CN116176557A - Energy management method and device for hybrid off-road vehicle and electronic equipment - Google Patents
Energy management method and device for hybrid off-road vehicle and electronic equipment Download PDFInfo
- Publication number
- CN116176557A CN116176557A CN202310168058.6A CN202310168058A CN116176557A CN 116176557 A CN116176557 A CN 116176557A CN 202310168058 A CN202310168058 A CN 202310168058A CN 116176557 A CN116176557 A CN 116176557A
- Authority
- CN
- China
- Prior art keywords
- road vehicle
- power
- required power
- hybrid
- analysis model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W20/00—Control systems specially adapted for hybrid vehicles
- B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W20/00—Control systems specially adapted for hybrid vehicles
- B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
- B60W20/11—Controlling the power contribution of each of the prime movers to meet required power demand using model predictive control [MPC] strategies, i.e. control methods based on models predicting performance
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W20/00—Control systems specially adapted for hybrid vehicles
- B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
- B60W20/15—Control strategies specially adapted for achieving a particular effect
Abstract
The invention relates to an energy management method and device for a hybrid off-road vehicle and electronic equipment, wherein the method comprises the following steps: acquiring target characteristic parameters of the off-road vehicle in real time, and calculating main component scores of the off-road vehicle according to the target characteristic parameters; performing cluster analysis on the principal component scores of the off-road vehicle, and determining the driving condition type of the off-road vehicle according to the result of the cluster analysis; determining a required power analysis model according to the driving condition type of the off-road vehicle, and inputting the main component score of the off-road vehicle into the required power analysis model to obtain the required power of the off-road vehicle; and calculating the output power of the off-road vehicle according to the target characteristic parameters, the required power of the off-road vehicle and preset constraint conditions. According to the invention, the accuracy of determining the driving condition type is improved through the principal component score, the instantaneity and the accuracy of the required power are improved through the required power analysis model, and finally the output power is determined through the constraint condition, so that the balance between the power and economy of the off-road vehicle is realized.
Description
Technical Field
The invention relates to the technical field of vehicle energy management, in particular to an energy management method and device for a hybrid off-road vehicle and electronic equipment.
Background
With the rapid development of vehicle power system technology, the proportion of electric power in automobile power is higher and higher, and the hybrid power technology has become one of effective technologies for reducing oil consumption. Because of a plurality of reasons such as small occupancy, special use scene and the like, a working condition construction system aiming at the off-road vehicle is not mature, key research is still lacking, and the current research aiming at working condition identification mostly uses working conditions as known information to perform performance optimization, so that a control strategy has a certain limitation.
In the prior art, most of energy management of hybrid vehicles is based on a rule energy management method, and is mainly formulated according to a control curve MAP of main components of the vehicle such as an engine, a motor and the like and engineering practice experience. And the identification of the running condition of the passing vehicle adopts an energy management strategy corresponding to the running condition of the vehicle so as to control the output torque of the engine and the motor of the vehicle in real time.
However, the running road conditions of the hybrid off-road vehicle are complex and changeable, the running state of the hybrid off-road vehicle is frequently changed, and the energy management method for the hybrid off-road vehicle in the prior art is difficult to meet the requirements of accuracy and instantaneity of the identification of the working conditions of the hybrid off-road vehicle. And when the main flow research direction of the energy management strategy of the lower hybrid power vehicle is based on the working condition of the urban vehicle and the fuel consumption of the whole vehicle is an optimization target, only the strategy analysis design of economy is completed, and the balance between the power and economy of the hybrid power off-road vehicle cannot be met.
Disclosure of Invention
In view of the foregoing, there is a need for an energy management method, an apparatus and an electronic device for a hybrid off-road vehicle, which are used for solving the problems that the energy management method of the hybrid off-road vehicle in the prior art cannot maintain the balance between the power and the economy of the hybrid off-road vehicle and cannot meet the requirements of the accuracy and the instantaneity of the identification of the working condition of the hybrid off-road vehicle.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a method of energy management for a hybrid off-road vehicle, comprising:
acquiring target characteristic parameters of the off-road vehicle in real time, and calculating main component scores of the off-road vehicle according to the target characteristic parameters;
performing cluster analysis on the principal component scores of the off-road vehicle, and determining the driving condition type of the off-road vehicle according to the result of the cluster analysis;
determining a required power analysis model according to the driving condition type of the off-road vehicle, and inputting the main component score of the off-road vehicle into the required power analysis model to obtain the required power of the off-road vehicle;
and calculating the output power of the off-road vehicle according to the target characteristic parameters, the required power of the off-road vehicle and the preset constraint conditions.
In some possible implementations, obtaining target feature parameters of the off-road vehicle in real time, calculating a principal component score of the off-road vehicle from the target feature parameters, comprising:
the method comprises the steps of collecting target characteristic parameters of the off-road vehicle in real time with a preset sampling period, and performing dimension reduction processing on the target characteristic parameters of the off-road vehicle based on a preset analysis method to obtain main component scores of the off-road vehicle.
In some possible implementations, performing cluster analysis on the principal component scores of the off-road vehicle, determining a driving condition type of the off-road vehicle according to a result of the cluster analysis, including:
setting a plurality of clustering centers according to historical driving data of the off-road vehicle;
calculating the working condition similarity between the principal component scores and all the clustering centers;
and setting the clustering center corresponding to the maximum value of the working condition similarity as the driving working condition type of the off-road vehicle.
