Energy management system and method for a hybrid vehicle

Abstract

The present application relates to an energy management system and method for a hybrid vehicle. The energy management system for a hybrid vehicle comprises a trip plan planning module, a state of charge monitoring module and a history record correction module. The trip plan planning module is configured to receive a trip plan of a user using the hybrid vehicle, and to plan a driving mode of the hybrid vehicle during a trip according to the trip plan. The state of charge monitoring module is configured to monitor a state of charge of a battery of the hybrid vehicle; the history record correction module is communicatively connected with the state of charge monitoring module, and the history record correction module is configured to receive historical driving data of the hybrid vehicle, and to adjust the driving mode of the hybrid vehicle based on the historical driving data when a value of the state of charge monitored by the state of charge monitoring module exceeds a first fluctuation range.

Description

Technical Field

[0001] This invention relates to the field of vehicles, and more specifically to the field of hybrid vehicle optimization and power battery energy management. Background Technology

[0002] As the country's requirements for energy conservation and emission reduction become increasingly stringent, new energy vehicles are gradually becoming the mainstream for urban driving. Among them, hybrid electric vehicles (HEVs) inherit the high energy density characteristics of traditional fuels, overcome the range limitations of pure electric vehicles, and also meet more stringent emission standards, making them widely favored by users. The performance of HEVs is closely related to the vehicle's battery; a reasonable energy management strategy can effectively extend the battery life of HEVs. Summary of the Invention

[0003] The energy management system and method for hybrid vehicles disclosed in this application provide an active energy management strategy based on travel plan information and historical driving data, which manages energy by optimizing the fuel-electric ratio of the hybrid vehicle.

[0004] According to a first aspect of this application, an energy management system for a hybrid vehicle is provided, the energy management system comprising: a trip planning module, a state of charge (SCC) monitoring module, and a historical record correction module. The trip planning module is configured to receive a user's trip plan for using the hybrid vehicle and to plan the driving mode of the hybrid vehicle during the trip based on the trip plan. The SCC monitoring module is configured to monitor the state of charge of the hybrid vehicle's battery. The historical record correction module is communicatively connected to the SCC monitoring module and is configured to receive historical driving data of the hybrid vehicle and, when the SCC value monitored by the SCC monitoring module exceeds a first fluctuation range, adjust the driving mode of the hybrid vehicle based on the historical driving data.

[0005] According to one or more embodiments of the first aspect of this application, optionally, the travel plan includes the destination location and departure time, and the historical driving data includes the historical driving route records, vehicle speed records, and battery state of charge records of the hybrid vehicle.

[0006] According to one or more embodiments of the first aspect of this application, optionally, the first fluctuation range is 35%-65%.

[0007] According to one or more embodiments of the first aspect of this application, optionally, the travel planning module determines the driving mode of the hybrid vehicle based on minimizing the equivalent fuel consumption value of the hybrid vehicle; wherein the equivalent fuel consumption value is: the sum of the fuel consumption value of the hybrid vehicle's engine itself and the fuel consumption value obtained by equivalent conversion of the amount of electricity consumed by the hybrid vehicle's battery at a certain instant.

[0008] According to one or more embodiments of the first aspect of this application, optionally, the energy management system further includes a penalty factor adjustment module, the penalty factor adjustment module being communicatively connected to the state of charge monitoring module, the penalty factor adjustment module being configured to: when the value of the state of charge monitored by the state of charge monitoring module exceeds the second fluctuation range, use a penalty factor to reward or punish the charging and discharging of the battery in a short period of time, so that the value of the state of charge quickly returns to the second fluctuation range.

[0009] According to one or more embodiments of the first aspect of this application, optionally, the second fluctuation range is 32%-70%.

[0010] According to a second aspect of this application, an energy management method for a hybrid vehicle is provided, the energy management method comprising: a trip planning step, a state of charge (SCC) monitoring step, and a historical record correction step. The trip planning step comprises: receiving a user's trip plan for using the hybrid vehicle, and planning the driving mode of the hybrid vehicle during the trip based on the trip plan. The SCC monitoring step comprises: monitoring the SCC of the hybrid vehicle's battery. The historical record correction step comprises: receiving historical driving data of the hybrid vehicle, and adjusting the driving mode of the hybrid vehicle based on the historical driving data when the SCC value monitored by the SCC monitoring step exceeds a first fluctuation range.

