Oil consumption optimization method and device based on Kalman filtering, equipment and storage medium

Abstract

The invention discloses a fuel consumption optimization method, device and equipment based on Kalman filtering and a computer readable storage medium, and the method comprises the steps: carrying out the normalization processing of an obtained accelerator characteristic MAP according to a received optimization instruction, and obtaining a target accelerator characteristic MAP; acquiring an accelerator characteristic value based on the target accelerator characteristic MAP; a prior estimation covariance at the current moment is calculated; calculating a Kalman gain correction value according to the obtained preset state change value, the preset transposition value, the initial value of the preset observation noise covariance matrix and the initial value of the prior estimation covariance at the current moment; the target accelerator characteristic MAP is optimized according to the accelerator characteristic value, a preset measured value and the Kalman gain correction value, the optimized accelerator characteristic MAP is obtained, and the technical problem that in the prior art, the accelerator characteristic MAP is calibrated manually, and the finally output accelerator characteristic MAP is not the optimal oil consumption is solved.

Description

Technical Field

[0001] The present application relates to the field of automobile energy-saving technology, and in particular to a fuel consumption optimization method, device, equipment and computer-readable storage medium based on Kalman filtering. Background Art

[0002] As global climate and environmental issues become more prominent, energy conservation and emission reduction have become the main direction of development in various industries. For the automotive industry, reducing vehicle fuel consumption is of great significance. In order to achieve the reduction of vehicle fuel consumption, major automobile manufacturers have adopted various types of technical solutions in terms of vehicle drive systems, including electric vehicles, fuel cells, hybrid power, etc. Although the fuel consumption of the whole vehicle can be reduced by converting traditional fuel vehicles to new energy, the fuel consumption of the whole vehicle is still subject to the operating conditions of the whole vehicle, and the fuel saving rate of the whole vehicle under different operating conditions is not the same. How to use different control parameters according to the operating conditions of the whole vehicle to improve the fuel saving rate of the whole vehicle is of great significance. However, the existing method is to manually calibrate the throttle characteristic MAP, but the calibration range is large, the time period is long, the difficulty is high, and it cannot take into account the fuel consumption economy of each driver. The throttle characteristic MAP finally output is not the problem of optimal fuel consumption. Summary of the invention

[0003] The present application provides a fuel consumption optimization method, device, equipment and computer-readable storage medium based on Kalman filtering, which can solve the technical problem in the prior art that the throttle characteristic MAP is calibrated manually, but the calibration range is large, the time period is long, the difficulty is high and the fuel consumption economy of each driver cannot be taken into account well, and the throttle characteristic MAP finally outputted is not the one with the best fuel consumption.

[0004] In a first aspect, an embodiment of the present application provides a fuel consumption optimization method based on Kalman filtering, and the fuel consumption optimization method based on Kalman filtering includes:

[0005] Normalizing the acquired throttle characteristic MAP according to the received optimization instruction to acquire a target throttle characteristic MAP, wherein the throttle characteristic MAP is predicted based on the speed and throttle pedal opening carried in the optimization instruction according to a preset fuel consumption model;

[0006] Based on the target throttle characteristic MAP, obtaining a throttle characteristic value;

[0007] The prior estimation covariance at the current moment is calculated according to the initial value of the posterior estimation covariance at the previous moment and the initial value of the covariance matrix of the process noise;

[0008] Calculate the Kalman gain correction value according to the acquired preset state change value, preset transposition value, the initial value of the preset observation noise covariance matrix and the initial value of the priori estimated covariance at the current moment;

[0009] The target throttle characteristic MAP is optimized according to the throttle characteristic value, the preset measured value and the Kalman gain correction value to obtain an optimized throttle characteristic MAP.

[0010] In combination with the first aspect, in one implementation, optimizing the target throttle characteristic MAP according to the throttle characteristic value, the preset measured value and the Kalman gain correction value to obtain the optimized throttle characteristic MAP includes:

[0011] Transforming the target throttle characteristic MAP to transform the row-column format of the target throttle characteristic MAP into a matrix format;

[0012] An optimized throttle characteristic MAP is obtained according to the first preset formula, the throttle characteristic value, the preset measured value, the Kalman gain correction value and the target throttle characteristic MAP in matrix format.

[0013] In combination with the first aspect, in one implementation, the Kalman gain correction value is calculated based on the acquired preset state change value, preset transposition value, initial value of the preset observation noise covariance matrix and initial value of the priori estimated covariance at the current moment, including:

[0014] Obtaining a preset state change value, a preset transposed value, an initial value of a preset observation noise covariance matrix, and an initial value of the a priori estimated covariance at the current moment;

[0015] A Kalman gain correction value is calculated according to a second preset formula, the preset state change value, the preset transposed value, the initial value of the preset observation noise covariance matrix and the initial value of the a priori estimated covariance at the current moment.

[0016] In combination with the first aspect, in one implementation, the calculating of the a priori estimated covariance at the current moment according to the initial value of the a posteriori estimated covariance at the previous moment and the initial value of the covariance matrix of the preset process noise includes:

[0017] Obtain the initial value of the posterior estimation covariance at the previous moment and the initial value of the covariance matrix of the preset process noise;

[0018] The a priori estimated covariance at the current moment is calculated according to the third preset formula, the a priori estimated covariance at the previous moment and the initial value of the covariance matrix of the preset process noise.

