CN115071505A - Fuel cell automobile layered planning method, system, device and storage medium - Google Patents
Fuel cell automobile layered planning method, system, device and storage medium Download PDFInfo
- Publication number
- CN115071505A CN115071505A CN202210695589.6A CN202210695589A CN115071505A CN 115071505 A CN115071505 A CN 115071505A CN 202210695589 A CN202210695589 A CN 202210695589A CN 115071505 A CN115071505 A CN 115071505A
- Authority
- CN
- China
- Prior art keywords
- fuel cell
- vehicle speed
- power
- determining
- vehicle
- 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.)
- Granted
Links
- 239000000446 fuel Substances 0.000 title claims abstract description 183
- 238000013439 planning Methods 0.000 title claims abstract description 62
- 238000000034 method Methods 0.000 title claims abstract description 49
- 238000003860 storage Methods 0.000 title claims abstract description 18
- 238000005265 energy consumption Methods 0.000 claims abstract description 53
- 238000007726 management method Methods 0.000 claims abstract description 21
- 230000008569 process Effects 0.000 claims abstract description 12
- 230000006870 function Effects 0.000 claims description 47
- 238000005457 optimization Methods 0.000 claims description 28
- 238000009826 distribution Methods 0.000 claims description 25
- 239000001257 hydrogen Substances 0.000 claims description 24
- 229910052739 hydrogen Inorganic materials 0.000 claims description 24
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 claims description 23
- 238000004422 calculation algorithm Methods 0.000 claims description 23
- 238000011217 control strategy Methods 0.000 claims description 18
- 230000001133 acceleration Effects 0.000 claims description 14
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 5
- 238000004088 simulation Methods 0.000 claims description 4
- 238000013517 stratification Methods 0.000 claims 1
- 238000004364 calculation method Methods 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 239000000126 substance Substances 0.000 description 4
- 230000006855 networking Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000005096 rolling process Methods 0.000 description 3
- 230000009466 transformation Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 230000014509 gene expression Effects 0.000 description 2
- 239000001301 oxygen Substances 0.000 description 2
- 229910052760 oxygen Inorganic materials 0.000 description 2
- 238000013519 translation Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000003487 electrochemical reaction Methods 0.000 description 1
- 235000011389 fruit/vegetable juice Nutrition 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 150000002431 hydrogen Chemical class 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000010223 real-time analysis Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 230000007723 transport mechanism Effects 0.000 description 1
- 238000012384 transportation and delivery Methods 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/30—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling fuel cells
Landscapes
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Sustainable Development (AREA)
- Sustainable Energy (AREA)
- Power Engineering (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Fuel Cell (AREA)
Abstract
The invention discloses a fuel cell automobile hierarchical planning method, a system, a device and a storage medium, and relates to the technical field of vehicle control. According to the method, the optimal economic path is obtained by searching the path based on the energy consumption prediction model in the traffic network, the target speed constraint range is determined according to the traffic information on the optimal economic path so as to plan the optimal speed track, the required power is determined according to the optimal speed track, then the working high-efficiency area of the fuel cell automobile and the charge state quantity of the power cell are considered, the output power of the fuel cell of the automobile is determined by combining the required power and performing energy management, so that the real-time energy management can be performed on the automobile by combining the external traffic environment, and the use of the fuel cell in the running process of the fuel cell automobile is reasonably planned.
Description
Technical Field
The invention relates to the technical field of vehicle control, in particular to a fuel cell automobile hierarchical planning method, a system, a device and a storage medium.
Background
The energy management strategy is taken as a key technology of the fuel cell automobile, and directly influences the overall economy, the service life and other performances of the fuel cell automobile. At present, research on an energy management strategy of a fuel cell automobile mainly optimizes energy distribution of a power battery and the fuel cell according to the braking condition of the automobile to improve the economy of the whole automobile, but energy management cannot be carried out by combining an external traffic environment in the driving process of the automobile, and the use of the fuel cell in the driving process of the fuel cell automobile cannot be reasonably planned.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides a fuel cell automobile layered planning method, a system, a device and a storage medium, which can be used for carrying out real-time energy management on the automobile by combining with an external traffic environment and reasonably planning the use of the fuel cell in the driving process of the fuel cell automobile.
On one hand, the embodiment of the invention provides a fuel cell automobile layered planning method, which comprises the following steps:
acquiring an energy consumption prediction model of a fuel cell automobile in a traffic network;
searching a path based on the energy consumption prediction model to obtain an optimal economic path;
determining a target vehicle speed range according to the traffic information on the optimal economic path;
constructing a target optimization function by adopting a model predictive control algorithm, and determining an optimal vehicle speed track by taking the target vehicle speed range as a constraint;
determining required power according to the optimal vehicle speed track;
and determining the output power of the vehicle fuel cell according to the required power based on the working high-efficiency region of the fuel cell automobile and the state of charge quantity of the power cell.
According to some embodiments of the invention, the energy consumption prediction model is obtained by:
constructing a vehicle dynamic model according to power structure parameters of the fuel cell automobile, wherein the vehicle dynamic model comprises a vehicle longitudinal dynamic model, a fuel cell system model, a power battery system model and a motor system model;
constructing a traffic network model according to the actual observation data of the area where the fuel cell automobile is located;
importing the vehicle dynamics model into the traffic network model to carry out simulation to obtain a traffic information data set and a driving condition data set;
and inputting the traffic information data set and the driving condition data set into a time cycle neural network to obtain the energy consumption prediction model.
According to some embodiments of the present invention, the step of searching for the optimal economic path based on the energy consumption prediction model includes the following steps:
determining the energy consumption from the current road to the next road according to the energy consumption prediction model;
substituting the energy consumption from the current road to the next road and the distance from the current road to the next road into a road resistance cost function to obtain the road resistance from the current road to the next road;
substituting the road resistance obtained in the path searching process into a road resistance cost function matrix, taking energy consumption and distance as optimization targets, and utilizing A * And obtaining the optimal economic path by an algorithm.
