Four-wheel-drive plug-in hybrid electric vehicle self-adaptive energy management method considering working condition congestion

Abstract

The invention relates to the technical field of hybrid electric vehicle energy management, and discloses a four-wheel-drive plug-in hybrid electric vehicle self-adaptive energy management method considering working condition congestion, which comprises the steps of dynamically identifying a congestion condition based on a vehicle speed and an acceleration, dividing a travel into an electric quantity consumption section and a recovery section, and planning a corresponding linear SOC target track; dynamically adjusting an equivalent factor according to the SOC deviation by utilizing the PID which is subjected to offline optimization through a genetic algorithm; a linear programming model containing engine efficient constraints is constructed, a front and rear shaft power distribution ratio is introduced as an optimization variable, and the optimal power distribution of the engine and front and rear motors is solved online by adopting a simplex method. According to the method, the optimal distribution of energy in a full-travel time domain is realized through working condition accurate identification and closed-loop adaptive control, the defects that traditional strategy parameter setting depends on experience and the working condition adaptability is poor are overcome, and the fuel economy of the whole vehicle is improved while the real-time control requirement is met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hybrid electric vehicle energy management, in particular to a four-wheel drive plug-in hybrid electric vehicle adaptive energy management method considering congestion working conditions. BACKGROUND

[0002] With the continuous improvement of energy saving and environmental protection requirements, four-wheel drive plug-in hybrid electric vehicles (hybrid electric vehicles) have become an important direction of the development of the automobile industry due to their advantages of power, fuel economy and low emissions. Energy management strategy is one of the core technologies of hybrid electric vehicles, and its performance directly determines the fuel economy, emission level and driving experience of the vehicle.

[0003] Existing energy management strategies are mainly divided into rule-based control strategies and optimization theory-based control strategies. Although the rule-based control strategy is simple and reliable, it usually depends on the experience of engineers and is difficult to adapt to complex and variable actual driving conditions, especially when the city congestion condition and the smooth high-speed condition frequently switch, its fuel economy is often poor. The optimization theory-based method, such as the global optimization algorithm (such as dynamic programming), can obtain the theoretical optimal solution, but it depends on complete prior condition information and cannot be used for real-time control. Equivalent fuel consumption minimum strategy (ECMS) as a kind of instantaneous optimization method, through the introduction of equivalent factor, the electric energy consumption is equivalent to fuel consumption, realizes online optimization, but its core parameter is equivalent factor, usually for fixed value or simple mapping, it is difficult to maintain good adaptability in the whole working condition range, especially when the congestion and smooth working conditions differ significantly, it is easy to cause the battery state of charge (SOC) to fluctuate sharply or the energy distribution to be improper, affecting the vehicle economy and battery life.

[0004] In addition, the existing energy management strategies for four-wheel drive hybrid power systems mainly focus on front and rear axle torque distribution to improve power or stability, and pay insufficient attention to the optimization of energy flow of motor and engine working together in congestion conditions. The characteristics of low speed and high efficiency of motor in congestion section and high speed and high efficiency of engine in non-congestion section are not fully utilized. The working condition recognition, SOC planning and real-time power distribution are not closed-loop adaptive coordinated. At the same time, the existing strategies lack hardware-in-the-loop and other real vehicle level validation support, and the calculation efficiency of some algorithms is insufficient, which is difficult to meet the real-time requirements of real vehicle controller.

[0005] Therefore, how to design an energy management method that can identify the congestion state of working condition in real time, plan the SOC trajectory based on the difference of power source efficiency characteristics, and dynamically optimize the front and rear axle power distribution, has become a key problem to improve the real road fuel economy and real vehicle applicability of four-wheel drive plug-in hybrid electric vehicles. SUMMARY

[0006] To address the shortcomings of existing technologies, this invention provides an adaptive energy management method for four-wheel drive plug-in hybrid electric vehicles that takes into account traffic congestion. This method solves the problems of poor adaptability, reliance on experience for parameter tuning, and difficulty in achieving optimal energy consumption allocation while ensuring real-time performance in complex operating conditions where four-wheel drive plug-in hybrid electric vehicles are operating under alternating congested and non-congested conditions.

