DESC:METHOD AND SYSTEM FOR COMPUTING CARBON EMISSION SAVED
CROSS REFERENCE TO RELATED APPLICATIONS
The present application claims priority from Indian Provisional Patent Application No. 202421075525 filed on 05/10/2024, the entirety of which is incorporated herein by a reference.
TECHNICAL FIELD
Generally, the present disclosure relates to carbon emissions. Particularly, the present disclosure relates to a system and method of computing the carbon emissions saved for an electric vehicle.
BACKGROUND
Conventional fuel vehicles operate with internal combustion engines powered by fossil fuels such as petrol or diesel, producing mechanical energy through combustion. The operation of the fuel vehicles results in continuous emission of carbon dioxide and other greenhouse gases, contributing significantly to environmental pollution. Alternatively, electric vehicles operate with electric motors powered by energy stored in battery packs, eliminating direct combustion and tailpipe emissions. Overall environmental benefit of the electric vehicles depends on efficiency of operation and on the carbon intensity of the energy used for charging, linking vehicle performance with broader energy generation conditions.
The existing carbon emission assessment relies on static models that estimate equivalent fuel savings or emission reductions from the electric vehicle operation. Further, such models employ fixed coefficients for the carbon intensity of grid power or use pre-defined datasets of fuel vehicle performance to provide approximate comparisons. For instance, a fleet monitoring system technology for emission assessment estimates carbon savings based on battery usage and distance travelled. The fleeting monitoring system calculates energy consumption per kilometre and compares the energy consumption against standardized fuel economy datasets of internal combustion engine vehicles to derive an equivalent emission reduction value. The methodology employs fixed coefficients for grid carbon intensity, based on annualized national averages, and does not account for spatial or temporal variations in electricity generation sources. Further, the above-mentioned models provide operators with an approximate estimate of emission savings for large-scale fleet reporting; the approach remains dependent on pre-defined benchmarks and generalized assumptions rather than real-time operational and supply-side conditions.
However, there are certain problems associated with the existing or above-mentioned mechanism for determining carbon emission savings of an electric vehicle. For instance, the existing technology lacks adaptability to real-time operating conditions and the absence of integration with dynamically changing energy supply data. Further, the static datasets fail to reflect fluctuating grid carbon intensity or variations in driving behavior, reducing the accuracy of emission saving calculations. Furthermore, the inaccurate estimations create challenges for compliance, benchmarking, and optimization of vehicle operation.
Therefore, there exists a need for a secure, interoperable, and automated alternative for determining the carbon emission savings of an electric vehicle.
SUMMARY
An object of the present disclosure is to provide a system for determining carbon emission savings of an electric vehicle.
Another object of the present disclosure is to provide a method for determining carbon emission savings of an electric vehicle.

Yet another objective of this invention is to provide a system and method for determining carbon emission savings of an electric vehicle that accurately determines carbon emission savings by adaptively comparing real-time vehicle operational data with reference emissions of a conventional fuel vehicle.
In accordance with a first aspect of the present disclosure, there is provided a system for determining carbon emission savings of an electric vehicle, the system comprising:
- a plurality of sensors operatively coupled to a plurality of vehicle subsystems and configured to acquire real-time vehicle parameters for baseline operating conditions;
- a processing unit operatively coupled to the plurality of sensors, the processing unit is configured to:
- determine a reference carbon emission value based on the real-time vehicle parameters, corresponding to an operation of a conventional fuel vehicle for the baseline operating conditions; and
- determine an actual carbon emission value of the electric vehicle based on the real-time vehicle operational data and at least one energy source emission coefficient,
wherein the processing unit is configured to determine the carbon emission savings via an adaptive algorithm, and wherein the adaptive algorithm is based on the comparison of the reference carbon emission value and the actual carbon emission value.
The system for determining carbon emission savings of an electric vehicle, as described in the present disclosure, is advantageous in terms of providing accurate quantification of carbon emission savings by integrating real-time vehicle parameters with adaptive computational models. Further, the system ensures dynamic adjustment of emission calculations based on regional energy source emission coefficients, enhancing precision in sustainability assessment. Furthermore, the system enables reliable comparison between the electric vehicle operation and the conventional fuel vehicle benchmarks under predefined baseline criteria. Moreover, the system delivers real-time insights for emission compliance, energy optimization, and policy validation across multiple operating conditions.
In accordance with another aspect of the present disclosure, there is provided a method for determining carbon emission savings of an electric vehicle, the method comprising:
- acquiring real-time vehicle parameters from a plurality of vehicle subsystems, via a plurality of sensors;
- determining a reference carbon emission value based on the real-time vehicle parameters, corresponding to an operation of a conventional fuel vehicle, via a processing unit;
- determining an actual carbon emission value of the electric vehicle based on the real-time vehicle operational data and at least one energy source emission coefficient, via the processing unit;
- comparing the reference carbon emission value and the actual carbon emission value, via the processing unit; and
- calibrating the determination of carbon emission savings by iteratively adjusting the reference carbon emission value and the actual carbon emission value, via the processing unit.

Additional aspects, advantages, features, and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow.
It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.
BRIEF DESCRIPTION OF DRAWINGS
The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.
Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:
Figure 1 illustrates a block diagram of a system for determining carbon emission savings of an electric vehicle, in accordance with an embodiment of the present disclosure.
Figure 2 illustrates a flow chart of a method for determining carbon emission savings of an electric vehicle, in accordance with another embodiment of the present disclosure.
In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing.
DETAILED DESCRIPTION
The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practicing the present disclosure are also possible.
As used herein, the term “carbon emission saving” refers to a measurable reduction in greenhouse gas emissions achieved through the replacement of conventional fuel vehicle operation with electric vehicle operation. Specifically, the carbon emission saving is established as the difference between a reference carbon emission value and an actual carbon emission value. Further, the reference value is generated from calibration datasets of conventional fuel vehicles based on fuel consumption and emission profiles across various speeds and loads, and the actual value is determined from real-time operational parameters of the electric vehicle combined with updated energy source emission coefficients. Furthermore, the carbon emission saving exists in distinct types, including, but not limited to, instantaneous savings derived from real-time comparison of reference and actual values during active operation, cumulative savings obtained by aggregating results over multiple operational cycles, and adaptive savings refined through iterative calibration using historic vehicle operation data in conjunction with dynamically updated emission coefficients. Moreover, the multi-layered structure ensures accurate benchmarking of environmental benefits and establishes carbon emission savings as a reliable performance indicator for electric vehicle sustainability assessment.
As used herein, the terms “electric vehicle”, “EV”, and “vehicle” are used interchangeably and refer to a transportation system powered by one or more electric motors that draw energy from rechargeable battery packs instead of internal combustion engines. Specifically, the electric vehicle operates by converting electrical energy stored in the battery pack into mechanical energy through controlled motor torque, with real-time parameters such as, but not limited to, acceleration, orientation, speed, and battery discharge rate monitored by integrated IME sensors for performance evaluation and emission savings determination. Further, the electric vehicles are classified into types that include, but are not limited to, battery electric vehicles relying exclusively on onboard battery storage for propulsion, plug-in hybrid electric vehicles that combine electric drive with auxiliary fuel-based operation, and extended-range electric vehicles that employ electric motors for propulsion supported by auxiliary power generation systems. Furthermore, such classification ensures accurate computation of carbon emission savings across diverse electric vehicle configurations by integrating operational parameters with adaptive algorithms and energy source emission coefficients.
