Method and system for estimating power consumption and time consumption of electric-assisted bicycle

Abstract

The invention provides a method and a system for estimating power consumption and time consumption of an electric-assisted bicycle. A starting location and a destination location are received. And generating a planned path according to the starting position and the destination position. According to the path information of the planned path and the riding habit information of the rider, the riding time consumed by the electric-assisted bicycle to travel through the planned path is estimated. According to the riding time, the path information and the riding habit information, the power consumption consumed by the electric-assisted bicycle passing through the planned path is estimated. And displaying the riding time and the power consumption associated with the first riding mode through the user operation interface. Therefore, the situation that the destination cannot be smoothly reached due to insufficient electric quantity can be reduced.

Description

Technical Field

[0001] The present invention relates to an electric-assisted bicycle, and in particular to a method and system for estimating power consumption and time consumption of an electric-assisted bicycle. Background Art

[0002] Bicycling is becoming increasingly popular in modern society, driven by various reasons, including environmental protection, health, leisure, and economic reasons. Traditional bicycles typically rely solely on pedaling to propel the bike forward. In contrast, electric-assisted bicycles, with their electric powertrain, are less strenuous to ride and are therefore increasingly popular. As you can see, if an electric-assisted bicycle runs out of power, the motor will no longer provide assistance, making it just like a traditional bicycle. Therefore, riders generally need to ensure their electric-assisted bicycle has enough power to reach their destination. The safest approach is to fully charge the battery. However, in many scenarios, riders may find it difficult to accurately determine whether their electric-assisted bicycle has enough power to reach their destination. Furthermore, different riding styles can result in varying levels of power consumption, making power consumption estimates based solely on distance traveled quite inaccurate. Summary of the Invention

[0003] The present invention provides a method and system for estimating power consumption and time consumption of an electric-assisted bicycle, which can solve the above technical problems.

[0004] An embodiment of the present invention provides a method for estimating the power consumption and time consumption of an electric-assisted bicycle, comprising the following steps: receiving a starting location and a destination location; generating a planned route based on the starting location and the destination location; estimating the riding time of the electric-assisted bicycle along the planned route based on path information of the planned route and information about the rider's riding habits; estimating the power consumption of the electric-assisted bicycle along the planned route based on the riding time, the path information, and the riding habits; and displaying the riding time and power consumption associated with a first riding mode via a user interface.

[0005] An embodiment of the present invention provides a system for estimating power consumption and time consumption for an electric-assisted bicycle, comprising a storage device and a processor. The processor is coupled to the storage device and configured to perform the following operations: Receive a starting location and a destination location. Generate a planned path based on the starting location and the destination location. Estimate the riding time of the electric-assisted bicycle along the planned path based on path information of the planned path and information about the rider's riding habits. Estimate the power consumption of the electric-assisted bicycle along the planned path based on the riding time, path information, and riding habit information. The riding time and power consumption associated with a first riding mode are displayed through a user interface.

[0006] Based on the above, in this embodiment of the present invention, the riding time and power consumption of an electric-assisted bicycle along a planned route can be accurately estimated based on the planned route information and the rider's riding habits, allowing the rider to accurately determine whether the electric-assisted bicycle's battery has sufficient power. This allows the rider to more conveniently plan and arrange their riding itinerary, while also reducing the likelihood of being unable to reach their destination due to insufficient battery power. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 is a schematic diagram of a power consumption and time consumption estimation system for an electric-assisted bicycle according to an embodiment of the present invention;

[0008] Figure 2 is a block diagram of a power consumption and time consumption estimation system for an electric-assisted bicycle according to an embodiment of the present invention;

[0009] Figure 3 is a flow chart of a method for estimating power consumption and time consumption of an electric-assisted bicycle according to an embodiment of the present invention;

[0010] Figure 4 is a flow chart of a method for estimating power consumption and time consumption of an electric-assisted bicycle according to an embodiment of the present invention;

[0011] Figure 5 is a flow chart of a method for estimating power consumption and time consumption of an electric-assisted bicycle according to an embodiment of the present invention;

[0012] Figure 6 is a schematic diagram of a user operation interface according to an embodiment of the present invention;

[0013] Figure 7 is a schematic diagram of a user operation interface according to an embodiment of the present invention.

