Method of determining a condition of a battery of an electric vehicle

Abstract

There is provided a method of determining a condition of a battery of an electric vehicle, the method comprising: receiving location data in relation to a journey undertaken by an electric vehicle; and determining a condition of a battery of the electric vehicle based at least in part on the location data.

Description

[0001] METHOD OF DETERMINING A CONDITION OF A BATTERY OF AN ELECTRIC VEHICLE

[0002] Field of the disclosure

[0003] The present disclosure relates to a method and system for determining a condition of a battery, more specifically for determining a condition of a battery of an electric vehicle.

[0004] Background to the Disclosure

[0005] Electric vehicles have become increasingly popular as an alternative to vehicles running on fossil fuels, due in part to the lower impact of driving electric vehicles on the climate when compared with vehicles running on combustion only. However, it is known that electric batteries deteriorate with age and use, and this results in a corresponding reduction in the performance of electric vehicles as the condition of the battery worsens. Unlike the engine condition in vehicles running on combustion engines, the condition of the battery cannot be simply estimated by using the odometer reading or battery age as a proxy. Battery deterioration is influenced by many factors, such as thermal cycling and charging cycles, in a complex manner. It is therefore desirable to provide a method to determine the condition of a battery in an electric vehicle.

[0006] Summary of the Disclosure

[0007] According to a first aspect of the present disclosure, there is described a method of determining a condition of a battery of an electric vehicle, the method comprising: receiving location data in relation to a journey undertaken by an electric vehicle; and determining a condition of a battery of the electric vehicle (directly) based at least in part on the location data.

[0008] Location data in relation to a journey may comprise coordinates (e.g. latitude and longitude values) of a start and of an end location of the journey. Location data in relation to a journey may comprise a plurality of coordinates corresponding to a plurality of points along the path of the journey.

[0009] A condition of a battery may reflect the (quantity of) energy storable in the battery. For example, a condition of a battery may be good if a large quantity of energy is storable in the battery. A condition of a battery may be bad if only a small quantity of energy is storable in the battery. A condition of a battery may also reflect the energy storable in the battery compared to the energy storable in an equivalent battery that is new. For example, a condition of a battery may be good if the quantity of energy that is storable in the battery is close to the quantity of energy that is storable in a battery of the same model in its new state. In other words, the condition may indicate the capacity of the battery compared to its original capacity when new and / or may indicate the maximum battery charge compared to its rated capacity. It will be appreciated that the condition of the battery is a distinct parameter to the (transient) state of charge of the battery. The condition of the battery may indicate how the battery is ‘ageing’ in respect of any or all of: performance, capacity, round-trip-efficiency, and peak power delivery.

[0010] In an example, the method comprises receiving location data in relation to an electric vehicle; determining a distance travelled by the electric vehicle based at least in part on the location data; and determining a condition of a battery of the electric vehicle based at least in part on the distance travelled. Advantageously, the distance travelled by the electric vehicle may be used as a proxy for the energy demand on the battery. This may allow a determination of a condition of the battery to be made without requiring any direct measurements to be taken from the battery.

[0011] Preferably, the location data is collected via a data collection device that is operationally independent to the electric vehicle. For example, the data collection device may be operationally independent from an onboard diagnostics interface and / or the electric vehicle’s application programming interface. The data collection device may have no data or physical connection to the electric vehicle. In other words, the data collection device is not part of the electric vehicle. The data collection device may comprise at least one sensor, e.g. an accelerometer, and / or a receiver for receiving data, e.g. a global positioning system (GPS) receiver.

[0012] Preferably, the method further comprises determining energy consumed during the journey, wherein determining the condition of the electric vehicle is further based at least in part on the energy consumed during the journey, and more preferably comprises determining an energy consumption efficiency based on the location data and the energy consumed during the journey.

[0013] Preferably, determining energy consumed during the journey comprises determining a change in a charge level of the battery, wherein the change in a charge level of the battery is more preferably a difference between a charge level of the battery at the start of the journey and a charge level of the battery at the end of the journey.

[0014] Preferably, determining a condition of a battery of the electric vehicle further comprises adjusting an energy consumption efficiency based on at least one parameter. A parameter may be a factor affecting energy consumption. For example, environmental factors that may affect energy consumption include factors relating to the weather. More preferably, adjusting the energy consumption efficiency comprises receiving data relating to at least one parameter; determining at least one parameter score based on the data; and adjusting the energy consumption efficiency based on the at least one parameter score. For example, data relating to a temperature parameter may be a temperature reading in degrees Celsius. The parameter score may be obtained by inputting the temperature reading in degrees Celsius into a scoring function, and obtaining a parameter score for the temperature as an output. The parameter score may indicate a magnitude of the parameter. For example, 20 degrees Celsius may result in a parameter score of 2 for the temperature parameter, while 30 degrees Celsius may result in a parameter score of 5 for the temperature parameter. The parameter score may be determined for at least one parameter by a binning function. Use of a binning function may allow a parameter score to be determined from non-numerical data. For example, data relating to a cloud cover parameter may be “cloudy”, and this may result in a parameter score for the cloud cover parameter of 1 . Yet more preferably, adjusting the energy consumption efficiency further comprises applying a (respective) weighting factor to the at least one parameter score. For example, a parameter score for a temperature parameter may be more highly weighted than a cloud cover parameter. This may allow for the importance of parameters in relation to their impact on battery performance to be reflected in the weighting factors, which may ensure that more important parameters have a greater impact on a determination of a condition of a battery.