As used herein, the term “sensor” refers to a measurement device designed to detect physical or electrical parameters and convert the parameters into signals suitable for processing and analysis. Specifically, the sensor integrated within the system acquires real-time vehicle parameters such as, but not limited to, the speed, torque, acceleration, orientation, angular velocity, and battery discharge rate, enabling accurate determination of both reference and actual carbon emission values. Further, the types of sensors include, but are not limited to, speed sensors for monitoring wheel rotation, torque sensors for capturing motor output, temperature sensors for assessing ambient and battery conditions, voltage-current sensors for monitoring battery performance, and Inertial Measurement and Energy (IME) sensors, which provide combined measurement of linear acceleration, angular rotation, orientation, and simultaneous tracking of energy discharge from the battery pack. Furthermore, IME sensors supply high-resolution data on vehicle dynamics and energy usage, forming the core input set for adaptive algorithms that compute carbon emission savings through correlation with calibration datasets and dynamically updated energy source emission coefficients.
As used herein, the term “vehicle subsystem” refers to a functional unit within the vehicle that performs a specific operational role and generates measurable parameters for monitoring and control. Specifically, the vehicle subsystem contributes data through integrated sensors, enabling the acquisition of parameters required for determining the reference and the actual carbon emission values. Further, each subsystem interacts with the processing unit to provide continuous inputs such as, but not limited to, speed, torque, energy consumption, orientation, and thermal characteristics. Furthermore, the types of vehicle subsystems include, but are not limited to, the propulsion subsystem responsible for motor operation and torque delivery, the energy storage subsystem governing battery charge and discharge behavior, the thermal management subsystem maintaining optimal operating temperature of components, and the control subsystem coordinating communication between sensors and the processing unit. Moreover, the integration of the aforementioned subsystems ensures that the IME sensor output and other sensor data collectively define baseline operating conditions and provide the foundation for adaptive algorithms that compute accurate carbon emission savings.
As used herein, the term “vehicle parameters” refers to a measurable attribute of the vehicle operation that defines performance, energy usage, and environmental impact. Specifically, the vehicle parameter refers to data acquired in real time from a plurality of sensors integrated with the different vehicle subsystems, serving as the input for the computation of the reference and the actual carbon emission values. Further, each parameter reflects a specific operational characteristic, allowing the processing unit to establish the baseline operating conditions, execute the interpolation within the calibration datasets, and apply the adaptive algorithms for accurate emission savings determination. Furthermore, the types of vehicle parameters include, but are not limited to, dynamic parameters such as, but not limited to, speed, acceleration, orientation, and motor torque, electrical parameters such as, but not limited to, battery voltage, current, and discharge rate, and environmental parameters such as, but not limited to, ambient temperature and load conditions. Moreover, the continuous acquisition and correlation of the parameters ensure precise benchmarking of the electric vehicle operation against the conventional fuel vehicles, enabling accurate assessment of carbon emission savings under varying conditions.
As used herein, the term “baseline operating conditions” refers to a defined set of operational states under which the vehicle parameters are acquired for accurate comparison between the electric vehicle performance and equivalent conventional fuel vehicle performance. Specifically, the baseline operating conditions establish a controlled reference scenario by filtering real-time vehicle data through predefined criteria such as, but not limited to, the speed range, acceleration stability, battery state of charge thresholds, and energy consumption uniformity. Further, the types of baseline operating conditions include, but are not limited to, the speed-based baselines defined by velocity thresholds, the energy-based baselines defined by charge or discharge states of the battery, and the stability-based baselines defined by the absence of abrupt changes in acceleration or torque. Furthermore, the identification of such operating conditions enables the processing unit to generate accurate reference carbon emission values from calibration datasets and compute reliable carbon emission savings through the adaptive algorithms.
As used herein, the term “processing unit” refers to a computational module responsible for acquiring, analyzing, and interpreting sensor data to execute the defined algorithms and generate outputs for decision-making. Specifically, the processing unit receives the real-time vehicle parameters from the sensors, determines the reference carbon emission value through correlation with the calibration datasets of the conventional fuel vehicles, computes the actual carbon emission value of the electric vehicle using the operational data and the energy source emission coefficients, and applies the adaptive algorithms to determine the carbon emission savings. Further, the processing unit performs interpolation, regression modeling, dynamic coefficient updates, and iterative calibration, ensuring that the outputs remain aligned with real-world operating and energy supply conditions. Furthermore, the types of processing units include, but are not limited to, embedded microcontrollers integrated within the vehicle subsystems for localized data handling, centralized processors managing system-wide computations and the adaptive algorithms, and distributed processing architectures combining multiple controllers for parallel handling of the sensor data and the emission calculations. Moreover, the integration of such processing units ensures precise, dynamic, and reliable determination of carbon emission savings across varied operational environments.
As used herein, the term “reference carbon emission value” refers to a calculated benchmark of greenhouse gas emissions that the conventional fuel vehicle would generate under operating conditions equivalent to those of the electric vehicle. Specifically, the reference carbon emission value is determined by correlating real-time parameters such as, but not limited to, the speed, torque, acceleration, and orientation from the electric vehicle with the calibration dataset of the conventional fuel vehicles containing fuel consumption and emission profiles across varying speeds and loads. Further, the processing unit applies the algorithmic mapping and the multi-dimensional interpolation to estimate the emissions for the operating points not explicitly present in the dataset, ensuring a continuous and accurate representation of the conventional vehicle behavior. Furthermore, the types of the reference carbon emission value include, but are not limited to, instantaneous reference values computed for specific operational states such as, but not limited to, steady speed or acceleration, aggregated reference values generated over complete trips or cycles, and adaptive reference values recalibrated iteratively using historic operation data to improve long-term accuracy. Moreover, the reference carbon emission value establishes the baseline against which actual carbon emission values of electric vehicles are compared to quantify carbon emission savings.
As used herein, the term “conventional fuel vehicle” refers to a transport system powered primarily by internal combustion engines that convert chemical energy from fossil fuels into mechanical energy for propulsion. Specifically, the conventional fuel vehicle operates through controlled combustion of gasoline or diesel in engine cylinders, producing rotational torque that drives the powertrain and the vehicle subsystems. Further, such an operation generates direct carbon emissions as a byproduct of incomplete fuel combustion, with emission intensity varying based on parameters such as, but not limited to, engine load, vehicle speed, and driving conditions. Specifically, the conventional fuel vehicles serve as the comparative baseline for calculating reference carbon emission values, supported by calibration datasets containing the fuel consumption and the emission profiles under the diverse operating conditions. Furthermore, the types of conventional fuel vehicles include, but are not limited to, gasoline-powered vehicles that rely on spark-ignition engines, diesel-powered vehicles that employ compression-ignition engines, and hybrid vehicles that combine internal combustion engines with auxiliary electric power sources, thereby producing carbon emissions due to fuel dependency. Moreover, the conventional fuel vehicle framework provides the essential reference for assessing carbon emission savings achieved by the electric vehicle operation.
As used herein, the term “actual carbon emission value” refers to the quantified amount of greenhouse gas emissions associated with the real-time operation of the electric vehicle under specific driving and energy supply conditions. Specifically, the actual carbon emission value is derived by correlating the instantaneous vehicle parameters, such as, but not limited to, speed, torque, acceleration, orientation, and battery discharge characteristics, with the carbon intensity of the energy source supplying the electric vehicle. The carbon intensity is periodically updated from external datasets or APIs. Further, the processing unit integrates the data to compute precise emission values that reflect the environmental impact of energy consumed during operation. Furthermore, the types of actual carbon emission value include, but are not limited to, instantaneous values representing emissions at a specific time step, cumulative values representing total emissions over an entire trip or duty cycle, and dynamic values that adapt over time through recalibration with updated emission coefficients and the historic vehicle operation data. Moreover, the actual carbon emission value establishes the comparative metric against the reference carbon emission value, enabling accurate determination of carbon emission savings achieved through electric vehicle deployment.
