CN112078565B

CN112078565B - Energy management method and device for hydrogen fuel cell electric vehicle and storage medium

Info

Publication number: CN112078565B
Application number: CN202010977689.9A
Authority: CN
Inventors: 周健豪; 海滨; 赛影辉; 宋廷伦; 周之光; 王磊; 方石
Original assignee: Chery Automobile Co Ltd
Current assignee: Chery Automobile Co Ltd
Priority date: 2020-09-17
Filing date: 2020-09-17
Publication date: 2021-07-30
Anticipated expiration: 2040-09-17
Also published as: CN112078565A

Abstract

The embodiment of the application discloses energy management, device and storage medium of a hydrogen fuel cell electric automobile, and belongs to the technical field of hybrid electric automobiles. The method comprises the following steps: acquiring a first driving parameter and a second driving parameter; determining an adjustment coefficient of the SOC according to the first driving parameter; the second driving parameter is used as the input of a strategy determination network model, and an equivalent factor is determined through the strategy determination network model; the method comprises the steps that the required power of the whole vehicle, an adjusting coefficient and an equivalent factor are used as input of an energy management model, and the first required power of a fuel cell and the second required power of a storage battery are determined through the energy management model; and adjusting the output power of the fuel cell to be the first required power, adjusting the output power of the storage battery to be the second required power, and providing power for the hydrogen fuel cell electric automobile by adopting the fuel cell and the storage battery. According to the embodiment of the application, the output power is distributed according to the instantaneous equivalent factor, and the lowest fuel consumption can be ensured.

Description

Energy management method and device for hydrogen fuel cell electric vehicle and storage medium

Technical Field

The embodiment of the application relates to the technical field of hybrid electric vehicles, in particular to an energy management method, device and storage medium for a hydrogen fuel cell electric vehicle.

Background

The hydrogen fuel cell electric automobile provides the power required by the running for the automobile by the fuel cell and the storage battery, but when the two power sources are adopted to provide the running power, the distribution problem of the output power of the fuel cell and the storage battery needs to be considered. In order to achieve the lowest fuel Consumption, ECMS (instantaneous Equivalent Consumption Minimization Strategies) is generally used to achieve energy management of the fuel cell and the battery. In order to make the state of the energy of the accumulator reached at the future time coincide with the initial state, the electric energy consumed by the accumulator at the present time is compensated by the hydrogen consumed by the fuel cell at the future time. And the ECMS converts the electric energy consumption of the storage battery at the current moment into the hydrogen consumption of the fuel cell at the future moment through the equivalent factor, and takes the sum of the hydrogen consumption of the fuel cell and the equivalent hydrogen consumption of the storage battery as the equivalent hydrogen consumption of the whole vehicle. The fuel consumption of the vehicle is lowest under the condition that the equivalent hydrogen consumption of the whole vehicle is minimum.

In the related art, ECMS enables optimum conversion efficiency when compensating the electric power consumption of the battery at the present time with the hydrogen consumption at the future time by finding an optimum equivalent factor (constant value) for a given driving cycle. That is, when the conversion of the amount of electric power consumption and the amount of hydrogen consumption is performed using the optimal equivalence factor, the energy distribution at the present time can minimize the fuel consumption of the automobile.

Although the method is simple and feasible, the energy distribution is determined by adopting the optimal equivalent factor, the influence of the whole driving cycle is large, the working condition is poor, and the overall optimal characteristic is not achieved. Moreover, due to uncertainty of future operating conditions, under which operating conditions the compensation of the current time power consumption is performed is not known, and the equivalence factor is an uncertain value. The optimal equivalence factor cannot adapt to changes of working conditions, so that energy distribution realized by the optimal equivalence factor cannot guarantee the lowest fuel consumption.

Disclosure of Invention

The embodiment of the application provides an energy management method and device for a hydrogen fuel cell electric vehicle and a storage medium, and can solve the problems that the working condition is poor, the determined optimal equivalent factor does not have the globally optimal characteristic, and the lowest fuel consumption cannot be guaranteed in the power distribution result of the hydrogen fuel cell electric vehicle in the related technology. The technical scheme is as follows:

in one aspect, a method for energy management of a hydrogen fuel cell electric vehicle is provided, the method comprising:

acquiring a first driving parameter and a second driving parameter, wherein the first driving parameter comprises the required power Of the whole vehicle, the change rate Of the required power, the current SOC (State Of Charge) Of a storage battery, the tread amplitude Of an accelerator pedal and the tread amplitude Of a brake pedal, and the second driving parameter comprises the driving speed, the acceleration and the current SOC Of the storage battery;

determining an adjustment coefficient of the SOC according to the first driving parameter;

taking the second driving parameter as an input of a strategy determination network model, and determining an equivalent factor through the strategy determination network model, wherein the equivalent factor is used for indicating an equivalent relation between the electric energy of the storage battery and the hydrogen consumption of the fuel cell;

the required power of the whole vehicle, the regulating coefficient and the equivalent factor are used as input of an energy management model, and a first required power of the fuel cell and a second required power of the storage battery are determined through the energy management model, wherein the first required power is between the minimum power and the maximum power of the fuel cell, and the second required power is between the minimum power and the maximum power of the storage battery;

and adjusting the output power of the fuel cell to the first required power, adjusting the output power of the storage battery to the second required power, and providing power for the hydrogen fuel cell electric automobile by adopting the fuel cell and the storage battery.

Optionally, the determining an adjustment coefficient of the SOC according to the first driving parameter includes:

inputting the first driving parameter into a battery state prediction model, and determining the charge and discharge state of the storage battery through the battery state prediction model;

and determining the adjustment coefficient of the SOC according to the charge-discharge state of the storage battery.

Optionally, before the first driving parameter is input into a battery state prediction model and the charge-discharge state of the storage battery is determined by the battery state prediction model, the method further includes:

acquiring a plurality of groups of sample driving parameters and battery charge and discharge states corresponding to each group of sample driving parameters;

and taking the multiple groups of sample driving parameters as input of an initial battery state prediction model, taking battery charge and discharge states corresponding to the multiple groups of sample driving parameters as output of the initial battery state prediction model, and training the initial battery state prediction model to obtain the battery state prediction model.

Optionally, the energy management model is established based on ECMS, the objective function of ECMS is a minimum value of instantaneous equivalent hydrogen consumption of the entire vehicle, and the instantaneous equivalent hydrogen consumption of the entire vehicle is a sum of instantaneous hydrogen consumption of the fuel cell and instantaneous equivalent hydrogen consumption of the storage battery.

