Optimal running method of mountain electric tricycle

Abstract

The invention discloses a mountain electric tricycle optimal driving method, which comprises the following steps of: (1) obtaining altitude data of each node of a regional road, and establishing a regional road altitude model; obtaining single-road mileage energy consumption data of each node of a regional road, and establishing a regional road energy consumption model; (2) establishing a vehicle unit mileage energy consumption model; (3) obtaining a driving path and calculating the total energy consumption of the driving path; and (4) comparing the total energy consumption E obtained in the step (3) with the SOC of the battery PACK, and judging whether the current pre-driving path and speed are reasonable or not. Compared with the prior art, the method has the following beneficial effects that the method can be easily realized on embedded equipment, the development difficulty is reduced, and after the equipment is assembled on the electro-tricycle, the user experience of the electro-tricycle can be greatly improved, the mileage anxiety of a user is relieved, and the adaptability of the electro-tricycle in a mountainous area is improved.

Description

Technical Field

[0001] This invention relates to an optimal driving method for mountain electric tricycles, specifically an optimal driving method that obtains the optimal driving path and segmented driving speed by establishing an engineering mechanics mathematical model of the tricycle and combining it with vehicle condition data. This method belongs to the field of electronic information technology. Background Technology

[0002] Electric tricycles are increasingly being used as freight transport in mountainous areas. Tricycle drivers often rely on their driving experience to judge whether the remaining battery power is sufficient to reach the destination. However, compared to plains areas, mountain roads are undulating, have complex and varied surfaces, and it's difficult to find temporary charging points along the roadside. Therefore, ensuring the vehicle can reach its destination quickly and safely becomes exceptionally complex.

[0003] However, current widely used technologies can only estimate the SOC (State of Charge) of the battery pack. Specifically, there is no publicly available technology or method to provide the optimal driving route and segmented driving speeds for the vehicle; the SOC is simply displayed on the vehicle's instrument panel. However, in mountainous areas, the amount of electricity consumed going uphill and downhill varies significantly, and the amount of electricity consumed per unit distance also differs depending on the road surface (asphalt, concrete, gravel, mud). Therefore, relying solely on navigation to determine whether the vehicle can reach its destination carries considerable risk for the driver. Summary of the Invention

[0004] To address the aforementioned technical problems in existing technologies, this invention proposes an optimal driving method for mountain electric tricycles. Based on the establishment of a regional road network elevation map layer and a unit distance energy consumption map layer, an engineering mechanics mathematical model of the tricycle is established, and combined with vehicle condition data such as vehicle weight, battery charge, and health status, the optimal driving path and segmented driving speed are obtained.

[0005] To solve the above problems, the present invention adopts the following technical solution:

[0006] An optimal driving method for a mountain electric tricycle, characterized by comprising the following steps:

[0007] Step (1): Obtain elevation data of each node of the regional road at a certain granularity and establish a regional road elevation model; obtain energy consumption data of single-road mileage of each node of the regional road at a certain granularity and establish a regional road energy consumption model.

[0008] Step (II): Establish a vehicle energy consumption model per unit mileage based on vehicle dynamics:

[0009] e=F[e(x1),e(x2)…e(xm)] (1)

[0010] In the formula, x1, x2…xm are the various factors affecting energy consumption;

[0011] Step (3): Divide the pre-driving route into n segments. Calculate the energy consumption of each segment based on the vehicle unit mileage energy consumption model established in Step (2), and then obtain the total energy consumption E of the driving route.

[0012] E = e1 + e2 + e3 + ... + en

[0013] Where e1, e2, e3, ... en are the energy consumption of each road segment, and n is the road segment number;

[0014] Step (4): Compare the total energy consumption E obtained in step (3) with the SOC of the battery pack to determine whether the current pre-driving path and speed are reasonable.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] The technical method disclosed in this invention can be easily implemented on embedded devices, reducing development difficulty. When the device is installed on an electric tricycle, it significantly improves the user experience, alleviates range anxiety, and enhances the tricycle's adaptability in mountainous areas. Furthermore, this method provides the driving speed for each road segment, which guides the driver to drive correctly, preventing situations where high speeds consume excessive energy, preventing the vehicle from being unable to pass the next steep slope, and preventing the vehicle from being recharged and storing energy after continuous downhill driving. Attached Figure Description

[0017] Figure 1 This is an overall flowchart of the method proposed in this invention. Detailed Implementation

[0018] The present invention will now be described in further detail with reference to the accompanying drawings and examples.

[0019] As described in the background section, the existing technology requires a method to obtain the optimal driving path and segmented driving speed for mountain electric tricycles. In general, this invention enables the planning of the optimal driving path and segmented driving speed before vehicle departure, taking into account road conditions (vehicle weight, altitude, road surface conditions), to minimize the time required for the vehicle to reach its destination while ensuring safe arrival. Clearly, this method is particularly important for tricycle drivers unfamiliar with mountainous terrain.

[0020] like Figure 1 As shown, an optimal driving method for a mountain electric tricycle includes the following steps:

[0021] Step (1): Obtain elevation data of each node of the regional road at a certain granularity and establish a regional road elevation model; obtain energy consumption data of single-road mileage of each node of the regional road at a certain granularity and establish a regional road energy consumption model.

