Charging remaining time estimation method and device, vehicle, storage medium and program

Abstract

The invention relates to the technical field of vehicles, in particular to a charging remaining time estimation method and device, a vehicle, a storage medium and a program. The method comprises the steps that battery characteristic parameters and charging pile characteristic parameters of a vehicle are obtained, and the battery characteristic parameters comprise the current battery temperature and the current state of charge; on the basis of a preset optimal charging temperature interval, the charging heat management duration of the vehicle is calculated according to the current battery temperature, and the battery characteristic parameters and the charging pile characteristic parameters are input into a pre-established charging time prediction model to obtain estimated charging duration; and according to the charging heat management duration and the estimated charging duration, obtaining the final residual charging duration of the vehicle. Therefore, the problems that in the prior art, estimation deviation is large, user experience and credibility are damaged, complex application scenes are difficult to deal with, and user anxiety is aggravated are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, in particular to a charging remaining time estimation method and device, a vehicle, a storage medium and a program. BACKGROUND

[0002] The estimation of the charging remaining time of an electric vehicle is a core index of charging experience, and directly affects user trust and vehicle promotion.

[0003] In related technologies, the estimation of the charging remaining time is mainly divided into three categories: independent estimation by the battery management system (BMS) based on internal battery parameters, estimation by the pile end based on its own nominal power, and simple prediction based on historical data.

[0004] However, due to the information silos, neglect of the preparation work of the thermal management system, static solidification of the model, non-transparent results, jump cannot be explained, and scattered calculation topics in related technologies, the estimation deviation is large, the user experience and trust are damaged, it is difficult to cope with complex application scenarios and exacerbates user anxiety, which needs to be solved urgently. SUMMARY

[0005] The present application provides a charging remaining time estimation method, device, vehicle, storage medium and program to solve the problems of large estimation deviation, damage to user experience and trust, difficulty in coping with complex application scenarios and exacerbation of user anxiety in related technologies.

[0006] To achieve the above-mentioned purpose, the first aspect of the present application provides a charging remaining time estimation method, comprising the following steps: Obtaining battery characteristic parameters and charging pile characteristic parameters of a vehicle, wherein the battery characteristic parameters include a current battery temperature and a current state of charge; Based on a preset optimal charging temperature interval, calculating a charging thermal management duration of the vehicle according to the current battery temperature, and inputting the battery characteristic parameters and the charging pile characteristic parameters into a pre-established charging time prediction model to obtain a predicted charging duration; According to the charging thermal management duration and the predicted charging duration, obtaining a final remaining charging duration of the vehicle.

[0007] According to one embodiment of the present application, based on the preset optimal charging temperature interval, the charging thermal management duration of the vehicle is calculated according to the current battery temperature, comprising: determining whether the current battery temperature is in the preset optimal charging temperature interval; If the current battery temperature is not in the preset optimal charging temperature interval, the specific heat capacity of the battery, the total mass of the battery pack and the rated power of the TMS system are obtained. calculating an absolute value of a difference between the current battery temperature and a target optimal temperature, obtaining the charging thermal management time length based on a preset correction coefficient, according to the specific heat capacity of the battery, the total mass of the battery pack, the rated power and the absolute value of the difference.

[0008] According to an embodiment of the present application, the charging thermal management time length is: ΔT_tms=(C_p*M*|T_opt-T_bat|) / (P_tms*η); Wherein, C_p is the specific heat capacity of the battery, M is the total mass of the battery pack, T_opt is the target optimal temperature of the battery, T_bat is the current temperature of the battery, P_tms is the rated power of the TMS system, and η is the correction coefficient.

[0009] According to an embodiment of the present application, after judging whether the current battery temperature is in the preset optimal charging temperature range, the method further comprises: If the current battery temperature is in the preset optimal charging temperature range, the charging thermal management time length is a preset time length.

[0010] According to an embodiment of the present application, after obtaining the final remaining charging time length of the vehicle according to the charging thermal management time length and the estimated charging time length, the method further comprises: obtaining a current working stage of the vehicle; If the current working stage is a thermal management stage, controlling the vehicle to display an icon corresponding to the thermal management stage and the charging thermal management time length.

[0011] According to an embodiment of the present application, after obtaining the current working stage of the vehicle, the method further comprises: If the current working stage is a charging stage, controlling the vehicle to display an icon corresponding to the charging stage and the estimated charging time length.

[0012] According to the charging remaining time estimation method provided by the embodiment of the present application, the battery characteristic parameters and the charging pile characteristic parameters of the vehicle are obtained, the charging thermal management time length of the vehicle is calculated based on the preset optimal charging temperature range and the current battery temperature, the characteristic parameters are input into the pre-established charging time prediction model to obtain the estimated charging time length, and the final remaining charging time length of the vehicle is obtained. Thus, the problems of large estimation deviation, damage to user experience and trust, difficulty in coping with complex application scenarios and aggravation of user anxiety in the related art are solved.

[0013] To achieve the above object, a second aspect of the present application provides a charging remaining time estimation device, comprising: The acquisition module acquires a battery characteristic parameter and a charging pile characteristic parameter of the vehicle, wherein the battery characteristic parameter comprises a current battery temperature and a current state of charge; The calculation module calculates a charging thermal management time length of the vehicle according to the current battery temperature based on a preset optimal charging temperature interval, and inputs the battery characteristic parameter and the charging pile characteristic parameter into a pre-established charging time prediction model to obtain an estimated charging time length. The estimation module obtains a final remaining charging time length of the vehicle according to the charging thermal management time length and the estimated charging time length.

[0014] According to an embodiment of the present application, the calculation module is specifically configured to: determine whether the current battery temperature is in the preset optimal charging temperature interval; if the current battery temperature is not in the preset optimal charging temperature interval, acquire a specific heat capacity of the battery, a total mass of a battery pack, and a rated power of a TMS system; calculate an absolute value of a difference between the current battery temperature and a target optimal temperature, and obtain the charging thermal management time length based on a preset correction coefficient, according to the specific heat capacity of the battery, the total mass of the battery pack, the rated power, and the absolute value of the difference.

[0015] According to an embodiment of the present application, the charging thermal management time length is: ΔT_tms=(C_p*M*|T_opt-T_bat|) / (P_tms*η); wherein C_p is the specific heat capacity of the battery, M is the total mass of the battery pack, T_opt is the target optimal temperature of the battery, T_bat is the current temperature of the battery, P_tms is the rated power of the TMS system, and η is the correction coefficient.

[0016] According to an embodiment of the present application, the calculation module is further configured to: if the current battery temperature is in the preset optimal charging temperature interval, the charging thermal management time length is a preset time length.

[0017] According to an embodiment of the present application, the estimation module is further configured to: acquire a current working phase of the vehicle; if the current working phase is a thermal management phase, control the vehicle to display an icon corresponding to the thermal management phase and the charging thermal management time length.

[0018] According to an embodiment of the present application, the estimation module is further configured to: if the current working phase is a charging phase, control the vehicle to display an icon corresponding to the charging phase and the estimated charging time length.

[0019] The charging residual time estimation device provided by the embodiment of the present application obtains the battery characteristic parameters and the charging pile characteristic parameters of the vehicle, calculates the charging thermal management duration of the vehicle according to the current battery temperature based on the preset optimal charging temperature interval, inputs the characteristic parameters into the pre-established charging time prediction model to obtain the estimated charging duration, and obtains the final residual charging duration of the vehicle. Thus, the problems of large estimation deviation, damage to user experience and trust, difficulty in coping with complex application scenarios, and aggravation of user anxiety in the related art are solved.

[0020] To achieve the above object, the third aspect of the present application provides a vehicle, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the charging residual time estimation method as described in the above embodiments.

[0021] To achieve the above object, the fourth aspect of the present application provides a computer readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the charging residual time estimation method as described in the above embodiments.

[0022] To achieve the above object, the fifth aspect of the present application provides a computer program product, wherein the computer program is executed by a processor to implement the charging residual time estimation method as described in the above embodiments.

[0023] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0024] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, including the appended drawings. Figure 1 A flowchart of a charging residual time estimation method according to an embodiment of the present application; Figure 2 A schematic diagram of a collaborative system integrating vehicle-side, pile-side and cloud-side data and computing power according to an embodiment of the present application; Figure 3 A flowchart of a detailed calculation method of a cloud-side collaborative calculation engine according to an embodiment of the present application; Figure 4 A flowchart of a vehicle-machine-side and pile-side display screen user interface (UI) display method according to an embodiment of the present application; Figure 5 A flowchart of a charging residual time estimation method according to an embodiment of the present application; Figure 6 A block schematic diagram of a charging remaining time estimation device according to an embodiment of the present application is provided. Figure 7 A structural schematic diagram of a vehicle according to an embodiment of the present application is provided. DETAILED DESCRIPTION

[0025] Embodiments of the present application are described in detail below with reference to the accompanying drawings. The embodiments shown in the drawings are examples and are intended to explain the present application, and should not be understood as limiting the present application.

[0026] The charging remaining time estimation method, device, vehicle, storage medium and program according to embodiments of the present application are described below with reference to the accompanying drawings. First, the charging remaining time estimation method according to embodiments of the present application is described with reference to the accompanying drawings.

[0027] Figure 1 is a flowchart of the charging remaining time estimation method according to an embodiment of the present application.

[0028] As shown in Figure 1 , the charging remaining time estimation method comprises the following steps: In step S101, the battery characteristic parameters and the charging pile characteristic parameters of the vehicle are acquired, wherein the battery characteristic parameters comprise the current battery temperature and the current state of charge.

[0029] The battery characteristic parameters of the vehicle refer to a parameter set describing the current working state, core performance and key characteristics of the vehicle power battery. The charging pile characteristic parameters refer to a core parameter set describing the charging capacity, interface specification, working boundary and safety characteristics of the charging pile. The current battery temperature refers to the actual temperature of the vehicle power battery under the current working condition. The current state of charge (SOC) refers to the percentage of the current remaining available power of the vehicle power battery to the rated total power of the battery.

[0030] Specifically, the vehicle hardware foundation includes two core controllers, i.e., a BMS and a vehicle control unit (VCU), and is equipped with a high-precision sensor network (such as voltage, current, temperature sensors, etc.). The data acquisition module of the vehicle acquires real-time battery data, battery historical data, thermal management system (TMS) data and vehicle identity data in real time. Among them, the real-time battery data includes: the current state of charge of the battery pack (accuracy ± 1%), single cell voltage (sampling rate 10 Hz), temperature (at least 3 temperature measurement points, sampling rate 1 Hz), internal resistance, insulation resistance, etc.; the battery historical data includes: the state of health (SOH) of the battery, the historical charging curve atlas, the battery capacity attenuation curve, the cycle number, etc.; the thermal management system data includes: the current working state of the TMS (heating / cooling / off), the target temperature, the current power consumption, the estimated time required to reach the optimal temperature (this estimate is calculated by the vehicle according to the environmental temperature, the current battery temperature and the target temperature difference, and the heating / cooling power); the vehicle identity data: vehicle identification number (VIN), vehicle model, battery pack model, maximum acceptable charging power / current / voltage.

[0031] The hardware foundation of the charging pile is equipped with a high-performance processor and an accurate electric energy metering chip. The data acquisition module of the charging pile acquires output capability data, pile body state data and real-time output data in real time. Among them, the output capability data includes: the nominal maximum power / current / voltage of the pile body, the actual maximum power / current / voltage that can be output under the current power grid condition (reflecting the influence of power grid fluctuations); the pile body state data includes: the internal temperature of the pile body, the cooling system state, the interface connection state and the fault code; the real-time output data includes: the actual output voltage, current and power at the current time (sampling rate 1 Hz, accuracy 0.5 level).

[0032] Thus, through the data acquisition modules of the vehicle and the charging pile, the battery characteristic parameters of the vehicle and the charging pile characteristic parameters are obtained. And valuable data support can be provided for battery health management, charging network optimization, power grid scheduling, etc., thereby generating additional data value and expanding the possibility of business model.

[0033] In step S102, based on the preset optimal charging temperature interval, the charging thermal management duration of the vehicle is calculated according to the current battery temperature, and the battery characteristic parameters and the charging pile characteristic parameters are input into the pre-established charging time prediction model to obtain the estimated charging duration.

[0034] Further, in some embodiments, based on the preset optimal charging temperature interval, the charging thermal management duration of the vehicle is calculated according to the current battery temperature, including: judging whether the current battery temperature is in the preset optimal charging temperature interval; if the current battery temperature is not in the preset optimal charging temperature interval, obtaining the specific heat capacity of the battery, the total mass of the battery pack and the rated power of the TMS system; calculating the absolute value of the difference between the current battery temperature and the target optimal temperature, and obtaining the charging thermal management duration based on the preset correction coefficient, according to the specific heat capacity of the battery, the total mass of the battery pack, the rated power and the absolute value of the difference.

[0035] Optionally, in some embodiments, the charging thermal management duration is: ; Wherein, C_p is the specific heat capacity of the battery, M is the total mass of the battery pack, T_opt is the target optimal temperature of the battery, T_bat is the current temperature of the battery, P_tms is the rated power of the TMS system, and η is the correction coefficient.

[0036] Wherein, the preset optimal charging temperature interval can be an interval preset by the user, an interval obtained through a limited number of experiments, or an interval obtained through a limited number of computer simulations. The charging thermal management duration of the vehicle refers to the cumulative working time of the battery thermal management system for actively starting the heating or cooling function to control the temperature of the power battery in the ideal working interval during the whole charging process. The preset correction coefficient can be an interval preset by the user, an interval obtained through a limited number of experiments, or an interval obtained through a limited number of computer simulations.

[0037] Specifically, the cloud collaborative computing platform is adopted in the embodiments of the present application, and the intelligent vehicle terminal and the intelligent charging pile are connected through Vehicle-to-Everything (V2X) or Internet technology to realize high-speed and low-delay data interaction. As shown in Figure 2 , Figure 2 is a schematic diagram of an integrated vehicle terminal, pile terminal and cloud data and computing collaborative system according to an embodiment of the present application. Thus, the traditional information silos are broken, global data sharing and computing collaboration are realized, and a vehicle-pile-cloud deep collaborative data path and computing framework is constructed, so that the cloud computing center can obtain the global optimal information (vehicle battery full state + charging pile real-time maximum capacity + environmental conditions) required for estimation in real time, eliminate the asymmetry from the source of information, and lay a data foundation for accurate estimation. And the system can real-time perceive the actual output capacity of the charging pile, so it can timely find the performance degradation or failure of the pile body, thereby providing a basis for preventive maintenance of the charging pile and improving the reliability of the charging facility.

[0038] The cloud collaborative computing platform comprises a communication interface module, a real-time database, a history and knowledge database, a fleet learning engine, a collaborative computing engine and a result issuing module. The communication interface module realizes high-concurrency and low-latency communication with a large number of vehicles and charging piles by using a message queuing telemetry transport (MQTT) protocol, supports disconnection reconnection and message queuing; the real-time database stores real-time stream data from the vehicle side and the pile side by using a time series database (such as InfluxDB), supports high-speed reading and writing and querying; the history and knowledge database stores a large amount of historical charging data by using a distributed relational database (such as MySQL Cluster), including a standard charging curve library of different vehicle models and different battery models, a charging efficiency correction coefficient library under different temperatures and different SOH, and a historical performance database of different charging piles (evaluating the output stability and attenuation); the fleet learning engine is based on a machine learning algorithm (such as eXtreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM)), trains a high-precision charging prediction model by using historical big data, and can continuously optimize itself by using new data, supports online learning and batch learning two modes; the collaborative computing engine is the core, receives the vehicle and pile data of a specific charging session, executes the main algorithm process of the embodiment of the application, completes the precise charging remaining time calculation (TTC) calculation, adopts a micro-service architecture, and supports horizontal expansion; the result issuing module issues the calculated TTC and related information to the vehicle central control screen and the charging pile display screen through the Websocket protocol, and guarantees the real-time of the information. The machine learning model and the fleet learning mechanism are introduced in the cloud, so that the estimation model can continuously evolve by using the network data, continuously absorbs new charging scene data, adapts to battery aging, environmental changes and pile performance fluctuations, realizes the intelligent ability of getting more accurate, long-term maintains high-precision prediction, and the estimation stability is better.

[0039] The embodiment of the present application judges whether the current battery temperature is in the preset optimal charging temperature interval. If the current battery temperature is not in the preset optimal charging temperature interval, the charging thermal management duration of the vehicle is calculated based on a formula. The thermal management duration of the vehicle is the time required for energy transfer in theory. The specific heat capacity C_p of the battery needs to be obtained, which is a known parameter, and is usually 800-1100 J / kg·K; the total mass M of the battery pack is a known parameter, and is usually 200-600 kg according to the vehicle type; T_opt is the target optimal temperature of the battery, which is usually 20-25°C; |T_opt-T_bat| is the absolute difference between the target optimal temperature of the battery and the current battery temperature. The rated power of the TMS system, which can be a positive temperature coefficient thermistor (Positive Temperature Coefficient, PTC) heater or a refrigeration compressor, is usually 3-8 kW; the correction coefficient η is an efficiency coefficient considering heat loss, 0<η<1, usually 0.6-0.8, which is obtained by experimental calibration. This calculation is completed by the cloud, and is updated every second. As the TMS works, the thermal management duration gradually decreases.

[0040] The calculation of the estimated charging duration is completed by a cloud machine learning model, and the specific process is as follows: the current state of charge SOC, the battery health state SOH, the battery temperature T_bat, the actual available power P_avl of the charging pile, the battery model, the environmental temperature, the historical charging efficiency, etc. are input into the model; the model performs polynomial expansion, interaction item generation and standardization processing on the original features to improve the expression ability of the model; gradient boosting tree (such as XGBoost) or recurrent neural network (Recurrent Neural Network, RNN) algorithm is used for supervised learning, wherein, the XGBoost gradient boosting tree algorithm is preferred, which has excellent performance on structured data, and the LSTM neural network is used for data with strong time series characteristics; a large amount of successful charging session data (tens of thousands of complete charging curves, each data contains the whole process record from a certain starting SOC to full charge) in the historical database is used as the training set, K-fold cross-validation (5-fold cross-validation can be used) is used to prevent overfitting, and Bayesian optimization is used for hyperparameter tuning. The model can learn the complex nonlinear charging characteristics of the battery in the constant current stage and the constant voltage stage, and the accuracy is much higher than that of the linear formula; the model predicts the time ΔT_charge required to charge from the current state to 100% SOC, and the accuracy can reach ±2 minutes. A cloud-native architecture is adopted to concentrate the core computing power in the cloud, realize centralized optimization and unified deployment of the algorithm, reduce the requirement for hardware computing power of the vehicle end and the pile end, improve the economy and scalability of the system, and reduce the overall system cost. It should be noted that the specific training process of the charging time prediction model is similar to that of some prediction models in related technologies, and to avoid redundancy, it will not be described in detail here.

[0041] Thus, the charging thermal management time length AT_tms and the estimated charging time length AT_charge of the vehicle are calculated by the cloud collaborative computing platform.

[0042] In step S103, the final remaining charging time length of the vehicle is obtained according to the charging thermal management time length and the estimated charging time length.

[0043] The final remaining charging time length of the vehicle refers to the total time length required for the vehicle to complete charging of the remaining power from the current time.

[0044] Specifically, the final remaining charging time length TTC of the vehicle is calculated according to the following formula: In the formula, AT_tms is the charging thermal management time length of the vehicle, and AT_charge is the estimated charging time length.

[0045] Thus, the working state and time consumption of the TMS are explicitly included in the total timeline, a segmented joint estimation model is established, the total charging time is scientifically divided into a TMS preparation stage and an efficient charging stage, and the time of the two stages is accurately estimated and summed up to realize accurate prediction of the whole charging process without perceptual breakpoints, and the initial estimation shock problem is completely solved.

[0046] The above data acquisition and calculation process is repeated at a fixed time interval (such as 60 seconds) in the embodiments of the present application, so as to realize dynamic updating and convergence of the TTC value. Since the input data is updated in real time, even if the charging pile power fluctuates or the TMS work ends, the estimation result can be timely followed up to ensure the accuracy of the prediction.

[0047] Thus, the battery feature parameters and the charging pile feature parameters of the vehicle are obtained, the charging thermal management time length of the vehicle is calculated according to the current battery temperature based on the preset optimal charging temperature interval, the feature parameters are input into the pre-established charging time prediction model to obtain the estimated charging time length, and the final remaining charging time length of the vehicle is obtained. Thus, the problems of large estimation deviation, damage to user experience and trust, difficulty in coping with complex application scenarios, and aggravation of user anxiety in related technologies are solved.

[0048] Further, in order to ensure the comprehensiveness and accuracy of the estimation of the final remaining charging time length of the vehicle, the scenario in which the current battery temperature is already in the preset optimal charging temperature interval is also considered.

[0049] Optionally, in some embodiments, after judging whether the current battery temperature is in the preset optimal charging temperature interval, the method further includes: if the current battery temperature is in the preset optimal charging temperature interval, the charging thermal management time length is a preset time length.

[0050] ​Specifically, if the current battery temperature is within the preset optimal charging temperature interval, the thermal management duration ΔT_tms of the vehicle is 0, and the estimated charging duration ΔT_charge is the final remaining charging duration.

[0051] To make the cloud collaborative computing engine of the present application more understandable to those skilled in the art, the following will combine Figure 3 with specific examples to describe in detail.

[0052] As Figure 3 shown, Figure 3 is a flowchart of a detailed calculation method of the cloud collaborative computing engine according to an embodiment of the present application, which comprises the following steps: S301, model input features.

[0053] S302, model output estimated charging duration.

[0054] S303, calculate the final remaining charging duration TTC of the vehicle.

[0055] S304, deliver the TTC result to the vehicle and the charging pile.

[0056] S305, wait for a preset time interval Δt.

[0057] S306, obtain new round of real-time data.

[0058] S307, start calculation.

[0059] S308, read real-time data of the vehicle and the charging pile.

[0060] S309, judge whether the current battery temperature is in the optimal temperature interval, if yes, execute step S310; otherwise, execute step S311.

[0061] S310, set the thermal management duration ΔT_tms of the vehicle to 0.

[0062] S311, calculate the TMS working time, i.e. the thermal management duration ΔT_tms of the vehicle.

[0063] S312, start the TMS, ΔT_tms starts countdown, and return to execute step S301.

[0064] Thus, the calculation logic loop of the charging thermal management duration is perfected, and the accuracy and integrity of the final remaining charging duration estimation are improved. Through a set of collaborative system integrating vehicle-end, pile-end and cloud-end data and computing power, the TMS working period and the charging period are innovatively jointly modeled, and machine learning and fleet learning technologies are used to finally realize high-precision, high-stability and high-credibility charging remaining time estimation across the whole process and all working conditions, which fundamentally improves the charging experience of electric vehicles.

[0065] Further, in order to improve interaction transparency and user trust, the embodiment of the application can also be provided with a display screen for displaying charging time.

[0066] Optionally, in some embodiments, after obtaining the final remaining charging time of the vehicle according to the charging thermal management time and the estimated charging time, the method further comprises: obtaining the current working stage of the vehicle; and if the current working stage is the thermal management stage, controlling the vehicle to display the icon corresponding to the thermal management stage and the charging thermal management time.

[0067] Optionally, in some embodiments, after obtaining the current working stage of the vehicle, the method further comprises: if the current working stage is the charging stage, controlling the vehicle to display the icon corresponding to the charging stage and the estimated charging time.

[0068] Optionally, in some embodiments, after obtaining the current working stage of the vehicle, the method further comprises: if the current working stage is the charging stage, controlling the vehicle to display the icon corresponding to the charging stage and the estimated charging time.

[0069] Specifically, the embodiment of the application also provides a vehicle machine end and pile end display screen UI, which clearly distinguishes the two stages and provides explanatory information.

[0070] When the current working stage of the vehicle is in the thermal management stage, the user is explicitly informed that the current stage is the preparation stage through the display screen, and the estimated completion time of preheating / cooling is displayed, effectively managing the user's expectation.

[0071] When the current working stage of the vehicle is in the charging stage, detailed charging parameters and progress are displayed, and a charging curve is provided to enhance transparency.

[0072] When the estimated time changes, a brief explanation is provided (such as power reduction due to power grid fluctuations, time increase of 2 minutes), enhancing user trust. On the basis of providing accurate time values, the explanatory nature of the estimated results is increased, and the user is explicitly informed of the current stage and the reason for the time change through multi-stage UI design, thereby greatly improving user trust and peace of mind, effectively managing psychological expectations, and improving product competitiveness.

[0073] As shown in Figure 4 , the flowchart of the vehicle machine end and pile end display screen UI display method provided according to an embodiment of the application comprises the following steps: Figure 4 S401, charging interface state management.

[0074] S402, determining whether the current battery temperature is in the optimal charging interval, if yes, executing step S404; otherwise, executing step S403.

[0075] ​S403, the current working stage of the vehicle is in the TMS stage, the display screen displays icons, main titles, descriptions, important information and additional prompts.

[0076] S404, the current working stage of the vehicle is in the charging stage, the display screen displays icons, main titles, progress, important information, real-time parameters and state notes.

[0077] S405, display on the car and pile screen.

[0078] S406, the user views.

[0079] Therefore, by increasing the UI design, the user experience and trust can be significantly improved.

[0080] In order to facilitate the person skilled in the art to further understand the charging remaining time estimation method of the present application, the following will be described in detail in combination with Figure 5 .

[0081] As Figure 5 shown, Figure 5 is a flowchart of a charging remaining time estimation method according to an embodiment of the present application, the method comprising the following steps: S501, the user inserts the charging gun.

[0082] S502, the vehicle and the charging pile establish a connection, and upload the self data to the cloud.

[0083] S503, the cloud computing engine judges whether the battery temperature is in the best charging interval, if yes, step S506 is executed; otherwise, step S504 is executed.

[0084] S504, the cloud calculates the TMS working time ΔT_tms, and issues an instruction to start the TMS.

[0085] S505, the car / pile interface displays: "battery heating / cooling, estimated to take X minutes".

[0086] S506, the cloud synchronously performs charging stage estimation: through data preprocessing and feature engineering; calling a machine learning model; based on all real-time data, simulating a charging curve, calculating ΔT_charge.

[0087] S507, the cloud calculates the final remaining charging time.

[0088] S508, the cloud issues the final TTC result to the car and the pile screen for display.

[0089] S509, the cloud enters a monitoring cycle, regularly acquires new data and recalculates the dynamic updated TTC.

[0090] According to the charging remaining time estimation method proposed in this application, the vehicle's battery characteristic parameters and charging pile characteristic parameters are obtained. Based on a preset optimal charging temperature range, the charging thermal management duration of the vehicle is calculated according to the current battery temperature. The characteristic parameters are input into a pre-established charging time prediction model to obtain the estimated charging duration, and finally, the vehicle's final remaining charging duration is obtained. This solves the problems of large estimation deviations, damage to user experience and trust, difficulty in handling complex application scenarios, and increased user anxiety in related technologies.

[0091] Next, the charging remaining time estimation device according to the embodiments of this application is described with reference to the accompanying drawings.

[0092] Figure 6 This is a block diagram of a charging remaining time estimation device according to an embodiment of this application.

[0093] like Figure 6 As shown, the charging remaining time estimation device 20 includes: an acquisition module 100, a calculation module 200, and an estimation module 300.

[0094] The acquisition module 100 acquires the vehicle's battery characteristic parameters and the charging pile characteristic parameters, wherein the battery characteristic parameters include the current battery temperature and the current state of charge. The calculation module 200 calculates the vehicle's charging thermal management time based on the preset optimal charging temperature range and the current battery temperature, and inputs the battery characteristic parameters and charging pile characteristic parameters into the pre-established charging time prediction model to obtain the estimated charging time. The estimation module 300 obtains the final remaining charging time of the vehicle based on the charging thermal management duration and the estimated charging duration.

[0095] According to one embodiment of this application, the calculation module 200 is specifically used for: Determine if the current battery temperature is within the preset optimal charging temperature range; If the current battery temperature is not within the preset optimal charging temperature range, obtain the battery's specific heat capacity, the total mass of the battery pack, and the rated power of the TMS system. The absolute value of the difference between the current battery temperature and the target optimal temperature is calculated. Based on a preset correction coefficient, the charging thermal management duration is obtained according to the battery's specific heat capacity, the total mass of the battery pack, the rated power, and the absolute value of the difference.

[0096] According to one embodiment of this application, the charging thermal management duration is: ΔT_tms=(C_p*M*|T_opt-T_bat|) / (P_tms*η); Wherein, C p is the specific heat capacity of the battery, M is the total mass of the battery pack, T opt is the target optimal temperature of the battery, T bat is the current temperature of the battery, P tms is the rated power of the TMS system, and η is a correction coefficient.

[0097] According to an embodiment of the present application, the computing module 200 is further configured to: If the current battery temperature is in the preset optimal charging temperature range, the charging thermal management duration is a preset duration.

[0098] According to an embodiment of the present application, the estimating module 300 is further configured to: obtain the current working phase of the vehicle; If the current working phase is the thermal management phase, control the vehicle to display an icon corresponding to the thermal management phase and the charging thermal management duration.

[0099] According to an embodiment of the present application, the estimating module 300 is further configured to: If the current working phase is the charging phase, control the vehicle to display an icon corresponding to the charging phase and the estimated charging duration.

[0100] It should be noted that the above explanation of the charging remaining time estimation method embodiment is also applicable to the charging remaining time estimation device of this embodiment, which will not be described here.

[0101] According to the charging remaining time estimation device provided by the embodiment of the present application, the battery characteristic parameters and the charging pile characteristic parameters of the vehicle are obtained, the charging thermal management duration of the vehicle is calculated according to the current battery temperature based on the preset optimal charging temperature range, the characteristic parameters are input into the pre-established charging time prediction model to obtain the estimated charging duration, and the final remaining charging duration of the vehicle is obtained. Therefore, the problems of large estimation deviation, damage to user experience and trust, difficulty in coping with complex application scenarios, and aggravation of user anxiety in related technologies are solved.

[0102] Figure 7 The vehicle provided by the embodiment of the present application is shown in the structural schematic diagram. The vehicle can include: The memory 701, the processor 702, and the computer program stored in the memory 701 and executable on the processor 702.

[0103] The processor 702 executes the program to implement the charging remaining time estimation method provided in the above embodiments.

[0104] Further, the vehicle further includes: The communication interface 703 is configured to communicate between the memory 701 and the processor 702.

[0105] The memory 701 is configured to store the computer program executable on the processor 702.

[0106] The memory 701 can include a high-speed RAM (Random Access Memory) memory, and can also include a nonvolatile memory, such as at least one disk memory.

[0107] If the memory 701, the processor 702 and the communication interface 703 are implemented independently, the communication interface 703, the memory 701 and the processor 702 can be connected to each other through a bus and complete communication between each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, Figure 7 In the figure, only one thick line is used to represent, but it does not mean that there is only one bus or one type of bus.

[0108] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can complete communication between each other through an internal interface.

[0109] The processor 702 can be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present application.

[0110] The embodiments of the present application also provide a computer readable storage medium, having stored thereon a computer program, which is executed by a processor to implement the above charging remaining time estimation method.

[0111] The embodiments of the present application also provide a computer program product, the computer program product including a computer program, which is executed by a processor to implement the steps in any of the above charging remaining time estimation method embodiments.

[0112] In addition, the terms "first", "second", etc. are used only to describe the purpose and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "a plurality of" is at least two, for example, two, three, etc., unless otherwise explicitly and specifically limited.

[0113] In the description of the present application, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present application, the illustrative description of the above terms is not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. Furthermore, the person skilled in the art can combine and combine the different embodiments or examples described in the present application and the features of the different embodiments or examples without contradiction.

[0114] Although the embodiments of the present application have been shown and described above, it is understood that the above-described embodiments are exemplary and cannot be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above-described embodiments within the scope of the present application.

Claims

1. A method of estimating a charging remaining time, characterized by, The method comprises the following steps: obtaining battery characteristic parameters and charging pile characteristic parameters of a vehicle, wherein the battery characteristic parameters comprise a current battery temperature and a current state of charge; based on a preset optimal charging temperature interval, calculating a charging thermal management time length of the vehicle according to the current battery temperature, and inputting the battery characteristic parameters and the charging pile characteristic parameters into a pre-established charging time prediction model to obtain an estimated charging time length; obtaining a final remaining charging time length of the vehicle according to the charging thermal management time length and the estimated charging time length.

2. The method of claim 1, wherein, The step of calculating the charging thermal management time length of the vehicle according to the current battery temperature based on the preset optimal charging temperature interval comprises: determining whether the current battery temperature is in the preset optimal charging temperature interval; if the current battery temperature is not in the preset optimal charging temperature interval, obtaining the specific heat capacity of the battery, the total mass of the battery pack and the rated power of the TMS system; calculating the absolute value of the difference between the current battery temperature and a target optimal temperature, and obtaining the charging thermal management time length based on a preset correction coefficient, according to the specific heat capacity of the battery, the total mass of the battery pack, the rated power and the absolute value of the difference.

3. The method of claim 2, wherein, The charging thermal management time length is: ΔT_tms=(C_p*M*|T_opt-T_bat|) / (P_tms*η); wherein C_p is the specific heat capacity of the battery, M is the total mass of the battery pack, T_opt is the target optimal temperature of the battery, T_bat is the current temperature of the battery, P_tms is the rated power of the TMS system, and η is the correction coefficient.

4. The method of claim 2, wherein, After determining whether the current battery temperature is in the preset optimal charging temperature interval, the method further comprises: if the current battery temperature is in the preset optimal charging temperature interval, the charging thermal management time length is a preset time length.

5. The method of claim 1, wherein, After obtaining the final remaining charging time length of the vehicle according to the charging thermal management time length and the estimated charging time length, the method further comprises: obtaining a current working phase of the vehicle; if the current working phase is a thermal management phase, controlling the vehicle to display an icon corresponding to the thermal management phase and the charging thermal management time length.

6. The method of claim 5, wherein, After obtaining the current working phase of the vehicle, the method further comprises: if the current working phase is a charging phase, controlling the vehicle to display an icon corresponding to the charging phase and the estimated charging time length.

7. A charge remaining time estimating device characterized by comprising: The method comprises: an obtaining module, which obtains battery characteristic parameters and charging pile characteristic parameters of a vehicle, wherein the battery characteristic parameters comprise a current battery temperature and a current state of charge; a calculating module, which calculates a charging thermal management time length of the vehicle according to the current battery temperature based on a preset optimal charging temperature interval, and inputs the battery characteristic parameters and the charging pile characteristic parameters into a pre-established charging time prediction model to obtain an estimated charging time length; an estimating module, which obtains a final remaining charging time length of the vehicle according to the charging thermal management time length and the estimated charging time length.

8. A vehicle characterized by comprising: The method comprises: - a memory, a processor, and a computer program stored on the memory and runable on the processor, the processor executing the program to implement the charge remaining time estimation method according to any one of claims 1-6.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by a processor for implementing the charge remaining time estimation method according to any one of claims 1-6.

10. A computer program product comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the charge remaining time estimation method according to any one of claims 1-6.

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