[0019] In combination with the first aspect, in one implementation, the acquiring the throttle characteristic value based on the target throttle characteristic MAP includes:

[0020] Obtaining the rotation speed and accelerator pedal opening carried by the optimization instruction;

[0021] The rotation speed and the accelerator pedal opening in the target throttle characteristic MAP are queried by interpolation table lookup to obtain throttle characteristic values ​​corresponding to the rotation speed and the throttle pedal opening in the target throttle characteristic MAP.

[0022] In combination with the first aspect, in one implementation, the step of normalizing the acquired throttle characteristic MAP according to the received optimization instruction to acquire the target throttle characteristic MAP includes:

[0023] Receiving an optimization instruction, obtaining a rotation speed and an accelerator pedal opening carried by the optimization instruction;

[0024] Based on a preset fuel consumption model, a predicted throttle characteristic MAP is obtained according to the speed and the throttle pedal opening;

[0025] The throttle characteristic MAP is normalized according to a preset maximum throttle characteristic value to obtain a target throttle characteristic MAP.

[0026] In combination with the first aspect, in one implementation, after optimizing the target throttle characteristic MAP according to the throttle characteristic value, the preset measured value and the Kalman gain correction value and obtaining the optimized throttle characteristic MAP, the method further includes:

[0027] Acquire the Kalman gain correction value, the state change value, the initial value of the a priori estimated covariance at the current moment, and the unit matrix value of the target throttle characteristic MAP;

[0028] According to the fourth preset formula, the Kalman gain correction value, the state change value, the initial value of the prior estimated covariance at the current moment and the unit matrix value of the target throttle characteristic MAP, the initial value of the posterior estimated covariance at the current moment is calculated, and the initial value of the posterior estimated covariance at the current moment is used as the initial value of the posterior estimated covariance at the previous moment for the next optimization.

[0029] In a second aspect, an embodiment of the present application provides a fuel consumption optimization device based on Kalman filtering, and the fuel consumption optimization device based on Kalman filtering includes:

[0030] A first acquisition module is used to normalize the acquired throttle characteristic MAP according to the received optimization instruction to acquire a target throttle characteristic MAP, wherein the throttle characteristic MAP is predicted based on the speed and throttle pedal opening carried in the optimization instruction according to a preset fuel consumption model;

[0031] A second acquisition module, configured to acquire a throttle characteristic value based on the target throttle characteristic MAP;

[0032] A first calculation module is used to calculate the a priori estimated covariance at the current moment according to the initial value of the posterior estimated covariance at the previous moment and the initial value of the covariance matrix of the preset process noise;

[0033] A second calculation module is used to calculate a Kalman gain correction value according to the acquired preset state change value, preset transposition value, initial value of the preset observation noise covariance matrix and initial value of the priori estimated covariance at the current moment;

[0034] The optimization and acquisition module is used to optimize the target throttle characteristic MAP according to the throttle characteristic value, the preset measured value and the Kalman gain correction value, and acquire the optimized throttle characteristic MAP.

[0035] In the third aspect, an embodiment of the present application provides a fuel consumption optimization device based on Kalman filtering, wherein the fuel consumption optimization device based on Kalman filtering comprises a processor, a memory, and a fuel consumption optimization program based on Kalman filtering stored in the memory and executable by the processor, wherein when the fuel consumption optimization program based on Kalman filtering is executed by the processor, the steps of the fuel consumption optimization method based on Kalman filtering as described above are implemented.

[0036] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a fuel consumption optimization program based on Kalman filtering is stored. When the fuel consumption optimization program based on Kalman filtering is executed by a processor, the steps of the fuel consumption optimization method based on Kalman filtering as described above are implemented.

[0037] The beneficial effects brought by the technical solution provided in the embodiments of the present application include:

[0038] The target throttle characteristic MAP is obtained by normalizing the acquired throttle characteristic MAP according to the received optimization instruction, wherein the throttle characteristic MAP is predicted by the preset fuel consumption model for the rotation speed and the throttle pedal opening carried in the optimization instruction; based on the target throttle characteristic MAP, the throttle characteristic value is obtained; according to the preset initial value of the posterior estimation covariance at the previous moment and the initial value of the covariance matrix of the preset process noise, the prior estimation covariance at the current moment is calculated; according to the acquired preset state change value, preset transposed value, the initial value of the preset observation noise covariance matrix and the prior at the current moment, the a priori estimation covariance matrix is ​​calculated; The initial value of the covariance is estimated by experiment, and the Kalman gain correction value is calculated; the target throttle characteristic MAP is optimized according to the throttle characteristic value, the preset measured value and the Kalman gain correction value, and the optimized throttle characteristic MAP is obtained, so as to solve the technical problem that the throttle characteristic MAP is calibrated manually in the prior art, but the calibration range is large, the time period is long, the difficulty is high and the fuel consumption economy of each driver cannot be taken into account well, and the throttle characteristic MAP finally outputted is not the fuel consumption optimal one. The fuel consumption economy of each driver is taken into account, and the fuel consumption of the output throttle characteristic MAP is the optimal one. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of the first embodiment of the fuel consumption optimization method based on Kalman filtering of the present application;

[0040] Figure 2 This is a flow chart of a second embodiment of a fuel consumption optimization method based on Kalman filtering of the present application;

[0041] Figure 3 This is a functional module diagram of an embodiment of a fuel consumption optimization device based on Kalman filtering of the present application;

[0042] Figure 4 This is a schematic diagram of the hardware structure of the fuel consumption optimization device based on Kalman filtering involved in the embodiment of the present application. DETAILED DESCRIPTION

[0043] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0044] First, some technical terms in the present application are explained to facilitate those skilled in the art to understand the present application.

