Hybrid electric vehicle online adaptive energy management method and system

Abstract

The present application relates to a kind of hybrid vehicle online self-adaptive energy management method and system, method includes the following steps: obtaining vehicle speed, demand torque, battery information and whole vehicle demand power, select working mode;Engine power and battery power are distributed;Optimize engine operating point;Motor speed is optimized, and the operating point of motor is calculated;Output working mode, engine operating point and motor operating point.Compared with prior art, the offline optimization result of PSO+ECMS and DP+ECMS energy management strategy is analyzed, the change rule of control variable is found, the control rule is extracted, the working mode selection, power distribution and operating point optimization are corrected, meet the online control requirement, it is advantageous to apply the result of global optimization to guide real vehicle control strategy.

Description

Technical Field

[0001] This invention relates to the field of energy management for hybrid electric vehicles, and in particular to an online adaptive energy management method and system for hybrid electric vehicles. Background Technology

[0002] Hybrid electric vehicles (HEVs) have garnered widespread attention as a transitional product from traditional gasoline-powered vehicles to pure electric vehicles. As a key issue in the research field of hybrid vehicles, the quality of energy management strategies directly impacts the vehicle's reliability, maneuverability, economy, and emissions performance.

[0003] Currently, based on the control requirements of hybrid vehicles, their energy management strategies can be broadly classified into three categories. The first category is rule-based energy management strategies, specifically including deterministic rule energy management strategies, fuzzy rule energy management strategies, and load power filtering energy management strategies. The second category is optimization-based energy management strategies, specifically including global optimization energy management strategies and instantaneous optimization energy management strategies. The third category is operating condition adaptive energy management strategies, which are implemented by predicting future operating conditions using existing information.

[0004] Among existing technologies, RB+ECMS (rule-based and equivalent fuel consumption minimization), PSO+ECMS (particle swarm optimization algorithm and equivalent fuel consumption minimization), and DP+ECMS (dynamic programming and equivalent fuel consumption minimization) energy management strategies are relatively typical. The RB+ECMS-based energy management strategy uses a rule-based (RB) approach for operating mode selection and an ECMS approach for power allocation and engine / motor operating point optimization. The paper "Equivalent consumption minimization strategy for parallel hybrid powertrains" was an early proposer of ECMS, a commonly used instantaneous optimization energy management strategy that scientifically equates electricity consumption to fuel consumption and selects the operating point with the lowest overall fuel consumption in real time. The paper "Mode shift map design and integrated energy management control of a multi-mode hybrid electric vehicle" uses dynamic programming to optimize the energy management strategy of a general-purpose Volt with multiple operating modes. It considers the frequency of gear shifts and energy loss during mode switching, extracts the operating points of various optimized operating modes to form shift rules, and combines them with the ECMS strategy for actual vehicle control. The paper "Real-time Strategy for Plug-in Hybrid Bus Based on Particle Swarm Optimization (PSO)" utilizes PSO to offline optimize the equivalent factor and engine start-up speed under specific operating conditions, and establishes an equivalent fuel consumption minimization strategy (ECMS) based on equivalent factor optimization.

[0005] Furthermore, Chinese patent CN115071673A discloses a real-time energy management strategy for plug-in hybrid electric vehicles based on model-free adaptive control. The energy management strategy adopts a hierarchical optimization control architecture, which is divided into an upper layer and a lower layer. The upper layer is based on the feedback control principle, and can output the control variable λ in real time through the feedback between the vehicle's target state and the actual state for the control of the lower layer. The key control parameter of the lower layer is the costate variable λ. Through the online control adjustment of the model-free adaptive controller, the online optimization of λ under unknown operating conditions can be realized, thereby ensuring the adaptive capability of the lower-layer PMP strategy to unknown operating conditions, improving the algorithm performance while ensuring the vehicle's economy. Chinese patent CN113859219A relates to an adaptive energy management method for hybrid electric vehicles based on driving condition identification. The method first divides the driving condition into grid cells and calculates the typical characteristic parameters of each grid cell. It then uses PCA to reduce the dimensionality of the characteristic parameters and employs a clustering analysis algorithm to classify the driving conditions. A neural network-based driving condition identification algorithm is established, and the neural network model is trained offline using the characteristic parameters and driving condition types. Historical speed data is used to identify the driving condition type online, and the equivalent factor is updated in real time. An equivalent fuel consumption minimization strategy is used to obtain the engine-motor power allocation online, ensuring the engine operates in a high-efficiency region. This scheme considers the impact of driving conditions on energy management performance. By identifying driving conditions online, the equivalent factor can be optimized in real time, improving vehicle fuel economy and the adaptability of the energy management strategy to driving conditions.