As used herein, the term “energy source emission coefficient” refers to a quantified measure of carbon emissions released per unit of energy generated from a specific energy source. Specifically, the energy source emission coefficient is determined by analyzing lifecycle emissions from the fuel extraction, conversion, and power generation processes, expressed typically in grams of carbon dioxide equivalent per kilowatt-hour. Further, the processing unit retrieves updated emission coefficients from the external energy databases or the APIs, such as, but not limited to, carbon intensity data fetched at fixed intervals, and integrates the values into emission calculations for accurate assessment of environmental impact during the electric vehicle operation. Furthermore, the types of energy source emission coefficient include, but are not limited to, static coefficients derived from standardized datasets representing average values of coal, natural gas, or renewable energy sources, dynamic coefficients that vary based on temporal grid composition or load demand, and location-specific coefficients that reflect regional differences in power generation mix. Moreover, the energy source emission coefficient forms a critical input in determining actual carbon emission values, enabling precise estimation of carbon emission savings against reference baselines of conventional fuel vehicles.
As used herein, the term “adaptive algorithm” refers to a computational procedure designed to modify the operational parameters in response to the changing input conditions, ensuring optimized performance under the varying scenarios. Specifically, the adaptive algorithm within the system dynamically adjusts sampling rates, reference values, and emission coefficients by continuously processing the sensor data, calibration datasets, and external energy data inputs. Further, the algorithm operates through iterative refinement, learning from historical operational data, and applying regression models to improve the estimation accuracy of the carbon emission savings over time. Furthermore, the types of adaptive algorithms include, but are not limited to, rule-based adaptive algorithms that follow the pre-defined logic thresholds for the parameter adjustments, data-driven adaptive algorithms that rely on the regression or machine learning models for the predictive updates, and hybrid adaptive algorithms that combine the fixed rules with the predictive modeling to balance stability and flexibility. Moreover, the adaptive algorithm ensures efficient computation, accurate emission quantification, and improved alignment between reference carbon emission values and actual operating conditions of the electric vehicle.
As used herein, the term “predefined baseline criteria” refers to a structured set of operational thresholds established for defining normal or reference conditions of the vehicle. Specifically, the predefined baseline criteria include, but are not limited to, specific limits for the vehicle speed, acceleration patterns, battery state of charge, torque demand, and environmental parameters, forming the foundation for the comparison with actual operating data to evaluate deviations and estimate the emission savings. Further, such criteria are determined through the calibration datasets, historical driving cycles, and regulatory emission profiles, ensuring consistency in the measurement and the standardization of evaluation. Furthermore, the types of predefined baseline criteria include, but is not limited to, dynamic baseline criteria derived from the real-time adaptive learning models, static baseline criteria defined by the fixed values of speed, load, or state of charge, and hybrid baseline criteria integrating the static thresholds with the adaptive adjustments for varying road, traffic, and energy source conditions. Moreover, the predefined baseline criteria ensure accurate distinction between the optimal operation and the deviating conditions, thereby supporting precise carbon emission evaluation and efficient control strategies.
As used herein, the term “sampling rates” refers to the frequency at which the data points from the sensors or the subsystems are collected and processed for analysis within the vehicle system. Specifically, the sampling rates provide the frequency of recording of the parameters, such as, but not limited to, the motor torque, battery discharge, vehicle acceleration, and orientation, enabling precise tracking of the performance and the emission characteristics under different operating conditions. Further, a higher sampling rate, such as, but not limited to 100 Hz for the motor torque during baseline operation, ensures fine granularity of the data and accurate representation of dynamic variations, alternatively, a lower sampling rate, such as, but not limited to, 10 Hz during non-baseline conditions, optimizes the computational efficiency and conserves processing resources without compromising essential accuracy. Furthermore, the types of sampling rates include, but are not limited to, fixed sampling rates defined at constant frequencies regardless of the operational state, adaptive sampling rates that adjust the frequency based on the deviation from the baseline criteria, and the hierarchical sampling rates that assign different frequencies to different parameters depending on the criticality for emission estimation and vehicle performance assessment. Moreover, the sampling rates directly influence data fidelity, computational efficiency, and system responsiveness, forming a crucial element in achieving accurate and resource-efficient emission evaluation.
As used herein, the term “calibration dataset” refers to a structured collection of reference values used to align the system outputs with actual performance characteristics under defined operating scenarios. Specifically, the calibration dataset stores fuel consumption profiles, emission characteristics, and efficiency benchmarks across varying vehicle speeds, torque levels, load conditions, and ambient influences, allowing interpolation of emissions relative to the current electric vehicle operating parameters. Further, by referencing stored calibration points, the processing unit estimates emission outputs with improved accuracy, ensuring dynamic adjustments that reflect real driving conditions. Furthermore, the types of calibration datasets include, but are not limited to, static calibration datasets that remain constant and serve as the fixed benchmarks, dynamic calibration datasets that update periodically with new measurements or the external energy data inputs, and the hybrid calibration datasets that integrate predefined reference values with the real-time sensor feedback for enhanced precision. Moreover, the calibration dataset strengthens the linkage between modelled estimations and real-world outcomes, enabling consistent emission savings evaluation and reliable operational profiling.
As used herein, the term “algorithmic mapping model” refers to a computational framework that establishes structured relationships between the input parameters and the corresponding output variables to enable the precise prediction, estimation, or classification within the defined system. Specifically, the algorithmic mapping model correlates the vehicle parameters, such as but not limited to the speed, acceleration, torque demand, ambient temperature, and energy source emission coefficients, with carbon emission values, creating a structured representation that allows the emission savings to be determined with high fidelity. Further, the model processes the historical operational data, the real-time sensor inputs, and the calibration datasets to generate predictive mappings that evolve over time through the adaptive learning mechanisms. Furthermore, the types of algorithmic mapping models include, but are not limited to, deterministic mapping models that apply the fixed equations for parameter-output correlation, statistical mapping models that employ the regression-based approaches for handling the multi-variable dependencies, and adaptive mapping models that refine the parameter relationships dynamically based on the updated operational feedback. Moreover, the algorithmic mapping model establishes the precise computational foundation for generating accurate emission profiles and quantifying savings under varying operating conditions.
As used herein, the term “multi-dimensional interpolation” refers to the numerical technique that estimates unknown values within a defined domain by referencing known data points distributed across multiple variables. Specifically, the multi-dimensional interpolation generates accurate emission estimates by interpolating within the calibration datasets that include the fuel consumption and the emission profiles across varying speeds, torque loads, and ambient conditions, allowing the precise alignment of predicted emission values with the current electric vehicle operating parameters. Further, the method computes the intermediate values by leveraging the structured data grids or the scattered data points and applies the interpolation functions across several independent variables simultaneously, ensuring that the estimated outputs maintain continuity and smoothness across the operational ranges. Furthermore, the types of the multi-dimensional interpolation include, but is not limited to, linear interpolation that connects the known data points with straight-line relationships, polynomial interpolation that fits the higher-order curves across datasets, spline interpolation that ensures smooth transitions across the intervals with minimal error, and radial basis function interpolation that provides robust estimation across irregular data distributions. Moreover, the multi-dimensional interpolation ensures precise mapping between the vehicle operation states and corresponding emission characteristics, supporting accurate calculation of carbon emission savings.
