Control method for an electric bicycle

Abstract

The present invention relates to a control method for an electric bicycle for optimising motor support, comprising the steps of: calculating a range forecast with the help of a physical riding resistance equation for estimating an energy content of an electric energy store along a planned route to a destination, determining the motor support depending on the range forecast, and range checking during a journey on the planned route, wherein the determined motor support is adjusted if a current energy content deviates from a planned energy content along the route.

Description

[0001] Description

[0002] title

[0003] Control procedure for an electric bicycle

[0004] State of the art

[0005] The present invention relates to a control method for an electric bicycle for optimizing motor assistance and to an electric bicycle which is configured to carry out the control method.

[0006] For e-bike riders who cover longer distances on a single battery charge, “range anxiety” is often a familiar issue. The rider wants to cycle with the certainty that the battery will last until the destination or that the battery will still have enough energy at the destination for the onward journey. Currently, they have to rely on the current range estimate (based on past experience) and, if necessary, save energy by switching through the assist modes. The experience is usually less than optimal and can lead, for example, to less motor support than possible or to a depleted battery before reaching the destination. Furthermore, range estimation methods are often inaccurate, making it impossible to take into account factors such as trailers (greater mass), headwinds (greater air resistance), or different surfaces (forest paths instead of asphalt).

[0007] Disclosure of the invention

[0008] The inventive control method for an electric bicycle for optimizing motor assistance with the features of claim 1 has the advantage that motor assistance is automatically determined, so that the rider no longer has to worry about saving battery energy by manually switching the motor assistance during the ride. This is achieved according to the invention by calculating a range forecast using a physical driving resistance equation to estimate the energy content of an electrical energy storage device along a planned route to a destination. Depending on the range forecast, motor assistance is then determined, which preferably ensures that the electric bicycle rider arrives at the destination with a defined energy content.While driving on a planned route, a range check is carried out, whereby the determined motor assistance is adjusted if the current energy content deviates from the planned energy content along the planned route. In this way, a strategy is initially calculated which can then be adapted to the ambient conditions. This enables the motor assistance to be optimized and quickly adapted to unexpected events or incorrect assumptions. If, for example, the headwind increases during the ride, the motor assistance can be reduced in order to arrive at the destination with the defined energy content. The motor assistance is preferably determined such that the energy content of the electrical energy storage device at the destination of the route is greater than or equal to a minimum energy content or a predetermined energy content of the electrical energy storage device.This allows the maximum possible motor assistance for a planned energy content to be determined. The motor assistance can be constant along the planned route or dependent on the environment. For example, higher motor assistance may be provided on an uphill section than on a flat section. The riding resistance equation preferably determines the rolling resistance and / or gradient resistance and / or acceleration resistance and / or air resistance of the electric bicycle with rider along the planned route.

[0009] The subclaims show preferred developments of the invention.

[0010] Preferably, the physical riding resistance equation is based on rider parameters, bicycle parameters, and a route profile. This allows for the most accurate determination of the riding resistance equation. The rider parameters, bicycle parameters, and the route profile can be based on historical data, standard data, forecast data, and / or data determined by the electric bicycle.

[0011] The rider parameter preferably comprises a rider power and / or a rider weight and / or an average cadence and / or a rider air resistance. The rider parameters can comprise data from one or more riders. Furthermore, the rider parameters can be queried initially, be based on standard data, or be determined during the ride. Based on the rider power, for example, it can be determined how strong the motor assistance must be to reach a defined speed. The rider power can, for example, be an average rider power of the rider. The rider weight has a particular influence on the motor assistance required to accelerate the electric bike and when climbing an incline. The rider air resistance influences the motor assistance required at higher speeds.

[0012] It is also preferable to specify rider power as a function of a gradient as a sigmoid function. Ride power has a fundamental influence on energy consumption and thus on the required motor assistance. The advantage of representing rider power as a sigmoid function is that the three parameters—average power, gradient, and x-shift—can fully describe a rider's gradient-dependent power input. Two additional parameters can be used to limit the maximum and minimum values ​​of rider power, and the sigmoid function can be adjusted accordingly.

[0013] Particularly preferred is learning the driver parameter from previous rides. Based on data collected from previous rides, the driver parameter can be improved through parameter optimization. This allows a driving resistance equation to be specified, based on which the necessary motor assistance can be determined as accurately as possible.

[0014] The bicycle parameter preferably includes a current energy content and / or a maximum assistance speed and / or a maximum assistance power and / or a maximum battery current and / or an open-circuit voltage and / or a minimum assistance power and / or a bicycle weight and / or a rolling resistance and / or a bicycle air resistance. This allows the best possible determination of the riding resistance and the influence of the motor assistance on the energy content of the electrical energy storage device. Other bicycle parameters can include a maximum motor torque, a maximum and / or minimum assistance factor, an efficiency parameter, a rotational inertia of the bicycle, a wheel circumference, and / or an efficiency of the bicycle gear system.