In some possible implementations, the demand power analysis model includes a steady state operating condition analysis model and a transient operating condition analysis model; determining a required power analysis model according to the driving condition type of the off-road vehicle, inputting the main component score of the off-road vehicle into the required power analysis model to obtain the required power of the off-road vehicle, and comprising the following steps:
setting a steady-state working condition analysis model and a transient working condition analysis model according to historical driving data of the off-road vehicle;
inputting the principal component score into a corresponding required power analysis model according to a preset time window according to the driving condition type of the off-road vehicle to obtain the required power time sequence of the off-road vehicle.
In some possible implementations, inputting the principal component score to a corresponding demand power analysis model according to a preset time window according to a driving condition type of the off-road vehicle to obtain a demand power time sequence of the off-road vehicle, including:
based on a steady-state working condition analysis model, carrying out Markov time sequence prediction on the principal component score to determine the steady-state demand power time sequence of the off-road vehicle;
based on the transient condition analysis model, NAR neural network time sequence prediction is carried out on the principal component scores to determine the transient demand power time sequence of the off-road vehicle.
In some possible implementations, the target characteristic parameter includes a rate of change of accelerator pedal opening; calculating the output power of the off-road vehicle according to the target characteristic parameters, the required power of the off-road vehicle and the preset constraint conditions, wherein the method comprises the following steps:
carrying out fuzzy processing on the required power of the off-road vehicle and the change rate of the opening degree of the accelerator pedal to determine an optimization factor;
determining power demand power and economic demand power according to the optimization factors, the target characteristic parameters and the demand power of the off-road vehicle;
determining comprehensive required power according to a preset weight factor, power required power and economic required power;
and constraining the comprehensive required power through preset constraint conditions to obtain the output power of the off-road vehicle.
In some possible implementations, determining the optimization factor by blurring the rate of change of the power demand and the accelerator opening of the off-road vehicle includes:
calculating average demand power in the predicted time window according to the predicted time window and the demand power of the off-road vehicle;
and inputting the average required power and the change rate of the opening degree of the accelerator pedal to a preset fuzzy controller, and outputting to obtain an optimization factor.
In a second aspect, the present invention also provides an energy management apparatus of a hybrid off-road vehicle, comprising:
the main component scoring module is used for acquiring target characteristic parameters of the off-road vehicle in real time and calculating main component scores of the off-road vehicle according to the target characteristic parameters;
the working condition identification module is used for carrying out cluster analysis on the main component scores of the off-road vehicle and determining the driving working condition type of the off-road vehicle according to the result of the cluster analysis;
the demand power analysis module is used for determining a demand power analysis model according to the driving condition type of the off-road vehicle, and inputting the main component score of the off-road vehicle into the demand power analysis model to obtain the demand power of the off-road vehicle;
the output power calculation module is used for calculating the output power of the off-road vehicle according to the target characteristic parameters, the required power of the off-road vehicle and the preset constraint conditions.
In a third aspect, the invention also provides an electronic device comprising a memory and a processor, wherein,
a memory for storing a program;
a processor coupled to the memory for executing programs stored in the memory to implement steps in the energy management method of the hybrid off-road vehicle in any one of the implementations described above.
In a fourth aspect, the present invention also provides a computer readable storage medium storing a computer readable program or instructions that, when executed by a processor, enable implementation of the steps in the energy management method of the hybrid off-road vehicle in any one of the above implementations.
The beneficial effects of adopting the embodiment are as follows: the invention relates to an energy management method and device for a hybrid off-road vehicle and electronic equipment, wherein the method comprises the following steps: acquiring target characteristic parameters of the off-road vehicle in real time, and calculating main component scores of the off-road vehicle according to the target characteristic parameters; performing cluster analysis on the principal component scores of the off-road vehicle, and determining the driving condition type of the off-road vehicle according to the result of the cluster analysis; determining a required power analysis model according to the driving condition type of the off-road vehicle, and inputting the main component score of the off-road vehicle into the required power analysis model to obtain the required power of the off-road vehicle; and calculating the output power of the off-road vehicle according to the target characteristic parameters, the required power of the off-road vehicle and preset constraint conditions. According to the energy management method, the device and the electronic equipment for the hybrid off-road vehicle, which are provided by the invention, the main component score is calculated firstly, the driving condition type of the off-road vehicle is determined according to the main component score, the accuracy of driving condition type identification is improved, then the required power of the off-road vehicle can be accurately determined according to the required power analysis model, the required power of the off-road vehicle can be quickly calculated, the real-time performance of calculation is improved, and finally the output power of the off-road vehicle is determined according to the constraint condition, so that the balance between the power and economy of the off-road vehicle is realized.
Drawings
FIG. 1 is a flow chart of an embodiment of a method for energy management of a hybrid off-road vehicle according to the present invention;
FIG. 2 is a schematic diagram illustrating a relationship between a working condition identification period and an update period according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating an embodiment of the step S102 in FIG. 1;
FIGS. 4 (a), (b), (c), and (d) are schematic diagrams of vehicle speeds for one embodiment of the cluster center types provided by the present invention;
FIG. 5 is a diagram illustrating the relationship of step S104 in FIG. 1;
FIG. 6 is a schematic diagram illustrating an embodiment of an energy management apparatus for a hybrid off-road vehicle according to the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form a part hereof, and together with the description serve to explain the principles of the invention, and are not intended to limit the scope of the invention.
In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the described embodiments of the invention may be combined with other embodiments.
The invention provides an energy management method and device for a hybrid off-road vehicle and electronic equipment, and the method and the device and the electronic equipment are respectively described below.