According to some embodiments of the invention, the determining the target vehicle speed range according to the traffic information on the optimal economic path comprises:
determining the state of a traffic light closest to the front of the fuel cell automobile and the first distance between the fuel cell automobile and the traffic light according to the traffic information;
determining a target vehicle speed range according to the traffic signal lamp state and the first distance;
wherein, when the traffic light status is red, the target vehicle speed range is represented as:
when the traffic signal lamp is in a green state
The target vehicle speed range is expressed as:
when the traffic signal lamp is in a green state
The target vehicle speed range is expressed as:
wherein d (k) represents a first distance, k represents a current time, t total Representing cycles of a traffic signal, each cycle comprising two states, red and green, t r Indicating duration of red light state, t g Indicating the duration of the green state, T indicating an integer describing the cycle of the traffic light, v max Indicating the current road speed limit.
According to some embodiments of the present invention, the constructing the target optimization function by using the model predictive control algorithm, and determining the optimal vehicle speed trajectory by using the target vehicle speed range as a constraint, comprises the following steps:
determining the degree of the actual vehicle speed deviating from the target vehicle speed according to the actual vehicle speed and the target vehicle speed, wherein the upper limit value of the target vehicle speed range is taken as the target vehicle speed;
determining a target optimization function according to the degree of the actual vehicle speed deviating from the target vehicle speed;
constructing a vehicle speed constraint equation according to the target vehicle speed range to obtain a constraint equation set;
and solving the target optimization function by combining the constraint equation set based on the vehicle longitudinal dynamics model and adopting a model predictive control algorithm to obtain an optimal vehicle speed track.
According to some embodiments of the present invention, the constructing the target optimization function by using the model predictive control algorithm, and determining the optimal vehicle speed trajectory by using the target vehicle speed range as a constraint further comprises:
determining a hydrogen consumption amount per unit time based on a vehicle hydrogen consumption model;
determining the smoothness degree of the driving operation according to the current actual acceleration;
determining a target optimization function according to the degree of deviation of the actual vehicle speed from the target vehicle speed, the hydrogen consumption per unit time and the smoothness degree of the driving operation;
constructing an acceleration constraint equation, a fuel cell power constraint equation and a power cell power constraint equation;
and obtaining a constraint equation set according to the vehicle speed constraint equation, the acceleration constraint equation, the fuel cell power constraint equation and the power cell power constraint equation.
According to some embodiments of the present invention, the determining the vehicle fuel cell output power from the required power based on the operation high efficiency region of the fuel cell automobile and the state of charge quantity of the power cell comprises:
determining a first power distribution result according to the difference between the required power and the lower limit value of the high-efficiency area of the output power of the fuel cell and the state of charge quantity of the power cell by adopting a fuzzy control strategy;
determining the on-off state and a second power distribution result of the fuel cell according to the required power and the charge state quantity of the power cell by adopting an on-off control strategy, wherein the charge state quantity of the power cell exceeds an upper limit value, the fuel cell is in an off state, and the second power distribution result is that the required power is provided by the power cell, otherwise, the fuel cell is in an on state;
when the fuel cell is in an open state, determining the output power of the vehicle fuel cell according to the first power distribution result;
and when the fuel cell is in an off state, determining the output power of the vehicle fuel cell according to the second power distribution result.
In another aspect, an embodiment of the present invention further provides a fuel cell automobile hierarchical planning system, including:
the path planning layer is used for obtaining an energy consumption prediction model of the fuel cell automobile in a traffic network and searching a path based on the energy consumption prediction model to obtain an optimal economic path;
the vehicle speed planning layer is used for determining a target vehicle speed range according to the traffic information on the optimal economic path, constructing a target optimization function by adopting a model predictive control algorithm, and determining an optimal vehicle speed track by taking the target vehicle speed range as a constraint;
and the vehicle energy management layer is used for determining required power according to the optimal vehicle speed track, and determining the output power of the vehicle fuel cell according to the required power based on the working high-efficiency area of the fuel cell automobile and the state of charge quantity of the power cell.
On the other hand, the embodiment of the invention also provides a fuel cell automobile layered planning device, which comprises:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, causes the at least one processor to implement the fuel cell vehicle layer planning method as described above.
In another aspect, the present invention also provides a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the fuel cell automobile layer planning method as described above.
The technical scheme of the invention at least has one of the following advantages or beneficial effects: according to the method, the optimal economic path is obtained by searching the path based on the energy consumption prediction model in the traffic network, the target speed constraint range is determined according to the traffic information on the optimal economic path so as to plan the optimal speed track, the required power is determined according to the optimal speed track, then the working high-efficiency area of the fuel cell automobile and the charge state quantity of the power cell are considered, the output power of the fuel cell of the automobile is determined by combining the required power and performing energy management, so that the real-time energy management can be performed on the automobile by combining the external traffic environment, and the use of the fuel cell in the running process of the fuel cell automobile is reasonably planned.
Drawings
FIG. 1 is a flow chart of a fuel cell vehicle hierarchical planning method provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of a fuel cell vehicle hierarchical planning system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a fuel cell vehicle layer planning apparatus according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a vehicle energy management process provided by an embodiment of the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, it should be understood that the orientation or positional relationship referred to in the description of the orientation, such as the upper, lower, left, right, etc., is based on the orientation or positional relationship shown in the drawings, and is only for convenience of description and simplicity of description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
In the description of the present invention, if there are first, second, etc. described, they are only used for distinguishing technical features, and they are not to be interpreted as indicating or implying relative importance or implying number of indicated technical features or implying precedence of indicated technical features.
Before further detailed description of the embodiments of the present application, terms and expressions referred to in the embodiments of the present application will be described, and the terms and expressions referred to in the embodiments of the present application will be used for the following explanation.
SOC: the value range of the state of charge quantity, the ratio of the residual capacity of the power battery to the capacity of the power battery in a full charging state is 0-1.
LSTM: the long-short term neural network with selective structure is a time cycle neural network, which is used for solving the problem of processing and predicting time series in the deep learning field.
A * The algorithm is as follows: a direct search method for solving the optimal path in a static road network is the most effective, and the optimal path is solved through an estimation function consisting of a cost function and a distance function.
