DESC:SYSTEM AND METHOD FOR OPTIMIZING ROUTE PLAN FOR AN ELECTRIC VEHICLE
CROSS REFERENCE TO RELATED APPLICATIONS
The present application claims priority from Indian Provisional Patent Application No. 202421068720 filed on 11/09/2024, the entirety of which is incorporated herein by a reference.
TECHNICAL FIELD
Generally, the present disclosure relates to route planning for electric vehicle(s). Particularly, the present disclosure relates to optimizing route plans for electric vehicle(e).
BACKGROUND
In electric vehicles (EVs), route planning traditionally focuses on minimizing travel time or distance, often without accounting for the vehicle's real-time energy consumption characteristics, battery limitations, and environmental factors. As EV adoption grows, there is a pressing need to ensure that trip planning is aligned with energy constraints, particularly the available State of Charge (SoC), to avoid route failures, unnecessary range anxiety, or inefficient charging behavior.
Conventionally, some advanced navigation solutions incorporate charging station waypoints and modify the route to ensure station availability along the route. Further, another common approach uses SoC prediction models that estimate energy consumption between charging points and attempt to identify a sequence of hops between charging stations with minimal detour. The above-mentioned models further use a combination of road gradient data, average vehicle efficiency, and historical driver behavior. For instance, Google Maps provides energy-aware navigation for EVs using such estimates, but the computations are generally route-global, without fine-grained analysis of local path energy demands. Similarly, EV routing engines such as A Better Route Planner (ABRP) and Tesla’s Trip Planner analyze energy usage at a macro level, considering charging station density and elevation.
However, there are certain underlying problems associated with the above-mentioned existing mechanism for crash detection mechanism. For instance, traditional routing ways focus on linear or global energy prediction and overlook localized fluctuations in energy demand caused by micro-gradients, acceleration patterns, or short-term congestion, which significantly affect actual consumption. Secondly, the absence of a modular, hierarchical energy block structure limits the system’s ability to dynamically assess alternate paths during navigation or proactively compute rerouting options. Thirdly, route selection mechanisms do not account for the energy influence of neighbouring regions, which provide valuable fallback options or enhance energy prediction robustness. Consequently, current path routing systems are more prone to inefficient routing, frequent recharging interruptions, or failure to complete a trip within a given SoC. The above-mentioned limitations are especially critical in constrained environments with sparse charging infrastructure or varying terrain conditions within short distances.
Therefore, there exists a need for a mechanism for determining an energy-optimal route for a trip of an electric vehicle that is efficient and overcomes one or more problems as mentioned above.
SUMMARY
An object of the present disclosure is to provide a system for determining an energy-optimal route for a trip of an electric vehicle.
Another object of the present disclosure is to provide a method of determining an energy-optimal route for a trip of an electric vehicle.
Yet another object of the present disclosure is to provide a system and method for determining an energy-optimal route for an electric vehicle by evaluating multiple route options using localized energy block analysis and state of charge constraints.
In accordance with a first aspect of the present disclosure, there is provided a system for determining an energy-optimal route for a trip of an electric vehicle, the system comprising:
- an input interface configured to receive a start location and a destination location for the trip;
- a processing unit operatively coupled to the input interface, the processing unit comprises:
- a path segmentation module configured to divide a geographical area between the start location and the destination location into a plurality of energy blocks, and determine a plurality of routes from the start location and the destination location;
- an output module operatively coupled to the processing unit and configured to transmit the energy-optimal route to a vehicle control unit,
wherein the processing unit is configured to select an energy-optimal route including a plurality of first-order energy blocks and a plurality of second-order energy blocks, with total energy consumption for the trip constrained within the available state of charge.
The system and method for determining an energy-optimal route for a trip of an electric vehicle, as described in the present disclosure, are advantageous in terms of providing offers a significant advancement in electric vehicle navigation by shifting from traditional distance or time based routing to a more intelligent, energy-aware approach accounting for real-world driving constraints. Advantageously, by segmenting the geographical area into energy blocks and incorporating both first-order and second-order neighbouring regions into the route evaluation process, the system enhances prediction accuracy and route robustness under varying terrain, traffic, and environmental conditions. The localized and hierarchical energy modeling enables dynamic route selection that aligns closely with the vehicle’s available state of charge, reducing range anxiety and minimizing redundant detours or charging stops. Therefore, the system ensures energy efficiency with higher reliability and adaptability in EV trip planning.
In accordance with another aspect of the present disclosure, there is provided a method of determining an energy-optimal route for a trip of an electric vehicle, the method comprises:
- dividing a geographical area between the start location and the destination location into a plurality of energy blocks, via a path segmentation module;
- determining a set of first-order energy blocks and a set of second-order energy blocks for each route, from a plurality of routes, based on a minimum traversal distance criteria, via an order identification module;
- determining, for each route, a combination of a plurality of first order energy blocks, from the set of first-order energy blocks and a plurality of second order energy blocks, from the set of second-order energy blocks, via an energy computation module;
- determining the total energy consumption for the trip between the start location and the destination location, via the energy computation module; and
- comparing energy consumption values computed for each route, via a route selection module.
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:
Figures 1 and 2 illustrate a block diagram of a system for determining an energy-optimal route for a trip of an electric vehicle, in accordance with different embodiments of the present disclosure.
Figure 3 illustrates a flow chart of a method of determining an energy-optimal route for a trip 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 terms “energy-optimal route”, “optimal route”, and “efficient route” are used interchangeably and refer to a path between a start location and a destination location that minimizes total energy consumption of an electric vehicle while remaining within the available state of charge. Specifically, the route is selected based on a structured evaluation of spatially divided geographical segments called energy blocks, which are categorized into first-order and second-order types based on proximity and traversal relevance. Further, the first-order energy blocks represent immediate neighbours to a reference energy block on a given route, whereas second-order energy blocks represent adjacent neighbours to the first-order energy blocks. Each block is associated with an estimated energy consumption value, determined using environmental, elevation, traffic, and road condition data. The objective is to identify a combination of energy blocks across potential routes that collectively incur the least energy usage without exceeding the battery's state of charge limit. Furthermore, the system includes a path segmentation module that segments the geographical region into discrete energy blocks and identifies multiple potential routes. Subsequently, a route selection module compares total consumption values across all candidate routes and selects the route with the minimum energy cost that conforms to the vehicle's current energy availability. Therefore, the above-mentioned procedure enables optimized energy-aware navigation tailored to electric vehicles, ensuring route feasibility under real-world constraints of battery capacity.
