Battery energy management method and device of hybrid electric vehicle and storage medium

Abstract

The invention relates to the technical field of automobile energy control, in particular to a battery energy management method and device of a hybrid electric vehicle and a storage medium. The method comprises the steps that battery parameters and vehicle operation data of the hybrid electric vehicle are collected in real time, and the operation route of the hybrid electric vehicle and historical operation data in the operation route are collected; the method comprises the following steps: acquiring a power buffer coefficient of a hybrid electric vehicle, setting a running route feature map based on an acquisition result, determining a driving state of the hybrid electric vehicle according to the running route feature map and battery parameters of the hybrid electric vehicle acquired in real time, acquiring a real-time load variation of the hybrid electric vehicle, and setting the power buffer coefficient of the hybrid electric vehicle according to the real-time load variation. And managing the inter-wheel power and the motor driving weight of the hybrid electric vehicle based on the driving state and the power buffering coefficient of the hybrid electric vehicle. The battery control efficiency of the hybrid electric vehicle is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automotive energy control, and particularly to a battery energy management method, device, and storage medium for a hybrid vehicle. Background Art

[0002] With the increasing global awareness of environmental protection and the intensification of the energy crisis, hybrid vehicles have received extensive attention because they can not only reduce emissions but also improve fuel efficiency. However, how to effectively manage the energy usage of the battery has become one of the key factors affecting its performance.

[0003] In the prior art, the energy management of hybrid vehicles needs to balance the battery output and power demand, but the prior art has deficiencies such as poor dynamic adaptability, limited data utilization, and neglect of environmental factors. Summary of the Invention

[0004] The purpose of the present invention is to provide a battery energy management method, device, and storage medium for a hybrid vehicle to solve at least one of the problems existing in the prior art.

[0005] To achieve the above object, according to one aspect of the present application, the present invention provides a battery energy management method for a hybrid vehicle, including: Real-time collecting battery parameters and vehicle operation data of the hybrid vehicle; Collecting the operation route of the hybrid vehicle and historical operation data within the operation route, and setting an operation route feature map based on the collection results; Determining the driving state of the hybrid vehicle according to the operation route feature map and the battery parameters of the hybrid vehicle collected in real time; Obtaining the real-time load change amount of the hybrid vehicle, and setting a power buffer coefficient for the hybrid vehicle according to the real-time load change amount; Managing the inter-wheel power and motor drive weight of the hybrid vehicle based on the driving state and power buffer coefficient of the hybrid vehicle.

[0006] Optionally, setting an acceleration feature index a(i) for each different road, and setting a drive feature index for each different road based on the acceleration feature index a(i) of each different road and the average braking frequency of each different road: when f(i) is less than or equal to a preset braking frequency, setting the drive feature index of the i-th different road as a(i); otherwise, setting the drive feature index of the i-th different road as [a(i) - fv(i) / t(i)]; In the formula, f(i) represents the average braking frequency of the i-th different road, fv(i) represents the average braking loss speed of the i-th different road, and t(i) represents the average passing time of the hybrid vehicle on the i-th different road; Integrate the driving characteristic indexes of each differentiated road into the driving route, and use the integration result as the driving route feature map.

[0007] Optionally, determine the differentiated road on which the current hybrid vehicle is traveling, and extract the driving characteristic indexes corresponding to the differentiated road in the driving route feature map; Based on the extraction result and combined with the vehicle operation data of the hybrid vehicle, judge the driving state of the hybrid vehicle in real time. According to the magnitude relationship between {[α(i) - sv] / sf} and the first preset state judgment index K1 and the second preset state judgment index K2, judge the driving state of the hybrid vehicle as three judgment results: stable driving, normal driving, and frequent start-stop driving; Wherein, α(i) represents the driving characteristic index of the i-th differentiated road, sv represents the average acceleration of the hybrid vehicle within the current differentiated road, and sf represents the braking frequency of the hybrid vehicle within the current differentiated road.