Model predictive control algorithm: a control strategy is provided for solving a finite time domain open loop optimal control problem at each sampling instant to obtain a current control solution, and optimizing the control solution through online rolling time domain optimization.
Real-time wavelet transformation: a time-frequency real-time analysis method for signals is characterized in that multi-scale refinement is carried out on the signals step by step through a telescopic translation operation, and finally a control strategy of time subdivision at a high frequency and frequency subdivision at a low frequency is achieved.
Referring to fig. 1, the fuel cell automobile hierarchical planning method according to the embodiment of the present invention includes, but is not limited to, step S110, step S120, step S130, step S140, step S150, and step S160.
Step S110, acquiring an energy consumption prediction model of the fuel cell automobile in a traffic network;
step S120, searching a path based on the energy consumption prediction model to obtain an optimal economic path;
step S130, determining a target vehicle speed range according to the traffic information on the optimal economic path;
step S140, constructing a target optimization function by adopting a model predictive control algorithm, and determining an optimal vehicle speed track by taking a target vehicle speed range as a constraint;
step S150, determining required power according to the optimal vehicle speed track;
and step S160, determining the output power of the vehicle fuel cell according to the required power based on the work high-efficiency area of the fuel cell automobile and the state of charge quantity of the power cell.
In this embodiment, a fuel cell vehicle is divided into an upper layer, a middle layer and a lower layer for planning, the upper layer is a path planning layer, the middle layer is a layer of vehicle speed planning layer, the lower layer is a vehicle energy management layer, the path planning layer performs path search based on an energy consumption prediction model in a traffic network to obtain an optimal economic path, the vehicle speed planning layer determines a target vehicle speed constraint range according to traffic information on the optimal economic path to plan an optimal vehicle speed track, the vehicle energy management layer determines required power according to the optimal vehicle speed track, and then performs energy management by considering a working high-efficiency area of a fuel cell of the fuel cell vehicle and a charge state quantity of a power cell and combining the required power to determine output power of the vehicle fuel cell. The embodiment of the invention can carry out real-time energy management on the vehicle by combining with the external traffic environment and reasonably plan the use of the fuel cell in the driving process of the fuel cell vehicle.
In other embodiments, in the intelligent networking system, the vehicle can be connected with the cloud server in real time, the cloud server can acquire external traffic environment information in real time, the external traffic environment information needs to be utilized in upper-layer path planning and middle-layer vehicle speed planning, the path planning layer and the vehicle speed planning layer can be arranged in the cloud server, and the vehicle energy management layer of the lower layer can be arranged in the vehicle.
According to some embodiments of the present invention, the energy consumption prediction model in step S110 is obtained by:
step S210, constructing a vehicle dynamic model according to power structure parameters of the fuel cell automobile, wherein the vehicle dynamic model comprises a vehicle longitudinal dynamic model, a fuel cell system model, a power cell system model and a motor system model;
step S220, constructing a traffic network model according to actual observation data of the area where the fuel cell automobile is located;
step S230, importing a vehicle dynamics model into the traffic network model to carry out simulation to obtain a traffic information data set and a driving condition data set;
and step S240, inputting the traffic information data set and the driving condition data set into a time cycle neural network to obtain an energy consumption prediction model.
Specifically, the power structure parameters of the fuel cell automobile include, but are not limited to, the mass of the whole automobile, the windward area, the rolling resistance coefficient, the efficiency of the whole automobile, the efficiency of a fuel cell system, the state of charge of a power cell, and the like, and the vehicle dynamic model includes a vehicle longitudinal dynamic model, a fuel cell system model, a power cell system model and a motor system model.
Determining a vehicle longitudinal dynamics model representing the required power of the whole vehicle based on the vehicle longitudinal dynamics:
wherein, P req Is the required power of the whole vehicle, u is the running speed, eta T Is the efficiency of the whole vehicle, m is the mass of the whole vehicle, g is the acceleration of gravity, f is the rolling resistance coefficient, i is the road gradient, C d Is the air resistance coefficient, a is the windward area, δ is the rotating mass conversion coefficient, and a is the acceleration.
The fuel cell system electrochemically reacts hydrogen and oxygen to generate chemical energy
Converting into electric energy and outputting power P of fuel cell system fc Based on this, the fuel cell system model is expressed as follows:
wherein E represents an open circuit of the fuel cellVoltage, V fc And i fc Representing output voltage and output current, R ohm Indicates the internal resistance i 0 Representing the exchange current, a, c 2 、c 3 、i max Are empirical constants, Lhv is the lower heating value of hydrogen,
is the instantaneous hydrogen consumption of the fuel cell system, eta fcs Is the efficiency of the fuel cell system and,
is the chemical energy generated by the fuel cell system through the hydrogen-oxygen electrochemical reaction.
Simplifying the power battery system into an internal resistance equivalent circuit model, and based on the internal resistance equivalent circuit model, representing the power battery system model as follows:
P bat ＝V oc I bat -R bat I bat 2 ；
wherein, V oc Is the open circuit voltage of the power battery, I bat Is the power cell current, R bat Is the internal resistance, SOC, of the power battery t Is the next moment of the power battery state of charge, SOC 0 Is the state of charge, Q, of the power battery at the current moment bat Is the power battery capacity.
The motor system model is represented as follows:
η motor ＝f motor (T motor ，ω motor )；
P motor ＝T motor ω motor ；
wherein, T motor Is the motor output torque, ω motor Is the motor output speed, f motor The method is an efficiency lookup function, and according to the output torque and the output rotating speed of the motor at the moment, an efficiency Map table is searched to obtain the working efficiency of the motor at the moment.
And (3) importing a traffic network of the area where the automobile is located into SUMO high-precision urban traffic simulation software, manually setting traffic network parameters according to actual observation data of the area in a certain time period, and establishing a traffic network model. The actual observation data includes the average traveling speed of the vehicle, the number of roads, the average traffic flow, the average traffic density, and the like.
And introducing a vehicle dynamics model into the constructed traffic network model for joint simulation to obtain a traffic information data set and a driving condition data set of the fuel cell automobile. The traffic information data set may include a road length, an average traveling vehicle speed, an average traffic flow, an average traffic density, and the like, and the traveling condition data set may include a fuel cell system power, a state of charge amount of the power cell, a power cell voltage, a power cell current, and the like.