Referring to fig. 1, fig. 1 is a schematic flow chart of an embodiment of an energy management method for a hybrid off-road vehicle according to the present invention, and in one embodiment of the present invention, an energy management method for a hybrid off-road vehicle is disclosed, including:
s101, acquiring target characteristic parameters of the off-road vehicle in real time, and calculating main component scores of the off-road vehicle according to the target characteristic parameters;
s102, performing cluster analysis on the principal component scores of the off-road vehicle, and determining the driving condition type of the off-road vehicle according to the result of the cluster analysis;
s103, determining a required power analysis model according to the driving condition type of the off-road vehicle, and inputting the main component score of the off-road vehicle into the required power analysis model to obtain the required power of the off-road vehicle;
s104, calculating the output power of the off-road vehicle according to the target characteristic parameters, the required power of the off-road vehicle and preset constraint conditions.
In the above embodiment, the acquisition of the target characteristic parameters of the off-road vehicle running can be realized through various sensors and detection devices or equipment arranged on the off-road vehicle, and it can be understood that the acquired characteristic parameters are characteristic parameters in a period of time (working condition section), so that not only the running speed of the vehicle needs to be reflected, but also the fluctuation condition of the vehicle speed and the comprehensive occupation ratio of the running state need to be represented, and therefore, the construction of the characteristic parameter set is mainly based on three dimensions: speed, acceleration, driving state duty ratio.
In this embodiment, 14 feature parameters are collected, and the 14 feature parameters are processed into 3 main components K1, K2 and K3 in a dimension-reducing manner by a main component analysis method (PCA) to obtain a main component score of the working condition section. K1 is positively correlated with parameters such as average vehicle speed, maximum acceleration and deceleration, but the correlation with average acceleration and standard deviation thereof is not great, and the method can be used for quantifying a driving scene for relieving driving intention during medium and high vehicle speeds; k2 is positively correlated with average acceleration, acceleration standard deviation, acceleration and deceleration section duty ratio and the like, and is used for quantifying a driving scene of strong driving braking intention of medium and low vehicle speeds; k3 emphasizes the driving scene for alleviating the driving intention when the vehicle speed is low or medium.
The mode of cluster analysis in the embodiment is that the driving condition type cluster analysis is based on a K-Means algorithm, the main component scoring condition of each working condition section is clustered through the K-Means algorithm, the driving condition type of the hybrid off-road vehicle is extracted from the main component scoring condition, the degree of closeness between the real-time working condition and each cluster working condition is calculated by utilizing Euclidean distance, and the method is used in the follow-up working condition type identification.
The required power analysis model in the embodiment is a Markov-NAR required power composite prediction model, and for steady-state working conditions, the Markov prediction model has lower prediction errors depending on a state transition probability matrix, and for transient working conditions, due to rapid acceleration/deceleration of a vehicle and frequent jump of longitudinal speed information, the accuracy of the Markov prediction model is obviously reduced due to solidification of the state transition probability matrix, so that the prediction effect of the multi-step NAR model is better than that of the Markov prediction model in the full prediction domain.
Combining the characteristics of two time sequence prediction methods, firstly, the current working condition type is identified on line through a sliding window by a working condition identification-based demand power composite prediction model, and then the vehicle running working condition type is tightly combined with the characteristics of different prediction methods: aiming at a transient working condition with high nonlinear characteristics, performing power prediction by using a multi-step NAR neural network model good at nonlinear fitting; aiming at a steady state working condition that the time sequence change is relatively stable, a Markov time sequence prediction model is adopted to accurately grasp the state transition process of the vehicle, and meanwhile, related prediction parameters are dynamically changed to improve the time sequence prediction effect.
Respectively determining a power performance function J through target characteristic parameters and the required power of the off-road vehicle 1 And economic performance function J 2 Further combine with the dynamic performance function J 1 And economic performance function J 2 And determining the comprehensive output power, and finally, restricting the comprehensive output power by combining preset restriction conditions to determine the final output power of the off-road vehicle.
Compared with the prior art, the energy management method of the hybrid off-road vehicle provided by the embodiment comprises the following steps: acquiring target characteristic parameters of the off-road vehicle in real time, and calculating main component scores of the off-road vehicle according to the target characteristic parameters; performing cluster analysis on the principal component scores of the off-road vehicle, and determining the driving condition type of the off-road vehicle according to the result of the cluster analysis; determining a required power analysis model according to the driving condition type of the off-road vehicle, and inputting the main component score of the off-road vehicle into the required power analysis model to obtain the required power of the off-road vehicle; and calculating the output power of the off-road vehicle according to the target characteristic parameters, the required power of the off-road vehicle and preset constraint conditions. According to the energy management method, the device and the electronic equipment for the hybrid off-road vehicle, which are provided by the invention, the main component score is calculated firstly, the driving condition type of the off-road vehicle is determined according to the main component score, the accuracy of driving condition type identification is improved, then the required power of the off-road vehicle can be accurately determined according to the required power analysis model, the required power of the off-road vehicle can be quickly calculated, the real-time performance of calculation is improved, and finally the output power of the off-road vehicle is determined according to the constraint condition, so that the balance between the power and economy of the off-road vehicle is realized.
Referring to fig. 2, fig. 2 is a schematic diagram showing a relationship between a working condition identification period and an update period according to an embodiment of the present invention, in some embodiments of the present invention, a target feature parameter of an off-road vehicle is obtained in real time, and a principal component score of the off-road vehicle is calculated according to the target feature parameter, including:
the method comprises the steps of collecting target characteristic parameters of the off-road vehicle in real time with a preset sampling period, and performing dimension reduction processing on the target characteristic parameters of the off-road vehicle based on a preset analysis method to obtain main component scores of the off-road vehicle.
