Energy recovery method, device, equipment and storage medium

Abstract

The present invention relates to the field of new energy vehicle technology, and in particular to an energy recovery method, device, equipment and storage medium. The energy recovery method divides the electromotive force generated during the braking process of the vehicle into a high back electromotive force and a low back electromotive force, and based on the high back electromotive force and the low back electromotive force, uses the gray wolf optimization algorithm to regulate the braking torque distribution of the wheel hub drive motor to obtain the optimal target braking torque of the target wheel, thereby distributing the braking force to the target wheel and outputting the primary energy recovery efficiency at the same time, and based on the primary energy recovery efficiency, obtains the target energy recovery efficiency and directly reverse charges the high back electromotive force and stores it in the power battery pack through a hybrid energy storage system combined with a supercapacitor through a power battery pack, and the low back electromotive force is transferred to the supercapacitor, which effectively improves the energy recovery efficiency of electric vehicles driven by multiple motors, reduces energy consumption, extends the cruising range, and improves the economy and environmental protection of the vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and in particular to an energy recovery method, device, equipment and storage medium. Background Art

[0002] With the continuous development of new energy vehicles, compared with traditional vehicles, new energy vehicles can convert the kinetic energy of the vehicle during sliding or braking into electrical energy and recharge it into the vehicle battery during braking due to the presence of wheel hub drive motors. However, the current energy recovery of multiple wheel hub drive motors is to directly recycle energy into batteries or energy storage systems. However, when the wheel hub drive motor decelerates and brakes, a back electromotive force will be generated. Different wheel hub drive motors have different speeds, inertias, and braking times, so there will be inconsistent back electromotive force. If this back electromotive force is lower than the voltage of the battery or energy storage system, it becomes a low back electromotive force. The energy of the low back electromotive force motor cannot be recharged into the battery or energy storage system, resulting in energy waste, and even in order to balance the low back electromotive force, additional energy consumption occurs. Summary of the invention

[0003] The present invention provides an energy recovery method, device, equipment and storage medium to solve the problem that energy with low back electromotive force cannot be recharged into a battery or an energy storage system.

[0004] To achieve the above object, the present invention provides an energy recovery method, comprising:

[0005] Detecting the back electromotive force of the wheel hub drive motor, setting the wheel hub drive motor whose back electromotive force is higher than the motor back electromotive force signal threshold as a high back electromotive force group, setting the wheel hub drive motor whose back electromotive force is lower than the motor back electromotive force signal threshold as a low back electromotive force group, and outputting the back electromotive force signal and the motor grouping signal;

[0006] The current braking torque of the target wheel, the back electromotive force signal, and the motor grouping signal are obtained, and the target braking torque of the target wheel is calculated and obtained, and the target wheel motor force distribution and primary energy recovery efficiency are output according to the target braking torque of the target wheel. The target wheel motor force is used to regulate the wheel hub drive motor to brake the vehicle, and the primary energy recovery efficiency is used to regulate the recovery of the back electromotive force of the target wheel;

[0007] Based on the primary energy recovery efficiency, the back electromotive force signal, and the motor grouping signal, a target energy recovery efficiency is calculated to recover the back electromotive force of the hub drive motor, wherein the back electromotive force of the high back electromotive force group is stored in a power battery pack, and the back electromotive force of the low back electromotive force group is stored in a supercapacitor.

[0008] Optionally, a back-electromotive force detection circuit is used to detect the back-electromotive force of the hub drive motor and the back-electromotive force is set to a high back-electromotive force group or a low back-electromotive force group.

[0009] Optionally, when there are multiple wheel hub drive motors, each of the wheel hub drive motors is provided with a back electromotive force detection circuit.

[0010] Optionally, the step of calculating and obtaining the target braking torque of the target wheel based on the current braking torque of the target wheel, the back electromotive force signal, and the motor grouping signal includes:

[0011] Obtain the back-electromotive force signal, and determine whether the back-electromotive force signal is equal to the target function voltage value. If equal, output the current braking torque of the target wheel corresponding to the target function voltage value as the target braking torque of the target wheel; if not equal, calculate based on the Gray Wolf optimization algorithm until the number of iterations is reached to obtain the target braking torque of the target wheel.

