Method and device for optimizing fuel consumption of a hybrid electric vehicle

Abstract

This disclosure describes a method and apparatus for optimizing fuel consumption of a hybrid electric vehicle, wherein a method includes: determining road information for a road segment to be driven by the electric vehicle corresponding to a first time length; predicting the total power demand of the electric vehicle at each preset time point within the first time length based on the road information and vehicle model parameters of the electric vehicle; determining an equivalent fuel consumption factor of the power battery at each time point based on the current state of charge, target state of charge, and the predicted total power demand at each time point of the electric vehicle's power battery; and providing a first equivalent fuel consumption factor obtained based on the equivalent fuel consumption factor of the power battery at each time point to a control unit of the electric vehicle for controlling the power distribution between the electric vehicle's fuel engine and power battery.

Description

Technical Field

[0001] This disclosure generally relates to vehicle control systems, and more specifically, to methods and apparatus for optimizing fuel consumption in hybrid electric vehicles. Background Technology

[0002] The rapid development of electric vehicles has met the urgent need for diversified and efficient energy utilization, effectively promoting environmental protection and sustainable development. As one of the main types of electric vehicles, hybrid electric vehicles refer to vehicles that can obtain power from at least two types of energy stored on-board: consumed fuel and rechargeable electrical energy storage devices. The flexible energy management of hybrid electric vehicles allows fuel engines to operate for extended periods under more efficient or less polluting conditions.

[0003] Typical hybrid electric vehicles typically include hybrid electric vehicles (HEVs), which include both an internal combustion engine powered by hydrocarbon fuels and a rechargeable battery. Hybrid electric vehicles also include fuel cell electric vehicles with a hybrid drive system, where the battery is typically used as another energy source in addition to the fuel cell system.

[0004] For hybrid electric vehicles, there is always a need to improve energy management and control, and further enhance fuel economy. Summary of the Invention

[0005] In the Summary section, some selected concepts are presented in a simplified form, and will be further elaborated in the Detailed Description section below. This Summary section is not intended to identify any key or essential features of the claimed subject matter, nor is it intended to help determine the scope of the claimed subject matter.

[0006] According to one aspect of this disclosure, a method for optimizing the fuel consumption of a hybrid electric vehicle is provided. The method includes: determining road information for a road segment to be driven by the electric vehicle corresponding to a first time length, wherein the road information includes predicted speed information of the electric vehicle within the first time length; predicting the total power demand of the electric vehicle at each preset time point within the first time length based on the road information and vehicle model parameters of the electric vehicle; determining an equivalent fuel consumption factor of the power battery at each time point based on the current state of charge, target state of charge, and predicted total power demand at each time point of the electric vehicle's power battery; and providing a first equivalent fuel consumption factor obtained based on the equivalent fuel consumption factor of the power battery at each time point to a control unit of the electric vehicle for controlling the power distribution between the fuel engine and the power battery of the electric vehicle.

[0007] According to another aspect of this disclosure, a computing device is provided, the computing device comprising: at least one processor; and a memory coupled to the at least one processor and used to store instructions, wherein, when executed by the at least one processor, the instructions cause the at least one processor to perform the methods described in this disclosure.

[0008] According to another aspect of this disclosure, a computer-readable storage medium is provided having instructions stored thereon that, when executed by at least one processor, cause the at least one processor to perform the methods described in this disclosure.

[0009] According to another aspect of this disclosure, a computer program product is provided, comprising instructions that, when executed by at least one processor, cause the at least one processor to perform the methods described in this disclosure. Attached Figure Description

[0010] Implementations of this disclosure are illustrated in the accompanying drawings by way of example rather than limitation, and similar reference numerals in the drawings denote the same or similar parts, wherein:

[0011] Figure 1 An exemplary operating environment in which some implementations of this disclosure may be implemented is shown;

[0012] Figure 2 A flowchart of an exemplary method according to some implementations of this disclosure is shown;

[0013] Figure 3 A block diagram of an exemplary apparatus according to some implementations of the present disclosure is shown;

[0014] Figure 4 A block diagram of an exemplary computing device according to some implementations of this disclosure is shown. Detailed Implementation

[0015] In the following sections of the specification, numerous specific details are set forth for purposes of explanation in order to provide a more thorough understanding of this disclosure. However, these specific details are merely exemplary and not restrictive, and it will be apparent to those skilled in the art that implementations of this disclosure can be carried out without these specific details. In other instances, well-known structures and techniques have not been shown in detail so as not to unnecessarily affect the understanding of the specification.

[0016] Throughout this specification, references to "an implementation," "implementation," "exemplary implementation," "some implementations," "various implementations," etc., indicate that the implementations of this disclosure described may include specific features, structures, or characteristics. However, it is not implied that every implementation must include these specific features, structures, or characteristics. Furthermore, some implementations may have some, all, or none of the features described for other implementations.

[0017] In a manner most conducive to understanding the claimed subject matter, various operations may be described as multiple discrete actions or processes in sequence. However, the order in which they are described should not be construed as implying that these operations necessarily depend on that order. Rather, these operations may be performed in a different order. In other implementations, various additional operations may be performed, and / or various operations already described may be omitted.