[0011] According to one or more embodiments of the second aspect of this application, optionally, the travel plan includes the destination location and departure time, and the historical driving data includes the historical driving route records, vehicle speed records, and battery state of charge records of the hybrid vehicle.

[0012] According to one or more embodiments of the second aspect of this application, optionally, the first fluctuation range is 35%-65%.

[0013] According to one or more embodiments of the second aspect of this application, optionally, the travel planning module determines the driving mode of the hybrid vehicle based on minimizing the equivalent fuel consumption value of the hybrid vehicle; wherein the equivalent fuel consumption value is: the sum of the fuel consumption value of the hybrid vehicle's engine itself and the fuel consumption value obtained by equivalent conversion of the amount of electricity consumed by the hybrid vehicle's battery at a certain instant.

[0014] According to one or more embodiments of the second aspect of this application, optionally, the energy management method further includes a penalty factor adjustment step, the penalty factor adjustment step including: when the value of the state of charge monitored by the state of charge monitoring step exceeds the second fluctuation range, using a penalty factor to reward or punish the charging and discharging of the battery in a short period of time, so that the value of the state of charge quickly returns to the second fluctuation range.

[0015] According to one or more embodiments of the second aspect of this application, optionally, the second fluctuation range is 32%-70%.

[0016] According to a third aspect of this application, a hybrid vehicle is provided, the hybrid vehicle including an energy management system as described in the first aspect of this application.

[0017] According to a fourth aspect of this application, a computer storage medium is provided, the computer storage medium including instructions that, when executed, perform the energy management method as described in the second aspect of this application.

[0018] The energy management system and method for hybrid vehicles in this application introduce user historical driving information and vehicle travel plans as new control input sources on the basis of basic drive mode planning strategies, thereby managing energy by optimizing the fuel-electric ratio of hybrid vehicles. Attached Figure Description

[0019] The above and other objects and advantages of the present invention will become more fully clear from the following detailed description taken in conjunction with the accompanying drawings, wherein the same or similar elements are indicated by the same reference numerals.

[0020] Figure 1 The structure of the powertrain system of a hybrid vehicle according to one embodiment of this application is shown; Figure 2 An embodiment of the present application is shown for an energy management system for a hybrid vehicle; Figure 3 An embodiment of this application illustrates an energy management method for a hybrid vehicle; Figure 4 A flowchart of an energy management method 300 according to an embodiment of this application is shown. Detailed Implementation

[0021] The energy management system and method for hybrid vehicles according to this application will be described in further detail below with reference to the accompanying drawings. It should be noted that the following specific embodiments are exemplary and not limiting, and are intended to provide a basic understanding of this application, and are not intended to identify key or decisive elements of the invention or limit the scope of protection.

[0022] The present application is described below with reference to block diagrams, diagrams, and / or flowcharts illustrating methods and apparatus according to embodiments of the present application. It will be understood that each block of these flowchart illustrations and / or block diagrams, and combinations thereof, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to form a machine, such that these instructions, executable by the processor of the computer or other programmable data processing apparatus, create components for implementing the functions / operations specified in these flowchart illustrations and / or blocks and / or one or more flowchart illustrations.

[0023] These computer program instructions may be stored in a computer-readable storage medium, which may instruct a computer or other programmable processor to perform functions in a particular manner, such that the instructions stored in the computer-readable storage medium constitute an instruction set comprising one or more boxes of an implementation flowchart and / or block diagram that specify the functions / operations.

[0024] These computer program instructions can be loaded onto a computer or other programmable data processor to cause a series of operational steps to be executed on the computer or other programmable processor, thereby constituting a computer-implemented process, such that these instructions, which execute on the computer or other programmable data processor, provide steps for implementing the functions or operations specified in one or more boxes of this flowchart and / or block diagram. It should also be noted that in some alternative implementations, the functions / operations shown in the boxes may not occur in the order shown in the flowchart. For example, two boxes shown sequentially may actually be executed substantially simultaneously, or these boxes may sometimes be executed in reverse order, depending on the functions / operations involved.

