New energy vehicle control method and control system based on generator joint control

Abstract

This invention relates to the field of hybrid vehicle control technology, and more particularly to a control method and control system for new energy vehicles based on generator-coupled control. The method first generates a basic operating trajectory for the vehicle when the generator intervention increment is zero, based on engine operating parameters, generator operating parameters, and vehicle operating status parameters. Then, it determines the required power generation based on battery status parameters and vehicle electrical load parameters, and determines the intervention trajectory segment based on the basic operating trajectory. On this basis, a load intervention container is generated, and the generator load intervention focus is determined within the load intervention container. At least two candidate coordinated control trajectories are generated based on the generator load intervention focus, and the target coordinated control trajectory is determined based on the trajectory feature point matching results. Finally, vehicle operation control commands and generator control commands are output based on the target coordinated control trajectory, and transition corrections are performed on the access boundary and reconnection boundary. This invention matches the generator intervention process with the vehicle operation process.

Description

Technical Field

[0001] This invention relates to the field of hybrid vehicle control technology, and in particular to a new energy vehicle control method and control system based on generator co-control. Background Technology

[0002] With the development of vehicle electrification and intelligent technologies, vehicle control schemes equipped with generators are widely used in hybrid vehicles, range-extended vehicles, and other vehicles with onboard power generation capabilities. As a crucial component in vehicle energy management, the generator not only affects the battery recharging process but also influences engine load distribution, the supply of electrical loads to the vehicle, and the vehicle's operating status. Therefore, how to rationally control the timing of generator intervention and the power generation process during vehicle operation has gradually become a significant technical issue in the field of vehicle control.

[0003] In existing new energy vehicles or range-extended vehicles, the generator typically starts, stops, and adjusts its power based on the battery's energy replenishment needs, the vehicle's electrical load requirements, or the engine's current status. This type of solution mainly focuses on whether the generator needs to generate electricity and the amount of electricity it can generate. However, the generator's intervention adds a load to the engine side and further affects the vehicle's driving torque, speed response, and running smoothness.

[0004] Therefore, if the generator is controlled to intervene solely based on the energy replenishment demand, it is easy for the generator load change to become disconnected from the original vehicle operation process, resulting in a lack of continuous connection between vehicle operation control and generator control. Summary of the Invention

[0005] To address this issue, the present invention provides a new energy vehicle control method based on generator co-control, which solves the problem in the prior art where generator intervention is controlled solely based on energy replenishment demand, which easily leads to a disconnect between generator load changes and the vehicle's original operating process, resulting in a lack of continuous connection between vehicle operation control and generator control. This method achieves the goal of continuously and controllably embedding the generator's additional load into the vehicle's original operating trajectory while meeting the battery's energy replenishment demand, thus avoiding sudden changes in vehicle drive torque, engine load, and vehicle speed response caused by generator intervention.

[0006] To achieve the above objectives, on the one hand, the present invention provides a new energy vehicle control method based on generator co-control, comprising:

[0007] Based on engine operating parameters, generator operating parameters, and vehicle operating status parameters, a basic operating trajectory of the vehicle is generated when the generator intervention increment is zero. The basic operating trajectory includes predicted vehicle speed, predicted drive torque, and predicted engine load arranged according to sampling time.

[0008] The required power generation is determined based on the battery status parameters and the vehicle electrical load parameters, and the trajectory segment to be intervened is determined based on the required power generation and the basic operating trajectory.

[0009] Based on the start sampling time and end sampling time of the trajectory segment to be intervened and the corresponding vehicle status in the basic operating trajectory, a load intervention container is generated. The load intervention container includes an access sub-area, an intervention sub-area, and a return sub-area.

[0010] Within the load intervention container, the generator load intervention focus is determined based on the battery charging demand, engine load margin, and vehicle power maintenance margin.

[0011] Based on the generator load intervention focus, at least two candidate joint control trajectories are generated within the load intervention container. Each candidate joint control trajectory includes a vehicle torque adjustment trajectory, an engine load distribution trajectory, and a generator output trajectory.

[0012] Identify the trajectory feature points between each candidate joint control trajectory and the basic operation trajectory, and determine the target joint control trajectory from at least two candidate joint control trajectories based on the trajectory feature point matching results;

[0013] Based on the target control trajectory, vehicle operation control commands and generator control commands are output, and transition corrections are made to the access boundary and reconnection boundary between the target control trajectory and the basic operation trajectory.

[0014] As a preferred technical solution for a new energy vehicle control method based on generator-controlled joint operation, the generation of the vehicle's basic operating trajectory when the generator intervention increment is zero includes:

[0015] The current vehicle speed, target vehicle speed, driving torque demand, engine output torque, engine load, current generator output power, and vehicle electrical load power are obtained at continuous sampling times according to a preset sampling period.

[0016] The generator load torque corresponding to the current generator output power is taken as the base load torque;

[0017] While keeping the base load torque constant, the predicted drive torque, predicted vehicle speed, and predicted engine load at each sampling time are calculated based on the drive demand torque, engine output torque, and vehicle operating state parameters.

[0018] The predicted driving torque, predicted vehicle speed, and predicted engine load are arranged in the order of sampling time to form the basic operating trajectory.

[0019] As a preferred technical solution for a new energy vehicle control method based on generator-driven control, the step of determining the required power generation based on the battery state parameters and vehicle electrical load parameters, and determining the trajectory segment to be intervened based on the required power generation and the basic operating trajectory, includes:

[0020] Based on the current state of charge of the battery, the target state of charge, the allowable charging power of the battery, and the electrical load power of the vehicle, determine the required power generation at each sampling time.

[0021] Based on the difference between the required power generation and the current generator output power, determine the generator intervention increment corresponding to each sampling time.

[0022] Based on the predicted engine load corresponding to each sampling time in the basic operating trajectory, determine the engine load margin corresponding to each sampling time.

[0023] The continuous sampling interval in which the increment of the generator to be intervened is greater than zero and the load margin of the engine is not less than the load demand corresponding to the increment of the generator to be intervened is determined as the trajectory segment to be intervened.

[0024] As a preferred technical solution for a new energy vehicle control method based on generator-controlled joint operation, the generated load intervention container includes:

[0025] The number of sampling times prior to the start sampling time of the trajectory segment to be intervened is determined as the access sub-region;

[0026] The trajectory segment to be intervened in is defined as the intervention sub-region;

[0027] The number of sampling times after the termination sampling time of the trajectory segment to be intervened is determined as the reconnection sub-region;

[0028] Based on the basic operating trajectories within the access sub-region, intervention sub-region, and reconnection sub-region, determine the vehicle speed boundary, driving torque boundary, engine load boundary, and generator output power boundary corresponding to each sampling moment;

[0029] The load intervention container is generated from the access sub-region, intervention sub-region, reconnection sub-region, and the vehicle speed boundary, driving torque boundary, engine load boundary, and generator output power boundary corresponding to each sampling time.

[0030] As a preferred technical solution for the new energy vehicle control method based on generator joint control, the step of determining the generator load intervention focus includes:

[0031] Within the load intervention container, the battery replenishment demand, engine load margin, and vehicle power maintenance margin are calculated at each sampling time.

[0032] Based on the battery replenishment demand, engine load margin, and vehicle power maintenance margin, normalized values ​​for replenishment demand, load margin, and power maintenance are generated respectively.

[0033] The sampling time when the normalized values ​​of replenishment demand, load margin, and power maintenance are all not less than the corresponding preset lower limit is determined as the candidate focus time.