As used herein, the terms “electric vehicle”, “EV”, “battery-powered vehicle”, and “vehicle” are used interchangeably and refer to a vehicle that is driven by an electric motor that draws its electrical energy from a battery and is charged from an external source. The electric vehicle includes both a vehicle that is only driven by the electric motor that draws electrical energy from the battery (all-electric vehicle) and a vehicle that may be powered by an electric motor that draws electricity from the battery and by an internal combustion engine (hybrid vehicle). Moreover, the ‘electric vehicle’ as mentioned herein may include electric two-wheelers, electric three-wheelers, electric four-wheelers, electric trucks, electric pickup trucks, and so forth.
As used herein, the terms “input interface” and “user interface” are used interchangeably and refer to a hardware and software component configured to receive user-defined trip parameters, including a start location and a destination location for determining an energy-optimal route. Specifically, the interface operates as the initial point of interaction between the user and the route computation system and ensures standardized input acquisition for consistent processing. The start and destination coordinates are digitized and relayed to the processing unit, initiating the segmentation of the geographical area into energy blocks. Further, the interface ensures compatibility with vehicle-mounted display units, remote mobile applications, or centralized fleet control terminals, depending on deployment context. Furthermore, the input interface supports multiple input types, including manual entry via touchscreens, voice-activated commands, or automated retrieval from pre-scheduled route data stored within vehicle control systems. Upon receiving valid input parameters, the interface triggers downstream modules within the processing unit to begin path segmentation and energy computation. The interface ensures parameter validation to avoid incomplete or ambiguous entries, thereby enabling precise geographical referencing. Consequently, integration with navigation databases and geospatial mapping systems ensures that received coordinates correspond to valid navigable regions, enabling seamless initiation of the energy-optimal route determination process.
As used herein, the terms “processing unit” and “processor” are used interchangeably and refer to an integrated computational module operatively coupled to the input interface and configured to perform route segmentation, block classification, energy computation, and route selection based on received trip parameters. Specifically, the unit forms the core of the energy-optimal route determination system and executes algorithmic operations necessary for transforming input coordinates into a viable route output. The processing unit operates on structured geographical data, battery state information, and environmental variables to ensure that selected routes conform to energy constraints imposed by the available state of charge. The types of submodules within the processing unit include a path segmentation module, an order identification module, an energy computation module, and a route selection module. The path segmentation module divides the region between the start and destination locations into a grid of energy blocks. Subsequently, the order identification module classifies blocks into first-order and second-order sets based on spatial adjacency criteria. The energy computation module assigns energy values to each block and evaluates total route consumption using candidate combinations. The route selection module compares computed totals and selects the route with minimum energy usage that remains within available energy reserves. The processing unit executes all computations in real time, interfacing directly with the vehicle control unit to support dynamic routing decisions.
As used herein, the terms “path segmentation module” and “segmentation module” are used interchangeably and refer to a functional component within the processing unit configured to divide a geographical area between a start location and a destination location into discrete spatial segments known as energy blocks. The module enables the transformation of a continuous travel space into quantifiable units for energy-based analysis. Each energy block represents a bounded region with defined coordinates and traversal attributes relevant to electric vehicle energy consumption. The segmentation provides a structural basis for subsequent classification and energy computation by organizing the route domain into indexed entities. The types of segmentation implemented by the module include grid-based division, vector-tile partitioning, or adaptive clustering based on road network density. The technique involves mapping the geographical area onto a coordinate matrix, assigning each matrix cell as an energy block. Further, neighbouring relationships are established to define adjacency for order classification. The segmentation output includes metadata for each block, such as terrain type, elevation profile, traffic density, and historical energy consumption patterns. Therefore, the module ensures consistent spatial resolution and prepares the segmented data for processing by order identification and energy computation modules in the selection of an energy-optimal route.
As used herein, the terms “energy blocks” and “blocks” are used interchangeably and refer to spatially bounded units in a geographical area between a start location and a destination location, divided for the purpose of evaluating energy consumption in electric vehicle routing. Specifically, each energy block represents a defined region associated with specific energy usage characteristics based on road gradient, surface condition, traffic patterns, and environmental factors. The use of energy blocks enables localized analysis of energy demand and facilitates modular computation across potential routes. Further, a single energy block forms the basic unit of route assessment, allowing the system to isolate and quantify the impact of individual segments on total trip energy consumption. Further, the types of energy blocks include first-order energy blocks and second-order energy blocks. The first-order energy blocks are immediate spatial neighbours of reference blocks, forming a primary path between the start and destination. The second-order energy blocks are adjacent to the corresponding first-order energy blocks and provide auxiliary traversal options. The technique of identifying energy blocks involves overlaying a uniform or adaptive grid over the travel region, extracting relevant spatial features, and assigning energy consumption metrics derived from vehicle telemetry or external datasets. Further, each route is evaluated as a combination of energy blocks, and total energy requirements are computed based on the summation of block-wise energy costs. The structured classification of energy blocks supports optimized route selection within the available energy constraints of the vehicle.
As used herein, the terms “output module” and “display module” are used interchangeably and refer to a system component operatively coupled to the processing unit and configured to transmit the selected energy-optimal route to a vehicle control unit. The module serves as the communication endpoint for delivering computed routing decisions and ensures synchronization between computational logic and vehicle-level execution. The output includes route coordinates, traversal instructions, and energy-related metadata, formatted for integration with onboard navigation systems. Further, the output parameters reflect the selected route's energy efficiency profile, structured to guide vehicle motion within the constraints of the available state of charge. The types of output supported by the module include direct control signals to the vehicle control unit, route maps for driver interfaces, and data streams for logging or remote monitoring. The technique of operation involves receiving route selection results from the processing unit, formatting the data into predefined protocol standards, and dispatching the data through wired or wireless communication links. Error-checking and acknowledgement protocols ensure delivery integrity. Therefore, the output module maintains operational compatibility with dynamic routing updates and facilitates real-time adjustments by issuing refreshed instructions based on revised energy computations or changing environmental inputs.