Optionally, the Policy-determined network model is a DDPG (Deep Deterministic Policy Gradient) model;

the determining the equivalence factor through the strategy determination network model by taking the second driving parameter as an input of the strategy determination network model comprises:

acquiring the SOC of the storage battery, the accumulated quantity of the equivalent hydrogen consumption of the whole vehicle, the required power of the fuel cell and the required power of the storage battery at the previous moment;

determining a feedback reward according to the SOC of the storage battery at the previous moment, the accumulated amount of the equivalent hydrogen consumption of the whole vehicle, the required power of the fuel cell and the required power of the storage battery through a reward function of the DDPG model;

and taking the second driving parameter and the feedback reward as the input of the DDPG model, and determining the equivalence factor through the DDPG model.

Optionally, before the obtaining the first driving parameter, the method further comprises:

acquiring Driving parameters of a virtual hydrogen fuel cell electric automobile in a simulation model under target working conditions, wherein the target working conditions comprise NEDC (New European Driving Cycle) working conditions;

carrying out importance sorting on the driving parameters under the target working condition through a Relieff algorithm to obtain an importance sorting result;

and determining the first driving parameter according to the importance ranking result.

In another aspect, there is provided an energy management apparatus of a hydrogen fuel cell electric vehicle, the apparatus including:

the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring a first driving parameter and a second driving parameter, the first driving parameter comprises the required power of the whole vehicle, the change rate of the required power, the current SOC of a storage battery, the tread amplitude of an accelerator pedal and the tread amplitude of a brake pedal, and the second driving parameter comprises the driving speed, the acceleration and the current SOC of the storage battery;

the first determining module is used for determining an adjusting coefficient of the SOC according to the first driving parameter;

a second determination module, configured to use the second driving parameter as an input of a policy determination network model, and determine an equivalence factor through the policy determination network model, where the equivalence factor is used to indicate an equivalence relation between electric energy of the battery and hydrogen consumption of a fuel cell;

a third determining module, configured to use the vehicle power demand, the adjustment coefficient, and the equivalence factor as inputs of an energy management model, and determine, through the energy management model, a first power demand of the fuel cell and a second power demand of the battery, where the first power demand is between a minimum power and a maximum power of the fuel cell, and the second power demand is between a minimum power and a maximum power of the battery;

and the adjusting module is used for adjusting the output power of the fuel cell to the first required power, adjusting the output power of the storage battery to the second required power, and providing power for the hydrogen fuel cell electric automobile by adopting the fuel cell and the storage battery.

Optionally, the first determining module includes:

the first determining submodule is used for inputting the first driving parameter into a battery state prediction model and determining the charge and discharge state of the storage battery through the battery state prediction model;

and the second determining submodule is used for determining the regulating coefficient of the SOC according to the charge-discharge state of the storage battery.

Optionally, the apparatus further comprises:

the second acquisition module is used for acquiring a plurality of groups of sample driving parameters and battery charge and discharge states corresponding to the sample driving parameters;

and the training module is used for taking the multiple groups of sample driving parameters as the input of an initial battery state prediction model, taking the battery charge-discharge states corresponding to the multiple groups of sample driving parameters as the output of the initial battery state prediction model, and training the initial battery state prediction model to obtain the battery state prediction model.

Optionally, the policy determines that the network model is a DDPG model;

the second determining module includes:

the acquisition submodule is used for acquiring the SOC of the storage battery, the accumulated quantity of the equivalent hydrogen consumption of the whole vehicle, the required power of the fuel cell and the required power of the storage battery at the previous moment;

the third determining submodule is used for determining feedback reward according to the SOC of the storage battery at the previous moment, the accumulated amount of the equivalent hydrogen consumption of the whole vehicle, the required power of the fuel cell and the required power of the storage battery through a reward function of the DDPG model;

a fourth determining sub-module, configured to use the second driving parameter and the feedback reward as inputs of the DDPG model, and determine the equivalence factor through the DDPG model.

Optionally, the apparatus further comprises:

the third acquisition module is used for acquiring driving parameters of the virtual hydrogen fuel cell electric automobile in the simulation model under target working conditions, wherein the target working conditions comprise NEDC working conditions;

the sorting module is used for sorting the importance of the driving parameters under the target working condition through a Relieff algorithm to obtain an importance sorting result;

and the fourth determination module is used for determining the first driving parameter according to the importance ranking result.

In another aspect, an electronic device is provided, which includes a memory for storing a computer program and a processor for executing the computer program stored in the memory to implement the steps of the energy management method for a hydrogen fuel cell electric vehicle according to any one of the above aspects.

In another aspect, a computer-readable storage medium is provided, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the energy management method for a hydrogen fuel cell electric vehicle according to any one of the above aspects.

In another aspect, a computer program product containing instructions is provided, which when run on a computer, causes the computer to perform the steps of the method for energy management of a hydrogen fuel cell electric vehicle according to any of the above aspects.

The beneficial effects brought by the technical scheme provided by the embodiment of the application at least comprise:

in the embodiment of the application, the energy management model determines the output power of the fuel cell and the output power of the storage battery according to the instantaneous equivalent factor, and the instantaneous equivalent factor is determined according to the driving parameters at the current moment, so that the working condition information at the current moment can be accurately reflected. And meanwhile, when the instantaneous equivalent factor is determined, the SOC of the storage battery is limited by using the regulating coefficient, so that the determined instantaneous electric quantity consumption of the storage battery is more accurate, and under the condition, the determined instantaneous equivalent factor can reflect the conversion relation between the electric energy consumed by the storage battery and the hydrogen consumption of the fuel cell, so that the output power is distributed according to the instantaneous equivalent factor, and the lowest fuel consumption can be ensured.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

FIG. 1 is a schematic diagram of an energy management device provided by an embodiment of the present application;

FIG. 2 is a flow chart of a method for energy management of a hydrogen fuel cell electric vehicle according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of an energy management device of a hydrogen fuel cell electric vehicle according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of a terminal according to an embodiment of the present application.

Detailed Description

To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.

Before explaining the energy management method of the hydrogen fuel cell electric vehicle provided in the embodiment of the present application in detail, terms, application scenarios and implementation environments related to the embodiment of the present application will be briefly described.