[0025] Below, we will combine Figure 1 The transmission system of a hybrid vehicle 100 according to one embodiment of this application will be described.

[0026] Figure 1 This is a schematic diagram of the transmission system of a hybrid vehicle 100 according to one embodiment of this application. In the embodiments of this application, the hybrid vehicle can be a hybrid electric vehicle. Figure 1As shown, the hybrid vehicle 100's transmission system includes an engine 110, a clutch 120, a generator 130, a battery 140, a drive motor 150, and wheels 160. On one hand, the engine 110 is connected to the clutch 120, which in turn is connected to the wheels 160, allowing the engine 110 to directly drive the vehicle using fuel. On the other hand, the engine 110 is connected to the generator 130, which is connected to the battery 140, which is connected to the drive motor 150, which is connected to the wheels 160. The generator 130 generates electrical energy through the engine 110, and this electrical energy can charge the battery 140. The battery 140 is a power battery that can use electrical energy to directly drive the vehicle.

[0027] Depending on the source of driving energy for the hybrid vehicle, the powertrain of the hybrid vehicle 100 can achieve the following driving modes: engine direct drive mode, range-extended electric vehicle (EREV) mode, pure electric vehicle (EV) mode, and hybrid electric vehicle (HEV) mode. In engine direct drive mode, the engine 110 directly drives the vehicle using fuel. In this mode, the battery 140 does not participate in driving the vehicle. In range-extended electric vehicle (EREV) mode, the engine 110 drives the generator 130 to generate electricity to charge the battery 140, which then directly drives the vehicle. In EREV mode, the battery 140 provides energy to the drive motor 150 to directly drive the vehicle; the engine 110 does not directly drive the vehicle but only serves as an energy source for the generator 130. In pure electric vehicle (EV) mode, the battery 140 provides energy to directly drive the vehicle. In this mode, the engine 110 does not participate in driving the vehicle. The hybrid electric vehicle (HEV) mode can drive the vehicle directly through the generator 130 or the battery 140. The electrical energy of the battery 140 can be obtained by generating electricity through the generator 130 or by energy recovery.

[0028] The energy management of the hybrid vehicle 100 is closely related to its driving mode, and the switching of the driving mode affects the state of charge (SOC) of the battery 140. In other words, the SOC of the battery 140 is a parameter closely related to the driving mode of the hybrid vehicle 100. As the hybrid vehicle 100 switches driving modes, the battery 140 is in a phased charging and discharging state, thus causing the SOC of the battery 140 to fluctuate. Since excessive charging or discharging may impair the energy regeneration of the battery 140, considering the aforementioned performance characteristics of the battery 140, the optimization strategy for the driving mode of the hybrid vehicle 100 focuses on how to maintain the battery SOC fluctuation range within a specific limit, i.e., how to maintain "SOC balance". In this application, the specific limit within which the battery SOC can fluctuate is referred to as the healthy state of charge region.

[0029] In order to maintain the state of charge (SOC) of the battery 140 within the healthy SOC range, this application provides an energy management system 200 for a hybrid vehicle 100 (see [link]). Figure 2 The energy management system 200 of this application can modify the driving mode of the hybrid vehicle 100 by combining active planning and dynamic adjustment, so that the state of charge (SOC) of the battery 140 is kept within the healthy state of charge range.

[0030] Next, we will combine Figure 2 To illustrate one embodiment of the energy management system 200 according to this application.

[0031] Figure 2 This is a schematic diagram of an energy management system 200 according to an embodiment of this application. Figure 2 As shown in the illustration, in this embodiment, the energy management system 200 includes a trip planning module 210, a state of charge monitoring module 220, and a historical record correction module 230. Optionally, the energy management system 200 may also include a penalty factor adjustment module 240.