As used herein, the term “carbon intensity” refers to a quantitative measure of carbon dioxide emissions produced per unit of energy generated, reflecting the environmental impact associated with energy consumption. Specifically, the carbon intensity represents the emission coefficient derived from the external energy data sources, where the system fetches updated values from the external application programming interface and integrates the aforementioned values into the calculation of the emission coefficients that directly influence real-time estimation of actual carbon emission values during the electric vehicle operation. Further, the measure accounts for variations in the grid composition, the renewable energy contribution, and the fossil fuel dependency, thereby establishing a dynamic linkage between the electricity supply characteristics and emission outcomes. Furthermore, the types of carbon intensity include, but are not limited to, grid carbon intensity that represents the average emissions associated with centralized electricity generation, marginal carbon intensity that reflects incremental emissions from the additional unit of demand, and regional carbon intensity that accounts for the location-specific generation mixes. Moreover, the carbon intensity provides the foundation for adaptive emission modeling, ensuring accurate determination of reference values and enhancing the precision of carbon emission savings computation.
As used herein, the term “energy source” refers to the origin from which usable energy is generated to power systems, processes, or devices, forming the fundamental input for energy conversion and utilization. Specifically, the energy source represents the supply medium that delivers power to the electric vehicle, with the associated emission coefficients dynamically updated based on the external carbon intensity data. Further, the processing unit integrates the energy source information with the vehicle parameters to establish the accurate actual carbon emission values and the reference comparisons, enabling precise determination of the emission savings. Furthermore, the types of energy source include, but are not limited to, a fossil-based energy such as, but not limited to, coal, oil, and natural gas that carry high carbon intensity values, renewable energy such as, but not limited to, solar, wind, and hydroelectric power that carry significantly lower carbon intensity values, and hybrid or mixed-grid energy sources that represent combinations of the conventional and the renewable generation. Moreover, the energy source directly defines the emission coefficient applied in the algorithmic models, establishing a critical factor in the adaptive calculations that refine the accuracy of carbon emission savings estimation.
As used herein, the term “carbon intensity data” refers to a quantified measure of the carbon dioxide emissions released per unit of energy generated from a particular source, serving as a key indicator of environmental impact associated with energy production. Specifically, the carbon intensity data represents the numerical values fetched at regular intervals from the external energy data sources through the application programming interface, with the system retrieving the carbon intensity data and updating the emission coefficient accordingly. Further, the carbon intensity data is processed in conjunction with the vehicle operational parameters and the adaptive algorithms to refine the actual emission values and enhance accuracy in the emission savings determination. Furthermore, the types of carbon intensity data include, but are not limited to, real-time grid carbon intensity reflecting the instantaneous power generation mixes, the averaged carbon intensity over the defined period representing the smoothed energy usage trends, and projected carbon intensity based on the forecasted energy demand and the supply conditions. Moreover, the carbon intensity data provides the essential dynamic variable for calibration of emission models, allowing the processing unit to adjust the reference and the actual emission values in line with evolving grid conditions, thereby improving the precision of carbon savings estimation in the electric vehicle operation.
As used herein, the term “external energy data source” refers to independent repositories or services that provide the quantified information regarding the energy generation, distribution, and associated carbon emissions, forming a foundation for accurate environmental assessment. Specifically, the external energy data sources deliver updated information on the grid conditions and the carbon intensity values through the application programming interfaces, with the system fetching the carbon intensity data and dynamically updating the emission coefficient in alignment with real-time energy mixes. Further, such integration allows the processing unit to refine reference and actual emission values based on evolving energy supply characteristics, enabling more accurate determination of emission savings. Furthermore, the types of external energy data sources include, but are not limited to, grid operator databases that publish the live carbon intensity metrics, governmental or regulatory platforms providing the standardized emission factors for different regions, and third-party analytics services aggregating data from multiple energy networks for enhanced precision. Moreover, the external energy data sources ensure that emission estimation and adaptive modeling remain synchronized with real-world energy dynamics, thereby strengthening the reliability of the carbon savings evaluation in the electric vehicle applications.
As used herein, the term “weighted averaging algorithm” refers to a computational technique that assigns proportional significance to multiple input variables to generate a consolidated output that reflects the relative influence rather than equal contribution. Specifically, the weighted averaging algorithm processes parameters such as, but not limited to, the vehicle speed, acceleration, ambient temperature, and the energy source emission coefficient, each assigned a weight based on the impact of the parameters on the overall emission savings estimation. Further, the algorithm integrates the sensor data, the historical records, and the dynamically updated coefficients from the external energy data sources, ensuring that the higher-impact variables exert a stronger influence on the final emission savings calculation. Furthermore, the types of weighted averaging algorithms include, but are not limited to, static weighted averaging, with fixed weights are pre-assigned based on the predefined baseline criteria, the dynamic weighted averaging where the weights are adaptively recalibrated through the learning mechanisms using the calibration datasets, and the hybrid weighted averaging where the static assignments are combined with adaptive updates to maintain both stability and responsiveness. Moreover, the weighted averaging algorithm ensures enhanced precision of emission modeling by mitigating the distortions caused by the variable fluctuations and aligning the estimation accuracy with the real-world operational dynamics of the electric vehicles.
As used herein, the term “historical vehicle operation” refers to a structured record of the past driving patterns, energy consumption, and emission behavior associated with a specific vehicle or fleet. Specifically, the historic vehicle operation forms the foundational dataset for the emission savings calibration by capturing the parameters such as, but not limited to, the distance travelled, average speed, acceleration cycles, stop-start frequencies, fuel or energy usage profiles, and the external conditions such as, but not limited to, ambient temperature. Further, the data from historic vehicle operation is systematically integrated with the updated emission coefficients to iteratively refine the reference and the actual carbon emission values, allowing the improved alignment of modelled outcomes with real-world performance. Furthermore, the types of historic vehicle operation include, but are not limited to, longitudinal operation data that spans the extended timeframes to capture the seasonal and the lifecycle variations, short-term operation data that emphasizes recent performance for the adaptive algorithmic tuning, and segmented operation data that categorizes driving behavior into urban, highway, or mixed cycles to provide a multi-dimensional basis for the regression modeling and interpolation methods. Moreover, the historic vehicle operation enables learning-based adjustment of the baseline operating conditions, enhancing precision and robustness in the determination of carbon emission savings.
As used herein, the term “multi-parameter regression model” refers to a statistical framework that correlates multiple independent variables with a dependent variable to achieve accurate prediction or estimation. Specifically, the multi-parameter regression model integrates variables such as, but not limited to, vehicle speed, acceleration, ambient temperature, and energy source emission coefficient to generate refined estimations of the carbon emission savings. Further, the model processes diverse sensor inputs and the external energy data sources simultaneously, applying weighted coefficients to each parameter to reflect the contribution toward the overall emission outcome. Furthermore, by incorporating a broader variable set, the model reduces estimation error, improves adaptability across the varying baseline operating conditions, and supports the precise alignment between the reference carbon emission values and the actual carbon emission values. Moreover, the types of multi-parameter regression models include, but not limited to, linear regression models that assume a direct proportionality between the parameters and the emission outcomes, polynomial regression models that capture the non-linear relationships within the vehicle operation data, and regularized regression models that manage the high-dimensional datasets by reducing the overfitting through penalty terms. Additionally, the integration of such models within the processing unit strengthens predictive robustness, enabling more accurate quantification of emission reduction linked to electric vehicle operation.
