Hybrid vehicle energy management strategy optimization method and system

Abstract

The invention relates to the technical field of vehicle control, in particular to an optimization method and system for an energy management strategy of a hybrid vehicle, and aims to determine a calibration working condition of an oil consumption test through vehicle type information of the hybrid vehicle, and perform a driving cycle test and collect vehicle operation data under the working condition. The calibration working condition is divided into a plurality of stages, the motor torque and the gear in each stage are used as control variables, the SOC of the battery is used as a state variable, and the control variables and the state variable are discretized to obtain a state transfer function; and constructing a cost function corresponding to each stage by taking the minimum fuel consumption as a target, taking the sum of the cost functions from the end stage to the starting stage as a target function, taking the minimum value of the target function, reversely calculating an optimal control variable and an accumulated cost function corresponding to each state variable from the end stage, and forming a query table. And a dynamic control switching threshold value is designed through an offline global optimal solution, so that the fuel consumption of the vehicle in each stage is close to a theoretical optimal value.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle control, and specifically to an optimization method and system for the energy management strategy of hybrid vehicles. Background Art

[0002] The statements in this part only provide background technical information related to the present invention and do not necessarily constitute prior art.

[0003] Hybrid electric vehicles (HEVs) are equipped with two power sources simultaneously, usually a thermal power source (such as a gasoline engine or a diesel engine) and an electric power source (including a battery and a motor). Currently, the mainstream energy management strategy for HEVs is the charge depletion - charge sustaining (CD - CS) strategy, that is, the working mode is switched according to the state of charge (SOC) of the battery. In the charge depletion (CD) mode, electric energy is preferentially used for driving; in the charge sustaining (CS) mode, the engine dominates the driving to maintain the charge balance.

[0004] For example, when the battery SOC > 0.8, the vehicle is in a pure - electric working state; when 0.3 < SOC < 0.8 of the battery, the motor is the main power source; when the battery SOC < 0.3, the engine is the main power source.

[0005] However, the SOC thresholds of the CD - CS strategy rely on static calibration by engineers based on laboratory standard cycles (such as regulatory driving cycles), which differ greatly from the real - world road conditions. This leads to the following problems with the strategy: Decision fragmentation: The power distribution between the CD stage and the CS stage lacks coordination and cannot be dynamically optimized in combination with future road conditions (such as the energy recovery opportunity when going downhill soon); Limited fuel economy: Static rules are difficult to adjust in real - time, may miss the best opportunity to use hybrid power, and reduce fuel efficiency.

[0006] Therefore, the traditional CD - CS strategy is limited by manual calibration and laboratory conditions and is difficult to achieve optimal fuel economy in actual driving. Summary of the Invention

[0007] To solve the technical problems existing in the above - mentioned background art, the present invention provides an optimization method and system for the energy management strategy of hybrid vehicles. For known working conditions, an offline global optimal solution is obtained by analyzing based on the dynamic programming algorithm, and the global optimal energy distribution rule is refined to optimize the traditional CD - CS - type strategy to achieve better fuel economy.

[0008] To achieve the above object, the present invention adopts the following technical solutions: The first aspect of the present invention provides an optimization method for the energy management strategy of hybrid vehicles, including the following steps: Obtain information about the hybrid vehicle to be optimized, determine the calibration operating conditions for the fuel consumption test, conduct a driving cycle test under these operating conditions, and simultaneously collect vehicle operating status data; The calibration working condition is divided into several stages. The motor torque and gear position in each stage are used as control variables, and the battery SOC is used as the state variable. The control variables and state variables are discretized to obtain the state transfer function. With the goal of minimizing fuel consumption, a cost function corresponding to each stage is constructed, and the sum of the cost functions from the terminal stage to the starting stage is used as the objective function. When the objective function takes the minimum value, the optimal control variable and cumulative cost function corresponding to each state variable are calculated in reverse from the terminal stage and saved as a lookup table for optimizing the energy management strategy.

[0009] Furthermore, the calibration condition is divided into N stages, each stage has a set sampling time, and the motor torque in each stage is and gear position as control variables u(k) , battery SOC is the state variable x(k) , for motor torque Discretize SOC and determine the state transfer function.

[0010] Furthermore, the state transfer function is shown below; ; Where, Indicates the SOC value of the Kth stage, I ( K +1) represents the battery current in the K+1th stage, and C is the battery capacity.

