Hybrid vehicles and methods for managing energy for those vehicles, including tools, intermediaries, and electronics.

Abstract

DEPCT68 Hybrid vehicles (10) and methods for managing energy for such vehicles, tools (100), Intermediaries and electronic devices related to the field of automotive technical science; methodology. Includes: S12, categorizing and sequencing the usage conditions of the current navigation route. Hybrid vehicles (10) and the mileage of each current navigation route operating condition, which is sequenced. The categories of usage conditions and the mileage for each usage condition are derived from the characteristic parameters. The road of the current navigation path; S13, achieving the target charge sequence according to the category. Terms of use and mileage for each term of use; and achieving equivalent factor sequences. According to the category order of operating conditions and the order of target charging conditions; and S14, according to the factor order. Equivalent, the instantaneous output power of the hybrid vehicle's battery (10) at each moment; and control of hybrid vehicles (10) based on the instantaneous output power of the battery, patent application. This achieves online control of hybrid vehicles (10) and achieves optimal control. Universality of hybrid vehicles (10) thus ensures fuel efficiency;

Description

Hybrid vehicle and energy management method, device and medium thereof, and electronic equipment

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This disclosure claims priority to Chinese patent application No. 202211203935.0, filed on September 29, 2022, entitled “Hybrid vehicle and its energy management method, device and medium, electronic device,” the entire contents of which are incorporated by reference into this disclosure. Technical Field

[0003] The present disclosure relates to the field of vehicle technology, and in particular to a hybrid vehicle and its energy management method, device and medium, and electronic equipment. Background Art

[0004] In related technologies, hybrid vehicle energy management strategies are based on dynamic programming algorithms, employing an equivalent consumption minimization strategy (ECMS) with a fixed equivalent factor. However, this strategy only achieves optimal control under specific operating conditions and cannot guarantee vehicle fuel economy when driving conditions change.

[0005] Summary of the Invention

[0006] The present disclosure aims to at least partially address one of the technical issues in the related art. To this end, the present disclosure is directed to a hybrid vehicle and its energy management method, apparatus, medium, and electronic device, to achieve online control of the hybrid vehicle and global optimal control of the hybrid vehicle to ensure fuel economy.

[0007] To achieve the above-mentioned objectives, an embodiment of the first aspect of the present disclosure proposes an energy management method for a hybrid vehicle, the method comprising: obtaining a working condition category sequence of a current navigation route of the hybrid vehicle and the mileage of each working condition in the current navigation route, the working condition category sequence and the mileage of each working condition being obtained based on road characteristic parameters of the current navigation route; obtaining a target state of charge sequence based on the working condition category sequence and the mileage of each working condition; obtaining an equivalent factor sequence based on the working condition category sequence and the target state of charge sequence; obtaining the instantaneous output power of the power battery of the hybrid vehicle at each moment based on the equivalent factor sequence; and controlling the hybrid vehicle based on the instantaneous output power of the power battery.

[0008] In addition, the energy management method for a hybrid vehicle according to the above embodiment of the present disclosure may also have the following additional technical features:

[0009] According to one embodiment of the present disclosure, obtaining a target state of charge sequence based on the operating condition category sequence and the mileage of each operating condition includes: obtaining the actual state of charge of the power battery at the starting point of the current navigation route; determining a range of change in the state of charge of the hybrid vehicle at the end of each operating condition based on the actual state of charge, the operating condition category sequence and the mileage of each operating condition; and obtaining the target state of charge sequence based on the range of change in the state of charge at the end of each operating condition.

[0010] According to one embodiment of the present disclosure, the range of charge change at the end of the first operating condition of the current navigation route is obtained based on the actual state of charge, the road characteristic data of the first operating condition and the mileage of the first operating condition; the range of charge change at the end of a non-first operating condition of the current navigation route is obtained based on the range of charge change at the end of the previous operating condition of the non-first operating condition, the road characteristic data of the non-first operating condition and the mileage of the non-first operating condition.

[0011] According to one embodiment of the present disclosure, the road characteristic data includes slope data and speed limit data.

[0012] According to one embodiment of the present disclosure, the upper limit value of the charge state variation range of the operating condition is the charge state of the hybrid vehicle at the end of operating the operating condition in the power generation mode, and the lower limit value of the charge state variation range of the operating condition is the charge state of the hybrid vehicle at the end of operating the operating condition in the pure electric mode.

[0013] According to an embodiment of the present disclosure, the target state of charge sequence is obtained by selecting a target state of charge from the state of charge variation range corresponding to each operating condition, and the target state of charge sequence is obtained according to the selected target states of charge.