[0045] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0046] In a first aspect, an embodiment of the present application provides a fuel consumption optimization method based on Kalman filtering.

[0047] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the fuel consumption optimization method based on Kalman filtering in this application. Figure 1 As shown, the fuel consumption optimization method based on Kalman filtering includes:

[0048] Step S10: normalizing the acquired throttle characteristic MAP according to the received optimization instruction to acquire a target throttle characteristic MAP, wherein the throttle characteristic MAP is predicted based on the speed and throttle pedal opening carried in the optimization instruction according to a preset fuel consumption model;

[0049] Exemplarily, an optimization instruction is received, and the optimization instruction carries the speed and the accelerator pedal opening. The speed and the accelerator pedal opening are predicted based on a preset fuel consumption model, and a throttle characteristic MAP output by the preset fuel consumption model for the speed and the accelerator pedal is obtained. The obtained throttle characteristic MAP is normalized to obtain a target throttle characteristic MAP.

[0050] Specifically, the method of normalizing the acquired throttle characteristic MAP according to the received optimization instruction to obtain the target throttle characteristic MAP includes: receiving the optimization instruction, obtaining the rotational speed and throttle pedal opening carried by the optimization instruction; based on a preset fuel consumption model, obtaining a predicted throttle characteristic MAP according to the rotational speed and throttle pedal opening; and normalizing the throttle characteristic MAP according to a preset maximum throttle characteristic value to obtain a target throttle characteristic MAP.

[0051] For example, after receiving an optimization instruction, the speed and accelerator pedal opening carried by the optimization instruction are obtained, and the speed and accelerator pedal opening are input into a preset fuel consumption model to obtain a predicted throttle characteristic MAP. A preset maximum throttle characteristic value is obtained to normalize the throttle characteristic MAP to obtain a target throttle characteristic MAP. For example, the preset maximum throttle characteristic value Max_value is obtained, and the throttle characteristic MAPN is normalized. premap Divide by the preset maximum throttle characteristic value Max_value to get the target throttle characteristic MAPN premap1 .

[0052] Step S20: acquiring a throttle characteristic value based on the target throttle characteristic MAP;

[0053] Exemplarily, when the target throttle characteristic MAP is obtained, the throttle characteristic value in the target throttle characteristic MAP is acquired.

[0054] Specifically, obtaining the throttle characteristic value based on the target throttle characteristic MAP includes: obtaining the rotational speed and throttle pedal opening carried by the optimization instruction; querying the rotational speed and the throttle pedal opening in the target throttle characteristic MAP by interpolation table lookup, and obtaining the throttle characteristic values ​​corresponding to the rotational speed and the throttle pedal opening in the target throttle characteristic MAP.

[0055] Exemplarily, the speed and accelerator pedal opening carried by the optimization instruction are obtained, and the speed and accelerator pedal opening in the target throttle characteristic MAP are queried by interpolation table lookup to obtain the throttle characteristic value corresponding to the speed and accelerator pedal opening in the target throttle characteristic MAP. For example, the speed is 1000, and the accelerator pedal opening is 75. The target throttle characteristic MAP is queried, and the output value corresponding to the speed of 1000 and the accelerator pedal opening of 75 in the target throttle characteristic MAP is 0.84906, and the output value 0.84906 is used as the throttle characteristic value.

[0056] Step S30: Calculate the a priori estimated covariance at the current moment according to the preset initial value of the a priori estimated covariance at the previous moment and the preset initial value of the covariance matrix of the process noise;

[0057] Exemplarily, the initial value p_t_KF_Before of the posterior estimation covariance at the previous moment and the initial value Q of the covariance matrix of the preset process noise are obtained, wherein the initial value p_t_KF_Before of the posterior estimation covariance at the previous moment is initially a preset value, and the initial value p_t of the prior estimation covariance at the current moment is calculated.

[0058] Specifically, the method of calculating the prior estimation covariance at the current moment based on the initial value of the posterior estimation covariance at the previous moment and the initial value of the covariance matrix of the preset process noise includes: obtaining the initial value of the posterior estimation covariance at the previous moment and the initial value of the covariance matrix of the preset process noise; calculating the prior estimation covariance at the current moment based on a third preset formula, the posterior estimation covariance at the previous moment and the initial value of the covariance matrix of the preset process noise.

[0059] Exemplarily, the initial value p_t_KF_Before of the posterior estimation covariance at the previous moment and the initial value Q of the covariance matrix of the preset process noise are obtained, and the a priori estimation covariance p_t at the current moment is calculated by calculating the third preset formula P_t=P_t_KF_Before+Q.

[0060] Step S40: Calculate the Kalman gain correction value according to the acquired preset state change value, preset transposition value, the initial value of the preset observation noise covariance matrix and the initial value of the a priori estimated covariance at the current moment;

[0061] Exemplarily, the preset state change value H and the preset transposition value H are obtained. 2 , preset the initial value R of the observation noise covariance matrix and the initial value p_t of the prior estimation covariance at the current moment, wherein the preset state change value H is used to make the Kalman gain correction value Kt (measured value N testl -Throttle characteristic value N opl ) state and target throttle characteristic MAPN premap1 The status is consistent. By obtaining the preset status change value H and the preset transposition value H 2 , preset the initial value R of the observation noise covariance matrix and the initial value p_t of the prior estimation covariance at the current moment, and calculate the Kalman gain correction value Kt.