[0007] To achieve the above objectives, the present invention provides an adaptive energy management method for four-wheel drive plug-in hybrid vehicles that takes into account traffic congestion.

[0008] This method first acquires vehicle operating status parameters in real time, including vehicle speed, total vehicle power demand, and current battery state of charge. Based on the acquired vehicle speed information, combined with speed duration and acceleration information, the current congestion situation is identified, and the entire vehicle driving process is divided into a power consumption segment and a power recovery segment. For each of the defined power consumption and recovery segments, the system plans target trajectories for the battery state of charge, one linearly decreasing and the other linearly increasing, providing a global reference benchmark for energy management.

[0009] During the control process, the deviation between the current battery state of charge and the planned target trajectory is calculated, and the equivalent factor is dynamically adjusted using a proportional-integral-differential algorithm to achieve adaptive correction of the weights for fuel and electric energy consumption. Based on the adjusted equivalent factor and the vehicle's required power, a linear programming model is constructed with the goal of minimizing equivalent fuel consumption. The optimal power allocation among the engine, front-drive motor, and rear-drive motor is solved online, and the operation of the hybrid vehicle's drive system is controlled according to the solution results.

[0010] Furthermore, when identifying congestion, this method sets a boundary time between congested and non-congested conditions. The operating condition stage is determined by comparing the current time with this boundary time: when the current time is less than or equal to the boundary time, it is determined to be a congested condition, corresponding to the power consumption phase; when the current time is greater than the boundary time, it is determined to be a non-congested condition, corresponding to the power recovery phase. The boundary time is not fixed but is preset based on historical driving data and dynamically corrected during driving based on the rate of change of vehicle speed and the average acceleration, thereby improving the timeliness and accuracy of operating condition identification.

[0011] Furthermore, regarding the planning of the target trajectory for the battery state of charge, for the energy consumption phase, the system uses the battery state of charge at the start of the journey as the starting point and the preset battery state of charge at the end of the congestion as the ending point, constructing a function relationship that decreases linearly with time to guide the system to prioritize the use of electrical energy; for the energy recovery phase, the system uses the battery state of charge at the end of the congestion as the starting point and the preset battery state of charge at the end of the journey as the ending point, constructing a function relationship that increases linearly with time to guide the system to recover electrical energy through engine drive or load point shifting.

[0012] Furthermore, when dynamically adjusting the equivalent factor, the system performs a weighted summation of the proportional term, time integral term, and time derivative term of the deviation value, and then superimposes the summation onto an initial benchmark value to obtain the equivalent factor at the current moment. The gain coefficients corresponding to each adjustment term are not selected empirically, but are optimized offline using a genetic algorithm. In the genetic algorithm, a comprehensive evaluation index containing the weighted sum of the instantaneous absolute value of the deviation and the cumulative absolute value of the deviation is established as the fitness function. The algorithm iteratively searches for the parameter combination that minimizes this index, thereby ensuring the optimality of the PID control parameters and the robustness of the system.

[0013] Furthermore, when constructing a linear programming model for power allocation optimization, the system selects engine output power and the front-to-rear drive power allocation ratio as optimization variables. The objective function is constructed as a weighted sum of engine fuel consumption and battery energy consumption terms, where the weight coefficient of the engine fuel consumption term is set to one minus an equivalent factor, and the weight coefficient of the battery energy consumption term is set to an equivalent factor. To improve solution efficiency, this method uses the continuously variable transmission efficiency, the front final drive efficiency, and the rear final drive efficiency to correct the battery energy consumption term, transforming the originally nonlinear power allocation problem into a linear function of the optimization variables.

[0014] Furthermore, to ensure the fuel economy of engine operation, a constraint on the engine's efficient operating range is introduced when solving for the optimal power allocation. Specifically, by pre-setting a data chart of the engine's efficient operating range, the corresponding power range and speed range are set as hard boundary conditions and incorporated into the constraint set of the linear programming problem. During the solution process, the algorithm automatically eliminates candidate solutions that exceed these hard boundary conditions, forcing the engine to always operate in the region with better fuel economy.