1. Industrial model of hydrogen fuel cell electric automobile

In a hydrogen fuel cell electric vehicle, hydrogen and oxygen in the air react in a specific environment of a fuel cell system to generate electric energy, the electric energy is used as a main power source of the hydrogen fuel cell electric vehicle, and is connected with a storage battery (an auxiliary power source) in series or in parallel after impedance matching and voltage conversion of a direct current-direct current (DC/DC) converter, and the two are used as input energy of a motor driving system together to provide power required by vehicle running.

The hydrogen fuel cell electric automobile can be powered by a fuel cell and/or a storage battery during running, and the power source for supplying running power can be different under different driving modes.

(1) Battery only drive mode

The vehicle is driven only by the power supply of the storage battery, and the mode is mainly used for low-speed running and reverse working conditions of the vehicle.

(2) Pure fuel cell drive mode

The running power required for driving the vehicle comes from a fuel cell, and the storage battery neither provides energy nor receives energy, and the mode is mainly used for medium-speed and high-speed running conditions of the vehicle.

(3) Hybrid drive mode

The running power required by the vehicle driving is from the fuel cell and the storage battery, the electric energy output by the fuel cell and the electric energy provided by the storage battery are coupled by the motor controller to jointly provide the power required by the vehicle running, and the mode is mainly used for the vehicle acceleration and the climbing running working condition.

(4) Driven by fuel cell and in charging mode

After the fuel cell consumes hydrogen to generate electric energy, the energy is distributed by the motor controller, one part is used for driving the vehicle, the other part is used for charging the storage battery, and the mode is mainly used for the working condition that the vehicle runs at low load and the SOC of the storage battery is low.

The SOC of the storage battery is used for reflecting the amount of energy stored in the storage battery, and is numerically defined as the ratio of the residual energy to the total energy which can be stored in the storage battery, and is expressed by a common percentage.

(5) Regenerative braking mode

The motor works in a power generation mode, converts kinetic energy from wheels into electric energy and charges a storage battery, and the mode is mainly used for vehicle braking and downhill working conditions.

(6) Battery charging mode

The motor does not receive energy, the fuel cell consumes hydrogen to generate electric energy to charge the storage battery, and the mode is mainly used for the working condition that the vehicle is static and the SOC of the storage battery is low.

2. Equivalent hydrogen consumption minimization strategy

Under the condition of not considering external energy input, the energy consumption of the hydrogen fuel cell electric automobile is finally provided by the consumption of hydrogen by the fuel cell, and the equivalent hydrogen consumption is the hydrogen consumption of the fuel cell by converting the electric energy consumed by the storage battery from the aspect of energy conservation. And the ECMS converts the electric energy consumed by the storage battery at the current moment into the hydrogen consumption of the fuel cell at the future moment through the equivalent factor, and takes the sum of the hydrogen consumption of the fuel cell and the equivalent hydrogen consumption of the storage battery as the equivalent hydrogen consumption of the whole vehicle. The fuel consumption of the vehicle is lowest under the condition that the equivalent hydrogen consumption of the whole vehicle is minimum.

That is, ECMS includes two layers of meaning: equivalent hydrogen consumption and transient optimization.

(1) Equivalent hydrogen consumption

For the electric quantity maintaining type hybrid electric vehicle, the electric energy consumed by the battery at the current moment is compensated by the hydrogen consumed by the fuel battery at the future moment. Therefore, an equivalent relationship between the consumed electric energy and the hydrogen consumption required for compensating the electric energy needs to be established, and the hydrogen consumption of the fuel cell and the equivalent hydrogen consumption of the battery consumed electric energy at a certain moment are summarized as a unified energy consumption index to be used as a control target of the optimization control.

(2) Instantaneous optimization

And according to the actual running state of the hybrid electric vehicle, optimizing the distribution result of the driving power required by the running of the vehicle between the fuel cell and the storage battery in real time within each control time so as to minimize the equivalent hydrogen consumption of the whole vehicle as a control target, thereby determining the working mode and the power distribution of the power assembly.

The objective function for ECMS is:

wherein M is_eqh(t) instantaneous equivalent hydrogen consumption of the entire vehicle, M_fch(t) instantaneous hydrogen consumption of the fuel cell, M_bh(t) instantaneous hydrogen consumption of the battery, S (t) instantaneous equivalent factor, pb (t) instantaneous output power of the battery, Q_H2Is the heating value of hydrogen.

That is, the core of the ECMS strategy is to obtain the equivalent factor s when the hydrogen consumption of the whole vehicle is minimum. In a hydrogen fuel cell electric vehicle, a storage battery is used as an energy storage element, and in order to make the state of the energy of the storage battery reached at the future time coincide with the initial state, the electric energy consumed by the battery at the current time is compensated by the consumption of hydrogen gas by the fuel cell at the future time. Since the future operating conditions are unknown, it is unknown under what operating conditions this portion of energy compensates, i.e. the equivalence factor should be an indeterminate value. The key of the ECMS is to find out the optimal equivalent factor at the current moment, so that the electric energy consumed by the current storage battery can have the optimal conversion efficiency in the future compensation, and on the premise, the energy at the current moment has the optimal condition.

Next, application scenarios and implementation environments related to the application embodiments are briefly described.

With the increasing attention of people to the environment, new energy automobiles such as hydrogen fuel cell electric automobiles and the like have attracted wide attention due to the characteristic of small environmental pollution. In order to ensure that the hydrogen fuel cell electric vehicle has the lowest fuel consumption during running, the electric energy provided by the fuel cell in the hydrogen fuel cell electric vehicle and the electric energy provided by the storage battery need to be distributed, that is, the output power of the fuel cell and the output power of the storage battery are adjusted in real time, so that the fuel cell and the storage battery jointly provide the driving power required by running for the hydrogen fuel cell electric vehicle.

In order to facilitate energy unified management, the ECMS converts the electric energy consumption of the storage battery at the current moment into the hydrogen consumption of the fuel cell at the future moment by the equivalent factor, so that the sum of the hydrogen consumption of the fuel cell and the equivalent hydrogen consumption of the storage battery can be used as the equivalent hydrogen consumption of the whole vehicle. The fuel consumption of the vehicle is lowest under the condition that the equivalent hydrogen consumption of the whole vehicle is minimum.