[0062] Specifically, the method of calculating the Kalman gain correction value based on the acquired preset state change value, preset transposed value, initial value of the preset observation noise covariance matrix and initial value of the prior estimated covariance at the current moment includes: obtaining the preset state change value, preset transposed value, initial value of the preset observation noise covariance matrix and initial value of the prior estimated covariance at the current moment; calculating the Kalman gain correction value based on a second preset formula, the preset state change value, the preset transposed value, initial value of the preset observation noise covariance matrix and initial value of the prior estimated covariance at the current moment.

[0063] Exemplarily, the preset state change value H and the preset transposition value H are obtained. 2 , preset the initial value R of the observation noise covariance matrix and the initial value p_t of the prior estimation covariance at the current moment, wherein the preset state change value H is used to make the Kalman gain correction value Kt (measured value N testl -Throttle characteristic value N opl ) state and target throttle characteristic MAP N premap1 By obtaining the second preset formula Kt=P_t*H 2 / (H*P_t*H 2 +R) to calculate the Kalman gain correction value Kt.

[0064] Step S50: optimizing the target throttle characteristic MAP according to the throttle characteristic value, the preset measured value and the Kalman gain correction value, and obtaining an optimized throttle characteristic MAP.

[0065] Exemplarily, the throttle characteristic value N is obtained opl , preset measured value Ntestl and Kalman gain correction value Kt, through the obtained throttle characteristic value N opl , preset measured value N testl and Kalman gain correction value Kt to target throttle characteristic MAPN premap1 Optimize and obtain the optimized throttle characteristic MAPN premap_d .

[0066] Specifically, the target throttle characteristic MAP is optimized according to the throttle characteristic value, the preset measured value and the Kalman gain correction value to obtain the optimized throttle characteristic MAP, including: deforming the target throttle characteristic MAP to transform the row and column format of the target throttle characteristic MAP into a matrix format; and obtaining the optimized throttle characteristic MAP according to a first preset formula, the throttle characteristic value, the preset measured value, the Kalman gain correction value and the target throttle characteristic MAP in matrix format.

[0067] Exemplarily, the acquired target throttle characteristic MAP is transformed to transform the row and column format of the target throttle characteristic MAP into a matrix format. Obtain the first preset formula Npremap_d=Npremap1+Kt*(Ntest1-Nop1), the throttle characteristic value N opl , preset measured value N testl and Kalman gain correction value Kt and target throttle characteristic MAPN in matrix format premap1 , by calculating the first preset formula, the optimized throttle characteristic MAPN is obtained premap_d .

[0068] In this embodiment, the target throttle characteristic MAP is obtained by normalizing the acquired throttle characteristic MAP according to the received optimization instruction, wherein the throttle characteristic MAP is predicted by the preset fuel consumption model for the speed and the throttle pedal opening carried in the optimization instruction; based on the target throttle characteristic MAP, the throttle characteristic value is obtained; according to the preset initial value of the posterior estimated covariance at the previous moment and the initial value of the covariance matrix of the preset process noise, the prior estimated covariance at the current moment is calculated; according to the acquired preset state change value, the preset transposed value, the initial value of the preset observation noise covariance matrix and the current state change value, the preset ... The initial value of the a priori estimated covariance at the previous moment is used to calculate the Kalman gain correction value; the target throttle characteristic MAP is optimized according to the throttle characteristic value, the preset measured value and the Kalman gain correction value to obtain the optimized throttle characteristic MAP, so as to solve the technical problem that the throttle characteristic MAP is manually calibrated in the prior art, but the calibration range is large, the time period is long, the difficulty is high and the fuel consumption economy of each driver cannot be taken into account well, and the throttle characteristic MAP finally outputted is not the fuel consumption optimal one, so as to achieve the fuel consumption economy of each driver being taken into account and the fuel consumption of the output throttle characteristic MAP being the fuel consumption optimal one.

[0069] In one embodiment, referring to Figure 2 , Figure 2 This is a flow chart of the second embodiment of the fuel consumption optimization method based on Kalman filtering in this application. Figure 2 As shown, the fuel consumption optimization method based on Kalman filtering includes:

[0070] Step S21: normalizing the acquired throttle characteristic MAP according to the received optimization instruction to acquire a target throttle characteristic MAP, wherein the throttle characteristic MAP is predicted based on the speed and throttle pedal opening carried in the optimization instruction according to a preset fuel consumption model;

[0071] Exemplarily, an optimization instruction is received, and the optimization instruction carries the speed and the accelerator pedal opening. The speed and the accelerator pedal opening are predicted based on a preset fuel consumption model, and a throttle characteristic MAP output by the preset fuel consumption model for the speed and the accelerator pedal is obtained. The obtained throttle characteristic MAP is normalized to obtain a target throttle characteristic MAP.