[0015] Furthermore, the set of constraints for the linear programming model also includes upper and lower limits for engine power, upper and lower limits for front-drive motor power, upper and lower limits for rear-drive motor power, and a numerical range constraint for the front-to-rear drive power distribution ratio. Among these, the front-to-rear drive power distribution ratio explicitly defines the proportional relationship between the front-drive motor and the rear-drive motor when undertaking the total electric drive power, thus achieving coordinated control of the dual-motor four-wheel drive configuration.

[0016] Furthermore, this method employs the simplex method to solve the linear programming model online. By searching for the optimal solution among the vertices of the polyhedral feasible region composed of constraints, iterative calculations quickly find the engine output power and front-rear drive power distribution ratio that minimize the objective function value, meeting the millisecond-level real-time requirements of real vehicle control.

[0017] This invention provides an adaptive energy management method for four-wheel drive plug-in hybrid vehicles that takes into account traffic congestion. It has the following beneficial effects: 1. This invention identifies congested conditions by combining multi-dimensional parameters such as vehicle speed, acceleration, and efficiency MAP, and plans a segmented SOC target trajectory accordingly. This mechanism utilizes the characteristics of high efficiency of electric motor drive under congested conditions and high efficiency of engine drive under non-congested conditions to achieve optimal energy allocation over the entire journey time dimension.

[0018] 2. This invention utilizes a genetic algorithm to optimize PID control parameters offline and adjusts the equivalent factor in real time based on the SOC deviation according to the optimal PID parameters. This method overcomes the limitations of traditional empirical calibration, enabling the control strategy to dynamically adapt to changing driving conditions. The equivalent factor adjustment response is rapid, ensuring accurate tracking of the target trajectory by the actual battery state of charge.

[0019] 3. This invention constructs a linear programming model that includes constraints on the engine's efficient operating range and uses the simplex method to transform the power allocation problem into an online solution problem. This method improves the solution efficiency, has extremely low computation time, and can fully meet the stringent requirements of real-time vehicle control. At the same time, by constraining the engine's operating point, the fuel economy of the entire vehicle is improved compared to traditional strategies. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of a CVT plug-in four-wheel drive hybrid vehicle powertrain system according to an embodiment of the present invention; Figure 2 This is a flowchart of the working condition identification and battery charge state trajectory planning according to an embodiment of the present invention; Figure 3 This is a flowchart of PID parameter optimization based on genetic algorithm according to an embodiment of the present invention; Figure 4 This is a flowchart illustrating the power allocation process based on adaptive adjustment of the equivalent factor, according to an embodiment of the present invention. Figure 5 This is a schematic diagram of the energy management system for a hybrid electric vehicle according to an embodiment of the present invention.

[0021] Among them, 1. Engine; 2. ISG motor; 3. CVT; 4. Front axle; 5. Front main reducer; 6. Rear drive motor; 7. Rear axle; 8. Rear main reducer. Detailed Implementation

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] See attached document Figure 1 This invention provides an adaptive energy management method for four-wheel drive plug-in hybrid vehicles that takes into account traffic congestion. This method is based on... Figure 1 The illustrated plug-in hybrid vehicle platform implementation. The vehicle's powertrain architecture includes an engine 1, an ISG motor 2, a CVT 3, a front axle 4, a front final drive reduction gear 5, a rear drive motor 6, a rear axle 7, and a rear final drive reduction gear 8. The powertrain also includes a battery pack for storing and supplying electrical energy, and a clutch C1 for controlling power on / off.

[0024] Engine 1, as one of the front-drive power sources, has its output shaft coaxially connected to the rotor of ISG motor 2 via clutch C1. ISG motor 2 is located between engine 1 and CVT3, forming a P2 hybrid power configuration. The output end of ISG motor 2 is mechanically connected to the input end of CVT3. The output end of CVT3 is connected to the front final drive 5 via a drive shaft, and the front final drive 5 drives the front axle 4 and the front wheels via half-shafts.

[0025] The rear-drive motor 6 serves as an independent rear-drive power source, forming a P4 hybrid configuration. The output shaft of the rear-drive motor 6 is directly connected to the input end of the rear main reducer 8, which drives the rear axle 7 and rear wheels via half-shafts. The battery pack is electrically connected to the controllers of the ISG motor 2 and the rear-drive motor 6 via high-voltage wiring harnesses. The battery pack is equipped with a charging interface, allowing it to be connected to an external power source (charger) for recharging.

