Method and system for controlling regenerative braking stability of electric vehicle facing non-standard road surface

Abstract

The invention relates to an electric vehicle regenerative braking stability control method and system oriented to a non-standard road surface. The regenerative braking stability control method in the vehicle emergency braking scene comprises the steps that according to information obtained by a wheel speed sensor, in combination with vehicle parameters and the vehicle speed, processing is conducted through a dynamic model, and the relation between the current slip rate and the optimal slip rate is obtained. And inputting the identified slip rate at the peak attachment state moment as a control reference quantity into a model prediction controller, solving a torque required by motor braking in the controller, inputting the torque into a motor control unit, and performing regenerative braking through an electric driving system. On the basis of identifying the slip rate, the method gets rid of the problem of dependence on a tire model existing in most response-based methods, does not need to consider a tire ground model, damping rigidity and other difficult-to-obtain quantities, is not limited by various information acquired by multiple sensors, and is high in accuracy. And the applicability of the method under different working conditions is improved while the identification method is simplified.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle electronic control, and in particular to a regenerative braking stability control method and system for an electric vehicle on non-standard roads. Background Art

[0002] Amidst the explosive growth of my country's electric vehicle industry, the industry is placing higher demands on energy efficiency due to the limitations of electric vehicle energy sources. Regenerative braking technology, a key means of improving energy efficiency, converts part of the vehicle's kinetic energy during braking into electrical energy stored in the battery, thereby increasing energy utilization. Compared to hydraulic and mechanical braking systems, electric brake systems offer the advantages of fast response and high control precision, effectively improving braking stability and safety. Therefore, research on regenerative braking stability control in emergency braking scenarios for electric vehicles is of great significance.

[0003] The design of regenerative braking stability control systems for electric vehicles in emergency braking scenarios primarily involves identifying the optimal slip ratio. Current research is primarily divided into cause-based and response-based approaches. Cause-based identification methods require the measurement of external information such as optical and acoustic information, temperature, and humidity. Their advantage is predictive road surface identification, enabling tire-road surface identification before tire-road contact. However, these methods require the installation of additional sensors, resulting in high costs, complex data processing, demanding operating environments, and susceptibility to external factors, leading to poor robustness. Furthermore, these methods struggle to accurately estimate road surfaces on untested surfaces. Response-based identification methods estimate the road adhesion coefficient by measuring and analyzing the vehicle's response to road surface variations. These methods generally do not require additional sensors, but they rely heavily on complex tire-road models. Different models have significantly different scopes and conditions of application, making accurate identification difficult under diverse road conditions. This significantly limits the performance of regenerative braking stability control systems. Summary of the Invention

[0004] Based on this, it is necessary to provide an electric vehicle regenerative braking stability control method and system for non-standard roads, in response to the problems of high cost and poor robustness of existing cause-based control methods, and poor adaptability and difficulty in obtaining tire parameters of response-based control methods that rely on high-precision tire models.

[0005] In a first aspect, the present invention provides a regenerative braking stability control method for an electric vehicle on a non-standard road surface, comprising:

[0006] After detecting the regenerative braking demand in the emergency braking scenario, the current wheel speed information and the current wheel braking torque T are collected in real time. b[k]; calculate the driving wheel slip rate λ2[k] and the driving wheel utilization adhesion coefficient μ[k] based on the current wheel speed information; construct the derivative of μ[k] with respect to λ2[k] and will The state at the moment when the derivative changes sign is identified as the optimal braking state, and the λ2[k] corresponding to this moment is used as the optimal slip rate λ opt ,Will The μ[k] corresponding to the moment when the derivative changes sign is taken as the peak road adhesion coefficient μ max ;Through the wheel braking torque T at the previous moment b [k-1], current wheel speed information and λ opt Predict the torque increment Δu[k] at the current moment; through T b [k-1] and Δu[k] are used to calculate the ideal motor braking torque T at the current moment. m [k]; T m [k] is input to the motor control unit and regenerative braking is performed through the electric drive system.