[0032] The travel planning module 210 is configured to receive travel plans from users using vehicle 100. The travel plan may include the destination, departure time, estimated arrival time, and trip length. In some embodiments, the travel plan may only include the destination and departure time. The sources of information regarding the travel plan can be diverse. For example, in some embodiments, users can input their travel plans (such as plans for the next day) into their mobile phones or personal computers before traveling. In this case, the travel plan information can be stored in the user's application or shared via the cloud, allowing the vehicle 100's energy management system 200 to receive the travel plan information from the user via the interface between the vehicle software and the user or via the cloud.

[0033] After receiving the travel plan, the travel plan planning module 210 can plan the driving mode of the vehicle 100 during the travel process according to the travel plan. The control strategies for the driving mode of the hybrid vehicle 100 include two types: rule-based strategies and optimization strategies. Among them, the rule-based hybrid control strategy has a simple algorithm and good real-time performance, but the control effect is generally average. In contrast, the optimization strategy has a better control effect. The optimization strategy includes the Equivalent Fuel Consumption Minimum Control Strategy (ECMS). In this embodiment, the travel plan planning module 210 can determine the vehicle's driving mode based on the Equivalent Fuel Consumption Minimum Control Strategy (ECMS). The Equivalent Fuel Consumption Minimum Control Strategy (ECMS) proposes the concept of an "equivalent fuel factor" to convert the energy loss of the battery 140 into "equivalent fuel consumption," and uses the converted equivalent fuel consumption together with the fuel consumption of the engine 110 as the objective function of the fuel consumption optimization strategy, thereby obtaining a more reasonable fuel-electric ratio allocation. In other words, ECMS can calculate the equivalent fuel consumption value in the following way: First, the amount of electricity consumed by the battery 140 at a certain instant is equivalently converted into the fuel consumption value of the engine 110. Then, the equivalent fuel consumption value of the engine 110 and the actual fuel consumption value of the engine 110 are combined into a unified energy consumption index, which is the equivalent fuel consumption value. ECMS is an instantaneous optimization strategy that aims to minimize the equivalent fuel consumption value of the vehicle 100 to determine the driving mode of the vehicle 100, thereby achieving optimal fuel economy for the vehicle 100. In this embodiment, ECMS can be applied throughout the entire travel plan cycle of the vehicle 100 to plan and adjust the driving mode of the vehicle 100 over a long period.

[0034] The state of charge (SCC) monitoring module 220 is configured to monitor the SCC of the battery 140. Since deep discharge or overcharging of the battery 140 can cause permanent damage to its lifespan, the vehicle 100 needs to monitor the SCC of the battery 140 in real time via the SCC monitoring module 220. When the SCC monitoring module 220 detects that the battery 140's charge level exceeds the healthy SCC range, it needs to further limit the charge / discharge capacity of the battery 140 to prevent damage due to further deterioration of the charging state. Optionally, the healthy SCC range is between 32% and 70%. That is, when the SCC monitoring module 220 detects that the battery 140's SCC is below 32% or above 70%, it indicates that the charge / discharge capacity of the battery 140 needs to be further limited. In other embodiments, the upper limit of the healthy SCC range can be selected from 65% to 75%, and the lower limit can be selected from 25% to 35%.

[0035] The historical driving data correction module 230 is used to import the user's historical driving data and correct the vehicle's driving mode based on this data. In this embodiment, the historical driving data correction module 230 is configured to receive the historical driving data of the vehicle 100. Optionally, the historical driving data includes the vehicle's historical driving route records, speed records, and battery state of charge records. In some embodiments, the speed records and battery state of charge records can be uploaded to the cloud in real time as basic data of the vehicle 100. The speed records and battery state of charge records are only useful when combined with the driving route records; after obtaining the information from these three types of records, the vehicle can fully understand the operating conditions within the corresponding road segment. Therefore, in this embodiment, the corresponding driving route records during the vehicle 100's driving process also need to be uploaded to the cloud via navigation for use by the energy management system 200.