[0006] Although the above energy management strategy takes into account factors such as fuel consumption, feedback, and operating condition identification, its analysis of the current vehicle status is still insufficient, and there is still room for improvement in the determined energy management strategy. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a hybrid electric vehicle online adaptive energy management method and system.

[0008] The objective of this invention can be achieved through the following technical solutions:

[0009] According to a first aspect of the present invention, an online adaptive energy management method for a hybrid electric vehicle is provided, comprising the following steps:

[0010] S1. Obtain vehicle speed, required torque, battery information, and total vehicle power requirement, and select the operating mode;

[0011] S2. If the operating mode is pure electric mode, then execute step S4; otherwise, the operating mode is hybrid mode, and the engine power and battery power are allocated.

[0012] S3. Optimize the engine operating point;

[0013] S4. Optimize the motor speed and calculate the motor operating point;

[0014] S5, Output Operating Mode, Engine Operating Point, and Motor Operating Point.

[0015] Further, step S1 specifically includes:

[0016] Based on the particle swarm optimization algorithm and the energy management strategy of minimizing equivalent fuel consumption, a working mode table of PSO+ECMS is established.

[0017] The operating mode can be obtained by looking up the table in the PSO+ECMS operating mode table according to the vehicle speed and required torque.

[0018] If the lookup result indicates pure electric mode, determine whether the battery SOC is lower than the preset low charge threshold SOC. low If yes, select hybrid mode; otherwise, keep pure electric mode.

[0019] If the lookup result indicates hybrid mode, determine whether the battery SOC is higher than the preset high charge threshold SOC. high And whether the total power demand of the vehicle is less than the preset high power threshold P out_high If yes, select pure electric mode; otherwise, keep hybrid mode.

[0020] Furthermore, in step S2, the power allocation specifically involves:

[0021] Based on dynamic programming algorithm and equivalent fuel consumption minimum energy management strategy, the power split ratio under different vehicle power demand is calculated;

[0022] Cluster analysis was performed on multiple power shunt ratios obtained under different vehicle power requirements, and curve fitting was performed on the cluster centers of the power shunt ratios obtained under different vehicle power requirements. The curve was used as the online power allocation rule for medium SOC.

[0023] The upper and lower boundaries of the power shunt ratio for different vehicle power requirements are fitted and used as online power allocation rules for low SOC and high SOC, respectively.

[0024] Based on the current vehicle power demand and battery SOC, a power shunt ratio is determined, and engine power and battery power are allocated based on the power shunt ratio.

[0025] Furthermore, in step S3, if the hybrid power mode is a power split mode, the engine operating point is optimized as follows:

[0026] Take the isopower line of the engine's target power, and take the intersection of the isopower line and the optimal fuel consumption line as the engine's optimal operating point.

[0027] Furthermore, in step S3, if the hybrid mode is a fixed gear ratio mode, the engine speed is calculated from the vehicle speed, and the engine torque is calculated from the engine target power and the engine speed, as shown in the following formula:

[0028]

[0029]

[0030] Where, n ICE T is the engine speed. ICE V represents the engine torque. spd For vehicle speed, P ICE For the engine target power, i g For the gearbox ratio, i 01 Main reducer transmission ratio, r wheel The radius is the wheel radius.