[0011] Furthermore, during the driving cycle of the hybrid vehicle to be optimized under the calibration conditions, the engine and motor speeds are obtained, and the torque relationship between the wheels and the engine and motor, as well as the speed relationship between the wheels and the engine and motor are determined based on the speed ratio between the motor and the engine, the transmission efficiency, and the speed ratio of the final reducer in different gears.

[0012] Furthermore, the constraints during the driving cycle are determined based on the torque relationship and the speed relationship, as shown in the following equation: ; Where, Indicates the SOC value of the Kth stage, 、 are the upper and lower limits of the power battery capacity respectively; and are the speeds of the engine and motor at stage K, and are the output torques of the engine and motor in the Kth stage respectively, 、 are the minimum and maximum speeds of the motor respectively; 、 is the minimum and maximum output torque of the engine; 、 are the minimum and maximum speeds of the engine respectively; 、 The minimum and maximum torques that the motor can provide.

[0013] Furthermore, with the goal of minimizing fuel consumption, the cost function corresponding to each stage is constructed as shown in the following formula: ( x(k),u(k) )= ; Where, is the fuel consumption, expressed as the motor torque in each stage and gear position as control variables u(k) , battery SOC as a state variable x(k) .

[0014] Furthermore, the sum of the cost functions from the end stage to the starting stage is used as the objective function, specifically: the objective function of the Kth stage Defined as the sum of the cost functions from stage K to stage N, .

[0015] Furthermore, when the objective function takes the minimum value, the optimal control variable and cumulative cost function corresponding to each state variable are calculated in reverse from the end stage, specifically: hour, is the optimal objective function of the K stage, and the optimal control variable and cumulative cost function corresponding to each state variable are calculated.

[0016] Furthermore, the optimal control variable and cumulative cost function corresponding to each state variable are saved to form an optimal control table and a cumulative cost table. According to the current battery SOC value and the current operating stage, the corresponding optimal motor torque and gear are obtained by looking up the table to optimize the energy management strategy.

[0017] A second aspect of the present invention provides a system for optimizing a hybrid vehicle energy management strategy, comprising: The calibration module is configured to: obtain information about the hybrid vehicle to be optimized, determine a calibration operating condition for a fuel consumption test, perform a driving cycle test under the operating condition, and simultaneously collect vehicle operating status data; The optimization module is configured to: divide the calibration working condition into several stages, use the motor torque and gear position in each stage as control variables, and the battery SOC as the state variable, discretize the control variables and state variables to obtain the state transfer function; The optimization module is also configured to: construct a cost function corresponding to each stage with the goal of minimizing fuel consumption, use the sum of the cost functions from the end stage to the starting stage as the objective function, and when the objective function takes the minimum value, reversely calculate the optimal control variable and cumulative cost function corresponding to each state variable from the end stage, save them as a lookup table, and use them to optimize the energy management strategy.

[0018] Compared with the existing technology, one or more of the above technical solutions have the following beneficial effects: During calibration, the vehicle operates under conditions that meet emission requirements. Traditional methods require engineers to empirically determine the various thresholds for hybrid vehicle control within the CD-CS strategy. This reliance on subjective experience lacks a global optimization basis. Furthermore, due to discrepancies between calibration conditions and actual driving conditions, the thresholds struggle to align with the vehicle's optimal operating conditions. Furthermore, the lack of predictive information regarding future operating conditions prevents optimal cumulative fuel economy. This proposed solution, under known calibration conditions, uses a backward recursive method to calculate the optimal torque distribution and gear selection at each moment (stage) to minimize total fuel consumption from the starting point to the end point. This approach also comprehensively considers power demand fluctuations across the entire operating condition to avoid short-sighted decision-making. The state transfer function accurately describes the dynamic characteristics of the hybrid vehicle at each stage, extracts the distribution of engine / motor operating points from the optimal solution, and transforms the complex optimal control sequence into conditional judgment rules. Dynamic CD→CS switching thresholds can be designed based on the SOC variation trend in the optimal solution, enabling the hybrid vehicle to achieve fuel consumption close to the theoretical optimal value at each stage of the selected operating condition. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0020] Figure 1 This is a schematic diagram of the overall process of a hybrid vehicle energy management strategy optimization method provided by one or more embodiments of the present invention; Figure 2 is a schematic diagram of a prior art hybrid vehicle energy management strategy provided by one or more embodiments of the present invention; Figure 3 Schematic diagram of the optimization process of the hybrid vehicle energy management strategy provided by one or more embodiments of the present invention. DETAILED DESCRIPTION

[0021] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0022] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0023] Explanation of terms: Hybrid vehicle: A vehicle that has two sources of power, usually a thermal power source (gasoline or diesel engine) and an electric power source (battery and electric motor).