[0034] Calculate the focus evaluation value corresponding to each candidate focus time. The focus evaluation value is obtained by multiplying the normalized value of replenishment demand, the normalized value of load margin, and the normalized value of power maintenance.

[0035] The candidate focus moment with the highest focus evaluation value is determined as the generator load intervention focus.

[0036] As a preferred technical solution for the new energy vehicle control method based on generator-controlled joint control, the generation of at least two candidate joint control trajectories includes:

[0037] Taking the generator load intervention focus as the center, a preset number of sampling times before and after the generator load intervention focus are selected as candidate intervention starting points;

[0038] Based on the preset set of generator output change rates, the generator output trajectory corresponding to each candidate intervention starting point is generated respectively.

[0039] Based on the generator load torque corresponding to the generator output trajectory, an engine load distribution trajectory is generated;

[0040] Based on the generator load torque and the preset torque compensation coefficient, a vehicle torque adjustment trajectory is generated;

[0041] The generator output trajectory, engine load distribution trajectory, and vehicle torque adjustment trajectory under the same candidate intervention starting point are combined to form a candidate joint control trajectory.

[0042] As a preferred technical solution for the new energy vehicle control method based on generator joint control, the step of identifying trajectory feature points between each candidate joint control trajectory and the basic operating trajectory, and determining the target joint control trajectory from at least two candidate joint control trajectories based on the trajectory feature point matching results, includes:

[0043] In each candidate joint control trajectory, the access start point, the generator load intervention focus, the sampling point where the generator output reaches the required power generation, and the reconnection end point are determined as the trajectory feature points of the candidate joint control trajectory.

[0044] The candidate vehicle speed, candidate drive torque, candidate engine load, and candidate generator output power corresponding to each trajectory feature point are obtained respectively.

[0045] Obtain the basic vehicle speed, basic drive torque, and basic engine load corresponding to the sampling time of each trajectory feature point in the basic operating trajectory;

[0046] Based on the absolute values ​​of the differences between candidate vehicle speed and base vehicle speed, candidate drive torque and base drive torque, and candidate engine load and base engine load, calculate the trajectory matching difference corresponding to each candidate joint control trajectory.

[0047] The candidate joint control trajectory with the smallest trajectory matching difference that is less than the preset matching upper limit is determined as the target joint control trajectory.

[0048] As a preferred technical solution for the new energy vehicle control method based on generator-controlled linkage, the transition correction of the access boundary and reconnection boundary between the target control trajectory and the basic operating trajectory includes:

[0049] At the access boundary, calculate the changes in vehicle operation control commands and generator control commands between adjacent sampling times;

[0050] At the connection boundary, calculate the changes in vehicle operation control commands and generator control commands between adjacent sampling times;

[0051] When the change in vehicle operation control command exceeds the preset upper limit of vehicle command change, the vehicle operation control command between the corresponding adjacent sampling times will be replaced with a sequence of vehicle operation control commands that are either increasing or decreasing in segments.

[0052] As a preferred technical solution for the control method of new energy vehicles based on generator joint control, when the change in generator control command exceeds the preset upper limit of generator command change, the generator control command between adjacent sampling times is replaced with a sequence of generator control commands that are segmented increasing or segmented decreasing.

[0053] In the segmented increasing or decreasing vehicle operation control command sequence, the absolute value of the difference between two adjacent vehicle operation control commands is not greater than the preset upper limit of vehicle command change; in the segmented increasing or decreasing generator control command sequence, the absolute value of the difference between two adjacent generator control commands is not greater than the preset upper limit of generator command change.

[0054] On the other hand, the present invention also provides a new energy vehicle control system based on generator co-control, comprising:

[0055] The parameter acquisition module is used to acquire battery status parameters, engine operating parameters, vehicle electrical load parameters, generator operating parameters, and vehicle operating status parameters.

[0056] The basic trajectory generation module is used to generate the basic operating trajectory of the vehicle when the generator intervention increment is zero, based on the engine operating parameters, generator operating parameters and vehicle operating status parameters.

[0057] The section to be intervened in module is used to determine the required power generation based on the battery status parameters and the vehicle electrical load parameters, and to determine the trajectory section to be intervened in based on the required power generation and the basic operating trajectory.

[0058] The load intervention container generation module is used to generate a load intervention container based on the start sampling time, end sampling time of the trajectory segment to be intervened, and the corresponding vehicle status in the basic running trajectory.

[0059] The intervention focus determination module is used to determine the generator load intervention focus within the load intervention container based on the battery charging demand, engine load margin, and vehicle power maintenance margin.

[0060] The candidate trajectory generation module is used to generate at least two candidate control trajectories within the load intervention container, based on the generator load intervention focus.

[0061] The target trajectory determination module is used to identify trajectory feature points between each of the candidate joint control trajectories and the basic operating trajectory, and to determine the target joint control trajectory from at least two candidate joint control trajectories based on the trajectory feature point matching results;

[0062] The instruction output module is used to output vehicle operation control instructions and generator control instructions according to the target control trajectory, and to perform transition correction on the access boundary and reconnection boundary between the target control trajectory and the basic operation trajectory.

[0063] Compared with existing technologies, the beneficial effects of this invention are that it does not directly control the generator to intervene after detecting a battery charging demand. Instead, it first generates a basic operating trajectory for the vehicle when the generator's incremental charging demand is zero, and uses this basic operating trajectory as a reference for the vehicle's original operating state. Subsequently, this invention determines the segment of the trajectory to be intervened in the basic operating trajectory and defines the vehicle's state range before, during, and after generator intervention through a load intervention container. Since the changes in engine load, vehicle torque, and vehicle speed response caused by generator intervention are all processed within the same local trajectory, generator control is no longer a charging action independent of the vehicle's operating process, but rather an embedded, matchable, splicable, and transitionable joint control trajectory within the vehicle's basic operating trajectory. This reduces the disturbance of the generator's additional load on the vehicle's original operating process, creating a continuous connection between vehicle operation control and generator charging control.

[0064] Furthermore, this invention divides the generator intervention process into an access sub-region, an intervention sub-region, and a reconnection sub-region by setting a load intervention container, and sets vehicle speed boundaries, drive torque boundaries, engine load boundaries, and generator output power boundaries at each sampling moment. Since generator intervention is not an instantaneous action but a continuous process involving load superposition, power establishment, and vehicle state reconnection, judging whether to intervene only at a single moment is insufficient to constrain state changes before and after intervention. This invention, through the load intervention container, confines this continuous process within a defined sampling interval and state boundaries, ensuring that generator load changes can only be generated, compared, and corrected within the container's defined range, thereby reducing the possibility of unexpected impacts from generator load adjustments on the vehicle's overall operating state.

[0065] Furthermore, this invention determines the generator load intervention focus within the load intervention container. This generator load intervention focus is not determined solely by battery charging needs, nor solely by engine load margin, but rather by a combination of battery charging needs, engine load margin, and vehicle power maintenance margin. In other words, a sampling location is only designated as the core location for generator load intervention when it simultaneously meets the requirements for charging, engine load conditions, and vehicle power maintenance conditions. This avoids the generator intervening at unsuitable locations solely due to battery charging needs, ensuring that the primary superposition location of the generator load more closely aligns with the load conditions of the vehicle's powertrain.