[0031] Furthermore, in step S4, if the electric mode is a power-split mode, the motor speed is determined based on the principle of minimizing power loss. The formula for calculating the power loss is:

[0032] P elec_loss =P M / G1_loss +P M / G2_loss +P batt_loss

[0033] Among them, P elec_loss For the total power loss, P M / G1_loss P M / G2_loss and P batt_loss These are the power losses of motor M / G1, motor M / G2, and battery, respectively.

[0034] Furthermore, when the motor operates in generator mode, the motor power loss is as shown in the following equation:

[0035]

[0036] When the motor is in drive mode, the power loss of the motor is as shown in the following formula:

[0037]

[0038] When the battery is in the charging state, the battery power loss is as shown in the following formula:

[0039] P batt_loss =P batt ·(1-η chg )

[0040] When the battery is operating in a discharging state, the battery power loss is as shown in the following formula:

[0041]

[0042] In the formula, P M / G_loss n represents the power loss of the motor. M / G T represents the motor's rotational speed. M / G η represents the torque of the motor. gen and η mot These are the generator efficiency and drive efficiency of the motor, respectively, P batt For battery output power, η chg and η dchg These represent the battery's charging efficiency and discharging efficiency, respectively.

[0043] Furthermore, in step S4, if the electric mode is a fixed transmission ratio mode, then the motor speed and torque are the speed and torque determined under the fixed transmission ratio mode.

[0044] Furthermore, it also includes: identifying the current operating condition or driving style, correcting the operating mode in step S1 and the power distribution in step S2 according to the current operating condition or driving style, and performing calculations in step S3 according to the corrected operating mode and power distribution, specifically as follows:

[0045] Based on particle swarm optimization and the energy management strategy of minimizing equivalent fuel consumption, with the goal of minimizing equivalent fuel consumption and achieving 50% battery SOC at the end of the operating condition, the optimization results of the working mode and power shunt ratio under different operating conditions are obtained.

[0046] Based on particle swarm optimization and equivalent fuel consumption minimum energy management strategy, with the goal of reducing vehicle fuel consumption and maintaining battery SOC balance, the working mode and power shunt ratio optimization results under different driving styles are determined.

[0047] Identify the current operating conditions or driving style, and adjust the working mode in step S1 and the power distribution in step S2 according to the current operating conditions or driving style.

[0048] According to a second aspect of the present invention, an online adaptive energy management system for hybrid electric vehicles is provided, based on the online adaptive energy management method for hybrid electric vehicles as described in the first aspect of the present invention, comprising:

[0049] The operating mode selection unit is used to obtain vehicle speed, required torque, battery information and vehicle power requirements, and select the operating mode.

[0050] A power distribution unit is used to distribute engine power and battery power.

[0051] The engine operating point optimization unit is used to optimize the engine operating point.

[0052] The motor speed optimization unit is used to optimize the motor speed and calculate the motor operating point;

[0053] The output unit is used to output the operating mode, engine operating point, and motor operating point.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] (1) The offline optimization results of PSO+ECMS and DP+ECMS energy management strategies were analyzed to find the changing patterns of control variables, extract control rules, and modify the working mode selection, power allocation and operating point optimization to meet the online control requirements. This is beneficial to applying the results of global optimization to guide the actual vehicle control strategy.

[0056] (2) Integrate the working condition recognition and driving style recognition modules into the vehicle control model, and select the corresponding control rules according to the recognition results to meet the adaptive control requirements. Attached Figure Description

[0057] Figure 1 This is a flowchart illustrating an online adaptive energy management method for hybrid electric vehicles.