[0018] In the specification and claims, the phrase "A and / or B" may appear to mean one of the following: (A), (B), (A and B). Similarly, the phrase "A, B and / or C" may appear to mean one of the following: (A), (B), (C), (A and B), (A and C), (B and C), (A and B and C).

[0019] The goal of energy management control in hybrid electric vehicles is to minimize fuel consumption while maintaining the state of charge (SOC) of the battery near a desired target value. Taking fuel cell electric vehicles (FCEVs) as an example, these systems typically use a rechargeable battery as an additional energy source, building upon a fuel cell system that directly converts the chemical energy of hydrogen into electrical energy through an electrochemical reaction. For such FCEVs, effectively reducing hydrogen consumption remains a crucial area for technological improvement. Existing energy management controls primarily optimize hydrogen consumption based on the vehicle's current power demand during operation; however, such mechanisms often fail to achieve optimal results.

[0020] This disclosure provides a mechanism that can effectively optimize the fuel consumption of hybrid electric vehicles from a global perspective.

[0021] Although the illustrations herein and below primarily use fuel cell electric vehicles in a hybrid drive configuration as examples, it should be noted that the technologies described in this disclosure are equally applicable to other types of hybrid electric vehicles, including hybrid electric vehicles, such as series hybrid electric vehicles.

[0022] The following is a reference. Figure 1This illustrates an exemplary operating environment 100 in which some implementations of this disclosure may be implemented. For example... Figure 1 As shown, the operating environment 100 may include a hybrid electric vehicle (hereinafter also referred to as "electric vehicle") 110 and a remote server 120 that is communicatively coupled to the electric vehicle 110 via a network 130.

[0023] Network 130 is typically implemented as a wireless network based on any suitable radio communication technology and / or standard. For example, network 130 may include a telecommunications network of a certain standard provided by a telecommunications operator. Although Figure 1 The diagram shows a single network 130, but network 130 can be configured to include multiple networks. Figure 1 The operating environment 100 shown may be a typical example of a vehicle-to-everything (V2X) system; however, this disclosure is not limited to this particular architecture.

[0024] According to some implementations of this disclosure, the electric vehicle 110 may be equipped with a vehicle data acquisition module (not shown), which is capable of communicating with one or more components of the electric vehicle 110 to acquire real-time operating data of the electric vehicle 110. The one or more components may include various sensors installed at various locations within the electric vehicle 110 and / or various control units of the electric vehicle 110 itself. Taking a fuel cell electric vehicle as an example, examples of control units may include, but are not limited to, the fuel cell control unit of the fuel cell system, the battery management system of the power battery, the motor control unit of the motor, and so on. In some implementations, the vehicle data acquisition module may be incorporated into the vehicle control unit (VCU) of the electric vehicle 110, or at least some of its functions may be implemented by such a control unit; however, using a separate vehicle data acquisition module is also feasible.

[0025] The electric vehicle 110 can send related data, including but not limited to data collected by the vehicle data acquisition module, such as the current state of charge (SOC) of the electric vehicle 110's power battery and the unique identifier of the electric vehicle 110 (e.g., VIN), to the remote server 120 through the electric vehicle 110's external communication unit (not shown). With this and other data, the remote server 120 can implement the mechanisms described in this disclosure for optimizing the fuel consumption of the electric vehicle 110.

[0026] Although Figure 1The remote server 120 is shown as a single server, but it is understood that it can also be implemented as a server array or server cluster. In some implementations, the remote server 120 can be deployed in a distributed computing environment and can also be implemented using cloud computing technology. Here, the remote server 120 can be used as an example of a cloud computing platform, and the computing and storage resources used are not limited to being provided by a specific one or more servers.

[0027] Furthermore, according to some implementations of this disclosure, the processing operations performed by the remote server 120 described herein can also be incorporated into the electric vehicle 110. For example, these functions can be performed through the VCU of the electric vehicle 110 to implement the mechanism described herein for optimizing the fuel consumption of the electric vehicle 110.

[0028] Next reference Figure 2 The diagram illustrates a flowchart of an exemplary method 200 according to some implementations of this disclosure. According to some implementations of this disclosure, the exemplary method 200 can be implemented in... Figure 1 The remote server 120 shown is implemented in a computing device to optimize the fuel consumption of the electric vehicle 110.

[0029] Method 200 begins at step 210, in which road information for the electric vehicle corresponding to a first time length can be determined by remote server 120.

[0030] Taking a fuel cell electric vehicle in a hybrid drive configuration as an example of electric vehicle 110, and considering the scenario where electric vehicle 110 plans to travel from a starting point A to a destination B, in some implementations of this disclosure, electric vehicle 110 can send relevant requests, such as control requests to optimize hydrogen consumption for the entire journey from point A to point B, to a remote server 120.

[0031] In some implementations of this disclosure, the information provided to the remote server 120 along with such a request may also include the unique identifier of the electric vehicle 110, the model of the electric vehicle 110, and the current state of charge (SOC) of the power battery of the electric vehicle 110.