As used herein, the terms “vehicle control unit” and “VCU” are used interchangeably and refer to an onboard electronic control system configured to receive and execute energy-optimal route instructions transmitted by the output module. The VCU interprets routing data in real time and translates it into drive commands, navigation cues, or system-level adjustments, such as, but not limited to, throttle modulation and regenerative braking activation. The control unit ensures adherence to the selected energy-efficient path and aligns vehicle behavior with energy management objectives defined by the processing unit. Further, the input received by the control unit includes waypoints, directional changes, and energy consumption constraints embedded in the transmitted route structure. Furthermore, the types of control logic within the vehicle control unit include path-following algorithms, battery management coordination, and adaptive drive mode selection. The technique involves continuous parsing of the received route data, comparing the current vehicle status with the prescribed trajectory, and initiating mechanical or software responses to maintain alignment. Further, the feedback from sensors and battery management systems supports closed-loop control, allowing for energy-aware navigation execution. The vehicle control unit also interfaces with user display systems, enabling visual representation of the energy-optimal route and alerts related to deviations, estimated energy usage, or route reassessment triggers.
As used herein, the terms “first-order energy blocks” and “first-order blocks” are used interchangeably and refer to spatial units directly adjacent to a reference energy block within a segmented geographical area used for determining an energy-optimal route. Specifically, each first-order energy block lies along a primary traversal path between the start and destination locations and serves as an immediate neighbour within the block connectivity graph. The identification of first-order energy blocks is based on minimum-distance adjacency, ensuring that each block contributes directly to the core structure of candidate routes. The first-order energy blocks form the foundational path set for energy consumption analysis and are prioritized during the computation of minimum-energy trajectories. Specifically, the types of first-order energy blocks are determined based on directional adjacency, such as north, south, east, west, or diagonals, depending on the spatial segmentation strategy applied during route modeling. The technique for determining first-order energy blocks includes defining a central reference block, evaluating all spatially adjacent blocks using distance metrics, and classifying the blocks within a defined proximity threshold as first-order. Further, each first-order energy block is assigned an energy profile using terrain data, traffic metrics, and elevation changes, which is further used in computing route-level energy costs. The processing unit uses the above-mentioned classified blocks to construct base-level candidate routes, which are further analysed in conjunction with second-order energy blocks for optimization.
As used herein, the terms “second-order energy blocks” and “second-order blocks” are used interchangeably and refer to spatial units that are adjacent to first-order energy blocks within a segmented geographical region used for energy-optimal route computation. Specifically, the second-order blocks do not lie on the immediate path between the start and destination locations but serve as auxiliary traversal options for path refinement and energy optimization. Further, the second-order energy blocks expand the search space for route computation, enabling identification of alternate segments that offer lower energy consumption under certain constraints, such as, but not limited to, elevation gain, traffic conditions, or road surface quality. The types of second-order energy blocks are derived based on adjacency to identified first-order energy blocks in all feasible directions permitted by the segmentation model, including orthogonal and diagonal neighbours. The way of selecting second-order energy blocks involves mapping each first-order block, identifying the neighbouring units using coordinate-based spatial relationships, and tagging the units that fall within a second-level adjacency radius. Subsequently, each selected block is evaluated for energy characteristics, and blocks contributing positively to total energy minimization are incorporated into routes. The combination of first-order and second-order energy blocks allows the route selection module to evaluate a broader and more adaptable set of paths while ensuring energy efficiency across varying environmental conditions.
As used herein, the terms “state of charge” and “SOC” are used interchangeably and refer to a quantifiable measure of the remaining electrical energy stored within an electric vehicle’s battery, expressed as a percentage of the total battery capacity. The SOC parameter represents the available energy reserve for vehicle operation and serves as a critical constraint in determining feasible routes for energy-efficient travel. Further, the accurate assessment of the state of charge enables route computation systems to limit total energy consumption within the bounds of battery availability, ensuring the vehicle completes the trip without depleting the energy storage below safe operational thresholds. The types of state of charge measurements include real-time instantaneous values derived from battery management system sensors and predictive estimates based on historical usage patterns and environmental conditions. The technique for utilizing state of charge in route optimization involves receiving the current charge value from the battery management system, integrating the charge value as a constraint within the processing unit, and selecting routes where cumulative energy consumption across combined first-order and second-order energy blocks does not exceed the available charge. Consequently, the above-mentioned approach guarantees energy feasibility while optimizing route selection according to energy consumption profiles generated from segmented spatial analysis.
As used herein, the term “order identification module” refers to a specialized processing component within the route determination system responsible for classifying energy blocks based on the spatial relationship to a reference block along candidate routes. The module systematically identifies and categorizes energy blocks into first-order and second-order sets, establishing hierarchical adjacency necessary for structured route analysis. The classification enables prioritization of blocks for energy consumption evaluation and supports the construction of optimized traversal paths between start and destination locations. Further, the accurate order identification is essential for organizing segmented geographical data into meaningful route components. The types of order identification include adjacency-based classification using distance metrics and connectivity analysis within the segmented spatial grid. The way for selecting a reference energy block along a route involves determining immediate neighbours within a predefined proximity as first-order energy blocks, and subsequently identifying neighbours of the first-order blocks as second-order energy blocks. The module applies minimum traversal distance criteria to ensure relevance and efficiency in classification. Resultant ordered sets of energy blocks provide input to the energy computation module for precise evaluation of energy consumption across hierarchical route segments.
As used herein, the term “energy computation module” refers to a core processing element configured to calculate energy consumption values associated with traversing defined energy blocks along the possible routes. The module quantifies the electrical energy required for an electric vehicle to move through each first-order and second-order energy block by analyzing factors such as, but not limited to, terrain elevation, road conditions, traffic density, and vehicle dynamics. The calculations form the basis for determining the total energy expenditure of each potential route, enabling selection of an energy-optimal path within the constraints of the vehicle’s state of charge. The types of energy computation include deterministic models based on physical parameters, statistical estimations from historical data, and machine learning approaches utilizing real-time inputs. The energy computation involves receiving sets of ordered energy blocks from the order identification module, applying energy consumption formulas or predictive models to each block, and aggregating block-level energy values to compute total route consumption. Further, the module evaluates multiple combinations of first-order and second-order energy blocks to identify routes with minimal energy cost. Subsequently, the computed energy metrics are provided to the route selection module for comparison and final route determination.
As used herein, the term “route selection module” refers to a decision-making component within the processing unit responsible for evaluating computed energy consumption values across multiple candidate routes and selecting the most energy-efficient route for the electric vehicle trip. The module compares total energy costs associated with combinations of first-order and second-order energy blocks and ensures that the selected route’s cumulative energy demand remains within the available state of charge. The output of the module directly influences the routing instructions transmitted to the vehicle control unit for execution. Further, the types of selection algorithms include deterministic minimum-energy comparison, threshold-based filtering, and multi-criteria optimization integrating energy consumption with additional constraints, such as, but not limited to, travel time or distance. The route-selection involves receiving energy consumption data for each candidate route from the energy computation module, applying selection criteria to identify feasible routes that do not exceed energy availability, and ranking the routes to determine the optimal path. Consequently, the module finalizes route choice by balancing energy efficiency against route viability, delivering the selected route information to the output module for transmission.