[0014] According to one embodiment of the present disclosure, the operating condition category sequence and the mileage of each operating condition are obtained using a trained neural network model based on the road characteristic parameters of the current navigation route. The training process of the neural network model includes: obtaining historical driving parameters of the hybrid vehicle on the current navigation route, determining historical road characteristic parameters based on the historical driving parameters, and clustering the historical road characteristic parameters to obtain multiple operating condition categories; constructing a training data set based on the historical road characteristic parameters and the operating condition categories; constructing a neural network model, and training the neural network model using the training data set.

[0015] According to one embodiment of the present disclosure, the operating condition categories include: ordinary urban roads, lightly congested urban roads, moderately congested urban roads, severely congested urban roads, expressways, highways, suburban roads, and township roads.

[0016] According to one embodiment of the present disclosure, the road characteristic parameters include at least one of the following: average vehicle speed, maximum vehicle speed, speed standard deviation, average acceleration, maximum acceleration, minimum acceleration, acceleration standard deviation, acceleration time ratio, deceleration time ratio, uniform speed time ratio, idling time ratio, and accumulated mileage.

[0017] According to one embodiment of the present disclosure, for each equivalent factor in the equivalent factor sequence, the instantaneous output power of the battery of the hybrid vehicle under the operating condition corresponding to the equivalent factor is calculated according to the following formula:

[0018] Wherein, H(u, SOC(t), t) is the Hamiltonian function established according to the equivalent fuel consumption minimum strategy, arg H(u, SOC(t), t) is the instantaneous output power of the power battery at time t, is the engine fuel consumption rate of the hybrid vehicle, s(t) is the equivalent factor at time t, SOC(t) is the state of charge of the power battery at time t, is the rate of change of state of charge, and u is the fuel consumption.

[0019] According to one embodiment of the present disclosure, the controlling of the hybrid vehicle based on the instantaneous output power of the power battery includes: obtaining the instantaneous power demand of the hybrid vehicle at time t; subtracting the instantaneous output power of the power battery at time t from the instantaneous power demand at time t to obtain the instantaneous power demand of the engine of the hybrid vehicle at time t; and controlling the power battery and the engine based on the instantaneous output power of the power battery at time t and the instantaneous output power of the engine at time t.

[0020] To achieve the above-mentioned objectives, a second embodiment of the present disclosure proposes a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the above-mentioned hybrid vehicle energy management method is implemented.

[0021] To achieve the above-mentioned objectives, an embodiment of the third aspect of the present disclosure proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the above-mentioned hybrid vehicle energy management method is implemented.

[0022] To achieve the above-mentioned objectives, an embodiment of the fourth aspect of the present disclosure proposes an energy management device for a hybrid vehicle, the device comprising: an acquisition module, for acquiring a working condition category sequence of a current navigation route of the hybrid vehicle and the mileage of each working condition in the current navigation route, the working condition category sequence and the mileage of each working condition being obtained based on road characteristic parameters of the current navigation route; a target state of charge sequence is obtained based on the working condition category sequence and the mileage of each working condition; an equivalent factor sequence is obtained based on the working condition category sequence and the target state of charge sequence; the instantaneous output power of the power battery of the hybrid vehicle at each moment is obtained based on the equivalent factor sequence; an energy management module, for controlling the hybrid vehicle based on the instantaneous output power of the power battery.

[0023] To achieve the above-mentioned objectives, a fifth embodiment of the present disclosure proposes a hybrid vehicle, including the above-mentioned energy management device for the hybrid vehicle.

[0024] The hybrid vehicle, energy management method, apparatus, medium, and electronic device of the disclosed embodiments achieve online control of the hybrid vehicle by acquiring road characteristic parameters of the hybrid vehicle's current navigation route and utilizing a pre-trained neural network model to determine a sequence of operating condition categories and mileage for each operating condition based on the road characteristic parameters. Furthermore, after acquiring the sequence of operating condition categories and mileage for each operating condition, a target state of charge sequence is acquired based on the sequence of operating condition categories and mileage for each operating condition. Based on the sequence of operating condition categories and target state of charge, an equivalent factor sequence is derived, thereby enabling the equivalent factor to vary based on the operating condition category of the road section, thereby ensuring fuel economy. Furthermore, because the current navigation route is first acquired, the equivalent factor sequence is derived based on the current navigation route, and the instantaneous output power of the hybrid vehicle's power battery at each moment is determined based on the equivalent factor sequence, the hybrid vehicle is controlled based on the instantaneous output power of the power battery, achieving energy management along the current navigation route based on the equivalent factor sequence. This ensures that energy management is not affected by the frequency of navigation map data output, requires minimal computational effort, and can better ensure fuel economy under varying operating conditions.