[0007] Real-time calculation of μ[k] and μ at the previous moment max The relative error Δμ is calculated and whether Δμ is greater than the threshold; if Δμ is greater than the threshold and μ[k] is greater than μ at the previous moment max , then increase T m [k]; If Δμ is greater than the threshold, and μ at the previous moment max Greater than μ[k], then reduce T m [k].

[0008] In a second aspect, the present invention further provides a regenerative braking stability control system for electric vehicles on non-standard roads, which utilizes the regenerative braking stability control method for electric vehicles on non-standard roads described in the first aspect. The regenerative braking stability control system for electric vehicles on non-standard roads includes an optimal slip ratio identifier, a model predictive controller, and a road adhesion monitor.

[0009] The optimal slip ratio identifier is used to collect the current wheel speed information and the current wheel braking torque T in real time after detecting the regenerative braking demand in the emergency braking scenario. b [k]; calculate the driving wheel slip rate λ2[k] and the driving wheel utilization adhesion coefficient μ[k] based on the current wheel speed information; construct the derivative of μ[k] with respect to λ2[k] and will The state at the moment when the derivative changes sign is identified as the optimal braking state, and the λ2[k] corresponding to this moment is used as the optimal slip rate λ opt ,Will The μ[k] corresponding to the moment when the derivative changes sign is taken as the peak road adhesion coefficient μ maxThe model predictive controller is used to calculate the wheel braking torque T at the previous moment. b [k-1], current wheel speed information and λ opt Predict the torque increment Δu[k] at the current moment; through T b [k-1] and Δu[k] are used to calculate the ideal motor braking torque T at the current moment. m [k]; T m [k] is input to the motor control unit and regenerative braking is performed through the electric drive system. The road adhesion monitor is used to calculate μ[k] and μ at the previous moment in real time. max The relative error Δμ is calculated and whether Δμ is greater than the threshold; if Δμ is greater than the threshold and μ[k] is greater than μ at the previous moment max , then increase T according to the braking torque adjustment strategy m [k]; If Δμ is greater than the threshold, and μ at the previous moment max If it is greater than μ[k], then reduce T according to the braking torque adjustment strategy. m [k].

[0010] The beneficial effects of the present invention are:

[0011] 1. This invention improves upon response-based road surface recognition methods, eliminating the reliance on tire models that plagues most response-based methods when identifying slip rate. While maintaining the simplicity and efficiency of response-based road surface recognition methods, this approach eliminates the need to consider tire ground models and difficult-to-obtain quantities like damping stiffness. Furthermore, it is unaffected by the current situation of multiple sensors collecting diverse information. This simplifies the recognition method while also enhancing its applicability under different operating conditions and strengthening the robustness of the control system.

[0012] 2. The present invention combines a model predictive control algorithm for braking control, uses the identified optimal slip rate as a reference value, derives a more accurate state-space model, obtains the ideal motor torque requirement by solving the control change that minimizes the cost function, and inputs the motor torque command into the motor control unit for control tracking of the optimal slip rate, so that the vehicle's longitudinal braking force is maintained at the maximum value, thereby shortening the braking distance and improving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0014] Figure 1Flowchart of a regenerative braking stability control method for an electric vehicle on a non-standard road surface in an embodiment;

[0015] Figure 2 This is a data diagram of vehicle speed and acceleration under the docking road conditions in the simulation experiment;

[0016] Figure 3 This is the data diagram of the slip rate under the docking road condition in the simulation experiment;

[0017] Figure 4 This is a data diagram of the motor braking torque and the utilized adhesion coefficient under the docking road conditions in the simulation experiment;

[0018] Figure 5 This is a data diagram of vehicle speed and acceleration under high-adhesion road conditions in the simulation experiment;

[0019] Figure 6 This is the data graph of the slip rate under high adhesion road conditions in the simulation experiment;

[0020] Figure 7 This is a data diagram of the motor braking torque and the utilized adhesion coefficient under high-adhesion road conditions in the simulation experiment;

[0021] Figure 8 2 is a structural diagram of a regenerative braking stability control system for an electric vehicle on non-standard roads in an embodiment. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used herein in the specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "or / and" as used herein includes any and all combinations of one or more of the associated listed items.

[0024] This embodiment provides a regenerative braking stability control method for electric vehicles on non-standard roads. The information obtained by the wheel speed sensor is combined with the vehicle parameters and processed through a dynamic model to obtain the relationship between the current slip rate and the optimal slip rate. The identified optimal slip rate is input into the model prediction controller, and the torque required for motor braking is solved in the controller and input into the motor control unit to perform regenerative braking through the electric drive system. It is mainly divided into three stages S1 to S3. In S1, after detecting the regenerative braking demand in an emergency braking scenario, the motor braking torque increases rapidly, the wheel speed sensor collects the wheel speed information, and the wheel speed information is imported into the optimal slip rate identifier, and the derivative of the adhesion coefficient with respect to the slip rate is calculated in real time. At this moment Compared with the previous sampling moment When the product is less than zero, the current state is identified as the optimal braking state, and the rear wheel slip rate at the current moment is output as the optimal slip rate λ opt , and store the current moment's utilization adhesion coefficient as the road peak adhesion coefficient μ max In S2, the identified optimal slip ratio is input as a reference value into the model predictive controller, which outputs the required motor braking torque for motor regenerative braking. In S3, the road adhesion monitor calculates the relative difference between the adhesion coefficient and the stored peak road adhesion coefficient in real time to determine whether the road adhesion coefficient has changed. If so, the motor braking torque is adjusted accordingly to achieve the new optimal slip ratio and re-identify and control. In the next cycle, the above process is repeated.

[0025] like Figure 1 As shown, the process and principles of each stage are described in detail below.

[0026] In S1, after detecting the regenerative braking demand in the emergency braking scenario, the wheel speed sensor collects the wheel speed information and the current wheel braking torque T in real time. b [k], and the current driving wheel slip ratio λ2[k] and the driving wheel's utilization adhesion coefficient μ[k] are calculated in combination with the basic vehicle parameters. In this embodiment, the entire vehicle's slip ratio can be represented by the driving wheel slip ratio λ2[k], which is calculated as follows:

[0027]

[0028] Where ω2 represents the driving wheel angular velocity, v represents the vehicle longitudinal velocity obtained by the vehicle speed sensor, and r represents the wheel radius. Taking a rear-wheel drive electric vehicle as an example, the wheel torque balance and the vehicle's overall axle load transfer formula are then used to obtain:

[0029]

[0030] Where I represents the wheel moment of inertia, represents the driving wheel angular acceleration, T b Indicates the braking torque acting on the driving wheel, F x2 is the ground tangential reaction force on the driving wheel, F z2 represents the ground normal reaction force on the driving wheel, L represents the vehicle wheelbase, G represents the vehicle gravity, a represents the distance from the vehicle center of mass to the front axle, m represents the vehicle mass, and h represents the height of the vehicle center of mass. Represents the longitudinal acceleration of the vehicle. Combining the above formula, the utilization adhesion coefficient μ[k] can be obtained in real time. The calculation formula is:

[0031]

[0032] Where, represents the longitudinal acceleration of the vehicle. Of course, if the vehicle has other drive modes, the same method is used to calculate the utilization coefficient of the drive wheel. After obtaining λ2[k] and μ[k], the derivative of μ[k] with respect to λ2[k] can be calculated. Thus, the relationship between λ2[k] and μ[k] at this moment can be determined as follows:

[0033]

[0034] The derivative The state at the time of sign change is identified as the optimal braking state, and the λ2[k] corresponding to this moment is used as the optimal slip rate λ opt Output, the corresponding μ[k] is used as the peak adhesion coefficient μ max Storage, the expression is:

[0035]

[0036] Where t0 represents the time to enter the emergency braking state, and n represents the time from the start of braking to the acquisition of λ opt The number of sampling intervals, t s represents the sampling interval. If the electric vehicle used has a different drive system, the same method can be used to obtain the drive wheel slip ratio and adhesion coefficient, as well as their optimal state time, and output the optimal slip ratio. This completes the first phase.

[0037] In S2, the wheel braking torque T at the previous moment is first b [k-1] (based on the continuous collection of T b [k] can be obtained), the current wheel speed information and λ opt The torque increment Δu[k] at the current moment is predicted. Then, T b The motor braking torque T is calculated by [k-1] and Δu[k] m [k]. Finally, use Tm [k] controls the motor to perform regenerative braking. Specifically, first define the prediction equation for the state variable x and output variable y at time k+1. x and y are values ​​determined according to vehicle parameters and wheel speed, and are both equal to [2+(mr 2 ) / (2I)-λ2]v / r. T b As the input control variable u, the state space equation is constructed by deriving the wheel dynamics model, and its expression is:

[0038]

[0039] in, represents the derivative of x, coefficient A c =0, coefficient B cu = -1 / I. Substitute The prediction equation can be obtained to predict the state variable x and output variable y at the next moment at time k. The expression of the prediction equation is:

[0040]

[0041] Among them, coefficient A=1, B u =B cu t s In addition, in model predictive control, the general cost function can be expressed as:

[0042]

[0043] Where q1 and q2 represent the weight coefficients of control following and control input change, respectively, and y r [k+j] represents the output reference value in the prediction time domain, and its value is equal to [2+(mr 2 ) / (2I)-λ opt ]v[k+j|k] / r. Where v[k+j|k] can be approximately written as v[k]+j(v[k]-v[k-1]). j represents the sequence number. p and n represent the prediction time domain and the control time domain respectively. In this embodiment, p and n are set to 1, so that the cost function at this time is expressed as:

[0044] J'=q1(y[k+1|k]-y r [k+1]) 2 +q2Δu 2 [k].

[0045] in

[0046] Substitute J' into the prediction equation, so that the new cost function J is:

[0047] J=q1(Ax[k]+B uu[k]-y r [k+1]) 2 +q2Δu 2 [k].

[0048] Substitute u(k)=u(k-1)+Δu(k) into J and transform J into a quadratic function to obtain:

[0049]

[0050] Where D, E and F are coefficients. The cost function J can be regarded as a quadratic function of Δu[k], where D is greater than zero, so the horizontal coordinate of the extreme point of J is It is the Δu[k] that minimizes J, and its expression is:

[0051]

[0052] Then, according to the transmission ratio of the motor, the ideal motor braking torque T can be obtained. m [k], and T m [k] is input to the motor control unit and regenerative braking is performed through the electric drive system. m The expression for [k] is:

[0053]

[0054] Where u represents the wheel braking torque T b , i0 represents the transmission ratio of the reducer, η T Indicates mechanical transmission efficiency.

[0055] In S3, when the vehicle speed is greater than 7 km / h (due to the characteristics of electric vehicles, the hydraulic braking system is generally used to assist when the vehicle speed is below a certain speed, which is not discussed in this case), the current μ[k] and the stored μ are calculated in real time. max The relative error Δμ. Δμ satisfies the following conditions:

[0056]

[0057] It is considered that under the new adhesion conditions (the vehicle travels on different roads, causing the adhesion conditions to change), opt 、μ max are greater than the storage value at the previous moment, at this time T m [k] needs to be increased. Δμ satisfies the following conditions:

[0058]

[0059] It is considered that under the new attachment condition λ opt 、μ max are greater than the storage value at the previous moment, at this time Tm [k] needs to be reduced.

[0060] T m The magnitude of increase and decrease of [k] is determined according to the new attachment condition. Specifically, in this embodiment, after Δμ meets the condition, the above calculation process is first repeated according to the current λ2[k] and μ[k], and the derivative after the current moment is updated. The data at the time of sign change, i.e. At time t1:

[0061]

[0062] At this time, the output λ2[t1+t s ] as the updated λ opt Based on the updated λ opt Calculate the updated T m [k], and make T before update m [k-1] (equivalent to the motor braking torque at the previous moment) follows the updated T m [k] to increase or decrease T m [k].