[0066] Furthermore, this invention generates multiple candidate control trajectories around the generator load intervention focus and determines the target control trajectories through trajectory feature point matching. Each candidate control trajectories include a vehicle torque adjustment trajectory, an engine load distribution trajectory, and a generator output trajectory. Therefore, generator output changes, engine load changes, and vehicle torque compensation are not calculated separately but are formed collaboratively within the same trajectory. Subsequently, by comparing the vehicle speed difference, drive torque difference, and engine load difference between the candidate control trajectories and the basic operating trajectory using trajectory feature points such as the access start point, generator load intervention focus, sampling point where the generator output reaches the required power generation, and reconnection end point, the target control trajectories with a higher degree of connection to the basic operating trajectory are selected. This reduces the misalignment between the generator output trajectory and the vehicle torque adjustment trajectory, making it easier to continuously connect the generator intervention process with the vehicle's original operating trajectory. Attached Figure Description

[0067] Figure 1 This is a flowchart illustrating the steps of a new energy vehicle control method based on generator co-control, as described in an embodiment of the present invention.

[0068] Figure 2 This is a structural block diagram of a new energy vehicle control system based on generator co-control, according to an embodiment of the present invention. Detailed Implementation

[0069] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0070] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0071] Please see Figure 1 The diagram illustrates the steps of a new energy vehicle control method based on generator co-control according to an embodiment of the present invention, including the following steps:

[0072] Step S1: Based on the engine operating parameters, generator operating parameters, and vehicle operating status parameters, generate the basic operating trajectory of the vehicle when the generator intervention increment is zero. The basic operating trajectory includes the predicted vehicle speed, predicted drive torque, and predicted engine load arranged according to the sampling time.

[0073] Step S2: Determine the required power generation based on the battery status parameters and the vehicle electrical load parameters, and determine the trajectory section to be intervened based on the required power generation and the basic operating trajectory.

[0074] Step S3: Based on the start sampling time, end sampling time and corresponding vehicle status in the basic operating trajectory of the trajectory segment to be intervened, a load intervention container is generated. The load intervention container includes an access sub-area, an intervention sub-area and a return sub-area.

[0075] Step S4: Within the load intervention container, determine the generator load intervention focus based on the battery charging demand, engine load margin, and vehicle power maintenance margin.

[0076] Step S5: Based on the generator load intervention focus, generate at least two candidate control trajectories within the load intervention container. Each candidate control trajectories includes the vehicle torque adjustment trajectory, the engine load distribution trajectory, and the generator output trajectory.

[0077] Step S6: Identify the trajectory feature points between each candidate joint control trajectory and the basic operation trajectory, and determine the target joint control trajectory from at least two candidate joint control trajectories based on the trajectory feature point matching results;

[0078] Step S7: Output vehicle operation control commands and generator control commands according to the target control trajectory, and perform transition corrections on the access boundary and reconnection boundary between the target control trajectory and the basic operation trajectory.

[0079] Specifically, in step S1, the controller can acquire the current vehicle speed, target vehicle speed, drive demand torque, engine output torque, engine load, current generator output power, and vehicle electrical load power corresponding to the continuous sampling time at a preset sampling period.

[0080] The preset sampling period can be determined based on the data refresh cycle of the vehicle control system. For example, if the control cycle of the vehicle controller is 100ms, the preset sampling period can be set to 100ms; if the power regulation cycle between the vehicle controller and the generator controller is 200ms, the preset sampling period can also be set to 200ms. Generally, the preset sampling period should not be longer than the control response cycle of the generator output power, so that changes in generator load can be reflected in subsequent trajectory calculations in a timely manner.

[0081] The current vehicle speed can be obtained from wheel speed sensors or the vehicle controller; the target vehicle speed can be obtained from driver input, cruise control system or vehicle driving strategy; the required driving torque can be determined based on accelerator pedal opening, target vehicle speed, vehicle mass and driving resistance; the engine output torque and engine load can be provided by the engine controller; the current generator output power can be provided by the generator controller; the vehicle electrical load power can be obtained from statistics of the vehicle electrical system.

[0082] After obtaining the current generator output power, the controller uses the generator load torque corresponding to the current generator output power as the base load torque. The relationship between generator output power and generator load torque can be calculated using a power-torque conversion method commonly used in vehicle control. For example, when there is a mechanical coupling between the engine and generator, the following relationship can be used for calculation:

[0083] Generator load torque = 9550 × current generator output power / engine speed.

[0084] The unit for the current generator output power can be kW, the unit for engine speed can be r / min, and the unit for generator load torque can be N·m. If the controller uses other unit systems, conversion can be performed based on the relationship that power equals the product of torque and angular velocity.

[0085] While keeping the base load torque constant, the controller calculates the predicted drive torque, predicted vehicle speed, and predicted engine load at each sampling time based on the drive demand torque, engine output torque, and vehicle operating status parameters.

[0086] For example, for the k-th sampling time, the corresponding predicted drive torque can be obtained based on the engine output torque, the base load torque, and the torque distribution relationship of the vehicle's powertrain. When the vehicle is directly driven by the engine, the predicted drive torque can be obtained by subtracting the base load torque from the engine output torque and then combining it with the transmission system efficiency; when the vehicle is a range-extended electric vehicle, the predicted drive torque can be obtained based on the torque required by the drive motor.

[0087] Vehicle speed can be predicted using a longitudinal dynamics model. This type of model is commonly used in vehicle control and can calculate the vehicle speed at the next sampling moment based on vehicle driving force, rolling resistance, air resistance, gradient resistance, vehicle mass, and sampling period. If the vehicle control system already stores a vehicle speed prediction model, it can also directly call that model to calculate the predicted vehicle speed.

[0088] Engine load can be predicted based on the engine output torque and the maximum available torque at the current engine speed. For example: Predicted engine load = Engine output torque / Maximum available torque at the current engine speed × 100%.

[0089] The maximum available torque of the engine at the current speed can be obtained from the engine characteristic table. The engine characteristic table can usually be pre-stored in the engine controller or vehicle controller.

[0090] Arrange the predicted vehicle speed, predicted drive torque, and predicted engine load corresponding to each sampling time in chronological order to form the basic operating trajectory. For example, sampling times k, k+1, k+2, and k+3 correspond to predicted vehicle speeds v(k), v(k+1), v(k+2), and v(k+3), predicted drive torques T(k), T(k+1), T(k+2), and T(k+3), and predicted engine loads L(k), L(k+1), L(k+2), and L(k+3), respectively. Arranging these data according to the sampling times provides the basic operating trajectory.

[0091] The significance of using a baseline operating trajectory is that subsequent increases in generator load are not directly added to the vehicle control process, but are first compared with this baseline operating trajectory. This allows for a clear determination of the changes in vehicle speed, drive torque, and engine load relative to the original operating state after generator intervention.

[0092] After obtaining the basic operating trajectory, the controller executes step S2, which determines the required power generation based on the battery status parameters and the vehicle electrical load parameters, and determines the trajectory segment to be intervened in based on the required power generation and the basic operating trajectory.

[0093] Demand power generation capacity represents the generator output power required by the vehicle to meet the vehicle's electrical load and charge the battery. This power can be determined by the battery's current state of charge, target state of charge, battery's allowable charging power, and the vehicle's electrical load power.

[0094] For example, the battery charging power can be calculated first. The battery charging power can be determined as follows:

[0095] Target replenishment power = max{0, target state of charge - current state of charge} × battery rated energy / target recovery time;

[0096] Battery charging power = min{battery allowable charging power, target charging power}.