[0032] In response to receiving such a request from the electric vehicle 110, the remote server 120 can request and receive corresponding navigation information from a navigation system (e.g., an external navigation service provider). For example, the navigation system can inform the remote server 120 that the journey from point A (the current location and origin) to point B (the destination), following its recommended 150-kilometer route and based on current road conditions, will take a total of 3 hours. It is understood that the navigation system typically provides more than just this simple overall information; it can break down the entire journey, for example, dividing the 150-kilometer route into numerous sub-segments based on factors such as current road conditions, road curves, or forks, and providing corresponding predicted travel times for each segment. Furthermore, in some implementations, the navigation system can also provide more detailed information on travel length and travel time. Additionally, in some implementations, the navigation system can also provide gradient information related to the route.

[0033] According to some implementations of this disclosure, the remote server 120 can determine the road information corresponding to the first time length of the road segment to be traveled based on the received navigation information.

[0034] In some implementations of this disclosure, the road segment to be traveled can always correspond to the entire distance from the current location of the electric vehicle 110 (which is the starting point A, or of course, a current location during the journey from the starting point to the destination) to the destination (i.e., point B); correspondingly, the first time length here corresponds to the predicted time length required for the electric vehicle 110 to travel from its current location (which is the starting point A) to the destination point B (which is 3 hours in this case).

[0035] In some implementations of this disclosure, the section to be traveled may also correspond to only a portion of the entire journey from the current location of the electric vehicle 110 (currently the starting point A) to the destination (i.e., point B), which is particularly suitable for cases where the entire journey is long. In some implementations, the section to be traveled may correspond to a preset first time length, such as 1.5 hours. In other words, the section to be traveled corresponds to the portion of the journey that takes 1.5 hours to travel from the current location of the electric vehicle 110.

[0036] According to some implementations of this disclosure, the road information determined in step 210 may include predicted speed information of the electric vehicle within the first time length. Again, taking the first time length of 1.5 hours as an example, the remote server 120 can determine the predicted speed curve of the electric vehicle 110 over the next 1.5 hours based on the received navigation information, making it possible to predict the power demand of the electric vehicle 110 over the next 1.5 hours. Furthermore, in some implementations of this disclosure, the road information determined in step 210 may also include an acceleration curve derived from the speed curve.

[0037] Furthermore, according to some implementations of this disclosure, the determination of road information in step 210 may also include obtaining road information from historical driving data (e.g., previously stored driving data from point A to point B). In some implementations, the road information obtained from historical driving data may include, but is not limited to, the slope information of the road segment to be driven.

[0038] Method 200 then proceeds to step 220, in which the remote server 120 can predict the total power demand of the electric vehicle at each preset time point within the first time length, based on the road information and the vehicle model parameters of the electric vehicle.

[0039] According to some implementations of this disclosure, the remote server 120 may store, or may obtain from, vehicle model parameters of the electric vehicle 110 from other locations. For example, this can be accurately identified based on the unique identifier and / or model of the electric vehicle 110. The vehicle model parameters of the electric vehicle 110 may include various vehicle parameters related to determining the power requirements of the electric vehicle 110, such as, but not limited to, vehicle mass, air density, frontal area, drag coefficient, wheel radius, transmission efficiency, motor efficiency, maximum / minimum motor power / torque, preset accessory power, etc.

[0040] According to some implementations of this disclosure, the preset time points are spaced one second apart. For an example where the first time length is 1.5 hours, this means that there could be 1.5 * 60 * 60 = 5400 time points, denoted as t0 to t... 5399 It should be noted that, depending on the specific implementation, the interval between the preset time points can be longer or shorter, for example, an interval of 0.5 seconds or an interval of 2 seconds, which is also within the scope of this disclosure.

[0041] In some implementations, for each of the 5400 time points, using the speed information contained in the determined road information and the vehicle model parameters of the electric vehicle 110, the remote server 120 can determine the rolling resistance, air resistance, and / or the rolling resistance power and air resistance power consumed to overcome the corresponding resistances at the corresponding time point. In some implementations, where the road information also includes acceleration information, the remote server 120 can also determine the acceleration resistance and / or the corresponding acceleration resistance power consumed when the electric vehicle 110 accelerates at the corresponding time point. Furthermore, in some implementations, where the road information also includes slope information, the remote server 120 can also determine the slope resistance and / or the corresponding slope resistance power consumed when the electric vehicle 110 climbs a slope at the corresponding time point, and can update other resistances and / or resistance powers, such as rolling resistance and / or rolling resistance power, based on the slope information. The values ​​determined in this way can be regarded as the wheel-side parameters of the electric vehicle 100.

[0042] Furthermore, taking a fuel cell electric vehicle with a hybrid drive configuration as an example, based on the determined wheel-side parameters of the electric vehicle 110, these parameters can be converted into motor output parameters of the electric vehicle 110 by considering factors such as the drive system efficiency. Then, by considering factors such as the motor efficiency of the electric vehicle 110, the motor output parameters can be further converted into the motor's power demand. Based on this, combined with preset accessory power parameters, the total power demand of the electric vehicle 110 at each time point can be determined, which can be denoted as p. req0 ~P req5399 .

[0043] Those skilled in the art will understand that, depending on the specific implementation, the total power demand of the electric vehicle 110 at each point in time may take into account more or fewer factors, without departing from the scope of this disclosure. According to some implementations of this disclosure, based on speed information for the predicted next period of time (1.5 hours in this example) contained in the determined road information, and combined with the vehicle model parameters of the electric vehicle 110, the remote server 120 can predict the total power demand of the electric vehicle 110 at each point in time during the next period with relatively high accuracy.