[0014] Description of Reference Numerals

[0015] 10: Power consumption and time consumption estimation system of electric assisted bicycle;

[0016] 100: server device;

[0017] 200: electronic device;

[0018] 300: Electric assisted bicycle;

[0019] 310: bicycle control system;

[0020] N1: Network;

[0021] 110, 210, 311: processor;

[0022] 120, 220, 315: storage device;

[0023] 130, 230, 316: transceiver;

[0024] 240: input device;

[0025] 250: display;

[0026] 312: stepping sensor;

[0027] 313: motor controller;

[0028] 314: motor;

[0029] S310~S350,S410~S460,S441~S444,S451~S452,S510~S580,S541~S543: Steps

[0030] UI_1, UI_2, UI_3: user interface;

[0031] N2: prompt box. DETAILED DESCRIPTION

[0032] Reference will now be made in detail to exemplary embodiments of the present invention, examples of which are illustrated in the accompanying drawings. Whenever possible, the same reference numerals are used in the drawings and the description to refer to the same or like parts.

[0033] Please refer to Figure 1 as well as Figure 2 , Figure 1 FIG. 2 is a schematic diagram of a system for estimating power consumption and time consumption of an electric-assisted bicycle according to an embodiment of the present invention. Figure 2 FIG. 4 is a block diagram of a system for estimating power consumption and time consumption of an electric-assisted bicycle according to an embodiment of the present invention.

[0034] The power consumption and time estimation system 10 for an electric-assisted bicycle includes a server device 100, an electronic device 200, and an electric-assisted bicycle 300. The server device 100 can be connected to the electronic device 200 via a network N1. The electronic device 200 can establish a communication connection with a bicycle control system 310 of the electric-assisted bicycle 300. For example, the electronic device 200 can establish a Bluetooth connection with the bicycle control system 310 of the electric-assisted bicycle 300.

[0035] Network N1 may include any combination of public and / or private networks, local area networks, and / or wide area networks. Furthermore, network N1 may utilize one or more wired and / or wireless communication technologies. In some embodiments, network N1 may include, for example, a cellular or other mobile network, a wireless local area network (WLAN), a wireless wide area network (WWAN), and / or the Internet. Examples of network N1 include a Long Term Evolution (LTE) wireless network, a fifth generation (5G) wireless network (also known as a New Radio (NR) wireless network or a 5G NR wireless network), Wi-Fi WLAN, and the Internet.

[0036] The server device 100 is an electronic device with data storage capabilities, computing capabilities, and networking capabilities. The server device 100 may include (but is not limited to) a storage device 120, a transceiver 130, and a processor 110. The storage device 120 is used to store data, instructions, software modules, or programs. The processor 110 can access and execute instructions, software modules, or programs in the storage device 120. The transceiver 130 is used to connect to the network N1 to receive and send data. In some embodiments, the server device 100 can be implemented by one or more cloud servers of a cloud computing platform. The cloud computing platform can be any cloud computing platform known in the art, such as Amazon Web Services (AWS), Microsoft Azure, GOOGLECLOUD, or other cloud computing platforms.

[0037] The electronic device 200 is, for example, a smartphone, a smart watch, a wearable electronic device, or other user terminal device. The electronic device 200 may include (but is not limited to) a processor 210, a storage device 220, a transceiver 230, an input device 240, and a display 250. The storage device 220 is used to store data, instructions, software modules, or programs. The processor 210 can access and execute instructions, software modules, or programs in the storage device 220. The transceiver 230 may include a transceiver circuit for connecting to the network N1 to receive and send data and a transceiver circuit for connecting to the bicycle control system 310. The input device 240 is, for example, a touch screen or a button, etc., for receiving rider operations. The display 250 is used to display the user operation interface of the application.