[0012] Optionally, the step of calculating and obtaining the target braking torque of the target wheel based on the Grey Wolf Optimization Algorithm includes:

[0013] Step S10, initialize the gray wolf population X∈R G×D , set the maximum number of iterations based on the gray wolf optimization algorithm to 1 max , the maximum braking torque of each wheel hub drive motor is T max The number of iterations based on the gray wolf optimization algorithm is I t And the initial state t is 1, where X is a gray wolf individual, G is the population size, D is the number of gray wolf individuals, G=D, the population size is equal to the number of gray wolves is equal to the number of wheel hub drive motors, i is the number of gray wolves, j is the number of wheel hub drive motors, i and j are both natural numbers greater than 1, and R is a natural number;

[0014] Step S20, determining whether the number of iterations based on the gray wolf optimization algorithm is greater than or equal to the maximum number of iterations based on the gray wolf optimization algorithm, if greater, outputting the target braking torque of the target wheel after iteration as the target braking torque of the target wheel, if not greater, executing step S30;

[0015] Step S30, using a first calculation formula to calculate the fitness to obtain the fitness value of each individual gray wolf, wherein the first calculation formula is:

[0016]

[0017] Among them, X i is the power distribution of the wheel drive motor of the i-th wolf individual, X i As the gray wolf individual of the gray wolf optimization algorithm, η jis the weight coefficient of the energy recovery efficiency of the high back-EMF group motor, η j ' is the weight coefficient of the energy recovery efficiency of the low back-EMF group motor, T ij is the braking torque assigned by the i-th gray wolf individual to the j-th motor, H is the high back-EMF group, L is the low back-EMF group, f(x i ) is the wheel drive motor force distribution function, i≤D, T ij ≤T max , i is the number of the individual gray wolf, j is the number of the wheel hub drive motor, and both i and j are natural numbers greater than 1;

[0018] Step S40, based on the obtained fitness value of each individual gray wolf, select the three best fitness values ​​as the leader wolf, follower wolf, and common wolf of the gray wolf optimization algorithm, where x α To lead the wolf, x β To follow the wolf, x δ For common wolf;

[0019] Step S50, based on the three optimal fitness values, the gray wolf position is updated using a second calculation formula to obtain the target braking torque distribution of the target wheel after iteration, and the second calculation formula is:

[0020]

[0021] Among them, A is the convergence coefficient, which decreases with the number of iterations. 1 is a random number, random number r 1 The range of is [0, 1], and t is the number of iterations;

[0022] Step S60, using t+1 to calculate the number of iterations of the gray wolf optimization algorithm, and executing step S20.

[0023] Optionally, the step of obtaining the target energy recovery efficiency includes:

[0024] Obtaining the primary energy recovery efficiency;

[0025] Acquire energy storage parameters of the power battery pack and energy storage parameters of the supercapacitor, and determine charging efficiency thresholds of the power battery pack and the supercapacitor;

[0026] The target energy recovery efficiency is determined based on the output energy recovery efficiency and charging efficiency thresholds of the power battery pack and the supercapacitor.

[0027] Optionally, the step of determining the charging efficiency thresholds of the power battery pack and the supercapacitor includes:

[0028] Determining the remaining power of the power battery pack and the supercapacitor according to the current states of the power battery pack and the supercapacitor;

[0029] Determining charging current thresholds of the power battery pack and the supercapacitor according to the remaining power of the power battery pack and the supercapacitor;

[0030] The charging efficiency thresholds of the power battery pack and the supercapacitor are determined according to the remaining power of the power battery pack and the supercapacitor and the charging current thresholds of the power battery pack and the supercapacitor.

[0031] The present invention also provides an energy recovery device, comprising:

[0032] Energy detection module, energy balance module, energy recovery module;

[0033] The energy detection module is used to detect the back electromotive force of the wheel hub drive motor, set the wheel hub drive motor whose back electromotive force is higher than the motor back electromotive force signal threshold to a high back electromotive force group, set the wheel hub drive motor whose back electromotive force is lower than the motor back electromotive force signal threshold to a low back electromotive force group, and output a back electromotive force signal and a motor grouping signal;

[0034] The energy balance module is used to obtain the current braking torque of the target wheel, the back electromotive force signal, and the motor grouping signal, calculate and obtain the target braking torque of the target wheel, distribute the target wheel motor force according to the target braking torque of the target wheel, and output the primary energy recovery efficiency. The target wheel motor force is used to regulate the wheel hub drive motor to brake the vehicle, and the primary energy recovery efficiency is used to regulate the recovery of the back electromotive force of the target wheel;

[0035] The energy recovery module is used to calculate and obtain the target energy recovery efficiency based on the primary energy recovery efficiency, the back electromotive force signal, and the motor grouping signal, and to recover the back electromotive force of the hub drive motor, wherein the back electromotive force of the high back electromotive force group is stored in a power battery pack, and the back electromotive force of the low back electromotive force group is stored in a supercapacitor.