[0025] Additional aspects and advantages of the present disclosure will be given in part in the description below and in part will be obvious from the description below, or will be learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] FIG1 is a flow chart of an energy management method for a hybrid vehicle according to an embodiment of the present disclosure;

[0027] FIG2 is a schematic diagram of a charge state change path according to an example of the present disclosure;

[0028] FIG3 is a schematic diagram of the structure of a neural network model of an example of the present disclosure;

[0029] FIG4 is a structural block diagram of an energy management device for a hybrid vehicle according to an embodiment of the present disclosure;

[0030] FIG5 is a structural block diagram of a hybrid vehicle according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0031] The following describes a hybrid vehicle, energy management method, apparatus, medium, and electronic device according to embodiments of the present disclosure with reference to the accompanying drawings. Identical or similar reference numerals throughout the disclosure represent identical or similar elements or elements having identical or similar functions. The embodiments described with reference to the accompanying drawings are illustrative only and are not to be construed as limiting the present disclosure.

[0032] FIG1 is a flowchart of an energy management method for a hybrid vehicle according to an embodiment of the present disclosure.

[0033] As shown in FIG1 , the energy management method for a hybrid vehicle includes:

[0034] S11, obtaining road characteristic parameters of the current navigation route of the hybrid vehicle.

[0035] S12, using a pre-trained neural network model to obtain a sequence of working condition categories and mileage of each working condition of the current navigation route according to road characteristic parameters.

[0036] S13, obtaining a target state of charge sequence based on the operating condition category sequence and the mileage of each operating condition, and using an equivalent fuel consumption minimization strategy to obtain an equivalent factor sequence based on the operating condition category sequence, the mileage of each operating condition, and the target state of charge sequence.

[0037] S14, obtaining the instantaneous output power of the power battery of the hybrid vehicle at each moment according to the equivalent factor sequence, and controlling the hybrid vehicle according to the instantaneous output power of the power battery.

[0038] Specifically, a full-vehicle simulation model is pre-built, and offline calculations of the hybrid vehicle's performance, such as energy consumption, are performed under various operating conditions. Based on these calculations, the hybrid vehicle's operating conditions are categorized into multiple operating condition categories. Furthermore, a neural network model is pre-trained so that it can output a sequence of operating condition categories after inputting road characteristic parameters from a navigation map.

[0039] When a hybrid vehicle needs to travel, it obtains road characteristic parameters for its current route from a navigation map. These parameters are then fed into a pre-trained neural network model to determine the operating condition category and mileage for each section of the route. The operating condition categories are then sorted to create a sequence of operating condition categories. Based on the sequence of operating condition categories and the mileage of each operating condition, a target state of charge sequence corresponding to the route is derived. This sequence of equivalent factors is then derived using a strategy to minimize equivalent fuel consumption, and energy management for the hybrid vehicle is performed based on this equivalent factor sequence.

[0040] Thus, by obtaining the road characteristic parameters of the hybrid vehicle's current navigation route and utilizing a pre-trained neural network model to derive a sequence of operating condition categories and the mileage for each operating condition for the current navigation route based on the road characteristic parameters, online control of the hybrid vehicle can be achieved. Furthermore, after obtaining the sequence of operating condition categories and the mileage for each operating condition, a target state of charge sequence is also obtained based on the sequence of operating condition categories and the mileage for each operating condition. Thus, an equivalent factor sequence is obtained based on the sequence of operating condition categories, the mileage for each operating condition, and the target state of charge sequence. This allows the equivalent factor to vary based on the operating condition category of the road section, thereby ensuring fuel economy. Furthermore, because the current navigation route is first obtained, the equivalent factor sequence is derived based on the current navigation route, and energy management is performed on the current navigation route based on this equivalent factor sequence. This ensures that energy management is not affected by the frequency of navigation map data output, requires minimal computational effort, and can better ensure fuel economy under varying operating conditions.

[0041] In some embodiments of the present disclosure, the above-mentioned obtaining of the target state of charge sequence based on the operating condition category sequence and the mileage of each operating condition includes: obtaining the current actual state of charge of the power battery; determining the range of charge change of the hybrid vehicle at the end of each operating condition based on the actual state of charge, the operating condition category sequence and the mileage of each operating condition; and obtaining the target state of charge sequence based on the range of charge change at the end of each operating condition.

[0042] Specifically, after receiving the operating condition category sequence and the mileage of each operating condition, the operating condition category sequence and the mileage of each operating condition can be sent to a third-party platform to obtain a target state of charge sequence, or the target state of charge sequence can be directly calculated locally.

