cost than the IE1 baseline motor, the subtraction yields a positive value. Conversely, if the upgraded motor proves more expensive over its lifetime, the result is negative. Formally, this decision rule can be expressed as follow:

LCC _IE1 - LCC _IE3/IE4 > 0

LCC _IE1 - LCC _IE3/IE4 < 0

The duration required to recover the additional capital outlay associated with selecting a higher‑efficiency motor over a standard alternative is quantified by the simple payback period. This metric is computed as the ratio of the

  incremental expenditure comprising the extra purchase price and its associated installation to the annual savings realized from reduced energy consumption and lower maintenance costs:

Payback = ΔC _acq+inst/ΔC _annual --------4

Where

• ΔC _acq+inst= additional acquisition and installation cost of the upgraded motor (€) 

• ΔC _annual = annual operating cost savings resulting from reduced energy consumption and maintenance expenses (€)

2.2 Motor Efficiency Classes Analyzed

Three motor types were selected for comparative analysis, all with identical rated output power of 75 kW, corresponding to the most common power range in industrial applications [3]:

The 75-kW motor rating was selected because it represents a commonly used motor with this rated power in industrial applications such as pumps, compressors, fans, conveyors, and material-handling systems. Motors within this power range are frequently operated for extended periods and therefore offer significant opportunities for energy-efficiency improvements and life-cycle cost optimization. Furthermore, the selected rating provides a representative case for medium-to-large industrial facilities where motor-driven systems account for a substantial share of electricity consumption. Although the numerical results presented in this study are specific to a 75-kW motor, the proposed life-cycle cost methodology and MATLAB simulation framework can be readily applied to motors with different power ratings adjustable to the relevant technical and economic input parameters. Therefore, the findings provide valuable insights that can support motor replacement decisions across a wide range of industrial applications. In Table 1 represent, side by side, the defining characteristics of three industrial motor efficiency classes the baseline IE1 (standard), the intermediate IE3 (premium), and the top‑tier IE4 (super‑premium). Each motor shares an identical rated output of 75 kW, a common power bracket in manufacturing settings. The table also shows four types of parameters. The financial entries such as the purchase price climbs from €4,500 for the IE1 to €6,800 for the IE3 and further to €9,200 for the IE4. The efficiency at three load levels (100 %, 75 %, and 50  %) reveals a steady upward progression. The IE4 outperforms its less efficient counterparts by roughly two to four percentage points, a difference that grows more pronounced under partial load.

The annual maintenance cost descends from €300 (IE1) to €200 (IE4), reflecting the generally superior build quality of premium motors. The two proportional installation costs (15 % of purchase price) and salvage value (10 % of purchase price) remain constant across all three classes, ensuring that the incremental investment is driven solely by the base price.

Table 1: Motor Technical and Economic Parameters

2.3 Operating Scenario Parameters

In Table 2 Three operating scenarios were defined to represent typical industrial duty cycles and load profiles. These scenarios cover continuous, single‑shift, and variable load operations, each with distinct annual operating hours, load factors, and electricity tariffs. The parameters are summarized as follows:

Table 2: Operating Scenarios for Comparative Analysis.

Table 2 establishes three distinct industrial duty cycles, each characterized by a unique combination of annual running hours, mechanical load factor, and electricity tariff. These scenarios are designed to mirror real‑world factory conditions, ranging from around the clock operation to part time, single shift schedules.

Scenario A Continuous Operation: In this Scenario, the motor runs for 8,000 hours every year which is equivalent to more than 90 % of the calendar under a demanding load factor of 0.85. Electricity is priced at €0.20 per kilowatt hour. The Scenario represent motors used in pumps, fans, and compressors that must operate almost without interruption to sustain production processes.

Scenario B Single‑Shift Operation: Scenario B represent 2,500 annual operating hours (roughly one shift per working day) and a load factor of 0.70, this scenario represents the duty cycle of machine tools and general manufacturing equipment. The electricity tariff is lower (€0.12/kWh), reflecting the off‑peak or medium‑demand rates often negotiated for such facilities.

Scenario C Variable Load Operation: In this Scenario, the motor accumulates 5,000 hours per year under a softer load factor of 0.60, again at an electricity price of €0.12/kWh. This pattern is characteristic of conveyors and material handling systems motors, where power demand fluctuates with product flow and where motors frequently operate at partial load.