[0097] The target state of charge (SOC) can be set according to the vehicle's energy management strategy. For example, range-extended vehicles can set the target SOC to a calibrated value between 50% and 70%; hybrid vehicles can adjust the target SOC based on driving mode, road gradient, or battery life protection strategy. The target recovery time indicates the desired time to recharge the battery to the target SOC, and can be set to 20 minutes, 30 minutes, or 60 minutes, or calibrated according to the vehicle mode.

[0098] For example, if a battery has a rated energy of 20kWh, a current state of charge of 45%, a target state of charge of 55%, and a target recovery time of 30 minutes, then the target replenishment power is:

[0099] (55%-45%)×20kWh / 0.5h=4kW.

[0100] If the battery's allowable charging power is 20kW, then the battery's recharge power is taken as 4kW. If the vehicle's electrical load power is 2kW, then the required power generation can be determined as 6kW.

[0101] After determining the required power generation, the controller determines the generator intervention increment for each sampling time based on the difference between the required power generation and the current generator output power. For example:

[0102] The generator waiting to be intervened increment = max{0, required power generation - current generator output power}.

[0103] If the required power generation is 6kW and the current generator output is 2kW, then the required generator increment is 4kW. If the required power generation is 3kW and the current generator output is 4kW, then the required generator increment is 0, indicating that the current generator output already covers the demand and no additional generator is needed.

[0104] The incremental generator load to be added is used here instead of the demand power output to distinguish between the existing generator output and the output that needs to be added later. Subsequent trajectory processing only embeds the newly added generator load, avoiding the duplication of calculations for the existing generator load.

[0105] Subsequently, the controller determines the engine load margin for each sampling time based on the predicted engine load corresponding to each sampling time in the basic operating trajectory. The engine load margin can be expressed in terms of power or load percentage.

[0106] When using power form, it can be determined as follows:

[0107] Engine load margin = Available engine output power at current speed - Predicted engine output power in the basic operating trajectory.

[0108] When using the load percentage method, it can be determined as follows:

[0109] Engine load margin = Maximum allowable engine load - Predicted engine load.

[0110] For example, if the permissible engine load limit is 85% and the predicted engine load is 70%, then the engine load margin is 15 percentage points. The permissible engine load limit can be calibrated based on engine thermal protection, fuel economy range, noise and vibration requirements, and emission control requirements. For example, in economy-priority mode, the permissible engine load limit can be set to 75%-85%; in power-priority mode, the permissible engine load limit can be set to 85%-95%.

[0111] When comparing engine load margin with the incremental generator demand, it is necessary to convert the incremental generator demand into the corresponding load requirement on the engine side. For example, the additional generating power can be converted into the additional power that the engine side needs to provide based on the generator efficiency.

[0112] The load demand corresponding to the incremental generator to be engaged = incremental generator to be engaged / generator efficiency.

[0113] For example, if the generator's incremental load to be added is 4kW and the generator efficiency is 0.9, then the engine side needs to handle approximately 4.44kW of additional power. If the available power corresponding to the engine load margin at this sampling time is 10kW, then the engine side has the capacity to handle the additional generator load at this sampling time.

[0114] When multiple consecutive sampling times all satisfy the condition that the generator intervention increment is greater than zero and the engine load margin is not less than the load demand corresponding to the generator intervention increment, the continuous sampling interval is determined as the intervention trajectory segment.

[0115] To avoid false triggering caused by a single sampling point, a minimum number of consecutive samples can be set. For example, with a sampling period of 100ms and a generator power set-up time of 300ms, the minimum number of consecutive samples can be set to 3; if a more robust intervention judgment is desired, it can also be set to 5 or 10. This number can be determined by the generator response time, engine load response time, and vehicle test calibration.

[0116] For example, if the sampling period is 100ms, and the above conditions are met for 7 consecutive sampling times from k+3 to k+9, then k+3 to k+9 can be identified as the trajectory segment to be intervened in.

[0117] The section of trajectory to be intervened does not necessarily mean that the generator will intervene immediately. Rather, it means that within this continuous sampling interval, the vehicle has both new power generation needs and engine load bearing capacity. Therefore, this section can serve as the basis for generating a load intervention container in the future.

[0118] S3 is the process of generating a load intervention container. After determining the trajectory segment to be intervened in, the controller generates a load intervention container based on the start and end sampling times of the trajectory segment and the corresponding vehicle status in the basic operating trajectory. The load intervention container includes an access sub-area, an intervention sub-area, and a return sub-area.

[0119] Load is the data range used in the controller to describe the local process of generator intervention. This data range includes both the sampling time range and the allowable vehicle speed, drive torque, engine load, and generator output power range at the corresponding sampling time. By using a load intervention container, the generator intervention process can be limited to a defined local control range.

[0120] Specifically, the controller defines the access sub-region as a preset number of sampling times prior to the initial sampling time of the trajectory segment to be intervened in. The access sub-region is used to provide preparation time for the generator to add load into the vehicle's powertrain, allowing vehicle torque adjustment and engine load distribution to be established gradually.

[0121] The trajectory segment to be intervened in is designated as the intervention sub-region. The intervention sub-region is used to perform generator output increase, engine load distribution, and vehicle torque adjustment.

[0122] The preset number of sampling times after the termination sampling time of the trajectory segment to be intervened is defined as the reconnection sub-region. The reconnection sub-region is used to reconnect the vehicle status after the generator intervention to the basic operating trajectory, avoiding control breakpoints at the end of the joint control process.

[0123] The aforementioned preset quantities can be determined based on the generator output power change time and vehicle control response time. For example, if the sampling period is 100ms, and it typically takes 500ms for the generator output power to adjust from the current output power to the required power, the access sub-zone and the reconnection sub-zone can each be set to 5 sampling times. If the vehicle powertrain response time is 300ms, it can be set to 3 sampling times. The preset quantities can also be calibrated through vehicle road tests or bench tests.

[0124] For example, if the trajectory segment to be intervened in is from k+3 to k+9, the preset number of sampling times for the access sub-region is 3, and the preset number of sampling times for the return sub-region is 3, then the access sub-region is from k to k+2, the intervention sub-region is from k+3 to k+9, and the return sub-region is from k+10 to k+12. These three sub-regions together constitute the time range of the load intervention container.

[0125] After determining the access sub-region, intervention sub-region, and reconnection sub-region, the controller determines the vehicle speed boundary, drive torque boundary, engine load boundary, and generator output power boundary corresponding to each sampling time based on the basic operating trajectory within each of the above sub-regions.

[0126] The vehicle speed boundary can be determined based on the difference between the predicted vehicle speed in the basic operating trajectory and the preset allowable vehicle speed. For example, if the predicted vehicle speed at a certain sampling time is 60 km / h and the preset allowable vehicle speed difference is 0.5 km / h, then the vehicle speed boundary can be set to 59.5 km / h to 60.5 km / h. The preset allowable vehicle speed difference can be set according to cruise control accuracy, driving comfort requirements, or vehicle calibration results, for example, it can be set to 0.3 km / h, 0.5 km / h, or 1 km / h.

[0127] The drive torque boundary can be determined based on the predicted drive torque in the basic operating trajectory and the preset allowable difference in drive torque. For example, if the predicted drive torque at a certain sampling moment is 120 N·m and the preset allowable difference in drive torque is 10 N·m, then the drive torque boundary can be set to 110 N·m to 130 N·m. The preset allowable difference in drive torque can be set according to the vehicle's torque control accuracy, transmission system load capacity, and driving smoothness requirements, or it can be set to 3%–8% of the vehicle's rated drive torque.