[0015] Optionally, the weighting factors and / or the parameter scores are determined by a trained machine learning model. When a trained machine learning model determines at least the parameter scores, the inputs to said machine learning model may comprise the data relating to at least one parameter; the location data; and preferably the energy consumed during the journey. The machine learning model may be trained on historical data to output an energy consumption efficiency based on the inputs. Preferably, the machine learning model is a neural network.

[0016] Preferably, when the location data is collected via a data collection device that is operationally independent to the electric vehicle and when the condition of the battery of the electric vehicle is determined based on at least one parameter, at least a portion of the data relating to the at least one parameter is collected via the data collection device, more preferably wherein the at least one parameter comprises information pertaining to motion of the vehicle, most preferably wherein the at least one parameter comprises at least one of: speed, elevation, and acceleration.

[0017] Preferably, at least a portion of the data relating to at least one parameter is determined on the basis of at least a portion of the location data.

[0018] Preferably, environmental data is determined on the basis of at least a portion of the location data, more preferably wherein the at least one parameter comprises at least one of: temperature, humidity, precipitation, wind speed, and wind direction. Preferably, the location data is associated with at least one timestamp, and environmental data for a location indicated by the location data is retrieved (e.g. via an API) for the location during a time period preceding the timestamp, more preferably for a time period of up to three hours preceding the timestamp. For example, at least a portion of the location data may be a single location datapoint indicating an end point of a journey, and environmental data may be determined for this single location datapoint. Preferably, the at least one parameter comprises driving style; and the method further comprises determining a driver style based at least in part on data collected via the data collection device, preferably wherein said data comprises at least one of: location data; average speed; maximum speed; average acceleration; and maximum acceleration.

[0019] Preferably, at least one parameter relates to a weight of a vehicle.

[0020] Optionally, at least one parameter relates to whether an air conditioning system is in use in the electric vehicle.

[0021] Preferably, determining the energy consumption efficiency comprises calculating an average energy consumption efficiency in relation to a plurality of journeys.

[0022] Preferably, the method further comprises determining a range of the electric vehicle based on the energy consumption efficiency. For example, the range of the electric vehicle may be calculated by multiplying the energy consumption efficiency by a usable battery capacity. The usable battery capacity may be retrieved from a reference database, and may be reference / benchmark data provided by a manufacturer.

[0023] Preferably, determining the condition of the battery further comprises comparing at least one of the energy consumption efficiency and the range to benchmark data.

[0024] Preferably, determining the energy consumed during the journey comprises receiving image data using the data collection device. For example, the image data may be a photograph of a dashboard display. The method may further comprise analysing the image data to extract information relevant to energy consumption, preferably wherein said analysing comprises applying optical character recognition techniques, for example to determine charge data displayed on a dashboard depicted in the image data.

[0025] Preferably, the method further comprises receiving identification data on at least one of the make, model, trim, and battery size of the electric vehicle; and retrieving benchmark data from a database based on said identification data.

[0026] Preferably, the journey is defined by the user of the electric vehicle. That is, the journey is controlled by the user I is not a prescribed ‘test’ which must be carried out for the method to be performed. Preferably, the location data comprises a plurality of data points corresponding to the respective locations of the electric vehicle at a plurality of respective points in time.

[0027] Preferably, the method further comprises predicting a future condition of the battery. More preferably, predicting a future condition of the battery is done using a trained machine learning model.

[0028] Preferably, the condition of the battery is stored in a user account. Preferably, the user account stores a plurality of image data related to the charge level displayed inside the electric vehicle, such as photographs of a dashboard displaying the charge level of the battery of the electric vehicle. The image data may comprise timestamps. Advantageously, timestamped photographs of the dashboard allow the charge level of the battery to be verified. This means that, were a user to fraudulently enter charge levels to generate an incorrect condition of a battery, this could be detected by inspection of the image data stored to the user’s account.

[0029] According to a second aspect of the present disclosure, there is described a device to determine a condition of a battery of an electric vehicle, wherein the device is configured to carry out the method of the first aspect.

[0030] According to a third aspect of the present disclosure, there is described a non-transitory computer- readable storage medium comprising instructions that, when executed, perform the method of the first aspect.

[0031] According to a fourth aspect of the present disclosure, there is described a system for use in determining a condition of a battery of an electric vehicle, the system comprising: a device for receiving location data in relation to a journey undertaken by an electric vehicle; and a module for determining a condition of a battery of the electric vehicle based at least in part on the location data; preferably wherein the module is located on a server and the device is configured to transmit the location data to the server.

[0032] Any feature in one aspect of the disclosure may be applied to other aspects of the invention, in any appropriate combination. In particular, method aspects may be applied to apparatus aspects, and vice versa.

[0033] Furthermore, features implemented in hardware may be implemented in software, and vice versa.

[0034] Any reference to software and hardware features herein should be construed accordingly. Any apparatus feature as described herein may also be provided as a method feature, and vice versa. As used herein, means plus function features may be expressed alternatively in terms of their corresponding structure, such as a suitably programmed processor and associated memory. It should also be appreciated that particular combinations of the various features described and defined in any aspects of the disclosure can be implemented and / or supplied and / or used independently.