[0058] Figure 2 A schematic diagram of the power split ratio cluster centers and fitting curves;

[0059] Figure 3 A schematic diagram of the power split ratio and the fitted curve;

[0060] Figure 4 The meaning of the query is to look up the target power of the battery;

[0061] Figure 5 A schematic diagram of the optimal operating point of an engine in power split mode;

[0062] Figure 6 This is a schematic diagram for identifying and correcting operating conditions / driving styles. Detailed Implementation

[0063] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are implemented based on the technical solutions of the present invention, providing detailed implementation methods and specific operating procedures. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them, and the scope of protection of the present invention is not limited to the following embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0064] As used herein, "an embodiment" or "embodiment" refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the invention. In the description of the invention, it should be understood that the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0065] This specification provides method operation steps as shown in the embodiments or flowcharts, but based on conventional or non-inventive labor, more or fewer operation steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual system or server products, the method can be executed in the order shown in the embodiments or drawings, or in parallel (e.g., in a parallel processor or multi-threaded processing environment), or the execution order of steps without timing constraints can be adjusted.

[0066] When the vehicle control adopts the RB+ECMS (rule-based and equivalent fuel consumption minimization) energy management strategy, the algorithm is simple and easy to implement. However, in actual vehicle operation, the following problems exist:

[0067] (1) The working mode selection is separated from power allocation and operating point optimization, and the mutual influence of the three is not considered. The mode switching rules are based on engineering experience, which requires a lot of debugging and cannot guarantee the optimization effect of the vehicle's energy consumption.

[0068] (2) Without feedback control of battery status, the battery SOC is prone to be too high or too low, making it impossible to keep the battery working in a healthy state, and the battery working efficiency is low, increasing power loss.

[0069] (3) The control parameters were not adaptively adjusted according to different working conditions and driving styles.

[0070] (4) The engine operating points are relatively dispersed and are prone to appear in the low efficiency zone.

[0071] To address the above problems, this invention proposes an online adaptive energy management strategy, the flowchart of which is shown below. Figure 1 As shown, this strategy extracts rules from the results of offline optimization using PSO+ECMS and DP+ECMS, modifies the operating mode selection, power allocation, and operating point optimization, and adaptively adjusts these rules according to operating conditions and driving style.

[0072] A hybrid electric vehicle online adaptive energy management method, such as Figure 1 As shown, it includes the following steps:

[0073] S1. Obtain vehicle speed, required torque, battery information, and total vehicle power requirement, and select the operating mode;

[0074] Step S1 is as follows:

[0075] Based on the particle swarm optimization algorithm and the energy management strategy of minimizing equivalent fuel consumption, a working mode table of PSO+ECMS is established.

[0076] The working mode can be obtained by looking up the working mode table in PSO+ECMS based on vehicle speed and required torque.

[0077] If the lookup result indicates pure electric mode, determine whether the battery SOC is lower than the preset low charge threshold SOC. low If yes, select hybrid mode; otherwise, keep pure electric mode.

[0078] If the lookup result indicates hybrid mode, determine whether the battery SOC is higher than the preset high charge threshold SOC. high And whether the total power demand of the vehicle is less than the preset high power threshold P out_high If yes, select pure electric mode; otherwise, keep hybrid mode.

[0079] In summary, the operating mode selection in this application is based on offline optimization using PSO+ECMS, summarizing patterns to extract control rules. For a given driving state, multiple operating modes are available, and each operating mode has multiple operating points that can meet the control requirements. According to the ECMS strategy, the equivalent fuel consumption for each operating point under all operating modes is calculated. During the calculation process, the equivalent factor is input from the outer PSO algorithm, and its value affects the equivalent fuel consumption calculation result. The operating mode and operating point corresponding to the lowest equivalent fuel consumption are selected as the control strategy, forming a corresponding operating mode table.

[0080] This application, based on the PSO+ECMS operating mode table, further incorporates online correction of the lookup results to ensure that the vehicle achieves the dual goals of reducing overall energy consumption and maintaining battery SOC balance during actual road operation. The correction approach is as follows: The operating mode is determined by looking up the PSO+ECMS operating mode table based on vehicle speed and required torque. When the lookup result indicates pure electric mode, it is then determined whether the battery SOC is below the required SOC level. low If yes, select hybrid mode; otherwise, maintain pure electric mode. If the table result indicates hybrid mode, determine if the battery SOC is higher than the SOC value. high And the total power requirement of the vehicle is less than P out_high If so, select pure electric mode; otherwise, maintain hybrid mode. It is understandable that the above-mentioned threshold SOC... low SOChigh P out_high All settings can be adjusted according to actual needs.