[0043] In order to calculate the target state of charge sequence, the first state of charge and the second state of charge at the end of the first operating condition can be determined based on the current actual state of charge of the hybrid vehicle, the road characteristic data corresponding to the first operating condition in the operating condition category sequence, and the mileage of the first operating condition. The state of charge change range at the end of the first operating condition is obtained based on the first state of charge and the second state of charge, wherein the road characteristic data includes slope data and speed limit data, the first state of charge is the state of charge of the hybrid vehicle at the end of the first operating condition in the power generation mode, and the second state of charge is the state of charge of the hybrid vehicle at the end of the first operating condition in the pure electric mode; for each operating condition except the first operating condition in the operating condition category sequence, the state of charge change range of the hybrid vehicle at the end of the operating condition is determined based on the state of charge change range at the end of the operating condition corresponding to the previous operating condition, the road characteristic data corresponding to the operating condition, and the mileage of the operating condition.

[0044] In this embodiment, a target state of charge may be selected from the state of charge variation range corresponding to each operating condition; and a target state of charge sequence may be obtained based on each target state of charge.

[0045] In some examples, referring to FIG2 , the first operating condition is operating condition 1 corresponding to segment AB. The actual state of charge of the hybrid vehicle at point A is F. Assuming the hybrid vehicle is in power generation mode, i.e., in operating condition 1 from point A to point B, the hybrid vehicle uses all fuel and the battery is in a charging state, the first state of charge at point B is determined to be G, i.e., the upper limit of the state of charge for operating condition 1 is G. Assuming the hybrid vehicle is in pure electric mode, i.e., in operating condition 1 from point A to point B, the hybrid vehicle uses all power and the battery is in a discharging state, the second state of charge at point B is determined to be I, i.e., the lower limit of the state of charge for operating condition 1 is I. Therefore, the range of the state of charge under operating condition 1 can be determined to be [I, G]. Assuming that the actual state of charge F under operating condition 1 is 70%, the upper limit of the state of charge G at point B is 75%, and the lower limit of the state of charge I is 65%, the range of the state of charge under operating condition 1 is [65%, 75%).

[0046] Then, based on the operating condition data corresponding to Operating Condition 2 and the SOC range corresponding to Operating Condition 1, the preceding operating condition of Operating Condition 2, the SOC range of the hybrid vehicle under Operating Condition 2 is determined. First, the SOC upper limit value of Operating Condition 1 is G, which is used as the actual SOC for Operating Condition 2. The hybrid vehicle is in power generation mode, i.e., in Operating Condition 2, the hybrid vehicle is fully fueled and the battery is in a charging state. The third SOC at point C is determined to be J, i.e., the SOC upper limit value of Operating Condition 2 is J. Next, the SOC lower limit value of Operating Condition 1 is I, which is used as the actual SOC for Operating Condition 2. The hybrid vehicle is in pure electric mode, i.e., in Operating Condition 2, from points B to C, the hybrid vehicle is fully fueled and the battery is in a discharging state. The fourth SOC at point C is determined to be L, i.e., the SOC lower limit value of Operating Condition 2 is L. Thus, the SOC range under Operating Condition 2 is determined to be [L, J].

[0047] Continuing with FIG2 , the actual state of charge at point A is F. Assuming that point H is selected as the target state of charge value within the state of charge variation range [I, G], then FH is a state of charge variation path for operating condition 1.

[0048] In addition to point H, which is within the SOC range, the target SOC value for AB segment condition 1 also includes an upper SOC value G and an lower SOC value I. Under this condition, AB segment condition 1 can generate three SOC paths: FG, FH, and FI. The actual SOC for BC segment condition 2 can be the target SOC value for AB segment, which includes G, H, and I. The preset reference SOC for BC segment includes J, K, and L. Under this condition, nine SOC paths can be generated for BC segment: GJ, GK, GL, HJ, HK, HL, IJ, IK, and IL. Similarly, CD segment condition 3 can generate fifteen SOC paths, and DE segment condition 4 can generate thirty SOC paths. Based on the above, the hybrid vehicle can generate 3 × 9 × 15 × 30 = 12,150 SOC paths under the AE condition of the current road.

[0049] It should be noted that the greater the number of target state of charge values ​​for each operating condition, the greater the number of state of charge change paths generated, and the higher the accuracy of determining the state of charge change path with the lowest energy consumption under the operating condition, and the better the energy management effect of the hybrid vehicle achieved.

[0050] By calculating the fuel and electricity consumption of different SOC paths, the path with the lowest fuel and electricity consumption is compared and selected as the optimal SOC path. This optimal path is then used as the target SOC path for the current operating condition, enabling energy management of the hybrid vehicle. Taking operating condition 1 (segment AB) as an example, the fuel and electricity consumption of SOC paths FG, FH, and FI are calculated, and SOC path FI is found to have the lowest fuel and electricity consumption. Therefore, SOC path FI is selected as the target SOC path for segment AB, with I being the optimal SOC at the end of operating condition 1. Similarly, the target SOC path for segment BC is determined to be IJ, the target SOC path for segment CD is JP, and the target SOC path for segment DE is PU. The target SOC path for pre-traveled road AE is FIJPU.