And fusing the traffic information data set and the fuel cell automobile multi-dimensional driving condition data set based on a sequence pair sequence technology, taking a fusion result as the input of the LSTM, taking road energy consumption as the output of the LSTM, and establishing a fuel cell automobile energy consumption prediction model.
According to some embodiments of the invention, step S120 includes, but is not limited to, the following steps:
step S310, determining the energy consumption from the current road to the next road according to the energy consumption prediction model;
step S320, substituting the energy consumption from the current road to the next road and the distance from the current road to the next road into a road resistance cost function to obtain the road resistance from the current road to the next road;
step S330, substituting the road resistance obtained in the path searching process into a road resistance cost function matrix, taking energy consumption and distance as optimization targets, and utilizing A * And obtaining an optimal economic path by an algorithm.
Specifically, the energy consumption from the current road to the next road can be predicted according to the theoretical energy consumption prediction model of the fuel cell automobile, and the energy consumption from the current road to the next road can be predictedAnd substituting the distance from the previous road to the next road into the road resistance cost function to obtain the road resistance from the current road to the next road. To calculate the road x 1 To road x 2 Taking the road resistance as an example, the energy consumption and distance coupled road resistance cost function is constructed by using the energy consumption cost function and the distance function with the same weight and is expressed as:
t＝5·q(min)，q＝1，2，...，N；
wherein the content of the first and second substances,
is the time t road x 1 To road x 2 The energy consumption of (2) is reduced,
is a road x 1 To road x 2 A and b are weighting coefficients, a equals 50%, q is the number of segments in five minutes after the vehicle starts, f (x) 1 ，x 2 And t) is a road resistance cost function of vehicle energy consumption coupled with distance.
With A * The algorithm carries out path search, and the process is as follows: and determining a search range according to the estimated road resistance of the current point, so that the search point is closer to the optimal point. Selection A * Taking the algorithm as a main solver, taking the road resistance calculated in the road resistance cost function as the input of a road resistance cost function matrix, and taking the energy consumption and the distance as optimization targets to obtain an optimal economic path, wherein the road resistance cost function matrix is expressed as:
in some embodiments, a vehicle dynamic model is established in a path planning layer by combining a vehicle power system structure and self parameters, a traffic network model is established by combining actual observation data of a certain area, a fuel cell automobile energy consumption prediction model is established by utilizing a long-short term neural network (LSTM) with a selective structure based on the vehicle dynamic model and the traffic network model, and energy consumption information obtained from the energy consumption prediction model is processed through A * And solving the path planning problem of optimal coupling of energy consumption and distance by an algorithm to obtain an optimal economic path.
According to some embodiments of the present invention, step S130 includes, but is not limited to, the following steps:
step S410, determining the state of a traffic light closest to the front of the fuel cell automobile and the first distance between the fuel cell automobile and the traffic light according to the traffic information;
and step S420, determining a target vehicle speed range according to the traffic light state and the first distance.
Specifically, the traffic information on the optimal economic path is obtained, and the traffic information may include a first distance from the current automobile to the nearest traffic light in front, a road speed limit value, and information such as a phase and timing of the nearest traffic light in front of the automobile.
By utilizing the traffic information of the intelligent network connection, the signal lamp state of the current moment can be calculated at any moment k according to the traffic signal lamp cycle period, wherein the signal lamp cycle period is t total Each period comprises two states of red light and green light, and the duration of the red light state and the duration of the green light state are respectively t r 、t g ，t total ＝t r +t g The traffic signal status at the current time is represented as:
where 1 represents the red light state, 0 represents the green light state, and the function mod produces k divided by t total The remainder of (1).
At any time k, to ensureThe fuel cell automobile does not stop when passing through the traffic intersection, and a target speed range is set l (k)，v u (k)]，v u (k) And v l (k) Is the upper and lower limits of the target vehicle speed, and the target vehicle speed v at the moment is calculated according to the signal lamp state at the current moment target (k) And a target vehicle speed lower limit v l (k) At this time v target (k)＝v u (k) Target vehicle speed range [ v ] l (k)，v target (k)]The following were used:
when the traffic light status is red, the target vehicle speed range is represented as:
when the traffic signal lamp is in a green state
The target vehicle speed range is expressed as:
when the traffic signal lamp is in a green state
The target vehicle speed range is expressed as:
wherein d (k) represents a first distance, k represents a current time, t total Representing cycles of a traffic signal, each cycle comprising two states, red and green, t r Indicating duration of red light state, t g Indicating the duration of the green state, T represents an integer describing the traffic signal cycle,
v max indicating the current road speed limit.
According to some embodiments of the invention, step S140 includes, but is not limited to, the following steps:
step S510, determining the degree of the actual vehicle speed deviating from the target vehicle speed according to the actual vehicle speed and the target vehicle speed, wherein the upper limit value of the target vehicle speed range is taken as the target vehicle speed;
step S520, determining a target optimization function according to the degree of deviation of the actual vehicle speed from the target vehicle speed;
step S530, constructing a vehicle speed constraint equation according to the target vehicle speed range to obtain a constraint equation set;
and S540, solving an objective optimization function by combining a constraint equation set and a model predictive control algorithm based on the vehicle longitudinal dynamics model to obtain an optimal vehicle speed track.
Further, step S140 further includes, but is not limited to, the following steps:
step S610, determining hydrogen consumption amount per unit time based on the vehicle hydrogen consumption model;
step S620, determining the smoothness degree of the driving operation according to the current actual acceleration;
step S630, determining a target optimization function according to the degree of deviation of the actual vehicle speed from the target vehicle speed, the hydrogen consumption per unit time and the smoothness degree of the driving operation;
step S640, constructing an acceleration constraint equation, a fuel cell power constraint equation and a power cell power constraint equation;
and step S650, obtaining a constraint equation set according to the vehicle speed constraint equation, the acceleration constraint equation, the fuel cell power constraint equation and the power cell power constraint equation.