[0008] Optionally, it further includes a driving state update method for the hybrid vehicle, including: Construct a road driving difficulty level based on the collected road surface friction coefficient, road slope, and curve radius, and then update the driving state according to the road driving difficulty level; Judge the battery output state of the hybrid vehicle based on the real-time collected battery temperature, construct a correlation function of battery drive with respect to the inter-wheel torque, and then adjust the construction process of the road driving difficulty level.

[0009] Optionally, calculate the road driving pressure index β based on the real-time collected road surface friction coefficient, road slope, and curve radius; Set each preset interval of the road driving pressure index to judge the road driving difficulty level: when the road driving pressure index belongs to the first preset interval, set the road driving level to level one. At this time, set the third preset state judgment index K3 to set the driving state when {[α(i) - sv] / sf} is greater than or equal to K1 and less than K3 as power-balanced driving; when the road driving pressure index belongs to the second preset interval, set the road driving level to level two. At this time, update the second preset state judgment index to [(β / right value of the third preset interval) - 1] times the original second state index; when the road driving pressure index belongs to the third preset interval, set the road driving level to level three. At this time, do not update the driving state.

[0010] Optionally, a temperature threshold is set for the battery temperature to determine whether the battery temperature meets the battery operating temperature range. When it does not meet the battery operating temperature range, a correlation function between the battery driving force and the torque between wheels is set: F = η×|△P|×G. In this function, F represents the torque between wheels, G represents the motor driving torque, △P represents the temperature value by which the battery temperature exceeds or is lower than the battery operating temperature range, and η represents the temperature decay correlation number; The temperature decay correlation number at the current time is obtained according to the correlation function between the battery driving force and the torque between wheels, and the product of the left value of the first preset interval and the temperature decay correlation number is used as the adjusted left value of the first preset interval.

[0011] Optionally, the real-time load change of the hybrid vehicle is set as △T, and the power buffer coefficients in different driving states are set according to the real-time load change: when the driving state is steady driving, the power buffer coefficient is set as w1; when the driving state is normal driving, the power buffer coefficient is set as w2; when the driving state is power balanced driving, the power buffer coefficient is not set; when the driving state is frequent start and stop, the power buffer coefficient is set as w3.

[0012] Optionally, the wheel power of the hybrid vehicle after the current time is set according to the driving state and the power buffer coefficient of the hybrid vehicle: when the driving state is steady driving, the wheel power is set as {w×w1}; when the driving state is normal driving, the wheel power is set as {w×w2}; when the driving state is power balanced driving, the wheel power is set as {w}; when the driving state is frequent start and stop, the wheel power is set as {w×w3}; where w is the user demand power; The drive weight of the hybrid vehicle is set according to the driving state of the hybrid vehicle: when the driving state is steady driving, the motor drive weight is set as 0; when the driving state is normal driving, the motor drive weight is set as 1 / 2×exp{-β}; when the driving state is power balanced driving, the motor drive weight is set as exp{-β}; when the driving state is frequent start and stop, the motor drive weight is set as 1.

[0013] According to another aspect of the present application, a battery energy management device for a hybrid vehicle is provided, including: An automotive data acquisition unit, which is used for: real-time collecting battery parameters and vehicle operation data of the hybrid vehicle; A route feature extraction unit, which is used for: collecting the running route of the hybrid vehicle and the historical operation data within the running route, and setting a running route feature map based on the collection results; A driving state judgment unit, which is used for: determining the driving state of the hybrid vehicle according to the running route feature map and the battery parameters of the hybrid vehicle collected in real time; A power buffer analysis unit, which is used to: obtain the real-time load change of a hybrid vehicle, and set the power buffer coefficient of the hybrid vehicle according to the real-time load change; A battery energy management unit, which is used to: manage the inter-wheel power and motor drive weight of a hybrid vehicle based on the driving state and power buffer coefficient of the hybrid vehicle.