[0051] In this way, it is possible to obtain a target state of charge sequence based on the operating condition category sequence and the mileage of each operating condition. Moreover, this process can be carried out on a third-party platform, which can reduce the vehicle's calculation amount, reduce the vehicle's calculation time, and ensure fuel economy when the operating conditions change.

[0052] In some embodiments of the present disclosure, the training process of the neural network model includes: obtaining historical driving parameters of the hybrid vehicle on the current navigation route, determining historical road characteristic parameters based on the historical driving parameters, and clustering the historical road characteristic parameters to obtain multiple operating condition categories; constructing a training dataset based on the historical road characteristic parameters and operating condition categories; constructing a neural network model, and training the neural network model using the training dataset. The neural network model includes an input layer, a hidden layer, and an output layer. The input layer is used to input the road characteristic parameters after dimensionality reduction processing, and the output layer is used to output the operating condition category.

[0053] Specifically, we first construct a neural network model as shown in Figure 3, including input layer, hidden layer and output layer, and the input layer inputs x1, x2, ...x n is the road characteristic parameter, and in the specific example shown in Figure 3, n is 4. The neural network model supports road characteristic parameters with a dimension of 4, and the output y of the output layer is the working condition category.

[0054] In order to train the neural network model, firstly, historical driving parameters of the hybrid vehicle on the current navigation route are obtained, and historical road characteristic parameters are determined according to the historical driving parameters.

[0055] Moreover, a cluster analysis algorithm is used to classify working conditions, that is, the historical road characteristic parameters are divided into different working condition categories using the Euclidean distance method, so that the neural network model is trained according to the historical road characteristic parameters and the corresponding working condition categories.

[0056] Optionally, before training the neural network model, historical road characteristic parameters can be subjected to dimensionality reduction using principal component analysis, so that the neural network model can be trained using the reduced dimensionality historical road characteristic parameters. Furthermore, the neural network model can also derive a sequence of operating condition categories and the mileage of each operating condition for the current navigation route based on the reduced dimensionality road characteristic parameters.

[0057] In some embodiments of the present disclosure, the operating condition categories include: ordinary urban roads, lightly congested urban roads, moderately congested urban roads, severely congested urban roads, expressways, highways, suburban roads, and township roads.

[0058] In some embodiments of the present disclosure, the road characteristic parameters include at least one of the following: average vehicle speed, maximum vehicle speed, speed standard deviation, average acceleration, maximum acceleration, minimum acceleration, acceleration standard deviation, acceleration time ratio, deceleration time ratio, uniform speed time ratio, idling time ratio, and cumulative mileage.

[0059] In some embodiments of the present disclosure, obtaining the instantaneous output power of the power battery of the hybrid vehicle at each moment according to the equivalent factor sequence includes: for each equivalent factor in the equivalent factor sequence, calculating the instantaneous output power of the battery of the hybrid vehicle corresponding to the equivalent factor according to the following formula:

[0060] Where H(u, SOC(t), t) is the Hamiltonian function established based on the equivalent fuel consumption minimum strategy, arg H(u, SOC(t), t) is the instantaneous output power of the hybrid vehicle's power battery at time t, is the engine fuel consumption rate of the hybrid vehicle, s(t) is the equivalent factor at time t, SOC(t) is the state of charge of the power battery at time t, is the rate of change of state of charge, and u is the fuel consumption.

[0061] Specifically, a Hamiltonian function is established, and using the principle of equivalent fuel consumption minimization strategy, each operating condition is calculated and the relationship between the operating condition category, target state of charge, and equivalent factor is constructed. Solving the Hamiltonian function establishes the relationship between the battery's target state of charge and the equivalent factor for the current operating condition category, thereby determining the optimal equivalent factor for each target state of charge in the current operating condition category. Once the optimal equivalent factor is obtained, the hybrid vehicle's battery instantaneous output power corresponding to the optimal equivalent factor can be obtained, thereby controlling the hybrid vehicle based on the instantaneous output power of the power battery. For example, after obtaining the instantaneous output power of the power battery at time t, the hybrid vehicle's instantaneous power requirement at time t can be obtained. The instantaneous output power of the power battery at time t is subtracted from the instantaneous power requirement to obtain the instantaneous power requirement of the hybrid vehicle's engine at time t. The power battery and engine are then controlled based on the instantaneous output power of the power battery and the engine at time t.