[0024] The CD-CS strategy: Charge Depletion mode and Charge Sustain mode. By controlling battery state of charge (SOC) usage in stages, fuel economy and power performance are optimized. In Charge Depletion (CD) mode, battery energy is prioritized for driving the vehicle, reducing engine intervention and lowering fuel consumption. In Charge Sustain (CS) mode, the battery SOC is maintained at a low but stable level to avoid excessive battery discharge while optimizing engine efficiency.

[0025] SOC: State of Charge, the remaining power of the battery.

[0026] Commonly used calibration cycles include the NEDC (New European Commission Driverless Car Cycle), the WLTP (World Light Vehicle Test Procedure), and the CLTC (China Light Vehicle Test Cycle). These cycles, also known as regulatory cycles, are derived from existing regulations and are standard test cycles used to evaluate vehicle fuel economy (fuel consumption), emissions performance (such as CO2 and NO3), and electric range (such as for EVs / PHEVs). Testing methods vary across countries and regions, influencing energy efficiency calibration strategies during vehicle manufacturing.

[0027] The following embodiments provide a hybrid vehicle energy management strategy optimization method and system. Based on known regulatory operating conditions, the DP algorithm is used to analyze and obtain an offline global optimal solution. The globally optimal energy allocation rule is extracted to optimize the traditional CD-CS strategy to achieve better fuel economy.

[0028] Example 1: like Figure 1 As shown, the optimization method of the hybrid vehicle energy management strategy includes the following steps: Obtain the vehicle model information of the hybrid vehicle to be optimized, determine the calibration working conditions for fuel consumption testing, conduct a driving cycle test under these conditions, and collect vehicle operating state data at the same time; Divide the calibration working conditions into several stages. Take the motor torque and gear in each stage as control variables, and the SOC of the battery as the state variable. Discretize the control variables and state variables to obtain the state transfer function; With the goal of minimizing fuel consumption, construct the cost function corresponding to each stage. Take the sum of the cost functions from the end stage to the start stage as the objective function. When the objective function takes the minimum value, calculate backward from the end stage to obtain the optimal control variable and the cumulative cost function corresponding to each state variable, and save them as a lookup table for optimizing the energy management strategy.

[0029] As a further implementation method, save the optimal control variable and the cumulative cost function corresponding to each state variable, form an optimal control table and a cumulative cost table. According to the current battery SOC value and the current working condition stage, look up the table to obtain the corresponding optimal motor torque and gear, and realize the optimization of the energy management strategy.

[0030] This solution is based on known regulatory working conditions, uses the dynamic programming algorithm to obtain the offline global optimal solution, analyzes and extracts rules from the optimal solution, and uses these rules to optimize and improve the traditional CD-CS type strategy to improve fuel economy.

[0031] As Figure 2 shown, the existing CD-CS type energy management strategy determines the respective working states of the engine and the motor according to the battery SOC. When the battery SOC > 0.8, it is in the pure electric working state. When 0.3 < SOC < 0.8 of the battery, the motor is the main power source. When the battery SOC < 0.3, the engine is the main power source.

[0032] The threshold points in the CD-CS strategy, such as "0.3", "0.8", etc., are determined depending on the subjective experience of engineers during calibration. At the same time, the calibration working conditions are laboratory conditions that meet regulatory emission requirements, and the actual operating conditions of the vehicle are dynamically changing, resulting in decision fragmentation when the vehicle switches states (such as switching to charge sustaining after the power is exhausted), and it is also impossible to predict future road conditions (such as being able to recover energy when going downhill soon), which may miss a better opportunity to use hybrid power and lead to unsatisfactory fuel economy.

[0033] This solution aims at known regulatory working conditions, analyzes and obtains the offline global optimal solution based on the dynamic programming algorithm, extracts the global optimal energy distribution rule, and optimizes the traditional CD-CS type strategy to achieve better fuel economy.

[0034] The principle of the dynamic programming algorithm (DP algorithm) is: if the path through N stages is the optimal path, the sub-route from any stage K to the last stage N of this path must be x k The best path among all paths starting from the starting point.