In the above embodiment, in the real-time running process of the vehicle, assuming that the current time is T, Δt is taken as the sampling time window to perform feature parameter extraction, and Δσ is taken as the working condition identification update period. The sliding window identification method has traceability, and when the condition type is updated at the next moment, part of historical vehicle state information is still reserved in the condition identification period delta T, so that sample fragments are prevented from being fragmented, the identification result frequently jumps, and the vehicle control effect is reduced. Meanwhile, proper selection of the delta sigma and the delta T is beneficial to accurately tracking the running state of the off-road vehicle, reducing the calculated amount and improving the on-line working condition identification efficiency, and as a preferred embodiment, the invention selects the value of delta sigma to be 5s and the value of delta T to be 50s.
In the real-time running process of the vehicle, taking delta T as a period, and calculating in real time to obtain a characteristic parameter set; and then mapping the target working condition section into a principal component space according to a principal component space analysis theory, and calculating to obtain the score values of the principal components K1, K2 and K3.
Referring to fig. 3, fig. 3 is a flowchart illustrating an embodiment of step S102 in fig. 1, in some embodiments of the present invention, performing cluster analysis on principal component scores of an off-road vehicle, and determining a driving condition type of the off-road vehicle according to a result of the cluster analysis, including:
s301, setting a plurality of clustering centers according to historical driving data of the off-road vehicle;
s302, calculating the working condition similarity between the principal component scores and all the clustering centers;
s303, setting a clustering center corresponding to the maximum value of the working condition similarity as the driving working condition type of the off-road vehicle.
In the above embodiments, please refer to fig. 4 (a), (b), (c), and (d), and fig. 4 (a), (b), (c), and (d) are examples of the type of clustering center provided in the present inventionThe method comprises the steps of (1) extracting kinematic segments of typical working conditions by adopting a travel analysis method, writing a script program by Matlab software, extracting the kinematic segments of the typical working conditions, accumulating 205 segments after the extraction is finished, recording 205 segments as 205 segment typical working condition segment sets, carrying out component analysis on the 205 segment typical working condition segment sets, and determining 4 class typical clustering centers C 1 、C 2 、C 3 、C 4 。
Working condition similarity variable ζ (C) i ,X Δt ) The calculation formula of (2) is as follows:
ξ(X ΔT )＝max{ξ(C 1 ,X ΔT ),ξ(C 2 ,X ΔT ),…,ξ(C 4 ,X ΔT )}；
wherein m is the number of main components, X Δt The main component score set of the target working condition section is recorded as xi (X ΔT ) K represents the time.
In some embodiments of the invention, the demand power analysis model includes a steady state operating condition analysis model and a transient operating condition analysis model; determining a required power analysis model according to the driving condition type of the off-road vehicle, inputting the main component score of the off-road vehicle into the required power analysis model to obtain the required power of the off-road vehicle, and comprising the following steps:
setting a steady-state working condition analysis model and a transient working condition analysis model according to historical driving data of the off-road vehicle;
inputting the principal component score into a corresponding required power analysis model according to a preset time window according to the driving condition type of the off-road vehicle to obtain the required power time sequence of the off-road vehicle.
In the embodiment, the steady-state working condition analysis model is a Markov prediction model, the transient working condition analysis model is a multi-step NAR neural network model, and the Markov prediction model and the multi-step NAR neural network model are determined to analyze different types of working conditions according to historical driving data of the hybrid off-road vehicle.
Inputting the main component scores into corresponding demand power analysis models through set sliding windows to obtain demand power time sequences of the off-road vehicle, wherein the steady states are divided into medium-low speed steady states and medium-high speed steady states of C1 and C4, and the transients are divided into medium-low speed transients and medium-high speed transients of C2 and C3.
In some embodiments of the present invention, inputting the principal component score into a corresponding demand power analysis model according to a preset time window according to a driving condition type of the off-road vehicle to obtain a demand power time sequence of the off-road vehicle, including:
based on a steady-state working condition analysis model, carrying out Markov time sequence prediction on the principal component score to determine the steady-state demand power time sequence of the off-road vehicle;
based on the transient condition analysis model, NAR neural network time sequence prediction is carried out on the principal component scores to determine the transient demand power time sequence of the off-road vehicle.
In the embodiment, the principal component score of the steady-state working condition is input to the steady-state working condition analysis model, and the steady-state demand power time sequence of the off-road vehicle is determined through Markov time sequence prediction.
Inputting the principal component score of the transient working condition into a transient working condition analysis model, and determining the transient demand power time sequence of the off-road vehicle through NAR neural network time sequence prediction.
It should be noted that, the markov time sequence prediction and the NAR neural network time sequence prediction are the prior art, and the invention does not need to make excessive description.
Referring to fig. 5, fig. 5 is a schematic diagram illustrating an embodiment of step S104 in fig. 1, and in some embodiments of the present invention, the target characteristic parameter includes a rate of change of an accelerator pedal opening; calculating the output power of the off-road vehicle according to the target characteristic parameters, the required power of the off-road vehicle and the preset constraint conditions, wherein the method comprises the following steps:
s501, carrying out fuzzy processing on the required power of the off-road vehicle and the change rate of the opening degree of an accelerator pedal to determine an optimization factor;
s502, determining power demand power and economic demand power according to the optimization factors, the target characteristic parameters and the demand power of the off-road vehicle;
s503, determining comprehensive required power according to a preset weight factor, power required power and economic required power;
s504, restraining the comprehensive required power through preset constraint conditions to obtain the output power of the off-road vehicle.