As used herein, the term “minimum traversal distance criteria” refers to a set of parameters and thresholds used to evaluate and select energy blocks based on the shortest possible distance between consecutive blocks along candidate routes. The criteria ensure that selected paths maintain spatial continuity and minimize overall travel distance with supporting energy optimization objectives. The criteria function as a constraint within the route segmentation and order identification processes, prioritizing adjacency relationships that reduce unnecessary detours or circuitous routing. Further, the types of traversal distance evaluation include Euclidean distance measurements, Manhattan distance calculations, and network-based shortest path algorithms incorporating road topology. The determination of minimum traversal distance criteria involves calculating distances between reference energy blocks and neighbouring blocks, filtering neighbours to identify only those within a predefined minimum distance threshold. The selection limits the pool of first-order blocks and second-order energy blocks to those that maintain efficient spatial progression toward the destination. Further, employing minimum traversal distance criteria ensures that route construction favours practical and direct paths, improving the accuracy and feasibility of the energy-optimal route determination process.
As used herein, the term “given route” refers to a predefined sequence of connected energy blocks representing a potential path between the start location and the destination location within the segmented geographical area. Further, each given route consists of an ordered set of energy blocks classified as first-order and second-order based on spatial adjacency and traversal priority. The route encapsulates spatial, topographical, and energy consumption attributes necessary for a comprehensive analysis of total energy demand. Furthermore, the types of given routes include shortest-distance routes, energy-efficient routes, and alternative detour routes that offer trade-offs between energy consumption and travel time. The identification of the given route involves selecting multiple routes by applying path segmentation and adjacency rules, and analyzing each route’s constituent energy blocks using energy computation techniques. Each route undergoes energy evaluation against the state of charge constraints, and total energy consumption is calculated by summing the energy values of included first-order and second-order energy blocks. The given routes form the basis for comparison and selection by the route selection module to determine the most energy-efficient path for the electric vehicle trip.
As used herein, the terms “reference energy block” and “current energy block” are used interchangeably and refer to a specific spatial segment within the segmented geographical area that serves as a focal point for classification and adjacency determination in the route optimization process. The reference energy block acts as the central node from which neighbouring energy blocks are identified and categorized into first-order and second-order sets. The reference energy block anchors the hierarchical structure used to organize energy blocks along candidate routes, facilitating precise analysis of energy consumption across connected segments. Further, the types of reference energy blocks include initial blocks corresponding to the start location, terminal blocks near the destination, and intermediate blocks along potential traversal paths. Specifically, a block is selected along a route as the reference, and spatial adjacency rules are applied to identify immediate neighbours as first-order energy blocks and neighbours of these first-order blocks as second-order energy blocks. Energy consumption data associated with the reference block and the neighbouring blocks contribute to calculating the total route energy usage. The reference energy block thus provides a critical spatial context for structuring route segmentation and supporting efficient energy-aware navigation.
As used herein, the term “minimum total energy consumption threshold” refers to a predefined energy value that establishes the lower bound for acceptable cumulative energy usage across a combination of first-order and second-order energy blocks within candidate routes. The threshold functions as a criterion to identify routes that optimize energy efficiency while ensuring feasibility within the electric vehicle’s available state of charge. Further, the types of thresholds include fixed numeric values based on battery capacity percentages, adaptive thresholds derived from real-time vehicle performance data, and predictive thresholds calculated from historical consumption patterns under varying environmental conditions. The total energy consumption for each route is evaluated by aggregating energy costs associated with selected energy blocks. Furthermore, the routes with cumulative energy consumption below or equal to the minimum total energy consumption threshold qualify as candidates for selection. Therefore, the above-mentioned approach facilitates filtering out inefficient paths and directs the processing unit to prioritize routes offering the greatest energy savings within operational constraints.
In accordance with a first aspect of the present disclosure, there is provided a system for determining an energy-optimal route for a trip of an electric vehicle, the system comprising:
- an input interface configured to receive a start location and a destination location for the trip;
- a processing unit operatively coupled to the input interface, the processing unit comprises:
- a path segmentation module configured to divide a geographical area between the start location and the destination location into a plurality of energy blocks, and determine a plurality of routes from the start location and the destination location;
- an output module operatively coupled to the processing unit and configured to transmit the energy-optimal route to a vehicle control unit,
wherein the processing unit is configured to select an energy-optimal route including a plurality of first-order energy blocks and a plurality of second-order energy blocks, with total energy consumption for the trip constrained within the available state of charge.
Referring to figure 1, in accordance with an embodiment, there is described a system 100 for determining an energy-optimal route for a trip of an electric vehicle. The system 100 comprises an input interface 102 configured to receive a start location and a destination location for the trip, a processing unit 104 operatively coupled to the input interface. The processing unit 100 comprises a path segmentation module 106 configured to divide a geographical area between the start location and the destination location into a plurality of energy blocks, and determine a plurality of routes from the start location and the destination location. Further, an output module 108 is operatively coupled to the processing unit 104 and configured to transmit the energy-optimal route to a vehicle control unit 110. Furthermore, the processing unit 104 is configured to select an energy-optimal route including a plurality of first-order energy blocks and a plurality of second-order energy blocks, with total energy consumption for the trip constrained within the available state of charge.
The system 100 receives a start location and a destination location for a trip via the input interface 102. Upon receiving the input, the processing unit 104 activates the path segmentation module 106, which divides the geographical region between the start location and the destination location into a plurality of energy blocks. Specifically, each energy block represents a discrete geographic segment characterized by topography, traffic conditions, road type, and other dynamic energy-affecting parameters. The path segmentation module 106 also determines a plurality of potential routes by arranging combinations of the energy blocks, enabling the processing unit 104 to perform an initial filtering of viable paths based on route connectivity and segment availability. Further, the order identification module 112 identifies first-order energy blocks as the immediate neighbors of a reference energy block within a given route and classifies second-order energy blocks as immediate neighbors of the corresponding first-order energy blocks. Furthermore, the energy computation module 114 computes energy consumption values for each energy block using real-time or stored energy models that account for gradient, traffic, and speed profiles. Subsequently, for each determined route, the energy computation module 114 selects combinations of first-order and second-order energy blocks and computes total energy consumption based on a minimum total energy threshold. The evaluation ensures that energy-inefficient segments are excluded from the selected combinations. The energy computation module 114 further calculates the total energy consumption for the full route between the start location and the destination location, constrained by the available state of charge of the electric vehicle. Furthermore, the route selection module 116 compares the computed total energy values for all candidate routes. Advantageously, the route selection module 116 selects the optimal route comprising a combination of first-order and second-order energy blocks that maintains the total energy consumption within the bounds of the available state of charge. The output module 108 transmits the selected energy-optimal route to the vehicle control unit 110, which executes the trip accordingly. The system 100 enables route selection with higher energy efficiency by exploiting hierarchical segmentation and block-level energy evaluation, thereby reducing energy waste, extending vehicle range, and improving route adaptability under varying operating conditions.