Combining a vehicle longitudinal dynamics model, solving the optimal vehicle speed track of the current vehicle on the road by using a model predictive control Method (MPC), wherein the optimization target comprises hydrogen consumption, target vehicle speed and acceleration and deceleration times, and obtaining the optimal economic vehicle speed of the current vehicle at the next moment by optimizing the acceleration in the prediction domain, and the target function and the constraint condition are represented as follows:
J 2 (t)＝[v h (t)-v target (t)] 2 ；
J 3 (t)＝a h (t) 2 ；
s.t.v min ≤v l (t)≤v h (t)≤v target (t)≤v max
a min ≤a h (t)≤a max
P fcmin ≤P fc (t)≤P fcmax
P batmin ≤P bat (t)≤P batmax
wherein, J 1 Is the hydrogen consumption per unit time, mainly considers the energy consumption economy,
is the equivalent hydrogen consumption of the fuel cell system, m bat (t) is the equivalent hydrogen consumption of the power cell system, J 2 The target speed is the economic speed for avoiding stopping at a traffic intersection planned according to traffic signals and the like, so that the smaller the deviation between the actual speed and the target speed is, the better the traffic smoothness is mainly considered, and v h Is the current actual vehicle speed, J 3 Mainly considering the smoothness of driving operation, a h Is the current actual acceleration, when v target (k)-v l (k) Smaller, i.e., smaller target vehicle speed range, α 1 、α 2 、α 3 Desirable constant, J h The function is optimized for the objective.
Equivalent hydrogen consumption of fuel cell system
The juice formula is:
wherein the content of the first and second substances,
is the amount of hydrogen consumption of the fuel cell system,
is the molar mass of hydrogen, N cell Is the number of cells in the fuel cell stack and F is the avogalois constant.
Equivalent hydrogen consumption m of power battery system bat (t) the calculation formula is:
wherein m is bat Is the hydrogen consumption of the power battery system, m fcavg Is the average hydrogen consumption, P, of the fuel cell system fcavg Is the average power, η, of the fuel cell system dis Is the discharge power of the power battery, eta cha Is the power of charging the power battery.
The setting of the state variables and the control variables in the solving process of the objective optimization function is as follows:
the equation of state is as follows:
in some embodiments, intelligent networking traffic information of an optimal path is obtained by utilizing a vehicle networking technology at a vehicle speed planning layer, a target vehicle speed range is determined, and then a model prediction control method is utilized to solve a global vehicle speed planning problem by combining a vehicle longitudinal dynamics model and an equivalent hydrogen consumption calculation formula to obtain an optimal vehicle speed track.
According to some specific embodiments of the present invention, the fuel cell automobile layer planning method of the embodiments of the present invention further includes, but is not limited to, the following steps:
and step S710, extracting low-frequency required power from the acquired required power of the whole vehicle by adopting a real-time wavelet transform strategy.
Specifically, a real-time wavelet transform strategy is formulated, a Haar wavelet is selected as a wavelet basis function, the number of wavelet decomposition layers is 5, the length of a sliding window is 32, and the required power of the whole vehicle is a discrete signal, so that the required power of the whole vehicle is transformed by using a discrete wavelet transform formula as follows:
wherein S (t) is the original power demand signal, λ is the scale factor, μ is the translation factor,
w is the wavelet basis function and the wavelet coefficient.
Then, decomposing and reconstructing the signals through a Mallat algorithm to obtain the low-frequency required power, and finally realizing the real-time processing of the low-frequency required power by utilizing a mode of combining a sliding window with wavelet transformation.
According to some embodiments of the invention, step S160 includes, but is not limited to, the following steps:
step S810, determining a first power distribution result according to the difference between the required power and the lower limit value of the high-efficiency area of the output power of the fuel cell and the state of charge quantity of the power cell by adopting a fuzzy control strategy;
step S820, determining the on-off state and the second power distribution result of the fuel cell according to the required power and the charge state quantity of the power cell by adopting an on-off control strategy, wherein the charge state quantity of the power cell exceeds an upper limit value, the fuel cell is in an off state, and the second power distribution result is that the required power is provided by the power cell, otherwise, the fuel cell is in an on state;
step S830, when the fuel cell is in an on state, determining the output power of the vehicle fuel cell according to the first power distribution result;
and step 840, when the fuel cell is in the off state, determining the output power of the vehicle fuel cell according to the second power distribution result.
Specifically, referring to fig. 4, after the required power of the entire vehicle is determined according to the optimal vehicle speed trajectory, the required power of the entire vehicle is subjected to real-time wavelet transform to obtain low-frequency required power.
And (4) inputting the low-frequency required power into a power difference calculation module for calculation to obtain the difference P between the required power and the lower limit value of the high-efficiency area of the output power of the fuel cell.
And inputting the low-frequency required power and the SOC value of the power battery into the switch control strategy model for analysis to obtain the switch state of the fuel battery and a second power distribution result. In the switch control strategy module, when the SOC value of the power battery increases and exceeds the upper limit value, the fuel battery is closed, and the power battery provides required power at the moment, so that the power battery is prevented from being overcharged.
And inputting the difference value P and the SOC value of the power battery into a fuzzy control strategy module to obtain a proportional coefficient K of the output power of the fuel battery, wherein the discourse domain of the SOC value is [0, 1] and the discourse domain of K is [0, 1 ]. In the fuzzy control strategy module, fuzzy rules are designed by analyzing the working conditions affecting the durability of the fuel cell to consider the fuel cell output power proportionality coefficient K of the durability of the fuel cell.
Inputting the output power proportionality coefficient K of the fuel cell into a fuel cell power calculation module for calculation to obtain the output power P of the fuel cell fc Thereby obtaining a first power allocation result. In the fuel cell power calculation module, the fuel cell outputs power P fc Calculated by the following formula:
P fc ＝a+(b-a)K；
wherein a is a lower limit of the high efficiency region of the output power of the fuel cell, and b is an upper limit of the high efficiency region of the output power of the fuel cell. For example, a may take the value 10 and b may take the value 40.