[0062] In this way, the output power can be obtained based on the equivalent factor of real-time optimization, and the optimal energy management of the entire navigation route in the entire time domain is achieved.

[0063] In summary, the energy management method for a hybrid vehicle according to the disclosed embodiment of the present invention achieves online control of the hybrid vehicle by acquiring road characteristic parameters for the hybrid vehicle's current navigation route and utilizing a pre-trained neural network model to obtain a sequence of operating condition categories and the mileage for each operating condition for the current navigation route based on the road characteristic parameters. Furthermore, after acquiring the sequence of operating condition categories and the mileage for each operating condition, a target state of charge sequence is also acquired based on the sequence of operating condition categories and the mileage for each operating condition. Thus, an equivalent factor sequence is obtained based on the sequence of operating condition categories, the mileage for each operating condition, and the target state of charge sequence, thereby enabling the equivalent factor to vary according to the operating condition category of the road section, thereby ensuring fuel economy. Furthermore, because the current navigation route is first acquired, an equivalent factor sequence is obtained based on the current navigation route, and energy management is performed on the current navigation route based on this equivalent factor sequence, energy management is not affected by the frequency of navigation map data output, the required computational effort is minimal, and fuel economy can be better ensured under varying operating conditions.

[0064] Based on the energy management method of the hybrid vehicle according to the above embodiment, the present disclosure proposes a computer-readable storage medium.

[0065] In the embodiment of the present disclosure, a computer program is stored on the processor, and when the computer program is executed by the processor, the above-mentioned energy management method for the hybrid vehicle is implemented.

[0066] The computer-readable storage medium of the disclosed embodiment implements the above-mentioned hybrid vehicle energy management method. By acquiring road characteristic parameters of the hybrid vehicle's current navigation route, a pre-trained neural network model is used to obtain a sequence of operating condition categories and the mileage of each operating condition for the current navigation route based on the road characteristic parameters, thereby achieving online control of the hybrid vehicle. Furthermore, after obtaining the sequence of operating condition categories and the mileage of each operating condition, a target state of charge sequence is also obtained based on the sequence of operating condition categories and the mileage of each operating condition. Thus, an equivalent factor sequence is obtained based on the sequence of operating condition categories, the mileage of each operating condition, and the target state of charge sequence, thereby achieving a change in the equivalent factor based on the operating condition category of the road section, thereby ensuring fuel economy. Furthermore, because the current navigation route is first acquired, an equivalent factor sequence is obtained based on the current navigation route, and energy management is performed on the current navigation route based on the equivalent factor sequence, energy management is not affected by the frequency of navigation map data output, the required computational effort is small, and fuel economy can be better ensured under varying operating conditions.

[0067] Based on the energy management method of the hybrid vehicle according to the above embodiment, the present disclosure proposes an electronic device.

[0068] In an embodiment of the present disclosure, the electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the above-mentioned hybrid vehicle energy management method is implemented.

[0069] The electronic device of the disclosed embodiment, by implementing the above-described hybrid vehicle energy management method, can obtain road characteristic parameters of the hybrid vehicle's current navigation route and utilize a pre-trained neural network model to obtain a sequence of operating condition categories and mileage for each operating condition for the current navigation route based on the road characteristic parameters, thereby achieving online control of the hybrid vehicle. Furthermore, after obtaining the sequence of operating condition categories and mileage for each operating condition, a target state of charge sequence is also obtained based on the sequence of operating condition categories and mileage for each operating condition. Based on the sequence of operating condition categories, mileage for each operating condition, and target state of charge sequence, an equivalent factor sequence is obtained, thereby enabling the equivalent factor to vary according to the operating condition category of the road section, thereby ensuring fuel economy. Furthermore, because the current navigation route is first obtained, an equivalent factor sequence is obtained based on the current navigation route, and energy management is performed on the current navigation route based on the equivalent factor sequence, energy management is not affected by the frequency of navigation map data output, requires minimal computational effort, and can better ensure fuel economy under varying operating conditions.

[0070] FIG4 is a structural block diagram of an energy management device for a hybrid vehicle according to an embodiment of the present disclosure.

[0071] As shown in FIG4 , the energy management device 100 of a hybrid vehicle includes an acquisition module 101 and an energy management module 102 .

[0072] Specifically, the acquisition module 101 is used to obtain road characteristic parameters of the current navigation route of the hybrid vehicle, and to use a pre-trained neural network model to obtain the operating condition category sequence and the mileage of each operating condition of the current navigation route based on the road characteristic parameters, and to obtain a target state of charge sequence based on the operating condition category sequence and the mileage of each operating condition, and to use the equivalent fuel consumption minimum strategy to obtain an equivalent factor sequence based on the operating condition category sequence, the mileage of each operating condition and the target state of charge sequence, and to obtain the instantaneous output power of the power battery of the hybrid vehicle at each moment based on the equivalent factor sequence; the energy management module 102 is used to control the hybrid vehicle based on the instantaneous output power of the power battery.