[0038] The electric-assisted bicycle 300 is a vehicle that combines human pedaling with electrical assistance. When a rider pedals, the bicycle 300 provides power to the rider, allowing the rider to rotate the tires of the bicycle 300 with less effort. The bicycle 300 includes a bicycle control system 310. The bicycle control system 310 includes a processor 311, a pedaling sensor 312, a motor controller 313, a motor 314, a storage device 315, and a transceiver 316. Furthermore, the bicycle 300 includes a rechargeable battery (not shown), such as a lithium battery, that provides power to the motor 314.

[0039] The pedaling sensor 312 is used to sense the pedaling status of the rider. For example, the pedaling sensor 312 may include a cadence sensor and a torque sensor. The torque sensor can be used to sense the force applied by the rider to the pedals. The cadence sensor can be used to sense the rider's pedaling frequency. The motor controller 313 can be used to control the start, stop, speed, and direction of the motor 314. The motor 314 is used to provide the driving torque required for the electric-assisted bicycle 300 to move forward, thereby driving at least one wheel of the electric-assisted bicycle 300. The storage device 315 is used to store data, instructions, software modules, or programs. The processor 310 can access and execute the instructions, software modules, or programs in the storage device 315, and the processor 310 can monitor and control the operating status of the entire electric-assisted bicycle 300. The transceiver 316 is used to connect to the electronic device 200 to receive and send data.

[0040] The processors 110, 210, and 311 are, for example, a central processing unit (CPU), an application processor, or other programmable general-purpose or special-purpose microprocessors, digital signal processors (DSPs), programmable controllers, application-specific integrated circuits (ASICs), programmable logic devices (PLDs), or other similar devices or combinations of these devices, which can respectively execute instructions, software modules, or programs in the storage devices 120, 220, and 315.

[0041] Figure 3 This is a flow chart of a method for estimating power consumption and time consumption of an electric-assisted bicycle according to an embodiment of the present invention. Figure 1 and Figure 3 , the following is matched Figure 1The steps of the method for estimating power consumption and time consumption of an electric-assisted bicycle according to this embodiment are described with reference to the components of FIG.

[0042] In step S310 , the server device 100 receives a starting location and a destination location. Specifically, the electronic device 200 receives the starting location and destination location set by the rider via the input device 240 and transmits the starting location and destination location set by the rider to the server device 100 via the network N1 .

[0043] In step S320, the server device 100 generates a planned route based on the starting location and the destination location. The server device 100 may create the planned route based on map data provided by a map server. Alternatively, the server device 100 may provide the starting location and the destination location to a navigation server providing navigation services to obtain the planned route.

[0044] In step S330, the server device 100 estimates the riding time consumed by the electric-assisted bicycle 300 to travel along the planned path based on the path information of the planned path and the riding habit information of the rider. The path information of the planned path may include route distance, terrain height change, slope value, traffic light information or the number of forks, etc. The rider's riding habit information can be obtained based on the rider's past riding record data. The rider's riding habit information may include riding speeds and motor assist parameters corresponding to different slope values. In some embodiments, the motor assist parameters may include maximum assist power (MaxAssist Power), assist ratio (Assist Ratio Percentage) and other motor control parameters. The maximum assist power is a parameter used to limit the maximum assist force of the motor 314. The assist ratio represents the ratio between the assist provided by the motor 314 and human pedaling.

[0045] In some embodiments, the server device 100 can estimate the ride time by inputting route information of the planned route and the rider's riding habits into a specific function. For example, the rider's average riding speed can be calculated based on the rider's riding history. The server device 100 can calculate an initial ride time based on the route distance and the rider's average riding speed, and adjust the initial ride time based on terrain elevation changes, slope values, traffic light information, or the number of intersections to obtain a final estimated ride time.

[0046] In some embodiments, the server device 100 may input the path information of the planned path and the riding habit information of the rider into a machine learning model to estimate the riding time consumed by the electric-assisted bicycle 300 along the planned path. The machine learning model is, for example, a linear regression model, a neural network model, or a support vector machine (SVM) model, etc. This machine learning model can be established based on training data and a machine learning algorithm, and the input features of this machine learning model may include the path information of the planned path and the riding habit information of the rider. The training data can be generated by collecting data from multiple testers who actually ride electric-assisted bicycles along multiple test paths. The training data may include the path information of the multiple test paths, the riding habit information of the test riders, and the actual riding time of the multiple test paths. The model parameters of the trained machine learning model can be recorded in the storage device 120 of the server device 100. The model parameters determined by the machine learning algorithm may include the regression coefficients or weight values ​​of the linear regression model, etc.