[0128] The engine load boundary is used to limit the permissible range of engine load after generator intervention. For example, the upper limit of the engine load boundary can be set to the maximum permissible engine load, such as 85%; the lower limit can be set to the predicted engine load in the basic operating trajectory, or to the predicted engine load minus a preset load drop. Using this boundary can prevent the engine load from exceeding the maximum permissible engine load of the current operating mode after generator intervention.

[0129] The generator output power boundary can be determined based on the current generator output power, the required power generation, and the generator's permissible output power. For example, if the current generator output power is 2kW, the required power generation is 6kW, and the generator's permissible output power is 8kW, then the generator output power boundary can be set to 2kW to 6kW. If the required power generation exceeds the generator's permissible output power, the upper limit can be set to the generator's permissible output power.

[0130] The load intervention container is formed by the access sub-region, intervention sub-region, reconnection sub-region, and the vehicle speed boundary, driving torque boundary, engine load boundary, and generator output power boundary corresponding to each sampling time.

[0131] The controller, located within the load intervention container, determines the generator load intervention focus based on the battery charging demand, engine load margin, and vehicle power maintenance margin.

[0132] The generator load intervention focus is a sampling moment within the load intervention container. This sampling moment is not simply the starting point of the segment to be intervened, nor is it selected solely based on battery charging demand. Instead, it is determined after comprehensively considering charging demand, engine load capacity, and vehicle power maintenance capability. This avoids the generator intervening at locations where charging demand exists but vehicle power connection conditions are insufficient.

[0133] Inside the load intervention container, the controller calculates the battery replenishment demand, engine load margin, and vehicle power maintenance margin at each sampling time.

[0134] The energy demand for battery replenishment can be expressed as the energy difference between the battery's current state of charge and its target state of charge. For example:

[0135] Battery recharge demand = max{0, target state of charge - current state of charge} × battery rated energy.

[0136] If the target state of charge (SOC) is 55%, the current SOC is 45%, and the battery's rated energy is 20 kWh, then the battery recharge requirement is 2 kWh. Alternatively, if evaluated in terms of power within the controller, the aforementioned battery recharge power can be used as the battery recharge requirement.

[0137] The engine load margin can be the difference between the engine's available output power at the current speed and the predicted engine output power in the basic operating trajectory, or the difference between the upper limit of the allowed engine load and the predicted engine load.

[0138] The vehicle power maintenance margin represents the available drive torque space that the vehicle can still use for coordination while maintaining its basic operating trajectory at the sampling time. This amount is a calculation component in this embodiment used to determine whether generator intervention will crowd out the vehicle's driving capability. For example, it can be calculated as follows:

[0139] Vehicle power maintenance margin = upper limit of drive torque boundary - predicted drive torque in the basic operating trajectory.

[0140] For example, if the upper limit of the driving torque boundary at a certain sampling moment is 130 N·m, and the predicted driving torque in the basic operating trajectory is 120 N·m, then the vehicle's power maintenance margin is 10 N·m. This value indicates that the vehicle still has 10 N·m of torque adjustment space without exceeding the driving torque boundary. If the vehicle's power maintenance margin is 0, then there is no torque space at that sampling moment to further coordinate the increased load from the generator.

[0141] Because the battery charging demand, engine load margin, and vehicle power maintenance margin use different units, they cannot be directly compared or multiplied. Therefore, the controller generates normalized values ​​for charging demand, engine load margin, and power maintenance margin, respectively.

[0142] In one example, the maximum value of the corresponding physical quantity within the container corresponding to the load intervention can be used as the normalization benchmark:

[0143] Normalized value of replenishment demand = Battery replenishment demand at the current sampling time / Maximum battery replenishment demand when load enters the container;

[0144] Normalized load margin = Engine load margin at the current sampling time / Maximum engine load margin in the load intervention container;

[0145] Power maintenance normalized value = vehicle power maintenance margin at the current sampling time / maximum vehicle power maintenance margin within the load intervention container.

[0146] When the corresponding maximum value is 0, the normalized value of this type can be set to 0 to indicate that there is no effective demand or effective margin in the load intervention container.

[0147] In another example, normalization can also be performed using a calibration reference. For instance, the normalized value for charging demand can be based on the battery's allowable charging power or the target charging power; the normalized value for load margin can be based on the engine's allowable load margin; and the normalized value for power sustaining can be based on the allowable adjustment of drive torque. The choice of normalization reference can be determined based on the vehicle controller's calibration method.

[0148] Next, the controller determines the sampling time when the normalized values ​​of replenishment demand, load margin, and power maintenance are all not less than the corresponding preset lower limit as the candidate focus time.

[0149] The preset lower limit is used to filter sampling times with incomplete conditions. For example, if the normalized value of the recharge demand at a certain sampling time is 0.9, but the normalized value of the load margin is 0.1, it indicates that although there is a recharge demand at that time, the engine's load capacity is insufficient. Similarly, if the normalized value of the load margin at a certain sampling time is 0.8, but the normalized value of the power maintenance is 0.05, it indicates that the vehicle's drive torque adjustment space is insufficient. Neither of these sampling times is suitable as the focus for generator load intervention.

[0150] The preset lower limits can be determined through vehicle calibration. For example, the preset lower limit for the normalized value of refueling demand can be set to 0.2, the preset lower limit for the normalized value of load margin can be set to 0.3, and the preset lower limit for the normalized value of power maintenance can be set to 0.3. If the vehicle control strategy prioritizes refueling response, the lower limit or weight corresponding to the normalized value of refueling demand can be increased; if driving smoothness is prioritized, the lower limit corresponding to the normalized value of power maintenance can be increased. The above parameters can be determined through road tests, bench tests, or simulation tests.

[0151] For each candidate focus moment, the controller calculates the corresponding focus evaluation value. The focus evaluation value can be determined as follows:

[0152] The key evaluation value = normalized value of replenishment demand × normalized value of load margin × normalized value of power maintenance.

[0153] The reason for using product calculation is that the generator load intervention focus needs to simultaneously meet the energy replenishment demand, engine load conditions, and vehicle power maintenance conditions. If any of these factors is close to 0, the focus evaluation value will also be close to 0, and thus that sampling moment will not be prioritized. Compared to simple summation, the product method can reduce the possibility of a high value for a single condition masking deficiencies in other conditions.

[0154] For example, there are three candidate focal moments within the load intervention container. At the first candidate focal moment, the normalized value of the replenishment demand is 0.8, the normalized value of the load margin is 0.6, and the normalized value of the power sustaining capacity is 0.5, resulting in a focal moment evaluation value of 0.24. At the second candidate focal moment, the normalized value of the replenishment demand is 0.7, the normalized value of the load margin is 0.8, and the normalized value of the power sustaining capacity is 0.7, resulting in a focal moment evaluation value of 0.392. At the third candidate focal moment, the normalized value of the replenishment demand is 0.9, the normalized value of the load margin is 0.4, and the normalized value of the power sustaining capacity is 0.4, resulting in a focal moment evaluation value of 0.144. In this case, the second candidate focal moment can be determined as the generator load intervention focal moment.

[0155] Therefore, the generator load intervention focus can be clearly mapped to a sampling moment within the load intervention container, and its determination process can be calculated from the battery replenishment demand, engine load margin, and vehicle power maintenance margin.

[0156] After determining the generator load intervention focus, the controller uses this generator load intervention focus as a reference to generate at least two candidate control trajectories within the load intervention container. Each candidate control trajectories includes a vehicle torque adjustment trajectory, an engine load distribution trajectory, and a generator output trajectory.

