Electric vehicle power consumption forecasting analysis and improvement suggestion system using LLM

Abstract

In predictive analysis and improvement suggestions for electric vehicle power consumption, we aim to predict power consumption with greater accuracy and provide drivers with solutions for improving power consumption. [Solution] The LLM-based power consumption forecast analysis and improvement suggestion system for an electric vehicle (100) comprises an estimation unit (202) that estimates the power consumption of the battery to be consumed on a planned route in an electric vehicle equipped with a battery (150) based on the current driving conditions and past driving history, and also estimates improvement measures using the LLM to bring the estimated power consumption closer to the minimum value, and a suggestion unit (203) that presents the estimated improvement measures.

Description

Technical Field

[0001] The present invention is applicable to electric vehicles using batteries, including, for example, battery electric vehicles (so-called BEVs: Battery Electric Vehicles), and relates to the technical field of a system that pre-reads and analyzes the power consumption of a battery and presents an improvement in the power consumption so as to be able to reach a destination as needed.

Background Art

[0002] As a technology related to this type of system, as a cruising range notification device in an electric vehicle navigation device, a device has been developed that displays how far a vehicle can travel according to the remaining battery level of an in-vehicle battery and notifies whether it is possible to return home or to a predetermined charging facility (see Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, according to the above-described background art, in a navigation device, simply displaying information regarding the remaining cruising range or the ability to reach a destination is likely to not convince the user or driver. There are also problems with the accuracy and correctness of the information displayed in this way. In particular, when there is no explanation of the detailed breakdown of power consumption and the influence of each element, it is unclear which part is the most problematic. There is also a technical problem that satisfactory countermeasures cannot be presented when it is impossible to reach the destination.

[0005] The present invention aims to provide an electric vehicle power consumption predictive analysis and improvement suggestion system using LLM that can predict power consumption with greater accuracy and present power consumption improvement measures in human-understandable natural language. [Means for solving the problem]

[0006] One aspect of the LLM-based power consumption forecast analysis and improvement suggestion system for electric vehicles according to the present invention solves the above problem by comprising: an estimation unit that estimates the power consumption of the battery to be consumed on a planned route when traveling to a destination in an electric vehicle equipped with a battery, based on information related to the driver and driving of the electric vehicle, including the current driving state and past driving history of the electric vehicle, as well as past driving history of an electric vehicle of the same type as the electric vehicle, and estimates improvement measures to bring the estimated power consumption closer to the minimum value using the LLM; and a presentation unit that presents the estimated improvement measures to the driver. [Effects of the Invention]

[0007] According to one aspect of the system according to the present invention, by using LLM, it becomes possible to predict power consumption with greater accuracy through high-dimensional learning including characters, and to present power consumption improvement measures in human-understandable natural language.

[0008] The effects and benefits of the present invention will be further clarified by the embodiments of the invention described below. [Brief explanation of the drawing]

[0009] [Figure 1] This is a block diagram showing the overall configuration of the system according to the embodiment. [Figure 2] This is a flowchart showing an example of processing in the system according to the embodiment. [Modes for carrying out the invention]

[0010] First, with reference to Figure 1, the overall configuration of the electric vehicle power consumption predictive analysis and improvement suggestion system (hereinafter simply referred to as the "analysis and improvement suggestion system") using LLM (Large Language Models) according to the embodiment will be described.

[0011] The LLM used for analysis and estimation or for linguistic presentation according to this embodiment may be a so-called single-modal LLM or a multimodal LLM. This embodiment is constructed as a system that performs LLM analysis or AI analysis based on LLM training data that appropriately includes various information such as the driving status of the electric vehicle, past driving history and specifications, various information on the route or map of the planned route, information on the past driving history of electric vehicles of the same or similar type as the electric vehicle, various personal information of the electric vehicle's user or driver (i.e., the user or driver of the electric vehicle and other electric vehicles), traffic laws and regulations and common sense information regarding traffic laws, and general common sense information.

[0012] Specifically, as detailed below, the system will be designed to provide highly accurate information regarding remaining range and the feasibility of reaching the destination, based on battery level, driving style, and driving operations, in a way that is easily understandable to the user or driver. Furthermore, by providing a detailed breakdown of power consumption and explanations of the impact of each element in this manner, the system will clearly help the user or driver understand which part is the biggest problem. In addition, the system will be designed to suggest countermeasures in cases where the destination cannot be reached.

[0013] Furthermore, such AI learning or LLM learning can employ not only traditional AI learning systems such as supervised learning, unsupervised learning, or reinforcement learning, but also new technologies such as generative AI or LLM that have recently been put into practical use, are currently under development, or will be developed in the future. For example, the AI ​​learning or LLM learning described here may be constructed using a neural network that performs efficient learning through representation learning, transfer learning, feature selection, fine tuning or hyperparameter tuning, ensemble learning, etc.

[0014] As shown in Figure 1, the analysis and improvement suggestion system according to the embodiment comprises an on-board unit 101 mounted on an electric vehicle 100 and a server unit 200. The on-board unit 101 and the server unit 200 are connected to a communication network 10, such as the Internet or a dedicated network line. The communication network 10 also connects to multiple or many other electric vehicles 100. Furthermore, the communication network 10 also connects to an external related knowledge collection unit 301 that collects information obtained outside the electric vehicle 100 that can be used to perform fine-tuning or hyper-tuning in the analysis and improvement suggestion system to impart domain knowledge (i.e., "external related knowledge"). The external related knowledge collection unit 301 may be at least partially located within the server unit 200 or within the facility where the server unit 200 is located, or it may be located within the on-board unit 101 or inside the vehicle.

[0015] The server unit 200 is connected to a database (DB) 300 that stores various data, including data used in the analysis and improvement suggestion system. The DB 300 may be connected to the server unit 200 or the vehicle unit 101 via a communication network 10. The server unit 200 is composed of various computer-equipped devices and various computer devices that perform centralized or distributed processing. In other words, the analysis and improvement suggestion system is constructed as a system that performs centralized or distributed processing using the large-scale data in the DB 300.

[0016] In Figure 1, the electric vehicle 100 is equipped with a battery 150 and is configured as a BEV, for example. The electric vehicle 100 may also be a so-called HEV (Hybrid Electric Vehicle), PHEV (Plugin HEV), FCEV (Fuel Cell EV), etc., which use a battery.

[0017] The in-vehicle unit 101 comprises a sensor unit 102 including various sensors positioned in predetermined locations inside the vehicle, a processing unit 103 including a computer, a communication unit 104 including a modem etc. configured to communicate with the outside of the vehicle via a communication network 10, and an interface unit 106 configured to exchange voice and images with a user or driver inside the vehicle.

[0018] One of its detection functions is to detect the remaining charge data of the battery 150 and pass it to the processing unit 103. The sensor unit 102 is also configured to detect the current driving status of the electric vehicle 100 and various information 102a related to the driver of the electric vehicle 100, and pass it to the processing unit 103 as CAN (Controlled Are Network) data, etc.

[0019] The processing unit 103 has a CPU, memory, etc., that controls the sensor unit 102, the communication unit 104, and the interface unit 106, and transmits various information related to the driver and driving of the electric vehicle 100, including the planned route of the electric vehicle 100, the current driving status of the electric vehicle 100, and past driving history, from the communication unit 104 to the server unit 200 in data in a predetermined format. Furthermore, the interface unit 106 is configured to present improvement measures, etc., indicated by improvement measure data, etc., received from the server unit 200 after processing via the communication unit 104 to the user or driver.

[0020] Under the control of the processing unit 103, the communication unit 104 transmits data necessary for predictive analysis of power consumption collected in the electric vehicle 100 and improvement suggestions to the server device 200 via the communication network 10. Further, it is configured to receive, via the communication network 10, the results of predictive analysis of power consumption of the electric vehicle 100 and data related to improvement suggestions generated by using the LLM in the server unit 200.

[0021] The interface unit 106 is configured to be able to input, by voice input or a predetermined operation on an image, etc., the destination of the electric vehicle 100 and conditions when selecting a planned route to the destination. Regarding the selection of the planned route here (i.e., the navigation function), all or part of it may be configured to be executed by the processing unit 103, or part or all of it may be configured to be executed by the processing unit 202 on the server unit 200 side (in other words, the in-vehicle unit 101 side solely performs the browser function). The interface unit 106 is further configured to be able to output, in a voice output or a predetermined format on an image, the data related to the results of predictive analysis of power consumption and improvement suggestions obtained from the server unit 200 side.

[0022] In FIG. 1, the server device 200 includes a communication unit 201 including a modem etc. capable of communicating with each electric vehicle 100 and the external related knowledge collection unit 301 via the communication network 10, a processing unit 202 including a computer capable of executing estimation processing of power consumption by the LLM to be described in detail later, and a presentation unit 203 capable of generating presentation data for presenting improvement measures according to the estimation results by the processing unit 202.

[0023] Under the control of the processing unit 202, the communication unit 201 receives, via the communication network 10, data necessary for the prediction analysis of the power consumption collected by the electric vehicle 100 and for presenting improvement suggestions. Under the control of the processing unit 202, the communication unit 201 receives, via the communication network 10, external related knowledge collected by the external related knowledge collection unit 301 as part of the data necessary for the prediction analysis of the power consumption and for presenting improvement suggestions. The communication unit 201 is further configured to transmit, via the communication network 10, to the electric vehicle 100 side, data related to the result of the prediction analysis of the power consumption and improvement suggestions for the electric vehicle 100 which is the object of this analysis and processed and generated by the processing unit 202 and the presentation unit 203.

[0024] The processing unit 202 estimates, using an LLM, the power consumption of the battery consumed during the planned route when traveling to the destination of the electric vehicle 100 which is the object of this analysis, based on information related to the driver and driving of the electric vehicle 100, including the current driving state and past driving history of the electric vehicle 100, and the past driving history of other electric vehicles of the same type as the electric vehicle 100. The processing unit 202 is further configured to estimate, using an LLM, improvement measures to bring the power consumption estimated here closer to the minimum value.

[0025] The presentation unit 203 generates presentation data for presenting the improvement measures estimated in this way to the driver or user of the electric vehicle 100 in a predetermined format corresponding to the interface unit 106 provided in the vehicle of the electric vehicle 100, and is configured to pass it to the communication unit 201. In this embodiment, the "presentation unit" is configured in a form including the presentation unit 203 on the server unit 200 side and the interface unit 106 on the in-vehicle unit 101 side in this way. The in-vehicle unit 101 side mainly takes on the browser function regarding the presentation function.

[0026] DB300 is configured to include a large-scale and high-speed data input / output storage device that stores various data received by the server unit 200 via the communication network 10, in particular various data necessary for estimation processing using LLM, data related to the estimation results or intermediate results generated by the processing unit 202, and presentation data generated by the presentation unit 203.

[0027] Next, with reference to the block diagram in Figure 1 and the flowchart in Figure 2, an example of processing in the analysis and improvement suggestion system according to this embodiment (in particular, processing performed using LLM in the processing unit 202 of the server unit 200) will be explained.

[0028] In Figure 2, first, the user inputs the "destination" for the current trip using the browser function of the navigation device in the interface unit 106 of the in-vehicle unit 101. Then, the processing unit 103 or the processing unit 202 via the communication network 10 performs a route search to the destination (step S1).

[0029] Next, the processing unit 202 in the server unit 201 determines whether or not there is a driving history for each link of the searched planned route (step S2). This determination function may be partially or entirely performed by the processing unit 103 on the in-vehicle unit 101 side. The data related to the driving history is basically stored in DB300. Note that some of the data related to the driving history may be stored in the memory of the processing unit 103 on the in-vehicle unit 101 side or in an in-vehicle memory equipped separately from the processing unit 103.

[0030] If the determination in step S2 indicates that there is no driving history (step S2: No), the "current driving status" is acquired by the sensor unit 102 and processing unit 103 on the vehicle unit 101 side and passed to the processing unit 202 on the server unit 200 side (step S3). Subsequently, the processing proceeds to processes such as estimating improvement measures using LLM in the processing unit 202 (step S4 onwards).

[0031] On the other hand, if the determination in step S2 indicates that there is a driving history (step S2: Yes), the processing unit 202 obtains the "past driving history of electric vehicles BEVs of the same type as the electric vehicle 100 for each link" from the DB300, etc. (step S5). Here, "electric vehicles BEVs of the same type" can include not only BEVs that are completely the same type or model as the BEV itself, but also BEVs that have pre-set common specifications or similar specifications. For example, if the powertrains are the same for both vehicles, or if the navigation numbers are the same, they can be treated as the same type here. Furthermore, LLM may be used to correct for minor differences between the two vehicles so that they can be treated as the same type of vehicle, and then the corrected driving history may be used for processing related to power consumption (step S6 and later).

[0032] Next, the processing unit 202 extracts the data corresponding to the "minimum power consumption" from the past driving history obtained in this way (step S6). After that, the processing proceeds to the estimation of improvement measures using LLM in the processing unit 202 (step S4 onwards). Here, in order to estimate improvement measures using LLM based on past driving history (step S4 onwards), the relationship graphs and numerical data of elements and power consumption accumulated for each road link are all converted into text using LLM and then vectorized.

[0033] In parallel with, before, or concurrently with, the processing in steps S1 to S6 described above, external related knowledge is acquired by the external related knowledge acquisition unit 301 (step S11), and the processing unit 202 performs a process to impart domain knowledge by performing fine-tuning or hyper-tuning (step S12). Subsequently, the processing proceeds to processes such as estimating improvement measures using LLM in the processing unit 202 (step S4 onwards). In this way, by using LLM fine-tuned with domain knowledge related to electric vehicles, such as BEV domain knowledge, it becomes possible to generate improvement measures by comparing them with the best driving settings.

[0034] Next, the processing unit 202 performs an estimation process to identify the difference between the driving state obtained in step S3 and the driving state extracted in step S6 (step S4). The identification of the difference using LLM here is performed based on various data obtained from the in-vehicle unit 101 and various data obtained or extracted from the DB, as well as the domain knowledge provided in step S12.

[0035] Next, as driver-specific data for the electric vehicle 100, data related to personal preferences that had been previously stored in DB300 is retrieved from DB300 and passed to processing unit 202 (step S7).

[0036] Next, based on the various data acquired or generated in steps S3, S4, S6, S7, etc., the processing unit 2020 generates power consumption improvement measures by estimation using LLM (step S8). Here, LLM estimates improvement measures that bring power consumption closer to the minimum value.

[0037] The LLM performed in steps S4 and S8 described above uses a large amount of information, including, as a large amount of text data, various information relating to the driving status, past driving history and specifications of the electric vehicle 100, various information relating to the route or map on the planned route, information relating to the past driving history of electric vehicles of the same or similar type as the electric vehicle 100, various personal information relating to the user or driver of the electric vehicle 100 (i.e., the user or driver of the electric vehicle 100 and other electric vehicles 100), information relating to traffic laws and common sense relating to traffic laws (e.g., driving on the left, speed limits in residential areas, etc.), and information relating to general common sense (e.g., it is dark at night, traffic congestion is likely during commuting hours and holidays, the presence of landmarks near the planned route, etc.), and is expressed in language.

[0038] However, a multimodal LLM capable of AI learning based not only on verbalized information but also on non-verbalized information may be employed in steps S4, S8, etc. That is, the data used in the processing of steps S4, S8, etc. includes text data, but is not limited to text data.

[0039] In steps S4, S8, and other processes described above, a large amount of text data is used to fine-tune a large-scale language model using LLM. As a result, it can be adapted to various natural language processing (NLP) tasks such as text classification, sentiment analysis, information extraction, text summarization, text generation, and question answering.

[0040] In step S8, presentation data indicating that the improvement measures generated by LLM are presented is generated by the presentation unit 203, and the "improvement measures" are output as audio or image by the interface unit 106 on the in-vehicle unit 101 side. In addition, the processing unit 202 and the presentation unit 203 also output presentation data indicating that the "basis" for the improvement measures is presented, as well as as audio or image by the interface unit 106. It is also preferable to use LLM to generate the presentation data for outputting the improvement measures in audio or image by the presentation unit 203. That is, here too, presentations using AI voice or AI image may be performed for the user or driver, adapted to various natural language processing tasks such as text classification, sentiment analysis, information extraction, text summarization, text generation, and question answering.

[0041] Next, it is determined whether or not there is feedback (step S9: Yes), and if there is (step S9: Yes), the process returns to step S7 and the subsequent steps are repeated, and an "improvement plan" updated by AI learning is presented (step S8).

[0042] The suggested improvements include, for example, "On this highway, to reduce power consumption, keep your lane in the left lane and maintain a constant speed of 80 km / h," "The battery level is low, so please avoid sudden acceleration and deceleration until the next charge," and "Charge at the charging station 25 km ahead." Ultimately, the interface unit 106 on the in-vehicle unit 101 will present these suggestions to the driver via voice output and image output using AI voice and AI images.

[0043] In this embodiment, it is preferable to present not only the improvement measures but also the rationale for those improvements, from the standpoint of convincing the user or driver, or in other words, from the standpoint of getting the driver to follow the improvement measures. Examples of rationale presented here include: "(The rationale is that) the car's fuel efficiency is best at 80 km / h," "(The rationale is that) if you continue to accelerate and decelerate, the charge will not last until the next charging station," and "(The rationale is that) you can drive as usual to the charging station 25 km away without any problems."

[0044] After processing related to the presentation of such improvement measures, if the result of the determination in step S9 is that there is no feedback (step S9: No), the series of processes is terminated.

[0045] As described in detail above, according to this embodiment, past driving history of electric vehicles such as BEVs of the same type is extracted for each road link (steps S5 and S6), the relationship between driving conditions (air conditioning, vehicle speed, vehicle weight, interior temperature, etc.) and power consumption is input to the LLM (step S3), the difference between the current settings and the driving conditions with minimum power consumption is output from the LLM (step S4), and improvement measures for saving power (i.e., improvement measures to bring power consumption closer to the minimum value) are presented in language or voice (steps S7 and S8).

[0046] In addition, based on user feedback (step S9), improvements that take personal preferences into even greater consideration can be presented in subsequent steps (steps S7 and S8). In this way, personal preferences can be reflected by utilizing LLM-specific feedback mechanisms such as RLHF (i.e., reinforcement learning from human feedback). Therefore, it becomes possible to collect feedback for each element that affects power consumption and generate improvements that are more tailored to the individual.

[0047] Unlike previous technologies or background technologies, which only use numerical data, this embodiment allows for the extraction of relevant features from textual data such as papers and related literature by using LLM, enabling the calculation or estimation of power consumption at a higher level. Furthermore, this embodiment allows for the simultaneous provision of results, justifications, and improvement measures to the user in natural language or voice, thereby increasing the user's understanding and satisfaction with the response.

[0048] Note The following additional information is disclosed regarding the embodiments described above.

[0049] [Note 1] The analysis and improvement suggestion system described in Appendix 1 of the present invention is characterized by comprising: an estimation unit that estimates the power consumption of the battery consumed on a planned route when traveling to a destination in an electric vehicle equipped with a battery, based on information related to the driver and driving of the electric vehicle, including the current driving state and past driving history of the electric vehicle, and past driving history of an electric vehicle of the same type as the electric vehicle, and estimates improvement measures to bring the estimated power consumption closer to a minimum value using LLM; and a presentation unit that presents the estimated improvement measures to the driver.

[0050] According to the analysis and improvement suggestion system described in Appendix 1, using LLM allows for more accurate prediction of power consumption through high-dimensional learning that includes or incorporates characters, and enables the explanation of the factors that most affect power consumption and improvement measures in human-understandable natural language. Furthermore, it becomes possible to provide collected driving history as useful information for other vehicles.

[0051] [Note 2] The analysis and improvement suggestion system described in Appendix 2 of the present invention is a power consumption predictive analysis and improvement suggestion system described in Appendix 1, characterized in that the estimation unit estimates the improvement measures by identifying the difference between the current driving state and the lowest power consumption in the past driving history using the LLM.

[0052] According to the analysis and improvement suggestion system described in Appendix 2 of the present invention, by identifying the difference between the current driving state and the past driving history with the lowest power consumption using LLM, it becomes possible to estimate improvement measures that bring power consumption closer to the minimum value with relatively efficient efficiency.

[0053] [Note 3] The analysis and improvement suggestion system described in Appendix 3 of the present invention is a power consumption predictive analysis and improvement suggestion system described in Appendix 1 or 2, characterized in that the estimation unit acquires external relevant knowledge other than information relating to the current driving state and the past driving history, and estimates the improvement measures taking into account domain knowledge relating to the electric vehicle using a finely tuned LLM using the acquired external relevant knowledge.

[0054] According to the analysis and improvement suggestion system described in Appendix 3 of the present invention, improvement measures are estimated not only based on information related to the current driving state and past driving history, but also by considering domain knowledge with an LLM that has been fine-tuned using external relevant knowledge, thus enabling more accurate estimation.

[0055] [Note 4] The analysis and improvement suggestion system described in Appendix 4 of the present invention is a power consumption predictive analysis and improvement suggestion system described in any one of Appendix 1 to 3, characterized in that the estimation unit acquires information relating to the personal preferences of the electric vehicle driver and estimates the improvement measures in an LLM that reflect the personal preferences using a mechanism that provides the acquired information relating to personal preferences as dedicated feedback to the LLM.

[0056] According to the analysis and improvement suggestion system described in Appendix 4 of the present invention, improvement measures are estimated using LLM not only based on information relating to the current driving state and past driving history, but also in a way that reflects individual preferences. Therefore, it becomes possible to present highly accurate improvement measures appropriate to the driver or user in a more convincing manner.

[0057] [Note 5] The analysis and improvement suggestion system described in Appendix 5 of the present invention is a power consumption predictive analysis and improvement suggestion system described in any one of Appendix 1 to 4, characterized in that the suggestion unit presents information indicating the basis for the estimation of the improvement measures together with the improvement measures.

[0058] According to the analysis and improvement suggestion system described in Appendix 5 of the present invention, not only improvement measures but also the rationale for those measures are presented, making it possible to present highly accurate improvement measures in a way that is easier for drivers or users to understand.

[0059] [Note 6] The analysis and improvement suggestion method described in Appendix 6 of the present invention is characterized by comprising the steps of: estimating the power consumption of the battery to be consumed on a planned route when traveling to a destination in an electric vehicle equipped with a battery, using LLM based on information related to the driver and driving of the electric vehicle, including the current driving state and past driving history of the electric vehicle, and the past driving history of an electric vehicle of the same type as the electric vehicle, and using LLM to estimate improvement measures to bring the estimated power consumption closer to the minimum value; and presenting the estimated improvement measures to the driver.

[0060] According to the analysis and improvement suggestion method described in Appendix 6 of the present invention, similar to the analysis and improvement suggestion system described in Appendix 1, by using LLM, it is possible to predict power consumption with greater accuracy through high-dimensional learning that includes or also includes characters, and to explain the factors that have the greatest impact on power consumption and improvement measures in human-understandable natural language.

[0061] The present invention may be modified as appropriate, without contradicting the gist or spirit of the invention as can be inferred from the claims and the entire specification, and such modifications, along with the analysis and improvement suggestion system and method, are also included in the technical concept of the present invention. [Explanation of Symbols]

[0062] Electric vehicles (BEVs)...100 Vehicle-mounted unit...101 Sensor unit...102 Processing section...103 Battery... 150 Server section...200 Processing section...202 Presentation section...203 DB...300 External Knowledge Acquisition Department...301

Claims

1. An estimation unit that estimates the battery power consumption along the planned route to a destination in an electric vehicle equipped with a battery, based on information related to the driver and driving of the electric vehicle, including the current driving status and past driving history of the electric vehicle, as well as the past driving history of an electric vehicle of the same type as the electric vehicle, and also estimates improvement measures to bring the estimated power consumption closer to the minimum value using LLM, A presentation unit that presents the estimated improvement measures to the driver. A system for predictively analyzing and suggesting improvements to the power consumption of electric vehicles using LLM, characterized by having the following features.

2. The estimation unit estimates the improvement measures by identifying the difference between the current driving state and the past driving history with the lowest power consumption using the LLM. A power consumption forecast analysis and improvement suggestion system for electric vehicles using LLM according to the description in 1, characterized in that it is a system for predicting and analyzing the power consumption of electric vehicles.

3. The estimation unit acquires external relevant knowledge other than the information relating to the current driving state and the past driving history, and uses the acquired external relevant knowledge to fine-tune the LLM and estimate the improvement measures taking into account the domain knowledge relating to the electric vehicle. A power consumption forecast analysis and improvement suggestion system for electric vehicles using LLM according to claim 1 or 2, characterized in that it is a system for predicting power consumption and suggesting improvements.

4. The estimation unit acquires information relating to the personal preferences of the electric vehicle driver and uses a mechanism to provide LLM-specific feedback of the acquired personal preference information to estimate the improvement measures in LLM in a manner that reflects the personal preferences. A power consumption forecast analysis and improvement suggestion system for electric vehicles using LLM as described in any one of claims 1 to 3.

5. The display unit presents information indicating the basis for the estimation of the improvement measures, along with the improvement measures themselves. A power consumption forecast analysis and improvement suggestion system for electric vehicles using LLM as described in any one of claims 1 to 4.

6. In an electric vehicle equipped with a battery, the steps include: estimating the battery power consumption along the planned route to the destination using LLM based on information related to the driver and driving of the electric vehicle, including the current driving status and past driving history of the electric vehicle, as well as the past driving history of an electric vehicle of the same type as the electric vehicle; and estimating improvement measures using LLM to bring the estimated power consumption closer to the minimum value; A step of presenting the estimated improvement measures to the driver. A method for predictively analyzing and suggesting improvements to the power consumption of an electric vehicle using LLM, characterized by comprising the following features.

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