[0036] The present invention also provides an energy recovery device, comprising: a memory, a processor, and an energy recovery program stored in the memory and executable on the processor, wherein the energy recovery program is configured to implement the steps of the energy recovery method as described above.

[0037] The present invention further provides a storage medium, comprising: an energy recovery program is stored on the storage medium, and the energy recovery program implements the above-mentioned energy recovery method when executed by a processor.

[0038] In summary, the advantages and beneficial effects of the present invention are:

[0039] The present invention provides an energy recovery method, device, equipment and storage medium. The energy recovery method divides the electromotive force generated during the braking process of the vehicle into high back electromotive force and low back electromotive force. Based on the high back electromotive force and low back electromotive force, the gray wolf optimization algorithm is used to adjust the braking force distribution of the wheel hub drive motor to obtain the optimal target braking torque of the target wheel, thereby distributing the braking force to the target wheel and outputting the primary energy recovery efficiency at the same time. Based on the primary energy recovery efficiency, the target energy recovery efficiency is obtained and the high back electromotive force is directly reversely charged and stored in the power battery pack through a hybrid energy storage system combining a power battery pack and a supercapacitor, and the low back electromotive force is transferred to the supercapacitor, which effectively improves the energy recovery efficiency of electric vehicles driven by multiple motors, reduces energy consumption, extends the cruising range, and improves the economy and environmental protection of the vehicle. Compared with the existing energy recovery method, it avoids the back electromotive force being lower than the voltage of the energy storage system, and cannot be effectively recharged to the energy storage system to cause energy waste, and even in order to balance the back electromotive force, additional energy consumption occurs, which effectively improves the energy recovery efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A schematic diagram of a process flow of an energy recovery method provided by an embodiment of the present invention;

[0041] Figure 2 A schematic diagram of an energy recovery method provided in an embodiment of the present invention for calculating a target braking torque of a target wheel based on a Grey Wolf optimization algorithm. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in combination with the specific implementation methods of the present invention and the corresponding drawings. Obviously, the described implementation methods are only part of the implementation methods of the present invention, not all implementation methods. Based on the implementation methods of the present invention, all other implementation methods obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0043] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0044] The present invention provides an energy recovery method, such as Figure 1 As shown, including:

[0045] Step S100, detecting the back electromotive force of the wheel hub drive motor, setting the wheel hub drive motor whose back electromotive force is higher than the motor back electromotive force signal threshold as a high back electromotive force group, setting the wheel hub drive motor whose back electromotive force is lower than the motor back electromotive force signal threshold as a low back electromotive force group, and outputting the back electromotive force signal and the motor grouping signal;

[0046] Step S200, obtaining the current braking torque of the target wheel, the back electromotive force signal, and the motor grouping signal, calculating and obtaining the target braking torque of the target wheel, distributing the target wheel motor force according to the target braking torque of the target wheel, and outputting the primary energy recovery efficiency, wherein the target wheel motor force is used to regulate the wheel hub drive motor to brake the vehicle, and the primary energy recovery efficiency is used to regulate the recovery of the back electromotive force of the target wheel;

[0047] Step S300, based on the primary energy recovery efficiency, the back electromotive force signal, and the motor grouping signal, calculate and obtain the target energy recovery efficiency, and recover the back electromotive force of the hub drive motor, wherein the back electromotive force of the high back electromotive force group is stored in a power battery pack, and the back electromotive force of the low back electromotive force group is stored in a supercapacitor.

[0048] Specifically, execute step S100, detect the back electromotive force of the wheel hub drive motor, set the wheel hub drive motor whose back electromotive force is higher than the motor back electromotive force signal threshold as a high back electromotive force group, set the wheel hub drive motor whose back electromotive force is lower than the motor back electromotive force signal threshold as a low back electromotive force group, and output the back electromotive force signal and the motor grouping signal.