[0073] It should be noted that for other specific implementations of the energy management device for a hybrid vehicle according to the embodiment of the present disclosure, reference may be made to the above-mentioned energy management method for a hybrid vehicle.

[0074] The energy management device for a hybrid vehicle according to the disclosed embodiment acquires road characteristic parameters of the hybrid vehicle's current navigation route and utilizes a pre-trained neural network model to obtain a sequence of operating condition categories and mileage for each operating condition for the current navigation route based on the road characteristic parameters, thereby enabling online control of the hybrid vehicle. Furthermore, after acquiring the sequence of operating condition categories and mileage for each operating condition, a target state of charge sequence is also acquired based on the sequence of operating condition categories and mileage for each operating condition. Thus, an equivalent factor sequence is acquired based on the sequence of operating condition categories, mileage for each operating condition, and target state of charge sequence, thereby enabling the equivalent factor to vary according to the operating condition category of the road section, thereby ensuring fuel economy. Furthermore, because the current navigation route is first acquired, an equivalent factor sequence is obtained based on the current navigation route, and energy management is performed on the current navigation route based on the equivalent factor sequence, energy management is not affected by the frequency of navigation map data output, requires minimal computational effort, and can better ensure fuel economy under varying operating conditions.

[0075] Based on the energy management method and apparatus of a hybrid vehicle in the above-mentioned embodiment, the present disclosure proposes a hybrid vehicle.

[0076] FIG5 is a structural block diagram of a hybrid vehicle according to an embodiment of the present disclosure.

[0077] As shown in FIG. 5 , a hybrid vehicle 10 includes the above-described hybrid vehicle energy management device 100 .

[0078] The hybrid vehicle of the disclosed embodiment, through the above-described hybrid vehicle energy management device, can obtain road characteristic parameters of the hybrid vehicle's current navigation route and, using a pre-trained neural network model, obtain a sequence of operating condition categories and mileage for each operating condition for the current navigation route based on the road characteristic parameters, thereby achieving online control of the hybrid vehicle. Furthermore, after obtaining the sequence of operating condition categories and mileage for each operating condition, it is also necessary to obtain a target state of charge sequence based on the sequence of operating condition categories and mileage for each operating condition. Thus, an equivalent factor sequence is obtained based on the sequence of operating condition categories, mileage for each operating condition, and target state of charge sequence, thereby achieving a change in the equivalent factor based on the operating condition category category, mileage for each operating condition, and the target state of charge sequence. This ensures that fuel economy is maintained by varying the equivalent factor according to the operating condition category of the road section. Furthermore, because the current navigation route is first obtained, the equivalent factor sequence is obtained based on the current navigation route, and energy management is performed on the current navigation route based on the equivalent factor sequence. This ensures that energy management is not affected by the frequency of navigation map data output, requires minimal computational effort, and can better maintain fuel economy under varying operating conditions.

[0079] It should be noted that the logic and / or steps represented in the flowcharts or otherwise described herein can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such an instruction execution system, apparatus, or device. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, apparatus, or device, or in conjunction with such an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion having one or more wires (electronic device), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0080] It should be understood that various parts of the present disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0081] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0082] In the description of this specification, the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and should not be understood as a limitation to the present disclosure.

[0083] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. Throughout the present disclosure, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0084] In the description of this specification, unless otherwise specified, terms such as "installed," "connected," "connect," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will understand the specific meanings of the above terms in this disclosure based on specific circumstances.

[0085] In the present disclosure, unless otherwise expressly specified or limited, when a first feature is "above" or "below" a second feature, it may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Furthermore, when a first feature is "above," "above," or "above" a second feature, it may mean that the first feature is directly above or diagonally above the second feature, or simply means that the first feature is at a higher level than the second feature. When a first feature is "below," "below," or "below" a second feature, it may mean that the first feature is directly below or diagonally below the second feature, or simply means that the first feature is at a lower level than the second feature.

[0086] Although the embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are illustrative and are not to be construed as limitations on the present disclosure. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present disclosure.