[0047] In step S340 , the server device 100 estimates the power consumption of the electric-assisted bicycle 300 while traveling along the planned route based on the riding time, route information, and riding habit information.

[0048] In some embodiments, the server device 100 can estimate power consumption by inputting riding time, route information, and riding habit information into a specific function. As can be seen, riding time is positively correlated with power consumption, and the output power of motor 314 is also positively correlated with power consumption. For example, the motor assist parameter in the riding habit information can be used to estimate the output power of motor 314. Based on the output power of motor 314 and riding time, the server device 100 can calculate an initial power consumption. Furthermore, the server device 100 can adjust this initial power consumption based on terrain elevation changes, slope values, traffic light information, or the number of intersections to obtain a final power consumption estimate.

[0049] In some embodiments, the server device 100 may input riding time, path information, and riding habit information into a machine learning model to estimate the power consumption of the electric-assisted bicycle 300 traveling along the planned path. The machine learning model is, for example, a linear regression model, a neural network model, or a support vector machine model, etc. This machine learning model can be established based on training data and a machine learning algorithm, and the input features of this machine learning model may include riding time, path information, and riding habit information. The training data can be generated by collecting data from multiple testers who actually ride electric-assisted bicycles along multiple test paths. The training data may include path information and riding time of the multiple test paths, riding habit information of the test riders, and actual power consumption corresponding to the multiple test paths. The model parameters of the trained machine learning model can be recorded in the storage device 120 of the server device 100. The model parameters determined by the machine learning algorithm may include regression coefficients or weight values ​​of a linear regression model, etc.

[0050] In step S350, the electronic device 200 displays the riding time and power consumption associated with the first riding mode via a user interface. The riding time and power consumption associated with the first riding mode are displayed via the user interface. Specifically, the server device 100 can transmit its estimated riding time and power consumption to the electronic device 200 via the network N1, and provide them to the rider via the user interface displayed on the electronic device 200. This allows the rider to determine the estimated riding time and power consumption required to reach the destination using the electric-assisted bicycle 300 in the first riding mode, thereby confirming whether the remaining power of the electric-assisted bicycle 300 is sufficient and facilitating the planning of a riding itinerary.

[0051] Figure 4 This is a flow chart of a method for estimating power consumption and time consumption of an electric-assisted bicycle according to an embodiment of the present invention. Figure 1 and Figure 4 , the following is matched Figure 1 The steps of the method for estimating power consumption and time consumption of an electric-assisted bicycle according to this embodiment are described with reference to the components of FIG.

[0052] In step S410, the server device 100 collects the rider's riding habit information. The riding habit information includes multiple riding speeds and multiple motor assist parameters corresponding to multiple slope values. In detail, during the rider's actual riding of the electric-assisted bicycle 300, the electric-assisted bicycle 300 can periodically report the rider's riding data and the motor parameters of the motor 314 to the server device 100. The above-mentioned riding data may include riding speed, cadence, GPS position, etc. The above-mentioned motor parameters may include the average torque, average output power or average current of the motor 314, etc. The server device 100 can collect the slope values ​​of multiple unit sections based on these GPS positions and terrain data, and can calculate the average riding speed corresponding to these unit sections. The server device 100 can calculate the average riding speed, motor assist parameters and other motor parameters corresponding to the same slope value. For example, based on the rider's riding data, the server device 100 may obtain a first riding speed and a first motor assist parameter corresponding to a first slope value, and obtain a second riding speed and a second motor assist parameter corresponding to a second slope value.

[0053] In step S420, the server device 100 receives a starting location and a destination location. In step S430, the server device 100 generates a planned route based on the starting location and the destination location. Steps S420 to S430 can be described with reference to the previous embodiment and are not further described here.

[0054] In step S440 , the server device 100 estimates the riding time of the electric-assisted bicycle 300 along the planned route based on the route information of the planned route and the rider's riding habit information. In some embodiments, step S440 can be implemented as steps S441 to S444 .