[0049] In the embodiments of the present invention, when the electric vehicle is in a braking state, a gliding state or a deceleration state, unused or consumed kinetic energy will be generated. Taking the braking state as an example, the wheel hub drive motor is rotating, and the back electromotive force generated by the electromagnetic induction phenomenon is recovered to improve energy utilization and extend battery life.

[0050] In the embodiment of the present invention, a back electromotive force detection circuit is used to detect the back electromotive force of the wheel hub drive motor and the back electromotive force is set to a high back electromotive force group or a low back electromotive force group.

[0051] Grouping the back electromotive force generated during the braking process of the electric vehicle is conducive to the subsequent use of the back electromotive force grouping based on the Grey Wolf optimization algorithm, as well as the weight control of high back electromotive force and low back electromotive force, to obtain the optimal target braking torque of the target wheel, thereby obtaining the optimal electric motor force distribution and output energy recovery efficiency.

[0052] In an embodiment of the present invention, the motor back electromotive force signal threshold is an average value of the back electromotive force currently output by the hub motor.

[0053] In other embodiments, the motor back electromotive force signal threshold is a median value of the back electromotive force output by the hub motor or other suitable voltage values.

[0054] In an embodiment of the present invention, when there are multiple wheel hub drive motors, each of the wheel hub drive motors is provided with a back electromotive force detection circuit. For example, a six-wheel electric vehicle uses six independent wheel hub drive motors, and each wheel hub drive motor is configured with a back electromotive force detection circuit.

[0055] Execute step S200, obtain the current braking torque of the target wheel, the back electromotive force signal, and the motor grouping signal, calculate and obtain the target braking torque of the target wheel, and distribute the target wheel motor force and output energy recovery efficiency according to the target braking torque of the target wheel. The target wheel motor force is used to regulate the wheel hub drive motor to brake the vehicle, and the output energy recovery efficiency is used to regulate the recovery of the back electromotive force of the target wheel.

[0056] In an embodiment of the present invention, the step of calculating and obtaining the target braking torque of the target wheel based on the current braking torque of the target wheel, the back electromotive force signal, and the motor grouping signal includes:

[0057] The back-electromotive force signal is obtained, and it is determined whether the back-electromotive force signal satisfies the target function voltage value. If so, the current braking torque of the target wheel corresponding to the target function voltage value is output as the target braking torque of the target wheel. If not, the target braking torque of the target wheel is obtained by calculating based on the Gray Wolf Optimization Algorithm until the number of iterations is reached.

[0058] In an embodiment of the present invention, the target function voltage value is an average value of the back electromotive force currently output by the hub motor.

[0059] In other embodiments, the target function voltage value is a median value of the back electromotive force currently output by the hub motor or other suitable voltage values.

[0060] In the embodiment of the present invention, Figure 2 As shown, the steps of calculating and obtaining the target braking torque of the target wheel based on the Grey Wolf optimization algorithm include:

[0061] Step S10, initialize the gray wolf population X∈R G×D , set the maximum number of iterations based on the gray wolf optimization algorithm to 1 max , set the maximum braking torque of each wheel hub drive motor to T max , set the number of iterations based on the gray wolf optimization algorithm to 1 tAnd the initial state t is 1, where X is a gray wolf individual, G is the population size, D is the number of gray wolf individuals, G=D, the population size is equal to the number of gray wolves equal to the number of wheel hub drive motors, and R is a natural number.

[0062] In the embodiment of the present invention, the population size G is the number of gray wolf individuals existing at the same time. The larger the population size G is, the wider the search breadth of the gray wolf optimization algorithm is, and the better the target braking torque of the target wheel obtained is.

[0063] In an embodiment of the present invention, the gray wolf individual is the braking force distribution of the motor, each gray wolf individual corresponds to a group of braking force distribution schemes of the motor, and the number of gray wolf individuals D is equal to the number of hub drive motors, corresponding to the number of braking force distribution schemes of the motor.

[0064] In the embodiment of the present invention, the population size G is equal to the number of gray wolf individuals D is equal to the number of wheel hub drive motors.