[0014] According to another aspect of the present application, there is provided a computer-readable storage medium storing a computer program, wherein the computer program is used to control an electronic device where the computer-readable storage medium is located to execute the battery energy management method of the hybrid vehicle when running.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: By multi-dimensional data collection and dynamic analysis, an accurate mapping between the driving state and road conditions is constructed, and the battery output strategy is optimized in real time in combination with variables such as load and temperature. The present invention takes into account both energy consumption efficiency and driving experience, significantly improves the battery control accuracy and comprehensive performance of hybrid vehicles, extends the battery life and reduces the operating cost. Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] Figure 1 It is a flowchart of the battery energy management method for the hybrid vehicle in this embodiment.

[0018] Figure 2 It is a flowchart of the method for setting the running route feature map in this embodiment.

[0019] Figure 3 It is a flowchart of the driving state update method for the hybrid vehicle in this embodiment.

[0020] Figure 4 It is a schematic structural diagram of the electronic device provided in this embodiment.

[0021] Figure 5 It is a schematic structural diagram of the battery energy management device for the hybrid vehicle provided in this embodiment. Detailed Embodiments

[0022] To more clearly illustrate the present invention, the present invention will be further described below in conjunction with preferred embodiments and the accompanying drawings. Similar components in the drawings are denoted by the same reference numerals. Those skilled in the art should understand that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the protection scope of the present invention.

[0023] It should be noted that although terms such as first, second, and third may be used in the embodiments of the present application for description, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0024] In the technical solution of the present application, the acquisition, storage, use, processing, etc. of data all comply with the relevant regulations of national laws and regulations.

[0025] Specifically, the battery energy management method of the hybrid vehicle provided in the present application is applied to the energy management of the power battery in the hybrid vehicle; the working mode of the hybrid vehicle in the present application is a hybrid vehicle in a parallel urban bus system; at the same time, the hybrid vehicle in the urban bus system described in the present application is specifically for cities in high-latitude regions.

[0026] Based on the above application scenarios, please refer to Figure 1 As shown, it is a schematic flowchart of the battery energy management method of the hybrid vehicle described in the present application, including: Step S101, collect the battery parameters and vehicle operation data of the hybrid vehicle in real time; Specifically, the battery parameters include battery temperature and air pressure inside the battery pack; the vehicle operation data includes: road surface friction coefficient, road slope, curve radius, load weight, inter-wheel torque, motor drive torque, braking frequency, braking loss speed, and acceleration.

[0027] Exemplarily, the present application does not specifically limit the acquisition method of the battery parameters and vehicle operation data of the hybrid vehicle. Those skilled in the art can freely set it as long as the acquisition requirements of the battery parameters and the vehicle operation data are met. In the present application, the battery parameters can be collected according to temperature sensors and pressure sensors, and the road slope and curve radius can be collected according to a level device and an in-vehicle camera; the remaining vehicle operation data can be obtained according to intelligent sensors.

[0028] Please continue to refer to Figure 1 As shown, the battery energy management method of the hybrid vehicle further includes: Step S102: Collect the running route of the hybrid vehicle and the historical running data within the running route, and set a running route feature map based on the collection results. By generating a feature map through the running route and historical data, predict the driving requirements of different sections, optimize the energy distribution strategy, reduce ineffective energy consumption, and extend the battery life.

[0029] Specifically, the running route of the hybrid vehicle is a fixed traffic route that the hybrid vehicle has as a bus. In this application, it is collected in the form of traffic map data. At the same time, the specific meaning of the historical running data within the running route in this application is as follows: taking the stopping points of the hybrid vehicle in the running route as division nodes, dividing the running route into multiple different roads, and using the speed and braking frequency of the hybrid vehicle in each different road as the historical running data of each different road. In this application, for each stopping point of the hybrid vehicle, there is only one preceding stopping point and one following stopping point of the hybrid vehicle.

[0030] To implement the above setting of the running route feature map, please refer to Figure 2 shown in the following figure, which is a schematic flow chart of the running route feature map setting method provided by this application, including: Step S201: Collect the running route of the hybrid vehicle and the historical running data within the running route. Exemplarily, in this application, the urban traffic network is used as the data source to collect the running route of the hybrid vehicle and the historical running data within the running route. Step S202: According to the collected historical running data of the hybrid vehicle, statistically calculate the average speed and average braking frequency of each different road.