[0044] Method 200 proceeds to step 230, in which the remote server 120 determines the equivalent fuel consumption factor of the power battery at each time point based on the current state of charge, the target state of charge, and the predicted total power demand at each time point of the electric vehicle's power battery.

[0045] Based on some implementations of this disclosure, the Hamiltonian function for solving the energy management problem of hybrid electric vehicles using the Pontryagin minimum principle is:

[0046]

[0047] Here, H(·) represents the Hamiltonian function; P req P represents the total power demand of electric vehicles 110; batt This represents the power output of the battery in electric vehicle 110. λ represents instantaneous fuel consumption; λ is the costate variable in the formula, representing the equivalent fuel consumption factor of the power battery. Furthermore, SOC represents the current state of charge of the power battery, and the dynamics of SOC are as follows:

[0048]

[0049] In some implementations, It can be calculated using the following formula:

[0050]

[0051] Here, Q c The nominal charge capacity of the power battery is represented by _batt_u; the voltage of the power battery is represented by _batt_u; and the internal resistance of the power battery is represented by _r0. These two are related to the state of charge (SOC) of the power battery.

[0052] Here, solving for λ involves solving the following equation:

[0053]

[0054] Therefore, for a discrete system, given an initial value λ0, each λ can be iteratively obtained using the following formula:

[0055]

[0056] According to some implementations of this disclosure, the target state of charge (SOC) of the power battery of the electric vehicle 110 can be set by a remote server 120 based on the current SOC of the power battery, road information of the route to be traveled, etc. In other implementations, the target SOC of the power battery can also be set by the user of the electric vehicle 110, for example, especially when the route to be traveled corresponds to the entire distance from the current location of the electric vehicle 110 to the destination. In this case, the user can determine the degree to which the SOC of the power battery needs to be maintained upon reaching the destination.

[0057] According to some implementations of this disclosure, the λ0 of the power battery of the electric vehicle 110 at an initial time point t0 can be set by a remote server 120 based on the current state of charge and the target state of charge of the power battery.

[0058] In some implementations of this disclosure, the total power demand P of the predicted electric vehicle 110 at t0 is used. req0 Multiple different power allocation schemes can be designed, each corresponding to satisfying the total power demand P. req0 fuel cell system power (P) fc0 ) and power battery power (P) batt0 A combination of ) . For example, with P req0 For example, with a power output of 100kW, multiple power allocation schemes could include: P fc0 =20kW and P batt0 =80kW, P fc0 =30kW and P batt0 =70kW, P fc0 =40kW and P batt0 =60kW, P fc0 =50kW and P batt0 =50kW, ..., and so on. According to some implementations of this disclosure, multiple different power allocation schemes are designed with preset power variation intervals (e.g., every 10kW). According to some implementations of this disclosure, the starting point and / or ending point of the power allocation for the fuel cell system can be set taking into account the efficient operating range of the fuel cell system.

[0059] According to some implementations of this disclosure, for each power allocation scheme, the corresponding total fuel consumption cost can be determined, which in the above example includes the fuel cell system power (P). fc0 The corresponding hydrogen consumption cost and power battery power (P) batt0 The corresponding hydrogen consumption cost. Here, the fuel cell system power (P) fc0 The corresponding hydrogen consumption cost can be determined by referring to the efficiency curve of the fuel cell system, and the power battery power (P) batt0 The corresponding hydrogen consumption cost can be determined by referring to the charge and discharge efficiency of the power battery and the equivalent fuel consumption factor λ0 of the power battery at this point in time.

[0060] In some implementations of this disclosure, the power allocation scheme that minimizes the total fuel consumption cost is selected from multiple different power allocation schemes, thereby determining the predicted P for the initial time point t0. req0 Corresponding P fc0 and P batt0 Based on the SOC0 at the initial time point t0 and the predicted Pbatt0 This allows us to determine λ1 at the next time point t1. Simultaneously, based on SOC0 and P at the initial time point t0... batt0 This allows us to determine the State of Charge (SOC) of the power battery at the next time point t1. Similarly, we can sequentially determine the SOC at subsequent time points t2, t3, ..., t... 5399 λ2,λ3,…,λ 5399 .

[0061] Therefore, according to some implementations of this disclosure, the operation of determining the equivalent fuel consumption factor of the power battery at each time point in step 230 may include: for each time point within the first time length other than the initial time point, determining the equivalent fuel consumption factor of the power battery at that time point based on the optimal power allocation scheme determined for the previous time point, and updating the state of charge of the power battery at that time point. Wherein, the optimal power allocation scheme determined for the previous time point is the power allocation scheme that minimizes the total fuel consumption cost, selected from multiple different power allocation schemes combining the fuel engine power and the power battery power that meet the total power demand, based on the predicted total power demand of the electric vehicle at the previous time point and the equivalent fuel consumption factor of the power battery at the previous time point.

[0062] After determining the equivalent fuel consumption factor of the power battery at each time point within the first time length using the aforementioned method, method 200 proceeds to step 240, in which the remote server 120 can provide the first equivalent fuel consumption factor obtained based on the equivalent fuel consumption factor of the power battery at each time point to the control unit of the electric vehicle for controlling the power distribution between the fuel engine and the power battery of the electric vehicle.