And inputting the fuel cell switch state and the second power distribution result output by the switch control strategy module and the first power distribution result output by the fuel cell power calculation module into the selection module to obtain the final output power of the fuel cell. In the fuel cell selection module, when the fuel cell is in an on state, the output power of the vehicle fuel cell is determined according to the first power distribution result, and when the fuel cell is in an off state, the output power of the vehicle fuel cell is determined according to the second power distribution result.
And finally, inputting the output power of the fuel cell into a vehicle model so as to realize the optimal energy management of the vehicle.
In the embodiment, because the control details of the fuzzy control strategy considering the durability of the fuel cell are not controllable, the energy management strategy of the embodiment of the invention is obtained by combining the real-time wavelet transform, the switching control strategy and the fuzzy control strategy, and the durability of the fuel cell and the safety of the power cell are improved on the basis of ensuring the dynamic property and the economical property of a vehicle.
Referring to fig. 2, the fuel cell vehicle hierarchical planning system according to the embodiment of the present invention includes:
the path planning layer is used for acquiring an energy consumption prediction model of the fuel cell automobile in a traffic network and searching a path based on the energy consumption prediction model to obtain an optimal economic path;
the vehicle speed planning layer is used for determining a target vehicle speed range according to the traffic information on the optimal economic path, constructing a target optimization function by adopting a model predictive control algorithm, and determining an optimal vehicle speed track by taking the target vehicle speed range as a constraint;
and the vehicle energy management layer is used for determining required power according to the optimal vehicle speed track, and determining the output power of the vehicle fuel cell according to the required power based on the working high-efficiency area of the fuel cell automobile and the state of charge quantity of the power cell.
Specifically, referring to fig. 2, the path planning layer, the vehicle speed planning layer and the vehicle energy management layer are respectively an upper layer, a middle layer and a lower layer. In a path planning layer, a vehicle dynamic model is obtained by using self structural parameters of a vehicle, a traffic network model is obtained by using actual observation data, an energy consumption prediction model is obtained by analyzing through an LSTM solver according to the vehicle dynamic model and the traffic network model, and road energy consumption of the energy consumption prediction model is combined through A * And the algorithm searches a path to obtain the optimal economic path of the vehicle. In a vehicle speed planning layer, acquiring network connection dynamic traffic information on an optimal economic path based on an intelligent network connection technology, determining a target vehicle speed range according to the traffic information, constructing a target optimization function based on a vehicle longitudinal dynamic model and equivalent hydrogen consumption models of a fuel cell and a power cell by adopting a model prediction control method, and solving the target optimization function by taking the target vehicle speed range as a constraint equation to obtain an optimal vehicle speed track. In a vehicle energy management layer, determining the required power of the whole vehicle according to an optimal vehicle speed track, determining the low-frequency required power by adopting real-time wavelet transformation, determining a second power distribution result and the on-off state of a fuel cell according to the low-frequency required power and the SOC value of the power cell by adopting an on-off control strategy, determining a proportional coefficient K of the output power of the fuel cell according to the difference P between the required power and the lower limit value of the high-efficiency area of the output power of the fuel cell and the SOC value of the power cell by adopting a fuzzy control strategy, calculating the output power of the fuel cell according to the proportional coefficient K to obtain a second power distribution result, and selecting the first power distribution result or the second power distribution result in a selection module according to the on-off state of the fuel cell so as to obtain the final output power of the fuel cell and outputting the final output power to a vehicle model.
It can be understood that the contents in the embodiment of the fuel cell automobile hierarchical planning method are all applicable to the embodiment of the system, the functions specifically implemented by the embodiment of the system are the same as those in the embodiment of the fuel cell automobile hierarchical planning method, and the beneficial effects achieved by the embodiment of the system are also the same as those achieved by the embodiment of the fuel cell automobile hierarchical planning method.
Referring to fig. 3, fig. 3 is a schematic diagram of a fuel cell automobile layer planning apparatus according to an embodiment of the present invention. The fuel cell automobile hierarchical planning device of the embodiment of the invention comprises one or more control processors and a memory, and a control processor and a memory are taken as an example in fig. 3.
The control processor and the memory may be connected by a bus or other means, as exemplified by the bus connection in fig. 3.
The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory remotely located from the control processor, and the remote memory may be connected to the fuel cell vehicle hierarchical planning apparatus via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Those skilled in the art will appreciate that the configuration of the apparatus shown in fig. 3 does not constitute a limitation of a fuel cell automotive hierarchy planner and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
The non-transitory software programs and instructions required to implement the fuel cell automobile layer planning method applied to the fuel cell automobile layer planning apparatus in the above embodiments are stored in a memory, and when executed by a control processor, the fuel cell automobile layer planning method applied to the fuel cell automobile layer planning apparatus in the above embodiments is executed.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, where computer-executable instructions are stored, and executed by one or more control processors, so as to enable the one or more control processors to execute the fuel cell automobile hierarchical planning method in the above method embodiment.
One of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.
Claims (10)
1. A fuel cell automobile layered planning method is characterized by comprising the following steps:
acquiring an energy consumption prediction model of a fuel cell automobile in a traffic network;
searching a path based on the energy consumption prediction model to obtain an optimal economic path;
determining a target vehicle speed range according to the traffic information on the optimal economic path;
constructing a target optimization function by adopting a model predictive control algorithm, and determining an optimal vehicle speed track by taking the target vehicle speed range as a constraint;
determining required power according to the optimal vehicle speed track;
and determining the output power of the vehicle fuel cell according to the required power based on the high-efficiency working area of the fuel cell automobile and the state of charge quantity of the power cell.
2. The fuel cell vehicle hierarchical planning method according to claim 1, wherein the energy consumption prediction model is obtained by:
constructing a vehicle dynamic model according to power structure parameters of the fuel cell automobile, wherein the vehicle dynamic model comprises a vehicle longitudinal dynamic model, a fuel cell system model, a power battery system model and a motor system model;
constructing a traffic network model according to the actual observation data of the area where the fuel cell automobile is located;
importing the vehicle dynamics model into the traffic network model to carry out simulation to obtain a traffic information data set and a driving condition data set;
and inputting the traffic information data set and the driving condition data set into a time cycle neural network to obtain the energy consumption prediction model.