In the above embodiment, the power source of the hybrid off-road vehicle is an APU system (auxiliary power system) and a diesel engine, and the efficient operation of the APU system is beneficial to rapid supply of electric energy, that is, the SOC is ensured to be maintained in a high-efficiency discharge interval, so that the dynamic potential of the hybrid off-road vehicle is better exerted, and a good power response effect is achieved. Taking the sudden acceleration working condition of the off-road vehicle as an example, when the whole vehicle has a high-power request, the power battery should be discharged preferentially in the early driving stage, the operation efficiency of the APU power generation system should be improved as soon as possible in the later driving stage, the power supply is realized efficiently, the SOC is maintained in a high-efficiency working region, and the design optimization factor determines the required power.
For a distributively driven hybrid off-road vehicle, electrical energy from the APU power generation system or power cell is transferred to the in-wheel motor to provide a source of power for the entire vehicle. Considering that the power battery has stronger charge and discharge capability in a high-efficiency SOC working range, and meanwhile, the output response speed is obviously superior to that of an APU power generation system, and the power performance function J is designed by combining the special requirements of a research object on the power performance 1 The power demand power is determined, and the calculation formula is as follows:
wherein P is APU () For the output power of the APU power generation system at the kth moment, P req () For the kth moment of the off-road vehiclePower demand, P bat () The power battery is output power at the kth moment, lambda (k) is charge/discharge multiplying power, zeta (k) is charge/discharge factor, U (k) is terminal voltage at the kth moment, SOC (k) is residual electric quantity at the kth moment, and E bat For power battery capacity, SOC low And SOC (System on chip) hiigh Respectively a minimum threshold value and a maximum threshold value of the residual electric quantity of the power battery.
When the hybrid off-road vehicle runs in a working condition with gentle power demand change (such as a cruising working condition and a suburban working condition), the fuel economy of the whole vehicle is stressed. Wherein the fuel consumption is the most representative evaluation index of fuel economy, and an economic performance function J is designed 2 The economic demand power is determined, and the calculation formula is as follows:
wherein f eng () F is the fuel consumption of the diesel engine bat () Is equivalent fuel consumption of power battery, P eng () For the output power of the diesel engine at the kth moment, be () is the fuel consumption rate of the diesel engine at the kth moment, which is a function of torque and rotation speed, i.e., be () = [ eng (),T m ()]Obtainable by interpolation;
the equivalent fuel consumption conversion coefficient is; η (eta) APU Work efficiency, eta, of an APU power generation system when charging a power battery bat The working efficiency of the power battery is; h μ The fuel oil of the diesel engine has low heat value, and I (k) is the charge and discharge current of the power battery at the kth moment.
In order to improve the working condition adaptability of the hybrid off-road vehicle, the real-time working condition type of the vehicle is tightly combined with the power generation decision of the whole vehicle and the APU, and an adaptive factor lambda is introduced 1 ，λ 2 Comprehensively regulating priority relation between dynamic property and fuel economy 1 、λ 2 The expression of (2) is as follows:
wherein d (C (k), C i ) And expressing Euclidean distance between the principal component score of the working condition identification window at the moment k and the principal component score of the ith clustering center.
The adaptive factor lambda 1 ，λ 2 Can be respectively regarded as an optimized weight coefficient for power responsiveness and fuel economy, and an online working condition identification model based on a sliding window can divide working conditions into C 1 To C 4 Four classes. Wherein C is 1 And C 4 All belong to transient working condition types, and along with the purposes of sudden acceleration/deceleration or sudden overtaking and obstacle crossing, the time sequence variation fluctuation of the working condition is large, and the power requirement of the off-road vehicle is mainly met at the moment, so that J is promoted 1 In J * The ratio of (3); and C is 2 And C 3 All belong to the type of steady-state working conditions, the whole vehicle is in a cruising or sliding state, and the time sequence change of the working conditions is relatively stable, so J should be lifted 2 The ratio of the ratio is designed to optimize the economic performance of the whole vehicle, and a predictive control performance function J is designed * Determining the comprehensive required power:
according to MPC theory, the optimal solution of the objective function should be limited to certain constraints, which might otherwise lead to performance degradation of the control system. The constraint condition is set by an objective limiting factor of a control system, and the following constraint condition is added from the whole vehicle safety problem during multi-energy source cooperation:
1) Safety constraint of the power battery:
wherein P is bat_max 、P bat_min Representing the maximum/small discharge power of the power battery; u (k) max 、U(k) min Representing the maximum/small terminal voltage; i const (k)、I peak (k) Respectively representing continuous charge/discharge and peak charge/discharge current of the power battery, the values of which are smaller than the respective real-time allowable limit value I allow_const (k)、I allow_peak (k)。
2) APU power generation and driving motor safety constraints:
wherein P is eng_max 、P eng_min Maximum/small output power for diesel engine; temp eng_max 、temp eng_min -upper/lower operating temperature limit of the diesel engine; p (P) motor For the safe power of the generator, in order to avoid irreversible high-temperature demagnetization failure caused by the overhigh working temperature of the driving motor/generator, the value of the safe power of the generator is ensured to be always larger than the peak power of the driving motor/generator; temp motor_max 、temp motor_min To drive the upper/lower motor/generator operating temperature limits.
For the multi-objective optimization problem, a dynamic programming algorithm can be utilized to solve, and the idea is to consider all calculation solving processes as a plurality of related sub-processes, and sequentially calculate the optimal control sequence of each sub-process, so as to obtain the optimal solution of a single prediction time domain. MPC multi-objective Performance function J for the present invention * Considering constraints, the inverse solution process can be expressed as:
J * (k+p)＝min{λ 1 J 1 (k+p)+λ 2 J 2 (k+p)}
J * (k+p-1)＝min{λ 1 J 1 (k+p-1)+λ 2 J 2 (k+p-1)+J * (k+p)}
J * (k)＝min{λ 1 J 1 (k)+λ 2 J 2 (k)+J * (k+1)}；
the multi-objective optimization equation under the security constraint is optimally solved in the prediction time domain, expressed as:
u * the result of (t) is the output power of the off-road vehicle.