[0065] Step S20, determining whether the number of iterations based on the Gray Wolf Optimization Algorithm is greater than or equal to the maximum number of iterations based on the Gray Wolf Optimization Algorithm; if so, outputting the target braking torque of the target wheel after iteration as the target braking torque of the target wheel; if not, executing step S30.

[0066] Step S30, using a first calculation formula to calculate the fitness to obtain the fitness value of each individual gray wolf, wherein the first calculation formula is:

[0067]

[0068] Among them, X i is the power distribution of the i-th gray wolf individual in the wheel hub drive motor, η j is the weight coefficient of the energy recovery efficiency of the high back-EMF group motor, η j ' is the weight coefficient of the energy recovery efficiency of the low back-EMF group motor, T ij is the braking torque assigned by the i-th gray wolf individual to the j-th motor, H is the high back-EMF group, L is the low back-EMF group, f(x i ) is the wheel drive motor force distribution function, i≤D, T ij ≤T max , i is the number of the individual gray wolf, j is the number of the hub drive motor, and both i and j are natural numbers greater than 1.

[0069] In the embodiment of the present invention, the fitness value of each individual gray wolf is obtained by calculating the fitness, and the fitness value of each individual gray wolf is used to evaluate the pros and cons of each individual gray wolf, and provide a basis for subsequent iterative optimization. The adaptability value represents the corresponding individual gray wolf, that is, the corresponding motor braking force distribution strategy. The larger the adaptability value, the higher the corresponding energy recovery efficiency.

[0070] In the embodiment of the present invention, since the back electromotive force of different motors is different, their energy recovery efficiency will also be different. When calculating the fitness using the first calculation formula, by setting different weight coefficients for the back electromotive force, the energy recovery efficiency of motors with different back electromotive forces can be distinguished, and the energy recovery efficiency of different motors can be evaluated more accurately. At the same time, the algorithm will select the optimal braking torque distribution scheme according to the high back electromotive force and the low back electromotive force in the formula, so as to maximize the energy recovery efficiency.

[0071] Step S40, based on the fitness value of each individual gray wolf, select the three best fitness values ​​as the leader wolf (Alpha wolf), the follower wolf (Beta wolf), and the common wolf (Delta wolf) of the gray wolf optimization algorithm, where x α To lead the wolf, x β To follow the wolf, x δ An ordinary wolf.

[0072] In the embodiment of the present invention, the x is obtained α 、x β 、x δ The wolf steps include:

[0073] Step S401, sorting, sorting is performed based on the fitness value of each gray wolf individual obtained in step 30, wherein the gray wolf individual with the highest fitness value is ranked first, and the gray wolf individual with the lowest fitness value is ranked last.

[0074] Step S402, select leader wolf x α , select the gray wolf individual with the highest fitness value as the leader wolf, and the leader wolf represents the optimal hub motor power distribution.

[0075] Step S403, select to follow wolf x β , select the gray wolf with the second highest fitness value as the follower wolf x β , Follow the Wolf x β Represents suboptimal wheel motor power distribution.

[0076] Step S404, select common wolf x δ , select the gray wolf individual with the third highest fitness value as the common wolf x δ , Common Wolf x δ Represents the third best in-wheel motor power distribution.

[0077] By selecting x α 、x β 、x δ Wolf, find the three best performing gray wolf individuals in the gray wolf population, and provide a reference for subsequent iterative optimization, so that in the algorithm iteration process, other gray wolf individuals will be based on the leader wolf x α 、Follow the wolf x β and ordinary wolf x δ The position information is used to update the position and gradually approach the optimal solution.

[0078] Step S50, based on the three optimal fitness values, the gray wolf position is updated using a second calculation formula to obtain the target braking torque distribution of the target wheel after iteration, and the second calculation formula is:

[0079]

[0080] Among them, A is the convergence coefficient, which decreases with the number of iterations. 1 is a random number, random number r 1 The range is [0, 1], and t is the number of iterations.

[0081] In the embodiment of the present invention, the random number r 1 It is used to control the randomness of the gray wolf optimization algorithm to prevent the gray wolf optimization algorithm from falling into a local optimal solution. The convergence coefficient A and the random number r in the second calculation formula 1 It works together with the number of iterations t to control the position update direction and step size of the individual gray wolf, thereby guiding the gray wolf optimization algorithm to gradually approach the optimal solution.

[0082] Step S60, using t+1 to calculate the number of iterations of the grey wolf optimization algorithm.