[0035] The disclosure also provides a computer program and a computer program product comprising software code adapted, when executed on a data processing apparatus, to perform any of the methods described herein, including any or all of their component steps.

[0036] The disclosure also provides a computer program and a computer program product comprising software code which, when executed on a data processing apparatus, comprises any of the apparatus features described herein.

[0037] The disclosure also provides a computer program and a computer program product having an operating system which supports a computer program for carrying out any of the methods described herein and / or for embodying any of the apparatus features described herein.

[0038] The disclosure also provides a computer readable medium having stored thereon the computer program as aforesaid.

[0039] The disclosure also provides a signal carrying the computer program as aforesaid, and a method of transmitting such a signal.

[0040] As used herein, the term ‘location data’ preferably connotes data indicating a geographical position of a device; more preferably wherein said location data comprises one of more of: the latitude of the device; the longitude of the device; the altitude of the device; the direction of travel of the device; and the time at which the location information was recorded. The location data may be collected by I processed in an electronic communications network / system, where the device is a device subscribed to I using the electronic communications network / system. As used herein, references to the location of the device, the location of the vehicle, and the location of the user are typically interchangeable, on the basis that these three different entities are generally in the same location when the system / method of the present invention is performed.

[0041] As used herein, the term ‘journey’ preferably connotes an act of travelling from one place to another. The disclosure extends to methods and / or apparatus substantially as herein described with reference to the accompanying drawings.

[0042] The disclosure will now be described, by way of example, with reference to the accompanying drawings.

[0043] Description of the Drawings

[0044] Figure 1 shows a flowchart of a method according to an example of the present invention.

[0045] Figure 2 shows a flowchart of a method according to a further example of the present invention.

[0046] Figure 3 shows the schematic flow of data through the system.

[0047] Figure 4 shows a flowchart of part of a method according to an example of the present invention. Figure 5 shows a flowchart of part of a method according to a further example of the present invention.

[0048] Figure 6 shows an electric vehicle in an environment.

[0049] Figures 7A and 7B show exemplary user interfaces.

[0050] Figure 8 is a schematic diagram of a user device for use with the invention.

[0051] Description of the preferred examples

[0052] Referring to Figure 1 , there is shown a flowchart of an example method 100 for determining a condition of a battery. At step 110, the system receives location data in relation to a journey. This may be a single data point indicating an end location of the journey or a start location of the journey. Alternatively, the location data may comprise a plurality of data points corresponding to locations at various times in the journey. The location data may comprise at least one data point corresponding to each of the start location and the end location of the journey.

[0053] At step 120, the system determines a distance travelled in the journey. The distance may be a distance between the start and end point ‘as the crow flies’ (i.e. in a straight line), or alternatively may be determined by evaluating an actual distance travelled by summing the distances between each data point.

[0054] At step 130, the system determines a condition of the battery based on the location data. This may be determined based on a ratio between the distance travelled during the journey and the energy consumed during the journey by the battery. For example, a difference in charge level of the battery between the charge level at the start location and the end location may be used to determine the energy consumed during the journey by the battery by multiplying the change in charge level by a usable battery capacity of the electric vehicle. The ratio between the distance travelled and the energy consumed during the journey by the battery may be adjusted to account for factors affecting battery performance over the journey. Figure 2 shows a flowchart of a second example method 200 for determining a condition of a battery. At step 210, the system receives a first battery charge level, for example based on sensors or on a user input. This may be the charge level of the battery at the start of a journey. This may be a value representing the energy stored in the battery as a percentage of the total storage capacity of the battery. Alternatively and / or additionally, this may be an energy value representing a total energy stored in the battery.

[0055] At step 220, the system receives a second battery charge level, for example based on sensors or a user input. The second battery charge level may be the charge level at the end of a journey.

[0056] At step 230, the system receives location data based on sensors and / or GPS (or similar) receivers. In one example, this comprises geographical coordinates of the electric vehicle at a plurality of points in time. In one example, the location data comprises geographical coordinates of the electric vehicle at the position at which the first battery charge level is taken. In one example, the location data comprises geographical coordinates of the electric vehicle at the position at which the second battery charge level is taken. In one example, the location data comprises geographical coordinates of a start point and an end point of a journey.

[0057] At step 240, the system fetches environmental data. This may comprise data on one or more parameters, such as the road surface, the weather, cloud cover, air pressure, wind speed and wind direction. In one example, the system queries a database of weather data using an application programming interface (API) to fetch environmental data for a location indicated by the location data and a time indicated by a timestamp, preferably a timestamp of the location data. For example, the system may query a weather API using a GET method by sending a HTTP (hypertext transfer protocol) GET request with parameters including the location and the time desired to an endpoint of an online weather database to retrieve weather data on the weather that was current at the spatial coordinates and time of the end of a journey.

[0058] At step 250, the system determines a condition of the battery. The determination by the system at step 250 may take the distance travelled and the energy consumed during the journey to generate a raw consumption efficiency score, and then may generate an adjusted consumption efficiency score by increasing the raw consumption efficiency score if any of the following factors are present: precipitation; a high surface roughness of the road; a road surface material known to reduce the vehicle efficiency, such as dirt; a wind opposing the direction of travel; an increase or multiple increases in elevation over the course of the journey; a high speed of travel during the journey; and significant acceleration or deceleration during the journey. The system may decrease the raw consumption efficiency score if any of the following factors are present: a decrease or multiple decreases in elevation, or a net decrease in elevation, over the course of the journey; a wind in the same direction as the direction of travel; a low speed of travel during the journey; and low acceleration or deceleration during the journey. Note that these lists are not intended to be exhaustive, and other factors may occur to the skilled reader.