[0055] In step S441, the server device 100 divides the planned path into multiple sub-paths. In some embodiments, the server device 100 may divide the planned path into multiple sub-paths based on a fixed distance (e.g., 200 meters), i.e., the path distances of these sub-paths are the same. Alternatively, in some embodiments, the server device 100 may divide the planned path into multiple sub-paths corresponding to different slope values ​​based on the slope information of the planned path, i.e., the path distances of these sub-paths may be different.

[0056] In step S442, the server device 100 determines the predicted riding parameters for each sub-path of the first riding mode based on the path information of each sub-path and the rider's riding habit information. In some embodiments, the predicted riding parameters for each sub-path may include a predicted riding speed and a predicted motor assistance parameter. Specifically, these sub-paths may include a first sub-path. The server device 100 may obtain a slope value for the first sub-path. The slope value for the first sub-path may be generated based on the altitude of the GPS location of the first sub-path. The server device 100 may determine the predicted riding speed corresponding to the first sub-path and the predicted motor assistance parameter corresponding to the first sub-path based on the slope value for the first sub-path. In other words, the rider's riding habit information includes riding speeds and motor assistance parameters corresponding to different slope values. Therefore, the server device 100 may search the rider's riding habit information based on the slope value for the first sub-path to obtain the corresponding predicted riding speed and predicted motor assistance parameter. Specifically, based on the rider's riding habit information, the server device 100 can estimate the predicted riding speed of the electric-assisted bicycle 300 along the first sub-route and the predicted motor assist parameter of the electric-assisted bicycle 300 based on the slope value of the first sub-route.

[0057] In step S443, the server device 100 determines the segment riding time of each sub-path based on the path information and predicted riding parameters of each sub-path. In some embodiments, the server device 100 inputs the path information and predicted riding parameters of the first sub-path into a machine learning model to generate the segment riding time of the first sub-path. It can be seen that the segment riding time of the first sub-path is positively correlated with the slope value of the first sub-path. The segment riding time of the first sub-path is positively correlated with the number of traffic lights on the first sub-path. Similarly, the server device 100 can use the machine learning model to estimate the segment riding time of each sub-path. By using the slope value and the number of traffic lights of each sub-path as input features of the machine learning model, the segment riding time of each sub-path can be more accurately estimated. In addition, by using the predicted riding parameters generated based on the rider's riding habit information as input features of the machine learning model, the segment riding time of each sub-path can be more accurately estimated.

[0058] In addition, in some embodiments, the server device 100 can also estimate the output power of the motor 314 based on the predicted motor assist parameter and other motor parameters in the predicted riding parameters, and use the output power of the motor 314 as an input feature of the machine learning model to estimate the segment riding time of each sub-path.

[0059] In step S444, the server device 100 determines the riding time of the electric-assisted bicycle 300 along the planned route based on the segment riding time of each sub-route. In some embodiments, the server device 100 may sum up the segment riding time of all sub-route to obtain the riding time of the planned route.

[0060] In step S450 , the server device 100 estimates the power consumption of the electric-assisted bicycle 300 traveling along the planned route based on the riding time, route information, and riding habit information. In some embodiments, step S450 can be implemented as steps S451 and S452 .

[0061] In step S451, the server device 100 determines the power consumption of each sub-path segment based on the riding time of each sub-path segment, the route information of each sub-path segment, and the predicted riding parameters of each sub-path segment. Specifically, in some embodiments, the server device 100 inputs the route information, predicted riding parameters, and rider information of the first sub-path segment into a machine learning model to determine the power consumption of the first sub-path segment. The rider information may include the rider's weight. Similarly, the server device 100 can use the machine learning model to estimate the power consumption of each sub-path segment segment.

[0062] Furthermore, in some embodiments, the server device 100 may use motor parameters (e.g., upper limit of motor speed, etc.) of the motor 314 as input features of the machine learning model to estimate the power consumption of each sub-path segment. In some embodiments, the server device 100 may also estimate the output power of the motor 314 based on the predicted motor assist parameter and other motor parameters in the predicted riding parameters, and use the output power of the motor 314 as an input feature to estimate the power consumption of each sub-path segment.