[0026] The vehicle is equipped with a sensor network and a vehicle controller. The sensor network is used to collect vehicle operating parameters in real time, including but not limited to engine speed, vehicle speed, acceleration, battery state of charge, accelerator pedal position, and brake pedal position. The vehicle controller receives the signals collected by the sensors and executes the adaptive energy management method of this invention, sending torque control commands to the engine controller, ISG motor controller, and rear drive motor controller.

[0027] The system achieves single-motor drive, dual-motor four-wheel drive, engine direct drive, vehicle charging, and hybrid drive modes through the coordinated operation of the front and rear axle drive units. The ISG motor 2 has both electric and regenerative braking capabilities. In electric mode, it outputs positive torque to assist drive; in regenerative braking mode, it outputs negative torque to store energy through the battery pack. The rear drive motor 6 also features electric and energy recovery functions. The CVT3 can continuously adjust the gear ratio based on the engine operating point and vehicle speed, optimizing the engine's operating range.

[0028] The power distribution between the front axle drive unit and the rear axle drive unit is adjusted by the vehicle controller. The vehicle controller calculates the total power demand based on real-time operating conditions and calculates the optimal power distribution value between engine 1, ISG motor 2, and rear drive motor 6 based on a linear programming model, controlling each component to output torque according to the distribution value. This architecture utilizes the combined advantages of the P2 and P4 configurations, enabling independent adjustment of the front and rear axle torques and flexible adjustment of the engine load rate.

[0029] See attached document Figure 3 This embodiment describes how to obtain the optimal gain coefficient of the PID controller through offline optimization before the vehicle is actually running.

[0030] First, set the initial parameters for the genetic algorithm. Set the population size. Set the maximum number of iterations. The initialization process involves randomly generating 100 sets of PID parameter individuals within the parameter search range based on a uniform random distribution, forming the initial population. Each set of individuals contains parameter values ​​in three dimensions, corresponding to the scaling factor. Integral coefficient and differential coefficients For example, the parameters of a generated initial individual are... , , Parameter constraint operations are performed on the initially generated PID parameter population. To ensure the physical feasibility of the PID controller in practical applications, the search range for the PID parameters is set as follows: , , All individual parameter values ​​involved in subsequent calculations are restricted to the range defined above.

[0031] Input a constrained PID parameter population and SOC tracking deviation data. The deviation data is provided by the simulation platform and includes a sequence of deviations between the actual and target SOC values ​​under alternating congestion and non-congestion conditions. For each individual parameter, its fitness value F is calculated using the fitness function.

[0032] A comprehensive evaluation index is established as the fitness function, which is the weighted sum of the absolute values ​​of instantaneous deviations and the cumulative absolute values ​​of deviations. The formula for calculating the fitness function is: ; In the formula, This represents the deviation between the target value and the actual value of the battery's state of charge at the current moment. and These represent the weights of the instantaneous SOC deviation term and the weights of the deviation accumulation term, respectively. In this embodiment, , The smaller the fitness value F, the smaller the control error of the corresponding individual parameters, and the better the control effect.

[0033] Input the calculated individual fitness value and the current iteration number G, and perform a convergence check. The check condition is set to meet either of the following: the number of iterations reaches ≥500; or, the rate of change of fitness value over 20 consecutive generations is less than or equal to 10. -5 .

[0034] If the convergence condition is not met, the genetic operations of selection, crossover, and mutation are sequentially performed on the current population to generate a new generation. The selection operation uses a roulette wheel algorithm, selecting the top 60% of superior individuals based on their fitness values ​​from highest to lowest. The crossover operation sets a probability. The selected individuals are cross-coded using binary encoding to generate 20 new individuals. The mutation operation has a set probability. Genetic mutation is performed on the crossover individuals to generate 20 new individuals. The selected, crossover, and mutated individuals are merged to form a new generation of PID parameter population containing 100 individuals, and the step of calculating fitness values ​​is returned.