[0063] In order to verify the effectiveness of the regenerative braking stability control method for electric vehicles on non-standard roads, simulation experiments were carried out under high adhesion road conditions and docking road conditions. The results are as follows: Figure 3 、 Figure 4 、 Figure 5 、 Figure 6 、 Figure 7 、 Figure 8 As shown in the figure, it can be seen that the vehicle maintains a relatively good slip ratio under both adhesion road conditions and docking road conditions, and the regenerative braking stability control system achieves good braking performance under both conditions, effectively shortening the braking distance, proving the effectiveness and feasibility of the scheme.

[0064] In another embodiment, a regenerative braking stability control system for electric vehicles on non-standard roads is also proposed, which uses the regenerative braking stability control method for electric vehicles on non-standard roads in the above embodiment. Figure 2 As shown in FIG, the regenerative braking stability control system for the vehicle regenerative braking emergency braking scenario includes: an optimal slip ratio identifier, a model predictive controller, and a road adhesion monitor. The optimal slip ratio identifier is used to collect the current wheel speed information and the current wheel braking torque T in real time after detecting the regenerative braking demand in the emergency braking scenario. b [k]; calculate the driving wheel slip rate λ2[k] and the driving wheel utilization adhesion coefficient μ[k] based on the current wheel speed information; construct the derivative of μ[k] with respect to λ2[k] and will The λ2[k] corresponding to the moment when the derivative changes sign is taken as the optimal slip rate λ opt ,Will The μ[k] corresponding to the moment when the derivative changes sign is taken as the peak road adhesion coefficient μ max The model predictive controller is used to calculate the wheel braking torque T at the previous moment. b [k-1], current wheel speed information and λ opt Predict the torque increment Δu[k] at the current moment; through T b [k-1] and Δu[k] are used to calculate the ideal motor braking torque T at the current moment. m [k]; T m [k] is input to the motor control unit and regenerative braking is performed through the electric drive system. The road adhesion monitor is used to calculate μ[k] and μ at the previous moment in real time. max The relative error Δμ is calculated and whether Δμ is greater than the threshold; if Δμ is greater than the threshold and μ[k] is greater than μ at the previous moment max , then increase T m [k]; If Δμ is greater than the threshold, and μ at the previous moment max Greater than μ[k], then reduce T m [k].

[0065] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0066] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A regenerative braking stability control method for electric vehicles on non-standard roads, characterized in that: It includes: After detecting the regenerative braking demand in the emergency braking scenario, the current wheel speed information and the current wheel braking torque T are collected in real time. b [k]; The slip ratio λ2[k] of the driving wheel and the utilization adhesion coefficient μ[k] of the driving wheel are calculated based on the wheel speed information at the current moment; Construct the derivative of μ[k] with respect to λ2[k] and will The state at the moment when the derivative changes sign is identified as the optimal braking state, and the λ2[k] corresponding to this moment is used as the optimal slip rate λ opt ,Will The μ[k] corresponding to the moment when the derivative changes sign is taken as the peak road adhesion coefficient μ max ; The wheel braking torque T at the previous moment b [k-1], current wheel speed information and λ opt Predict the torque increment Δu[k] at the current moment; By T b [k-1] and Δu[k] are used to calculate the ideal motor braking torque T at the current moment. m [k]; T m [k] Input to the motor control unit, which performs regenerative braking through the electric drive system; Real-time calculation of μ[k] and μ at the previous moment max The relative error Δμ is calculated and whether Δμ is greater than the threshold; if Δμ is greater than the threshold and μ[k] is greater than μ at the previous moment max , then increase T m [k]; If Δμ is greater than the threshold, and μ at the previous moment max Greater than μ[k], then reduce T m [k].

2. The electric vehicle regenerative braking stability control method for non-standard roads according to claim 1, characterized in that; The slip ratio of the entire vehicle is characterized by the slip ratio λ2[k] of the driving wheel; The calculation formula of λ2[k] is: Where ω2 represents the driving wheel angular velocity, and v is the vehicle longitudinal velocity, which is obtained from the vehicle speed sensor.