[0081] S2. If the working mode is pure electric mode, the entire vehicle's driving power is provided by the battery, and there is no need to consider power distribution and engine operating point optimization. You can directly proceed to step S4. Otherwise, if the working mode is hybrid mode, it is necessary to distribute the engine power and battery power.

[0082] In step S2, the power allocation is specifically performed as follows:

[0083] Based on dynamic programming algorithm and equivalent fuel consumption minimum energy management strategy, the power split ratio under different vehicle power demand is calculated;

[0084] Cluster analysis was performed on multiple power shunt ratios obtained under different vehicle power requirements, and curve fitting was performed on the cluster centers of the power shunt ratios obtained under different vehicle power requirements. The curve was used as the online power allocation rule for medium SOC.

[0085] The upper and lower boundaries of the power shunt ratio for different vehicle power requirements are fitted and used as online power allocation rules for low SOC and high SOC, respectively.

[0086] Based on the current vehicle power demand and battery SOC, the power shunt ratio is determined, and the engine power and battery power are allocated based on the power shunt ratio.

[0087] The hybrid power distribution is based on the offline optimization of DP+ECMS, from which control rules are extracted. The output power of the power split system, battery power, and engine power are extracted from the optimization results of the global energy management strategy DP+ECMS (including power distribution and engine operating point), and the distribution relationship among the three is represented by the Power Split Ratio (PSR). The main steps include: extracting the hybrid power distribution relationship from the DP+ECMS optimization results and calculating the Power Split Ratio (PSR) (representing the ratio of engine power to the output power of the power split system); performing cluster analysis on the PSR; and fitting curves to the obtained cluster centers, such as... Figure 2 As shown, this serves as the online power allocation rule for a medium SOC; curve fitting is performed on the upper and lower boundaries of the PSR, as follows: Figure 3 As shown, these serve as online allocation rules for low SOC and high SOC, respectively; a target PSR rule table is generated based on the fitted curve.

[0088] When controlling online, such as Figure 4 As shown, the target PSR is obtained by looking up a table based on the vehicle's required power and battery SOC. The target battery power P is then obtained based on the vehicle's power balance relationship.batt_ini With the battery target power P batt_ini The offline optimization results are stored in the control model as a three-dimensional table as outputs, and the inputs are battery SOC and ΔSOC. ΔSOC is the difference between the current battery SOC and the SOC at time d. This parameter is included in the planning to avoid prolonged high-power charging and discharging of the battery, ensuring that the battery operates in a healthy state.

[0089] S3. Optimize the engine operating point;

[0090] Hybrid power modes include power split mode (HEV4) and fixed gear ratio mode (HEV2), and the methods for determining the engine operating point are different for the two modes.

[0091] For the power split mode, the engine's operating state has two degrees of freedom: engine speed and engine torque. Therefore, once the target engine power is determined, its operating point can be selected on the isopower line. To ensure the lowest fuel consumption at the selected operating point, the isopower line P is chosen. ICE The intersection point A with the optimal fuel consumption line (where the horizontal and vertical axes represent engine speed and torque, respectively) is taken as the engine's optimal operating point. Figure 5 As shown, its specific fuel consumption satisfies the following formula:

[0092] be A =minbe(P ICE )

[0093] Among them, be A P represents the specific fuel consumption at engine operating point A. ICE This indicates the engine's target power.

[0094] If the hybrid mode is a fixed gear ratio mode, the engine speed is calculated from the vehicle speed, and the engine torque is calculated from the engine target power and the engine speed, as shown in the following formula:

[0095]

[0096]

[0097] Where, n ICE T is the engine speed. ICE V represents the engine torque. spd For vehicle speed, P ICE For the engine target power, i g For the gearbox ratio, i 01 Main reducer transmission ratio, r wheel The radius is the wheel radius.