In accordance with a first aspect of the present disclosure, there is provided a system for determining carbon emission savings of an electric vehicle, the system comprising:
- a plurality of sensors operatively coupled to a plurality of vehicle subsystems and configured to acquire real-time vehicle parameters for baseline operating conditions;
- a processing unit operatively coupled to the plurality of sensors, the processing unit is configured to:
- determine a reference carbon emission value based on the real-time vehicle parameters, corresponding to an operation of a conventional fuel vehicle for the baseline operating conditions; and
- determine an actual carbon emission value of the electric vehicle based on the real-time vehicle operational data and at least one energy source emission coefficient,
wherein the processing unit is configured to determine the carbon emission savings via an adaptive algorithm, and wherein the adaptive algorithm is based on the comparison of the reference carbon emission value and the actual carbon emission value.
Referring to figure 1, in accordance with an embodiment, there is described a system 100 for determining carbon emission savings of an electric vehicle 102 is described. The system 100 comprises a plurality of sensors 104 operatively coupled to a plurality of vehicle subsystems 106 and configured to acquire real-time vehicle parameters for baseline operating conditions. Further, the system 100 comprises a processing unit 108 operatively coupled to the plurality of sensors 104. Furthermore, the processing unit 108 is configured to determine a reference carbon emission value based on the real-time vehicle parameters, corresponding to an operation of a conventional fuel vehicle for the baseline operating conditions. Moreover, the processing unit 108 is configured to determine an actual carbon emission value of the electric vehicle 102 based on the real-time vehicle operational data and at least one energy source emission coefficient. Additionally, the processing unit 108 is configured to determine the carbon emission savings via an adaptive algorithm, wherein the adaptive algorithm is based on the comparison of the reference carbon emission value and the actual carbon emission value.
The system 100 operates by employing a plurality of sensors 104 configured as at least one of Inertial Measurement and Energy (IME) sensors, each integrated with multiple vehicle subsystems 106 to capture precise real-time vehicle parameters under defined baseline operating conditions. The IME sensors 104 measure acceleration, angular velocity, and orientation to determine the vehicle’s 102 movement profile, and simultaneously monitor battery discharge rate. Further, the processing unit 108 receives and correlates the real-time vehicle parameters with a calibration dataset representing conventional fuel vehicle operation. Further, the algorithmic mapping model, combined with multi-dimensional interpolation, generates a reference carbon emission value that replicates the equivalent emissions of the conventional fuel vehicle under identical conditions. In parallel, the processing unit 108 calculates the actual carbon emission value of the electric vehicle 102 by processing operational data from the IME sensors 104 along with at least one dynamically updated energy source emission coefficient. Furthermore, the energy source emission coefficient represents the carbon intensity of the energy supplied to the electric vehicle 102 and undergoes continuous updates in response to variations in the energy generation mix. Moreover, the adaptive algorithm performs a comparison between the computed reference carbon emission value and the actual carbon emission value, enabling accurate determination of carbon emission savings. The IME sensors 104 provide high-fidelity data on motion dynamics, orientation changes, and power consumption patterns, ensuring that both reference and actual emission values are aligned with true operational behavior. Consequently, the precise and context-aware computation of carbon emission savings is enabled through the integration of IME sensor data, baseline operating condition detection, and adaptive algorithmic processing. Additionally, the advantages of the system 100 include improved accuracy of carbon emission assessments, effective benchmarking of electric vehicle 102 efficiency against the conventional fuel vehicles, enhanced reliability through continuous updates to emission coefficients, and reduced computational overhead by focusing measurements within predefined operational contexts. Subsequently, the system 100 delivers accurate, real-time insights for environmental impact reporting, regulatory compliance, and performance optimization in diverse operational scenarios.
In an embodiment, the processing unit 108 is configured to identify the baseline operating conditions based on a comparison of the real-time vehicle parameters with a predefined baseline criterion. Specifically, the baseline criteria include, but is not limited to, constraints such as, but not limited to, a specific speed range, a stable acceleration profile, battery state of charge thresholds, and steady energy consumption patterns. Further, real-time data from the sensors 104, including, but not limited to, speed, orientation, acceleration, and battery discharge rate, undergo filtering and normalization before being matched against the aforementioned criteria. Furthermore, the identification process ensures that subsequent calculations of carbon emission savings are based on data captured under consistent and representative operating conditions. Moreover, the identification method involves sequential data sampling, signal conditioning, and threshold evaluation for each parameter. For instance, the IME sensor 104 outputs for acceleration and angular velocity are analysed to confirm stability of motion, and the battery monitoring inputs verify compliance with state-of-charge requirements. Subsequently, once all parameters fall within the defined ranges, the processing unit 108 flags the operational state as baseline compliant. The detection process remains active throughout the trip, allowing immediate recognition of deviations from the baseline and enabling segmentation of data into qualified and non-qualified sets for further processing. Consequently, the relevant operational data is precisely isolated, which reflects the intended baseline scenario, thereby improving the accuracy of reference carbon emission value generation. Additionally, the advantages of identifying the baseline operating conditions include the elimination of variations caused by inconsistent driving patterns, higher correlation accuracy between electric vehicle data and calibration datasets of conventional fuel vehicles, and optimized computation efficiency through selective data processing. Ultimately, the approach ensures that carbon emission savings are determined under strictly defined and verifiable operating conditions, leading to more reliable environmental performance assessments.
In an embodiment, the processing unit 108 is configured to adjust sampling rates of the plurality of sensors 104 based on the identified baseline operating conditions. The processing unit 108 adjusts the sampling rates of the plurality of sensors 104 based on the detected baseline operating conditions by dynamically controlling data acquisition frequency. For instance, under baseline operating conditions, such as during a trip from place A to place B with speed maintained between 40–60 km/h, absence of sudden accelerations, and battery state of charge above 80%, motor torque data from the sensors is sampled at 100 Hz to capture fine-grained variations required for precise reference carbon emission calculations. Further, upon detection of operational states deviating from the baseline, such as, but not limited to, significant speed variations, abrupt accelerations, or battery state of charge falling below the threshold, the processing unit 108 reduces the sampling rate to 10 Hz. Furthermore, the aforementioned high-frequency sampling ensures detailed mapping of torque fluctuations, acceleration profiles, and energy consumption patterns, supporting accurate correlation with calibration datasets of conventional fuel vehicles. Moreover, the aforementioned adjustment conserves computational resources and minimizes unnecessary data processing overhead without impacting the integrity of the carbon emission savings computation. The reduced frequency still captures essential parameters for tracking overall vehicle operation but eliminates excessive data density in non-critical intervals. Simultaneously, the IME sensors 104 continue to monitor orientation, angular velocity, and discharge rate under both high and low sampling regimes, ensuring consistent input quality for the adaptive algorithm. Consequently, an optimal balance is achieved between measurement precision and processing efficiency through intelligent sampling rate management. Additionally, the advantages include a significant reduction in processing load and storage requirements, improved responsiveness of the system by allocating resources to relevant operational periods, and sustained accuracy of emission savings determination during critical baseline intervals. Subsequently, the adaptive control of sampling rates enables the system 100 to remain both performance-oriented and resource-efficient across varying operational scenarios.