[0063] According to some implementations of this disclosure, the first equivalent fuel consumption factor provided to the control unit of the electric vehicle 110 may be the average of all equivalent fuel consumption factors of the determined power battery of the electric vehicle 110 within a first time length. Continuing the previous example, after obtaining 5400 λ values ​​of the power battery of the electric vehicle 110, namely λ0, λ1, λ2, λ3, ..., λ 5399 Then, remote server 120 can calculate the average of these 5400 λ values. This is provided to the control unit of the electric vehicle 110 as an equivalent fuel consumption factor corresponding to the next 1.5 hours. By providing only one equivalent fuel consumption factor for the next 1.5 hours, i.e. This effectively reduces the communication load between the remote server 120 and the electric vehicle 110 and improves processing efficiency.

[0064] According to some implementations of this disclosure, instead of providing the average of all equivalent fuel consumption factors of the power battery within a first time length, the determined equivalent fuel consumption factors of the power battery at each time point can also be directly provided to the control unit of the electric vehicle 110. That is, the first equivalent fuel consumption factor provided to the control unit of the electric vehicle 110 can include the determined equivalent fuel consumption factors of the power battery at each time point within the first time length. Using the previous example, the obtained 5400 λ values, i.e., λ0, λ1, λ2, λ3, ..., λ 5399 All of these are provided to the control unit of the electric vehicle 110. Therefore, the control unit of the electric vehicle 110 has a corresponding equivalent fuel consumption factor for each point in time during the next 1.5 hours, making it possible to achieve more precise control.

[0065] Furthermore, according to some implementations of this disclosure, the first equivalent fuel consumption factor provided to the control unit of the electric vehicle 110 can also be implemented in other ways. For example, considering the balance between efficiency and performance, the first equivalent fuel consumption factor can be implemented for λ0, λ1, λ2, λ3, ..., λ 5399 An average value is calculated for every 100 values, and the resulting 54 average values ​​are provided to the control unit of the electric vehicle 110 as the first equivalent fuel consumption factor. Therefore, the control unit of the electric vehicle 110 will have a corresponding equivalent fuel consumption factor for every 100 time points in the next 1.5 hours.

[0066] In some implementations of this disclosure, the control unit of the electric vehicle 110 may refer to the vehicle control unit (VCU) of the electric vehicle, which is responsible for implementing the energy management strategy of the electric vehicle. In some implementations, during the actual driving of the electric vehicle 110, the VCU controls the power distribution over the next 1.5 hours based on the actual total power demand of the electric vehicle 110 (e.g., which may depend on the actual speed of the electric vehicle 110 at that time, the actual situation of the user pressing the accelerator pedal at that time, the actual power consumption of vehicle accessories such as air conditioning, etc.) and a first equivalent fuel consumption factor obtained from the remote server 120.

[0067] The vehicle control unit of electric vehicle 110 controls power distribution based on the actual total power demand and the first equivalent fuel consumption factor. It can be done in the same way as described above, that is, selecting the power distribution scheme that minimizes the total fuel consumption cost from multiple different power distribution schemes that combine the power of the fuel engine and the power of the power battery to meet the total power demand. This will not be elaborated here.

[0068] The first equivalent fuel consumption factor provided to the electric vehicle 110 using the aforementioned mechanism can effectively reflect road information and the corresponding predicted total power demand within a predicted longer time window (i.e., the first time length, which in some implementations may also correspond to the time required for the electric vehicle 110 to travel from its current location to its destination, as described above). Therefore, by using this determined first equivalent fuel consumption factor to control the power allocation of the electric vehicle 110, overall optimization of the fuel consumption of the electric vehicle 110 over a longer time period can be achieved, and even global optimization over the entire journey from the origin to the destination becomes possible.

[0069] Furthermore, according to some implementations of this disclosure, the operation of the method can be re-executed after a second time length to update the first equivalent fuel consumption factor provided to the control unit of the electric vehicle, wherein the second time length is shorter than the first time length.

[0070] In some implementations, the second time length can be set to 15 minutes, shorter than the 1.5 hours used as an example of the first time length. That is, instead of waiting until 1.5 hours have elapsed or are approaching that time before calculating the first equivalent fuel consumption factor for the next 1.5 hours, steps 210-240 of method 200 are re-executed every 15 minutes to update the first equivalent fuel consumption factor for the next 1.5 hours following those 15 minutes. In this way, by calculating every 15 minutes in conjunction with road information from the next 1.5 hours, the road information used can be updated promptly (as it is always dynamically changing), and adjustments can be made in a timely manner to address uncertainties in driving conditions. This approach, using the optimal method for each segment, strives to get as close as possible to the global optimum, thereby effectively reducing hydrogen consumption.

[0071] Furthermore, according to some implementations of this disclosure, when recalculating every second time length, if the time required to reach the destination is less than the first time length, then road information that is less than the first time length is considered during the calculation.

[0072] Furthermore, according to some implementations of this disclosure, the operation of determining the equivalent fuel consumption factor of the power battery at each time point in step 230 may further include: in response to updating the state of charge of the power battery at that time point, determining whether the updated state of charge falls within a preset state of charge range; and in response to the updated state of charge not falling within the preset state of charge range, redetermining the equivalent fuel consumption factor of the power battery at each time point by adjusting the magnitude of the equivalent fuel consumption factor of the power battery at the initial time point.