In the related art, the equivalence factor may use a constant value s for a given driving cycle₀(optimal equivalence factor) to optimize the energy distribution, wherein the equivalence factor at any moment is the optimal equivalence factor in the driving cycle. The method is simple and easy to implement, but cannot explain the influence of the equivalent factors on which factors are influenced, the influence of the whole driving cycle is large, and the working condition adaptability is poor.

Based on the application scenario, in order to improve the global optimality of the equivalent factor, improve the working condition adaptability of the strategy and ensure that the energy distribution under various working conditions can reach the lowest fuel consumption, the embodiment of the application provides an energy management method of a hydrogen fuel cell electric vehicle.

Fig. 1 is a schematic diagram of an energy management apparatus 100 provided in an embodiment of the present application, where the apparatus is located in an electronic device. A virtual hydrogen fuel cell electric automobile model is established in an MATLAB/Simulink simulation environment of electronic equipment, and a standard NEDC working condition is adopted in a driving working condition. In the virtual hydrogen fuel cell electric vehicle, the energy management device 100 distributes the output power of the fuel cell and the battery according to the driving power required by the virtual hydrogen fuel cell electric vehicle during the simulated driving, thereby realizing the distribution and management of energy. Referring to fig. 1, the energy management device 100 includes: a driving module 101, a policy determination network module 102, and an energy management module 103.

The driving module 101 collects driving parameters generated in the simulation driving process, and the strategy determination network module 102 is used for acquiring the driving parameters from the driving module 101, and further determining the instantaneous equivalent factor by analyzing and processing the driving parameters. The energy management module 103 determines the output power of the storage battery and the output power of the fuel cell according to the instantaneous equivalent factor, and completes energy management.

Optionally, the driving module 101 may include a driver behavior module and a virtual electric vehicle module. The driver behavior module is used for acquiring the accelerator pedaling condition and the brake pedaling condition of a driver under different working conditions and outputting an accelerator pedaling amplitude and a brake pedaling amplitude. The virtual electric vehicle module is used for acquiring driving parameters generated by the virtual hydrogen fuel cell electric vehicle in the simulation environment in the process of simulating the actual vehicle running.

It should be noted that, when the energy management device provided in the embodiment of the present application is explained, only the three functional modules are taken as examples, and the energy management device may further include other functional modules, and the above examples do not limit the embodiment of the present application.

Based on the above energy management device, the energy management method of the hydrogen fuel cell electric vehicle according to the embodiment of the present application will be explained in detail. Referring to fig. 2, fig. 2 is a flowchart of an energy management method for a hydrogen fuel cell electric vehicle according to an embodiment of the present application, where the method is applied to the energy management apparatus shown in fig. 1 to implement energy allocation and management in a virtual hydrogen fuel cell electric vehicle, and the method includes the following steps:

step 201: the method comprises the steps of obtaining a first driving parameter and a second driving parameter, wherein the first driving parameter comprises the required power of the whole vehicle, the change rate of the required power, the current state of charge (SOC) of a storage battery, the tread amplitude of an accelerator pedal and the tread amplitude of a brake pedal, and the second driving parameter comprises the driving speed, the acceleration and the current SOC of the storage battery.

It should be noted that the required power change rate is an instantaneous change rate of the required power of the entire vehicle, the stepping amplitude of the accelerator pedal is between 0 and 1, and the stepping amplitude of the brake pedal is between-1 and 0. The first driving parameter and the second driving parameter may be obtained from a simulation model of the hydrogen fuel cell electric vehicle.

In a possible implementation manner, since the simulation model can simulate various driving parameters generated by the physical electric vehicle during driving, when the virtual hydrogen fuel cell electric vehicle is driven by simulation through the simulation model, the driving parameters of the virtual hydrogen fuel cell electric vehicle are obtained from the simulation model, and the first driving parameters and the second driving parameters are determined according to the obtained driving parameters.

It should be noted that the above-mentioned "first" and "second" merely classify the driving parameters for use in subsequent calculations, and do not limit the driving parameters in the embodiments of the present application.

In addition, in order to determine the core factors affecting the equivalent factor and reduce the subsequent data processing amount, the data output by the simulation model needs to be preprocessed before the first driving parameter is acquired. That is, the acquired first driving parameter may be previously processed.

In one possible implementation manner, the process of obtaining the first driving parameter may be: the method comprises the steps of obtaining driving parameters of a virtual hydrogen fuel cell electric automobile in a simulation model under a target working condition, wherein the target working condition comprises a NEDC working condition, conducting importance ranking on the driving parameters under the target working condition through a Relieff algorithm to obtain an importance ranking result, and determining a first driving parameter according to the importance ranking result.

The driving parameters of the virtual hydrogen fuel cell electric vehicle in the simulation model under the target working condition include but are not limited to: the required power of the whole vehicle, the change rate of the required power, the current state of charge (SOC) of the storage battery, the stepping amplitude of an accelerator pedal, the stepping amplitude of a brake pedal, the driving speed and the acceleration.

As an example, the driving parameters under the target condition include a plurality of data sets, the same data set belongs to the same category, the ReliefF algorithm selects one data from all driving parameters as the target data, selects N neighbor data (neighbor Hits) from the data set belonging to the same category as the target data, selects N neighbor data (neighbor Misses) from each data set belonging to different categories as the target data, gives different weights to each category according to the correlation between the N neighbor data and the charge and discharge state of the storage battery under each category, and if the weight of a certain category is less than the importance threshold, the category is considered to have little influence on the energy distribution of the virtual hydrogen fuel cell electric vehicle and can be ignored.

The neighbor data and the target data have certain similarity under the same category, the similarity can be represented by the distance between the neighbor data and the target data, all the distances are sorted, and N neighbor data which are closer to the target data are selected. The distance may be an euclidean distance or a minkowski distance, which is not limited by the embodiments of the present application.

Therefore, the obtained multiple driving parameters are screened, the subsequent data processing amount is reduced, and meanwhile, the core driving parameters influencing the charge and discharge state of the storage battery are determined and serve as the first driving parameters.

In addition, the working condition is used for reflecting the running condition of the automobile on different road conditions, and the test working condition in the simulation is substantially to simulate the running condition of the automobile in urban areas or suburbs in different areas, including accelerating climbing, decelerating, uniform speed and the like. Different driving condition graphs can be used for representing different working conditions, and the driving condition graph is a speed-time curve and reflects the speed change condition of the automobile on the road.

The conditions include an ECE (Economic Commission for Europe), a UDC (Urban circulation), an EUDC (Extra Urban circulation) and a NEDC.