Referring to figure 2, in accordance with an embodiment, there is described a system 100 for determining an energy-optimal route for a trip of an electric vehicle. The system 100 comprises an input interface 102 configured to receive a start location and a destination location for the trip, a processing unit 104 operatively coupled to the input interface. The processing unit 100 comprises a path segmentation module 106 configured to divide a geographical area between the start location and the destination location into a plurality of energy blocks, and determine a plurality of routes from the start location and the destination location. Further, an output module 108 is operatively coupled to the processing unit 104 and configured to transmit the energy-optimal route to a vehicle control unit 110. Furthermore, the processing unit 104 is configured to select an energy-optimal route including a plurality of first-order energy blocks and a plurality of second-order energy blocks, with total energy consumption for the trip constrained within the available state of charge. Furthermore, the processing unit comprises an order identification module 112, an energy computation module 114, and a route selection module 116. The processing unit 104 integrates the order identification module 112, the energy computation module 114, and the route selection module 116 to execute a layered and energy-aware route optimization process. Further, the order identification module 112 processes each potential route generated by the path segmentation module 106 and categorizes energy blocks into first-order and second-order levels. A first-order energy block is determined as an immediate neighbor of a reference energy block in a route sequence, and a second-order energy block is determined as a neighboring segment of the respective first-order energy block. Advantageously, the above-mentioned hierarchical classification enables localized energy profiling within route contexts and facilitates an accurate estimation of energy transitions between adjacent route segments. Further, the energy computation module 114 receives classified sets of energy blocks and computes energy requirements for each segment using predefined models that incorporate route gradient, surface characteristics, vehicle parameters, and traffic data. Furthermore, each energy block's energy requirement is aggregated to determine total route energy consumption. The energy computation module 114 identifies the optimal set of first-order and second-order energy blocks that produce the minimum cumulative energy consumption value for each candidate route. Specifically, the block combinations that satisfy a minimum energy threshold are retained for final evaluation, eliminating high-consumption path segments and improving overall energy prediction accuracy. Further, the route selection module 116 receives energy values for all valid route candidates and selects the energy-optimal route constrained by the available state of charge. The selected route contains the lowest energy combination of first-order and second-order blocks, providing a high-efficiency traversal path. Therefore, the above-mentioned configuration ensures dynamic adaptability to changing route conditions and enhances vehicle range by prioritizing low-energy paths. Further, the modular processing within the processing unit 104 enables efficient parallel operation, high computational accuracy, and real-time response capabilities, delivering a technically robust and energy-conscious navigation strategy for electric vehicles.
In an embodiment, the order identification module 112 is configured to determine a set of first-order energy blocks and a set of second-order energy blocks for each of the plurality of routes, based on a minimum traversal distance criterion, between the start location and the destination location. The order identification module 112 receives each of the plurality of routes identified by the path segmentation module 106 and evaluates the spatial relationship between energy blocks within those routes based on the minimum traversal distance criterion. The module 112 analyzes the continuity and proximity of energy blocks with respect to a reference energy block in each route and designates immediate neighbors as first-order energy blocks. Subsequently, the distance threshold is dynamically adjusted based on road geometry and route density to ensure meaningful segmentation. Further, once the first-order blocks are established, the module 112 identifies second-order energy blocks as the next level of neighbors, directly linked to the first-order blocks and within an extended distance range, forming a two-level adjacency structure. Furthermore, the module 112 performs graph-based spatial filtering using weighted edges, where edge weights represent inter-block traversal distances. A minimum distance spanning approach is applied to identify first-order blocks that maintain compactness around the reference block, reducing intra-route dispersion and preserving route fidelity. Furthermore, the second-order blocks are determined by extending the traversal logic outward from each first-order block while excluding overlapping or redundant segments. The hierarchical organization enables localized grouping of route segments and supports scalable computation of route-level energy metrics across complex route networks. In working, the traversal distance between energy blocks is evaluated using network-based shortest path algorithms that incorporate actual road topology. The network-based approach models the transportation network as a directed graph, with intersections being represented as nodes and road segments as weighted edges. The weights are dynamically assigned based on real-world parameters such as, but not limited to, road length, speed limits, elevation changes, traffic congestion, and surface conditions. Further, algorithms such as, but not limited to, Dijkstra’s or A* are applied to compute the minimum cumulative cost path between energy blocks, ensuring that the calculated traversal distance accurately reflects the energy impact of road characteristics encountered during navigation. The method allows the system to identify practical and feasible routes that align with the electric vehicle’s physical constraints and energy consumption profile, thereby enhancing the reliability and realism of energy-optimal route selection. Advantageously, the use of minimum traversal distance criteria ensures that only spatially coherent and energetically relevant energy blocks are selected for further analysis. The approach eliminates non-contributing segments and reduces computational overhead by narrowing the evaluation scope to high-relevance blocks. The selective block identification improves the resolution of energy consumption prediction, increases route stability under varying external factors, and enhances real-time performance of the route optimization process. The integration of the order identification module 112 within the processing unit 104 provides structured input for downstream energy computation and ensures efficient segmentation of the energy-optimal route.
In an exemplary embodiment, the traversal distance between energy blocks is computed using a network-based shortest path algorithm that incorporates real-world road topology. For instance, energy blocks A1, A2, and A3 are considered. The system 100 identifies multiple road-based paths between A1 and A2, such as a 1.0 km flat segment with a speed limit of 50 km/h, and an alternative 1.2 km route with a mild uphill gradient and a speed limit of 30 km/h. Further, between A2 and A3, a single 2.0 km path is identified, which includes a steep downhill segment and a speed limit of 60 km/h. The algorithm evaluates the options using weighted cost functions that consider geometric distance with elevation and permissible speed, assigning traversal weights of 1.0 weighted unit for A1 to A2 and 1.5 for A2 to A3. The resulting cumulative traversal distance between A1 and A3 via A2 is determined as 2.5 weighted units. In contrast, a Euclidean or straight-line approach may yield a shorter apparent distance (example, 2.3 km) but do not capture the true energy-relevant cost of travel. The network-based method, therefore, ensures that traversal distances reflect realistic driving conditions and energy impact, enabling more accurate route selection under state-of-charge constraints.
