Patent Description:
One of the main problems to be solved in relation to battery electric vehicles, BEVs, is to estimate how much energy that is needed for the BEV to reach a certain target destination from its current location. This is because in contrast to Internal Combustion Engine, ICE, vehicles in which the fuel consumption to a certain target destination may be easily determined, the energy need of a BEV and range estimation may differ greatly dependent on several different factors.

The different factors may, for example, be divided into four main categories. The first main category may concern the vehicle itself. This may comprise factors such as the vehicle mass, the vehicle tires, aerodynamics of the vehicle, the powertrain efficiency of the vehicle, etc. The second main category may concern a speed profile of the vehicle. A speed profile of a vehicle is commonly estimated from one or more reference speed value (such as, e.g. the different road speed limitations along the determined route) combined with current or expected weather and traffic information. The third main category may concern the road resistance along the route. This may comprise factors such as the road gradients (e.g. flat roads, uphill roads or downhill roads) and road surface conditions (e.g. cold weather road conditions, wet weather road conditions, sunny weather road conditions, etc.). The fourth main category may concern auxiliary energy consumption of the vehicle. This may comprise all on-board energy consumption that does not contribute to the propulsion of the vehicle (e.g. climate control systems, thermal energy storage systems, air pressure systems, Power Take-off, PTO, systems, etc.). Based on standard vehicle dynamics formulas associated with these four categories, an energy consumption value of the vehicle for the route may be estimated. Examples of such standard vehicle dynamics formulas may, for example, be found in <NPL>. Another option is to exchange the standard vehicle dynamics formulas, entirely or partially, with machine-learning algorithms being trained on data from vehicles that previously has travelled along the same or similar roads of the route.

However, due to the volatile nature or changeability in the data concerning all these different factors, it should be noted that the estimated energy consumption value of the vehicle for the route is equally volatile and changeable and therefore not a particularly reliable estimate. Furthermore, performing continuous re-calculations of the estimated energy consumption value along the route also significant increases the processing load and power consumption on any on-board processing system and puts high requirements on its processing capability. This may lead to non-feasible or unrealistic, high cost implementations. Hence, there is a need to provide an efficient and reliable estimation of the energy consumption of a vehicle along a route in order to enable a reliable range estimation.

Thorgeirsson <NPL> discloses a probabilistic prediction of energy demand and driving range for electric vehicles based on historical mean traffic speed and current mean traffic speed.

<CIT> discloses probabilistic range estimation based on road segments.

<CIT> discloses a device for determining a journey guidance policy based at least in part on a plurality of probabilistic states and an end objective.

<CIT> discloses a calculation method of electric vehicle driving range based on working condition recognition.

It is an object of embodiments herein to provide a processing unit and method therein, along with computer program products and a vehicle, for enabling reliable range estimation synchronous remote vehicle diagnostics for a plurality of vehicles that seeks to mitigate, alleviate, or eliminate all or at least some of the above-discussed drawbacks of presently known solutions.

According to a first aspect of embodiments herein, the object is achieved by a according to claim <NUM>.

By splitting the calculation of the energy consumption of the vehicle into factors that may be calculate separately, i.e. for each route section, instead of for the entire route and by estimating energy consumption probability distributions instead of distinct values, a significant reduction is the number of recalculations of the energy consumption of the vehicle along the route due to changes in different conditions is achieved, while also allowing for asymmetric conditions and large uncertainties in the data of the factors affecting the energy consumption of the vehicle to be handled. Furthermore, merging the different estimated energy consumption probability distributions of each route section will generate a complete estimation of the energy consumption probability distribution for the entire route having the same advantages as mentioned above. Hence, an efficient and reliable estimation of the energy consumption of a vehicle along a route is provided that enables a reliable range estimation of the vehicle to be obtained.

According to the invention, the method comprises determining one or more range estimations having a determined level of reliability for the vehicle along the route based on the estimated PR. This means for example that a confidence value on the range estimations may be set or configured, such as, for example, setting the confidence to <NUM>% for the range estimation of the vehicle along the route in case of normal route mission operation (e.g. standard delivery), or an increased confidence of <NUM>% in case of sensitive or important route mission operations (e.g. organ transportation, transporting refrigerated goods, etc.). This may then, for example, be used is in determining the expected range to be indicated to a driver of the vehicle. According to some embodiments, the estimation of P<NUM>, P<NUM> and ITF may comprise obtaining an expected value and a variance value for the energy consumption for the vehicle by adding uncertainty values for one or more of RSCV and VECV, as well as, T<NUM>, T<NUM>, and T<NUM>,.

In some embodiments, RSCV may comprise one or more of: one or more road resistance values within the section, one or more road weather condition values within the section, one or more road reference speed values within the section, and a travel time for the section. Also, in some embodiments, T<NUM> may comprise one or more of: one or more speed limitations set within the section, one or more static traffic rules applied within the section, one or more speed bump occurrences within the section, traffic intensity values within the section indicating a best case scenario according to previously stored traffic intensity values for the section, speed average values of vehicles within the section indicating a best case scenario according to previously stored speed average values for the section, and speed variance values of vehicles within the section indicating a best case scenario according to previously stored speed variance values for the section. Further, in some embodiments, T<NUM> may comprise one or more of: traffic intensity values within the section indicating a worst case scenario according to previously stored traffic intensity values for the section, speed average values of vehicles within the section indicating a worst case scenario according to previously stored speed average values for the section, and speed variance values of vehicles within the section indicating a worst case scenario according to previously stored speed variance values for the section. Here, according to some embodiments, the best case scenario may be represented by a combination of one or more of the lowest traffic intensity values, the highest speed average values, and the lowest speed variance values, and the worst case scenario may be represented by a combination of one or more of the highest traffic intensity values, the lowest speed average values, and the highest speed variance values. This enables energy consumption probability distributions to be calculated that incorporate "no traffic" scenarios, as well as, a "follow the current traffic flow" scenarios.

Furthermore, in some embodiments, T<NUM> may comprise one or more of: an estimated traffic value for the section indicating the current traffic situation in the section, an estimated current travel time for the section, and one or more additional estimated traffic values for the section indicating previous traffic situations in the section. According to some embodiments, one or more of RSCV, T<NUM>, T<NUM>, and T<NUM> may be obtained from a server accessible via wireless communications network. This means that some information may be pre-processed elsewhere and retrieved on-demand.

In some embodiments, Vεcv comprise one or more of: characteristic values of the vehicle that will impact the energy consumption of the vehicle whilst driving within the section, and energy consumption values of auxiliary systems on-board the vehicle. In this case, the characteristic values of the vehicle comprises one or more of: a mass value of the vehicle, tire characteristic values of the vehicle, aerodynamic values of the vehicle, and characteristic values of powertrain of the vehicle.

According to some embodiments, the processing unit may be located in a remote server accessible via wireless network. In this case, the method may also comprise transmitting the determined one or more range estimations to the vehicle. This means that the complete processing of the range estimation may be outsourced or performed in a connected server or cloud-solution accessible via a wireless communications network.

According to a second aspect of embodiments herein, the object is achieved by a processing unit according to claim <NUM>. A preferred embodiment is set out in a dependent claim.

According to the invention, the processing unit is configured to determine one or more range estimations having a determined level of reliability for the vehicle along the route based on the estimated PR. In some embodiments, the processing unit may be configured to estimate P<NUM>, P<NUM> and ITF by determining an expected value and a variance value for the energy consumption for the vehicle by adding uncertainty values for one or more of RSCV and VECV, as well as, T<NUM>, T<NUM>, and T<NUM>,.

In some embodiments, Rscv comprise one or more of: one or more road resistance values within the section, one or more road weather condition values within the section, one or more road reference speed values within the section, and a travel time for the section. In some embodiments, T<NUM> comprise one or more of: one or more speed limitations set within the section, one or more static traffic rules applied within the section, one or more speed bump occurrences within the section, traffic intensity values within the section indicating a best case scenario according to previously stored traffic intensity values for the section, speed average values of vehicles within the section indicating a best case scenario according to previously stored speed average values for the section, and speed variance values of vehicles within the section indicating a best case scenario according to previously stored speed variance values for the section. In some embodiments, T<NUM> comprise one or more of: traffic intensity values within the section indicating a worst case scenario according to previously stored traffic intensity values for the section, speed average values of vehicles within the section indicating a worst case scenario according to previously stored speed average values for the section, and speed variance values of vehicles within the section indicating a worst case scenario according to previously stored speed variance values for the section. According to some embodiments, the best case scenario is represented by a combination of one or more of the lowest traffic intensity values, the highest speed average values, and the lowest speed variance values, and the worst case scenario is represented by a combination of one or more of the highest traffic intensity values, the lowest speed average values, and the highest speed variance values.

In some embodiments, T<NUM> comprise one or more of: an estimated traffic value for the section indicating the current traffic situation in the section, an estimated current travel time for the section, and one or more additional estimated traffic values for the section indicating previous traffic situations in the section. Further, in some embodiments, the processing unit may be configured to obtain one or more of Rscv, T<NUM>, T<NUM>, and T<NUM> from a server accessible via wireless communications network.

In some embodiments, Vεcv comprise one or more of: characteristic values of the vehicle that will impact the energy consumption of the vehicle whilst driving within the section, and energy consumption values of auxiliary systems on-board the vehicle. In some embodiments, the characteristic values of the vehicle comprises one or more of: a mass value of the vehicle, tire characteristic values of the vehicle, aerodynamic values of the vehicle, and characteristic values of a powertrain of the vehicle.

In some embodiments, the processing unit may be located in a remote server accessible via wireless network. In this case, the processing unit may be configured to transmit the determined one or more range estimations to the vehicle.

According to a third aspect of the embodiments herein, the object is achieved by a computer program according to claim <NUM>. A preferred embodiment is set out in a dependent claim.

<FIG> illustrates an example of a vehicle <NUM> where the herein disclosed embodiments may be applied with advantage. In this case, the vehicle <NUM> is exemplified as a heavy-duty vehicle combination for cargo transport. The vehicle <NUM> exemplified in <FIG> comprises a truck or towing vehicle configured to tow a trailer unit in a known manner, e.g., by a fifth wheel connection. Herein, a heavy-duty vehicle is taken to be a vehicle designed for the handling and transport of heavier objects or large quantities of cargo. The vehicle <NUM> may further comprise any number of auxiliary systems that are connected to and consumes energy in the vehicle <NUM>. Examples of such auxiliary systems on-board the vehicle <NUM> may comprise one or more of: electronic control units (ECUs), a climate control system, a thermal Energy Storage System, ESS, an air pressure system, a Power Take-off, PTO, system, and a cabin comfort system, etc. It is appreciated that the embodiments disclosed herein may be applied to a wide variety of electrically powered vehicle units. For example, while the embodiments disclosed herein are also applicable in, for example, rigid trucks, working machines, multi-trailer electric heavy-duty vehicles comprising one or more dolly vehicle units, etc., the embodiments disclosed herein are also applicable for use in any electrically powered vehicle, such as, e.g. any Battery Electric Vehicles, BEVs. Thus, the embodiments herein should not be considered limited to a particular type of vehicle, but should also be considered applicable in other types of vehicles.

A processing unit <NUM> on the vehicle <NUM> may be in communication with a remote server <NUM> via wireless link <NUM> over an access point <NUM> that could form part of a cellular access network such as a fifth generation (<NUM>) or sixth generation (<NUM>) wireless access network. The processing unit <NUM> may comprise processing circuitry, as will be described in more detail below with reference to <FIG>.

As part of developing the embodiments described herein, it has been realized that one of the underlying problems of providing reliable range estimations is how to handle all of the uncertainties in all of the different factors affecting the energy consumption of vehicle along a determined route. While previously known solutions may be said to address this problem by inefficient continuous re-calculations to obtain an up-to-date single value that estimates the complete energy consumption or range of the vehicle along the route, the embodiments herein provide probabilistic distribution of the energy consumption or range of the vehicle along the route depending on the arrival time. This enables the uncertainties in the different factors to be handled when optimizing the speed of the vehicle, while also enabling reliable measurements on the energy consumption and range estimations. For example, this enables the speed to be optimized towards a maximized expected net energy consumption rather than a minimized energy consumption with set reference speed values.

Examples of embodiments of a method performed by a processing unit <NUM> for enabling reliable range estimations for a vehicle <NUM> along a route R from a source location S to a target destination T, will now be described with reference to the flowchart depicted in <FIG> is an illustrated example of actions, steps or operations which may be performed by the processing unit <NUM> as described above with reference to <FIG>. According to some embodiments, the processing unit <NUM> may be located in the vehicle <NUM>, as described in the example of <FIG>, but may also be located in the remote server <NUM> accessible via wireless communications network. The method may comprise the following actions, steps or operations.

Action <NUM>. The processing unit <NUM> segments the route R into a plurality of sections R<NUM>-R<NUM>. This may be performed in several different ways, but it is preferable to split the route R into sections where the driving conditions are as homogenous as possible. This means, for example, that a route R may be suitably divided into different shorter sections Rn, such as, e.g. the sections R<NUM>-R<NUM> shown in <FIG>, in which the conditions in terms of road resistance may essentially be assumed to be constant, e.g. in regards to constant road slope, constant weather conditions, constant traffic situation, etc. This may advantageously reduce the uncertainties in the calculations for each section. After the segmentation in this action, the processing unit <NUM> may perform Actions 202a-202f for each segmented section Rn. Here, the total number of route sections is denoted by N.

Action 202a. For each section Rn, the processing unit <NUM> obtains a set of route section characteristic values, Rscv, that will impact the energy consumption of the vehicle <NUM> whilst driving within the section Rn. This means that, for each section Rn, a number of time varying road resistance factors, road weather factors and traffic information factors may be assigned. In some embodiment, the RSCV may comprise one or more of: one or more road resistance values within the section Rn, one or more road weather condition values within the section Rn, one or more road reference speed values within the section Rn, and an expected travel time for the section Rn. For example, the road resistance values may comprise: a road gradient value within the section Rn, a road surface condition value within the section Rn, an air density value within the section Rn, a wind speed value within the section Rn, and a wind direction indicator value, etc. This may quantify the road resistance conditions for the section Rn. Additionally, the one or more road weather condition values may comprise: an ambient temperature value, a sun intensity value, an ambient humidity value, an air pressure value, etc. This may quantify the road weather conditions for the section Rn. Further, the one or more road reference speed values may, for example, comprise speed limit values for the road within the section Rn or be an averaged value thereof. Also, the expected travel time for the section Rn may be calculated based on the other route section characteristic values in the set, RSCV, according to standard vehicle dynamics formulas known in the art. Here, it should be noted that this set of route section characteristic values, Rscv, is vehicle independent and may therefore be collected and stored in a shared server or cloud solution, such as, in a road information hub. Thus, in some embodiments, the set of route section characteristic values, RSCV, may be obtained by the processing unit <NUM> by retrieving it from a remote server <NUM> accessible via wireless access network.

Action 202b. In addition to obtaining the route section characteristic values, Rscv, for the section Rn in Action 202a, the processing unit <NUM> also obtains a set of vehicle energy consumption values, VECV, that will impact the energy consumption of the vehicle <NUM> whilst driving within the section Rn at least partly based on Rscv. This means that, for each section Rn, a number of vehicle energy consumption sensitivity factors may be assigned. These factors may be considered as the main vehicle contributors to the overall vehicle energy consumption. In some embodiments, the VECV may comprise one or more of: characteristic values of the vehicle <NUM> that will impact the energy consumption of the vehicle <NUM> whilst driving within the section Rn, and energy consumption values of auxiliary systems on-board the vehicle <NUM>. Information indicating these factors may, in some cases, be stored on in a memory on-board the vehicle <NUM> or in a vehicle specific server slot in the remote server <NUM> and be retrievable by the processing unit <NUM>. This information may also be continuously updated in case some of the factors are estimated during operation. Here, according to some embodiments, the characteristic values of the vehicle <NUM> comprises one or more of: a mass value of the vehicle <NUM>, tire characteristic values of the vehicle <NUM>, aerodynamic values of the vehicle <NUM>, and characteristic values of powertrain of the vehicle <NUM>. For example, the tire characteristic values of the vehicle <NUM> may comprise a tire rolling resistance coefficient, and characteristic values of powertrain of the vehicle <NUM> may comprise an efficiency value of the powertrain and/or an energy recuperation capability value.

Action 202c. After obtaining Rscv and VECV in Actions 202a-202b, the processing unit <NUM> estimates a first probability distribution, P<NUM>, of the energy consumption for the vehicle <NUM> whilst driving within the section Rn based on Rscv, VECV, and a first set of traffic information values, T<NUM>, within the section Rn. This means that the processing unit <NUM> may estimate a probability distribution that represent the energy consumption of the vehicle <NUM> for the section Rn when there is no traffic in the section Rn. In other words, T<NUM> and P<NUM> may represent ideal conditions for the vehicle <NUM> travelling the section Rn. Similar to RSCV, the traffic information values T<NUM> are vehicle-independent and may, in some cases, be stored on in a memory on-board the vehicle <NUM> or in the remote server <NUM> and be retrievable by the processing unit <NUM>. Thus, in some embodiments, the traffic information values, T<NUM>, within the section Rn may be obtained by the processing unit <NUM> by retrieving it from a remote server <NUM> accessible via wireless access network. This information may also be continuously updated during operation of the vehicle <NUM>.

In this case, the traffic information values, T<NUM>, within the section Rn may comprise one or more of: one or more speed limitations set within the section Rn, one or more static traffic rules applied within the section Rn, one or more speed bump occurrences within the section Rn, traffic intensity values within the section Rn indicating a best case scenario according to previously stored traffic intensity values for the section Rn, speed average values of vehicles within the section Rn indicating a best case scenario according to previously stored speed average values for the section Rn, and speed variance values of vehicles within the section Rn indicating a best case scenario according to previously stored speed variance values for the section Rn. Here, it should also be noted that the best case scenario may be represented by a combination of one or more of the lowest traffic intensity values, the highest speed average values, and the lowest speed variance values.

According to one example, the first probability distribution P<NUM> of the energy consumption for the vehicle <NUM> whilst driving within the section Rn may be determined by setting an expected speed of the vehicle <NUM> within the section Rn based on the set of route section characteristic values Rscv, e.g. the one or more road reference speed values and/or any other values in the set. Using the expected speed of the vehicle <NUM> within the section Rn, the energy consumption of the vehicle <NUM> within the section Rn may be estimated using a formula similar to Eq. <NUM>: <MAT> wherein.

In some sections Rn, wherein the road surface rolling resistance Cγ-road is negative and energy may be can be recuperated, the energy consumption of the vehicle <NUM> within the section Rn may be estimated using a formula similar to Eq. <NUM>: <MAT> wherein Pcap is the maximum energy recuperation power of the vehicle's powertrain.

It should be noted that many of the parameters in Eq. <NUM> and <NUM> above are not exactly known. However, by adding the uncertainties in each parameter in the Eq. <NUM> and <NUM> according to some embodiments herein, an expected value of the energy consumption of the vehicle <NUM> within the section Rn and a variance in the energy consumption of the vehicle <NUM> within the section Rn may be determined for the section Rn. This means that according to some embodiments, estimating P<NUM> may comprise determining an expected value and a variance value for the energy consumption for the vehicle <NUM> by adding uncertainty values for one or more of the values in RSCV and VECV. In other words, by assuming that the energy consumption of the vehicle <NUM> within the section Rn is normal distributed, the first probability distribution P<NUM> of the energy consumption for the vehicle <NUM> whilst driving within the section Rn may be fully defined by its variance and expected value.

Action 202d. Furthermore, the processing unit <NUM> may estimate a second probability distribution, P<NUM>, of the energy consumption for the vehicle <NUM> whilst driving within the section Rn based on RSCV, VECV, and a second set of traffic information values, T<NUM>, within the section Rn. This means that the processing unit <NUM> may estimate a probability distribution that represent the energy consumption of the vehicle <NUM> for the section Rn when the vehicle <NUM> adheres to heavy traffic in the section Rn. In other words, T<NUM> and P<NUM> may represent non-ideal, or severe, conditions for the vehicle <NUM> travelling the section Rn at a particular point in time. The traffic information values, T<NUM>, within the section Rn may be collected, e.g. at the remote server <NUM>, from other vehicles that has previously travelled within the section Rn during corresponding points in time. Similar to T<NUM>, the traffic information values T<NUM> are vehicle-independent and may, in some cases, be stored on in a memory on-board the vehicle <NUM> or in the remote server <NUM> and be retrievable by the processing unit <NUM>. Thus, in some embodiments, the traffic information values, T<NUM>, within the section Rn may be obtained by the processing unit <NUM> by retrieving it from a remote server <NUM> accessible via wireless access network. This information may also be continuously updated during operation of the vehicle <NUM>.

In some cases, the traffic information values, T<NUM>, within the section Rn may comprise one or more of: traffic intensity values within the section Rn indicating a worst case scenario according to previously stored traffic intensity values for the section Rn, speed average values of vehicles within the section Rn indicating a worst case scenario according to previously stored speed average values for the section Rn, and speed variance values of vehicles within the section Rn indicating a worst case scenario according to previously stored speed variance values for the section Rn. This means that the processing unit <NUM> may estimate a probability distribution that represent the energy consumption of the vehicle <NUM> for the section Rn when the vehicle <NUM> follows the worst possible traffic in the section Rn. In other words, T<NUM> and P<NUM> may represent the worst possible conditions for the vehicle <NUM> travelling the section Rn at a particular point in time. Here, it should also be noted that the worst case scenario may be represented by a combination of one or more of the highest traffic intensity values, the lowest speed average values, and the highest speed variance values.

According to one example, the second probability distribution, P<NUM>, of the energy consumption for the vehicle <NUM> whilst driving within the section Rn may be determined in the same way as the first probability distribution P<NUM> of the energy consumption for the vehicle <NUM> whilst driving within the section Rn, except in that the expected speed of the vehicle <NUM> within the section Rn is based on the average speed of the traffic within the section Rn. In other words, formulas similar to Eq. <NUM> and <NUM> may be used to estimate P<NUM> in a similar manner as when estimating P<NUM> in Action 202c, but instead using the average speed of the traffic within the section Rn. Further, in some embodiments, by adding uncertainties in each parameter in the Eq. <NUM> and <NUM>, an expected value of the energy consumption of the vehicle <NUM> within the section Rn and a variance in the energy consumption of the vehicle <NUM> within the section Rn may be determined for the section Rn. This means that, according to some embodiments, estimating P<NUM> may comprise determining an expected value and a variance value for the energy consumption for the vehicle <NUM> by adding uncertainty values for one or more of the values in RSCV and VECV. In other words, by assuming that the energy consumption of the vehicle <NUM> within the section Rn is normal distributed, the second probability distribution P<NUM> of the energy consumption for the vehicle <NUM> whilst driving within the section Rn may be fully defined by its variance and expected value.

Action 202e. The processing unit <NUM> may also estimate a traffic flow indicator, ITF, for the section Rn based on RSCV, VECV and a third set of traffic information values, T<NUM>, within the section Rn. This traffic flow indicator ITF may advantageously be used to determine how the actual traffic situation within the section Rn compares to the ideal conditions assumed in Action 202c and the non-ideal conditions assumed in Action 202d. In some embodiments, the traffic information values, T<NUM>, within the section Rn may comprise one or more of: an estimated traffic value for the section Rn indicating the current traffic situation in the section Rn ,an estimated current travel time for the section Rn, and one or more additional estimated traffic values for the section Rn indicating previous traffic situations in the section Rn. This means that different factor may be used or averaged to compiled a traffic density value representing the actual traffic situation within the section Rn. In some embodiments, the traffic information values, T<NUM>, within the section Rn may be obtained by the processing unit <NUM> by retrieving it from a remote server <NUM> accessible via wireless access network. This information may also be continuously updated during operation of the vehicle <NUM>.

For example, in some cases, the traffic flow indicator ITF may then be based on this traffic density value, such as, e.g. ITF = f(td) wherein td represents the traffic density. Here, according to some embodiments, a machine learning algorithm or model may be trained to determine the function f based on data comprising a number of vehicle speed profiles for different traffic density values. Generally, it may be seen that a low traffic density value within the section Rn will result in low ITF, while a high traffic density value within the section Rn will result in high ITF. It should also be noted that the ITF may here be either a deterministic value or represented as a probability distribution. In other words, estimating ITF may comprise determining an expected value and a variance value for the energy consumption for the vehicle <NUM> by adding uncertainty values for one or more of the values in RSCV and VECV.

Action 202f. The processing unit <NUM> may further determine a route section probability distribution, PRS, of the energy consumption for the vehicle <NUM> whilst driving within the section Rn based on the relation between P<NUM>, P<NUM>, and ITF. This means that the ITF may be used to interpolate between P1 and P2, to reflect the actual traffic situation within the section Rn. For example, if ITF is limited to be within the interval [<NUM>, <NUM>], the route section energy consumption for the vehicle <NUM> whilst driving within the section Rn may be estimated using a formula similar to Eq. <NUM>: <MAT> wherein.

Here, by adding the uncertainties in Eq. <NUM> according to some embodiments herein, an expected value of the energy consumption of the vehicle <NUM> within the section Rn and a variance in the energy consumption of the vehicle <NUM> within the section Rn may be determined for the section Rn. In other words, by assuming that the energy consumption of the vehicle <NUM> within the section Rn is normal distributed, the route section probability distribution PRS of the energy consumption for the vehicle <NUM> whilst driving within the section Rn may be fully defined by its variance and expected value.

Action <NUM>. Furthermore, the processing unit <NUM> may determine a route probability distribution, PR, of the energy consumption for the vehicle <NUM> whilst driving along the route R based on the estimated PRS for all sections Rn. This means that the estimated PRS for all sections Rn may be merged into a complete route probability distribution PR of the energy consumption for the vehicle <NUM> whilst driving the route R. This has the advantage of only requiring recalculations of the current section Rn in which the vehicle <NUM> is travelling instead of recalculating the entire route R. This may significantly reduce the computational load on the processing unit <NUM>.

For example, the energy consumption for the vehicle <NUM> whilst driving along the route R from a source location S to a target destination T as shown in <FIG>, may be determined based on all of the estimated energy consumptions for the vehicle <NUM> whilst driving within all sections Rn. This may be performed by adding up the estimated energy consumptions for all sections Rn along the route R between the source location S to the target destination T in a sum using a formula similar to Eq. <NUM>: <MAT>.

Since all Wsections are assumed to be normal distributed, the route probability distribution, PR, of the energy consumption for the vehicle <NUM> whilst driving along the route R may be determined based on the expected value and the variance of sum of all Wsections.

Action <NUM>. After the determination the route probability distribution, PR, in Action <NUM>, the processing unit <NUM> may determine one or more range estimations having a determined level of reliability for the vehicle <NUM> along the route R based on the estimated PR. For example, by being able to handle uncertainties, the range estimations herein will obtain a higher level of reliability than other deterministic range estimations.

Action <NUM>. Optionally, in case the processing unit <NUM> is located in a remote server <NUM> accessible via wireless communications network, the processing unit <NUM> may transmit the determined one or more range estimations to the vehicle <NUM>. This means that the processing may advantageously be, at least partly, performed elsewhere according to some embodiments, and not necessarily on-board the vehicle <NUM>.

To perform the method actions for enabling reliable range estimations for a vehicle <NUM> along a route R from a source location S to a target destination T, the processing unit <NUM> may comprise the following arrangement depicted in <FIG> shows a schematic block diagram of embodiments of the processing unit <NUM>. It should also be noted that, although not shown in <FIG>, known conventional features for operating a processing unit <NUM>, such as, for example, a connection to a power source, e.g. a battery or the mains, may be assumed to be comprised in the processing unit <NUM>, but is not shown or described in any further detail in regards to <FIG>.

The processing unit <NUM> may comprise processing circuitry <NUM> and a memory <NUM>. It should also be noted that some or all of the functionality described in the embodiments above as being performed by the processing unit <NUM> may be provided by the processing circuitry <NUM> executing instructions stored on a computer-readable medium, such as, e.g. the memory <NUM> shown in <FIG>. Alternative embodiments of the processing unit <NUM> may comprise additional components, such as, for example, an segmenting module <NUM>, an obtaining module <NUM>, an estimating module <NUM>, and a determining <NUM>, whereby each module may be configured and responsible for providing its dedicated functionality to support the embodiments described herein. Here, it should be noted that the obtaining module <NUM> may form part of, or be connected to, the I/O module <NUM>, or transceiver, for receiving and transmitting data information over a wireless network. The I/O module <NUM> may also be connected to one or more antennas <NUM>.

The processing unit <NUM> or processing circuitry <NUM> is configured to, or may comprise the segmenting module <NUM> being configured to, segment the route R into a plurality of sections Rn. Also, the processing unit <NUM> or processing circuitry <NUM> is configured to, or may comprise the obtaining module <NUM> being configured to, for each section Rn, obtain a set of route section characteristic values, Rscv, that will impact the energy consumption of the vehicle <NUM> whilst driving within the section Rn. Further, the processing unit <NUM> or processing circuitry <NUM> is configured to, or may comprise the obtaining module <NUM> being configured to, for each section Rn, obtain a set of vehicle energy consumption values, VECV, that will impact the energy consumption of the vehicle <NUM> whilst driving within the section Rn at least partly based on Rscv. The processing unit <NUM> or processing circuitry <NUM> is also configured to, or may comprise the estimating module <NUM> being configured to, for each section Rn, estimate a first probability distribution, P<NUM>, of the energy consumption for the vehicle <NUM> whilst driving within the section Rn based on RSCV, VECV, and a first set of traffic information values, T<NUM>, within the section Rn. The processing unit <NUM> or processing circuitry <NUM> is further configured to, or may comprise the estimating module <NUM> being configured to, for each section Rn, estimate a second probability distribution, P<NUM>, of the energy consumption for the vehicle <NUM> whilst driving within the section Rn based on Rscv, Vεcv, and a second set of traffic information values, T<NUM>, within the section Rn. Furthermore, the processing unit <NUM> or processing circuitry <NUM> is further configured to, or may comprise the estimating module <NUM> being configured to, for each section Rn, estimate a traffic flow indicator, ITF, for the section Rn based on RSCV, VECV and a third set of traffic information values, T<NUM>, within the section Rn. Additionally, the processing unit <NUM> or processing circuitry <NUM> is further configured to, or may comprise the determining module <NUM> being configured to, for each section Rn, determine a route section probability distribution, PRS, of the energy consumption for the vehicle <NUM> whilst driving within the section Rn based on the relation between ITF, P<NUM>, and P<NUM>. Finally, the processing unit <NUM> or processing circuitry <NUM> is also configured to, or may comprise the determining module <NUM> being configured to, determine a route probability distribution, PR, of the energy consumption for the vehicle <NUM> whilst driving along the route R based on the estimated PRS for all sections Rn.

In some embodiments, the processing unit <NUM> or processing circuitry <NUM> may be configured to, or may comprise the determining module <NUM> being configured to, determine one or more range estimations having a determined level of reliability for the vehicle <NUM> along the route R based on the estimated PR. Further, the processing unit <NUM> or processing circuitry <NUM> may be configured to, or may comprise the estimating module <NUM> being configured to, estimate P<NUM>, P<NUM> and ITF by determining an expected value and a variance value for the energy consumption for the vehicle <NUM> by adding uncertainty values for one or more of RSCV and VECV, as well as, T<NUM>, T<NUM>, and T<NUM>, respectively.

In some embodiments, RSCV may comprise one or more of: one or more road resistance values within the section Rn, one or more road weather condition values within the section Rn, one or more road reference speed values within the section Rn, and a travel time for the section Rn. Also, in some embodiments, T<NUM> may comprise one or more of: one or more speed limitations set within the section Rn, one or more static traffic rules applied within the section Rn, one or more speed bump occurrences within the section Rn, traffic intensity values within the section Rn indicating a best case scenario according to previously stored traffic intensity values for the section Rn, speed average values of vehicles within the section Rn indicating a best case scenario according to previously stored speed average values for the section Rn, and speed variance values of vehicles within the section Rn indicating a best case scenario according to previously stored speed variance values for the section Rn. According to some embodiments, T<NUM> may comprise one or more of: traffic intensity values within the section Rn indicating a worst case scenario according to previously stored traffic intensity values for the section Rn, speed average values of vehicles within the section Rn indicating a worst case scenario according to previously stored speed average values for the section Rn, and speed variance values of vehicles within the section Rn indicating a worst case scenario according to previously stored speed variance values for the section Rn. Here, according to some embodiments, the best case scenario may be represented by a combination of one or more of the lowest traffic intensity values, the highest speed average values, and the lowest speed variance values, and the worst case scenario may be represented by a combination of one or more of the highest traffic intensity values, the lowest speed average values, and the highest speed variance values. In some embodiments, T<NUM> may comprise one or more of: an estimated traffic value for the section Rn indicating the current traffic situation in the section Rn, an estimated current travel time for the section Rn, and one or more additional estimated traffic values for the section Rn indicating previous traffic situations in the section Rn.