In some embodiments of the present invention, determining an optimization factor for a fuzzy process for a rate of change of a required power and an accelerator opening of an off-road vehicle includes:
calculating average demand power in the predicted time window according to the predicted time window and the demand power of the off-road vehicle;
and inputting the average required power and the change rate of the opening degree of the accelerator pedal to a preset fuzzy controller, and outputting to obtain an optimization factor.
In the above embodiment, the average value is predicted from the required power
Rate of change Δα from accelerator pedal opening Acc And designing a responsiveness optimization factor kappa (k), and obtaining a predicted value of t-p to t-1 for t time according to a demand power analysis model. the accuracy of the power prediction is gradually increased from time t-p to time t-1, so the power prediction value of the demand at time t needs to be self-corrected according to the rolling of a time sequence window, and the power prediction value is calculated as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
the predicted average value after the correction of the required power is obtained; p is the predicted time domain length.
The fuzzy arguments of the preset fuzzy controller are respectively set as [0, 160], [0, 300], and the fuzzy language variable quantity is as follows: { S, M, B, VB }, the average required power and the change rate of the opening degree of the accelerator pedal are input to a preset fuzzy controller, and then the optimization factor kappa (k) can be obtained.
The significance of kappa (k) is that the power output duty ratio of different energy sources of the whole vehicle can be dynamically coordinated according to the driving intention of a driver and the change condition of the required power. Δα Acc Can characterize the intensity of the driver's driving intention when Δα Acc When the variable language after blurring is VB or B, the kappa (k) is approximately in the interval [1,1.3 ] after deblurring]Thus J 1 P in the reduced term APU (k) The power battery output duty ratio is increased when the power battery is smaller; in the same way, the processing method comprises the steps of,
characterizing a power prediction mean value extracted from the operating mode information, when +.>
When the variable language after blurring is VB or B, the kappa (k) deblurred is basically in the interval [0.6,1 ]]，J 1 P in the reduced term APU (k) The power supply system is increased, so that the output power duty ratio of the APU power generation system is improved, the energy storage and electric energy output efficiency of the whole vehicle are ensured, and the power response performance of the whole vehicle is optimized. />
In order to better implement the energy management method of the hybrid off-road vehicle according to the embodiment of the present invention, referring to fig. 6, fig. 6 is a schematic structural diagram of an embodiment of the energy management device of the hybrid off-road vehicle according to the present invention, and the embodiment of the present invention provides an energy management device 600 of the hybrid off-road vehicle, which includes:
the principal component score module 610 is configured to obtain a target feature parameter of the off-road vehicle in real time, and calculate a principal component score of the off-road vehicle according to the target feature parameter;
the working condition identification module 620 is configured to perform cluster analysis on the principal component scores of the off-road vehicle, and determine a driving working condition type of the off-road vehicle according to a result of the cluster analysis;
the required power analysis module 630 is configured to determine a required power analysis model according to a driving condition type of the off-road vehicle, and input a principal component score of the off-road vehicle into the required power analysis model to obtain required power of the off-road vehicle;
the output power calculation module 640 is configured to calculate the output power of the off-road vehicle according to the target feature parameter, the required power of the off-road vehicle, and the preset constraint condition.
What needs to be explained here is: the apparatus 600 provided in the foregoing embodiments may implement the technical solutions described in the foregoing method embodiments, and the specific implementation principles of the foregoing modules or units may be referred to the corresponding content in the foregoing method embodiments, which is not repeated herein.
Referring to fig. 7, fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the invention. Based on the energy management method of the hybrid off-road vehicle, the invention also correspondingly provides energy management equipment of the hybrid off-road vehicle, wherein the energy management equipment of the hybrid off-road vehicle can be computing equipment such as a mobile terminal, a desktop computer, a notebook computer, a palm computer, a server and the like. The energy management apparatus of the hybrid off-road vehicle includes a processor 710, a memory 720, and a display 730. Fig. 7 shows only some of the components of the electronic device, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead.
The memory 720 may be an internal storage unit of the energy management device of the hybrid off-road vehicle in some embodiments, such as a hard disk or memory of the energy management device of the hybrid off-road vehicle. The memory 720 may also be an external storage device of the energy management device of the hybrid off-road vehicle in other embodiments, such as a plug-in hard disk equipped on the energy management device of the hybrid off-road vehicle, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), etc. Further, the memory 720 may also include both an internal storage unit and an external storage device of the energy management device of the hybrid off-road vehicle. The memory 720 is used to store application software of the energy management device mounted on the hybrid off-road vehicle and various types of data, such as program code for mounting the energy management device of the hybrid off-road vehicle, and the like. The memory 720 may also be used to temporarily store data that has been output or is to be output. In one embodiment, the memory 720 has stored thereon an energy management program 740 of the hybrid off-road vehicle, and the energy management program 740 of the hybrid off-road vehicle is executable by the processor 710 to implement the energy management method of the hybrid off-road vehicle of the embodiments of the present application.
The processor 710 may be, in some embodiments, a central processing unit (Central Processing Unit, CPU), microprocessor or other data processing chip for executing program code or processing data stored in the memory 720, such as performing energy management methods for hybrid off-road vehicles, etc.