[0015] The route profile preferably includes a gradient profile and / or a route surface and / or a route length and / or a weather forecast. The gradient profile has a significant influence on the riding resistance, and the route surface has a significant influence on the rolling resistance of the electric bicycle. Thus, by taking into account the route profile, the route surface, and the route length, the riding resistance equation can be used to better estimate the energy content along the planned route in order to determine appropriate motor assistance. Incorporating the weather forecast (e.g., wind direction, wind strength, temperature) can further improve the riding resistance equation.

[0016] Further preferably, the control method is configured to use the driving resistance equation to predict the remaining energy content of an electrical energy storage device at the destination and / or the progression of the energy content along the route. This allows for the determination of optimized motor assistance so that the driver receives optimal support at every point in their journey and can be assured that the electrical energy storage device has a sufficiently high energy content at the destination.

[0017] Preferably, a maximum speed and / or a maximum power and / or a support profile of the motor assistance is limited based on the range forecast and / or the range control. In particular, the motor assistance is limited if it is determined based on the range forecast and / or the range control that the planned energy content at the destination cannot be achieved with the desired motor assistance. By limiting the maximum speed and / or the maximum power and / or the maximum torque and / or the support profile of the motor assistance, it can be ensured that the electrical energy storage device has a defined energy content at the destination. The support profile can, for example, provide that the rider receives more support at low speeds than at high speeds.Further preferably, the motor assistance is reduced to a minimum value on the basis of the range forecast and / or the range control if the destination cannot be reached with the motor assistance. In this way, the driver can be supported by the motor assistance for as long as possible until the energy content of the electrical energy storage device has reached a minimum value and no further motor assistance is possible.

[0018] Furthermore, the invention relates to an electric bicycle comprising an electric energy storage device, an electric auxiliary drive, and a control unit. The control unit is configured to execute the method described above. This allows the motor assistance of the electric auxiliary drive to be adjusted in such a way that a rider can be certain of reaching the destination with maximum assistance and a defined energy content of the electric energy storage device.

[0019] Short description of the drawings

[0020] An embodiment of the invention will be described in detail below with reference to the accompanying drawings. In the drawing:

[0021] Figure 1 is a schematic flow diagram of a method according to a first embodiment,

[0022] Figure 2 is a diagram showing the driver performance as a function of the gradient, and

[0023] Figure 3 is a schematic representation of an electric bicycle according to the first embodiment.

[0024] Embodiment of the invention

[0025] Preferably, all identical components, elements and / or units in all figures are provided with the same reference numerals.

[0026] A control method for an electric bicycle 1 and an electric bicycle 1 according to a first preferred embodiment of the invention will be described in detail below with reference to Figures 1 to 3. As can be seen from Figure 1, the control method comprises a first step S1, in which a range forecast is calculated using a physical driving resistance equation. The range forecast serves to estimate an energy content along a planned route of the electric bicycle 1 to a defined destination.

[0027] The driving resistance equation preferably consists of a value for air resistance, rolling resistance, gradient resistance and acceleration resistance.

[0028] The riding resistance equation is based primarily on rider parameters, bicycle parameters, and a route profile. Rider parameters are individually dependent on a specific rider, for example, riding power, rider weight, average cadence, or rider air resistance. The rider parameters can be based on default values ​​or values ​​entered manually by the rider. However, the rider values ​​are preferably determined during the ride, so the riding resistance equation can be adapted to changes in rider values.

[0029] Bicycle parameters depend on the specific electric bicycle type and are generally constant values ​​stored in a storage unit. Bicycle parameters include, for example, the current energy content of the electric energy storage unit 2, maximum assistance speed, maximum assistance power, maximum battery current, open circuit voltage, minimum assistance power, bicycle weight, rolling resistance, or bicycle air resistance.

[0030] The route profile preferably includes a gradient profile, a route surface, and a route length. Additionally, the route profile can include information about curves, traffic lights, intersections, or other traffic conditions. The route information is also preferably stored on a storage unit or can be retrieved via a radio connection.

[0031] The range forecast can be calculated for one route to a destination, multiple routes to one destination, or multiple routes to multiple destinations. Using the driving resistance equation, the remaining energy content of the electrical energy storage device 2 at the destination and / or a progression of the energy content along the route can be determined. The energy content along the route can be determined either for defined intervals or continuously.

[0032] In a second step S2, motor assistance is determined based on the range forecast, so that the electric bicycle reaches the destination with a defined energy content in the electrical energy storage device 2. The motor assistance is preferably provided by an electric auxiliary drive 3, which is supplied with electrical energy from an electrical energy storage device 2. The energy requirement of the motor assistance depends on the driving resistance equation.

[0033] Motor assistance can preferably be provided at different levels. The maximum motor assistance is usually limited by the power of the electric auxiliary drive 3 and / or legal guidelines.

[0034] Strong motor support of the electric auxiliary drive 3 results in a high energy demand of the electrical energy storage device, so that along the planned route the energy content of the electrical energy storage device 2 decreases more quickly than with low motor support.