[0022] Step (II): Establish a vehicle energy consumption model per unit mileage based on vehicle dynamics:

[0023] e=F[e(h),e(r),e(g),e(u)](1)

[0024] e(h) is the energy consumption related to altitude, e(r) is the energy consumption related to road conditions, e(g) is the energy consumption related to vehicle weight, and e(u) is the energy consumption related to speed.

[0025] e(h), e(g), and e(u) are implemented using conventional techniques and are not improvements to this invention, so they will not be elaborated upon. As one implementation method, the main influencing factor of energy consumption e(r) related to road conditions is the road gradient r. Accelerometers are used to measure the vehicle's accelerations ax, ay, and az in three directions; the total acceleration a is then calculated. The relationship between the gradient r and the total acceleration a is expressed as: a = ax + gsinr, from which the gradient r is calculated. e(r) can then be obtained using conventional techniques.

[0026] Step (3): Divide the pre-driving route into n segments. Calculate the energy consumption of each segment based on the vehicle unit mileage energy consumption model established in Step (2), and then obtain the total energy consumption E of the driving route.

[0027] E = e1 + e2 + e3 + ... + en

[0028] Where e1, e2, e3, ... en are the energy consumption of each road segment, and n is the road segment number;

[0029] Step (4): Compare the total energy consumption E obtained in step (3) with the SOC of the battery pack to determine whether the current pre-driving path and speed are reasonable.

[0030] As a data processing method, step (ii) includes the following data processing method: as mentioned above, divide the road into n segments and m factors affecting energy consumption to form an energy consumption sample matrix E: in the above embodiment, m is 4 (e(h), e(r), e(g), e(u)) representing the influence of four factors on energy consumption. More influencing factors can be added according to the specific application scenario.

[0031]

[0032] (1) Standardize the energy consumption sample E to obtain the standardized energy consumption sample matrix X:

[0033]

[0034]

[0035] S j Let be the mean and standard deviation of the j-th indicator, respectively;

[0036] (2) Calculate the correlation coefficient Rij among the various indicators in X, and obtain the correlation coefficient matrix R = (rij)n×n

[0037] (3) Calculate the eigenvalues ​​λ of R and arrange them in order of magnitude: λ1≥λ2≥λ3...≥λn. Calculate the corresponding eigenvectors ω1,ω2,ω3...ωn and extract the principal component factors.

[0038] (4) Determine the top k principal components according to their cumulative contribution rates, thereby obtaining the principal component energy consumption sample matrix Y:

[0039]

[0040] Y=(Yij)m×k

[0041] Contribution rate can be defined as follows: The ratio is set according to the application scenario to determine the value of k. The total energy consumption E of the driving path is calculated using the principal component energy consumption sample matrix Y.

[0042] Therefore, those skilled in the art should understand that although the steps in the above flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows, unless explicitly stated herein, i.e., there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least some steps in the above flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages do not necessarily need to be completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages does not necessarily have to be sequential, but can be executed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0043] As another specific embodiment, the present invention provides an optimal driving control system for a mountain electric tricycle, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-described optimal driving method for a mountain electric tricycle.

[0044] As another specific embodiment, the present invention provides a vehicle including the above-described optimal driving control system for mountain electric tricycles.

[0045] As another specific embodiment, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described optimal driving method for a mountain electric tricycle.

[0046] Those skilled in the art will understand that any references to memory, storage medium, or database in the above embodiments of this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be obtained in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), synchronous link DRAM (SLDRAM), direct memory bus RAM (RDRAM), enhanced SDRAM (ESDRAM), and direct memory bus dynamic RAM (DRDRAM), etc.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An optimal driving method for a mountain electric tricycle, characterized in that, It includes the following steps: Step (1): Obtain elevation data of each node of the regional road at a certain granularity and establish a regional road elevation model; obtain energy consumption data of single-road mileage of each node of the regional road at a certain granularity and establish a regional road energy consumption model. Step (II): Establish a vehicle energy consumption model per unit mileage: e=F[e(x1),e(x2)…e(xm)] (1) In the formula, x1, x2…xm are the various factors affecting energy consumption; Step (3): Divide the pre-driving route into n segments. Calculate the energy consumption of each segment based on the vehicle unit mileage energy consumption model established in Step (2), and then obtain the total energy consumption E of the driving route. E = e1 + e2 + e3 + ... + en Where e1, e2, e3, ... en are the energy consumption of each road segment, and n is the road segment number; Step (4): Compare the total energy consumption E obtained in step (3) with the SOC of the battery pack to determine whether the current pre-driving path and speed are reasonable.

2. The optimal driving method for a mountain electric tricycle according to claim 1, characterized in that, The main influencing factor of the energy consumption e(r) related to road conditions is the road slope r; the acceleration of the vehicle in three directions is measured using an acceleration sensor ax, ay and az; and then the total acceleration a is calculated; the relationship between the slope value r and the total acceleration a is expressed as: a=ax+gsinr.

3. The optimal driving method for a mountain electric tricycle according to claim 1, characterized in that, The road gradient r is obtained by combining digital elevation modeling with acceleration sensor measurements.

4. An optimal driving control system for a mountain electric tricycle, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the optimal driving method for a mountain electric tricycle as described in any one of claims 1 to 3.

5. A vehicle, characterized in that, Including the optimal driving control system for mountain electric tricycles as described in claim 5.

6. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the optimal driving method for a mountain electric tricycle as described in any one of claims 1 to 3.

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