The display 730 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like in some embodiments. The display 730 is used to display information on the energy management device of the hybrid off-road vehicle and to display a visual user interface. The components 710-730 of the energy management device of the hybrid off-road vehicle communicate with each other over a system bus.
In one embodiment, the steps in the energy management method of a hybrid off-road vehicle as described above are implemented when the processor 710 executes the energy management program 740 of the hybrid off-road vehicle in the memory 720.
The present embodiment also provides a computer readable storage medium having stored thereon an energy management program for a hybrid off-road vehicle, which when executed by a processor, performs the steps of:
acquiring target characteristic parameters of the off-road vehicle in real time, and calculating main component scores of the off-road vehicle according to the target characteristic parameters;
performing cluster analysis on the principal component scores of the off-road vehicle, and determining the driving condition type of the off-road vehicle according to the result of the cluster analysis;
determining a required power analysis model according to the driving condition type of the off-road vehicle, and inputting the main component score of the off-road vehicle into the required power analysis model to obtain the required power of the off-road vehicle;
and calculating the output power of the off-road vehicle according to the target characteristic parameters, the required power of the off-road vehicle and the preset constraint conditions.
In summary, the present embodiment provides an energy management method, an apparatus and an electronic device for a hybrid off-road vehicle, where the method includes: acquiring target characteristic parameters of the off-road vehicle in real time, and calculating main component scores of the off-road vehicle according to the target characteristic parameters; performing cluster analysis on the principal component scores of the off-road vehicle, and determining the driving condition type of the off-road vehicle according to the result of the cluster analysis; determining a required power analysis model according to the driving condition type of the off-road vehicle, and inputting the main component score of the off-road vehicle into the required power analysis model to obtain the required power of the off-road vehicle; and calculating the output power of the off-road vehicle according to the target characteristic parameters, the required power of the off-road vehicle and preset constraint conditions. According to the energy management method, the device and the electronic equipment for the hybrid off-road vehicle, which are provided by the invention, the main component score is calculated firstly, the driving condition type of the off-road vehicle is determined according to the main component score, the accuracy of driving condition type identification is improved, then the required power of the off-road vehicle can be accurately determined according to the required power analysis model, the required power of the off-road vehicle can be quickly calculated, the real-time performance of calculation is improved, and finally the output power of the off-road vehicle is determined according to the constraint condition, so that the balance between the power and economy of the off-road vehicle is realized.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention.
Claims (10)
1. A method of energy management for a hybrid off-road vehicle, comprising:
acquiring target characteristic parameters of the off-road vehicle in real time, and calculating main component scores of the off-road vehicle according to the target characteristic parameters;
performing cluster analysis on the principal component scores of the off-road vehicle, and determining the driving condition type of the off-road vehicle according to the result of the cluster analysis;
determining a required power analysis model according to the driving condition type of the off-road vehicle, and inputting the main component score of the off-road vehicle into the required power analysis model to obtain the required power of the off-road vehicle;
and calculating the output power of the off-road vehicle according to the target characteristic parameters, the required power of the off-road vehicle and preset constraint conditions.
2. The method of energy management of a hybrid off-road vehicle of claim 1, wherein the obtaining in real time a target feature parameter of the off-road vehicle, calculating a principal component score of the off-road vehicle from the target feature parameter, comprises:
the method comprises the steps of collecting target characteristic parameters of the off-road vehicle in real time in a preset sampling period, and performing dimension reduction processing on the target characteristic parameters of the off-road vehicle based on a preset analysis method to obtain main component scores of the off-road vehicle.
3. The method for energy management of a hybrid off-road vehicle according to claim 1, wherein the performing cluster analysis on the principal component scores of the off-road vehicle, determining the driving condition type of the off-road vehicle according to the result of the cluster analysis, comprises:
setting a plurality of clustering centers according to historical driving data of the off-road vehicle;
calculating the working condition similarity between the principal component scores and all the clustering centers;
and setting the clustering center corresponding to the maximum value of the working condition similarity as the driving working condition type of the off-road vehicle.
4. The energy management method of a hybrid off-road vehicle of claim 3, wherein the demand power analysis model includes a steady state operating condition analysis model and a transient operating condition analysis model; the method for determining the required power analysis model according to the driving condition type of the off-road vehicle, inputting the principal component score of the off-road vehicle into the required power analysis model to obtain the required power of the off-road vehicle comprises the following steps:
setting the steady-state working condition analysis model and the transient working condition analysis model according to the historical driving data of the off-road vehicle;
and inputting the principal component score into the corresponding required power analysis model according to a preset time window according to the driving condition type of the off-road vehicle to obtain the required power time sequence of the off-road vehicle.
5. The method for energy management of a hybrid off-road vehicle according to claim 4, wherein the inputting the principal component score into the corresponding required power analysis model according to a preset time window according to the driving condition type of the off-road vehicle to obtain a required power time sequence of the off-road vehicle comprises:
based on the steady-state working condition analysis model, carrying out Markov time sequence prediction on the principal component score to determine the steady-state demand power time sequence of the off-road vehicle;
and based on the transient condition analysis model, NAR neural network time sequence prediction is carried out on the principal component score to determine the transient demand power time sequence of the off-road vehicle.
6. The energy management method of a hybrid off-road vehicle of claim 1, wherein the target characteristic parameter includes a rate of change of accelerator pedal opening; the calculating the output power of the off-road vehicle according to the target characteristic parameter, the required power of the off-road vehicle and the preset constraint condition comprises the following steps:
carrying out fuzzy processing on the required power of the off-road vehicle and the change rate of the opening of the accelerator pedal to determine an optimization factor;
determining power demand power and economic demand power according to the optimization factors, the target characteristic parameters and the demand power of the off-road vehicle;
determining comprehensive required power according to a preset weight factor, the power required power and the economic required power;
and constraining the comprehensive required power through the preset constraint condition to obtain the output power of the off-road vehicle.