[0059] The determination by the system at step 250 may take into account the make, model , trim and / or battery size of the vehicle. For example, the condition of the battery may be determined as a comparison with benchmark data. The benchmark data may be data provided by the manufacturer, may be obtained from third party sources, and / or may be data obtained empirically from similar vehicles. The user may enter the vehicle make, model and trim directly into the system to allow benchmark data to be retrieved. Alternatively, the user may input a vehicle registration number, and the system may retrieve information on the vehicle (such as the make, model or trim) from a database. The vehicle make, model and / or trim may be stored in a user account, so that this information can be reused across multiple journeys without requiring re-entry of this data.

[0060] The determination by the system at step 250 may also take into account the total distance travelled by the vehicle during the vehicle’s lifetime to date. The determination by the system at step 250 may additionally take into account one or more previous conditions of the battery calculated by the system for the electric vehicle. For example, the method may be carried out for multiple journeys, and the condition of the battery may be adjusted based on the adjusted consumption efficiency scores obtained for recent journeys. For example, the condition of the battery may be determined using a rolling average of past adjusted consumption efficiency scores. Example methods for determining a condition of a battery are shown in figures 4 and 5.

[0061] Advantageously, the method may take into consideration multiple factors that may affect battery performance, allowing a more accurate determination of the energy consumption efficiency. An additional advantage of this method is that the location data is determined without requiring direct user input, reducing the number of user interactions that are required to determine the energy consumption efficiency.

[0062] At step 260, the system outputs a condition of the battery to a user. The condition of the battery may be output as a qualitative descriptor, such as “good”, “fair”, or “poor”, or as a rating, such as “A+”, “A”, “B”, “C” or “D”. In this example, numerical values of the energy consumption efficiency may be mapped to bands corresponding to the qualitative descriptors. Alternatively and / or additionally, the condition of the battery may be output as a numerical value, such as a value on a scale, or a percentage. The value may be calculated such that a new battery achieves a maximum score on the scale, or a percentage of 100%. Alternatively and / or additionally, the condition of the battery may be output as a pictorial indication. For example, the condition of the battery may be illustrated as a bar, which may be longer to indicate a better battery condition. The system may also output a predicted condition of the battery for a point in the future. For example, the system may output a predicted condition of the battery one year into the future. The prediction may be generated using machine learning, and may be based on a dataset of battery condition data for batteries of the same or similar make and / or model over time. Alternatively and / or additionally, the system may output a point in the future at which a predicted condition of the battery is expected to fall below a threshold. For example, the system may output a point in the future at which it is expected that the condition of the battery will fall from “good” to “fair” or from “A” to “B”. The prediction may similarly be generated using machine learning, and may be based on a dataset of battery condition data for similar batteries over time. Alternatively, the prediction may be generated based on a predetermined graph of current battery condition and expected battery condition at some future time, where the graph may be based on historic data.

[0063] The condition of the battery may be stored in a user account. The account may provide graphics visualising the data from each journey and / or over multiple journeys. Additionally, the system may incorporate the condition of the battery into a battery condition report. The battery condition report may provide an indication of the condition of the battery. The value of electric cars lies largely in the battery performance, so a battery condition report may be used to demonstrate the value of an electric car for resale or for rental. The battery condition report may display additional information. For example, the battery condition report may display the make, model and / or trim of the electric vehicle. The battery condition report may also display the projected range of the vehicle, a forecast for the projected range of the vehicle in the future (such as over the next ten years), and / or terms of a battery warranty on the battery. The report may further incorporate a date of issuance and a report reference number to allow reports to be checked and verified. In examples, a user account may record an audit trail such that events on the account may be tracked to determine any fraudulent activity.

[0064] In examples, the system may also determine the performance of different makes and / or models over time, and may determine the makes and / or models with the best performance over time. In examples, the condition of the battery may be an average over multiple journeys. The condition of the battery may be computed as a rolling average of the adjusted consumption efficiency score over the past three journeys.

[0065] Figure 3 is a schematic showing an exemplary flow of data used to determine a condition of a battery. The location data 310 and charge data 320 are used to determine a raw consumption efficiency score 330. Charge data may comprise a battery charge level at a start of a journey, and a battery charge level at an end of a journey. Alternatively and / or additionally, charge data may comprise an amount of charge consumed during a journey. The raw consumption efficiency score may be determined, in a simple example, by dividing the distance travelled in a journey by the energy consumed during the journey. The additional data 340 is used to determine a weighted impact value 350. This value reflects the impact that other parameters, such as ambient temperature, have had on the raw consumption efficiency score 330. The raw consumption efficiency score 330 is then adjusted using the weighted impact value 350 to determine a condition of the battery 360.