[0157] At this stage, the controller does not directly increase the generator output power to the required power output. Instead, it generates multiple possible intervention modes around the generator load intervention focus. For example, it can generate candidate control trajectories that start increasing the generator output power before the generator load intervention focus, candidate control trajectories that start increasing the generator output power from the generator load intervention focus, and candidate control trajectories that start increasing the generator output power after the generator load intervention focus.

[0158] The vehicle torque adjustment trajectory is used to represent how the vehicle drive side compensates for or coordinates the additional load of the generator at multiple sampling times; the engine load distribution trajectory is used to represent how the engine distributes drive demand and power generation demand at multiple sampling times; and the generator output trajectory is used to represent how the generator output power transitions from the current generator output power to the required power generation power.

[0159] In the above embodiments, the additional load on the generator is not directly superimposed as a single sampling point, but is converted into a candidate control trajectory composed of vehicle torque adjustment, engine load distribution, and generator output. The controller can then further identify trajectory feature points between each candidate control trajectory and the basic operating trajectory, and determine the target control trajectory based on the trajectory feature point matching results.

[0160] In detail, after determining the generator load intervention focus, the controller selects a preset number of sampling times before and after the generator load intervention focus as candidate intervention starting points.

[0161] The generator load intervention focus is used to represent the baseline sampling time at which the new generator load is suitable for intervention. Candidate intervention starting points are multiple sampling times selected around this baseline sampling time. This allows for the generation of multiple generator intervention schemes for subsequent comparison and selection.

[0162] For example, if the sampling period is 100ms, the generator load intervention focus is the kth sampling time, and the preset number is 2, then k-2, k-1, k, k+1, and k+2 can be used as candidate intervention starting points. The preset number can be set according to the generator power response time and the vehicle torque response time. For example, when the generator power response time is 300ms to 500ms, the preset number can be set to 2 to 5 sampling times.

[0163] Subsequently, the controller generates the generator output trajectory corresponding to each candidate intervention starting point based on the preset set of generator output change rates.

[0164] A preset generator output change rate set is used to define the rate at which the generator output power changes with the sampling time. For example, the preset generator output change rate set may include 0.5kW / 100ms, 1kW / 100ms, and 1.5kW / 100ms. These values ​​can be set according to the generator's allowable power ramp-up rate, generator temperature rise protection requirements, and engine load response capability.

[0165] For example, if the current generator output power is 2kW and the required power output is 6kW, and an output change rate of 1kW / 100ms is used, the generator output trajectory can be 2kW, 3kW, 4kW, 5kW, 6kW in sequence. If an output change rate of 0.5kW / 100ms is used, the generator output trajectory can be 2kW, 2.5kW, 3kW, 3.5kW... up to 6kW.

[0166] The controller generates the engine load distribution trajectory based on the generator load torque corresponding to the generator output trajectory. The generator load torque can be calculated from the generator output power and engine speed. For example:

[0167] Generator load torque = 9550 × generator output power / engine speed.

[0168] The engine load distribution trajectory is used to record the distribution of engine output capacity between vehicle drive demand and generator load demand at each sampling time. For example, if the engine output torque is 160 N·m and the generator load torque is 30 N·m at a certain sampling time, then the remaining torque available for vehicle drive or other accessory loads is 130 N·m.

[0169] The controller also generates a vehicle torque adjustment trajectory based on the generator load torque and a preset torque compensation coefficient. The preset torque compensation coefficient represents the proportion of the increased load on the generator that needs to be compensated by the vehicle's drive side. For example, the preset torque compensation coefficient can be set to 0.3, 0.5, or 0.8. This coefficient can be calibrated according to the vehicle's power mode: it can be set to 0.3 to 0.5 in economy mode and 0.6 to 0.9 in power mode.

[0170] For example, if the generator load torque increases by 20 N·m at a certain sampling moment, and the preset torque compensation coefficient is 0.5, then the vehicle torque adjustment amount is 10 N·m. This vehicle torque adjustment amount can be used to adjust the output torque of the drive motor or the engine drive-side torque, so that the change in vehicle drive torque remains within the aforementioned drive torque boundary.

[0171] The generator output trajectory, engine load distribution trajectory, and vehicle torque adjustment trajectory under the same candidate intervention starting point are combined to form a candidate joint control trajectory. Each candidate joint control trajectory corresponds to a generator intervention starting point and a generator output change rate.

[0172] After generating multiple candidate control trajectories, the controller identifies the trajectory feature points between each candidate control trajectory and the basic operating trajectory, and determines the target control trajectory based on the trajectory feature point matching results.

[0173] Trajectory feature points are key sampling points used to evaluate the degree of connection in candidate joint control trajectories. In each candidate joint control trajectory, the connection start point, the generator load intervention focus, the sampling point where the generator output reaches the required power generation, and the reconnection end point can be used as trajectory feature points.

[0174] Among them, the access start point is used to indicate the sampling point where the generator output trajectory begins to change; the generator load intervention focus is used to indicate the reference intervention position of the newly added generator load; the sampling point where the generator output reaches the required power generation is used to indicate the position where the supplementary power target is established; and the reconnection end point is used to indicate the sampling point where the candidate joint control trajectory ends and returns to the basic operating trajectory.

[0175] The controller acquires candidate vehicle speed, candidate drive torque, candidate engine load, and candidate generator output power corresponding to each trajectory feature point. Simultaneously, it acquires the base vehicle speed, base drive torque, and base engine load corresponding to the sampling time of each trajectory feature point in the base operating trajectory.

[0176] Then, the controller calculates the trajectory matching difference corresponding to each candidate joint control trajectory based on the absolute value of the difference between the candidate vehicle speed and the base vehicle speed, the absolute value of the difference between the candidate drive torque and the base drive torque, and the absolute value of the difference between the candidate engine load and the base engine load.

[0177] For example, it can be calculated as follows:

[0178] Trajectory matching difference = a × normalized absolute value of vehicle speed difference + b × normalized absolute value of drive torque difference + c × normalized absolute value of engine load difference.

[0179] Here, a, b, and c are weighting coefficients, and their sum can be 1. For example, a can be set to 0.4, b can be set to 0.4, and c can be set to 0.2. If the vehicle focuses more on maintaining speed, a can be increased; if the vehicle focuses more on torque smoothness, b can be increased; if the vehicle focuses more on engine load changes, c can be increased.

[0180] Alternatively, a simple addition method can be used for calculation, for example:

[0181] Trajectory matching difference = Normalized absolute value of vehicle speed difference + Normalized absolute value of drive torque difference + Normalized absolute value of engine load difference

[0182] The normalized value can be calculated using the preset allowable difference in vehicle speed, the preset allowable difference in drive torque, and the preset allowable difference in engine load as the denominator. For example, if the vehicle speed difference is 0.2 km / h and the preset allowable difference in vehicle speed is 1 km / h, then the normalized value of the absolute value of the vehicle speed difference is 0.2.

[0183] The preset matching upper limit is used to exclude candidate control trajectories that deviate from the basic operating trajectory beyond the allowable range. For example, if the above weighted calculation method is used, the preset matching upper limit can be set to 1. It can also be calibrated through road tests or simulation tests, for example, by selecting the trajectory matching difference corresponding to the condition that the vehicle has no obvious jerking, and the vehicle speed fluctuation and engine load fluctuation all meet the requirements as the preset matching upper limit.