3. The electric vehicle regenerative braking stability control method for non-standard roads according to claim 1, characterized in that: Obtain the basic vehicle parameters and calculate the driving wheel's utilization adhesion coefficient μ[k] based on the driving wheel torque balance equation and the vehicle axle load transfer equation; the calculation formula for μ[k] is: Where L is the vehicle wheelbase, T b represents the braking torque acting on the driving wheel, I represents the wheel moment of inertia, represents the angular acceleration of the driving wheel, G represents the gravity of the car, a represents the distance from the center of mass of the car to the front axle, m represents the mass of the car, and h represents the height of the center of mass of the car. Indicates the vehicle's longitudinal acceleration.

4. The electric vehicle regenerative braking stability control method for non-standard roads according to claim 1, characterized in that: The method for predicting Δu[k] includes the following steps: Define the prediction equation for predicting the state variable x and output variable y at time k+1; where x and y are values ​​determined by vehicle parameters and wheel speed; Substituting the prediction equation into the cost function in predictive control, we get a new cost function J, which is expressed as: J=q1(Ax[k]+B u u[k]-y r [k+1]) 2 +q2Δu 2 [k]; Where q1 and q2 are the weight coefficients of control following and control input variation respectively; A and B are u represents the coefficient, y r [k+1] represents the reference value in the prediction time domain; Solve for the minimum value of J to obtain Δu[k].

5. The electric vehicle regenerative braking stability control method for non-standard roads according to claim 4, characterized in that: The prediction equation is constructed from the state-space equation derived from the wheel dynamics model; the expression of the prediction equation is:

6. The electric vehicle regenerative braking stability control method for non-standard roads according to claim 4, characterized in that: Convert J into a quadratic function, and its expression is: J(Δu[k])=DΔu 2 [k]+EΔu[k]+F; In the formula, D, E and F are coefficients; by solving To get the minimum value of J.

7. The electric vehicle regenerative braking stability control method for non-standard roads according to claim 6, characterized in that: The calculation formula of Δu[k] is:

8. The electric vehicle regenerative braking stability control method for non-standard roads according to claim 1, characterized in that: T m The formula for [k] is: Where u represents the wheel braking torque T b , i0 represents the transmission ratio of the reducer, η T Indicates mechanical transmission efficiency.

9. The electric vehicle regenerative braking stability control method for non-standard roads according to claim 1, characterized in that: Increase or decrease T m The range of [k] is: The updated λ is calculated based on the current λ2[k] and μ[k] opt ; According to the updated λ opt Calculate the updated T m [k], and make T before update m [k-1] follows the updated T m [k] to increase or decrease T m [k].

10. A regenerative braking stability control system for electric vehicles on non-standard roads, characterized in that: It uses the electric vehicle regenerative braking stability control method for non-standard roads as described in any one of claims 1 to 9; It includes: The optimal slip ratio identifier is used to collect the current wheel speed information and the current wheel braking torque T in real time after detecting the regenerative braking demand in the emergency braking scenario. b [k]; calculate the driving wheel slip rate λ2[k] and the adhesion coefficient μ[k] based on the current wheel speed information; construct the derivative of μ[k] with respect to λ2[k] and will The state at the moment when the derivative changes sign is identified as the optimal braking state, and the λ2[k] corresponding to this moment is used as the optimal slip rate λ opt ,Will The μ[k] corresponding to the moment when the derivative changes sign is taken as the peak road adhesion coefficient μ max ; The model predictive controller is used to calculate the wheel braking torque T at the previous moment. b [k-1], current wheel speed information and λ opt Predict the torque increment Δu[k] at the current moment; through T b [k-1] and Δu[k] are used to calculate the ideal motor braking torque T at the current moment. m [k]; T m [k] Input to the motor control unit, which performs regenerative braking through the electric drive system; Road adhesion monitor, which is used to calculate μ[k] and μ at the previous moment in real time max The relative error Δμ is calculated and whether Δμ is greater than the threshold; if Δμ is greater than the threshold and μ[k] is greater than μ at the previous moment max , then increase T m [k]; If Δμ is greater than the threshold, and μ at the previous moment max Greater than μ[k], then reduce T m [k].

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