[0063] As can be seen, the power consumption of each segment of the first sub-path is positively correlated with the slope of the first sub-path. The power consumption of each segment of the first sub-path is also positively correlated with the number of traffic lights on the first sub-path. By using the slope and number of traffic lights of each sub-path as input features for the machine learning model, the power consumption of each segment of the sub-path can be more accurately estimated. Furthermore, by using predicted riding parameters generated based on the rider's riding habits as input features for the machine learning model, the power consumption of each segment of the sub-path can be more accurately estimated.

[0064] In step S452, the server device 100 determines the power consumption of the electric-assisted bicycle 300 along the planned route based on the power consumption of each sub-route. In some embodiments, the server device 100 may sum the power consumption of all sub-route segments to obtain the total power consumption of the planned route.

[0065] In step S460, the electronic device 200 displays the riding time and power consumption associated with the first riding mode through the user operation interface. In some embodiments, the electronic device 200 can compare the remaining power and power consumption of the electric-assisted bicycle 300. The electronic device 200 can display a visual prompt through the user operation interface based on the comparison result of the remaining power and power consumption of the electric-assisted bicycle 300. For example, assuming that the remaining power of the electric-assisted bicycle 300 is 40% and the power consumption estimated by the server device 100 to travel the planned route in the first riding mode is 50%, the electronic device 200 can provide a visual prompt of insufficient power to the rider through the user operation interface.

[0066] Figure 5 This is a flow chart of a method for estimating power consumption and time consumption of an electric-assisted bicycle according to an embodiment of the present invention. Figure 1 and Figure 5 , the following is matched Figure 1 The steps of the method for estimating power consumption and time consumption of an electric-assisted bicycle according to this embodiment are described with reference to the components of FIG.

[0067] It should be noted that in some embodiments, the electric-assisted bicycle 300 can be configured in different riding modes (also referred to as power-assistance modes) to provide the power assistance required for different riding scenarios. For example, these riding modes may include a power-saving mode, a normal mode, and a time-saving mode. These riding modes correspond to different motor-assistance parameters. In some embodiments, the server device 100 can estimate power consumption and riding time based on these riding modes.

[0068] In step S510, the server device 100 collects information about the rider's riding habits. In step S520, the server device 100 receives a starting location and a destination location. In step S530, the server device 100 generates a planned route based on the starting location and the destination location. Steps S510-S530 can be described with reference to the previous embodiment and are not further detailed here.

[0069] In step S540 , the server device 100 estimates the riding time and power consumption of the electric-assisted bicycle along the planned route based on the route information of the planned route and the rider's riding habits. In some embodiments, step S540 can be implemented as steps S541 and S542 .

[0070] In step S541, the server device 100 determines a first predicted riding parameter for the first riding mode based on the path information of the planned path and the rider's riding habit information. The first predicted riding parameter includes a predicted riding speed and a predicted motor assist parameter. In step S542, the server device 100 estimates the riding time consumed by the electric-assisted bicycle 300 to travel the planned path based on the path information of the planned path and the first predicted riding parameter of the first riding mode. In step S543, the server device 100 estimates the power consumption consumed by the electric-assisted bicycle 300 to travel the planned path based on the riding time, path information, and the first predicted riding parameter of the first riding mode. The server device 100 estimates the riding time and power consumption of the first riding mode based on multiple pre-trained machine learning models.

[0071] In step S550, the server device 100 determines a second predicted riding parameter for the second riding mode based on the first predicted riding parameter of the first riding mode and the adjustment parameter. The second predicted riding parameter includes a predicted riding speed and a predicted motor assist parameter. The adjustment parameter may be a proportional value.

[0072] For example, assuming the first riding mode is the normal mode and the second riding mode is the power saving mode, the adjustment parameter may be a proportional value less than 100% (e.g., 50%, 80%, etc.). By multiplying the default riding speed in the first predicted riding parameter by 80%, the server device 100 can obtain the default riding speed in the second predicted riding parameter. By multiplying the preset motor assist parameter in the first predicted riding parameter by 50%, the server device 100 can obtain the default riding speed in the second predicted riding parameter.