As an example, in order to consider traffic information as comprehensive as possible, the target condition adopted in the embodiment of the present application may be an NEDC condition. The NEDC working condition consists of four urban circulation and one suburban circulation, wherein in the urban circulation, the maximum speed is 50km/h, and the average speed is 19 km/h; the maximum vehicle speed is 120km/h and the average vehicle speed is 62km/h in a suburban cycle.

Step 202: and determining an adjustment coefficient of the SOC according to the first driving parameter.

In one possible implementation manner, the implementation procedure of step 302 may be: and inputting the first driving parameter into a battery state prediction model, determining the charge-discharge state of the storage battery through the battery state prediction model, and determining the adjustment coefficient of the SOC according to the charge-discharge state of the storage battery.

The charging and discharging state of the battery comprises a charging state and a discharging state. In addition, since the battery serves as an energy storage element, in order to match a state of energy of the battery reached at a future time with an initial state, it is necessary to determine a change in SOC of the battery with respect to the initial SOC and adjust the change. Therefore, the adjustment coefficient of the SOC at the present time is determined according to the charge/discharge state of the battery. The SOC regulation coefficient in the charging state is larger than 1, the SOC regulation coefficient in the discharging state is smaller than 1, and the specific value of the initial SOC can be preset.

As one example, the adjustment coefficient for the state of charge may be 1.6 and the adjustment coefficient for the SOC may be 0.4.

Before determining the charge/discharge state of the battery using the battery state prediction model, the initial battery state prediction model needs to be trained to obtain the battery state prediction model.

In one possible implementation mode, acquiring a plurality of groups of sample driving parameters and battery charge-discharge states corresponding to the sample driving parameters; and taking the multiple groups of sample driving parameters as the input of the initial battery state prediction model, taking the battery charge-discharge states corresponding to the multiple groups of sample driving parameters as the output of the initial battery state prediction model, and training the initial battery state prediction model to obtain the battery state prediction model.

It should be noted that, the battery charge-discharge state corresponding to the obtained multiple sets of sample driving parameters and each set of sample driving parameters may be determined by a planning algorithm (DP or MPC, etc.), and the DP algorithm needs to know the control requirement of the driving condition in advance. Then, the DP algorithm divides the whole running working condition into a plurality of ordered stages according to time or space characteristics, the optimization objective function of the DP and the ECMS objective function are always used, and under the condition that the lowest fuel consumption is guaranteed, namely the equivalent hydrogen consumption of the whole vehicle is the minimum, the driving parameters of each stage and the battery charge-discharge state of the storage battery are obtained and used as samples for training a battery state prediction model.

The training battery state prediction model may be a RUSBoost model, the RUSBoost is a clustering algorithm for unbalanced data sets, the RUS (random undersampling) is a training data set in which a certain amount of majority class samples and minority class samples are randomly extracted from the data set to form a balanced distribution, and the Boost refers to Adaboost. M2 algorithm. And the RUSBoost is to extract a training data set by using an RUS method according to each round of iteration data of the Adaboost. M2 algorithm, and then train a weak classifier according to the extracted training data set to finally obtain a trained RUSBoost model.

Step 203: and taking the second driving parameter as an input of the strategy determination network model, and determining an equivalent factor through the strategy determination network model, wherein the equivalent factor is used for indicating an equivalent relation between the electric energy of the storage battery and the hydrogen consumption of the fuel cell.

The second driving parameters comprise driving speed, acceleration and the current SOC of the storage battery and are used for reflecting the working condition of the automobile at the current moment.

In a possible implementation manner, if the policy determines that the network model is a depth deterministic gradient algorithm DDPG model, the implementation process of step 203 is as follows: acquiring the SOC of a storage battery at the previous moment, the accumulated quantity of the equivalent hydrogen consumption of the whole vehicle, the required power of a fuel cell and the required power of the storage battery, and determining a feedback reward through a reward function of a DDPG model according to the SOC of the storage battery at the previous moment, the accumulated quantity of the equivalent hydrogen consumption of the whole vehicle, the required power of the fuel cell and the required power of the storage battery; and taking the second driving parameter and the feedback reward as input of the DDPG model, and determining the equivalent factor through the DDPG model.

The DDPG model is a deepened intensity learning model, and can determine the optimal action value at the current moment according to various state parameters of the current working condition of the automobile, wherein the optimal action value is the equivalent factor in the embodiment of the application. Wherein the status parameters include, but are not limited to: SOC of the storage battery, accumulated quantity of equivalent hydrogen consumption of the whole vehicle, required power of the fuel cell and required power of the storage battery.

The DDPG model includes four networks: the network node comprises an Actor current network, an Actor target network, a criticic current network and a criticic target network, wherein the structures of 2 Actor networks are the same, and the structures of 2 criticic networks are the same. In addition, the DDPG model also comprises an experience pool, and the size of the experience pool can be preset and is used for storing the state parameters at each moment in the whole driving cycle and the optimal action value at each moment. The Actor current network is responsible for selecting the optimal action value at the previous moment from the experience pool according to the state parameter at the previous moment randomly selected from the experience pool; the Actor target network is responsible for selecting the optimal action value at the next moment according to the state parameter sampled in the experience pool at the next moment; the criticic current network is responsible for determining feedback rewards aiming at the hydrogen fuel cell electric vehicle simulation model according to state parameters at the previous moment, and the feedback rewards are used for adjusting the simulation output at the current moment to enable the simulation output to be more consistent with the actual working condition, so that the accuracy of determining the equivalent factors is improved. The criticic target network is responsible for adjusting the optimal action value of the Actor at the current time selected by the current network at the current time according to the second driving parameter acquired at the current time and the feedback reward at the current time, so as to obtain the adjusted action value, namely the equivalent factor.

The reward function of the DDPG model is as follows:

wherein, reward represents a feedback award,

c, accumulating the equivalent hydrogen consumption of the whole vehicle, and when the required power of the fuel cell and the required power of the storage battery are in respective power value ranges₁The value is 1, otherwise the value is 0. When the SOC value of the storage battery is between 0.4 and 0.7, c₂The value is 1, otherwise the value is 0.

Step 204: and the required power of the whole vehicle, the regulating coefficient and the equivalent factor are used as the input of an energy management model, and the first required power of the fuel cell and the second required power of the storage battery are determined through the energy management model.

Wherein the first required power is between the minimum power and the maximum power of the fuel cell, and the second required power is between the minimum power and the maximum power of the storage battery.