[0073] In some implementations of this disclosure, the state of charge (SOC) of the power battery of the electric vehicle 110 may be required to always fall within a preset allowable SOC range (which may be denoted as [SOC]). min SOC max Within [ ]). Therefore, the remote server 120 can determine whether the updated state of charge of the power battery exceeds the allowable range each time it updates the state of charge during the calculation process. If so, it can be considered that the series of λ calculated based on the previously set λ0 in the above manner is inappropriate. In this case, the size of λ0 can be adjusted, and a new series of λ can be determined based on the adjusted λ0. In some implementations of this disclosure, this process can be performed iteratively until the above constraints are met.

[0074] Furthermore, according to some implementations of this disclosure, the operation of determining the equivalent fuel consumption factor of the power battery at each time point in step 230 may further include: determining the difference between the state of charge of the power battery at the end of the first time length and the target state of charge by comparing and determining the difference between the state of charge of the power battery at the end of the first time length and the target state of charge; and in response to the difference exceeding a preset threshold, redetermining the equivalent fuel consumption factor of the power battery at each time point by adjusting the magnitude of the equivalent fuel consumption factor of the power battery at the initial time point.

[0075] In some implementations of this disclosure, the total power demand P of the electric vehicle 110 at each time point during the next 1.5 hours is predicted. req0 ~P req5399 And the correspondingly determined equivalent fuel consumption factor λ of the power battery of the electric vehicle 110 at each point in time. req0 ~λ req5399 The remote server 120 can determine the state of charge (SOC) of the power battery at the end of 1.5 hours. This SOC, determined based on prediction, can be compared with the target SOC. If the difference exceeds a preset threshold, the series of λ values ​​calculated using the previously set λ0 is considered inappropriate. In this case, the value of λ0 can be adjusted, and a new series of λ values ​​can be determined based on the adjusted λ0. In some implementations of this disclosure, this process can be iteratively executed until the aforementioned constraints are met before proceeding to the next step, 240.

[0076] Furthermore, according to some implementations of this disclosure, for the iterative adjustment of λ0 and the redetering of a series of λ based on the adjusted λ0, certain limits may be imposed on the number of adjustments and / or processing time, so as to avoid repeated adjustments affecting processing efficiency and failing to respond to the requests of electric vehicle 110 in a timely manner.

[0077] Furthermore, it should be understood that the first time length of 1.5 hours and the second time length of 15 minutes are given as examples. Depending on the specific implementation, the first time length can be set to be longer or shorter, such as 2 hours, 1 hour, or 0.8 hours; similarly, depending on the specific implementation, the second time length can also be set to be longer or shorter, such as 20 minutes, 10 minutes, or 5 minutes, all of which are within the scope of this disclosure.

[0078] Next reference Figure 3 The diagram illustrates a block diagram of an exemplary device 300 according to some implementations of the present disclosure. The exemplary device 300 is used to implement the mechanisms described in this disclosure for optimizing fuel consumption in hybrid electric vehicles. Those skilled in the art will understand that device 300 can be implemented using software, hardware, firmware, or any combination thereof. According to some implementations of the present disclosure, device 300 can be included in the aforementioned remote server 120 or similar entity.

[0079] like Figure 3 As shown, the device 300 may include a module 310 for determining road information for a road segment to be traveled by the electric vehicle corresponding to a first time length, wherein the road information includes predicted speed information of the electric vehicle within the first time length. The device 300 may also include a module 320 for predicting the total power demand of the electric vehicle at each preset time point within the first time length, based on the road information and vehicle model parameters of the electric vehicle. The device 300 may also include a module 330 for determining the equivalent fuel consumption factor of the power battery at each time point based on the current state of charge, target state of charge, and the predicted total power demand at each time point of the electric vehicle's power battery. Furthermore, the device 300 may also include a module 340 for providing a first equivalent fuel consumption factor obtained based on the equivalent fuel consumption factor of the power battery at each time point to the control unit of the electric vehicle for controlling the power distribution between the fuel engine and the power battery of the electric vehicle.

[0080] In some implementations of this disclosure, the above-described or additional modules of device 300 can be used to perform other operations already described in the specification, such as in conjunction with Figure 2 The operations described are illustrated in the flowchart of the exemplary method 200 and its various variations. Furthermore, in some implementations, the various modules of the apparatus 300 may be combined or separated as needed without departing from the scope of this disclosure.

[0081] Figure 4A block diagram of an exemplary computing device 400 according to some implementations of the present disclosure is shown. The exemplary computing device 400 is used to implement the mechanisms described in this disclosure for optimizing fuel consumption in hybrid electric vehicles. According to some implementations of the present disclosure, the computing device 400 may be included in the aforementioned remote server 120 or a similar entity.

[0082] like Figure 4 As shown, computing device 400 may include at least one processor 410. Processor 410 may include any type of general-purpose processing unit (such as CPU, GPU), dedicated processing unit, core, circuitry, controller, etc. Furthermore, computing device 400 may also include memory 420. Memory 420 may include any type of medium that can be used to store data. In some implementations, memory 420 is configured to store instructions that, when executed by at least one processor 410, cause processor 410 to perform the operations described herein, for example, in conjunction with... Figure 2 The flowcharts of exemplary method 200 and the various variations thereof describe those operations.