[0035] Using this principle, the multi-stage decision-making problem is transformed into the problem of finding the optimal solution to the single-stage decision-making problem, so that each stage can make the best decision corresponding to this stage, thereby achieving the overall optimal decision in the reverse state transfer process of the multi-stage problem, and the best decision in each stage constitutes the optimal strategy.

[0036] The purpose of the DP algorithm is to achieve the optimal control effect by reasonably distributing the torque of the engine and the motor. The key lies in determining the control variables in the k stage. u k , system state variables x k , optimal objective function f k , stage cost function v k and the state transfer function T .

[0037] Under the premise that the driving conditions are known, the torque required by the vehicle Torque output by the motor and engine output torque Provided jointly through a power coupling mechanism.

[0038] The relationship between the torque and speed of the wheel, engine and motor is shown in the following formula: ; ; Where, is the total efficiency of the transmission system, is the speed ratio between the motor and the engine, R(i) For the i The product of the speed ratio of the gear and the main reducer, 、 、 are the speeds of the engine, motor, and wheels, respectively.

[0039] The regulatory conditions are divided into N stages, and the sampling time is 1s. Considering the SOC and Directly related to the motor torque , gear position as the control variable of the system u(k) , the battery state of charge SOC is the system state variable x(k) , and By discretizing SOC and , the state transfer function can be obtained as follows: ; Where, Indicates the SOC value of the Kth stage, I ( K +1) represents the battery current in the K+1 stage, C is the battery capacity, A·h.

[0040] At the same time, the battery SOC, engine and motor speed and torque must meet the following constraints: ; Where, and are the speeds of the engine and motor at stage K, is the output torque of the motor in the Kth stage, is the output torque of the engine in stage K, 、 are the upper and lower limits of the power battery capacity respectively; 、 are the minimum and maximum speeds of the motor respectively; 、 is the minimum and maximum output torque of the engine; 、 are the minimum and maximum speeds of the engine respectively; 、 The minimum and maximum torques that the motor can provide.

[0041] The global optimal energy management strategy based on the DP algorithm aims to minimize fuel consumption without considering emissions. Preliminary research found that there is no obvious frequent gear shifting and engine start-stop phenomenon, so the stage cost function is defined as v k for: v k ( x(k),u(k) )= ; Where, is the fuel consumption, g / s.

[0042] The objective function of the Kth stage It is defined as the sum of the cost functions from stage K to stage N, as shown below: .

[0043] The optimal objective function of K stage is the minimum value of the objective function, as shown below: .

[0044] As mentioned above, the control variables, system state variables, state transfer function, stage cost function, and optimal objective function have all been confirmed. Based on the above conditions, a dynamic programming algorithm can be established based on the specified regulatory operating conditions to reasonably allocate engine torque and motor torque to obtain the lowest fuel consumption.

[0045] like Figure 3 As shown, the optimization method of the hybrid vehicle energy management strategy includes the following steps: Confirm vehicle-related parameters according to design requirements, such as vehicle mass and dimensions, and the mass and dimensions of components such as the engine, motor, transmission, and battery; Identify the regulatory operating conditions and driving cycles for the fuel consumption test of the vehicle to be optimized, for example, using international standard operating conditions such as NEDC (New European Car Regulation Cycle) and WLTP (World Light Vehicle Test Procedure); Obtain relevant information required by the DP algorithm, such as control variables, state variables, optimal objective function, stage cost function, and state transfer function; Run the DP algorithm to extract the optimal power distribution rule, and formulate the control strategy of the hybrid vehicle based on the obtained optimal power distribution rule.

[0046] Through existing software or tools, build a physical model of the whole vehicle and an energy management strategy control model for joint simulation, simulate the DP algorithm and extract rules. For example, use AMESIM to build a physical model of the whole vehicle and Simulink to build an energy management strategy control model for joint simulation.

[0047] This solution calculates the global optimal control strategy based on the dynamic programming algorithm and extracts the control rules to provide guidance for the formulation of the control strategy. It can approach the global optimal while meeting practical applications.