[0072] Specifically, the method of normalizing the acquired throttle characteristic MAP according to the received optimization instruction to obtain the target throttle characteristic MAP includes: receiving the optimization instruction, obtaining the rotational speed and throttle pedal opening carried by the optimization instruction; based on a preset fuel consumption model, obtaining a predicted throttle characteristic MAP according to the rotational speed and throttle pedal opening; and normalizing the throttle characteristic MAP according to a preset maximum throttle characteristic value to obtain a target throttle characteristic MAP.

[0073] For example, after receiving an optimization instruction, the speed and accelerator pedal opening carried by the optimization instruction are obtained, and the speed and accelerator pedal opening are input into a preset fuel consumption model to obtain a predicted throttle characteristic MAP. A preset maximum throttle characteristic value is obtained to normalize the throttle characteristic MAP to obtain a target throttle characteristic MAP. For example, the preset maximum throttle characteristic value Max_value is obtained, and the throttle characteristic MAPN is normalized. premap Divide by the preset maximum throttle characteristic value Max_value to get the target throttle characteristic MAPN premap1 .

[0074] Step S22: acquiring a throttle characteristic value based on the target throttle characteristic MAP;

[0075] Exemplarily, when the target throttle characteristic MAP is obtained, the throttle characteristic value in the target throttle characteristic MAP is acquired.

[0076] Specifically, obtaining the throttle characteristic value based on the target throttle characteristic MAP includes: obtaining the rotational speed and throttle pedal opening carried by the optimization instruction; querying the rotational speed and the throttle pedal opening in the target throttle characteristic MAP by interpolation table lookup, and obtaining the throttle characteristic values ​​corresponding to the rotational speed and the throttle pedal opening in the target throttle characteristic MAP.

[0077] Exemplarily, the speed and accelerator pedal opening carried by the optimization instruction are obtained, and the speed and accelerator pedal opening in the target throttle characteristic MAP are queried by interpolation table lookup to obtain the throttle characteristic value corresponding to the speed and accelerator pedal opening in the target throttle characteristic MAP. For example, the speed is 1000, and the accelerator pedal opening is 75. The target throttle characteristic MAP is queried, and the output value corresponding to the speed of 1000 and the accelerator pedal opening of 75 in the target throttle characteristic MAP is 0.84906, and the output value 0.84906 is used as the throttle characteristic value.

[0078] Step S23: Calculate the a priori estimated covariance at the current moment according to the preset initial value of the posterior estimated covariance at the previous moment and the preset initial value of the covariance matrix of the process noise;

[0079] Exemplarily, the initial value p_t_KF_Before of the posterior estimation covariance at the previous moment and the initial value Q of the covariance matrix of the preset process noise are obtained, wherein the initial value p_t_KF_Before of the posterior estimation covariance at the previous moment is initially a preset value, and the initial value p_t of the prior estimation covariance at the current moment is calculated.

[0080] Specifically, the method of calculating the prior estimation covariance at the current moment based on the initial value of the posterior estimation covariance at the previous moment and the initial value of the covariance matrix of the preset process noise includes: obtaining the initial value of the posterior estimation covariance at the previous moment and the initial value of the covariance matrix of the preset process noise; calculating the prior estimation covariance at the current moment based on a third preset formula, the posterior estimation covariance at the previous moment and the initial value of the covariance matrix of the preset process noise.

[0081] Exemplarily, the initial value p_t_KF_Before of the posterior estimation covariance at the previous moment and the initial value Q of the covariance matrix of the preset process noise are obtained, and the a priori estimation covariance p_t at the current moment is calculated by calculating the third preset formula P_t=P_t_KF_Before+Q.

[0082] Step S24: Calculate the Kalman gain correction value according to the acquired preset state change value, preset transposition value, the initial value of the preset observation noise covariance matrix and the initial value of the a priori estimated covariance at the current moment;

[0083] Exemplarily, the preset state change value H and the preset transposition value H are obtained. 2 , preset the initial value R of the observation noise covariance matrix and the initial value p_t of the prior estimation covariance at the current moment, wherein the preset state change value H is used to make the Kalman gain correction value Kt (measured value N testl -Throttle characteristic value N opl ) state and target throttle characteristic MAPN premap1 The status is consistent. By obtaining the preset status change value H and the preset transposition value H 2 , preset the initial value R of the observation noise covariance matrix and the initial value p_t of the prior estimation covariance at the current moment, and calculate the Kalman gain correction value Kt.

[0084] Specifically, the method of calculating the Kalman gain correction value based on the acquired preset state change value, preset transposed value, initial value of the preset observation noise covariance matrix and initial value of the prior estimated covariance at the current moment includes: obtaining the preset state change value, preset transposed value, initial value of the preset observation noise covariance matrix and initial value of the prior estimated covariance at the current moment; calculating the Kalman gain correction value based on a second preset formula, the preset state change value, the preset transposed value, initial value of the preset observation noise covariance matrix and initial value of the prior estimated covariance at the current moment.

[0085] Exemplarily, the preset state change value H and the preset transposition value H are obtained. 2, preset the initial value R of the observation noise covariance matrix and the initial value p_t of the prior estimation covariance at the current moment, wherein the preset state change value H is used to make the Kalman gain correction value Kt (measured value N testl -Throttle characteristic value N opl ) state and target throttle characteristic MAP N premap1 By obtaining the second preset formula Kt=P_t*H 2 / (H*P_t*H 2 +R) to calculate the Kalman gain correction value Kt.

[0086] Step S25: Optimizing the target throttle characteristic MAP according to the throttle characteristic value, the preset measured value and the Kalman gain correction value to obtain an optimized throttle characteristic MAP.