[0036] Optionally, the historical record correction module 230 is communicatively connected to the state of charge monitoring module 220. The historical record correction module 230 is further configured to: when the state of charge value of the battery 140 monitored by the state of charge monitoring module 220 exceeds the first fluctuation range of the healthy state of charge region, adjust the planning of the driving mode of the vehicle 100 based on the received historical driving data, so that the state of charge value of the battery 140 returns to the first fluctuation range of the healthy state of charge region. The healthy state of charge region can be 32% to 70%, and the first fluctuation range can be 35% to 65%. In other embodiments, the first fluctuation range can also be selected from other ranges within the healthy state of charge region based on factors such as the performance of the battery 140 or the driving state of the vehicle 100, for example, other ranges within the 32% to 70% range. The historical record correction module 230 selecting a sub-region within the healthy state of charge region as the critical range for initiating driving mode adjustment helps provide more operational space for correcting the driving mode, ensuring that the battery state of charge always remains within the healthy state of charge region. Additionally, in some embodiments, the historical record correction module 230 may initiate the correction of the driving mode planning for the vehicle 100 only after the monitored state of charge value exceeds the first fluctuation range for a certain period of time, in order to avoid correcting the driving mode planning for the vehicle 100 when the battery 140 experiences instantaneous fluctuations in the state of charge. Optionally, the period of time is 2-100 seconds.

[0037] The penalty factor adjustment module 240 introduces an additional penalty factor based on the ECMS to regulate short-term fluctuations in battery SOC. In this embodiment, the penalty factor adjustment module 240 is communicatively connected to the state of charge monitoring module 220. The penalty factor adjustment module 240 is configured to: when the state of charge value of battery 140 detected by the state of charge monitoring module 220 exceeds the second fluctuation range of the healthy state of charge region, use the penalty factor to reward or penalize the charging and discharging of battery 140 for a short period of time, so as to quickly pull the state of charge value of battery 140 back to the second fluctuation range. In some embodiments, the second fluctuation range overlaps with the healthy state of charge region, for example, 32%-70%. In other embodiments, the second fluctuation range may also be selected from other intervals within the healthy state of charge region.

[0038] However, the ECMS (Electronic Control System) executes control strategies based on instantaneous operating conditions. If a penalty factor is used to adjust the ECMS, the changes brought about by the penalty factor often lag behind the changes in the current operating conditions. In other words, the penalty factor calculated based on the previous moment and applied to the ECMS is no longer suitable for the current operating conditions. When the penalty factor is not suitable for the current operating conditions, the battery SOC will continue to change in one direction, making it impossible to guarantee the balance of the battery SOC. For example, when vehicle 100 travels on a highway for a long time, vehicle 100 will tend to use engine 110 for propulsion. If no control is implemented, the battery SOC will continue to rise. In this case, adjusting the ECMS with a penalty factor can suppress the charging of battery 140, but the adjustment of the penalty factor does not consider the driving conditions of vehicle 100 after that section of road. If vehicle 100 suddenly experiences a traffic jam after the highway section, a large amount of electricity will be needed to drive vehicle 100, which will instead cause the battery SOC to be in a sub-healthy state with a low SOC. To compensate for the shortcomings of using the penalty factor adjustment module 240 to adjust the ECMS, the energy management system 200 of this application provides a trip planning module 210 and a historical record correction module 230 to use the received user trip plans and vehicle historical driving data to perform self-learning proactive planning and proactive adjustment for users, thereby reserving electricity in advance for upcoming congested road sections.

[0039] This application expands the information acquisition interface for vehicle 100, thereby providing more comprehensive input signals for the hybrid optimization control strategy. Optionally, this application introduces user historical driving data and travel plans as new control input sources based on the Equivalent Minimum Fuel Consumption Control (ECMS) strategy. When there is a deviation between the ECMS value based on historical driving data and the vehicle's instantaneous ECMS value, the energy management system 200 of this application can use historical driving data and travel plans to perform self-learning correction to optimize the fuel-electric distribution ratio of the hybrid vehicle, thereby achieving "automated" and "user-customized" drive mode control.

[0040] Next, we will combine Figure 3 To illustrate an energy management method 300 for a hybrid vehicle 100 according to one embodiment of this application.

[0041] Figure 3 Is adopted Figure 2 The diagram shows the energy management method 300 implemented by the energy management system 200 shown. Figure 3 As shown, the energy management method 300 includes: a trip planning step 310, a state of charge monitoring step 320, and a historical record correction step 330. Optionally, the energy management method 300 also includes a penalty factor adjustment step 340.