It should be noted that the energy management model is established based on an instantaneous equivalent consumption minimum control strategy ECMS, an objective function of the ECMS is a minimum value of instantaneous equivalent hydrogen consumption of the whole vehicle, and the instantaneous equivalent hydrogen consumption of the whole vehicle is a sum of the instantaneous hydrogen consumption of the fuel cell and the instantaneous equivalent hydrogen consumption of the storage battery.

The energy management model constructed in the embodiment of the application has an objective function as follows:

wherein M is_eqh(t) instantaneous equivalent hydrogen consumption of the entire vehicle, M_fch(t) instantaneous hydrogen consumption of the fuel cell, M_bh(t) instantaneous hydrogen consumption of the battery, S (t) instantaneous equivalent factor, u adjustment coefficient for limiting the variation range of SOC of the battery, pb (t) instantaneous output power of the battery,

is the heating value of hydrogen.

Since the SOC of the battery in different charging and discharging states may be different, the SOC reflects the stored energy of the battery, and when the equivalent factor is used to convert the electric energy consumed by the battery into the amount of hydrogen consumed by the fuel cell, the current SOC of the battery also affects the accuracy of the equivalent factor determined at the current time. Therefore, when the objective function of the energy management model is constructed, the SOC of the storage battery is fully considered, the adjustment coefficient is adopted to limit the SOC, and when the SOC of the storage battery is at the optimal value, the electric energy consumption of the storage battery is more accurately counted, so that the determined equivalent factor is the optimal instantaneous equivalent factor at the current moment.

Step 205: and adjusting the output power of the fuel cell to be the first required power, adjusting the output power of the storage battery to be the second required power, and providing power for the hydrogen fuel cell electric automobile by adopting the fuel cell and the storage battery.

In one possible implementation manner, after the energy management model determines the output power of the fuel cell and the output power of the storage battery, the output power of the fuel cell in the virtual hydrogen fuel cell electric vehicle is adjusted to the first required power, and the output power of the storage battery is adjusted to the second required power. That is, the sum of the first required power and the second required power is used as the driving power of the entire vehicle, and the fuel consumption of the virtual hydrogen fuel cell electric vehicle is minimized when the virtual hydrogen fuel cell electric vehicle performs the simulation driving using the driving power.

Wherein the first required power is determined to be between the maximum power and the minimum power of the fuel cell, and the second required power is determined to be between the maximum power and the minimum power of the storage battery. During the running of the hydrogen fuel cell electric automobile, the sum of the first required power of the fuel cell and the second required power of the storage battery is not less than the total driving power during the running of the hydrogen fuel cell electric automobile.

In the above description, the output power of the fuel cell and the output power of the battery of the virtual hydrogen fuel cell electric vehicle are determined in the simulation environment, and the output power is provided to the virtual hydrogen fuel cell electric vehicle through the fuel cell and the battery. In practical application, the energy management device can be deployed in a real hydrogen fuel cell electric automobile, so that the fuel consumption of the real hydrogen fuel cell electric automobile during running is ensured to be minimum.

All the above optional technical solutions can be combined arbitrarily to form an optional embodiment of the present application, and the present application embodiment is not described in detail again.

Fig. 3 is a schematic structural diagram of an energy management device of a hydrogen fuel cell electric vehicle according to an embodiment of the present application, where the device 300 may be implemented by software, hardware, or a combination of the two. The apparatus 300 comprises: a first obtaining module 301, a first determining module 302, a second determining module 303, a third determining module 304 and an adjusting module 305.

The first obtaining module 301 is configured to obtain a first driving parameter and a second driving parameter, where the first driving parameter includes a required power of the entire vehicle, a required power change rate, a current SOC of the battery, a tread amplitude of an accelerator pedal, and a tread amplitude of a brake pedal, and the second driving parameter includes a driving speed, an acceleration, and the current SOC of the battery;

a first determining module 302, configured to determine an adjustment coefficient of the SOC according to the first driving parameter;

a second determining module 303, configured to determine an equivalence factor through the policy determination network model using the second driving parameter as an input of the policy determination network model, where the equivalence factor is used to indicate an equivalence relation between the electrical energy of the battery and the hydrogen consumption of the fuel cell;

a third determining module 304, configured to use the vehicle power demand, the adjustment coefficient, and the equivalent factor as inputs of an energy management model, and determine a first power demand of the fuel cell and a second power demand of the battery through the energy management model, where the first power demand is between a minimum power and a maximum power of the fuel cell, and the second power demand is between the minimum power and the maximum power of the battery;

and the adjusting module 305 is used for adjusting the output power of the fuel cell to the first required power, adjusting the output power of the storage battery to the second required power, and supplying power to the hydrogen fuel cell electric automobile by using the fuel cell and the storage battery.

Optionally, the first determining module 302 includes:

the first determining submodule is used for inputting the first driving parameter into the battery state prediction model and determining the charge and discharge state of the storage battery through the battery state prediction model;

and the second determining submodule is used for determining the adjustment coefficient of the SOC according to the charge-discharge state of the storage battery.

Optionally, the apparatus 300 further comprises:

and the training module is used for taking the multiple groups of sample driving parameters as the input of the initial battery state prediction model, taking the battery charge-discharge states corresponding to the multiple groups of sample driving parameters as the output of the initial battery state prediction model, and training the initial battery state prediction model to obtain the battery state prediction model.

Optionally, the policy determines that the network model is a DDPG model;

a second determining module 303, comprising:

the acquisition submodule is used for acquiring the SOC of the storage battery at the previous moment, the accumulated quantity of the equivalent hydrogen consumption of the whole vehicle, the required power of the fuel cell and the required power of the storage battery;

the third determining submodule is used for determining feedback reward according to the SOC of the storage battery at the previous moment, the accumulated quantity of equivalent hydrogen consumption of the whole vehicle, the required power of the fuel cell and the required power of the storage battery through a reward function of the DDPG model;

and the fourth determination sub-module is used for taking the second driving parameter and the feedback reward as the input of the DDPG model, and determining the equivalent factor through the DDPG model.

Optionally, the apparatus 300 further comprises:

the third acquisition module is used for acquiring driving parameters of the virtual hydrogen fuel cell electric automobile in the simulation model under a target working condition, wherein the target working condition comprises a NEDC working condition;

and the fourth determining module is used for determining the first driving parameter according to the importance ranking result.