In an embodiment, the order identification module 112 is configured to identify each first-order energy block, from the set of first-order energy blocks of a given route, as an immediate neighbor of a reference energy block of the given route. The order identification module 112 operates on each route generated by the path segmentation module 106 by selecting a reference energy block that lies centrally or strategically within the route. Further, the module 112 constructs a local neighborhood graph, with nodes representing energy blocks and edges denoting spatial or topological adjacency. Furthermore, using adjacency detection logic based on geographical coordinates and connectivity metadata, the module 112 evaluates each neighboring energy block directly connected to the reference block. Furthermore, any energy block with a direct and uninterrupted link to the reference block, within a defined spatial resolution, is classified as a first-order energy block. Specifically, the identification process applies a fixed distance threshold or dynamic proximity rule based on map granularity and route density. The immediate neighbors are selected using shortest path traversal over the graph structure, restricted to a single edge hop from the reference energy block, ensuring that only directly connected and energetically impactful segments are classified as first-order energy blocks. The redundant or non-adjacent blocks are excluded using geometric boundary checks and connectivity matrices. The resulting first-order set preserves the spatial and logical continuity of the route and establishes the foundation for further energy computation. Advantageously, the identification of first-order energy blocks based on immediate adjacency enhances the granularity and relevance of the route evaluation. Further, the identification ensures high spatial fidelity in block classification, enabling localized energy analysis around critical route segments. The focus on direct neighbors reduces computational complexity and limits propagation of estimation errors across non-influential segments. Furthermore, the output from the order identification module 112 provides a tightly bounded set of energy blocks with direct influence on routing energy characteristics, enabling the processing unit 104 to generate precise, route-specific energy profiles.
In an embodiment, the order identification module 112 is configured to identify each second-order energy block, from the set of second-order energy blocks of the given route, as an immediate neighbor of the corresponding first-order energy block. The order identification module 112 processes the set of first-order energy blocks previously determined for a given route and applies a second-level adjacency evaluation. For each first-order energy block, the module 112 constructs a localized neighbourhood structure using spatial indexing techniques such as R-trees or graph-based traversal. Further, energy blocks that share a direct spatial or topological edge with a first-order energy block are identified as second-order energy blocks. The identification logic excludes any energy block already classified as a first-order energy block or the initial reference energy block to maintain hierarchical distinction between energy block categories. Furthermore, the module 112 employs adjacency constraints based on immediate spatial continuity and route topology, using one additional edge traversal from each first-order energy block. Furthermore, the connectivity is verified through proximity matrices, geographic boundaries, or geohash-based lookups to ensure accuracy in second-order classification. Each second-order energy block is validated to confirm adjacency with only one corresponding first-order block, which ensures unambiguous block grouping. The result forms an extended ring of influence around each first-order block, capturing additional energy-affecting segments that indirectly impact the route’s total energy consumption. Advantageously, the classification of second-order energy blocks introduces a peripheral layer of analysis around the core route path, allowing the processing unit 104 to model energy variations due to surrounding environmental factors such as traffic spillover, gradient shifts, or detour influence. The hierarchical separation of first-order and second-order energy blocks enables scalable computation of route energy metrics and supports adaptive route planning. Further, the above-mentioned structure produced by the order identification module 112 enhances predictive accuracy, reduces false-positive energy anomalies, and increases resilience of the energy-optimal route determination against local disturbances or operational uncertainties.
In an embodiment, the energy computation module 114 is configured to receive the set of first-order energy blocks and the set of second-order energy blocks associated with each route, and compute energy consumption for each energy block. The energy computation module 114 receives the structured input from the order identification module 112, comprising the set of first-order energy blocks and the set of second-order energy blocks for each evaluated route. Each energy block is treated as a discrete unit with defined attributes, such as, but not limited to, gradient, road texture, curvature, traffic density, and elevation change. The module 114 retrieves or computes energy consumption values for individual energy blocks by applying a predefined energy model that integrates vehicle dynamics parameters such as mass, rolling resistance, regenerative braking capacity, and velocity profiles. Each energy block’s energy demand is quantified independently to preserve block-level resolution. Further, the computation process utilizes deterministic energy models or data-driven estimators trained on historical drive cycles. Energy consumption for each energy block is calculated as a function of the physical parameters and the vehicle’s operational state. First-order energy blocks are prioritized due to the direct influence on the route core, while second-order blocks are computed in parallel to account for contextual and indirect energy influences. Further, the module 114 employs a block-wise aggregation strategy to avoid overestimation from overlapping segments and ensures consistent energy contribution per unit distance. Computed energy values are stored in association with the corresponding energy blocks for route-level summation in later stages. Furthermore, the individual computation of energy consumption for each block enables fine-grained energy profiling, improving prediction accuracy and supporting adaptive path selection. The energy computation module 114 ensures real-time operability by parallelizing calculations across blocks and applying bounded models optimized for embedded automotive platforms. The structured computation enhances responsiveness, increases confidence in energy predictions, and forms the quantitative basis for route comparison. The granular output generated by the module 114 facilitates the selection of an energy-optimal route with minimized cumulative energy demand across varying driving conditions.
In an embodiment, the energy computation module 114 is configured to determine, for each route, a combination of a plurality of first order energy blocks, from the set of first-order energy blocks and a plurality of second order energy blocks, from the set of second-order energy blocks, based on a minimum total energy consumption threshold. The energy computation module 114 receives the energy consumption values associated with the set of first-order and second-order energy blocks for each route and initiates a combinatorial evaluation process. Specifically, the module 114 constructs possible groupings of first-order and second-order energy blocks by associating each first-order block with its adjacent second-order blocks. Each grouping is assessed for cumulative energy consumption using summation logic applied to the computed energy values of the constituent blocks. The module 114 applies a predefined total energy consumption threshold, filtering out block combinations that exceed the permitted energy limit for a given vehicle configuration. Further, for each valid route, the module 114 identifies a combination of energy blocks that achieves the minimum possible energy consumption while preserving route continuity and block adjacency. The computation process leverages dynamic programming or greedy optimization techniques to eliminate non-optimal paths early in the evaluation cycle. Furthermore, only those combinations with both the core traversal segments (first-order) and the peripheral context segments (second-order) that align with the minimum energy objective are retained. The result is a subset of energy block combinations that reflect the most efficient segment layout within the spatial boundaries of the route. Advantageously, the structured selection of energy block combinations based on a minimum energy threshold increases energy prediction precision and reduces propagation of inefficiencies through the routing framework. The energy computation module 114 enables intelligent exclusion of energy-intensive detours or block transitions, ensuring the final route configuration aligns with energy-saving objectives. The module outputs a reduced candidate space for final route selection, improving computation time, optimizing vehicle range utilization, and ensuring the selected route maintains energy viability under variable load, terrain, and operational scenarios.