In an embodiment, the processing unit 108 is configured to correlate the real-time vehicle parameters with a calibration dataset of the conventional fuel vehicle via an algorithmic mapping model. The processing unit 108 correlates the real-time vehicle parameters obtained from the plurality of sensors 104 with a calibration dataset of the conventional fuel vehicle through the algorithmic mapping model. Specifically, the calibration dataset represents the pre-recorded fuel consumption and emission profiles across a wide range of speeds, engine loads, and operating conditions, obtained through standardized testing and field measurements. Further, parameters such as, but not limited to, vehicle speed, motor torque, acceleration, and battery discharge rate from the electric vehicle 102 are matched with equivalent operating points in the dataset to establish a direct comparison framework. Furthermore, the aforementioned correlation ensures that each set of electric vehicle parameters is associated with a precise reference point in the conventional fuel vehicle dataset. Moreover, the technique applies interpolation within the calibration dataset to account for operating conditions that are similar to the recorded data points. Additionally, multi-dimensional interpolation across speed and load axes generates an estimated fuel consumption and emission value corresponding to the current electric vehicle parameters. In an instance, if the calibration dataset includes emissions data for 40 km/h and 50 km/h at a specific load, and the electric vehicle 102 operates at 45 km/h with a matching load, the algorithm interpolates between the two points to produce a reference value. Subsequently, the algorithmic mapping model integrates multiple parameters simultaneously, ensuring that the combined effects of speed, load, and acceleration are accurately represented in the reference emission calculation. Consequently, high-resolution estimation of conventional fuel vehicle emissions is achieved for precise benchmarking against electric vehicle 102 operation. Further, the advantages of correlation include improved accuracy in determining reference carbon emission values, the ability to accommodate a wide variety of real-world driving scenarios through interpolation, and enhanced adaptability of the system 100 to different vehicle categories without the need for direct on-road fuel vehicle measurement. The aforementioned approach enables reliable, repeatable, and scalable assessment of carbon emission savings in diverse operating environments.
In an embodiment, the processing unit 108 is configured to apply the algorithmic mapping model by implementing a multi-dimensional interpolation of the calibration dataset to generate the reference carbon emission value. The processing unit 108 applies multi-dimensional interpolation to the calibration dataset for generating the reference carbon emission value. The calibration dataset includes, but is not limited to, fuel consumption and emission profiles of the conventional fuel vehicles across varying speeds, torque levels, and acceleration profiles. The interpolation process fills gaps between recorded calibration points, enabling continuous estimation of emissions for intermediate operating states. For instance, the calibration dataset provides emission data at 40 km/h and 50 km/h at a specific torque, and the electric vehicle operates at 45 km/h with the same torque; the interpolation algorithm estimates the equivalent fuel vehicle emission value at 45 km/h. Consequently, precise and seamless reference emission values are generated, improving benchmarking accuracy between electric and conventional vehicles. Further, the processing unit 108 applies the algorithmic mapping model by implementing a multi-dimensional interpolation of the calibration dataset to generate the reference carbon emission value. Moreover, the calibration dataset includes fuel consumption and emission profiles recorded for conventional fuel vehicles across varying speeds, loads, and operational states. Furthermore, the multi-dimensional interpolation ensures that intermediate operating points not explicitly stored in the dataset are calculated with precision. For instance, the parameters such as vehicle speed, motor torque, and acceleration obtained from the electric vehicle 102 are mapped into the dataset axes, and the interpolation algorithm determines the equivalent emission output of the conventional fuel vehicle at the exact conditions. Additionally, the technique involves constructing interpolation surfaces across multiple dimensions of the dataset, with speed and load typically forming the primary axes and emission outputs forming the dependent variable. Real-time parameters acquired from the IME sensors 104 are continuously matched against the axes, and the interpolation algorithm computes values that lie between the recorded points. For instance, the torque values that fall between measured calibration entries are interpolated in conjunction with speed data, producing a continuous emission estimate across the entire operating spectrum. The interpolation process is executed in real time, ensuring that the reference carbon emission value remains consistent with the current vehicle operation. Consequently, precise and continuous estimation of reference carbon emission values is achieved without reliance on discrete dataset entries alone. Subsequently, the advantages include higher accuracy in emission savings determination, seamless representation of a wide range of operating conditions, elimination of gaps between calibration points, and improved scalability of the system to different datasets and vehicle types. Further, the interpolation approach enhances the reliability of benchmarking electric vehicle 102 performance against conventional fuel vehicles, leading to more accurate quantification of environmental benefits.
In an embodiment, the processing unit 108 is configured to dynamically update the at least one energy source emission coefficient based on the fluctuations in carbon intensity of the energy source supplying energy to the electric vehicle 102. Specifically, the energy source emission coefficient represents the amount of carbon released per unit of energy consumed, and its value varies according to the energy generation mix, which includes renewable, thermal, and other sources. Furthermore, real-time or near-real-time updates of grid composition are received and processed, ensuring the coefficient remains aligned with current supply conditions. For instance, an increased share of renewable energy in the supply reduces the coefficient, whereas a higher reliance on coal or gas elevates the coefficient. Furthermore, the method involves continuously monitoring energy supply characteristics through incoming data streams from external energy data sources, grid operators, or internal estimation models. Moreover, the processing unit 108 evaluates the data inputs, applies conversion factors, and further recalibrates the energy source emission coefficient in real time. Subsequently, during the electric vehicle 102 operation, every calculation of actual carbon emission value incorporates the updated coefficient, ensuring that the determined carbon emission savings accurately reflect prevailing energy conditions. Additionally, the dynamic adjustment process ensures synchronization between energy use by the vehicle and the carbon intensity of the supply network. Consequently, accurate representation of real-world variability in energy-related emissions is achieved, leading to a precise assessment of carbon savings across different temporal and geographical contexts. Further, the advantages include enhanced reliability of emission reporting, improved alignment of vehicle-level calculations with broader energy system conditions, support for compliance with evolving environmental regulations, and provision of actionable insights for optimizing charging strategies. Eventually, the dynamic updating of energy source emission coefficient establishes a direct link between vehicle operation and energy system sustainability, ensuring an adaptive and context-sensitive evaluation of emission savings.
In an embodiment, the processing unit 108 is configured to receive carbon intensity data from external energy data sources and apply a weighted averaging algorithm to at least one energy source emission coefficient. The processing unit 108 employs the weighted averaging algorithm to refine the energy source emission coefficient based on multiple external grid carbon intensity inputs. The carbon intensity data is retrieved periodically from regional energy operators or APIs, each carrying a reliability score or weight. The algorithm assigns a higher weight to more reliable data sources or geographically closer nodes, creating a composite coefficient that reflects current grid conditions. For instance, a first source reports 420 gCO2/kWh with 70% reliability and a second source reports 460 gCO2/kWh with 90% reliability; the weighted average generates a coefficient closer to the second value. The aforementioned approach ensures that short-term fluctuations in grid carbon intensity are incorporated into actual carbon emission value calculations, improving the fidelity of savings estimation. Specifically, the processing unit 108 fetches updated carbon intensity values from an external API, ensuring that the emission coefficient remains representative of current energy grid conditions. Further, the values reflect the changing mix of renewable, thermal, and other energy contributors within the grid. Furthermore, by integrating the time-stamped external data with operational data from the electric vehicle 102, the system 100 ensures that the actual carbon emission value incorporates the most recent energy composition information. Moreover, the technique applies a weighted averaging algorithm to the incoming carbon intensity data, which accounts for variations across multiple sources or regional energy nodes. Additionally, the processing unit 108 processes the values from the API, assigns weights based on the reliability and relevance of each source, and calculates a composite emission coefficient. The weighted coefficient is further embedded into the ongoing carbon emission computation, ensuring that short-term fluctuations in grid carbon intensity are represented in the determination of actual emissions. The 10-minute refresh interval strikes a balance between data granularity and computational efficiency, keeping the coefficient aligned with the dynamic grid without introducing unnecessary overhead. Consequently, higher fidelity in emission savings assessment is achieved by synchronizing vehicle-level calculations with continuously updated energy system data. Subsequently, the advantages include improved precision in real-time carbon emission reporting, stronger correlation between vehicle operation and regional energy sustainability, enhanced adaptability of the system 100 to fluctuating grid conditions, and provision of reliable data for both environmental compliance and operational decision-making. Ultimately, the integration of external energy data sources through periodic API updates ensures that the system 100 remains context-aware and responsive to real-world energy dynamics.