[0066] Figure 4 is a flow chart illustrating an exemplary method 400 for determining the condition of a battery using location and environmental data. At step 410, the system receives location data from a data collection device mounted on or contained in an electric vehicle. For example, the data collection device may be used to gather longitude and latitude coordinates every second for the duration of a journey, and these values may be transmitted to a server at the end of the journey. The data may also comprise instantaneous speed data and altitude data. At step 420, the system uses the final longitude and latitude coordinates and their corresponding timestamp to retrieve the environmental information for the location indicated by the coordinates during a preceding time period. This information may comprise: rainfall (in mm), snowfall (in mm), wind speed, wind direction, humidity, cloud cover, air pressure, and ambient temperature. For example, these values may be retrieved for the 3 hours preceding the timestamp.

[0067] At step 430, the system calculates the raw consumption efficiency score as the miles per kWh used during the journey. For example, the system may receive the level of charge at the start of the journey and the level of charge at the end of the journey. Alternatively, the system may receive as input a deficit in charge determined at the end of a journey. For example, the vehicle may start the journey on a full charge, and the system may receive only the level of charge at the end of the journey. The system may retrieve data from the manufacturer or a third-party on the Usable Battery Capacity for the electric vehicle, and may use this data to convert a change in percentage of battery charge into a kWh value. In alternative examples, the system may receive a charge value in kWh. In some examples, the energy consumed during the journey may be determined directly from a dashboard display at the end of a journey. Alternatively and / or additionally, the energy consumed during the journey may be determined by an application downloaded to a mobile phone, which may use information input by a user, such as the level of charge at the start and end of a journey, to determine the energy consumed during the journey.

[0068] At step 440, the average speed and maximum speed are calculated. In alternative methods, these values may be received directly from the data collection device or later via the server following post-processing of the GPS data. At this step, the system also determines a driving style of the user from the speed data, and / or from acceleration / deceleration data. For example, a user with a low average speed and low variance in speed may be determined to have an “eco-friendly” driving style. Conversely, a user with a high average speed and intensive acceleration and deceleration may be determined to have an “aggressive” driving style. This determination may be made, for example, using a binning function, wherein each bin has a defined lower bound and upper bound of acceleration frequency and intensity, and wherein each bin corresponds to a driving style. For example, a bin corresponding to “aggressive” driving style may be defined by average acceleration above a certain threshold, and frequency of acceleration above a certain threshold.

[0069] At step 450, the average speed, the maximum speed, the driving style, the ambient temperature, and the weather conditions are each mapped to a numerical scoring band to determine a parameter score. For example, no precipitation may be mapped to a score of 0 for this parameter, light rain may be mapped to a parameter score of 1 , and heavy rain may be mapped to a parameter score of 2. In this way, all of the parameters may be mapped to scoring bands to determine parameter scores indicating whether and to what degree the parameter score indicates a positive or a negative effect on battery consumption.

[0070] At step 460, the parameter scores are weighted by a weighting factor in proportion with the importance of that parameter in affecting the battery performance over a journey to obtain a weighted score for each parameter. For example, the humidity may be weighted with a weighting factor of 5%, while the average speed may be weighted with a weighting of 80%, as the speed may be a more important parameter for battery performance than the local humidity.

[0071] At step 470, the system calculates a weighted impact value from the weighted scores. This may be done by summing the weighted scores across all the parameters. It will be appreciated that, due to the weighting at step 460 of the parameter scores by weighting factors, some parameters will have a greater effect on the weighted impact value than others.

[0072] At step 480, the system calculates an adjusted consumption efficiency score. In examples, this may be a miles per kilowatt hour value that the vehicle would be expected to achieve under standard conditions. For example, standard conditions could be a trip carried out on a flat dry road at 20 degrees centigrade, with no wind rain or snow, at an average speed of 35 mph, with the maximum speed not exceeding 40 mph.

[0073] The table below shows an example of the adjustment process being applied to real data. As shown, the adjusted consumption efficiency score is higher than the raw consumption efficiency score, as the raw consumption efficiency score is adjusted due to the drive having taken place on a warm day. The speed of the journey and the weather conditions are similar to the standard conditions and so do not contribute to the weighted impact value for adjustment of the raw consumption efficiency score. A second example of the weighting process is shown in the table below. In this example, the average speed is extremely low, and so the raw consumption efficiency score is adjusted to give a lower adjusted consumption efficiency score to take into account the fact that the low average speed is below the average speed set as standard conditions, and the low average speed will inflate the raw consumption efficiency score relative to projected performance under the standard conditions.

[0074] The system may output the condition of the battery to a user. For example, the system may output a qualitative indication of the battery condition based on whether the adjusted consumption efficiency score falls within one of a number of result bands. The score may further factor in an adjusted consumption efficiency score expected from the battery when in perfect condition (e.g. based on a benchmark obtained from a manufacturer). If for example the battery is expected to provide 6 miles per kilowatt hour when new, and is found to provide 5.5, the score assigned might be “good”.

[0075] The weighting factors may be determined empirically from real-world data gathered on vehicle performance under different conditions.

[0076] Advantageously, the method (in particular as a result of its use of location data) does not require any connection to the vehicle battery, or to an onboard diagnostics interface, in order to determine a condition of the battery. The data collection device may be a user device such as a mobile phone or smartphone, which means that the method may be carried out without any specialist equipment.

[0077] An alternative method 500 of deriving an adjusted consumption efficiency score is shown in figure 5. As in the method of figure 4, the system receives data (including location data) from a device in step 410, retrieves environmental data in a step 420, and determines a raw consumption efficiency score in step 430. The system also receives speed data from the device in step 540. At step 550, the system inputs the environmental data and the speed data into a machine learning model. The machine learning model outputs a weighted impact value in step 560. The weighted impact value is used to adjust the raw consumption efficiency score to calculate an adjusted consumption efficiency score in step 570.

[0078] Although in this example method, a raw consumption efficiency score is generated and then adjusted to take into account other factors, in other examples the system may not generate a raw consumption efficiency score, and may determine an adjusted consumption efficiency score for the condition of the battery without this intermediate step.

[0079] The machine learning model may be trained on a large dataset comprising information on energy consumption efficiency for different vehicles under a variety of different conditions. For example, the dataset may comprise data on energy consumption across journeys taken when a vehicle is new or nearly new, and the machine learning model may use this dataset to determine the impact of different factors on the resulting energy consumption, assuming that a condition of a battery for new vehicles is roughly consistent for a particular make or model. The dataset may be processed to remove outliers before being used as the basis for training. The training may use regression and / or backpropagation to determine the optimum parameters for the machine learning model. For example, the machine learning model may be a neural network, and backpropagation of errors may be used to determine the optimum weights for input nodes and hidden nodes in the neural network.

[0080] To illustrate the various factors that may influence battery performance over a journey, Figure 6 shows a schematic of an electric vehicle 610 in an environment 600. The electric vehicle comprises a battery 612. The electric vehicle carries a driver 614.

[0081] Within the environment of the electric vehicle, there is a road 630. The road has a surface material, which may be asphalt, grit, or bare earth. The surface also has a surface texture, which may be smooth or may comprise potholes. The environment of the electric vehicle also comprises weather conditions. These include a temperature of the environment 634, a level of precipitation 636, a level of humidity and air pressure 637, cloud cover 638, and a wind speed and wind direction 639. For example, the environment may be dry, with no precipitation and low humidity, low air pressure, thick cloud cover, a temperature of 25 degrees Centigrade, and a wind speed of 2 miles per hour in a northerly direction.

[0082] The electric vehicle drives between point 645 and point 650. Between these points, there is a net change in elevation 655. The electric vehicle undertakes this journey with an average speed shown by the inset speedometer 660.

[0083] Figure 7A shows an example user interface 700 for a mobile application on a mobile device 710 in accordance with the present invention, where the mobile device is used as the data collection device. The mobile application takes as input from the user information such as the charge level of the battery. This may be input manually into the application using box 720. The application also takes as input from the GPS system a location of the device. The application collects this information directly from the mobile phone’s GPS system, meaning that direct user input of the location data is not required. The application may determine weather and road conditions that are current forthe location of the device. Alternatively, these conditions may be determined at a server receiving information from the application. For example, weather conditions may be retrieved from a weather API service. The application monitors the change in GPS location over time during the journey, and uses these measurements to determine an instantaneous speed of travel, acceleration I deceleration, and any change in elevation.

[0084] The user may commence recording of this information on starting a journey using button 750. The application may store the information during the journey or may transmit the information directly to a server as it is gathered. In one example, after pressing the button 750 the application commences collecting location data at regular intervals. In one example, after pressing the button 750 the application collects location data every second. The application may collect location data until a user provides an indication that the journey has finished.

[0085] Figure 7B shows a second example user interface. This user interface may be provided to a user at the end of a journey. The user may enter the battery charge level at the end of the journey into box 760. Pressing the button 770 may send the data gathered to a server. Alternatively and / or additionally, pressing the submit button 770 may prompt local calculation of a condition of the battery. In an example, pressing the button 770 may also halt further collection of location data. For example, a user may start collection of location data using the button 750 of figure 7A, and location data may be collected every second until the user presses the button 770 of figure 7B. Advantageously, calculating a condition of the battery at a server reduces the computational demands on the user device.

[0086] In examples, different vehicle makes and / or models may be served by the mobile application using different respective user interfaces. In examples, photo and video guides detailing how to use the application may be retrieved based on the vehicle model.

[0087] Referring to Figure 8, there is disclosed a computer device 1000 on which the methods and systems disclosed herein may be implemented. The computer device 1000 may be the data collection device which has been previously described.

[0088] The computer device 1000 comprises a processor in the form of a CPU 1002, a communication interface 1004, a memory 1006, storage 1008, a user interface 1010, and an input device 1012 coupled to one another by a bus 1014.

[0089] The CPU 1002 executes instructions, including instructions stored in the memory 1006 and / or the storage 1008. The communication interface 1004 enables the computer device 1000 to communicate with other computer devices. The communication interface may comprise a local area network interface, a WiFi interface, a 3G, 4G, or 5G interface, a Bluetooth® interface, and / or a near field communication (NFC) interface.

[0090] The memory 1006 stores instructions and other information for use by the CPU 1002. The memory typically comprises both Random Access Memory (RAM) and Read Only Memory (ROM).

[0091] The storage 1008 provides mass storage for the computer device 1000. In different implementations, the storage is an integral storage device in the form of a hard disk device, a flash memory or some other similar solid state memory device, or an array of such devices.

[0092] The user interface 1010 enables the user to interact with the computer device 1000, e.g. to provide instructions to the CPU 1002. The user interface may comprise a touchscreen, a mouse, and / or a keyboard.

[0093] The input device 1012 enables a user to input identifying information. The input device typically comprises a camera, which enables the user to take a photograph. Equally, the input device may comprise other inputs such as a fingerprint scanner.

[0094] The computer device typically comprises a smartphone and / or a personal computer. The systems and methods disclosed herein may be implemented using such a device, or using a plurality of such devices. In particular methods may be carried out in a system comprising a plurality of computer devices, where the methods are carried out in part on a first computer device and in part on a second computer device.

[0095] Although in the above description, the steps are given in an order, in examples the steps may not be performed in this order. For example, the location data may be received simultaneously with the charge levels, or may be received before the charge levels. The environmental data may be gathered before, simultaneously with, or after the charge levels.

[0096] In general, it is recognised herein that a condition of a battery may be diagnosed by observing the change in charge levels over the course of a journey. It is therefore possible to determine the condition of a battery to some extent through monitoring the distance travelled on a certain change in charge level of the battery. This may be expressed as an energy consumption efficiency score representing the miles per kilowatt hour achieved by the battery. However, there is not a simple linear relationship between miles per kilowatt hour achieved and the condition of the battery, as many parameters influence the energy consumed during the journey on a given trip. Therefore, an energy consumption efficiency may be adjusted based on data relating to at least one parameter affecting battery performance.

[0097] For example, when the external temperature is high, a battery will operate at a lower efficiency for a number of reasons, including the likelihood that the driver of the vehicle uses the air- conditioning inside the car, increasing the auxiliary energy consumption of the vehicle. This means that on a warm day, a battery will achieve a lower miles per kilowatt hour value than the same battery would achieve on a cool day, if other conditions for the journeys are the same. If the condition of a battery is measured by the miles per kilowatt hour achieved, then the performance on a warm day will result in an underestimate of the condition of the battery. It may therefore be advantageous to adjust the energy consumption efficiency to account for ambient temperature. Another factor that influences battery performance is load. Driving at high speed requires more energy to cover the same distance. Similarly, a driving style involving frequent acceleration and deceleration increases the energy required by a journey. It may therefore be similarly advantageous to adjust the energy consumption efficiency to account for the average speed and driving style. The energy consumption efficiency may be an adjusted consumption efficiency score calculated by first determining a raw consumption efficiency based on the distance travelled and energy consumed, and then adjusting this value based on data relating to at least one parameter.

[0098] Alternatives and modifications

[0099] It will be understood that the present invention has been described above purely by way of example, and modifications of detail can be made within the scope of the invention.

[0100] For example, although in examples the electric vehicle is driven by a human and so carries at least one driver, in alternative examples the electric vehicle may be autonomous. In this case, the vehicle may have no passengers.

[0101] Similarly, while in examples the user inputs the first charge level and the second charge level manually, in other examples the user may take a photograph of the dashboard, and the system may determine the charge levels by analysing the photograph using optical character recognition. In further examples, the system may obtain the first charge level and the second charge level directly from the vehicle. In examples, the system may accept charge levels from manual input but may require a photograph of the dashboard as corroborating evidence. Photographs of the dashboard may be stored in a user account and used to confirm the veracity of user inputs. In examples, after the user has travelled a minimum distance on a journey, the user is prompted by a notification. In examples, this notification allows the user to bring up the user interface of Figure 7B directly. In examples, the minimum distance is 10 miles.

[0102] In examples, the system may further collect data on a current mileage of the electric vehicle.

[0103] In examples, the location data may be GPS data. In examples, the location data may be gathered at multiple points in the journey, and a distance travelled may be calculated by determining the total path length travelled between all of the location data points. Although in examples the location data is gathered passively by a device (i.e. without requiring manual user input), in other examples the location data may be manually input by a user. For example, a user may indicate a location on a map.

[0104] In examples, the system receives an average speed and a maximum speed for the journey. In examples, these values are determined from location data gathered during the journey. In examples, the average speed is determined from the difference between the start and end times of the journey and the distance covered. In examples, the average speed is determined by averaging instantaneous speed data gathered during the journey.

[0105] In examples, the data collection device may be a mobile phone. In other examples, the location data may be gathered by another device, such as a device that acts as a vehicle accessory, or a device integrated into the electric vehicle, which may carry out the steps of the method, ortransmit the data to another device capable of carrying out the steps of the method. In examples, the device may be operatively coupled to the onboard diagnostics interface such that it may obtain information from the vehicle regarding the charge level of the battery. In examples, the location data may be gathered by the vehicle itself. The vehicle may send this data to a mobile phone application, and / or to a server.

[0106] The method and system described herein may be carried out on a mobile phone application. Alternatively and / or additionally, the method and system described herein may (with the exception of aspects involving the data collection device) be carried out at a server. In examples, user information from a journey may be stored in a user account. In examples, a user account stores information about the vehicle determined from a vehicle registration number, such as the make, model and / or trim of the electric vehicle. In examples, a user account stores data from multiple journeys. In examples, a user may have multiple accounts, each corresponding to a respective electric vehicle. In examples, a user may have a single account capable of storing data on multiple vehicles. In examples, one account may serve multiple users. For example, a plurality of users may share an electric vehicle, and may have shared access to a corresponding account. Where data is received from the vehicle or from a device that does not perform the steps of the method (such as a vehicle accessory), the data may be transmitted to a further device using WiFi, Ethernet, Bluetooth ®, or any other means.

[0107] In examples, the change in elevation may be calculated using location data gathered at multiple points in the journey or gathered continuously. In examples, the change in elevation may be calculated between multiple points in the journey to comprise data on all ascents and descents traversed.

[0108] In examples, the system also receives data on the use of an air conditioning system within the electric vehicle.

[0109] In examples, the environmental information may comprise a temperature of the surroundings. In examples, the environmental information may comprise precipitation information. For example, the precipitation information may indicate a level of rainfall, a level of snowfall, and / or a level of hail. In examples, the environmental information may comprise road surface information. For example, the road surface indication may indicate a rainwater presence on the road, an ice presence on the road, a proportion of potholes, or a surface material of the road such as gravel, asphalt, or cement.

[0110] In examples, the user inputs their vehicle reference number to the system in order for the system to determine the make, model and / or trim of the vehicle. In examples, this information is stored in a user account.

[0111] In examples, the electric vehicle may be a car. In other examples, the electric vehicle may be a truck, van, bicycle, scooter or any other vehicle containing a battery and electric running system.

[0112] It should be understood that the present invention has been described above purely by way of example, and modifications of detail can be made within the scope of the invention.

[0113] Each feature disclosed in the description, and (where appropriate) the claims and drawings may be provided independently or in any appropriate combination.

[0114] Reference numerals appearing in the claims are by way of illustration only and shall have no limiting effect on the scope of the claims.

Claims

Claims1 . A method of determining a condition of a battery of an electric vehicle, the method comprising: receiving location data in relation to a journey undertaken by an electric vehicle; and determining a condition of a battery of the electric vehicle based at least in part on the location data.

2. The method of claim 1 , wherein the location data is collected via a data collection device that is operationally independent to the electric vehicle.

3. The method of claim 1 or 2, further comprising determining energy consumed during the journey, wherein determining the condition of the electric vehicle is further based at least in part on the energy consumed during the journey.

4. The method of claim 3, wherein determining energy consumed during the journey comprises determining a change in a charge level of the battery.

5. The method of claim 3 or 4, wherein determining a condition of a battery of the electric vehicle comprises determining an energy consumption efficiency based on the location data and the energy consumed during the journey.

6. The method of claim 5, wherein determining a condition of a battery of the electric vehicle further comprises adjusting the energy consumption efficiency based on at least one parameter.

7. The method of claim 6, wherein adjusting the energy consumption efficiency comprises receiving data relating to at least one parameter; determining at least one parameter score based on the data; and adjusting the energy consumption efficiency based on the at least one parameter score.

8. The method of claim 7, wherein adjusting the energy consumption efficiency further comprises applying a weighting factor to the at least one parameter score.

9. The method of claim 7 or 8, wherein the weighting factors and / or the parameter scores are determined by a trained machine learning model.

10. The method of claim 9, wherein the inputs to said trained machine learning model comprise the data relating to the at least one parameter; the location data; and preferably the energy consumed during the journey.11 . The method of any of claims 6-10 when dependent on claim 2, wherein at least a portion of the data relating to the at least one parameter is collected via the data collection device, preferably wherein the at least one parameter comprises at least one of: speed; elevation, and acceleration.

12. The method of any of claims 6 to 11 , wherein at least a portion of the data relating to at least one parameter is determined on the basis of at least a portion of the location data.

13. The method of claim 12, wherein environmental data is determined on the basis of at least a portion of the location data, preferably wherein the at least one parameter comprises at least one of: temperature, humidity, precipitation, wind speed, wind direction, air pressure, and cloud cover.

14. The method of claim 13, wherein the location data is associated with at least one timestamp, and wherein the environmental data for a location indicated by the location data is retrieved for the location during a time period preceding the timestamp, preferably for a time period of up to three hours preceding the timestamp.

15. The method of any of claims 6 to 14 when dependent on claim 2, wherein the at least one parameter comprises driving style; and the method further comprises determining a driver style based at least in part on data collected via the data collection device, preferably wherein said data comprises at least one of: location data; average speed; maximum speed; average acceleration; and maximum acceleration.

16. The method of any claims 5 to 15, wherein determining the energy consumption efficiency comprises calculating an average energy consumption efficiency in relation to a plurality of journeys.

17. The method of any of claims 5 to 16, further comprising determining a range of the electric vehicle based on the energy consumption efficiency.

18. The method of any of claims 5 to 17, wherein determining the condition of the battery further comprises comparing at least one of the energy consumption efficiency and the range to benchmark data.

19. The method of claim 3 or any of claims 4 to 18 when dependent on claim 3, wherein determining the energy consumed during the journey comprises receiving image data using the data collection device.

20. The method of any preceding claim, further comprising receiving identification data on at least one of the make, model, trim, and battery size of the electric vehicle; and retrieving benchmark data from a database based on said identification data.21 . The method of any preceding claim, wherein the journey is defined by the user of the electric vehicle.

22. The method of any preceding claim, wherein the method further comprises predicting a future condition of the battery.

23. A device to determine a condition of a battery of an electric vehicle, wherein the device is configured to carry out the method of any of claims 1-22.

24. A non-transitory computer-readable storage medium comprising instructions that, when executed, perform the method of any of claims 1-22.

25. A system for use in determining a condition of a battery of an electric vehicle, the system comprising: a device for receiving location data in relation to a journey undertaken by an electric vehicle; and a module for determining a condition of a battery of the electric vehicle based at least in part on the location data; preferably wherein the module is located on a server and the device is configured to transmit the location data to the server.

Images