Together, all these three scenarios span the most common industrial motor applications, allowing the life‑cycle cost analysis to be tested under diverse and realistic operating conditions.

2.4 Economic Parameters

The economic assumptions applied in the life‑cycle cost analysis are consistent with standard industrial capital budgeting practices. These include a discount rate, an analysis period, escalation rates for electricity and maintenance costs, and installation and salvage value percentages. All parameters are listed in Table 3.

Table 3: Economic Assumptions

The discount rate of 5% reflects typical industrial hurdle rates for energy efficiency investments. The analysis period of 15 years corresponds to the expected economic life of a new industrial motor. Electricity price escalation of 2% per year accounts for long‑term trends in energy markets, while a 2% inflation rate is applied to maintenance costs to preserve their real value.

2.5 MATLAB Simulation Implementation

Figures illustrate dedicated MATLAB simulation tool that was developed to perform the life‑cycle cost calculations, sensitivity analyses, and graphical visualization. The implementation is organized into four functional modules:

1. Main calculation engine – Computes the 15‑year life‑cycle cost for each motor type (IE1, IE3, and IE4) across the three operating scenarios. The calculation uses present‑value formulas that incorporate electricity price escalation and inflation on maintenance costs.
   
   

   
   

Figure 1 MATLAB Simulation Framework

1. Sensitivity analysis module – Systematically varies two key input parameters (electricity price and annual operating hours) while holding all others constant. For each variation, the present net value (NPV) of upgrading from an IE1 baseline to IE3 or IE4 is recalculated.
2. Visualization module – Generates publication‑ready figures, including a four‑panel chart comparing LCC, savings, payback periods, and electricity price sensitivity, as well as a separate figure showing NPV as a function of operating hours with a break‑even line.
3. Reporting module – Outputs formatted summary tables to the MATLAB command window,
   
   

   
   

    presenting LCC results, incremental savings, payback periods, and break‑even analysis outcomes.

2.5.1 MATLAB Simulation Assumptions and Input Parameters

The selected economic assumptions were chosen to reflect commonly adopted engineering-economic practices for evaluating long-term industrial investments. A discount rate of 5% was applied because it is widely used in life-cycle cost analyses to account for the time value of money while representing a moderate cost of capital for industrial projects. An annual electricity price escalation rate of 2% was assumed to account for the gradual increase in energy prices resulting from inflation, market conditions, and regulatory changes over the motor's operational life. Similarly, a motor lifetime of 15 years was adopted because it represents the typical service life of industrial electric motors operating under normal maintenance conditions. These assumptions provide a realistic basis for comparing the long-term economic performance of different motor efficiency classes and are consistent with values commonly reported in previous life-cycle cost studies. To ensure transparency, consistency, and reliability of the life-cycle cost analysis, several technical, operational, and economic assumptions were integrated into the MATLAB simulation model. These assumptions were selected to represent realistic industrial operating conditions and are commonly accepted engineering-economic practices.

Motor Rated Power (75 kW): A rated power of 75 kW was selected because these motors are widely used in industrial applications such as pumps, compressors, ventilation systems, conveyors, and manufacturing equipment. The selected power rating provides a representative case for medium-to-large scale industrial facilities where energy consumption constitutes a major portion of operating expenses.

Motor Efficiency Classes (IE1, IE3, and IE4): Three motor efficiency classes were evaluated according to international efficiency standards. IE1 represents standard-efficiency motors commonly found in older installations, IE3 represents premium-efficiency motors, and IE4 represents super-premium-efficiency motors. These categories enable a comprehensive comparison between investment costs and long-term operational savings.

Purchase Price: The purchase prices of €4,500, €6,800, and €9,200 were assigned to the IE1, IE3, and IE4 motors, respectively. These values reflect the higher manufacturing and material costs associated with improved efficient technologies and provide a realistic basis for investment analysis.

Motor Efficiency Values: Motor efficiencies were defined at different loading conditions (100%, 75%, and 50% load) because industrial motors rarely operate continuously at full load. The selected values reflect typical performance characteristics reported by manufacturers and demonstrate how efficiency changes under varying operating conditions.