[0042] The trip planning step 310 can be executed by the trip planning module 210. The trip planning step 310 includes: receiving the user's trip plan for the hybrid vehicle 100, and planning the driving mode of the hybrid vehicle 100 during the trip based on the received trip plan. Specifically, the trip planning step 310 can determine the driving mode of the vehicle 100 by minimizing the equivalent fuel consumption value of the vehicle 100 based on an equivalent fuel consumption minimization control strategy (ECMS). In some embodiments, the vehicle 100 can obtain the user's trip plan via the network before the user's trip. The trip plan includes the destination, departure time, and trip length. After receiving the trip plan, the vehicle 100 can obtain information such as whether the target road segment is congested during the trip and the gradient value of the road segment through navigation. Based on this information, the vehicle 100 will proactively plan the electric and fuel consumption strategies for the subsequent driving conditions.

[0043] The state of charge monitoring step 320 can be performed by the state of charge monitoring module 220. The state of charge monitoring step 320 includes monitoring the state of charge of the battery 140 of the hybrid vehicle 100.

[0044] The historical record correction step 330 can be executed by the historical record correction module 230. The historical record correction step 330 includes: receiving historical driving data of the hybrid vehicle 100, and adjusting the driving mode of the vehicle 100 based on the received historical driving data when the state of charge (SOC) value of the battery 140 monitored by the state of charge (SOC) monitoring step 320 exceeds a first fluctuation range. Since different users have different driving styles, the fluctuations in the SOC of the battery 104 within the same road segment are also different. The historical record correction step 330 can correct the driving mode of the vehicle 100 by incorporating the user's historical driving data. In some embodiments, when the vehicle 100 is driving on a daily travel route, if the SOC monitoring step 320 detects that the battery SOC deviates from the normal value for a long period, it indicates that the instantaneous value of the ECMS does not match the current operating conditions. At this time, the energy management system 200 can start executing the historical record correction step 330 to trigger self-learning to correct the ECMS. During the self-learning process, Vehicle 100 calculates an ECMS value based on historical travel data in the cloud and compares this ECMS value with the current instantaneous ECMS value to obtain an error value. The goal of self-learning is to reduce this error value. The penalty factor adjustment step 340 can be executed by the penalty factor adjustment module 240. The penalty factor adjustment step 340 includes: when the state of charge value of the battery 140 detected by the state of charge monitoring step 320 exceeds the second fluctuation range, the penalty factor is used to reward or punish the charging and discharging of the battery 140 in a short period of time, so that the state of charge value of the battery 140 quickly returns to the second fluctuation range.

[0045] Considering the complex and ever-changing urban road conditions and the different driving styles of drivers, it is limited to rely solely on instantaneous ECMS for hybrid control of vehicle 100. The energy management method 300 of this application includes a trip planning step 310 and a historical data correction step 330. It can use user trip plans and vehicle historical driving data obtained from the cloud or terminal to correct the ECMS, thereby enabling systematic and global control of the driving mode of vehicle 100.

[0046] Next, we will combine Figure 4 The following describes the specific implementation process of an energy management method 300 according to one embodiment of this application. Figure 4 This is a flowchart illustrating an embodiment of an energy management method 300 of this application.

[0047] In one embodiment, a user can store their driving plan from home to work at 8:00 AM the next morning in a mobile application. Since the mobile application and the vehicle 100 application have established a cloud connection interface, this travel plan information can be obtained by the vehicle 100. The obtained travel plan information can be translated as follows: the route from the user's home to the company is 20 kilometers long, with the first 10 kilometers being a suburban highway and an estimated speed of 80 kilometers per hour; the last 10 kilometers will enter the city, and considering the departure time of 8:00 AM, traffic congestion is expected after entering the city; navigation information indicates that there are no significant inclines or declines along the entire route. The travel plan planning module 210 in the energy management system 200 of the vehicle 100 can proactively plan based on the above information: because the engine 110 is highly efficient at medium to high constant speeds but not so efficient in congested areas, it is suitable to use the engine 110 for driving on highways and electric power for driving in congested conditions. In other words, the travel planning module 210 plans that the vehicle 100 will use the engine direct drive mode to drive in the first half of the road segment, and use a portion of the driving energy to charge the battery 140 by the drive motor 150 to increase the battery SOC, thereby reserving energy for pure electric drive in the second half of the congested road segment.