3. The fuel cell vehicle hierarchical planning method according to claim 2, wherein the step of performing a path search based on the energy consumption prediction model to obtain an optimal economic path comprises the steps of:
determining the energy consumption from the current road to the next road according to the energy consumption prediction model;
substituting the energy consumption from the current road to the next road and the distance from the current road to the next road into a road resistance cost function to obtain the road resistance from the current road to the next road;
substituting the road resistance obtained in the path searching process into a road resistance cost function matrix, taking energy consumption and distance as optimization targets, and utilizing A * And obtaining the optimal economic path by an algorithm.
4. The fuel cell vehicle hierarchical planning method according to claim 1, wherein the determining a target vehicle speed range according to the traffic information on the optimal economic path includes the steps of:
determining the state of a traffic light closest to the front of the fuel cell automobile and the first distance between the fuel cell automobile and the traffic light according to the traffic information;
determining a target vehicle speed range according to the traffic signal lamp state and the first distance;
wherein, when the traffic light status is red, the target vehicle speed range is represented as:
when the traffic signal lamp is in a green state
The target vehicle speed range is expressed as:
when the traffic signal lamp is in a green state
The target vehicle speed range is expressed as:
wherein d (k) represents a first distance, k represents a current time, t total Representing cycles of a traffic signal, each cycle comprising two states, red and green, t r Indicating duration of red light state, t g Indicating the duration of the green state, T indicating an integer describing the cycle of the traffic light, v max Indicating the current road speed limit.
5. The fuel cell automobile hierarchical planning method according to claim 4, wherein the constructing an objective optimization function by using a model predictive control algorithm, and the determining an optimal vehicle speed trajectory with the objective vehicle speed range as a constraint, comprises the steps of:
determining the degree of the actual vehicle speed deviating from the target vehicle speed according to the actual vehicle speed and the target vehicle speed, wherein the upper limit value of the target vehicle speed range is taken as the target vehicle speed;
determining a target optimization function according to the degree of the actual vehicle speed deviating from the target vehicle speed;
constructing a vehicle speed constraint equation according to the target vehicle speed range to obtain a constraint equation set;
and based on the vehicle longitudinal dynamics model, solving the target optimization function by combining the constraint equation set and adopting a model predictive control algorithm to obtain an optimal vehicle speed track.
6. The fuel cell automobile hierarchical planning method according to claim 5, wherein the constructing an objective optimization function by using a model predictive control algorithm and determining an optimal vehicle speed trajectory with the objective vehicle speed range as a constraint further comprises the steps of:
determining a hydrogen consumption amount per unit time based on a vehicle hydrogen consumption model;
determining the smoothness degree of the driving operation according to the current actual acceleration;
determining a target optimization function according to the degree of deviation of the actual vehicle speed from the target vehicle speed, the hydrogen consumption per unit time and the smoothness degree of the driving operation;
constructing an acceleration constraint equation, a fuel cell power constraint equation and a power cell power constraint equation;
and obtaining a constraint equation set according to the vehicle speed constraint equation, the acceleration constraint equation, the fuel cell power constraint equation and the power cell power constraint equation.
7. The fuel cell vehicle layer planning method according to claim 1, wherein the determining the vehicle fuel cell output power from the required power based on the operating high efficiency region of the fuel cell vehicle and the state of charge quantity of the power cell comprises the steps of:
determining a first power distribution result according to the difference between the required power and the lower limit value of the high-efficiency area of the output power of the fuel cell and the state of charge quantity of the power cell by adopting a fuzzy control strategy;
determining the on-off state and the second power distribution result of the fuel cell according to the required power and the charge state quantity of the power cell by adopting an on-off control strategy, wherein the charge state quantity of the power cell exceeds an upper limit value, the fuel cell is in an off state, and the second power distribution result is that the required power is provided by the power cell, otherwise, the fuel cell is in an on state;
when the fuel cell is in an open state, determining the output power of the vehicle fuel cell according to the first power distribution result;
and when the fuel cell is in an off state, determining the output power of the vehicle fuel cell according to the second power distribution result.
8. A fuel cell automotive hierarchical planning system, comprising:
the path planning layer is used for obtaining an energy consumption prediction model of the fuel cell automobile in a traffic network and searching a path based on the energy consumption prediction model to obtain an optimal economic path;
the vehicle speed planning layer is used for determining a target vehicle speed range according to the traffic information on the optimal economic path, constructing a target optimization function by adopting a model predictive control algorithm, and determining an optimal vehicle speed track by taking the target vehicle speed range as a constraint;
and the vehicle energy management layer is used for determining required power according to the optimal vehicle speed track, and determining the output power of the vehicle fuel cell according to the required power based on the working high-efficiency area of the fuel cell automobile and the state of charge quantity of the power cell.
9. A fuel cell vehicle stratification planning apparatus, comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the fuel cell vehicle layer planning method of any one of claims 1 to 7.