[0035] If the convergence condition is met, the optimal solution extraction operation is performed. The parameter individual with the smallest fitness value is selected from the converged population as the optimal parameter combination for PID control. This optimal parameter combination will be stored in the vehicle controller for use in the online energy management process, adjusting the equivalent factor.

[0036] See attached document Figure 2 and attached Figure 4 This embodiment mainly describes the real-time operating condition identification and battery state of charge trajectory planning method in the energy management process.

[0037] The system first performs a real-time data acquisition step. It collects real-time operating information, including vehicle speed, through onboard sensors and the controller. Current state of charge of the power battery and the power required for the whole vehicle Simultaneously, the engine efficiency MAP, front-drive ISG motor efficiency MAP, and rear-drive motor efficiency MAP are synchronously acquired from memory. Based on the collected data, a driving condition stage determination step is performed. A congestion-free boundary time is set. This dividing moment The initial values ​​are preset based on historical driving data. The system monitors the rate of change of vehicle speed and the mean acceleration, and adjusts the boundary times at a frequency consistent with the control cycle. Dynamic adjustments are made. For the New European Driving Cycle (NEDC), the first 780 seconds are urban driving, and the last 400 seconds are suburban driving; the dividing time in this case... The value is 780s.

[0038] According to the current time and the dividing moment The comparison result determines the current driving stage. When At this time, it is determined that the current situation is a traffic jam, corresponding to a period of high energy consumption. During this phase, vehicles frequently start and stop and travel at low speeds, so the energy management strategy tends to consume electrical energy. When the system determines that the current situation is not congested, it corresponds to the battery recovery phase. During this phase, the vehicle speed is relatively high and stable, and the energy management strategy tends to maintain or restore battery power.

[0039] Based on the operating condition stage determination results, the SOC target trajectory planning step is executed. For congested sections (battery consumption sections), the battery state of charge at the start of the journey is used as the starting point, and the preset battery state of charge at the end of the congestion is used as the ending point, constructing a function relationship that decreases linearly with time. The calculation formula for the linear descent trajectory is set as follows: ; In the formula, For the current moment The target battery state of charge, The start time of the trip The actual state of battery charge. This is the decrease rate coefficient, with a value range of 0.5% to 3.0%. This coefficient varies with the current... The value is dynamically adapted when the current Approaching the upper limit When the value approaches the lower limit, the upper limit is used. Take the lower limit value at that time. This is the dividing moment.

[0040] For the non-congested segment (battery recovery segment), the battery state of charge at the end of the congestion is used as the starting point, and the preset battery state of charge at the end of the trip is used as the ending point, constructing a linearly increasing function relationship with time. The formula for calculating the linear upward trajectory is set as follows: ; In the formula, Boundary moment The state of charge of the battery. This marks the end of the trip. The increase coefficient is set to a range of 0.5% to 2.5%. The planned... The full-process SOC target curve is generated as a reference input for subsequent equivalent factor adjustments.

[0041] Perform the SOC deviation calculation step. Calculate the actual data collected at the current moment. With the planned target trajectory Deviation between The formula for calculating the deviation is: ; Calculated deviation value Used for real-time correction of the equivalent factor in subsequent control steps.

[0042] See attached document Figure 4 This embodiment mainly describes the power allocation optimization and execution process based on the adaptive adjustment of the equivalent factor.

[0043] After completing the SOC deviation calculation, the system executes the equivalent factor PID adjustment step. The optimal PID parameter combination, including the proportional gain, is optimized offline using a genetic algorithm. Integral coefficient and differential coefficients For equivalent factors Online dynamic adjustment is performed. Equivalent factor. The formula for calculating the control law is: ; In the formula, The initial benchmark value for the equivalence factor is set to [0.8, 2.1].

[0044] The SOC deviation value is calculated in the previous steps. .

[0045] The control law is corrected in real time based on the proportional, integral, and derivative terms of the SOC deviation. This is to adjust the equivalent conversion relationship between fuel and electricity.

[0046] When in reality Below target When, the deviation value If positive, the control law makes An increase in the numerical value increases the equivalent cost of electricity use; when the actual Exceeding the target When, the deviation value If it is negative, the control law makes The value is reduced, thus lowering the equivalent cost of electricity use.