[0031] Specifically, the historical running data of each different road in this application includes multiple groups of data, and the average speed and average braking frequency of each different road described in this application are the averages of the above multiple groups of data. Statistically calculate the average speed and braking frequency of the section, quantify the section characteristics, provide key parameters for calculating the driving feature index, and improve the scientificity of energy distribution.

[0032] Please continue to refer to Figure 2 shown in the following figure, the running route feature map setting method further includes: Step S203: Set the running route feature map according to the average speed and average braking frequency of each different road.

[0033] Specifically, the process of setting the running route feature map in this application is as follows: Set the acceleration feature index a(i) of each different road, and set: a(i) = [pv(i) - pv(i + 1)] / t(i); where i is a numerical identifier, specifically representing the i-th distinct road, pv(i) represents the average speed of the i-th distinct road, pv(i + 1) represents the (i + 1)-th distinct road, and t(i) represents the average passing duration of the hybrid vehicle on the i-th distinct road; Set the driving characteristic index of each distinct road based on the acceleration characteristic index a(i) of each distinct road and the average braking frequency of each distinct road: when f(i) is less than or equal to the preset braking frequency, set the driving characteristic index of the i-th distinct road to a(i); otherwise, set the driving characteristic index of the i-th distinct road to [a(i) - fv(i) / t(i)]; where f(i) represents the average braking frequency of the i-th distinct road, and fv(i) represents the average braking loss speed of the i-th distinct road; Integrate the driving characteristic index of each distinct road into the running route, and use the integration result as the running route characteristic map.

[0034] Exemplarily, the process of "integrating the driving characteristic index of each distinct road into the running route" in this application can be understood as integrating the driving characteristic index as the weight of each distinct road; at the same time, in this application, a preset braking frequency is used as the minimum braking frequency for normal driving to filter the interference of ordinary data. The setting value of the preset braking frequency in this application is specifically set according to the specific usage scenario. The method provided in this application is to set the preset braking frequency to min{f(i)}.

[0035] It should be noted that the running route in this application is a connected curve. Therefore, when i is the last distinct road, set i + 1 to 1 to make the logic of this method smooth.

[0036] Specifically, generate a route characteristic map by integrating the driving characteristic index, dynamically map the energy consumption requirements of road sections, assist the system in predicting driving scenarios, and optimize the response speed of energy scheduling.

[0037] Please continue to refer to Figure 1 As shown, the battery energy management method of the hybrid vehicle further includes: Step S103, determine the driving state of the hybrid vehicle based on the running route characteristic map and the battery parameters of the hybrid vehicle collected in real time.

[0038] Specifically, the process of determining the driving state of the hybrid vehicle in step S103 is as follows: Determine the distinct road on which the hybrid vehicle is currently traveling, and extract the driving characteristic index of the corresponding distinct road in the running route characteristic map; Based on the extraction results and combined with the vehicle operation data of the hybrid vehicle, the driving state of the hybrid vehicle is judged in real time: if {[α(i) - sv] / sf} is less than K1, it is judged that the driving state of the hybrid vehicle is stable driving; if {[α(i) - sv] / sf} is greater than or equal to K1 and less than K2, it is judged that the driving state of the hybrid vehicle is normal driving; if {[α(i) - sv] / sf} is greater than or equal to K2, it is judged that the driving state of the hybrid vehicle is frequent start-stop driving. Wherein, α(i) represents the driving characteristic index of the i-th differentiated road, sv represents the average acceleration of the hybrid vehicle within the current differentiated road, sf represents the braking frequency of the hybrid vehicle within the current differentiated road, K1 is the first preset state judgment index, K2 is the second preset state judgment index, and K1 < K2.