[0098] S4. Optimize the motor speed and calculate the motor operating point;

[0099] The pure electric mode also includes power-split mode (EV3) and fixed gear ratio mode (EV2). In fixed gear ratio mode, the motor speed and torque are fixed, while in power-split mode, the motor speed can be freely adjusted. For a known vehicle power requirement, different motor speeds correspond to different power losses; therefore, motor speed optimization is necessary to ensure minimal power loss. The formula for calculating power loss is:

[0100] P elec_loss =P M / G1_loss +P M / G2_loss +P batt_loss

[0101] Among them, P elec_loss For the total power loss, P M / G1_loss P M / G2_loss and P batt_loss These are the power losses of motor M / G1, motor M / G2, and battery, respectively.

[0102] When the motor is operating in generator mode, the power loss of the motor is as shown in the following formula:

[0103]

[0104] When the motor is in drive mode, the power loss of the motor is as shown in the following formula:

[0105]

[0106] When the battery is in the charging state, the battery power loss is as shown in the following formula:

[0107] P batt_loss =P batt ·(1-η chg )

[0108] When the battery is operating in a discharging state, the battery power loss is as shown in the following formula:

[0109]

[0110] In the formula, P M / G_loss n represents the power loss of the motor. M / G T represents the motor's rotational speed. M / G η represents the torque of the motor. gen and η mot These are the generator efficiency and drive efficiency of the motor, respectively, P batt For battery output power, η chg and η dchg These represent the battery's charging efficiency and discharging efficiency, respectively.

[0111] S5, Output Operating Mode, Engine Operating Point, and Motor Operating Point.

[0112] In addition, the above strategy also includes: identifying the current operating conditions or driving style, correcting the operating mode in step S1 and the power distribution in step S2 according to the current operating conditions or driving style, and performing calculations in step S3 according to the corrected operating mode and power distribution.

[0113] Since different driving conditions and driving styles have a significant impact on vehicle fuel economy, this application identifies the current driving conditions and style, and modifies the control strategy based on the identification results. Figure 6 As shown, the vehicle operating condition / driving style recognition and correction includes four parts: operating condition recognition, control rules for different operating conditions, driving style recognition, and control rules for various driving styles under different operating conditions. Vehicle operating condition recognition can be based on existing recognition methods, such as patents CN113859219A and CN113276829A, which analyze vehicle motion parameters to obtain the vehicle's current operating condition type. Driving style recognition can also be performed using existing recognition methods, such as patents CN115366891A and CN110254435A.

[0114] The correction process is as follows:

[0115] Based on particle swarm optimization and the energy management strategy of minimizing equivalent fuel consumption, with the goal of minimizing equivalent fuel consumption and achieving 50% battery SOC at the end of the operating condition, the optimization results of the working mode and power shunt ratio under different operating conditions are obtained.

[0116] Based on particle swarm optimization and equivalent fuel consumption minimum energy management strategy, with the goal of reducing vehicle fuel consumption and maintaining battery SOC balance, the working mode and power shunt ratio optimization results under different driving styles are determined.

[0117] Identify the current operating conditions or driving style, and adjust the working mode in step S1 and the power distribution in step S2 according to the current operating conditions or driving style.

[0118] The extraction of control rules for different operating conditions aims to minimize equivalent fuel consumption and achieve a battery SOC of 50% at the end of the operating condition. The equivalent factor is optimized using the PSO+ECMS energy management strategy to obtain the optimization results of the working mode and power split ratio under different operating conditions, which is the optimal control strategy for the corresponding operating condition.

[0119] The extraction of control rules for various driving styles under different operating conditions aims to reduce overall vehicle fuel consumption and maintain battery SOC balance. It analyzes vehicle speed, power demand, and operating point distribution, and uses the PSO+ECMS energy management strategy to obtain the optimal equivalent factor for different driving styles. The corresponding operating mode and power shunt ratio are then obtained, which are the results of the control rule extraction for different driving styles.