[0047] Based on the adjusted equivalent factor, a power allocation optimization step is performed. The system constructs a linear programming model with the objective of minimizing instantaneous equivalent fuel consumption. The objective function is defined as: ; In the formula, Represents engine output power. This represents the power required by the entire vehicle. To optimize the variable, it represents the front-to-rear axle power distribution ratio, that is, the proportion of drive power borne by the rear axle. , , These represent the transmission efficiencies of the continuously variable transmission (CVT), the front final drive, and the rear final drive, respectively, with typical values ​​ranging from 90% to 95%. The first term of the objective function... The first term represents the weighted cost of engine fuel consumption, and the second term represents the converted cost of electrical energy consumption.

[0048] The constraints of the linear programming model are established as follows: First, engine power constraints: ; Second, the power constraint of the front drive motor: ; Third, the power constraint of the rear drive motor: ; Fourth, constraints on the relationship between engine speed and torque (based on MAP): ; in, and , and , and These represent the minimum and maximum power limits for the engine, the front-drive ISG motor, and the rear-drive motor, respectively. Represents the engine's output torque; The engine speed is determined by the current vehicle speed and the CVT speed ratio. Furthermore, the range of values ​​for the front-to-rear axle power distribution ratio is constrained. .

[0049] The simplex method was used to solve the above linear programming model online, with the solution period set to be less than 1ms to meet the real-time control requirements.

[0050] The solution process outputs the optimal engine power at the current moment. and optimal power allocation ratio Based on these two optimization results, the optimal power command for the front-drive ISG motor and the rear-drive motor is calculated. Optimal power for the front-drive ISG motor. The calculation formula is: ; Optimal power of rear drive motor The calculation formula is: ; The torque command calculation and execution steps are as follows: Based on the calculated optimal power P of each component, and combined with the current speed feedback N of the engine, ISG motor, and rear drive motor, the power command is converted into a torque command T. The torque calculation formula is: ; The system generates corresponding torque control signals, which are sent to the engine ECU, ISG motor controller and rear drive motor controller respectively, to drive the power sources to work together to meet the needs of the vehicle.

[0051] Finally, the trip end determination step is executed. The system compares the current time with the preset trip end time. .like The system returns to the data acquisition step and continues the cycle of the next control period; if If the process ends, the energy management and control process is terminated, thus achieving energy optimization coverage throughout the entire process.

[0052] When performing the equivalent factor PID adjustment step, the control system follows the following adjustment logic based on the output of the PID controller to achieve a dynamic trade-off between electricity and fuel use: When in reality Below the target trajectory This indicates that the current power consumption is too rapid or has not reached the planned lower limit. At this time, the PID controller will output a smaller value. In subsequent power allocation optimization, smaller This will reduce the equivalent fuel cost of electric power, thus making energy management strategies more inclined to use electric motor drive, reducing engine work, and helping to slow down the decline in SOC or promote recovery (in the recovery phase).

[0053] When in reality Above the target trajectory This means there is a surplus of power. At this time, the PID controller will output a relatively large value. Larger This would increase the cost of electricity, thus making the strategy more inclined to use engine drive or enter a driving charging mode, consuming fuel to reduce the state of charge (SOC) and bring it closer to the target trajectory.

[0054] When constructing a linear programming model, in order to transform the multivariate problem into a standard form, the system performs the following variable substitution process: Defined by engine power and front and rear axle power distribution ratio To optimize the variables, let the total power demand be... The front axle requires power of The power required for the rear axle is Based on the transmission system structure, the power of the rear drive motor is... and front-drive ISG motor power Represented as control variables and Functions: ; ; Substituting the above relationships into the original objective function and rearranging, we obtain the final linear objective function used for solving the problem: ;

[0055] During the model solution phase, the problem, which includes engine power constraints, motor power constraints, and speed-torque coupling constraints, is transformed into a linear programming problem. The system employs the simplex method or intersection method for online solution. To ensure the real-time performance of onboard control, the system sets the solution cycle for a single optimization calculation to be strictly less than 1ms, and the actual calculation time for a single operation is controlled to be ≤1.5×10⁻⁶. −4 Within seconds. This speed indicator ensures that the energy management strategy can cover high-frequency operating condition changes.