[0039] Exemplarily, the present application does not specifically limit the values of the first preset state judgment index and the second preset state judgment index. Those skilled in the art can freely set them as long as the value requirements of the first preset state judgment index and the second preset state judgment index are met. In the present application, the optimal value of the first preset state index is 0.05, and the optimal value of the second preset state index is 0.1.

[0040] In order to make the driving state of the hybrid vehicle in the present application more accurate, please refer to Figure 3 As shown, it is a schematic flowchart of the driving state update method of the hybrid vehicle in the present application, including: Step S301, construct a road driving difficulty level based on the collected road surface friction coefficient, road slope, and curve radius, and then update the driving state according to the road driving difficulty level.

[0041] Specifically, the process of constructing the road driving difficulty level is as follows: Calculate the road driving pressure index β based on the real-time collected road surface friction coefficient, road slope, and curve radius. Set β = b1×μ / μdry + b2×Rmin / Rc + b3×θ / θmax; where μ represents the road surface friction coefficient, θ represents the road slope, Rc represents the curve radius, μdry represents the friction coefficient of the dry road surface, θmax represents the road slope threshold, Rmin represents the minimum turning radius, and b1, b2, and b3 respectively represent the friction weight, curve weight, and slope weight, and b1 + b2 + b3 = 1; Set preset intervals for the road driving pressure index to determine the road driving difficulty level: When the road driving pressure index belongs to the first preset interval, set the road driving level to level one. At this time, set the third preset state judgment index K3 to set the driving state when {[α(i)-sv] / sf} is greater than or equal to K1 and less than K3 as a balanced power driving state; When the road driving pressure index belongs to the second preset interval, set the road driving level to level two. At this time, update the second preset state judgment index to [(β / right value of the third preset interval)-1] times the original second state index; When the road driving pressure index belongs to the third preset interval, set the road driving level to level three. At this time, do not update the driving state.

[0042] It should be noted that the value range of the third preset state judgment index K3 in this application should be: K1 < K3 < K2; At the same time, the optimal value of the third preset state judgment index K3 in this application is 0.07; Construct the driving difficulty level based on the road condition parameters, dynamically correct the driving state judgment threshold, enhance the adaptability under complex road conditions, and ensure the rationality of battery output.

[0043] Specifically, in this application, no specific limitations are set for the boundaries of the first preset interval, the second preset interval, and the third preset interval. Those skilled in the art can freely set them as long as they meet the value requirements of the boundaries of each preset interval. In this application, the first preset interval is set as [0.85, 1], the second preset interval is set as [0.6, 0.85), and the third preset interval is set as [0, 0.6); At the same time, it can be understood that in this application, level one, level two, and level three respectively represent high, medium, and low road driving difficulties; In this application, b1 = 0.3, b2 = 0.3, and b3 = 0.4 are set.

[0044] Please continue to refer to Figure 3 As shown, the driving state update method of the hybrid vehicle further includes: Step S302, based on the battery temperature collected in real time, judge the battery output state of the hybrid vehicle to construct an association function of battery drive with respect to the inter-wheel torque, and then adjust the construction process of the road driving difficulty level.

[0045] Specifically, the adjustment process of the road difficulty level is as follows: Set a temperature threshold for the battery temperature to judge whether the battery temperature meets the battery operating temperature range, and when it does not meet the battery operating temperature range, set the association function of battery driving force with respect to the inter-wheel torque: F = η×|△P|×G. In this function, F represents the inter-wheel torque, G represents the motor driving torque, △P represents the temperature value of the battery temperature exceeding or falling below the battery operating temperature range, and η represents the temperature decay correlation number; Obtain the temperature decay correlation number at the current time based on the correlation function between the battery driving force and the torque between wheels, and use the product of the left value of the first preset interval and the temperature decay correlation number as the adjusted left value of the first preset interval; combine the battery temperature adjustment correlation function to compensate for the influence of temperature on torque in real time, avoid overheating of the battery or performance decay at low temperature, and ensure the stability of power output.

[0046] Exemplarily, in the correlation function between the battery driving force and the torque between wheels described in the present application, η is the dependent variable, and the torque between wheels, the motor driving torque, and △P are all obtainable independent variables.