[0048] Commonly used calibration cycles include the NEDC (New European Commission Driverless Car Cycle), the WLTP (World Light Vehicle Test Procedure), and the CLTC (China Light Vehicle Test Cycle). These cycles are derived from existing regulations and are also known as regulatory cycles. During calibration, the vehicle is operated under a specific regulatory cycle. Engineers use experience to determine the thresholds for hybrid vehicle control in the CD-CS strategy. This reliance on subjective experience lacks a basis for global optimization. Furthermore, regulatory cycles differ significantly from actual driving, making fixed thresholds difficult to address the dynamic demands of the vehicle during actual operation. Furthermore, real-time strategies only consider the current state (such as SOC) and fail to foresee future conditions, resulting in suboptimal cumulative fuel economy. This solution, however, uses a backward recursive method to calculate the optimal torque distribution and gear selection at each moment (stage) under known regulatory conditions, ensuring minimal total fuel consumption from the start to the end point. This approach considers the changing power demand across the entire cycle and avoids short-sighted decisions. The state transfer function accurately reflects the dynamic changes of the hybrid vehicle in each stage, and the distribution law of the engine / motor operating point is obtained from the optimal solution. The complex optimal control sequence is simplified into conditional judgment rules. According to the SOC change trend in the optimal solution, a dynamic CD→CS switching threshold can be designed, so that the fuel consumption of the hybrid vehicle in each stage of the selected operating conditions can approach the theoretical optimal value.

[0049] Example 2: Optimization system for hybrid vehicle energy management strategy, The calibration module is configured to: obtain information about the hybrid vehicle to be optimized, determine a calibration operating condition for a fuel consumption test, perform a driving cycle test under the operating condition, and simultaneously collect vehicle operating status data; The optimization module is configured to: divide the calibration working condition into several stages, use the motor torque and gear position in each stage as control variables, and the battery SOC as the state variable, discretize the control variables and state variables to obtain the state transfer function; The optimization module is also configured to: construct a cost function corresponding to each stage with the goal of minimizing fuel consumption, use the sum of the cost functions from the end stage to the starting stage as the objective function, and when the objective function takes the minimum value, reversely calculate the optimal control variable and cumulative cost function corresponding to each state variable from the end stage, save them as a lookup table, and use them to optimize the energy management strategy.

[0050] Under known calibration conditions, the system calculates the optimal torque distribution and gear selection at each moment (stage) through reverse recursion to minimize total fuel consumption from the starting point to the end point, comprehensively considering the power demand changes throughout the entire operating condition to avoid short-sighted decisions. During this process, the state transfer function accurately reflects the dynamic changes of the hybrid vehicle at each stage. The distribution pattern of the engine / motor operating points is derived from the optimal solution, simplifying the complex optimal control sequence into conditional judgment rules. Based on the SOC change trend in the optimal solution, dynamic CD→CS switching thresholds can be designed, ensuring that the hybrid vehicle's fuel consumption at each stage of the selected operating condition approaches the theoretical optimal value.

[0051] Example 3: An electronic device includes at least one processor and a memory connected to the processor, the memory being used to store a computer program; the processor being used to execute the computer program, so that the electronic device can implement the above-mentioned method for optimizing the hybrid vehicle energy management strategy. The method includes the following steps: Obtain information about the hybrid vehicle to be optimized, determine the calibration operating conditions for the fuel consumption test, conduct a driving cycle test under these operating conditions, and simultaneously collect vehicle operating status data; The calibration working condition is divided into several stages. The motor torque and gear position in each stage are used as control variables, and the battery SOC is used as the state variable. The control variables and state variables are discretized to obtain the state transfer function. With the goal of minimizing fuel consumption, a cost function corresponding to each stage is constructed, and the sum of the cost functions from the terminal stage to the starting stage is used as the objective function. When the objective function takes the minimum value, the optimal control variable and cumulative cost function corresponding to each state variable are calculated in reverse from the terminal stage and saved as a lookup table for optimizing the energy management strategy.

[0052] As a further implementation method, the calibration condition is divided into N stages, each stage has a set sampling time, and the motor torque in each stage is and gear position as control variables u(k) , battery SOC is the state variable x(k) , for motor torque Discretize SOC and determine the state transfer function.

[0053] As a further embodiment, the state transfer function is shown as follows: ; Where, Indicates the SOC value of the Kth stage, I ( K +1) represents the battery current in the K+1th stage, and C is the battery capacity.

[0054] As a further implementation method, during a driving cycle of the hybrid vehicle to be optimized under calibrated operating conditions, the engine and motor speeds are obtained, and based on the speed ratio of the motor to the engine, the transmission efficiency, and the speed ratio of the final reducer in different gears, the torque relationship between the wheels and the engine and motor, as well as the speed relationship between the wheels and the engine and motor are determined.