In an embodiment, the energy computation module 114 is configured to determine the total energy consumption for the trip between the start location and the destination location, for each route, based on the combination of a plurality of first-order energy blocks and the plurality of second-order energy blocks. The energy computation module 114 processes each route by aggregating energy consumption values derived from the selected combination of first-order and second-order energy blocks. Each block within the combination contributes a discrete energy value based on previously computed parameters such as gradient, traffic density, and vehicle dynamics. The module 114 applies sequential summation logic to accumulate the energy contributions along the full route trajectory, beginning at the start location and terminating at the destination location. The aggregated energy values produce a total energy consumption metric specific to the structural configuration of each route. Further, the computation accounts for dynamic energy transitions between adjacent energy blocks to refine cumulative estimates. The module 114 introduces correction factors for overlapping influence between first-order and second-order blocks to avoid redundancy in energy addition. Furthermore, the route integrity is preserved during aggregation by ensuring that all included blocks maintain spatial continuity and directional alignment. Each total energy value is bound to the respective route index and stored as a scalar metric, enabling direct comparison across all route candidates evaluated by the processing unit 104. Advantageously, the determination of total energy consumption based on structured energy block combinations enables high-accuracy route assessment under diverse environmental and operational contexts. The approach improves energy forecasting resolution, reduces estimation deviation under variable driving profiles, and supports real-time deployment in on-board systems. The output from the energy computation module 114 provides a quantifiable energy baseline for route selection and facilitates energy-constrained navigation planning, maximizing electric vehicle performance while maintaining safety and range compliance.
In an embodiment, the route selection module 116 is configured to compare energy consumption values computed for each route and select a route with the total energy consumption for the associated first-order and second-order energy blocks is within the available state of charge. The route selection module 116 receives total energy consumption values associated with each evaluated route, computed by the energy computation module 114 based on selected combinations of first-order and second-order energy blocks. Each energy value is compared against the available state of charge parameter, which is retrieved from the vehicle control unit 110 or the onboard battery management system. The module 116 eliminates all route options with the computed energy exceeds the available energy capacity, filtering out infeasible paths from the candidate set. Further, only the routes that maintain energy consumption within the defined state of charge limit proceed to the final selection phase. Furthermore, among the valid routes, the module 116 identifies the route associated with the lowest total energy consumption value while maintaining adherence to first-order and second-order energy block configuration rules. The evaluation is executed through deterministic sorting and threshold comparison logic that ensures repeatable and deterministic route selection behavior. Each selected route maintains block continuity, complies with traversal criteria, and aligns with energy availability constraints. The selected route is subsequently prioritized for transmission to the output module 108, which communicates the route to the vehicle control unit 110 for execution. Advantageously, the comparative evaluation performed by the route selection module 116 ensures optimal alignment between energy resource availability and route energy demand. The module prevents energy depletion scenarios by enforcing strict compliance with the available state of charge. The logic embedded in module 116 improves decision-making accuracy, supports energy-aware route planning, and enhances the electric vehicle’s operational reliability. By selecting the most energy-efficient and feasible route, the system 100 enables range optimization, minimizes recharging interruptions, and supports intelligent trip scheduling under energy constraints.
In an exemplary embodiment, the system 100 for determining an energy-optimal route for an electric vehicle trip includes an input interface 102 through which a user provides a start location S and a destination location D. The processing unit 104 receives the inputs and initiates the path segmentation process via a path segmentation module 106. Further, the path segmentation module 106 divides the geographical region between S and D into a grid of spatial units called energy blocks. Based on available road network data, traffic conditions, elevation profiles, and SoC constraints, the system determines four routes, labeled as Route A, Route B, Route C, and Route D, each comprising a sequence of energy blocks. For instance, route A includes energy blocks A1 to A2 and from A2 to A3, route B includes B1 to B2 and from B2 to B3, route C includes C1 to C2 and from C2 to C3, and Route D includes D1 to D2 and from D2 to D3. Each of the routes is processed by the order identification module, which identifies first-order and second-order energy blocks for each route independently. For instance, for Route A, the system determines: First-order energy blocks as blocks immediately adjacent to each of A1, A2, and A3. The first-order energy blocks are named A1.1, A2.1, and A3.1, respectively. Furthermore, the second-order energy blocks are blocks adjacent to the first-order blocks. The second-order blocks are named A1.2, A2.2, and A3.2, respectively. Similarly, the first-order and second-order energy blocks are named for route C and route D. Further, the energy computation module receives each route’s main blocks, first-order neighbors, and second-order neighbors. It calculates energy consumption per block based on various parameters, distance and gradient (uphill/downhill), surface friction, traffic or congestion levels, regenerative braking potential, local temperature and environmental effects, and energy loss from idling or deceleration events. Furthermore, for route A, the energy computation module estimates A1 as 3.2 kWh, A2 as 4.0 kWh, A3 as 2.5 kWh, a combination of A1.1, A2.1, and A3.1 as a total of 1.5 kWh, and a combination of A1.2, A2.2, and A3.2 as a total 0.8 kWh. Therefore, the total energy consumption for route A with the first-order and second order neighborhood is 12.0 kWh. Similarly, route B has a total of 10.5 kWh, route C 13.2 kWh, and route D 11.8 kWh. Subsequently, the route selection module compares all four candidate routes (A–D) and the associated total energy consumption, including the neighborhood blocks. In case the vehicle’s available SoC corresponds to a usable energy of 11.0 kWh, the system selects route B (10.5 kWh), as route B is the energy-optimal route that satisfies the constraint.
In accordance with a second aspect, there is described a method of determining an energy-optimal route for a trip of an electric vehicle, the method comprises:
- dividing a geographical area between the start location and the destination location into a plurality of energy blocks, via a path segmentation module;
- determining a set of first-order energy blocks and a set of second-order energy blocks for each route, from a plurality of routes, based on a minimum traversal distance criterion, via an order identification module;
- determining, for each route, a combination of a plurality of first-order energy blocks, from the set of first-order energy blocks, and a plurality of second-order energy blocks, from the set of second-order energy blocks, via an energy computation module;
- determining the total energy consumption for the trip between the start location and the destination location, via the energy computation module; and
- comparing energy consumption values computed for each route, via a route selection module.