[0047] Please continue to refer to Figure 1 As shown, the battery energy management method of the hybrid vehicle further includes: Step S104, obtain the real-time load change amount of the hybrid vehicle, and set the power buffer coefficient of the hybrid vehicle according to the real-time load change amount.

[0048] Specifically, the process of setting the power buffer coefficient interval of the hybrid vehicle in the step S104 is as follows: Set the real-time load change amount of the hybrid vehicle as △T, and set the power buffer coefficients in different driving states according to the real-time load change amount: when the driving state is steady driving, set the power buffer coefficient as w1, and set w1 = [1-(△T / T)]; when the driving state is normal driving, set the power buffer coefficient as w2, and set w2 = c1×[1-(△T / T)]; when the driving state is power balanced driving, do not set the power buffer coefficient; when the driving state is frequent start and stop, set the power buffer coefficient as w3, and set w3 = exp{△T / T}; where T is the load weight of the hybrid vehicle, and c1 is the comfort offset coefficient.

[0049] Specifically, in the present application, the real-time load change amount is the load of the hybrid vehicle at the previous time point minus the load of the hybrid vehicle at the current time point; at the same time, the present application does not specifically limit the value of the comfort offset coefficient c1, and those skilled in the art can freely set it as long as it meets the value requirements of the comfort offset coefficient. In the present application, the best value of c1 is set to 1.15.

[0050] Specifically, set the power buffer coefficient based on the real-time load change, balance the power output and energy consumption, avoid battery overload caused by sudden load changes, and enhance the stability and safety of the system.

[0051] Please continue to refer to Figure 1 As shown, the battery energy management method of the hybrid vehicle further includes: Step S105, manage the wheel power and motor driving weight of the hybrid vehicle based on the driving state and power buffer coefficient of the hybrid vehicle.

[0052] Specifically, the process of managing the inter-wheel power and motor drive weight of the hybrid vehicle in step S105 of the present application is as follows: Set the inter-wheel power of the hybrid vehicle after the current time based on the driving state and power buffer coefficient of the hybrid vehicle: when the driving state is smooth driving, set the inter-wheel power to {w×w1}; when the driving state is normal driving, set the inter-wheel power to {w×w2}; when the driving state is power-balanced driving, set the inter-wheel power to {w}; when the driving state is frequent start-stop, set the inter-wheel power to {w×w3}; where w is the user demand power; Set the drive weight of the hybrid vehicle based on the driving state of the hybrid vehicle: when the driving state is smooth driving, set the motor drive weight to 0; when the driving state is normal driving, set the motor drive weight to 1 / 2×exp{-β}; when the driving state is power-balanced driving, set the motor drive weight to exp{-β}; when the driving state is frequent start-stop, set the motor drive weight to 1.

[0053] Exemplarily, the drive weight of the hybrid vehicle in the present application includes the motor drive weight and the internal combustion engine drive weight, and the motor drive weight + the internal combustion engine drive weight = 1; the setting process of the internal combustion engine drive weight is abbreviated in the present application.

[0054] Specifically, manage the battery output weight according to the driving state and buffer coefficient to achieve the coordinated control of the motor and the engine, reduce redundant energy consumption, and improve energy utilization efficiency.

[0055] To implement the functions of the battery energy management method of the hybrid vehicle in a computer, the present application also provides a computer-readable storage medium, which is disposed in an electronic device. Please refer to Figure 4 As shown, the electronic device includes: A processor 1, a memory 2, a communication interface 3, and a bus 4; wherein, data transmission is performed between the processor 1 and the memory 2, and between the memory 2 and the communication interface 3 through the bus 4; the processor is used to process the data in the memory and generate commands, the memory is used for data storage, the communication interface is used to receive and send data, and the bus is used to realize data transmission between the processor, the memory, and the communication interface.