[0055] As a further embodiment, the constraints during the driving cycle are determined based on the torque relationship and the speed relationship, as shown in the following equation: ; Where, Indicates the SOC value of the Kth stage, 、 are the upper and lower limits of the power battery capacity respectively; and are the speeds of the engine and motor at stage K, and are the output torques of the engine and motor in the Kth stage respectively, 、 are the minimum and maximum speeds of the motor respectively; 、 is the minimum and maximum output torque of the engine; 、 are the minimum and maximum speeds of the engine respectively; 、 The minimum and maximum torques that the motor can provide.

[0056] As a further implementation method, with the goal of minimizing fuel consumption, a cost function corresponding to each stage is constructed as shown in the following formula: ( x(k),u(k) )= ; Where, is the fuel consumption, expressed as the motor torque in each stage and gear position as control variables u(k) , battery SOC as a state variable x(k) .

[0057] As a further implementation method, the sum of the cost functions from the end stage to the starting stage is used as the objective function, specifically: the objective function of the Kth stage Defined as the sum of the cost functions from stage K to stage N, .

[0058] As a further implementation method, when the objective function takes the minimum value, the optimal control variable and cumulative cost function corresponding to each state variable are calculated reversely from the end stage, specifically: hour, is the optimal objective function of the K stage, and the optimal control variable and cumulative cost function corresponding to each state variable are calculated.

[0059] As a further implementation method, the optimal control variable and cumulative cost function corresponding to each state variable are saved to form an optimal control table and a cumulative cost table. According to the current battery SOC value and the current operating stage, the corresponding optimal motor torque and gear are obtained by looking up the table to optimize the energy management strategy.

[0060] Under known calibration conditions, the system calculates the optimal torque distribution and gear selection at each moment (stage) through reverse recursion to minimize total fuel consumption from the starting point to the end point, comprehensively considering the power demand changes throughout the entire operating condition to avoid short-sighted decisions. During this process, the state transfer function accurately reflects the dynamic changes of the hybrid vehicle at each stage. The distribution pattern of the engine / motor operating points is derived from the optimal solution, simplifying the complex optimal control sequence into conditional judgment rules. Based on the SOC change trend in the optimal solution, dynamic CD→CS switching thresholds can be designed, ensuring that the hybrid vehicle's fuel consumption at each stage of the selected operating condition approaches the theoretical optimal value.

[0061] Example 4: A computer program product includes computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements the above-mentioned method for optimizing the hybrid vehicle energy management strategy. The method includes the following steps: Obtain information about the hybrid vehicle to be optimized, determine the calibration operating conditions for the fuel consumption test, conduct a driving cycle test under these operating conditions, and simultaneously collect vehicle operating status data; The calibration working condition is divided into several stages. The motor torque and gear position in each stage are used as control variables, and the battery SOC is used as the state variable. The control variables and state variables are discretized to obtain the state transfer function. With the goal of minimizing fuel consumption, a cost function corresponding to each stage is constructed, and the sum of the cost functions from the terminal stage to the starting stage is used as the objective function. When the objective function takes the minimum value, the optimal control variable and cumulative cost function corresponding to each state variable are calculated in reverse from the terminal stage and saved as a lookup table for optimizing the energy management strategy.

[0062] Under known calibration conditions, the system calculates the optimal torque distribution and gear selection at each moment (stage) through reverse recursion to minimize total fuel consumption from the starting point to the end point, comprehensively considering the power demand changes throughout the entire operating condition to avoid short-sighted decisions. During this process, the state transfer function accurately reflects the dynamic changes of the hybrid vehicle at each stage. The distribution pattern of the engine / motor operating points is derived from the optimal solution, simplifying the complex optimal control sequence into conditional judgment rules. Based on the SOC change trend in the optimal solution, dynamic CD→CS switching thresholds can be designed, ensuring that the hybrid vehicle's fuel consumption at each stage of the selected operating condition approaches the theoretical optimal value.