[0083] Those skilled in the art will understand that the above description of the structure of computing device 400 is merely exemplary and not restrictive, and other structures of devices are also feasible.

[0084] Various implementations of this disclosure may include or operate on multiple components, parts, units, modules, instances, or mechanisms that can be implemented in hardware, software, firmware, or any combination thereof. Examples of hardware may include, but are not limited to: devices, processors, microprocessors, circuits, circuit elements (e.g., transistors, resistors, capacitors, inductors, etc.), integrated circuits, application-specific integrated circuits (ASICs), programmable logic devices (PLDs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs), memory cells, logic gates, registers, semiconductor devices, chips, microchips, chipsets, etc. Examples of software may include, but are not limited to: software components, programs, applications, computer programs, application programs, system programs, machine programs, operating system software, middleware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, application programming interfaces (APIs), instruction sets, computer code, computer code segments, words, values, symbols, or any combination thereof. Determining whether an implementation is accomplished using hardware, software, and / or firmware can vary depending on a variety of factors, such as desired compute speed, power level, thermal tolerance, processing cycle budget, input data rate, output data rate, memory resources, data bus speed, and other design or performance constraints, as is expected of a given implementation.

[0085] Some implementations described herein may include an article of writing. The article of writing may include a storage medium. Examples of storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (e.g., computer-readable instructions, data structures, program modules, or other data). Storage media may include, but are not limited to: random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other semiconductor memory, optical disc (CD), digital multi-disc (DVD) or other optical storage, magnetic tape cassettes, magnetic tape, disk storage or other magnetic storage devices, or any other medium capable of storing information. In some implementations, the article of writing may store executable computer program instructions that, when executed by one or more processing units, cause the processing units to perform the operations described herein. Executable computer program instructions may include any suitable type of code, such as source code, compiled code, interpreted code, executable code, static code, dynamic code, etc. Executable computer program instructions can be implemented using any appropriate high-level, low-level, object-oriented, visual, compiled, and / or interpreted programming language.

[0086] Some exemplary implementations of this disclosure are described below.

[0087] Example 1 describes a method for optimizing the fuel consumption of a hybrid electric vehicle. The method includes: determining road information for a road segment to be driven by the electric vehicle corresponding to a first time length, wherein the road information includes predicted speed information of the electric vehicle within the first time length; predicting the total power demand of the electric vehicle at each preset time point within the first time length based on the road information and vehicle model parameters of the electric vehicle; determining an equivalent fuel consumption factor of the power battery at each time point based on the current state of charge, target state of charge, and the predicted total power demand at each time point; and providing a first equivalent fuel consumption factor obtained based on the equivalent fuel consumption factor of the power battery at each time point to a control unit of the electric vehicle for controlling the power distribution between the fuel engine and the power battery of the electric vehicle.

[0088] Example 2 may include the subject matter described in Example 1, wherein the first equivalent fuel consumption factor provided to the control unit of the electric vehicle is the average of all equivalent fuel consumption factors of the determined power battery over the first time length.

[0089] Example 3 may include the subject matter described in any of the preceding examples, wherein the operation of the method is re-executed after each second time length to update the first equivalent fuel consumption factor provided to the control unit of the electric vehicle, wherein the second time length is shorter than the first time length.

[0090] Example 4 may include the subject matter described in any of the foregoing examples, wherein determining the equivalent fuel consumption factor of the power battery at each time point includes: for each time point within the first time length other than the initial time point, determining the equivalent fuel consumption factor of the power battery at that time point based on the optimal power allocation scheme determined for the previous time point, and updating the state of charge of the power battery at that time point, wherein the optimal power allocation scheme determined for the previous time point is the power allocation scheme that minimizes the total fuel consumption cost selected from multiple different power allocation schemes that combine the power of the fuel engine and the power of the power battery to meet the total power demand, based on the predicted total power demand of the electric vehicle at the previous time point and the equivalent fuel consumption factor of the power battery at the previous time point, and wherein the equivalent fuel consumption factor of the power battery at the initial time point is set based on the current state of charge and the target state of charge of the power battery.

[0091] Example 5 may include the subject matter described in Example 4, wherein determining the equivalent fuel consumption factor of the power battery at each time point further includes: in response to updating the state of charge of the power battery at that time point, determining whether the updated state of charge falls within a preset state of charge range; and in response to the updated state of charge not falling within the preset state of charge range, redetermining the equivalent fuel consumption factor of the power battery at each time point by adjusting the magnitude of the equivalent fuel consumption factor of the power battery at the initial time point.

[0092] Example 6 may include the subject matter described in Example 4, wherein determining the equivalent fuel consumption factor of the power battery at each time point further includes: determining the difference between the state of charge of the power battery at the end of the first time length and the target state of charge by comparing and determining; and in response to the difference exceeding a preset threshold, redetermining the equivalent fuel consumption factor of the power battery at each time point by adjusting the magnitude of the equivalent fuel consumption factor of the power battery at the initial time point.

[0093] Example 7 may include the subject matter described in any of the preceding examples, wherein the first time length corresponds to the predicted time length required for the electric vehicle to travel from its current location to its destination.