[0056] In this application, the computer-readable storage medium is a tangible physical storage medium, which can store the above-mentioned computer program and various types of data used in the program; the physical storage medium includes, but is not limited to, existing physical storage media or combinations of media such as random access memory, read-only memory, optical discs, hard disks, etc.; at the same time, the battery energy management method of the hybrid vehicle can be implemented as a runnable computer program. When the computer program is loaded into the processor and when the computer program is loaded into the memory and processed by the processor through the bus, one or more steps of the battery energy management method of the hybrid vehicle can be executed.

[0057] Please refer to Figure 5, which is a schematic structural diagram of a battery energy management method for a hybrid vehicle provided by this application, including: An automotive data acquisition unit, which is used for: real-time acquisition of battery parameters and vehicle operation data of the hybrid vehicle; A route feature extraction unit, which is used for: collecting the running route of the hybrid vehicle and historical operation data within the running route, and setting a running route feature map based on the collection results; A driving state determination unit, which is used for: determining the driving state of the hybrid vehicle based on the running route feature map and the battery parameters of the hybrid vehicle collected in real time; A power buffer analysis unit, which is used for: obtaining the real-time load change amount of the hybrid vehicle, and setting a power buffer coefficient for the hybrid vehicle according to the real-time load change amount; A battery energy management unit, which is used for: managing the inter-wheel power and motor drive weight of the hybrid vehicle based on the driving state and power buffer coefficient of the hybrid vehicle.

[0058] The battery energy management device of the hybrid vehicle provided by the embodiment of this application can execute the battery energy management method of the hybrid vehicle provided by any embodiment of this application, and has the corresponding functional modules and beneficial effects for executing the method.

[0059] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

Claims

1. A battery energy management method for a hybrid vehicle, characterized in that, Including: Real-time collecting battery parameters and vehicle operation data of a hybrid vehicle; Collecting the operation route of the hybrid vehicle and historical operation data within the operation route, and setting an operation route feature map based on the collection results; Determining the driving state of the hybrid vehicle according to the operation route feature map and the battery parameters of the hybrid vehicle collected in real time; Obtaining the real-time load change of the hybrid vehicle, and setting a power buffer coefficient for the hybrid vehicle according to the real-time load change; Managing the inter-wheel power and motor drive weight of the hybrid vehicle based on the driving state and power buffer coefficient of the hybrid vehicle.

2. The battery energy management method for a hybrid vehicle according to claim 1, characterized in that, Setting an acceleration feature index a(i) for each different road, and setting a driving feature index for each different road based on the acceleration feature index a(i) of each different road and the average braking frequency of each different road: when f(i) is less than or equal to a preset braking frequency, setting the driving feature index of the i-th different road as a(i); otherwise, setting the driving feature index of the i-th different road as [a(i) - fv(i) / t(i)]; In the formula, f(i) represents the average braking frequency of the i-th different road, fv(i) represents the average braking loss speed of the i-th different road, and t(i) represents the average passing time of the hybrid vehicle on the i-th different road; Integrating the driving feature indices of each different road into the operation route, and using the integration result as the operation route feature map.

3. The battery energy management method for a hybrid vehicle according to claim 2, characterized in that, Determining the different road on which the current hybrid vehicle is traveling, and extracting the driving feature index of the corresponding different road in the operation route feature map; According to the extraction result, and in combination with the vehicle operation data of the hybrid vehicle, judging the driving state of the hybrid vehicle in real time, and judging the driving state of the hybrid vehicle as three judgment results: smooth driving, normal driving, and frequent start-stop driving according to the magnitude relationship between {[α(i) - sv] / sf} and a first preset state judgment index K1 and a second preset state judgment index K2; Wherein, α(i) represents the driving feature index of the i-th different road, sv represents the average acceleration of the hybrid vehicle within the current different road, and sf represents the braking frequency of the hybrid vehicle within the current different road.

4. The battery energy management method for a hybrid vehicle according to claim 3, characterized in that, It also includes a driving state update method for the hybrid vehicle, including: Constructing a road driving difficulty level according to the collected road surface friction coefficient, road slope, and curve radius, and then updating the driving state according to the road driving difficulty level; Judging the battery output state of the hybrid vehicle based on the battery temperature collected in real time to construct a correlation function of battery drive with respect to inter-wheel torque, and then adjusting the construction process of the road driving difficulty level.