[0056] Based on the complete operating condition identification, SOC planning, equivalent factor adjustment, and power allocation closed-loop framework described above, simulation and hardware-in-the-loop (HIL) testing verified that under complex operating conditions with alternating congestion and non-congestion, the method in this embodiment improves fuel economy by ≥10% compared to traditional energy management strategies, effectively solving the problems of traditional parameter tuning relying on experience and poor adaptability.

[0057] like Figure 5 As shown, this embodiment provides a hybrid electric vehicle energy management system. This system operates within the vehicle control unit (VCU) and is used to execute an adaptive energy management method for four-wheel-drive plug-in hybrid electric vehicles that considers traffic congestion. The system includes: Operating Condition Analysis Module: Used to receive sensor data in real time and analyze traffic congestion conditions. It has a pre-set logic for dividing points based on historical data, which can dynamically identify whether the current vehicle is in a congested condition with frequent starts and stops or a non-congested condition with stable speed based on the duration of vehicle speed, the average value of acceleration, and the rate of change. It also divides the driving trajectory into a power consumption segment and a power recovery segment.

[0058] Planning Module: Connected to the operating condition analysis module, this module sets a reference trajectory for the battery's state of charge (SOC) based on the currently identified operating condition stage. It plans a linear descent trajectory during congested sections and a linear ascent trajectory during uncongested sections, providing a benchmark for energy allocation throughout the entire journey.

[0059] Equivalent Factor Adaptive Module: This module contains a PID control algorithm unit. It receives the deviation between the actual SOC value and the reference trajectory, calculates and outputs the real-time equivalent factor using PID parameters optimized offline via a genetic algorithm. The module's function is to convert the SOC deviation into adjustment signals for fuel and electrical energy weights.

[0060] Power Allocation Module: The core calculation unit. This module constructs a linear programming model that includes constraints on the engine's high-efficiency range, based on the equivalence factor and the vehicle's required power. It transforms the power allocation problem into a mathematical optimization problem and uses a simplex method solver to quickly calculate the optimal power allocation values ​​for the engine, front-drive motor, and rear-drive motor.

[0061] Control execution module: It is used to convert the optimal power value output by the power distribution module into specific torque or speed control commands, and send them to the engine controller, front drive motor controller and rear drive motor controller via CAN bus to drive the coordinated operation of each power component and achieve the optimization goal of improving fuel economy by more than 10%.

Claims

1. A four-wheel drive plug-in hybrid vehicle adaptive energy management method considering traffic congestion, characterized in that, The method comprises the following steps: Real-time acquisition of vehicle speed, vehicle demand power and current battery state of charge; Based on the comparison result of vehicle speed and threshold, combined with vehicle speed duration and acceleration information, congestion condition is identified, and the journey is divided into power consumption section and power recovery section; Linearly descending and linearly ascending battery state of charge target trajectories are respectively planned for the power consumption section and the power recovery section; The deviation of the current battery state of charge and the target trajectory is calculated, and the equivalent factor is dynamically adjusted by using proportional-integral-derivative algorithm; Based on the equivalent factor and the vehicle demand power, a linear programming model with the minimum equivalent fuel consumption as the target is constructed, and the optimal power distribution of engine, front drive motor and rear drive motor is solved online; According to the optimal power distribution, the driving system of the hybrid vehicle is controlled to operate.

2. The adaptive energy management method for a four-wheel drive plug-in hybrid vehicle considering traffic congestion according to claim 1, characterized in that, The step of identifying congestion condition specifically comprises: Setting the demarcation time of congestion and non-congestion working conditions; Comparing the current time with the demarcation time; When the current time is less than or equal to the demarcation time, it is determined that the current is in congestion condition, corresponding to the power consumption section; When the current time is greater than the demarcation time, it is determined that the current is in non-congestion condition, corresponding to the power recovery section; Wherein, the demarcation time is pre-set based on historical driving data, and is dynamically corrected according to the average of vehicle speed change rate and acceleration. 3.The adaptive energy management method for a four-wheel drive plug-in hybrid vehicle considering traffic congestion according to claim 1, wherein, The step of respectively planning linearly descending and linearly ascending battery state of charge target trajectories specifically comprises: For the power consumption section, the battery state of charge at the start time of the journey is taken as the starting point, and the pre-set battery state of charge at the end time of the congestion condition is taken as the terminal point, to construct a function relationship of linearly decreasing with time; For the power recovery section, the battery state of charge at the end time of the congestion condition is taken as the starting point, and the pre-set battery state of charge at the end time of the journey is taken as the terminal point, to construct a function relationship of linearly increasing with time.