[0184] The controller identifies the candidate control trajectory with the smallest trajectory matching difference (less than a preset matching upper limit) as the target control trajectory. This target control trajectory is used to subsequently output vehicle operation control commands and generator control commands.

[0185] After determining the target control trajectory, the controller outputs vehicle operation control commands and generator control commands based on the target control trajectory. Vehicle operation control commands may include drive torque commands, drive motor torque commands, or engine drive-side torque commands. Generator control commands may include generator target output power commands, generator target torque commands, or generator excitation control commands.

[0186] The access boundary is the position where the target control trajectory begins to connect to the basic operating trajectory. The return boundary is the position where the target control trajectory ends and returns to the basic operating trajectory. These two boundaries are prone to concentrated command changes; therefore, the controller calculates the changes in vehicle operation control commands and generator control commands between adjacent sampling times at the access boundary and the return boundary, respectively.

[0187] The changes in vehicle operation control commands can be determined as follows:

[0188] Change in vehicle operation control command = Vehicle operation control command at the current sampling time - Absolute value of vehicle operation control command at the previous sampling time.

[0189] For example, if the vehicle operation control command at the previous sampling time was 120 N·m and the vehicle operation control command at the current sampling time is 138 N·m, then the change in the vehicle operation control command is 18 N·m.

[0190] The preset upper limit for vehicle command variation is used to limit the maximum allowable variation of vehicle operation control commands between adjacent sampling times. For example, when the sampling period is 100ms, the preset upper limit for vehicle command variation can be set to 10N·m / 100ms or 15N·m / 100ms. This upper limit can be set based on the vehicle's torque response capability, transmission system shock limits, and driving comfort test results.

[0191] When the change in vehicle operation control command exceeds the preset upper limit of vehicle command change, the controller will replace the vehicle operation control command between adjacent sampling times with a sequence of vehicle operation control commands that are either increasing or decreasing in segments.

[0192] For example, if a vehicle operation control command needs to change from 120 N·m to 150 N·m, and the preset upper limit for vehicle command change is 10 N·m / 100 ms, then the original single-change command can be replaced with a sequence of commands of 120 N·m, 130 N·m, 140 N·m, and 150 N·m. If the command needs to be reduced, a segmented decreasing method is used.

[0193] The controller also determines the changes in generator control commands at the access and reconnection boundaries. The changes in generator control commands can be determined as follows:

[0194] Change in generator control command = Generator control command at the current sampling time - Absolute value of generator control command at the previous sampling time.

[0195] When the generator control command is the generator target output power, the change in the generator control command can be expressed as the power change. For example, if the generator target output power was 2kW at the previous sampling time and 4kW at the current sampling time, then the change in the generator control command is 2kW.

[0196] The preset generator command change upper limit is used to limit the maximum allowable change in generator control commands between adjacent sampling times. For example, when the sampling period is 100ms, the preset generator command change upper limit can be set to 0.5kW / 100ms, 1kW / 100ms, or 1.5kW / 100ms. This upper limit can be set according to the generator's allowable power ramp-up rate, generator temperature rise protection requirements, and engine load response capability.

[0197] When the change in generator control command exceeds the preset upper limit of generator command change, the controller will replace the generator control command between adjacent sampling times with a sequence of generator control commands that are either incremented or decremented in segments.

[0198] For example, if the generator's target output power needs to change from 2kW to 6kW, and the preset upper limit for generator command change is 1kW / 100ms, then the original single-change command can be replaced with a command sequence of 2kW, 3kW, 4kW, 5kW, and 6kW. If the generator's target output power needs to be reduced, then a segmented decreasing method is used.

[0199] In the segmented increasing or decreasing vehicle operation control command sequence, the absolute value of the difference between two adjacent vehicle operation control commands is not greater than the preset upper limit of vehicle command change; in the segmented increasing or decreasing generator control command sequence, the absolute value of the difference between two adjacent generator control commands is not greater than the preset upper limit of generator command change.

[0200] See Figure 2 As shown, this embodiment also provides a new energy vehicle control system based on generator-driven control, which includes a parameter input layer, a control processing layer, and an execution output layer. The parameter input layer outputs battery status parameters, engine operating parameters, vehicle electrical load parameters, generator operating parameters, and vehicle operating status parameters. The control processing layer includes the following modules. The execution output layer sends the instructions obtained after processing by the control processing layer to the vehicle power system and the generator:

[0201] The control processing layer includes a parameter acquisition module, which is used to acquire battery status parameters, engine operating parameters, vehicle electrical load parameters, generator operating parameters, and vehicle operating status parameters.

[0202] The basic trajectory generation module is used to generate the basic running trajectory of the vehicle when the generator intervention increment is zero, based on the engine operating parameters, generator operating parameters, and vehicle operating status parameters.

[0203] The section to be intervened in module is used to determine the required power generation based on the battery status parameters and the vehicle electrical load parameters, and to determine the trajectory section to be intervened in based on the required power generation and the basic operating trajectory.

[0204] The load intervention container generation module is used to generate a load intervention container based on the start sampling time, end sampling time, and corresponding vehicle status in the basic operating trajectory of the trajectory segment to be intervened.

[0205] The intervention focus determination module is used to determine the generator load intervention focus within the load intervention container based on the battery charging demand, engine load margin, and vehicle power maintenance margin.

[0206] The candidate trajectory generation module is used to generate at least two candidate control trajectories within the load intervention container, based on the generator load intervention focus.

[0207] The target trajectory determination module is used to identify trajectory feature points between each candidate joint control trajectory and the basic operation trajectory, and to determine the target joint control trajectory from at least two candidate joint control trajectories based on the trajectory feature point matching results.

[0208] The instruction output module is used to output vehicle operation control instructions and generator control instructions according to the target control trajectory, and to perform transition corrections on the access boundary and reconnection boundary between the target control trajectory and the basic operation trajectory.

[0209] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0210] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A control method for new energy vehicles based on generator-driven control, characterized in that, include: Based on engine operating parameters, generator operating parameters, and vehicle operating status parameters, a basic operating trajectory of the vehicle is generated when the generator intervention increment is zero. The basic operating trajectory includes predicted vehicle speed, predicted drive torque, and predicted engine load arranged according to sampling time. The required power generation is determined based on the battery status parameters and the vehicle electrical load parameters, and the trajectory segment to be intervened is determined based on the required power generation and the basic operating trajectory. Based on the start sampling time and end sampling time of the trajectory segment to be intervened and the corresponding vehicle status in the basic operating trajectory, a load intervention container is generated. The load intervention container includes an access sub-area, an intervention sub-area, and a return sub-area. Within the load intervention container, the generator load intervention focus is determined based on the battery charging demand, engine load margin, and vehicle power maintenance margin. Based on the generator load intervention focus, at least two candidate joint control trajectories are generated within the load intervention container. Each candidate joint control trajectory includes a vehicle torque adjustment trajectory, an engine load distribution trajectory, and a generator output trajectory. Identify the trajectory feature points between each candidate joint control trajectory and the basic operation trajectory, and determine the target joint control trajectory from at least two candidate joint control trajectories based on the trajectory feature point matching results; Based on the target control trajectory, vehicle operation control commands and generator control commands are output, and transition corrections are made to the access boundary and reconnection boundary between the target control trajectory and the basic operation trajectory.