[0083] Based on the Grey Wolf optimization algorithm, the braking force distribution of each group of wheel hub motors is optimized as the basis for maximizing the energy recovery efficiency. The Grey Wolf optimization algorithm uses the braking force distribution of each wheel hub motor as an optimization variable, and through iterative optimization, ultimately obtains the optimal solution for the braking force distribution of the wheel hub motors, thereby improving the energy recovery efficiency.

[0084] Based on the high back electromotive force and the low back electromotive force, the Grey Wolf optimization algorithm is used to regulate the braking force distribution of the wheel hub drive motor to obtain the optimal target braking torque of the target wheel, thereby distributing the braking force to the target wheel while outputting the primary energy recovery efficiency.

[0085] Execute step S300, calculate and obtain the target energy recovery efficiency based on the primary energy recovery efficiency, the back EMF signal, and the motor grouping signal, and recover the back EMF of the hub drive motor, wherein the back EMF of the high back EMF group is stored in a power battery pack, and the back EMF of the low back EMF group is stored in a supercapacitor.

[0086] The present invention uses the supercapacitor to store low back electromotive force. The supercapacitor can quickly absorb and release energy, reducing energy loss during transmission, and avoiding the waste of energy caused by the low back electromotive force being unable to be recovered to the energy storage system due to being lower than the power battery pack. The flexibility of energy management is improved, energy waste is reduced, and the overall efficiency of energy recovery is improved, which has a direct positive impact on improving the cruising range and economy of electric vehicles.

[0087] By forming an energy storage system with the power battery pack and the supercapacitor and utilizing the high power characteristics of the supercapacitor and the high energy characteristics of the power battery pack, the purpose of recovering both high back electromotive force and low back electromotive force during vehicle braking is achieved, thereby improving the energy utilization rate of electric vehicles.

[0088] In the embodiment of the present invention, during the next braking process, the energy stored in the supercapacitor is preferentially output to the motor controller through the reverse charging circuit for braking the hub motor.

[0089] In an embodiment of the present invention, the step of obtaining the target energy recovery efficiency includes:

[0090] Obtaining primary energy recovery efficiency;

[0091] Acquire energy storage parameters of the power battery pack and energy storage parameters of the supercapacitor, and determine charging efficiency thresholds of the power battery pack and the supercapacitor;

[0092] The target energy recovery efficiency is determined based on the output energy recovery efficiency and charging efficiency thresholds of the power battery pack and the supercapacitor.

[0093] In an embodiment of the present invention, the step of determining the charging efficiency thresholds of the power battery pack and the supercapacitor includes:

[0094] Step S3011, determining the remaining power of the power battery pack and the supercapacitor according to the current states of the power battery pack and the supercapacitor.

[0095] Step S3012: determining charging current thresholds of the power battery pack and the supercapacitor according to the remaining capacities of the power battery pack and the supercapacitor.

[0096] When the energy storage system of the electric vehicle is fully charged, the charging current threshold of the electric vehicle is set to 0 to ensure that the energy storage system of the electric vehicle is no longer charged when the energy storage system is fully charged, thereby ensuring the stability of the energy storage system of the electric vehicle; when the vehicle battery is not fully charged, the corresponding charging current threshold is set according to the remaining power of the energy storage system to charge the energy storage system of the electric vehicle, wherein the lower the remaining power of the energy storage system, the more power the vehicle energy storage system can be charged with, and the charging current threshold is increased at this time; conversely, the higher the remaining power of the energy storage system, the less power the vehicle energy storage system can be charged with, and the charging current threshold is reduced at this time.

[0097] Step S3013: determining a charging efficiency threshold of the power battery pack and the supercapacitor according to the remaining power of the power battery pack and the supercapacitor and the charging current threshold of the power battery pack and the supercapacitor.