Maintenance Cost: Annual maintenance costs were assumed to be €300 for IE1, €250 for IE3, and €200 for IE4 motors. Higher-efficiency motors generally incorporate improved materials, enhanced thermal management, and better manufacturing quality, which can reduce maintenance requirements throughout their operational lifetime.

Analysis Period (15 Years): A 15-year analysis period was adopted to represent the typical economic service life of industrial electric motors. This duration is widely used in life-cycle cost studies and allows both initial investment and long-term operating costs to be evaluated comprehensively.

Discount Rate (5%): A discount rate of 5% was used to account for the time value of money and reflect common industrial investment evaluation practices. This rate represents the minimum acceptable return expected from capital investments in energy-efficiency projects.

Electricity Price (€0.20/kWh):The base electricity tariff was assumed to be €0.20 per kilowatt-hour, representing typical industrial electricity prices in many European countries. Electricity cost is the dominant component of motor life-cycle cost and therefore has a significant influence on the economic attractiveness of efficiency upgrades.

Electricity Price Escalation Rate (2%): An annual electricity price escalation rate of 2% was incorporated to account for long-term increases in energy prices resulting from market fluctuations, inflation, and regulatory changes.

Maintenance Cost Inflation Rate (2%): A maintenance inflation rate of 2% per year was assumed to reflect the gradual increase in labor, spare parts, and service costs over time.

Installation Cost (15% of Purchase Price): Installation costs were estimated as 15% of the motor purchase price. This assumption includes labor expenses, alignment activities, commissioning procedures, and associated installation services required during motor replacement.

Salvage Value (10% of Purchase Price): A residual value equal to 10% of the purchase price was assumed at the end of the motor lifetime. This represents the recoverable value obtained through resale, recycling, or material recovery.

Operating Scenarios: Three representative industrial operating scenarios were considered. The continuous-operation scenario represents motors operating 8,000 hours per year at 85% load. The single-shift scenario represents motors operating 2,500 hours per year at 70% load. The variable-load scenario represents motors operating 5,000 hours per year at 60% load. These scenarios cover a broad range of industrial applications and operating conditions.

Table 4 summarizes all key input parameters used in the MATLAB simulation model.

Table 4. Key MATLAB Simulation Input Parameters

The key parameters presented in Table 4 were used consistently throughout the MATLAB simulation framework to evaluate the life-cycle cost (LCC), net present value (NPV), payback period, and environmental benefits associated with different motor efficiency classes. These assumptions represent typical industrial operating conditions and provide a transparent and reliable basis for economic assessment and motor replacement decision-making.

2.6 Sensitivity Analysis Methodology

To identify the parameters that most strongly influence the optimal motor selection, sensitivity analyses were conducted on two critical variables: electricity price and annual operating hours.

• Electricity price variation: The electricity price was varied from €0.08 to €0.16 per kilowatt‑hour in nine equal increments. For each price level, the NPV of upgrading from IE1 to IE3 and from IE1 to IE4 was recalculated under the continuous operation scenario (8,000 hours per year, 85% load factor). This range captures typical industrial tariff variations across different regions and time periods.
• Annual operating hours variation: Operating hours were varied from 1,000 to 8,760 hours per year in 20 linear steps. For each step, the NPV of both upgrade options was recomputed under the continuous operation scenario, using the baseline electricity price. The break‑even point (where NPV equals zero) for each upgrade was identified by locating the hour value at which the NPV changes sign.

These analyses allow the determination of threshold conditions under which higher‑efficiency motors become economically advantageous.

2.7 Validation Approach

The simulation model was validated through a four‑step procedure to ensure accuracy, consistency, and logical behavior.

1. Comparison with published results:  The computed life‑cycle cost values and payback periods were compared against those reported in peer‑reviewed studies [4]. The results were found to lie within expected ranges, confirming qualitative alignment with established literature.
2. Cross‑checking with manual computations: A sample calculation (e.g., the life‑cycle cost for an IE1 motor under continuous operation) was reproduced manually using the same present‑value formulas. The manual result matched the simulation output exactly, verifying the correctness of the underlying arithmetic.
3. Verification of internal consistency – The outputs of the sensitivity analyses were examined for monotonic behavior. As expected from economic theory, the net present value of upgrading was found to increase strictly with both electricity price and annual operating hours. No non‑monotonic or unexpected trends were observed.
4. Testing of extreme cases – The simulation was executed with extreme input values (e.g., zero
   
   

   
   

    electricity price, very low operating hours of 100 hours per year). In such cases, the model returned negative NPV for both IE3 and IE4 upgrades, reflecting the logical outcome that energy savings become negligible and cannot recover the higher initial investment. This confirms the model’s robustness across the full range of plausible inputs.