It should be noted that: the energy management device for a hydrogen fuel cell electric vehicle provided in the above embodiment is only illustrated by dividing the above functional modules when managing the energy distribution of the hydrogen fuel cell electric vehicle, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the above described functions. In addition, the energy management device of the hydrogen fuel cell electric vehicle provided by the above embodiment and the energy management method embodiment of the hydrogen fuel cell electric vehicle belong to the same concept, and the specific implementation process thereof is described in the method embodiment and is not described herein again.

Fig. 4 is a block diagram of an electronic device 400 according to an embodiment of the present disclosure. The electronic device 400 may be: a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III, motion video Experts compression standard Audio Layer 3), an MP4 player (Moving Picture Experts Group Audio Layer IV, motion video Experts compression standard Audio Layer 4), a notebook computer, or a desktop computer. Electronic device 400 may also be referred to by other names as user equipment, portable electronic device, laptop electronic device, desktop electronic device, and so on.

In general, the electronic device 400 includes: a processor 401 and a memory 402.

Processor 401 may include one or more processing cores, such as a 4-core processor, an 8-core processor, or the like. The processor 401 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 401 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 401 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed by the display screen. In some embodiments, the processor 401 may further include an AI (Artificial Intelligence) processor for processing computing operations related to machine learning.

Memory 402 may include one or more computer-readable storage media, which may be non-transitory. Memory 402 may also include high speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 402 is used to store at least one instruction for execution by processor 401 to implement the method of energy management for a hydrogen fuel cell electric vehicle provided by the method embodiments herein.

In some embodiments, the electronic device 400 may further optionally include: a peripheral interface 403 and at least one peripheral. The processor 401, memory 402 and peripheral interface 403 may be connected by bus or signal lines. Each peripheral may be connected to the peripheral interface 403 via a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of radio frequency circuitry 404, touch screen display 405, camera 406, audio circuitry 407, positioning components 408, and power supply 409.

The peripheral interface 403 may be used to connect at least one peripheral related to I/O (Input/Output) to the processor 401 and the memory 402. In some embodiments, processor 401, memory 402, and peripheral interface 403 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 401, the memory 402 and the peripheral interface 403 may be implemented on a separate chip or circuit board, which is not limited by this embodiment.

The Radio Frequency circuit 404 is used for receiving and transmitting RF (Radio Frequency) signals, also called electromagnetic signals. The radio frequency circuitry 404 communicates with communication networks and other communication devices via electromagnetic signals. The rf circuit 404 converts an electrical signal into an electromagnetic signal to transmit, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 404 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and so forth. The radio frequency circuitry 404 may communicate with other electronic devices via at least one wireless communication protocol. The wireless communication protocols include, but are not limited to: metropolitan area networks, various generation mobile communication networks (2G, 3G, 4G, and 5G), Wireless local area networks, and/or WiFi (Wireless Fidelity) networks. In some embodiments, the radio frequency circuit 404 may further include a circuit related to NFC (Near Field Communication), which is not limited in this application.

The display screen 405 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. When the display screen 405 is a touch display screen, the display screen 405 also has the ability to capture touch signals on or over the surface of the display screen 405. The touch signal may be input to the processor 401 as a control signal for processing. At this point, the display screen 405 may also be used to provide virtual buttons and/or a virtual keyboard, also referred to as soft buttons and/or a soft keyboard. In some embodiments, the display screen 405 may be one, providing the front panel of the electronic device 400; in other embodiments, the display screen 405 may be at least two, respectively disposed on different surfaces of the electronic device 400 or in a folded design; in still other embodiments, the display screen 405 may be a flexible display screen disposed on a curved surface or a folded surface of the electronic device 400. Even further, the display screen 405 may be arranged in a non-rectangular irregular pattern, i.e. a shaped screen. The Display screen 405 may be made of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), and other materials.

The camera assembly 406 is used to capture images or video. Optionally, camera assembly 406 includes a front camera and a rear camera. Generally, a front camera is disposed on a front panel of an electronic apparatus, and a rear camera is disposed on a rear surface of the electronic apparatus. In some embodiments, the number of the rear cameras is at least two, and each rear camera is any one of a main camera, a depth-of-field camera, a wide-angle camera and a telephoto camera, so that the main camera and the depth-of-field camera are fused to realize a background blurring function, and the main camera and the wide-angle camera are fused to realize panoramic shooting and VR (Virtual Reality) shooting functions or other fusion shooting functions. In some embodiments, camera assembly 406 may also include a flash. The flash lamp can be a monochrome temperature flash lamp or a bicolor temperature flash lamp. The double-color-temperature flash lamp is a combination of a warm-light flash lamp and a cold-light flash lamp, and can be used for light compensation at different color temperatures.

The audio circuit 407 may include a microphone and a speaker. The microphone is used for collecting sound waves of a user and the environment, converting the sound waves into electric signals, and inputting the electric signals to the processor 401 for processing, or inputting the electric signals to the radio frequency circuit 404 for realizing voice communication. For stereo capture or noise reduction purposes, the microphones may be multiple and disposed at different locations of the electronic device 400. The microphone may also be an array microphone or an omni-directional pick-up microphone. The speaker is used to convert electrical signals from the processor 401 or the radio frequency circuit 404 into sound waves. The loudspeaker can be a traditional film loudspeaker or a piezoelectric ceramic loudspeaker. When the speaker is a piezoelectric ceramic speaker, the speaker can be used for purposes such as converting an electric signal into a sound wave audible to a human being, or converting an electric signal into a sound wave inaudible to a human being to measure a distance. In some embodiments, audio circuitry 407 may also include a headphone jack.

The positioning component 408 is used to locate the current geographic Location of the electronic device 400 for navigation or LBS (Location Based Service). The Positioning component 408 may be a Positioning component based on the GPS (Global Positioning System) of the united states, the beidou System of china, the graves System of russia, or the galileo System of the european union.

The power supply 409 is used to supply power to the various components in the electronic device 400. The power source 409 may be alternating current, direct current, disposable or rechargeable. When power source 409 comprises a rechargeable battery, the rechargeable battery may support wired or wireless charging. The rechargeable battery may also be used to support fast charge technology.

In some embodiments, the electronic device 400 also includes one or more sensors 410. The one or more sensors 410 include, but are not limited to: acceleration sensor 411, gyro sensor 412, pressure sensor 413, fingerprint sensor 414, optical sensor 415, and proximity sensor 416.