[0120] After adjusting the operating mode and power split ratio based on the driving conditions / style, the process proceeds to step S3. Engine operating point optimization includes two modules: engine target power calculation and engine operating point optimization. The engine target power calculation module obtains the engine target power based on the corrected power split ratio and the balance between the battery target power and the vehicle power, as follows:

[0121] P ICE =P demand +P loss -P batt_crrt

[0122] Among them, P ICE For the engine target power, P demand For the power required by the whole vehicle, P loss For power loss, P batt_crrt This is the target power for the battery.

[0123] Compared with energy management strategies based on RB+ECMS, this application has the following significant advantages:

[0124] (1) Based on the current working state of the battery and the changes in the battery SOC in the short term, the battery can be effectively protected and the battery working efficiency and life can be improved. The online adaptive energy management strategy provided in this application puts the control of the battery working state in a high priority. First, the battery power is determined, then the engine power is determined according to the power balance relationship of the whole vehicle, and then the engine working point is determined. In this way, the working state of the battery can be independent of the specific working conditions.

[0125] (2) The control process of the online adaptive energy management strategy is consistent with that of the DP+ECMS strategy, which is conducive to applying the results of global optimization to guide the actual vehicle control strategy. The DP+ECMS strategy obtains the target power of the battery based on the change of the state variable battery SOC, and then selects the working mode, engine and motor working points that meet the power requirement. This is consistent with the control process of the online adaptive energy management strategy.

[0126] (3) The engine operating point in the power split hybrid mode is selected from the optimal fuel consumption line, ensuring that the engine operating point is concentrated in the high efficiency zone.

[0127] (4) It can identify the operating conditions and driving style type according to the vehicle's operating conditions, and correct the control parameters according to the identification results to realize adaptive control of the adaptive energy management strategy.

[0128] The present invention also provides an online adaptive energy management system for hybrid electric vehicles, comprising:

[0129] The operating mode selection unit is used to obtain vehicle speed, required torque, battery information and vehicle power requirements, and select the operating mode.

[0130] A power distribution unit is used to distribute engine power and battery power.

[0131] The engine operating point optimization unit is used to optimize the engine operating point.

[0132] The motor speed optimization unit is used to optimize the motor speed and calculate the motor operating point;

[0133] The output unit is used to output the operating mode, engine operating point, and motor operating point.

[0134] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described unit can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0135] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for online adaptive energy management of hybrid electric vehicles, characterized in that, Includes the following steps: S1. Obtain vehicle speed, required torque, battery information, and total vehicle power requirement, and select the operating mode; S2. If the operating mode is pure electric mode, then execute step S4; otherwise, the operating mode is hybrid mode, and the engine power and battery power are allocated. S3. Optimize the engine operating point; S4. Optimize the motor speed and calculate the motor operating point; S5, Output working mode, engine operating point and motor operating point; Step S1 specifically involves: Based on the particle swarm optimization algorithm and the energy management strategy of minimizing equivalent fuel consumption, a working mode table of PSO+ECMS is established. The operating mode can be obtained by looking up the table in the PSO+ECMS operating mode table according to the vehicle speed and required torque. If the lookup result indicates pure electric mode, determine whether the battery SOC is lower than the preset low charge threshold. If yes, select hybrid mode; otherwise, keep pure electric mode. If the lookup result indicates hybrid mode, determine whether the battery SOC is higher than the preset high charge threshold. And whether the total power demand of the vehicle is less than the preset high power threshold. If yes, select pure electric mode; otherwise, keep hybrid mode.