[0087] Exemplarily, the throttle characteristic value N is obtained opl , preset measured value N testl and Kalman gain correction value Kt, through the obtained throttle characteristic value N opl , preset measured value N testl and Kalman gain correction value Kt to target throttle characteristic MAPN premap1 Optimize and obtain the optimized throttle characteristic MAPN premap_d .

[0088] Specifically, the target throttle characteristic MAP is optimized according to the throttle characteristic value, the preset measured value and the Kalman gain correction value to obtain the optimized throttle characteristic MAP, including: deforming the target throttle characteristic MAP to transform the row and column format of the target throttle characteristic MAP into a matrix format; and obtaining the optimized throttle characteristic MAP according to a first preset formula, the throttle characteristic value, the preset measured value, the Kalman gain correction value and the target throttle characteristic MAP in matrix format.

[0089] Exemplarily, the acquired target throttle characteristic MAP is transformed to transform the row and column format of the target throttle characteristic MAP into a matrix format. Obtain the first preset formula Npremap_d=Npremap1+Kt*(Ntest1-Nop1), the throttle characteristic value N opl , preset measured value N testl and Kalman gain correction value Kt and target throttle characteristic MAPN in matrix format premap1 , by calculating the first preset formula, the optimized throttle characteristic MAPN is obtained premap_d .

[0090] Step S26: acquiring the Kalman gain correction value, the state change value, the initial value of the a priori estimated covariance at the current moment, and the unit matrix value of the target throttle characteristic MAP;

[0091] Step S27: Calculate the initial value of the a priori estimated covariance at the current moment according to the fourth preset formula, the Kalman gain correction value, the state change value, the initial value of the a priori estimated covariance at the current moment, and the unit matrix value of the target throttle characteristic MAP, and use the initial value of the a priori estimated covariance at the current moment as the initial value of the a priori estimated covariance at the previous moment for the next optimization.

[0092] Exemplarily, the initial value p_t of the a priori estimated covariance at the current moment, the state change value H, the Kalman gain correction value Kt, and the unit matrix value I of the target throttle characteristic MAP are obtained, and the initial value P_t_KF of the posterior estimated covariance at the current moment is calculated according to the fourth preset formula P_t_KF=(I-Kt*H)*P_t, the initial value p_t of the a priori estimated covariance at the current moment, the state change value H, the Kalman gain correction value Kt, and the unit matrix value I of the target throttle characteristic MAP, and the initial value P_t_KF of the posterior estimated covariance at the current moment is calculated, and the initial value P_t_KF of the posterior estimated covariance at the current moment is used as the initial value of the posterior estimated covariance at the previous moment for the next optimization.

[0093] Exemplarily, by obtaining the Kalman gain correction value, the state change value, the initial value of the a priori estimated covariance at the current moment, and the unit matrix value of the target throttle characteristic MAP; according to the fourth preset formula, the Kalman gain correction value, the state change value, the initial value of the a priori estimated covariance at the current moment, and the unit matrix value of the target throttle characteristic MAP, the initial value of the posterior estimated covariance at the current moment is calculated, and the initial value of the posterior estimated covariance at the current moment is used as the initial value of the posterior estimated covariance at the previous moment of the next optimization, so as to improve the accuracy of the Kalman gain correction value of the next optimization, thereby obtaining the optimal fuel consumption of the output throttle characteristic MAP.

[0094] In a second aspect, an embodiment of the present application also provides a fuel consumption optimization device based on Kalman filtering.

[0095] In one embodiment, referring to Figure 3 , Figure 3 This is a functional module diagram of an embodiment of a fuel consumption optimization device based on Kalman filtering in this application. Figure 3 As shown, the fuel consumption optimization device based on Kalman filtering includes:

[0096] A first acquisition module 10 is used to normalize the acquired throttle characteristic MAP according to the received optimization instruction to acquire a target throttle characteristic MAP, wherein the throttle characteristic MAP is predicted based on a preset fuel consumption model for the speed and throttle pedal opening carried in the optimization instruction;

[0097] A second acquisition module 20, configured to acquire a throttle characteristic value based on the target throttle characteristic MAP;

[0098] A first calculation module 30 is used to calculate the a priori estimated covariance at the current moment according to the initial value of the a priori estimated covariance at the previous moment and the initial value of the covariance matrix of the preset process noise;

[0099] A second calculation module 40 is used to calculate a Kalman gain correction value according to the acquired preset state change value, preset transposed value, the initial value of the preset observation noise covariance matrix and the initial value of the priori estimated covariance at the current moment;

[0100] The optimization and acquisition module 50 is used to optimize the target throttle characteristic MAP according to the throttle characteristic value, the preset measured value and the Kalman gain correction value, and acquire the optimized throttle characteristic MAP.

[0101] Furthermore, in one embodiment, the optimization and acquisition module 50 is used to:

[0102] Transforming the target throttle characteristic MAP to transform the row-column format of the target throttle characteristic MAP into a matrix format;

[0103] An optimized throttle characteristic MAP is obtained according to the first preset formula, the throttle characteristic value, the preset measured value, the Kalman gain correction value and the target throttle characteristic MAP in matrix format.

[0104] Furthermore, in one embodiment, the second calculation module 40 is used to:

[0105] Obtaining a preset state change value, a preset transposed value, an initial value of a preset observation noise covariance matrix, and an initial value of the a priori estimated covariance at the current moment;

[0106] A Kalman gain correction value is calculated according to a second preset formula, the preset state change value, the preset transposed value, the initial value of the preset observation noise covariance matrix and the initial value of the a priori estimated covariance at the current moment.

[0107] Furthermore, in one embodiment, the first calculation module 30 is used to:

[0108] Obtain the initial value of the posterior estimation covariance at the previous moment and the initial value of the covariance matrix of the preset process noise;

[0109] The a priori estimated covariance at the current moment is calculated according to the third preset formula, the a priori estimated covariance at the previous moment and the initial value of the covariance matrix of the preset process noise.

[0110] Furthermore, in one embodiment, the second acquisition module 20 is used to:

[0111] Obtaining the rotation speed and accelerator pedal opening carried by the optimization instruction;

[0112] The rotation speed and the accelerator pedal opening in the target throttle characteristic MAP are queried by interpolation table lookup to obtain throttle characteristic values ​​corresponding to the rotation speed and the throttle pedal opening in the target throttle characteristic MAP.

[0113] Furthermore, in one embodiment, the first acquisition module 10 is used to:

[0114] Receiving an optimization instruction, obtaining a rotation speed and an accelerator pedal opening carried by the optimization instruction;

[0115] Based on a preset fuel consumption model, a predicted throttle characteristic MAP is obtained according to the speed and the throttle pedal opening;

[0116] The throttle characteristic MAP is normalized according to a preset maximum throttle characteristic value to obtain a target throttle characteristic MAP.

[0117] Furthermore, in one embodiment, the fuel consumption optimization device based on Kalman filtering also includes a new module for:

[0118] Acquire the Kalman gain correction value, the state change value, the initial value of the a priori estimated covariance at the current moment, and the unit matrix value of the target throttle characteristic MAP;

[0119] According to the fourth preset formula, the Kalman gain correction value, the state change value, the initial value of the prior estimated covariance at the current moment and the unit matrix value of the target throttle characteristic MAP, the initial value of the posterior estimated covariance at the current moment is calculated, and the initial value of the posterior estimated covariance at the current moment is used as the initial value of the posterior estimated covariance at the previous moment for the next optimization.

[0120] Among them, the functional implementation of each module in the above-mentioned fuel consumption optimization device based on Kalman filtering corresponds to the various steps in the above-mentioned fuel consumption optimization method embodiment based on Kalman filtering, and its functions and implementation processes will not be repeated here one by one.

[0121] In a third aspect, an embodiment of the present application provides a fuel consumption optimization device based on Kalman filtering. The fuel consumption optimization device based on Kalman filtering can be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0122] Reference Figure 4 , Figure 4 The hardware structure diagram of the fuel consumption optimization device based on Kalman filtering involved in the embodiment of the present application is shown in FIG. In the embodiment of the present application, the fuel consumption optimization device based on Kalman filtering may include a processor, a memory, a communication interface and a communication bus.

[0123] The communication bus may be of any type and is used to interconnect the processor, the memory, and the communication interface.

[0124] The communication interface includes an input / output (I / O) interface, a physical interface, and a logical interface, etc., which are used to interconnect the devices inside the fuel consumption optimization device based on Kalman filtering, and an interface used to interconnect the fuel consumption optimization device based on Kalman filtering with other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, an optical fiber interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.

[0125] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0126] The processor may be a general-purpose processor, which may call the fuel consumption optimization program based on Kalman filtering stored in the memory and execute the fuel consumption optimization method based on Kalman filtering provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the fuel consumption optimization program based on Kalman filtering is called may refer to the various embodiments of the fuel consumption optimization method based on Kalman filtering of the present application, which will not be repeated here.

[0127] Those skilled in the art will understand that Figure 4The hardware structure shown in the figure does not constitute a limitation on the present application, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0128] In a fourth aspect, an embodiment of the present application also provides a computer-readable storage medium.

[0129] The computer-readable storage medium of the present application stores a fuel consumption optimization program based on Kalman filtering, wherein when the fuel consumption optimization program based on Kalman filtering is executed by a processor, the steps of the fuel consumption optimization method based on Kalman filtering as described above are implemented.

[0130] Among them, the method implemented when the fuel consumption optimization program based on Kalman filtering is executed can refer to the various embodiments of the fuel consumption optimization method based on Kalman filtering in this application, and will not be repeated here.

[0131] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0132] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.

[0133] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[0134] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0135] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0136] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0137] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A fuel consumption optimization method based on Kalman filtering, characterized in that: The fuel consumption optimization method based on Kalman filtering includes: Normalizing the acquired throttle characteristic MAP according to the received optimization instruction to acquire a target throttle characteristic MAP, wherein the throttle characteristic MAP is predicted based on the speed and throttle pedal opening carried in the optimization instruction according to a preset fuel consumption model; Based on the target throttle characteristic MAP, obtaining a throttle characteristic value; The prior estimation covariance at the current moment is calculated according to the initial value of the posterior estimation covariance at the previous moment and the initial value of the covariance matrix of the process noise; Calculate the Kalman gain correction value according to the acquired preset state change value, preset transposition value, the initial value of the preset observation noise covariance matrix and the initial value of the priori estimated covariance at the current moment; The target throttle characteristic MAP is optimized according to the throttle characteristic value, the preset measured value and the Kalman gain correction value to obtain an optimized throttle characteristic MAP.