[0073] For example, assuming the first riding mode is the normal mode and the second riding mode is the time-saving mode, the adjustment parameter may be a proportional value greater than 100% (e.g., 150%, 120%, etc.). By multiplying the default riding speed in the first predicted riding parameters by 150%, the server device 100 can obtain the default riding speed in the second predicted riding parameters. By multiplying the preset motor assist parameter in the first predicted riding parameters by 120%, the server device 100 can obtain the default riding speed in the second predicted riding parameters.

[0074] In step S560, the server device 100 estimates another riding time for the electric-assisted bicycle 300 to travel the planned route based on the route information of the planned route and the second predicted riding parameters. In step S570, the server device 100 estimates another power consumption for the electric-assisted bicycle 300 to travel the planned route based on the other riding time, the route information, and the predicted riding parameters. The methods for estimating riding time and power consumption for different riding modes are similar. Reference may be made to the aforementioned embodiments regarding the method for estimating riding time and power consumption for the first riding mode, and will not be repeated here.

[0075] In step S580, the electronic device 200 displays the riding time and power consumption associated with the first riding mode through the user interface, and displays another riding time and another power consumption associated with the second riding mode through the user interface. In this way, the rider can know the riding time and power consumption of different riding modes through the user interface.

[0076] Figure 6 is a schematic diagram of a user operation interface according to an embodiment of the present invention. Figure 6 , the rider can enter the starting and destination locations in the user interface UI_1 of the electronic device 200. Next, assume that the electric-assisted bicycle 300 has three riding modes: time-saving mode, normal mode, and power-saving mode. The user interface UI_2 of the electronic device 200 can display the power consumption and riding time of the time-saving mode, the power consumption and riding time of the normal mode, and the power consumption and riding time of the power-saving mode, respectively. Then, in response to the rider selecting the normal mode, the electric-assisted bicycle 300 will provide assistance based on the motor assistance parameters corresponding to the normal mode. In response to the rider selecting the power-saving mode, the electric-assisted bicycle 300 will provide assistance based on the motor assistance parameters corresponding to the power-saving mode.

[0077] Figure 7 is a schematic diagram of a user operation interface according to an embodiment of the present invention. Figure 7 Assume that the electric-assisted bicycle 300 has three riding modes: time-saving mode, normal mode, and power-saving mode. The electronic device 200 can determine that the remaining battery power of the electric-assisted bicycle 300 is less than the power consumption of the time-saving mode. Therefore, the user interface UI_3 can provide a visual prompt for the time-saving mode. For example, the time-saving mode prompt box N2 may display a specific prompt color or a low-battery prompt text.

[0078] In summary, in this embodiment of the present invention, the riding time and power consumption of an electric-assisted bicycle along a planned route can be accurately estimated based on the planned route information and the rider's riding habits. This allows the rider to accurately determine whether the electric-assisted bicycle's battery has sufficient power. This allows the rider to more conveniently plan and schedule their rides, while reducing the likelihood of being unable to reach their destination due to insufficient battery power. Furthermore, this embodiment of the present invention can estimate riding time and power consumption for different riding modes, allowing the rider to decide their riding style based on their actual needs, further enhancing the riding experience.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for estimating power consumption and time consumption of an electric-assisted bicycle, characterized in that: include: Receive the starting position and destination position; generating a planned path according to the starting position and the destination position; estimating the riding time of the electric-assisted bicycle along the planned route based on the route information of the planned route and the riding habit information of the rider; estimating the power consumption of the electric-assisted bicycle when traveling along the planned route based on the riding time, the route information, and the riding habit information; as well as The riding time and the power consumption associated with the first riding mode are displayed through a user operation interface.

2. The method for estimating power consumption and time consumption of an electric-assisted bicycle according to claim 1, characterized in that: The path information includes a slope value and traffic light information.

3. The method for estimating power consumption and time consumption of an electric-assisted bicycle according to claim 1, wherein: The method further comprises: The riding habit information of the rider is collected, wherein the riding habit information includes a plurality of riding speeds and a plurality of motor assist parameters corresponding to a plurality of slope values ​​respectively.