2. The online adaptive energy management method for hybrid electric vehicles according to claim 1, characterized in that, In step S2, the power allocation specifically involves: Based on dynamic programming algorithm and equivalent fuel consumption minimum energy management strategy, the power split ratio under different vehicle power demand is calculated; Cluster analysis was performed on multiple power shunt ratios obtained under different vehicle power requirements, and curve fitting was performed on the cluster centers of the power shunt ratios obtained under different vehicle power requirements. The curve was used as the online power allocation rule for medium SOC. The upper and lower boundaries of the power shunt ratio for different vehicle power requirements are fitted and used as online power allocation rules for low SOC and high SOC, respectively. Based on the current vehicle power demand and battery SOC, a power shunt ratio is determined, and engine power and battery power are allocated based on the power shunt ratio.

3. The online adaptive energy management method for hybrid electric vehicles according to claim 1, characterized in that, In step S3, if the hybrid power mode is a power split mode, the engine operating point is optimized as follows: Take the isopower line of the engine's target power, and take the intersection of the isopower line and the optimal fuel consumption line as the engine's optimal operating point.

4. The online adaptive energy management method for hybrid electric vehicles according to claim 1, characterized in that, In step S3, if the hybrid mode is a fixed gear ratio mode, the engine speed is calculated from the vehicle speed, and the engine torque is calculated from the engine target power and the engine speed, as shown in the following formula: in, Engine speed, For engine torque, For vehicle speed, For the engine target power, For the gearbox ratio, Main reducer transmission ratio, The radius is the wheel radius.

5. The online adaptive energy management method for hybrid electric vehicles according to claim 1, characterized in that, In step S4, if the electric mode is a power-split mode, the motor speed is determined based on the principle of minimizing power loss. The formula for calculating power loss is as follows: in, For the total power loss, , and These are the power losses of motor M / G1, motor M / G2, and battery, respectively.

6. The online adaptive energy management method for hybrid electric vehicles according to claim 1, characterized in that, When the motor is operating in generator mode, the power loss of the motor is as shown in the following formula: When the motor is in drive mode, the power loss of the motor is as shown in the following formula: When the battery is in the charging state, the battery power loss is as shown in the following formula: When the battery is operating in a discharging state, the battery power loss is as shown in the following formula: In the formula, This represents the power loss of the motor. Represents the motor's rotational speed. Represents the torque of the motor. and These are the generator efficiency and drive efficiency of the motor, respectively. For battery output power, and These represent the battery's charging efficiency and discharging efficiency, respectively.

7. The online adaptive energy management method for hybrid electric vehicles according to claim 1, characterized in that, In step S4, if the electric mode is a fixed transmission ratio mode, then the motor speed and torque are the speed and torque determined under the fixed transmission ratio mode.

8. The online adaptive energy management method for hybrid electric vehicles according to claim 1, characterized in that, Also includes: Identify the current operating condition or driving style, and adjust the operating mode in step S1 and the power distribution in step S2 according to the current operating condition or driving style. In step S3, calculations are performed based on the adjusted operating mode and power distribution, specifically as follows: Based on particle swarm optimization and the energy management strategy of minimizing equivalent fuel consumption, with the goal of minimizing equivalent fuel consumption and achieving 50% battery SOC at the end of the operating condition, the optimization results of the working mode and power shunt ratio under different operating conditions are obtained. Based on particle swarm optimization and equivalent fuel consumption minimum energy management strategy, with the goal of reducing vehicle fuel consumption and maintaining battery SOC balance, the working mode and power shunt ratio optimization results under different driving styles are determined. Identify the current operating conditions or driving style, and adjust the working mode in step S1 and the power distribution in step S2 according to the current operating conditions or driving style.

9. An online adaptive energy management system for hybrid electric vehicles, characterized in that, The online adaptive energy management method for hybrid electric vehicles as described in any one of claims 1-8 includes: The operating mode selection unit is used to obtain vehicle speed, required torque, battery information and vehicle power requirements, and select the operating mode. A power distribution unit is used to distribute engine power and battery power. The engine operating point optimization unit is used to optimize the engine operating point. The motor speed optimization unit is used to optimize the motor speed and calculate the motor operating point; The output unit is used to output the operating mode, engine operating point, and motor operating point.

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