The acceleration sensor 411 may detect the magnitude of acceleration in three coordinate axes of a coordinate system established with the electronic apparatus 400. For example, the acceleration sensor 411 may be used to detect components of the gravitational acceleration in three coordinate axes. The processor 401 may control the touch display screen 405 to display the user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 411. The acceleration sensor 411 may also be used for acquisition of motion data of a game or a user.

The gyro sensor 412 may detect a body direction and a rotation angle of the electronic device 400, and the gyro sensor 412 may cooperate with the acceleration sensor 411 to acquire a 3D motion of the user on the electronic device 400. From the data collected by the gyro sensor 412, the processor 401 may implement the following functions: motion sensing (such as changing the UI according to a user's tilting operation), image stabilization at the time of photographing, game control, and inertial navigation.

The pressure sensors 413 may be disposed on a side bezel of the electronic device 400 and/or on a lower layer of the touch display screen 405. When the pressure sensor 413 is arranged on the side frame of the electronic device 400, a holding signal of the user to the electronic device 400 can be detected, and the processor 401 performs left-right hand identification or shortcut operation according to the holding signal collected by the pressure sensor 413. When the pressure sensor 413 is disposed at the lower layer of the touch display screen 405, the processor 401 controls the operability control on the UI interface according to the pressure operation of the user on the touch display screen 405. The operability control comprises at least one of a button control, a scroll bar control, an icon control and a menu control.

The fingerprint sensor 414 is used for collecting a fingerprint of the user, and the processor 401 identifies the identity of the user according to the fingerprint collected by the fingerprint sensor 414, or the fingerprint sensor 414 identifies the identity of the user according to the collected fingerprint. Upon recognizing that the user's identity is a trusted identity, processor 401 authorizes the user to perform relevant sensitive operations including unlocking the screen, viewing encrypted information, downloading software, paying, and changing settings, etc. The fingerprint sensor 414 may be disposed on the front, back, or side of the electronic device 400. When a physical button or vendor Logo is provided on the electronic device 400, the fingerprint sensor 414 may be integrated with the physical button or vendor Logo.

The optical sensor 415 is used to collect the ambient light intensity. In one embodiment, the processor 401 may control the display brightness of the touch display screen 405 based on the ambient light intensity collected by the optical sensor 415. Specifically, when the ambient light intensity is high, the display brightness of the touch display screen 405 is increased; when the ambient light intensity is low, the display brightness of the touch display screen 405 is turned down. In another embodiment, the processor 401 may also dynamically adjust the shooting parameters of the camera assembly 406 according to the ambient light intensity collected by the optical sensor 415.

Proximity sensor 416, also known as a distance sensor, is typically disposed on the front panel of electronic device 400. The proximity sensor 416 is used to capture the distance between the user and the front of the electronic device 400. In one embodiment, the processor 401 controls the touch display screen 405 to switch from the bright screen state to the dark screen state when the proximity sensor 416 detects that the distance between the user and the front surface of the electronic device 400 gradually decreases; when the proximity sensor 416 detects that the distance between the user and the front of the electronic device 400 is gradually increased, the processor 401 controls the touch display screen 405 to switch from the breath screen state to the bright screen state.

Those skilled in the art will appreciate that the configuration shown in fig. 4 does not constitute a limitation of the electronic device 400, and may include more or fewer components than those shown, or combine certain components, or employ a different arrangement of components.

The embodiment of the application also provides a computer readable storage medium, and a computer program is stored in the storage medium, and when being executed by a processor, the computer program realizes the steps of the energy management method of the hydrogen fuel cell electric vehicle in the embodiment. For example, the computer-readable storage medium may be a ROM (Read-Only Memory), a RAM (Random Access Memory), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

It is noted that the computer-readable storage medium referred to in the embodiments of the present application may be a non-volatile storage medium, in other words, a non-transitory storage medium.

The embodiment of the present application also provides a computer program product containing instructions, which when run on a computer, causes the computer to execute the steps of the energy management method of the hydrogen fuel cell electric vehicle in the above embodiment.

It should be understood that all or part of the steps for implementing the above embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The computer instructions may be stored in the computer-readable storage medium described above.

The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method of energy management for a hydrogen fuel cell electric vehicle, the method comprising:

acquiring a first driving parameter and a second driving parameter, wherein the first driving parameter comprises the required power of the whole vehicle, the change rate of the required power, the current state of charge (SOC) of a storage battery, the tread amplitude of an accelerator pedal and the tread amplitude of a brake pedal, and the second driving parameter comprises the driving speed, the acceleration and the current SOC of the storage battery;

2. The method of claim 1, wherein determining an adjustment factor for the SOC based on the first driving parameter comprises:

3. The method of claim 2, wherein prior to inputting the first driving parameter into a battery state prediction model from which the charge-discharge state of the battery is determined, the method further comprises:

4. The method of claim 1, wherein the energy management model is established based on an instantaneous equivalent consumption minimum control strategy (ECMS), the objective function of the ECMS is a minimum value of an entire vehicle instantaneous equivalent hydrogen consumption, and the entire vehicle instantaneous equivalent hydrogen consumption is a sum of a fuel cell instantaneous hydrogen consumption and a storage battery instantaneous equivalent hydrogen consumption.

5. The method of claim 1, wherein the policy-determined network model is a depth-deterministic gradient algorithm (DDPG) model;

6. The method of claim 1, wherein prior to obtaining the first driving parameter, the method further comprises:

acquiring driving parameters of a virtual hydrogen fuel cell electric automobile in a simulation model under a target working condition, wherein the target working condition comprises a new European automobile regulation circulation NEDC working condition;

7. An energy management device for a hydrogen fuel cell electric vehicle, the device comprising:

the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring a first driving parameter and a second driving parameter, the first driving parameter comprises the required power of the whole vehicle, the change rate of the required power, the current state of charge (SOC) of a storage battery, the tread amplitude of an accelerator pedal and the tread amplitude of a brake pedal, and the second driving parameter comprises the driving speed, the acceleration and the current SOC of the storage battery;

8. The apparatus of claim 7, wherein the first determining module comprises:

9. The apparatus of claim 8, wherein the apparatus further comprises:

10. A computer-readable storage medium, characterized in that the storage medium has stored therein a computer program which, when executed by a processor, implements the steps of the energy management method for a hydrogen fuel cell electric vehicle of any one of claims 1 to 6.