2. The new energy vehicle control method based on generator co-control according to claim 1, characterized in that, The generated vehicle's basic operating trajectory when the generator's waiting-to-intervention increment is zero includes: The current vehicle speed, target vehicle speed, driving torque demand, engine output torque, engine load, current generator output power, and vehicle electrical load power are obtained at continuous sampling times according to a preset sampling period. The generator load torque corresponding to the current generator output power is taken as the base load torque; While keeping the base load torque constant, the predicted drive torque, predicted vehicle speed, and predicted engine load at each sampling time are calculated based on the drive demand torque, engine output torque, and vehicle operating state parameters. The predicted driving torque, predicted vehicle speed, and predicted engine load are arranged in chronological order of sampling time to form the basic operating trajectory.

3. The new energy vehicle control method based on generator co-control according to claim 2, characterized in that, The step of determining the required power generation based on the battery status parameters and vehicle electrical load parameters, and determining the trajectory segment to be intervened based on the required power generation and the basic operating trajectory, includes: Based on the current state of charge of the battery, the target state of charge, the allowable charging power of the battery, and the electrical load power of the vehicle, determine the required power generation at each sampling time. Based on the difference between the required power generation and the current generator output power, determine the generator intervention increment corresponding to each sampling time. Based on the predicted engine load corresponding to each sampling time in the basic operating trajectory, determine the engine load margin corresponding to each sampling time. The continuous sampling interval in which the increment of the generator to be intervened is greater than zero and the engine load margin is not less than the load demand corresponding to the increment of the generator to be intervened is determined as the trajectory segment to be intervened.

4. The new energy vehicle control method based on generator co-control according to claim 3, characterized in that, The generated load intervention container includes: The number of sampling times prior to the start sampling time of the trajectory segment to be intervened is determined as the access sub-region; The trajectory segment to be intervened in is defined as the intervention sub-region; The number of sampling times after the termination sampling time of the trajectory segment to be intervened is determined as the reconnection sub-region; Based on the basic operating trajectories within the access sub-region, intervention sub-region, and reconnection sub-region, determine the vehicle speed boundary, driving torque boundary, engine load boundary, and generator output power boundary corresponding to each sampling moment; The load intervention container is generated from the access sub-region, intervention sub-region, reconnection sub-region, and the vehicle speed boundary, driving torque boundary, engine load boundary, and generator output power boundary corresponding to each sampling time.

5. The new energy vehicle control method based on generator co-control according to claim 4, characterized in that, The determination of the generator load intervention focus includes: Within the load intervention container, the battery replenishment demand, engine load margin, and vehicle power maintenance margin are calculated at each sampling time. Based on the battery replenishment demand, engine load margin, and vehicle power maintenance margin, normalized values ​​for replenishment demand, load margin, and power maintenance are generated respectively. The sampling time when the normalized values ​​of replenishment demand, load margin, and power maintenance are all not less than the corresponding preset lower limit is determined as the candidate focus time. Calculate the focus evaluation value corresponding to each candidate focus time. The focus evaluation value is obtained by multiplying the normalized value of replenishment demand, the normalized value of load margin, and the normalized value of power maintenance. The candidate focus moment with the highest focus evaluation value is determined as the generator load intervention focus.

6. The new energy vehicle control method based on generator co-control according to claim 5, characterized in that, The generation of at least two candidate joint control trajectories includes: Taking the generator load intervention focus as the center, a preset number of sampling times before and after the generator load intervention focus are selected as candidate intervention starting points; Based on the preset set of generator output change rates, the generator output trajectory corresponding to each candidate intervention starting point is generated respectively. Based on the generator load torque corresponding to the generator output trajectory, an engine load distribution trajectory is generated; Based on the generator load torque and the preset torque compensation coefficient, a vehicle torque adjustment trajectory is generated; The generator output trajectory, engine load distribution trajectory, and vehicle torque adjustment trajectory under the same candidate intervention starting point are combined to form a candidate joint control trajectory.

7. The new energy vehicle control method based on generator co-control according to claim 6, characterized in that, The process of identifying trajectory feature points between each candidate joint control trajectory and the basic operating trajectory, and determining the target joint control trajectory from at least two candidate joint control trajectories based on the trajectory feature point matching results, includes: In each candidate joint control trajectory, the access start point, the generator load intervention focus, the sampling point where the generator output reaches the required power generation, and the reconnection end point are determined as the trajectory feature points of the candidate joint control trajectory. The candidate vehicle speed, candidate drive torque, candidate engine load, and candidate generator output power corresponding to each trajectory feature point are obtained respectively. Obtain the basic vehicle speed, basic drive torque, and basic engine load corresponding to the sampling time of each trajectory feature point in the basic operating trajectory; Based on the absolute values ​​of the differences between candidate vehicle speed and base vehicle speed, candidate drive torque and base drive torque, and candidate engine load and base engine load, calculate the trajectory matching difference corresponding to each candidate joint control trajectory. The candidate joint control trajectory with the smallest trajectory matching difference that is less than the preset matching upper limit is determined as the target joint control trajectory.

8. The new energy vehicle control method based on generator co-control according to claim 7, characterized in that, The transition correction of the access boundary and reconnection boundary between the target control trajectory and the basic operation trajectory includes: At the access boundary, calculate the changes in vehicle operation control commands and generator control commands between adjacent sampling times; At the connection boundary, calculate the changes in vehicle operation control commands and generator control commands between adjacent sampling times; When the change in vehicle operation control command exceeds the preset upper limit of vehicle command change, the vehicle operation control command between the corresponding adjacent sampling times will be replaced with a sequence of vehicle operation control commands that are either increasing or decreasing in segments.

9. The new energy vehicle control method based on generator co-control according to claim 8, characterized in that, When the change in generator control command exceeds the preset upper limit of generator command change, the generator control command between the corresponding adjacent sampling times will be replaced with a sequence of generator control commands that are either incremented or decremented in segments. In the segmented increasing or decreasing vehicle operation control command sequence, the absolute value of the difference between two adjacent vehicle operation control commands is not greater than the preset upper limit of vehicle command change; in the segmented increasing or decreasing generator control command sequence, the absolute value of the difference between two adjacent generator control commands is not greater than the preset upper limit of generator command change.

10. A new energy vehicle control system based on generator-driven control, characterized in that, include: The parameter acquisition module is used to acquire battery status parameters, engine operating parameters, vehicle electrical load parameters, generator operating parameters, and vehicle operating status parameters. The basic trajectory generation module is used to generate the basic operating trajectory of the vehicle when the generator intervention increment is zero, based on the engine operating parameters, generator operating parameters and vehicle operating status parameters. The section to be intervened in module is used to determine the required power generation based on the battery status parameters and the vehicle electrical load parameters, and to determine the trajectory section to be intervened in based on the required power generation and the basic operating trajectory. The load intervention container generation module is used to generate a load intervention container based on the start sampling time, end sampling time of the trajectory segment to be intervened, and the corresponding vehicle status in the basic running trajectory. The intervention focus determination module is used to determine the generator load intervention focus within the load intervention container based on the battery charging demand, engine load margin, and vehicle power maintenance margin. The candidate trajectory generation module is used to generate at least two candidate control trajectories within the load intervention container, based on the generator load intervention focus. The target trajectory determination module is used to identify trajectory feature points between each of the candidate joint control trajectories and the basic operating trajectory, and to determine the target joint control trajectory from at least two candidate joint control trajectories based on the trajectory feature point matching results; The instruction output module is used to output vehicle operation control instructions and generator control instructions according to the target control trajectory, and to perform transition correction on the access boundary and reconnection boundary between the target control trajectory and the basic operation trajectory.

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