[0063] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for optimizing a hybrid vehicle energy management strategy, characterized in that: The following steps are involved: Obtain information about the hybrid vehicle to be optimized, determine the calibration operating conditions for the fuel consumption test, conduct a driving cycle test under these operating conditions, and simultaneously collect vehicle operating status data; The calibration working condition is divided into several stages. The motor torque and gear position in each stage are used as control variables, and the battery SOC is used as the state variable. The control variables and state variables are discretized to obtain the state transfer function. With the goal of minimizing fuel consumption, a cost function corresponding to each stage is constructed, and the sum of the cost functions from the terminal stage to the starting stage is used as the objective function. When the objective function takes the minimum value, the optimal control variable and cumulative cost function corresponding to each state variable are calculated in reverse from the terminal stage and saved as a lookup table for optimizing the energy management strategy.

2. The method for optimizing the energy management strategy of a hybrid vehicle according to claim 1, wherein: The calibration condition is divided into N stages, each stage has a set sampling time, and the motor torque in each stage is and gear position as control variables u(k) , battery SOC is the state variable x(k) , for motor torque Discretize SOC and determine the state transfer function.

3. The method for optimizing the energy management strategy of a hybrid vehicle according to claim 1, wherein: The state transfer function is shown below; ； Where, Indicates the SOC value of the Kth stage, I ( K +1) represents the battery current in the K+1 stage, C is the battery capacity.

4. The method for optimizing the energy management strategy of a hybrid vehicle according to claim 1, wherein: During the driving cycle of the hybrid vehicle to be optimized under the calibration conditions, the engine and motor speeds are obtained. Based on the speed ratio between the motor and the engine, the transmission efficiency, and the speed ratio of the final reducer in different gears, the torque relationship between the wheels and the engine and motor, as well as the speed relationship between the wheels and the engine and motor are determined.

5. The method for optimizing the energy management strategy of a hybrid vehicle according to claim 4, wherein: The constraints during the driving cycle are determined based on the torque relationship and the speed relationship, as shown in the following equation: ； Where, Indicates the SOC value of the Kth stage, 、 are the upper and lower limits of the power battery capacity respectively; and are the speeds of the engine and motor at stage K, and are the output torques of the engine and motor in the Kth stage respectively, 、 are the minimum and maximum speeds of the motor respectively; 、 is the minimum and maximum output torque of the engine; 、 are the minimum and maximum speeds of the engine respectively; 、 The minimum and maximum torques that the motor can provide.

6. The method for optimizing the energy management strategy of a hybrid vehicle according to claim 1, wherein: With the goal of minimizing fuel consumption, the cost function corresponding to each stage is constructed as shown below: v k ( x(k),u(k) )= ； Where, is the fuel consumption, expressed as the motor torque in each stage and gear position as control variables u (k) , battery SOC as a state variable x(k) .

7. The method for optimizing the energy management strategy of a hybrid vehicle according to claim 1, wherein: The sum of the cost functions from the end stage to the starting stage is used as the objective function, specifically: the objective function of the Kth stage Defined as the sum of the cost functions from stage K to stage N, , is the fuel consumption.

8. The method for optimizing the energy management strategy of a hybrid vehicle according to claim 1, wherein: When the objective function takes the minimum value, the optimal control variable and cumulative cost function corresponding to each state variable are calculated in reverse from the end stage, specifically: hour, is the optimal objective function of the K stage, and the optimal control variable and cumulative cost function corresponding to each state variable are calculated.

9. The method for optimizing the energy management strategy of a hybrid vehicle according to claim 8, wherein: The optimal control variable and cumulative cost function corresponding to each state variable are saved to form an optimal control table and a cumulative cost table. According to the current battery SOC value and the current operating stage, the corresponding optimal motor torque and gear are obtained by looking up the table to optimize the energy management strategy.

10. An optimization system for hybrid vehicle energy management strategy, characterized in that: include: The calibration module is configured to: obtain information about the hybrid vehicle to be optimized, determine a calibration operating condition for a fuel consumption test, perform a driving cycle test under the operating condition, and simultaneously collect vehicle operating status data; The optimization module is configured to: divide the calibration working condition into several stages, use the motor torque and gear position in each stage as control variables, and the battery SOC as the state variable, discretize the control variables and state variables to obtain the state transfer function; The optimization module is also configured to: construct a cost function corresponding to each stage with the goal of minimizing fuel consumption, use the sum of the cost functions from the end stage to the starting stage as the objective function, and when the objective function takes the minimum value, reversely calculate the optimal control variable and cumulative cost function corresponding to each state variable from the end stage, save them as a lookup table, and use them to optimize the energy management strategy.

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