Furthermore, the processing unit <NUM> or processing circuitry <NUM> may be configured to, or may comprise the obtaining module <NUM> being configured to, obtain one or more of RSCV, T<NUM>, T<NUM>, and T<NUM> from a remote server <NUM> accessible via wireless communications network. In some embodiments, VECV may comprise one or more of: characteristic values of the vehicle <NUM> that will impact the energy consumption of the vehicle <NUM> whilst driving within the section Rn, and energy consumption values of auxiliary systems on-board the vehicle <NUM>. Further, the characteristic values of the vehicle <NUM> comprises one or more of: a mass value of the vehicle <NUM>, tire characteristic values of the vehicle <NUM>, aerodynamic values of the vehicle <NUM>, and characteristic values of powertrain of the vehicle <NUM>. According to some embodiments, the processing unit <NUM> may be located in a server <NUM> accessible via wireless network. In this case, the processing unit <NUM> or processing circuitry <NUM> is configured to, or may comprise the obtaining module <NUM> being configured to, transmit the determined one or more range estimations to the vehicle <NUM>.

Furthermore, the embodiments for enabling reliable range estimations for a vehicle <NUM> along a route R from a source location S to a target destination T described above may be at least partly implemented through one or more processors, such as, the processing circuitry <NUM> in the processing unit <NUM> depicted in <FIG>, together with computer program code for performing the functions and actions of the embodiments herein. The program code mentioned above may also be provided as a computer program product, for instance in the form of a data carrier carrying computer program code or code means for performing the embodiments herein when being loaded into the processing circuitry <NUM> in the processing unit <NUM>. The data carrier, or computer readable medium, may be one of an electronic signal, optical signal, radio signal or computer-readable storage medium. The computer program code may e.g. be provided as pure program code in the processing unit <NUM> or on a server and downloaded to the processing unit <NUM>. Thus, it should be noted that the functions of the processing unit <NUM> may in some embodiments be implemented as computer programs stored in memory <NUM> in <FIG>, e.g. a computer readable storage unit, for execution by processors or processing modules, e.g. the processing circuitry <NUM> in the processing unit <NUM> of <FIG>.

Those skilled in the art will also appreciate that the processing circuitry <NUM> and the memory <NUM> described above may refer to a combination of analog and digital circuits, and/or one or more processors configured with software and/or firmware, e.g. stored in a memory, that when executed by the one or more processors such as the processing circuitry <NUM> perform as described above. One or more of these processors, as well as the other digital hardware, may be included in a single application-specific integrated circuit (ASIC), or several processors and various digital hardware may be distributed among several separate components, whether individually packaged or assembled into a system-on-a-chip (SoC).

Claim 1:
A method performed by a processing unit (<NUM>) for enabling reliable range estimations for a vehicle (<NUM>) along a route (R) from a source location (S) to a target destination (T), wherein the method comprises:
segmenting (<NUM>) the route (R) into a plurality of sections (Rn); for each section (Rn):
obtaining (302a) a set of route section characteristic values, Rscv, that will impact the energy consumption of the vehicle (<NUM>) whilst driving within the section (Rn), and
obtaining (302b) a set of vehicle energy consumption values, VECV, that will impact the energy consumption of the vehicle (<NUM>) whilst driving within the section (Rn) at least partly based on Rscv;
the method being characterized by further
for each section (Rn):
estimating (302c) a first probability distribution, P<NUM>, of the energy consumption for the vehicle (<NUM>) whilst driving within the section (Rn) based on RSCV, VECV, and a first set of traffic information values, T<NUM>, within the section (Rn), wherein the first set of traffic information values, T<NUM>, comprise values indicating a best case scenario for the section (Rn),
estimating (302d) a second probability distribution, P<NUM>, of the energy consumption for the vehicle (<NUM>) whilst driving within the section (Rn) based on RSCV, VECV, and a second set of traffic information values, T<NUM>, within the section (Rn), wherein the second set of traffic information values, T<NUM>, comprise values indicating a worst case scenario for the section (Rn),
estimating (302e) a traffic flow indicator, ITF, for the section (Rn) based on RSCV, VECV and a third set of traffic information values, T<NUM>, within the section (Rn), wherein the third set of traffic information values, T<NUM>, comprise values indicating a current traffic situation for the section (Rn), and
determining (302f) a route section probability distribution, PRS, of the energy consumption for the vehicle (<NUM>) whilst driving within the section (Rn) based on the relation between P<NUM>, P<NUM>, and ITF; and also
determining (<NUM>) a route probability distribution, PR, of the energy consumption for the vehicle (<NUM>) whilst driving along the route (R) based on the estimated PRS for all sections (Rn); and
determining (<NUM>) one or more range estimations having a determined level of reliability for the vehicle (<NUM>) along the route (R) based on the estimated PR.