10. A computer-readable storage medium in which a program executable by a processor is stored, wherein the program executable by the processor is used to implement the fuel cell automobile layer planning method according to any one of claims 1 to 7 when executed by the processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210695589.6A CN115071505B (en) | 2022-06-20 | 2022-06-20 | Fuel cell automobile layered planning method, system, device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210695589.6A CN115071505B (en) | 2022-06-20 | 2022-06-20 | Fuel cell automobile layered planning method, system, device and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115071505A true CN115071505A (en) | 2022-09-20 |
| CN115071505B CN115071505B (en) | 2024-06-25 |
Family
ID=83253623
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210695589.6A Active CN115071505B (en) | 2022-06-20 | 2022-06-20 | Fuel cell automobile layered planning method, system, device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115071505B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116048057A (en) * | 2023-04-03 | 2023-05-02 | 苏州英特模科技股份有限公司 | Thermal management test system and method |
| CN116729386A (en) * | 2023-07-07 | 2023-09-12 | 清华大学 | Method and device for hierarchical planning and control of vehicles in multi-intersection scene |
| CN116749813A (en) * | 2023-08-18 | 2023-09-15 | 上海重塑能源科技有限公司 | Method, device, apparatus and medium for regulating fuel cell charging system |
| CN116893664A (en) * | 2023-09-11 | 2023-10-17 | 苏州英特模科技股份有限公司 | Thermal management test system and method |
| CN117650257A (en) * | 2024-01-29 | 2024-03-05 | 北京稳力科技有限公司 | Fuel cell system control method and device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007200654A (en) * | 2006-01-25 | 2007-08-09 | Nissan Motor Co Ltd | Fuel cell system |
| CN109733248A (en) * | 2019-01-09 | 2019-05-10 | 吉林大学 | Pure electric automobile remaining mileage model prediction method based on routing information |
| CN111666715A (en) * | 2020-06-05 | 2020-09-15 | 北京理工大学 | Electric automobile energy consumption prediction method and system |
| CN112714729A (en) * | 2019-11-29 | 2021-04-27 | 华为技术有限公司 | Vehicle path planning method and vehicle path planning device |
-
2022
- 2022-06-20 CN CN202210695589.6A patent/CN115071505B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007200654A (en) * | 2006-01-25 | 2007-08-09 | Nissan Motor Co Ltd | Fuel cell system |
| CN109733248A (en) * | 2019-01-09 | 2019-05-10 | 吉林大学 | Pure electric automobile remaining mileage model prediction method based on routing information |
| CN112714729A (en) * | 2019-11-29 | 2021-04-27 | 华为技术有限公司 | Vehicle path planning method and vehicle path planning device |
| CN111666715A (en) * | 2020-06-05 | 2020-09-15 | 北京理工大学 | Electric automobile energy consumption prediction method and system |
| WO2021244632A1 (en) * | 2020-06-05 | 2021-12-09 | 北京理工大学 | Electric automobile energy consumption prediction method and system |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116048057A (en) * | 2023-04-03 | 2023-05-02 | 苏州英特模科技股份有限公司 | Thermal management test system and method |
| CN116729386A (en) * | 2023-07-07 | 2023-09-12 | 清华大学 | Method and device for hierarchical planning and control of vehicles in multi-intersection scene |
| CN116729386B (en) * | 2023-07-07 | 2023-12-26 | 清华大学 | Method and device for hierarchical planning and control of vehicles in multi-intersection scene |
| CN116749813A (en) * | 2023-08-18 | 2023-09-15 | 上海重塑能源科技有限公司 | Method, device, apparatus and medium for regulating fuel cell charging system |
| CN116749813B (en) * | 2023-08-18 | 2023-10-24 | 上海重塑能源科技有限公司 | Method, device, apparatus and medium for regulating fuel cell charging system |
| CN116893664A (en) * | 2023-09-11 | 2023-10-17 | 苏州英特模科技股份有限公司 | Thermal management test system and method |
| CN116893664B (en) * | 2023-09-11 | 2024-01-30 | 苏州英特模科技股份有限公司 | Thermal management test system and method |
| CN117650257A (en) * | 2024-01-29 | 2024-03-05 | 北京稳力科技有限公司 | Fuel cell system control method and device |
| CN117650257B (en) * | 2024-01-29 | 2024-04-05 | 北京稳力科技有限公司 | Fuel cell system control method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115071505B (en) | 2024-06-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115071505B (en) | Fuel cell automobile layered planning method, system, device and storage medium | |
| Yuan et al. | Intelligent energy management strategy based on hierarchical approximate global optimization for plug-in fuel cell hybrid electric vehicles | |
| Liu et al. | Online Markov Chain-based energy management for a hybrid tracked vehicle with speedy Q-learning | |
| CN110775065B (en) | Hybrid electric vehicle battery life prediction method based on working condition recognition | |
| Qi et al. | Development and evaluation of an evolutionary algorithm-based online energy management system for plug-in hybrid electric vehicles | |
| US9090255B2 (en) | Hybrid vehicle fuel efficiency using inverse reinforcement learning | |
| Huang et al. | Battery health-aware and naturalistic data-driven energy management for hybrid electric bus based on TD3 deep reinforcement learning algorithm | |
| CN108909702A (en) | A kind of plug-in hybrid-power automobile energy management method and system | |
| Taherzadeh et al. | A comprehensive study of the parameters impacting the fuel economy of plug-in hybrid electric vehicles | |
| Zhang et al. | Cloud computing-based real-time global optimization of battery aging and energy consumption for plug-in hybrid electric vehicles | |
| CN112526883B (en) | Vehicle energy management method based on intelligent networking information | |
| Ritter et al. | Long-term stochastic model predictive control for the energy management of hybrid electric vehicles using Pontryagin’s minimum principle and scenario-based optimization | |
| CN115158094A (en) | Plug-in hybrid electric vehicle energy management method based on long-short-term SOC (System on chip) planning | |
| CN116070783B (en) | Learning type energy management method of hybrid transmission system under commute section | |
| Shi et al. | Multi-objective tradeoff optimization of predictive adaptive cruising control for autonomous electric buses: A cyber-physical-energy system approach | |
| US20160362096A1 (en) | Method and System for Selecting Power Sources in Hybrid Electric Vehicles | |
| CN112018794B (en) | Management device, management method, and storage medium | |
| Yan et al. | Design of a deep inference framework for required power forecasting and predictive control on a hybrid electric mining truck | |
| Jiao et al. | Real-time energy management based on ECMS with stochastic optimized adaptive equivalence factor for HEVs | |
| Li et al. | Adaptive equivalent consumption minimization strategy and its fast implementation of energy management for fuel cell electric vehicles | |
| CN114872711A (en) | Driving planning method, system, device and medium based on intelligent networked vehicle | |
| Xia et al. | A predictive energy management strategy for multi-mode plug-in hybrid electric vehicle based on long short-term memory neural network | |
| Gong et al. | Energy Management for a DM-i Plug-in Hybrid Electric Vehicle via Continuous-Discrete Reinforcement Learning | |
| Lin et al. | AER adaptive control strategy via energy prediction for PHEV | |
| Chang et al. | An energy management strategy of deep reinforcement learning based on multi-agent architecture under self-generating conditions |
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 | ||
| GR01 | Patent grant |