5. The battery energy management method for a hybrid vehicle according to claim 4, characterized in that, Calculating a road driving pressure index β according to the road surface friction coefficient, road slope, and curve radius collected in real time; Set preset intervals for the road driving pressure index to determine the road driving difficulty level: When the road driving pressure index belongs to the first preset interval, set the road driving level to level one. At this time, set the third preset state judgment index K3 to set the driving state when {[α(i)-sv] / sf} is greater than or equal to K1 and less than K3 as the power-balanced driving; When the road driving pressure index belongs to the second preset interval, set the road driving level to level two. At this time, update the second preset state judgment index to [(β / right value of the third preset interval)-1] times the original second state index; When the road driving pressure index belongs to the third preset interval, set the road driving level to level three. At this time, do not update the driving state.

6. The battery energy management method for a hybrid vehicle according to claim 5, characterized in that, Set a temperature threshold for the battery temperature to determine whether the battery temperature meets the battery operating temperature range, and when it does not meet the battery operating temperature range, set the correlation function between the battery driving force and the wheel torque: F = η×|△P|×G. In this function, F represents the wheel torque, G represents the motor driving torque, △P represents the temperature value by which the battery temperature exceeds or is lower than the battery operating temperature range, and η represents the temperature decay correlation number; Obtain the temperature decay correlation number at the current time based on the correlation function between the battery driving force and the wheel torque, and use the product of the left value of the first preset interval and the temperature decay correlation number as the adjusted left value of the first preset interval.

7. The battery energy management method for a hybrid vehicle according to claim 6, characterized in that, Set the real-time load change amount of the hybrid vehicle as △T, and set the power buffer coefficients in different driving states based on the real-time load change amount: When the driving state is smooth driving, set the power buffer coefficient as w1; When the driving state is normal driving, set the power buffer coefficient as w2; When the driving state is power-balanced driving, do not set the power buffer coefficient; When the driving state is frequent start and stop, set the power buffer coefficient as w3.

8. The battery energy management method for a hybrid vehicle according to claim 7, characterized in that, Set the wheel power of the hybrid vehicle after the current time based on the driving state and power buffer coefficient of the hybrid vehicle: When the driving state is smooth driving, set the wheel power as {w×w1}; When the driving state is normal driving, set the wheel power as {w×w2}; When the driving state is power-balanced driving, set the wheel power as {w}; When the driving state is frequent start and stop, set the wheel power as {w×w3}; where w is the user demand power; Set the driving weight of the hybrid vehicle based on the driving state of the hybrid vehicle: When the driving state is smooth driving, set the motor driving weight as 0; When the driving state is normal driving, set the motor driving weight as 1 / 2×exp{-β}; When the driving state is power-balanced driving, set the motor driving weight as exp{-β}; When the driving state is frequent start and stop, set the motor driving weight as 1.

9. A battery energy management device for a hybrid vehicle, characterized in that, Include: An automotive data acquisition unit, which is used for: real-time acquisition of battery parameters and vehicle operation data of the hybrid vehicle; A route feature extraction unit, which is used for: collecting the running route of the hybrid vehicle and historical operation data within the running route, and setting a running route feature map based on the collection results; A driving state determination unit, which is configured to: determine the driving state of a hybrid vehicle based on a running route feature map and battery parameters of the hybrid vehicle collected in real time; A power buffer analysis unit, which is configured to: obtain the real-time load change amount of the hybrid vehicle and set the power buffer coefficient of the hybrid vehicle according to the real-time load change amount; A battery energy management unit, which is configured to: manage the inter-wheel power and motor drive weight of the hybrid vehicle based on the driving state and power buffer coefficient of the hybrid vehicle.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is used to control the electronic device where the computer-readable storage medium is located to execute the battery energy management method of the hybrid vehicle according to any one of claims 1-8 during operation.

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