In an embodiment, the processing unit 108 is configured to dynamically calibrate the determination of the carbon emission savings by iteratively adjusting the reference carbon emission value and the actual carbon emission value based on historic vehicle operation and the updated energy source emission coefficient. Specifically, the emission savings calculations are refined through integration of the historic vehicle operation data with updated energy source emission coefficients, ensuring that each successive computation reflects accumulated operational knowledge. For instance, reference values derived from calibration datasets of the conventional fuel vehicles are continuously fine-tuned by comparing previously computed estimates against observed operational outcomes of the electric vehicle 102. Further, the aforementioned iterative adjustment process establishes a feedback loop that improves the alignment between modelled reference emissions and real-world conditions. Furthermore, the technique involves storing historic datasets that include speed profiles, torque demands, orientation changes, and energy consumption patterns collected from IME sensors 104. The datasets are combined with temporally updated emission coefficients derived from fluctuating grid carbon intensity. Moreover, the processing unit 108 processes the combined data to identify systematic deviations or patterns in earlier estimations and recalibrates the computation models accordingly. Additionally, as new operational cycles are completed, the iterative adjustment process repeats, progressively enhancing the fidelity of both reference and actual carbon emission values. The adaptive recalibration ensures that emission savings estimation evolves with each additional operational input. Consequently, progressive improvement in emission savings accuracy is achieved over time, enabled by the learning-oriented recalibration approach. Subsequently, the advantages include minimization of long-term estimation errors, reliable benchmarking across varied operating scenarios, and enhanced robustness of emission savings determination under changing energy supply conditions. Ultimately, continuous calibration also strengthens the predictive capability of the system, supporting accurate long-term reporting of environmental benefits and enabling stakeholders to base decisions on progressively refined emission savings data.
In an embodiment, the processing unit 108 is configured to apply a multi-parameter regression model based on the historic vehicle operation and the updated energy source emission coefficient. Further, the processing unit 108 applies the multi-parameter regression model to adjust emission estimations based on combined operational and environmental factors. The model treats reference and actual emission values as dependent variables and correlates the variables with independent variables, including vehicle speed, acceleration, torque demand, ambient temperature, and the updated energy source emission coefficient. The historic datasets are used to train the model, enabling the identification of complex interdependencies across multiple parameters. For instance, an acceleration spike at 6000 rpm under 32°C ambient temperature is linked with a higher discharge rate, leading to an upward adjustment of the actual emission estimate. Over successive iterations, the regression model reduces prediction errors and improves the long-term reliability of carbon savings determination under varying real-world conditions. Specifically, the regression model is structured to correlate dependent variables such as, but not limited to, reference and actual carbon emission values with independent variables comprising, but not limited to, vehicle speed, acceleration, ambient temperature, and the current energy source emission coefficient. Further, the real-time parameters from IME sensors 104 combined with external energy data are processed as inputs, and the regression model outputs adjusted emission values that capture the combined effect of operational dynamics and energy supply conditions. Furthermore, the statistical modelling approach enables precise quantification of emission behavior across diverse scenarios. Furthermore, the technique involves training the regression model on historic vehicle operation data augmented with updated energy source emission coefficients. Moreover, the model continuously refines the coefficients by analyzing residual errors between predicted emission savings and previously computed values, improving prediction accuracy over successive iterations. For instance, a scenario involving higher acceleration under elevated ambient temperature is mapped against corresponding increases in energy consumption, with the regression model adjusting the emission estimate accordingly. Additionally, by simultaneously evaluating multiple parameters, the model generates outputs that reflect complex interdependencies rather than isolated parameter effects. Consequently, improved reliability and predictive capability of carbon emission savings determination is achieved through the integration of multiple operational and environmental variables. Subsequently, the advantages include enhanced robustness of emission estimations under varying driving patterns and external conditions, greater accuracy in benchmarking electric vehicle efficiency against conventional fuel vehicles, and adaptability of the system 100 across different environments and vehicle categories. Ultimately, the regression-based approach strengthens the system’s capacity to deliver precise, data-driven insights that support regulatory compliance, performance optimization, and transparent environmental reporting.
In an exemplary embodiment, an electric vehicle operates with a battery pack rated at 60 kWh, integrated with a system configured to estimate and compare carbon emissions in comparison with a conventional fuel vehicle of similar capacity. Further, a plurality of sensors 104 comprising current sensors, voltage sensors, IME sensors, and temperature sensors continuously acquire operational parameters such as, but not limited to, battery current of 120 A, battery voltage of 350 V, and motor speed of 6000 rpm under an acceleration cycle. The processing unit 108 receives sensor signals and applies an adaptive algorithm with a predefined baseline criterion derived from a calibration dataset of 50,000 km of vehicle operation. The system 100 retrieves external energy data sources reflecting a regional grid carbon intensity value of 450 gCO2/kWh and compares the values against a reference carbon emission value of 180 gCO2/km generated by a conventional fuel vehicle operating at a fuel efficiency of 15 km/litre and a fuel emission coefficient of 2.68 kgCO2/litre. The processing unit 108 applies multi-dimensional interpolation on calibration data to determine the reference emission value at intermediate operating states. The torque demand and vehicle speed at 45 km/h are mapped between recorded calibration points at 40 km/h and 50 km/h, producing an interpolated reference emission output of 180 gCO2/km. The interpolation procedure ensures accurate estimation of reference values for operational conditions not explicitly stored in the calibration dataset, thereby enabling precise benchmarking of the electric vehicle against conventional fuel vehicles. Furthermore, the processing unit 108 refines the determination of the actual carbon emission value through integration of dynamically updated energy source emission coefficients. A weighted averaging algorithm processes multiple grid intensity inputs, each assigned with a reliability score to generate a composite coefficient. For instance, a first source provides 420 gCO2/kWh and a second source provides 460 gCO2/kWh with higher reliability, the weighted coefficient is adjusted closer to 460 gCO2/kWh. The above-mentioned approach ensures that short-term variations in grid composition are embedded into the emission coefficient, providing an accurate reflection of prevailing energy supply conditions. The processing unit 108 additionally applies a multi-parameter regression model to adjust the emission estimation based on combined operational and environmental factors. The regression model correlates reference and actual emission values with parameters including vehicle speed, torque demand, acceleration profile, ambient temperature, and the updated emission coefficient. For instance, an acceleration event at 6000 rpm with a battery current of 120 A under 32°C ambient temperature increases discharge rate, and the regression model adjusts the actual emission estimate upward to reflect the higher energy consumption. Advantageously, the progressive refinement of regression coefficients across multiple cycles improves prediction accuracy and reduces long-term estimation errors, ensuring robustness of emission savings determination. Further, through the integration of interpolation, weighted averaging, and regression modelling, the processing unit determines the actual carbon emission value of the electric vehicle as 110 gCO2/km. A weighted averaging algorithm additionally processes historic operation data covering 12 months of daily commuting cycles, interpolates variations, and refines prediction accuracy through feedback-based calibration. The outcome establishes a carbon emission saving of 70 gCO2/km, translating to a reduction of 1050 kgCO2 over a driving distance of 15,000 km annually.