2. The fuel consumption optimization method based on Kalman filtering according to claim 1, characterized in that: The step of optimizing the target throttle characteristic MAP according to the throttle characteristic value, the preset measured value and the Kalman gain correction value to obtain the optimized throttle characteristic MAP includes: Transforming the target throttle characteristic MAP to transform the row-column format of the target throttle characteristic MAP into a matrix format; An optimized throttle characteristic MAP is obtained according to the first preset formula, the throttle characteristic value, the preset measured value, the Kalman gain correction value and the target throttle characteristic MAP in matrix format.

3. The fuel consumption optimization method based on Kalman filtering according to claim 1, characterized in that: The Kalman gain correction value is calculated based on the acquired preset state change value, preset transposition value, the initial value of the preset observation noise covariance matrix and the initial value of the priori estimated covariance at the current moment, including: Obtaining a preset state change value, a preset transposed value, an initial value of a preset observation noise covariance matrix, and an initial value of the a priori estimated covariance at the current moment; A Kalman gain correction value is calculated according to a second preset formula, the preset state change value, the preset transposed value, the initial value of the preset observation noise covariance matrix and the initial value of the a priori estimated covariance at the current moment.

4. The fuel consumption optimization method based on Kalman filtering according to claim 1, characterized in that: The method of calculating the a priori estimated covariance at the current moment according to the initial value of the a posteriori estimated covariance at the previous moment and the initial value of the covariance matrix of the preset process noise comprises: Obtain the initial value of the posterior estimation covariance at the previous moment and the initial value of the covariance matrix of the preset process noise; The a priori estimated covariance at the current moment is calculated according to the third preset formula, the a priori estimated covariance at the previous moment and the initial value of the covariance matrix of the preset process noise.

5. The fuel consumption optimization method based on Kalman filtering according to claim 1, characterized in that: The acquiring of the throttle characteristic value based on the target throttle characteristic MAP includes: Obtaining the rotation speed and accelerator pedal opening carried by the optimization instruction; The rotation speed and the accelerator pedal opening in the target throttle characteristic MAP are queried by interpolation table lookup to obtain throttle characteristic values ​​corresponding to the rotation speed and the throttle pedal opening in the target throttle characteristic MAP.

6. The fuel consumption optimization method based on Kalman filtering according to claim 1, characterized in that: The step of normalizing the acquired throttle characteristic MAP according to the received optimization instruction to acquire the target throttle characteristic MAP includes: Receiving an optimization instruction, obtaining a rotation speed and an accelerator pedal opening carried by the optimization instruction; Based on a preset fuel consumption model, according to the speed and the accelerator pedal opening, a predicted throttle characteristic MAP is obtained; The throttle characteristic MAP is normalized according to a preset maximum throttle characteristic value to obtain a target throttle characteristic MAP.

7. The fuel consumption optimization method based on Kalman filtering according to claim 1, characterized in that: After optimizing the target throttle characteristic MAP according to the throttle characteristic value, the preset measured value and the Kalman gain correction value and obtaining the optimized throttle characteristic MAP, the method further includes: Obtaining the Kalman gain correction value, the state change value, the initial value of the a priori estimated covariance at the current moment, and the unit matrix value of the target throttle characteristic MAP; According to the fourth preset formula, the Kalman gain correction value, the state change value, the initial value of the prior estimated covariance at the current moment and the unit matrix value of the target throttle characteristic MAP, the initial value of the posterior estimated covariance at the current moment is calculated, and the initial value of the posterior estimated covariance at the current moment is used as the initial value of the posterior estimated covariance at the previous moment for the next optimization.

8. A fuel consumption optimization device based on Kalman filtering, characterized in that: The fuel consumption optimization device based on Kalman filtering includes: A first acquisition module is used to normalize the acquired throttle characteristic MAP according to the received optimization instruction to acquire a target throttle characteristic MAP, wherein the throttle characteristic MAP is predicted based on the speed and throttle pedal opening carried in the optimization instruction according to a preset fuel consumption model; A second acquisition module, configured to acquire a throttle characteristic value based on the target throttle characteristic MAP; A first calculation module is used to calculate the a priori estimated covariance at the current moment according to the initial value of the posterior estimated covariance at the previous moment and the initial value of the covariance matrix of the preset process noise; A second calculation module is used to calculate a Kalman gain correction value according to the acquired preset state change value, preset transposition value, initial value of the preset observation noise covariance matrix and initial value of the priori estimated covariance at the current moment; The optimization and acquisition module is used to optimize the target throttle characteristic MAP according to the throttle characteristic value, the preset measured value and the Kalman gain correction value, and acquire the optimized throttle characteristic MAP.

9. A fuel consumption optimization device based on Kalman filtering, characterized in that: The Kalman filter-based fuel consumption optimization device includes a processor, a memory, and a Kalman filter-based fuel consumption optimization program stored in the memory and executable by the processor, wherein when the Kalman filter-based fuel consumption optimization program is executed by the processor, the steps of the Kalman filter-based fuel consumption optimization method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a fuel consumption optimization program based on Kalman filtering, wherein when the fuel consumption optimization program based on Kalman filtering is executed by a processor, the steps of the fuel consumption optimization method based on Kalman filtering as described in any one of claims 1 to 7 are implemented.

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