[0094] Example 8 may include the subject matter described in any of the preceding examples, wherein determining the road information includes determining the road information of the road segment to be driven based on navigation information received from the navigation system, and wherein the road information further includes the slope information of the road segment to be driven and acceleration information derived based on the speed information.

[0095] Example 9 may include the subject matter described in any of the foregoing examples, wherein the electric vehicle includes a hybrid-drive fuel cell electric vehicle, the fuel engine includes a fuel cell system, and the fuel includes hydrogen.

[0096] Example 10 describes a computing device comprising: at least one processor; and a memory coupled to the at least one processor and used to store instructions, wherein, when executed by the at least one processor, the instructions cause the at least one processor to perform the method according to any one of the preceding Examples 1-9.

[0097] Example 11 describes a computer-readable storage medium having instructions stored thereon that, when executed by at least one processor, cause the at least one processor to perform the method according to any one of the preceding Examples 1-9.

[0098] Example 12 describes a computer program product comprising instructions that, when executed by at least one processor, cause the at least one processor to perform the method according to any one of the preceding Examples 1-9.

[0099] Example 13 describes an apparatus comprising a module for performing the method described in any one of Examples 1-9 above.

[0100] The above description includes examples of the disclosed architecture. It is certainly impossible to describe every conceivable combination of components and / or methods, but those skilled in the art will understand that many other combinations and permutations are also possible. Therefore, this novel architecture is intended to cover all such alternatives, modifications, and variations that fall within the spirit and scope of the appended claims.

Claims

1. A method for optimizing the fuel consumption of a hybrid electric vehicle, the method comprising: Determine road information for the electric vehicle corresponding to a first time length of road segment to be driven, wherein the road information includes predicted speed information of the electric vehicle within the first time length; Based on the road information and the vehicle model parameters of the electric vehicle, the total power demand of the electric vehicle at each preset time point within the first time length is predicted. Based on the current state of charge (SOC), target SOC, and predicted total power demand at each time point of the electric vehicle's power battery, the equivalent fuel consumption factor of the power battery at each time point is determined; and The first equivalent fuel consumption factor, obtained based on the equivalent fuel consumption factor of the power battery at each time point, is provided to the control unit of the electric vehicle to control the power distribution between the fuel engine and the power battery of the electric vehicle.

2. The method according to claim 1, wherein, The first equivalent fuel consumption factor provided to the control unit of the electric vehicle is the average of all equivalent fuel consumption factors of the power battery determined within the first time length.

3. The method according to claim 1, wherein, Every second time period, the operation of the method is re-executed to update the first equivalent fuel consumption factor provided to the control unit of the electric vehicle, wherein the second time period is shorter than the first time period.

4. The method according to claim 1, wherein, Determining the equivalent fuel consumption factor of the power battery at each time point includes: For each time point within the first time length, excluding the initial time point, based on the optimal power allocation scheme determined for the previous time point, the equivalent fuel consumption factor of the power battery at that time point is determined, and the state of charge of the power battery at that time point is updated. Specifically, the optimal power allocation scheme determined for the previous time point is selected from multiple different power allocation schemes that combine the power of the fuel engine and the power of the battery to meet the predicted total power demand of the electric vehicle at the previous time point and the equivalent fuel consumption factor of the power battery at the previous time point. This selection minimizes the total fuel consumption cost. The equivalent fuel consumption factor of the power battery at the initial time point is set based on the current state of charge and the target state of charge of the power battery.

5. The method according to claim 4, wherein, Determining the equivalent fuel consumption factor of the power battery at each time point also includes: In response to updating the state of charge (SOC) of the power battery at that point in time, it is determined whether the updated SOC falls within a preset SOC range; and In response to the updated state of charge not falling within the preset state of charge range, the equivalent fuel consumption factor of the power battery at each time point is re-determined by adjusting the magnitude of the equivalent fuel consumption factor of the power battery at the initial time point.

6. The method according to claim 4, wherein, Determining the equivalent fuel consumption factor of the power battery at each time point also includes: The difference between the state of charge of the power battery and the target state of charge at the end of the first time period is determined by comparison; and In response to the difference exceeding a preset threshold, the equivalent fuel consumption factor of the power battery at each time point is re-determined by adjusting the magnitude of the equivalent fuel consumption factor of the power battery at the initial time point.

7. The method according to claim 1, wherein, The first time length corresponds to the predicted time required for the electric vehicle to travel from its current location to its destination.

8. The method according to claim 1, wherein, Determining the road information includes determining the road information of the road segment to be driven based on navigation information received from the navigation system, wherein the road information also includes the slope information of the road segment to be driven and the acceleration information derived based on the speed information.

9. The method according to any one of the preceding claims, wherein, The electric vehicle includes a hybrid-drive fuel cell electric vehicle, the fuel engine includes a fuel cell system, and the fuel includes hydrogen.

10. A computing device, the computing device comprising: At least one processor; as well as A memory coupled to the at least one processor and used to store instructions, wherein, when executed by the at least one processor, the instructions cause the at least one processor to perform the method according to any one of claims 1-9.

11. A computer-readable storage medium having instructions stored thereon, which, when executed by at least one processor, cause the at least one processor to perform the method according to any one of claims 1-9.

12. A computer program product comprising instructions that, when executed by at least one processor, cause the at least one processor to perform the method according to any one of claims 1-9.

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