[0098] The embodiment of the present invention also provides an energy recovery device, such as Figure 2 As shown, including:

[0099] Energy detection module 100, energy balance module 200, energy recovery module 300;

[0100] The energy detection module 100 is used to detect the back electromotive force of the wheel hub drive motor, set the wheel hub drive motor whose back electromotive force is higher than the motor back electromotive force signal threshold to a high back electromotive force group, set the wheel hub drive motor whose back electromotive force is lower than the motor back electromotive force signal threshold to a low back electromotive force group, and output a back electromotive force signal and a motor grouping signal;

[0101] The energy balance module 200 is used to obtain the current braking torque of the target wheel, the back electromotive force signal, and the motor grouping signal, calculate and obtain the target braking torque of the target wheel, distribute the target wheel motor force according to the target braking torque of the target wheel, and output the primary energy recovery efficiency. The target wheel motor force is used to regulate the wheel hub drive motor to brake the vehicle, and the primary energy recovery efficiency is used to regulate the recovery of the back electromotive force of the target wheel;

[0102] The energy recovery module 300 is used to calculate and obtain the target energy recovery efficiency based on the primary energy recovery efficiency, the back electromotive force signal, and the motor grouping signal, and to recover the back electromotive force of the hub drive motor, wherein the back electromotive force of the high back electromotive force group is stored in a power battery pack, and the back electromotive force of the low back electromotive force group is stored in a supercapacitor.

[0103] An embodiment of the present invention further provides an energy recovery device, comprising: a memory, a processor, and an energy recovery program stored in the memory and executable on the processor, wherein the energy recovery program is configured to implement the steps of the above-mentioned energy recovery method.

[0104] An embodiment of the present invention further provides a storage medium, comprising: an energy recovery program is stored on the storage medium, and the energy recovery program implements the above-mentioned energy recovery method when executed by a processor.

[0105] Finally, it is to be noted that any modification or equivalent replacement of part or all of the technical features based on the device structure of the present invention and the technical solution of the described embodiment, which does not deviate from the essence of the corresponding technical solution of the present invention, belongs to the patent scope of the device structure of the present invention and the described implementation scheme.

Claims

1. An energy recovery method, It is characterized in that include: Detecting the back electromotive force of the wheel hub drive motor, setting the wheel hub drive motor whose back electromotive force is higher than the motor back electromotive force signal threshold as a high back electromotive force group, setting the wheel hub drive motor whose back electromotive force is lower than the motor back electromotive force signal threshold as a low back electromotive force group, and outputting the back electromotive force signal and the motor grouping signal; The current braking torque of the target wheel, the back electromotive force signal, and the motor grouping signal are obtained, and the target braking torque of the target wheel is calculated and obtained, and the target wheel motor force distribution and primary energy recovery efficiency are output according to the target braking torque of the target wheel. The target wheel motor force is used to regulate the wheel hub drive motor to brake the vehicle, and the primary energy recovery efficiency is used to regulate the recovery of the back electromotive force of the target wheel; Based on the current braking torque of the target wheel, the back electromotive force signal, and the motor grouping signal, the step of calculating and obtaining the target braking torque of the target wheel includes: obtaining the back electromotive force signal, determining whether the back electromotive force signal is equal to the target function voltage value, and if so, outputting the braking torque corresponding to the target function voltage value as the target braking torque of the target wheel; if not, calculating based on the gray wolf optimization algorithm until the number of iterations is reached to obtain the target braking torque of the target wheel; the step of calculating and obtaining the target braking torque of the target wheel based on the gray wolf optimization algorithm includes: Step S10, initialize the gray wolf population X∈R G×D , set the maximum number of iterations based on the gray wolf optimization algorithm to 1 max , the maximum braking torque of each wheel hub drive motor is T max The number of iterations based on the gray wolf optimization algorithm is I t And the initial state t is 1, where X is a gray wolf individual, G is the population size, D is the number of gray wolf individuals, G=D, the population size is equal to the number of gray wolves equal to the number of wheel hub drive motors, and R is a natural number; Step S20, determining whether the number of iterations based on the gray wolf optimization algorithm is greater than or equal to the maximum number of iterations based on the gray wolf optimization algorithm, if satisfied, outputting the target braking torque of the target wheel after iteration as the target braking torque of the target wheel, if not satisfied, executing step S30; Step S30, using a first calculation formula to calculate the fitness to obtain the fitness value of each individual gray wolf, wherein the first calculation formula is: Among them, X i is the power distribution of the i-th gray wolf individual in the wheel hub drive motor, η j is the weight coefficient of the energy recovery efficiency of the high back-EMF group motor, η j ' is the weight coefficient of the energy recovery efficiency of the low back-EMF group motor, T ij is the braking torque assigned by the i-th gray wolf individual to the j-th motor, H is the high back-EMF group, L is the low back-EMF group, f(x i ) is the wheel drive motor force distribution function, i≤D, T ij ≤T max , i is the number of the individual gray wolf, j is the number of the wheel hub drive motor, and both i and j are natural numbers greater than 1; Step S40, based on the obtained fitness value of each individual gray wolf, select the three best fitness values ​​as the leader wolf, follower wolf, and common wolf of the gray wolf optimization algorithm, where x α To lead the wolf, x β To follow the wolf, x δ For common wolf; Step S50, based on the three optimal fitness values, the gray wolf position is updated using a second calculation formula to obtain the target braking torque distribution of the target wheel after iteration, and the second calculation formula is: Among them, A is the convergence coefficient, which decreases with the number of iterations. 1 is a random number, random number r 1 The range of is [0, 1], and t is the number of iterations; Step S60, using t+1 to calculate the number of iterations of the gray wolf optimization algorithm Based on the primary energy recovery efficiency, the back electromotive force signal, and the motor grouping signal, a target energy recovery efficiency is calculated to recover the back electromotive force of the wheel hub drive motor, wherein the back electromotive force of the high back electromotive force group is stored in a power battery pack, and the back electromotive force of the low back electromotive force group is stored in a supercapacitor; the step of obtaining the target energy recovery efficiency comprises: obtaining the primary energy recovery efficiency; obtaining the energy storage parameters of the power battery pack and the energy storage parameters of the supercapacitor, and determining the charging efficiency thresholds of the power battery pack and the supercapacitor; based on the primary energy recovery efficiency, the power battery pack and the charging efficiency threshold of the supercapacitor are determined; the step of determining the charging efficiency threshold of the power battery pack and the supercapacitor comprises: determining the remaining power of the power battery pack and the supercapacitor according to the current states of the power battery pack and the supercapacitor; determining the charging current threshold of the power battery pack and the supercapacitor according to the remaining power of the power battery pack and the supercapacitor; determining the charging efficiency threshold of the power battery pack and the supercapacitor by the remaining power of the power battery pack and the supercapacitor and the charging current threshold of the power battery pack and the supercapacitor.