[0048] During actual driving on the second day, vehicle 100 executes step 410 to perform battery SOC detection, and then executes step 420 to determine whether the battery SOC exceeds the healthy state of charge range, for example, whether the battery SOC is greater than 70% or less than 32%. If step 420 determines that the battery SOC is greater than 70% or less than 32%, then step 430 is executed to limit the charging / discharging power of battery 140 and return to step 410 to re-monitor SOC. If step 420 determines that the battery SOC does not exceed the healthy state of charge range (i.e., between 32% and 70%), then step 440 is executed to drive according to the actively planned travel schedule. After step 440, step 450 is executed to determine whether the battery SOC monitored by the state of charge monitoring module 220 fluctuates during the driving of vehicle 100. If step 450 determines that the fluctuation exceeds the second fluctuation range (e.g., 32%-70%), then step 460 is executed to use a penalty factor to reward or penalize the charging and discharging of battery 140 within a short period of time. If step 450 determines that the fluctuation does not exceed the second fluctuation range, then the process returns to step 440 to continue actively planning the trip according to the travel plan.

[0049] As vehicle 100 is about to exit the highway section, the battery's State of Charge (SOC) has been raised to 65% (the highest critical point of the first fluctuation range), thus reserving energy for subsequent congested sections. However, after entering the city, the roads are not particularly congested, and the driver's skills are excellent, with little change in driving conditions compared to before. If vehicle 100's energy management system 200 calculates the fuel-electric ratio based on the ECMS, the result will indicate that vehicle 100 does not need to prioritize electric power. However, vehicle 100 has already reserved a significant amount of energy in the previous section. If the current ECMS strategy continues to be applied, the battery's energy will accumulate, causing the battery SOC to remain excessively high, potentially exceeding the healthy SOC range.

[0050] To avoid the above situation, this embodiment executes step 470 after step 460 to determine whether there is fluctuation in the battery state of charge. If step 450 determines that the fluctuation does not exceed the second fluctuation range, it returns to step 440 to continue actively planning the driving according to the travel plan. If step 470 determines that the fluctuation exceeds the first fluctuation range (e.g., 65% to 75%), it executes step 480 to correct the planning of the driving mode of the vehicle 100 based on historical driving data through the historical record correction module 230.

[0051] During step 480, the energy management system 200 of vehicle 100 retrieves historical driving data of vehicle 100 on urban roads after exiting the highway from the cloud to calculate the ECMS equivalent factor value based on the historical data. It then uses a self-learning method to adjust the deviation between the ECMS value predicted based on operating conditions and the actual value, ensuring that the fuel-electric ratio of vehicle 100 matches the current actual operating conditions after learning and correction. After step 480, the energy management method 300 returns to step 470 to continue determining whether there are fluctuations in the battery state of charge.

[0052] This application discloses an active energy management strategy based on navigation and travel information. This strategy integrates the concept of intelligent travel into the traditional energy management strategy, so as to enable the vehicle 100 to make customized active driving plans based on user data and navigation information by expanding the ways in which the vehicle 100 obtains data.

[0053] After obtaining the data required to control the driving mode of vehicle 100, the energy management system 200 and method 300 of this application can select which resources to call based on the current SOC state to realize an automated energy management process based on user data. After applying vehicle historical data and travel plan data, the energy management system 200 and method 300 of this application can adapt to changing road conditions and different driving styles, realizing user-customized automatic hybrid control and improving the user's driving experience.

[0054] Although only some embodiments of the invention have been described, those skilled in the art will understand that the invention can be implemented in many other forms without departing from its spirit and scope. Therefore, the examples and embodiments shown are to be considered illustrative rather than restrictive, and the invention may encompass various modifications and substitutions without departing from the spirit and scope of the invention as defined by the appended claims.

Claims

1. An energy management system for hybrid vehicles, characterized in that, The energy management system includes: A travel planning module is configured to receive a user's travel plan for using the hybrid vehicle and plan the driving mode of the hybrid vehicle during the travel process based on the travel plan. A state-of-charge (POC) monitoring module, configured to monitor the state of charge of the battery of the hybrid vehicle; and Historical record correction module, which is communicatively connected to the state of charge monitoring module, is configured to: Receive historical driving data of the hybrid vehicle, and When the value of the state of charge monitored by the state of charge monitoring module exceeds the first fluctuation range, the corresponding historical road segment is matched from the historical driving data according to the current position of the hybrid vehicle, and the driving mode of the hybrid vehicle is adjusted based on the historical speed record and historical state of charge record corresponding to the historical road segment only when the historical road segment is matched. A penalty factor adjustment module is communicatively connected to the state of charge monitoring module. The penalty factor adjustment module is configured to: when the state of charge value detected by the state of charge monitoring module exceeds the second fluctuation range, use a penalty factor to reward or punish the charging and discharging of the battery in a short period of time, so that the state of charge value quickly returns to the second fluctuation range; The second fluctuation range is greater than the first fluctuation range.

2. The energy management system according to claim 1, characterized in that, The travel plan includes the destination and departure time.

3. The energy management system according to claim 1, characterized in that, The first fluctuation range is 35%-65%.

4. The energy management system according to claim 1, characterized in that, The travel planning module determines the driving mode of the hybrid vehicle based on minimizing the equivalent fuel consumption of the hybrid vehicle. The equivalent fuel consumption value is the sum of the fuel consumption value of the engine of the hybrid vehicle and the fuel consumption value obtained by equivalent conversion of the amount of electricity consumed by the battery of the hybrid vehicle at a certain instant.

5. The energy management system according to claim 1, characterized in that, The second fluctuation range is 32%-70%.

6. An energy management method for hybrid vehicles, characterized in that, The energy management method includes: The travel planning steps include: receiving a user's travel plan for using the hybrid vehicle, and planning the driving mode of the hybrid vehicle during the travel process based on the travel plan; The state of charge (SCC) monitoring step includes: monitoring the SCC of the battery of the hybrid vehicle; and The historical record correction step includes: Receive historical driving data of the hybrid vehicle, and When the value of the state of charge monitored in the state of charge monitoring step exceeds the first fluctuation range, the corresponding historical road segment is matched from the historical driving data according to the current position of the hybrid vehicle, and the driving mode of the hybrid vehicle is adjusted based on the historical speed record and historical state of charge record corresponding to the historical road segment only when the historical road segment is matched. The penalty factor adjustment step includes: when the value of the state of charge monitored by the state of charge monitoring step exceeds the second fluctuation range, the penalty factor is used to reward or punish the charging and discharging of the battery in a short period of time so that the value of the state of charge quickly returns to the second fluctuation range; The second fluctuation range is greater than the first fluctuation range.

7. The energy management method according to claim 6, characterized in that, The travel plan includes the destination and departure time, and the historical driving data includes the historical driving route records, vehicle speed records, and battery state of charge records of the hybrid vehicle.

8. The energy management method according to claim 6, characterized in that, The first fluctuation range is 35%-65%.

9. The energy management method according to claim 6, characterized in that, The travel planning steps determine the driving mode of the hybrid vehicle based on minimizing the equivalent fuel consumption of the hybrid vehicle. The equivalent fuel consumption value is the sum of the fuel consumption value of the engine of the hybrid vehicle and the fuel consumption value obtained by equivalent conversion of the amount of electricity consumed by the battery of the hybrid vehicle at a certain instant.

10. The energy management method according to claim 9, characterized in that, The second fluctuation range is 32%-70%.

11. A hybrid vehicle, characterized in that, The hybrid vehicle includes an energy management system as described in any one of claims 1 to 5.

12. A computer storage medium, characterized in that, The computer storage medium includes instructions that, when executed, perform the energy management method as described in any one of claims 6 to 10.

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