[0035] During the journey along the planned route, a range check is performed in a third step S3. This check determines whether the planned energy content of the electrical energy storage device 2 deviates from the current energy content along the planned route.

[0036] Depending on the ratio between the current energy content and the planned energy content, the motor support of the electric bicycle 1 is adjusted in step S4.

[0037] If the current energy content is below the planned energy content, the motor assistance is preferably reduced to reduce energy requirements. If a further reduction in motor assistance is no longer possible, the electric bicycle is preferably supported with minimal motor assistance until the energy content of the electrical energy storage device 2 is exhausted. The electric bicycle 1 can then be ridden further without motor assistance. In the event that motor assistance is not available until the destination of the planned route, a warning signal can be transmitted to the rider of the electric bicycle 1.

[0038] If the current energy level is above the planned energy level, the motor assistance is preferably increased to provide greater support to the rider.

[0039] If it is determined during the range check that the electric bicycle 1 deviates from the planned route, a new route with a new range forecast can be planned, with the motor support being adjusted depending on the new range forecast.

[0040] Figure 2 shows a diagram illustrating an example frequency distribution 13 of a cyclist's power 10 as a function of the gradient 11. It can be seen that on a gradient 11 of 0°, the cyclist averages a power 10 of between 150 and 200 W. On a gradient steeper than 8°, the cyclist performs no work. On a gradient steeper than 12°, however, the cyclist performs a power 10 of at least 250 W. For gradients between -5° and 5°, the power 10 exhibits a high variance and can range between 0 W and 500 W.

[0041] Using a regression analysis of the frequency distribution 13, an expected driver performance 14 can be determined as a function of the gradient 11. The expected driving performance 14 can then be represented by a sigmoid function 12.

[0042] The sigmoid function 12 is preferably described by an average power, a slope, an X-shift and a minimum and maximum value.

[0043] For the range prediction, a sigmoid function 12 can be used to estimate the energy content of the electrical energy storage device 2 along the planned route to a destination. The energy demand of the electrical energy storage device 2 is the difference between the total energy demand, which is determined based on the driving resistance equation, and the energy supplied by the cyclist to the electric bicycle 1.

[0044] As can be seen from Figure 3, the electric bicycle 1 comprises the electrical energy storage unit 2, an electric auxiliary drive 3, and a control unit 4. The control unit 4 is configured to execute the method according to the first exemplary embodiment. The control unit 4 can, for example, be part of a motor control unit or a battery management system.

[0045] Preferably, the control unit 4 comprises a storage unit on which driver parameters, bicycle parameters and route profiles can be stored.

[0046] Further preferably, the control unit 4 is connected to sensors of the electric bicycle 1, which can determine rider parameters, route parameters and bicycle parameters.

[0047] Particularly preferably, the control unit 4 is configured to communicate with other devices, such as mobile terminals, via a radio network.

Claims

Claims 1 . Control method for an electric bicycle (1) for optimizing motor assistance, comprising the steps: Calculating a range forecast (S1) using a physical driving resistance equation to estimate the energy content of an electrical energy storage device (2) along a planned route to a destination, Determining the motor support (S2) depending on the range forecast, and Range control (S3) during a journey on the planned route, whereby the determined motor support is adjusted (S4) if a current energy content deviates from a planned energy content along the route.

2. Control method according to claim 1, wherein the physical driving resistance equation is based on driver parameters, bicycle parameters and a route profile.

3. Control method according to claim 2, wherein the rider parameter comprises a rider power (10) and / or a rider weight and / or an average cadence and / or a rider air resistance.

4. Control method according to claim 3, wherein the driver performance (10) is specified as a sigmoid function (12) as a function of a gradient (11).

5. Control method according to one of claims 2 to 4, wherein the driver parameters are learned from previous trips.

6. Control method according to one of claims 2 to 5, wherein the bicycle parameter comprises a current energy content and / or a maximum assistance speed and / or a maximum assistance power and / or a maximum battery current and / or an open circuit voltage and / or a minimum assistance power and / or a bicycle weight and / or a rolling resistance and / or a bicycle air resistance.

7. Control method according to one of claims 2 to 6, wherein the route profile comprises a gradient profile and / or a route surface and / or a route length and / or a weather forecast.

8. Control method according to one of the preceding claims, wherein the control method is configured to predict, with the aid of the driving resistance equation, a remaining energy content of an electrical energy storage device at the destination and / or a course of the energy content along the route.

9. Control method according to one of the preceding claims, wherein a maximum speed and / or a maximum power and / or a support profile of the motor support is limited on the basis of the range forecast (S1) and / or the range control (S2).

10. Control method according to one of the preceding claims, wherein the motor assistance is reduced to a minimum value on the basis of the range forecast (S1) and / or the range control (S3) if the destination cannot be reached with the motor assistance.

11. Electric bicycle, comprising an electrical energy storage device (2), an electric auxiliary drive (3) and a control unit (4), wherein the control unit (4) is configured to carry out a method according to one of claims 1 to 10

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