Claims

DEPCT681. A method for managing the power of hybrid vehicles, comprising: determining the sequence of operating conditions of the hybrid vehicle's current route and the mileage of each operating condition; the sequence of operating conditions and the mileage of each operating condition are obtained according to the road characteristic parameters of the current route; determining the target charging condition sequence according to the operating condition sequence and the mileage of each operating condition; determining the equivalent factor sequence according to the operating condition sequence and the target charging condition sequence; determining the instantaneous output power of the hybrid vehicle's power battery at each moment according to the equivalent factor sequence; and controlling the hybrid vehicle based on the instantaneous output power of the power battery.The energy management method for hybrid vehicles under Claim 1, which involves achieving a target charging sequence based on operating condition categories and mileage for each operating condition, includes: achieving the true charging state of the power battery at the start of the current navigation route; determining the charging transition interval of the hybrid vehicle at the end of the operating condition based on the true charging state, operating condition categories, and mileage for each operating condition; and achieving the target charging sequence based on the charging transition interval at the end of the operating condition.The energy management method for hybrid vehicles under Claim 2, where the charging transition period at the end of the first operating condition of the current navigation route is derived from the actual charging condition, the road characteristics of the first operating condition, and the mileage of the first operating condition; and the charging transition period at the end of a non-first operating condition of the current navigation route is derived from the charging transition period at the end of the preceding operating condition of the non-first operating condition, the road characteristics of the non-first operating condition, and the mileage of the non-first operating condition.

4. The energy management method for hybrid vehicles under Claim 3, where the road characteristics include gradient information and speed limit information. 5.

6. The energy management method for hybrid vehicles under claim 3 or 4, where the upper limit of the charge transition range of the operating conditions is the charge condition of the hybrid vehicle at the end of operation of the operating conditions in power generation mode, and the lower limit of the charge transition range of the operating conditions is the charge condition of the hybrid vehicle at the end of operation of the operating conditions in electric-only mode.

7. The energy management method for hybrid vehicles under any one of claims 3 through 5, where the sequence of target charge conditions is achieved according to each target charge condition selected from the charge transition range corresponding to each operating condition.The energy management method for hybrid vehicles under any of the claims 1 through 6, where the sequence of operating condition categories and the mileage of each operating condition are obtained using a neural network model trained on the road characteristic parameters of the current navigation route, and the neural network model training process includes: obtaining the historical driving parameters of the hybrid vehicle on the current navigation route; determining historical road characteristic parameters based on the historical driving parameters and performing grouping processing with historical road characteristic parameters to obtain a number of operating condition categories; creating a training dataset based on historical road characteristic parameters and operating condition categories; and creating the neural network model and training the neural network model using the training dataset.

8. The energy management method for hybrid vehicles under claim 7, where the operating condition categories include: ordinary city roads, slightly congested city roads, moderately congested city roads, heavily congested city roads, expressways, highways, suburban roads, and urban community roads. 9.The energy management method for hybrid vehicles under any one of the claims 1 through 8, in which the road characteristic parameters include at least one of the following: average speed, maximum speed, speed standard deviation, average acceleration, maximum acceleration, minimum acceleration, acceleration standard deviation, acceleration time ratio, deceleration time ratio, steady speed time ratio, idle time ratio, and mileage.10.The power management method for hybrid vehicles under any one of the claims 1 through 9, where for each equivalent factor in the sequence of equivalent factors, calculates the instantaneous output power of the hybrid vehicle's battery under operating conditions corresponding to the equivalent factor according to the following formula: argH(u,SOC(t),t)=argmeng(u,t)+s(t)*SOC(t), where H(u,SOC(t),t) is the Hamiltonian function installed according to the minimum equivalent consumption strategy, argH(u,SOC(t),t) is the instantaneous output power of the power battery at t, meng(u,t) is the fuel consumption rate of the hybrid vehicle's engine, s(t) is the equivalent factor at t, SOC(t) is the charging condition of the power battery at t, SOC(t) is the rate of change of charging condition, and u is the fuel consumption.11.

12. A computer-readable storage medium on which a computer program is stored in memory and is executable by a processor, in which the program, when executed by the processor, enables any of the power management methods for hybrid vehicles under claims 1 to 11.

13. An electronic device comprising memory, a processor, and a computer program stored in memory and executable by a processor, in which the program, when executed by the processor, enables any of the power management methods for hybrid vehicles under claims 1 to 11.The power management tool for hybrid vehicles comprises: a supply module configured to achieve the sequence of operating conditions of the hybrid vehicle's current route and the mileage of each operating condition of the current route, with the sequence of operating conditions and mileage of each operating condition obtained according to the road characteristic parameters of the current route; to achieve the target charging condition sequence according to the operating condition sequence and mileage of each operating condition; to achieve the equivalent factor sequence according to the operating condition sequence and the target charging condition sequence; and to achieve the instantaneous output power of the hybrid vehicle's power battery at each moment according to the equivalent factor sequence; and a power management module configured to control the hybrid vehicle according to the instantaneous output power of the power battery.

15. Hybrid vehicles comprising the power management tool for hybrid vehicles as per claim 14;

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