4. The method for estimating power consumption and time consumption of an electric-assisted bicycle according to claim 1, wherein: The step of estimating the riding time of the electric-assisted bicycle along the planned route based on the route information of the planned route and the riding habit information of the rider includes: Splitting the planned path into multiple sub-paths; determining predicted riding parameters for each of the sub-paths of the first riding mode based on the path information of each of the sub-paths and the riding habit information of the rider; determining a segment riding time of each sub-path according to the path information of each sub-path and the predicted riding parameter; and The riding time consumed by the electric-assisted bicycle traveling along the planned route is determined according to the segment riding time of each sub-route.

5. The method for estimating power consumption and time consumption of an electric-assisted bicycle according to claim 4, characterized in that: The sub-paths include a first sub-path, and the step of determining predicted riding behavior information of each sub-path based on the path information of each sub-path and the riding habit information of the rider includes: Obtaining the slope value of the first subpath; and A predicted riding speed corresponding to the first sub-path and a predicted motor assist parameter corresponding to the first sub-path are determined according to the gradient value of the first sub-path.

6. The method for estimating power consumption and time consumption of an electric-assisted bicycle according to claim 4, wherein: The sub-paths include a first sub-path, and the step of determining the segment riding time of each sub-path according to the path information of each sub-path and the predicted riding parameter includes: The route information of the first sub-route and the predicted riding parameter are input into a machine learning model to generate the segment riding time of the first sub-route.

7. The method for estimating power consumption and time consumption of an electric-assisted bicycle according to claim 4, wherein: The step of estimating the power consumption of the electric-assisted bicycle traveling along the planned route based on the riding time, the route information, and the riding habit information includes: determining a segment power consumption of each sub-path according to the segment riding time of each sub-path, the route information of each sub-path, and the predicted riding parameter of each sub-path; and The power consumption consumed by the electric-assisted bicycle traveling along the planned route is determined according to the power consumption of each segment of the sub-route.

8. The method for estimating power consumption and time consumption of an electric-assisted bicycle according to claim 7, wherein: The sub-paths include a first sub-path, and the step of determining the segment power consumption of each sub-path according to the segment riding time of each sub-path, the path information of each sub-path, and the predicted riding parameter of each sub-path includes: The route information of the first sub-route, the predicted riding parameters, and the rider information are input into a machine learning model to determine the segment power consumption of the first sub-route.

9. The method for estimating power consumption and time consumption of an electric-assisted bicycle according to claim 1, wherein: The method further comprises: determining a first predicted riding parameter of the first riding mode based on the path information of the planned path and the riding habit information of the rider; determining a second predicted riding parameter for a second riding mode based on the first predicted riding parameter of the first riding mode and an adjustment parameter, wherein the second predicted riding parameter includes a predicted riding speed and a predicted motor assist parameter; estimating another riding time consumed by the electric-assisted bicycle to travel along the planned route based on the route information of the planned route and the second predicted riding parameter; estimating another amount of power consumed by the electric-assisted bicycle traveling along the planned route based on the another riding time, the route information, and the predicted riding parameter; and The other riding time and the other power consumption associated with the second riding mode are displayed through the user operation interface.

10. The method for estimating power consumption and time consumption of an electric-assisted bicycle according to claim 1, wherein: The method further comprises: comparing the remaining power of the electric-assisted bicycle with the power consumption; and A visual prompt is displayed through the user operation interface according to a comparison result of the remaining power of the electric-assisted bicycle and the power consumption.

11. A power consumption and time consumption estimation system for an electric-assisted bicycle, characterized in that: include: a storage device storing a plurality of instructions; as well as a processor coupled to the storage device and configured to: Receive the starting position and destination position; generating a planned path according to the starting position and the destination position; estimating the riding time of the electric-assisted bicycle along the planned route based on the route information of the planned route and the riding habit information of the rider; as well as estimating the power consumption of the electric-assisted bicycle when traveling along the planned route based on the riding time, the route information, and the riding habit information; The riding time and the power consumption associated with the first riding mode are displayed through a user operation interface.

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