4. The adaptive energy management method for a four-wheel drive plug-in hybrid vehicle considering traffic congestion according to claim 1, characterized in that, The step of dynamically adjusting the equivalent factor by using proportional-integral-derivative algorithm specifically comprises: The proportional term of the deviation value, the time integral term of the deviation value and the time differential term of the deviation value are weighted and summed, and the sum is added to an initial reference value to obtain the equivalent factor at the current time; Wherein, the gain coefficients corresponding to the proportional term, the time integral term and the time differential term are pre-adjusted fixed values.

5. The adaptive energy management method for a four-wheel drive plug-in hybrid vehicle considering traffic congestion according to claim 4, characterized in that, The gain coefficients are off-line optimized and adjusted by genetic algorithm, and the fitness function of the genetic algorithm is constructed as follows: A comprehensive evaluation index is established as the fitness function, and the comprehensive evaluation index is the weighted sum of instantaneous deviation absolute value and deviation absolute value cumulative amount; In the iteration process of genetic algorithm, the parameter combination that minimizes the comprehensive evaluation index is found as the gain coefficient. 6.The adaptive energy management method for a four-wheel drive plug-in hybrid vehicle considering traffic congestion according to claim 1, wherein, The step of constructing the linear programming model specifically comprises: Selecting engine output power and front and rear drive power distribution ratio as optimization variables; Constructing an objective function, the objective function including engine fuel consumption term and battery electric energy consumption term; Wherein, the weight coefficient of the engine fuel consumption term is set to one minus the equivalent factor, and the weight coefficient of the battery electric energy consumption term is set to the equivalent factor; The battery power consumption term is corrected by using the continuously variable transmission efficiency, the front main reducer efficiency and the rear main reducer efficiency, and a nonlinear power distribution problem is converted into a linear function form about the optimization variables.

7. The adaptive energy management method for a four-wheel drive plug-in hybrid vehicle considering traffic congestion according to claim 6, characterized in that, When the optimal power distribution of the engine, the front motor and the rear motor is solved online, an engine high-efficiency working zone constraint is introduced, which specifically includes: An engine high-efficiency working zone data chart is preset, and the power range and the speed range corresponding to the data chart are taken as hard boundary conditions; The hard boundary conditions are included in the constraint condition set of the linear programming; During the solving process, candidate solutions exceeding the hard boundary conditions are automatically removed. 8.The adaptive energy management method for a four-wheel drive plug-in hybrid vehicle considering traffic congestion according to claim 6, wherein, The constraint condition set of the linear programming model further includes: engine power upper and lower limit constraints, front motor power upper and lower limit constraints, rear motor power upper and lower limit constraints, and a numerical range constraint of the front and rear drive power distribution ratio; The front and rear drive power distribution ratio defines the proportional relationship of the front motor and the rear motor when they bear the total electric drive power. 9.The adaptive energy management method for a four-wheel drive plug-in hybrid vehicle considering traffic congestion according to claim 6, wherein, The optimal power distribution of the engine, the front motor and the rear motor is solved by using the simplex method, which specifically includes: The optimal solution is searched in the vertices of the polyhedral feasible region composed of the constraint conditions; Through iterative operation, the engine output power and the front and rear drive power distribution ratio that make the objective function value minimum are found. 10.The adaptive energy management method for a four-wheel drive plug-in hybrid vehicle considering traffic congestion according to claim 1, wherein, The specific logic of controlling the operation of the drive system of the hybrid vehicle is: When the equivalent factor increases, the engine output power is increased, and the output powers of the front motor and the rear motor are correspondingly reduced or the motors are controlled to enter the power generation state; When the equivalent factor decreases, the engine output power is reduced, and the output powers of the front motor and the rear motor are correspondingly increased.

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