2. An energy recovery method as claimed in claim 1, It is characterized in that A back electromotive force detection circuit is used to detect the back electromotive force of the wheel hub drive motor and the back electromotive force is set to a high back electromotive force group or a low back electromotive force group.

3. An energy recovery method as claimed in claim 2, It is characterized in that When there are a plurality of wheel hub drive motors, each of the wheel hub drive motors is provided with a back electromotive force detection circuit.

4. An energy recovery device, It is characterized in that Applying the energy recovery method as claimed in claim 1, the energy recovery device comprises: Energy detection module, energy balance module, energy recovery module; The energy detection module is used to detect the back electromotive force of the wheel hub drive motor, set the wheel hub drive motor whose back electromotive force is higher than the motor back electromotive force signal threshold to a high back electromotive force group, set the wheel hub drive motor whose back electromotive force is lower than the motor back electromotive force signal threshold to a low back electromotive force group, and output a back electromotive force signal and a motor grouping signal; The energy balance module is used to obtain the current braking torque of the target wheel, the back electromotive force signal, and the motor grouping signal, calculate and obtain the target braking torque of the target wheel, distribute the target wheel motor force according to the target braking torque of the target wheel, and output the primary energy recovery efficiency. The target wheel motor force is used to regulate the wheel hub drive motor to brake the vehicle, and the primary energy recovery efficiency is used to regulate the recovery of the back electromotive force of the target wheel; The energy recovery module is used to calculate and obtain the target energy recovery efficiency based on the primary energy recovery efficiency, the back electromotive force signal, and the motor grouping signal, and to recover the back electromotive force of the hub drive motor, wherein the back electromotive force of the high back electromotive force group is stored in a power battery pack, and the back electromotive force of the low back electromotive force group is stored in a supercapacitor.

5. An energy recovery device, It is characterized in that include: A memory, a processor, and an energy recovery program stored in the memory and executable on the processor, wherein the energy recovery program is configured to implement the steps of the energy recovery method according to any one of claims 1 to 3.

6. A storage medium, It is characterized in that include: An energy recovery program is stored on the storage medium, and when the energy recovery program is executed by the processor, the energy recovery method according to any one of claims 1 to 3 is implemented.

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