Figure 3 describes a method of determining an energy-optimal route for a trip of an electric vehicle. The method 200 starts at a step 202. At the step 202, the method comprises dividing a geographical area between the start location and the destination location into a plurality of energy blocks, via a path segmentation module 106. At a step 204, the method comprises determining a set of first-order energy blocks and a set of second-order energy blocks for each route, from a plurality of routes, based on a minimum traversal distance criterion, via an order identification module 112. At a step 206, the method comprises determining, for each route, a combination of a plurality of first-order energy blocks, from the set of first-order energy blocks, and a plurality of second-order energy blocks, from the set of second-order energy blocks, via an energy computation module 114. At a step 208, the method comprises determining the total energy consumption for the trip between the start location and the destination location, via the energy computation module 114. At a step 210, the method comprises comparing energy consumption values computed for each route, via a route selection module 116. The method 200 ends at the step 210.
In an embodiment, the method 200 comprises dividing a geographical area between the start location and the destination location into a plurality of energy blocks, via a path segmentation module 106.
In an embodiment, the method 200 comprises determining a plurality of routes from the start location and the destination location, via the path segmentation module 106.
In an embodiment, the method 200 comprises determining a set of first-order energy blocks and a set of second-order energy blocks for each route, from a plurality of routes, based on a minimum traversal distance criterion, via an order identification module 112.
In an embodiment, the method 200 comprises determining, for each route, a combination of a plurality of first-order energy blocks, from the set of first-order energy blocks, and a plurality of second-order energy blocks, from the set of second-order energy blocks, via an energy computation module 114.
In an embodiment, the method 200 comprises determining the total energy consumption for the trip between the start location and the destination location, via the energy computation module 114.
In an embodiment, the method 200 comprises comparing energy consumption values computed for each route, via a route selection module 116.
In an embodiment, the method 200 comprises dividing a geographical area between the start location and the destination location into a plurality of energy blocks, via a path segmentation module 106. Furthermore, the method 200 comprises In an embodiment, the method 200 comprises determining a plurality of routes from the start location and the destination location, via the path segmentation module 106. In an embodiment, the method 200 comprises determining a set of first-order energy blocks and a set of second-order energy blocks for each route, from a plurality of routes, based on a minimum traversal distance criterion, via an order identification module 112. Furthermore, the method 200 comprises determining, for each route, a combination of a plurality of first-order energy blocks, from the set of first-order energy blocks, and a plurality of second-order energy blocks, from the set of second-order energy blocks, via an energy computation module 114. Furthermore, the method 200 comprises determining the total energy consumption for the trip between the start location and the destination location, via the energy computation module 114. Furthermore, the method 200 comprises comparing energy consumption values computed for each route, via a route selection module 116.
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 to 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 an energy-optimal route for a trip of an electric vehicle, the system (100) comprises:
- an input interface (102) configured to receive a start location and a destination location for the trip;
- a processing unit (104) operatively coupled to the input interface (102), the processing unit (104) comprises:
- a path segmentation module (112) configured to divide a geographical area between the start location and the destination location into a plurality of energy blocks, and determine a plurality of routes from the start location and the destination location;
- an output module (108) operatively coupled to the processing unit (104) and configured to transmit the energy-optimal route to a vehicle control unit (110),
wherein the processing unit (104) is configured to select an energy-optimal route including a plurality of first-order energy blocks and a plurality of second-order energy blocks, with total energy consumption for the trip constrained within the available state of charge.

2. The system (100) as claimed in claim 1, wherein the processing unit comprises an order identification module (112), an energy computation module (114), and a route selection module (116).

3. The system (100) as claimed in claim 1, wherein the order identification module (112) is configured to determine a set of first-order energy blocks and a set of second-order energy blocks for each of the plurality of routes, based on a minimum traversal distance criteria, between the start location and the destination location.

4. The system (100) as claimed in claim 2, wherein the order identification module (112) is configured to identify each first-order energy block, from the set of first-order energy blocks of a given route, as an immediate neighbour of a reference energy block of the given route.

5. The system (100) as claimed in claim 2, wherein the order identification module (112) is configured to identify each second-order energy block, from the set of second-order energy blocks of the given route, as an immediate neighbour of the corresponding first-order energy block.

6. The system (100) as claimed in claim 2, wherein the energy computation module (114) is configured to receive the set of first-order energy blocks and the set of second-order energy blocks associated with each route, and compute energy consumption for each energy block.

7. The system (100) as claimed in claim 2, wherein the energy computation module (114) is configured to determine, for each route, a combination of a plurality of first order energy blocks, from the set of first-order energy blocks and a plurality of second order energy blocks, from the set of second-order energy blocks, based on a minimum total energy consumption threshold.

8. The system (100) as claimed in claim 2, wherein the energy computation module (114) is configured to determine the total energy consumption for the trip between the start location and the destination location, for each route, based on the combination of a plurality of first order energy blocks and the plurality of second order energy blocks.

9. The system (100) as claimed in claim 2, wherein the route selection module (116) is configured to compare energy consumption values computed for each route and select a route with the total energy consumption for the associated first-order and second-order energy blocks is within the available state of charge.

10. A method (200) of determining an energy-optimal route for a trip of an electric vehicle, the method (200) comprises:
- dividing a geographical area between the start location and the destination location into a plurality of energy blocks, via a path segmentation module (106);
- determining a set of first-order energy blocks and a set of second-order energy blocks for each route, from a plurality of routes, based on a minimum traversal distance criterion, via an order identification module (112);
- determining, for each route, a combination of a plurality of first-order energy blocks, from the set of first-order energy blocks, and a plurality of second-order energy blocks, from the set of second-order energy blocks, via an energy computation module (114);
- determining the total energy consumption for the trip between the start location and the destination location, via the energy computation module (114); and
- comparing energy consumption values computed for each route, via a route selection module (116).

11. The method (200) as claimed in claim 10, wherein the method (200) comprises determining a plurality of routes from the start location and the destination location, via the path segmentation module (106).

12. The method (200) as claimed in claim 10, wherein the method (200) comprises selecting a route with the total energy consumption for the associated first-order and second-order energy blocks within the available state of charge, via the route selection module (116).