To further validate the proposed framework, the quantitative results obtained in this study were compared with values reported in recent literature. Previous studies on industrial motor replacement have reported typical payback periods ranging from approximately 1 to 5 years for IE3 and IE4 motor upgrades under standard industrial operating conditions. In the present study, the calculated payback periods ranged from 0.84 to 4.36 years, which fall within the reported range. Similarly, published studies have identified energy costs as the dominant contributor to motor life-cycle cost, often accounting for more than 90% of the total ownership cost. The present analysis demonstrates a comparable trend, with energy consumption representing the largest component of the overall life-cycle cost and the primary source of economic savings achieved through higher-efficiency motors. These quantitative comparisons demonstrate that the proposed MATLAB-based framework produces results that are consistent with established findings reported in the literature, thereby providing additional confidence in the validity and reliability of the proposed methodology.

These validation steps collectively establish the reliability of the simulation framework for subsequent analysis and decision‑support applications.

3. Results

The simulation was performed for three operating scenarios: continuous operation (8,000 hours per year, 85% load), single‑shift operation (2,500 hours per year, 70% load), and variable load operation (5,000 hours per year, 60% load). The electricity price was set to €0.20/kWh for the continuous scenario and €0.12/kWh for the other two, reflecting typical industrial tariffs. The following subsections present the numerical outputs and graphical results.

3.1 Numerical Results

The simulation produced the life‑cycle costs (LCC), savings relative to IE1, and simple payback periods summarized in Tables 5 and 6.

Table 5: Life‑Cycle Cost Comparison (15‑year present value, €)

Table 5 presents the 15-year present-value life-cycle cost comparison for the three motor efficiency classes under the operating scenarios considered. The results clearly show that the IE4 motor consistently delivers the lowest total cost in all cases, followed closely by IE3, while IE1 remains the most expensive option throughout the analysis. Under continuous operation, where the motors run for the highest number of hours, the cost difference becomes most pronounced, indicating that higher efficiency motors provide the greatest economical advantage when operation demand is intensive. In the single-shift and variable-load operation scenarios, the same trend is maintained, although the gap between the alternatives becomes smaller due to reduced operating hours and energy consumption. Overall, the table 5 demonstrates that although IE4 and IE3 require a higher initial investment, their lower energy and operating costs lead to a superior long-term economic outcome, making them the more financially stable choices over the full life cycle of the motor.

Table 6: Savings and Payback Analysis

Table 6 summarizes the savings and payback performance of the IE3 and IE4 motors relative to the IE1 baseline motor across three operating scenarios. The results show that both higher-efficiency motors generate economic benefits, but the magnitude of the benefit depends mainly on duty cycle. Under continuous operation, the savings are higher and the payback periods are lower, confirming that efficiency upgrades are most effective when motor operates for long hours and energy consumption is substantial. In the single-shift scenario, the savings remain positive, but the payback period becomes noticeably longer because the annual operating time is lower, reducing the rate at which the additional investment is recovered. The variable-load

 scenario lies between these two extremes, offering moderate savings and intermediate payback periods. Overall, the table 6 demonstrates that IE4 Class provides the greatest total savings in every case, although IE3 recovers its investment faster in some scenarios. This indicates that the financial aspects of efficiency upgrades is closely linked to operating intensity, with continuous-duty applications offering the strongest business case for premium-efficiency motors.

3.2 Graphical Results

Two figures were generated from the simulation. Figure 2 presents an overall performance comparison across the three scenarios. Figure 3 shows the sensitivity of net present value (NPV) to annual operating hours and identifies break‑even thresholds.

Figure 2 :Overall Performance Comparison

Figure 2 represents four subplots:

• Subplot (a): LCC Comparison by Scenario – This bar chart compares the 15‑year life‑cycle cost (in thousand €) of IE1, IE3 and IE4 motors.  IE4 has the lowest LCC in all three scenarios, with the largest absolute difference in continuous operation.  The reduction from IE1 to IE4 exceeds €49,000 in continuous operation, confirming that the higher purchase price is rapidly recovered through energy savings.
• Subplot (b): 15‑Year Savings Relative to IE1: The chart represent absolute savings (in thousand €) when upgrading to IE3 or IE4. IE4 savings are approximately 40‑50% higher than IE3 savings in every scenario. The savings scale almost linearly with operating hours, indicating that facilities with longer shift patterns benefit disproportionately from super‑premium efficiency motors.
• Subplot (c): Payback Period for Efficiency Upgrades – Payback periods (in years) for IE3 and IE4 upgrades are displayed.  IE4 pays back in 1.19 years in continuous operation, while IE3 pays back in 0.84 years. In single‑shift operation, payback extends to 4.36 years for IE4.  Payback periods below 2 years are exceptionally attractive for industrial investments. For motors running more than 5,000 hours per year, IE4 is economically superior despite a slightly longer payback than IE3.
• Subplot (d): Sensitivity to Electricity Price – This line plot shows the NPV of upgrading from IE1 to IE3 (blue circles) and from IE1 to IE4 (red squares) as a function of electricity price (€/kWh) under continuous operation. Both upgrades have positive NPV across the entire price range (€0.08‑0.16/kWh), and NPV increases linearly with price. The IE4 upgrade becomes more attractive than IE3 at prices above approximately €0.10/kWh.  In regions with very low electricity prices (below €0.08/kWh), IE3 may be the better choice; at typical European industrial prices (€0.12‑0.20/kWh), IE4 dominates.
  
  

  
  

Figure 3 Sensitivity to Operating Hours and Break-Even Analysis

In Figure illustrate the NPV of upgrading from IE1 to IE3 (blue) and from IE1 to IE4 (red) against annual operating hours from 1,000 to 8,760 hours. A horizontal dashed black line marks NPV = 0 (break-even). Both upgraded options show positive NPV for all operating hours above 1,000 hours per year. The IE3 upgrade reaches break‑even at approximately 1,000 hours per year, and IE4 also becomes positive at approximately 1,000 hours per year. For hours above 2,500, IE4 yields consistently higher NPV than IE3. The simulation indicates that even at very low utilizations (1,000 hours per year, roughly 4 hours per working day), both IE3 and IE4 upgrades are economically viable under the assumed electricity price of €0.12/kWh. This result is more favorable than typical literature thresholds due to the high baseline electricity price used. A conservative decision rule derived from this analysis is:

• • • If operation is below 1,000 hours per year keeping IE1 is beneficial.
    • If Operation is above 1,000 hours per year upgrading to IE4 is crucial for maximum lifetime savings.

The break‑even thresholds are sensitive to electricity price; at lower prices (e.g., €0.08/kWh) the thresholds would be higher.

3.3 Additional Sensitivity Findings

• Discount rate impact: A lower discount rate (3%) increases the present value of future energy savings, making IE4 even more attractive. A higher discount rate (8%) reduces the advantage but does not eliminate it for continuous operation.
• Variable load benefit: Under variable load condition (60% load factor), the efficiency advantage of IE4 over IE1 is amplified because IE4 maintains higher efficiency at partial loads. This explains why the savings in the variable load
  
  

  
  

   scenario (€18,983) are proportionally larger than in the single‑shift scenario despite similar annual hours.

Maintenance Cost Uncertainty: Although the primary sensitivity analysis focused on electricity prices and operating hours, maintenance cost uncertainty may also influence the economic performance of motor replacement decisions. In practice, maintenance expenses can vary depending on operating conditions, equipment age, maintenance strategies, and environmental factors. However, because energy consumption represents the largest component of the total life-cycle cost, moderate variations in maintenance costs are expected to have a smaller impact on overall economic outcomes than changes in electricity prices or operating hours. Nevertheless, facilities with unusually high maintenance requirements may experience additional economic benefits from upgrading to higher-efficiency motor technologies.

Although the sensitivity analysis focused primarily on electricity prices and annual operating hours, uncertainty in other input parameters may also influence the reported life-cycle cost savings and payback periods. Variations in factors such as the discount rate, motor purchase price, maintenance costs, and electricity price escalation rate can affect the overall economic performance of motor replacement projects. For example, higher electricity prices or longer operating hours generally increase the economic benefits of high-efficiency motors and shorten the payback period, whereas lower electricity prices or shorter operating hours reduce the expected savings. Similarly, changes in maintenance costs or initial investment expenses may influence the magnitude of life-cycle cost savings, although their impact is generally smaller than that of energy-related parameters. Overall, the results indicate that the proposed framework remains robust under realistic variations in key economic assumptions while confirming that electricity cost and annual operating hours are the dominant factors influencing investment decisions.

3.4 Large‑Scale Implementation Potential

Extrapolating these findings to an industrial scale under the continuous-operation scenario of 8,000 hours per year and an electricity price of €0.20/kWh illustrates the substantial long-term benefits of high-efficiency motor replacement. Substituting a single 75 kW IE1 motor with an IE4 motor yields an estimated cost saving of approximately €49,721 over 15 years, while reducing electricity consumption by about 1.24 million kWh, based on the difference in energy cost.

3.4.1 CO₂ Emission Reduction Estimation

The environmental benefits of replacing standard-efficiency motors with higher-efficiency alternatives were estimated by converting the annual electricity savings into equivalent carbon dioxide (CO₂) emission reductions. The annual CO₂ reduction was calculated using the following equation:

Where:

• Energy Savings = annual electrical energy savings (kWh/year)
• Emission Factor = electricity-grid emission factor (kg CO₂/kWh)

An emission factor of 0.42 kg CO₂/kWh was adopted in this study, representing the average carbon intensity of the European electricity generation mix. This value was selected because the study is intended to represent typical industrial operating conditions within the European context. The estimated CO₂ reductions therefore reflect indirect emissions associated with electricity consumption rather than direct emissions from motor operation. It should be noted that electricity-grid emission factors vary among countries and regions depending on the share of renewable energy, nuclear generation, and fossil-fuel-based electricity production. Consequently, the environmental benefits reported in this study should be interpreted as representative values for the assumed electricity generation mix. When applying the proposed framework to other geographical locations, the emission factor can be replaced with locally applicable values to obtain more accurate estimates of CO₂ emission reductions. Using the EU grid emission factor of 0.42 kg CO₂/kWh, this corresponds to a reduction of roughly 520 metric tons of CO₂ emissions. It should be noted that the estimated CO₂ emission reductions are dependent on the selected electricity-grid emission factor. The value of 0.42 kg CO₂/kWh used in this study represents an average European electricity generation mix. However, emission factors can vary significantly across regions and countries depending on the proportion of renewable energy, nuclear power, and fossil-fuel-based generation. Consequently, the actual environmental benefits associated with motor efficiency improvements may be higher or lower than those reported in this study when applied to different geographical locations. The large-scale implementation analysis was developed by extrapolating the results obtained for a single motor to industrial facilities operating 50 and 500 identical motors. The extrapolation assumes that all motors have the same rated power, efficiency class, operating hours, load factor, maintenance requirements, electricity price, and economic parameters used in the single-motor analysis. Furthermore, it is

 assumed that the motors operate independently under similar industrial conditions without significant interactions affecting their individual energy consumption. This simplified approach allows the cumulative economic and environmental benefits of large-scale motor replacement programs to be estimated while providing a practical representation of industrial facilities with medium and large motor populations. When scaled to a medium-sized facility operating 50 such motors, the cumulative benefits rise to around €2.49 million in cost savings, 62 million kWh in energy savings, and 26,000 metric tons of CO₂ avoided. For a large industrial complex with 500 motors, the impact becomes even more pronounced, reaching approximately €24.9 million in financial savings, 620 million kWh in electricity savings, and 260,000 metric tons of CO₂ reduction. These results highlight that systematic motor replacement programs can serve as one of the most economically effective and environmentally impactful carbon abatement strategies available to industry.

4. Discussion

4.1 Interpretation of Key Findings

The simulation results provide a direct quantitative evidence. In the continuous operation scenario (8,000 hours per year, 85% load, electricity price €0.20/kWh), the IE4 motor achieves a payback period of 1.19 years and cumulative 15‑year savings of €49,721 compared to the IE1 motor. The IE3 motor under the same conditions pays back in 0.84 years with savings of €34,812. These results confirm that the higher initial investment in premium efficiency motors is rapidly recovered through energy savings.

For the single‑shift scenario (2,500 hours per year, 70% load, €0.12/kWh), the IE4 motor still yields positive savings of €9,406 with a payback of 4.36 years, while IE3 saves €7,453 with a payback of 3.11 years. In the variable load scenario (5,000 hours per year, 60% load, €0.12/kWh), IE4 savings reach €18,983 with a payback of 2.63 years, and IE3 saves €14,167 with a payback of 1.86 years. The sensitivity analysis shows that the net present value (NPV) of upgrading from IE1 to IE3 or IE4 increases linearly with electricity price. At the lowest simulated price (€0.08/kWh), the NPV is positive for both upgrades, and at €0.16/kWh the NPV for IE4 exceeds that of IE3 by a substantial margin. The break‑even analysis identifies that both IE3 and IE4 upgrades become economically viable at approximately 1,000 hours per year under the assumed economic parameters (discount rate 5%, price escalation 2%, and inflation 2%). This threshold is lower than typical literature values due to the inclusion of escalation and a baseline electricity price of €0.12/kWh for the single‑shift and variable scenarios. The results obtained in this study are generally consistent with findings reported in recent literature on industrial motor replacement and energy-efficiency investments. Previous studies have reported payback periods ranging from approximately 1 to 5 years for IE3 and IE4 motor upgrades, depending on operating hours, electricity prices, and motor loading conditions. The payback periods obtained in the present study, ranging from 0.84 to 4.36 years, fall within this reported range and confirm the economic viability of high-efficiency motor technologies. Similarly, the substantial life-cycle cost savings observed for IE3 and IE4 motors are consistent with published studies that identify electricity consumption as the dominant contributor to motor ownership costs. These comparisons provide additional confidence in the validity of the proposed life-cycle cost assessment framework and its applicability to industrial decision-making.

4.2 Implications for Industrial Practice

The numerical results support the following actionable recommendations for facility managers:

1. Prioritize high‑utilization motors – The continuous operation scenario shows that a single 75 kW IE4 motor saves nearly €50,000 over 15 years with a payback of just over one year. Facilities with many continuously running motors should prioritize IE4 replacements.
2. Use the break‑even chart for rapid screening – The simulation shows that for any motor operating more than 1,000 hours per year, upgrading to IE4 yields positive NPV under the assumed electricity prices. This provides a simple visual decision rule.
3. Consider variable load applications – In the variable load scenario (5,000 hours per year, 60% load), IE4 savings (€18,983) are proportionally larger than in the single‑shift scenario (€9,406 for 2,500 hours). This confirms that high‑efficiency motors are particularly advantageous for pumps, fans, and compressors with modulating demand.
4. Account for electricity price trends – The linear sensitivity implies that as electricity prices rise, the NPV of upgrades increases. Facilities expecting higher future tariffs should accelerate replacement programs.
5. Integrate with maintenance planning – The calculated payback periods (0.84–4.36 years) are short enough that even motors with remaining useful
   
   

   
   

    life can be economically replaced during scheduled maintenance outages.

4.3 Limitations and Future Research Directions

Several limitations of this study should be acknowledged:

1. Simplified maintenance modelling – Maintenance costs were treated as constant annual amounts (€300 for IE1, €250 for IE3, €200 for IE4). Actual maintenance requirements may increase non‑linearly with motor age and vary by application.
2. Deterministic approach – All input parameters (electricity price, operating hours, load factor, discount rate) were treated as fixed values. Stochastic modelling (Monte Carlo simulation) would better capture uncertainty and provide probability distributions of expected savings.
3. Limited motor types and power rating – Only three efficiency classes (IE1, IE3, and IE4) were evaluated for a single power rating (75 kW). The results are not directly generalizable to other power ratings without recalibration.
4. Grid‑specific emission factors – The CO₂ savings extrapolated in Section 3.4 used an average EU grid factor (0.42 kg/kWh). Actual emissions reductions depend on the local generation mix.
5. Installation complexity – The model assumes direct replacement with identical mounting configurations. Retrofits may require additional adaptations not captured in the cost model.

Future research directions emerging from this work include:

• Integration of Monte Carlo simulation to quantify uncertainty in LCC estimates and provide confidence intervals for expected savings.
• Multi‑objective optimization frameworks balancing economic and environmental objectives.
• Field validation studies tracking actual savings from implemented motor replacement programs.
• Extension to motor systems including variable frequency drives, gearboxes, and driven equipment.
• Life‑cycle assessment integration incorporating embodied carbon of manufacturing and disposal phases.

From a practical industrial perspective, the implementation of high-efficiency motor replacement programs should consider several operational factors beyond the economic analysis. Installation activities may require temporary production shutdowns or scheduled maintenance periods, which can result in short-term operational disruptions. In addition, retrofit complexity may vary depending on the compatibility of existing electrical systems, mechanical couplings, mounting arrangements, and control equipment. Although these factors may increase the initial implementation effort and cost, they are generally offset by the long-term reductions in energy consumption, maintenance requirements, and operating expenses demonstrated in this study. Therefore, industrial facilities should incorporate both technical feasibility and operational planning into their motor replacement strategies to maximize the overall benefits of high-efficiency motor upgrades.

5. Conclusions

This study has developed and validated a comprehensive life‑cycle cost optimization framework for industrial electric motor replacement decisions, implemented as a practical MATLAB simulation tool. Based directly on the simulation output and graphical results, the key conclusions are:

1. Premium efficiency motors deliver substantial savings – In continuous operation (8,000 hours per year, €0.20/kWh), the IE4 motor achieves €49,721 in 15‑year savings with a payback of 1.19 years, while IE3 saves €34,812 with a payback of 0.84 years. Even in single‑shift operation (2,500 hours per year, €0.12/kWh), IE4 saves €9,406 with a payback of 4.36 years.
2. The break‑even threshold is 1,000 annual operating hours – The sensitivity analysis (Figure 2) shows that both IE3 and IE4 upgrades become economically viable at approximately 1,000 hours per year under the assumed economic parameters (discount rate 5%, price escalation 2%, electricity price €0.12/kWh). This is a lower threshold than many previous estimates, due to the inclusion of escalation and realistic tariffs.
3. Variable loading enhances the advantage of IE4 – In the variable load scenario (5,000 hours per year, 60% load), IE4 savings (€18,983) are more than double those in the single‑shift scenario (€9,406 for 2,500 hours). This confirms that high‑efficiency motors are particularly beneficial for pumps, fans, and compressors with modulating demand.
4. Electricity price sensitivity is linear and critical – Figure 1 (d) demonstrates that the NPV of upgrades increases linearly with electricity price. At €0.08/kWh the NPV is positive but modest; at €0.16/kWh the IE4 upgrade yields an NPV
   
   

   
   

    exceeding €40,000 in continuous operation. Facilities in high‑tariff regions should prioritize IE4.
5. Large‑scale implementation potential is substantial – Extrapolating the continuous operation results, a medium‑sized facility with 50 motors can achieve €2.49 million in savings and 26,000 metric tons of CO₂ reduction over 15 years. A large industrial complex with 500 motors would save nearly €25 million and reduce emissions by 260,000 metric tons.

Despite the promising economic and environmental benefits identified in this study, several practical implementation challenges should be considered when planning motor replacement programs. These may include installation downtime, retrofit requirements, equipment compatibility issues, production interruptions during replacement activities, and budget constraints associated with large-scale investments. Furthermore, the analysis was based on a single motor rating and deterministic input parameters. Therefore, actual results may vary depending on operating conditions, maintenance practices, electricity market conditions, and regional characteristics. These limitations should be considered when applying the proposed framework to specific industrial facilities. The practical MATLAB framework presented here enables industrial decision makers to move beyond initial‑cost‑focused procurement toward economically optimal motor selection. By integrating established LCC principles with accessible implementation guidelines and customizable simulation capabilities, this work bridges the gap between academic optimization research and real‑world industrial application. The resulting improvements in motor system efficiency will contribute simultaneously to industrial competitiveness and environmental sustainability – a win‑win outcome achievable through systematic application of engineering economics. As global pressure mounts to reduce industrial energy consumption and carbon emissions, tools such as the one developed here will become increasingly essential for informed decision making.

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