In accordance with a second aspect, there is described a method for determining carbon emission savings of an electric vehicle, the method comprising:
- acquiring real-time vehicle parameters from a plurality of vehicle subsystems, via a plurality of sensors;
- determining a reference carbon emission value based on the real-time vehicle parameters, corresponding to an operation of a conventional fuel vehicle, via a processing unit;
- determining an actual carbon emission value of the electric vehicle based on the real-time vehicle operational data and at least one energy source emission coefficient, via the processing unit;
- comparing the reference carbon emission value and the actual carbon emission value, via the processing unit; and
- calibrating the determination of carbon emission savings by iteratively adjusting the reference carbon emission value and the actual carbon emission value, via the processing unit.
Referring to Figure 2, in accordance with an embodiment, there is described a method 200 for determining carbon emission savings of an electric vehicle 102. At step 202, the method 200 comprises acquiring real-time vehicle parameters from a plurality of vehicle subsystems 106 via a plurality of sensors 104. At step 204, the method 200 comprises determining a reference carbon emission value based on the real-time vehicle parameters, corresponding to an operation of a conventional fuel vehicle, via a processing unit 108. At step 206, the method 200 comprises determining an actual carbon emission value of the electric vehicle 102 based on the real-time vehicle operational data and at least one energy source emission coefficient, via the processing unit 108. At step 208, the method 200 comprises comparing the reference carbon emission value and the actual carbon emission value, via the processing unit 108. At step 210, the method 200 comprises calibrating the determination of carbon emission savings by iteratively adjusting the reference carbon emission value and the actual carbon emission value, via the processing unit 108.
In an embodiment, the method 200 comprises correlating the real-time vehicle parameters with a calibration dataset of the conventional fuel vehicle using an algorithmic mapping model, via the processing unit 108.
In an embodiment, the method 200 comprises applying the algorithmic mapping model by implementing a multi-dimensional interpolation of the calibration dataset to generate the reference carbon emission value, via the processing unit 108.
In an embodiment, the method 200 comprises receiving carbon intensity data from external energy data sources and applying a weighted averaging algorithm to at least one energy source emission coefficient, via the processing unit 108.
In an embodiment, the method 200 comprises applying a multi-parameter regression model based on the historic vehicle operation and the updated energy source emission coefficient, via the processing unit 108.
In an embodiment, the method 200 comprises acquiring real-time vehicle parameters from a plurality of vehicle subsystems 106, via a plurality of sensors 104. Further, the method 200 comprises correlating the real-time vehicle parameters with a calibration dataset of the conventional fuel vehicle using an algorithmic mapping model, via the processing unit 108. Furthermore, the method 200 comprises applying the algorithmic mapping model by implementing a multi-dimensional interpolation of the calibration dataset to generate the reference carbon emission value, via the processing unit 108. Moreover, the method 200 comprises determining a reference carbon emission value based on the real-time vehicle parameters, corresponding to an operation of a conventional fuel vehicle, via a processing unit 108. Additionally, the method 200 comprises receiving carbon intensity data from external energy data sources and applying a weighted averaging algorithm to at least one energy source emission coefficient, via the processing unit 108. Subsequently, the method 200 comprises determining an actual carbon emission value of the electric vehicle 102 based on the real-time vehicle operational data and at least one energy source emission coefficient, via the processing unit 108. Further, the method 200 comprises comparing the reference carbon emission value and the actual carbon emission value, via the processing unit 108. Furthermore, the method 200 comprises calibrating the determination of carbon emission savings by iteratively adjusting the reference carbon emission value and the actual carbon emission value, via the processing unit 108. Furthermore, the method 200 comprises applying a multi-parameter regression model based on the historic vehicle operation and the updated energy source emission coefficient, via the processing unit 108.
The system for determining carbon emission savings of an electric vehicle, as described in the present disclosure, is advantageous in terms of accurate quantification of carbon emission savings by integrating real-time vehicle parameters with adaptive computational models. Further, the system ensures dynamic adjustment of emission calculations based on regional energy source emission coefficients, enhancing precision in sustainability assessment.
It would be appreciated that all the explanations and embodiments of the system 100 also apply mutatis mutandis to the method 200.
In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms “disposed,” “mounted,” and “connected” are to be construed broadly, and may, for example, be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases by those skilled in the art.
Modifications to embodiments and combinations of different embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as “including”, “comprising”, “incorporating”, “have”, and “is” used to describe and claim the present disclosure are intended to be construed in a non-exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.
Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings, and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
,CLAIMS:WE CLAIM:
1. A system (100) for determining carbon emission savings of an electric vehicle (102), the system (100) comprising:
- a plurality of sensors (104) operatively coupled to a plurality of vehicle subsystems (106) and configured to acquire real-time vehicle parameters for baseline operating conditions;
- a processing unit (108) operatively coupled to the plurality of sensors (104), the processing unit (108) is configured to:
- determine a reference carbon emission value based on the real-time vehicle parameters, corresponding to an operation of a conventional fuel vehicle for the baseline operating conditions; and
- determine an actual carbon emission value of the electric vehicle (102) based on the real-time vehicle operational data and at least one energy source emission coefficient,
wherein the processing unit (108) is configured to determine the carbon emission savings via an adaptive algorithm, and wherein the adaptive algorithm is based on the comparison of the reference carbon emission value and the actual carbon emission value.
2. The system (100) as claimed in claim 1, wherein the processing unit (108) is configured to identify the baseline operating conditions based on a comparison of the real-time vehicle parameters with a predefined baseline criterion.

3. The system (100) as claimed in claim 1, wherein the processing unit (108) is configured to adjust sampling rates of the plurality of sensors (104) based on the identified baseline operating conditions.

4. The system (100) according to claim 1, wherein the processing unit (108) is configured to correlate the real-time vehicle parameters with a calibration dataset of the conventional fuel vehicle via an algorithmic mapping model.

5. The system (100) according to claim 1, wherein the processing unit (108) is configured to apply the algorithmic mapping model by implementing a multi-dimensional interpolation of the calibration dataset to generate the reference carbon emission value.

6. The system (100) as claimed in claim 1, wherein the processing unit (108) is configured to dynamically update the at least one energy source emission coefficient based on the fluctuations in carbon intensity of energy source supplying energy to the electric vehicle (102).

7. The system (100) as claimed in claim 1, wherein the processing unit (108) is configured to receive carbon intensity data from external energy data sources and apply a weighted averaging algorithm to at least one energy source emission coefficient.

8. The system as claimed in claim 1, wherein the processing unit (108) is configured to dynamically calibrate the determination of the carbon emission savings by iteratively adjusting the reference carbon emission value and the actual carbon emission value based on historic vehicle operation and the updated energy source emission coefficient.

9. The system (100) according to claim 1, wherein the processing unit (108) is configured to apply a multi-parameter regression model based on the historic vehicle operation and the updated energy source emission coefficient.

10. A method (200) for determining carbon emission savings of an electric vehicle (102), the method (200) comprising:
- acquiring real-time vehicle parameters from a plurality of vehicle subsystems, via a plurality of sensors (104);
- determining a reference carbon emission value based on the real-time vehicle parameters, corresponding to an operation of a conventional fuel vehicle, via a processing unit (108);
- determining an actual carbon emission value of the electric vehicle based on the real-time vehicle operational data and at least one energy source emission coefficient, via the processing unit (108);
- comparing the reference carbon emission value and the actual carbon emission value, via the processing unit (108); and
- calibrating determination of carbon emission savings by iteratively adjusting the reference carbon emission value and the actual carbon emission value, via the processing unit (108).