7. The method of energy management of a hybrid off-road vehicle of claim 6, wherein the blurring process determining an optimization factor for the required power of the off-road vehicle and the rate of change of the accelerator opening comprises:
calculating average demand power in a predicted time window according to the predicted time window and the demand power of the off-road vehicle;
and inputting the average required power and the change rate of the opening of the accelerator pedal to a preset fuzzy controller, and outputting to obtain an optimization factor.
8. An energy management apparatus for a hybrid off-road vehicle, comprising:
the main component scoring module is used for acquiring target characteristic parameters of the off-road vehicle in real time and calculating main component scores of the off-road vehicle according to the target characteristic parameters;
the working condition identification module is used for carrying out cluster analysis on the main component scores of the off-road vehicle and determining the driving working condition type of the off-road vehicle according to the result of the cluster analysis;
the demand power analysis module is used for determining a demand power analysis model according to the driving condition type of the off-road vehicle, and inputting the principal component score of the off-road vehicle into the demand power analysis model to obtain the demand power of the off-road vehicle;
and the output power calculation module is used for calculating the output power of the off-road vehicle according to the target characteristic parameters, the required power of the off-road vehicle and the preset constraint conditions.
9. An electronic device comprising a memory and a processor, wherein,
the memory is used for storing programs;
the processor, coupled to the memory, for executing the program stored in the memory to implement the steps in the energy management method of the hybrid off-road vehicle of any one of the above claims 1 to 7.
10. A computer readable storage medium storing a computer readable program or instructions which, when executed by a processor, enable the steps in the energy management method of a hybrid off-road vehicle according to any one of the preceding claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310168058.6A CN116176557A (en) | 2023-02-24 | 2023-02-24 | Energy management method and device for hybrid off-road vehicle and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310168058.6A CN116176557A (en) | 2023-02-24 | 2023-02-24 | Energy management method and device for hybrid off-road vehicle and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116176557A true CN116176557A (en) | 2023-05-30 |
Family
ID=86447376
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310168058.6A Pending CN116176557A (en) | 2023-02-24 | 2023-02-24 | Energy management method and device for hybrid off-road vehicle and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116176557A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116749946A (en) * | 2023-08-21 | 2023-09-15 | 新誉集团有限公司 | Vehicle energy management method, device, equipment and readable storage medium |
-
2023
- 2023-02-24 CN CN202310168058.6A patent/CN116176557A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116749946A (en) * | 2023-08-21 | 2023-09-15 | 新誉集团有限公司 | Vehicle energy management method, device, equipment and readable storage medium |
| CN116749946B (en) * | 2023-08-21 | 2023-10-20 | 新誉集团有限公司 | Vehicle energy management method, device, equipment and readable storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110775065B (en) | Hybrid electric vehicle battery life prediction method based on working condition recognition | |
| Tian et al. | An adaptive ECMS with driving style recognition for energy optimization of parallel hybrid electric buses | |
| Yuan et al. | Intelligent energy management strategy based on hierarchical approximate global optimization for plug-in fuel cell hybrid electric vehicles | |
| CN110281904B (en) | Energy management method and device for hybrid vehicle and terminal | |
| CN108528436B (en) | Inner-outer nested ECMS multi-target double-layer optimization method | |
| Ali et al. | Optimal control of multi-source electric vehicles in real time using advisory dynamic programming | |
| CN110775043B (en) | Hybrid electric vehicle energy optimization method based on battery life attenuation pattern recognition | |
| WO2022241898A1 (en) | Hierarchical energy-saving driving method for fuel cell vehicle | |
| Ganji et al. | A study on look-ahead control and energy management strategies in hybrid electric vehicles | |
| CN112319462B (en) | Energy management method for plug-in hybrid electric vehicle | |
| CN112224035B (en) | Driving torque optimization control method for pure electric vehicle | |
| Xu et al. | Optimization of energy management strategy for extended range electric vehicles using multi-island genetic algorithm | |
| CN116176557A (en) | Energy management method and device for hybrid off-road vehicle and electronic equipment | |
| Chen et al. | Power reserve predictive control strategy for hybrid electric vehicle using recognition-based long short-term memory network | |
| Fries et al. | Operational strategy of hybrid heavy-duty trucks by utilizing a genetic algorithm to optimize the fuel economy multiobjective criteria | |
| Wei et al. | Integrating intelligent driving pattern recognition with adaptive energy management strategy for extender range electric logistics vehicle | |
| Yan et al. | Design of a deep inference framework for required power forecasting and predictive control on a hybrid electric mining truck | |
| CN113135113A (en) | Global SOC (System on chip) planning method and device | |
| Gnanaprakasam et al. | Robust energy management technique for plug-in hybrid electric vehicle with traffic condition identification | |
| CN111824115B (en) | Vehicle multi-energy control method, system, device and storage medium | |
| Tao et al. | Terrain information-involved power allocation optimization for fuel cell/battery/ultracapacitor hybrid electric vehicles via an improved deep reinforcement learning | |
| CN105774797A (en) | Self-adaptive control method for plug-in type parallel hybrid electric vehicle | |
| CN113997925B (en) | Energy management method for plug-in hybrid power system | |
| Yang et al. | Variable optimization domain-based cooperative energy management strategy for connected plug-in hybrid electric vehicles | |
| CN113276829A (en) | Vehicle running energy-saving optimization weight-changing method based on working condition prediction |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |