CN115983134B - Battery power state prediction method and system based on neural network - Google Patents
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Abstract
The invention discloses a battery power state prediction method and a system based on a neural network, wherein the method comprises the following steps: establishing a generalized neural network prediction model of a new battery power state prediction and prediction correction coefficient aiming at a few samples of similar battery historical experimental data; optimizing the model by using a DSA algorithm according to the obtained generalized regression neural network prediction model; obtaining a battery power state prediction result according to the optimized model; and according to the obtained battery power state prediction result, performing intelligent regulation and control on the energy distribution of the vehicle. According to the invention, the GRNN neural network prediction model is used, the battery power state can be predicted through laboratory short-term data, accurate and reasonable data support is provided for energy distribution of vehicles and the like, and the method has the advantages of better generalization capability, stronger optimizing capability and no sinking into local optimum.
Description
Technical Field
The invention relates to a battery prediction technology, in particular to a battery power state prediction method and system based on a neural network.
Background
Due to the rising environmental awareness caused by environmental pollution in recent years and the exhaustion of traditional fuels such as petroleum, the international society is actively developing clean renewable energy sources to realize sustainable development of human society, new energy automobiles are generated, and the environmental pollution is greatly reduced compared with the traditional automobiles. With the development of new energy automobiles in recent years, pure Electric Vehicles (EVs), hybrid Electric Vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and the like are emerging on the market. The battery is used as a heart of the new energy automobile, and the accurate evaluation of the power state of the battery plays an important role in the safety of the new energy automobile.
Disclosure of Invention
The invention aims to: the invention aims to provide a battery power state prediction method and a battery power state prediction system based on a neural network, so that the high calculation cost and the storage capacity of the neural network are reduced, the cost is saved, and the battery power state is rapidly and accurately predicted.
The technical scheme is as follows: the invention relates to a battery power state prediction method based on a neural network, which comprises the following steps:
(1) And establishing a generalized neural network prediction model of a new battery power state prediction and prediction correction coefficient aiming at a few samples of similar battery historical experimental data.
And (1.1) dividing the GRNN neural network into a few-sample state prediction model and a compensation correction prediction model, and establishing a GRNN neural network battery power state prediction model by combining the two models.
(1.2) establishing a GRNN neural network battery power prediction model, wherein the formula is as follows:
Q(t)＝K(t)Q 1 (t)
wherein Q (t) represents the corrected prediction result, Q 1 (t) represents the prediction result of the low-sample prediction model, and K (t) represents the result of the compensation correction prediction model.
(1.3) calculating a GRNN neural network battery power prediction model output layer, wherein the formula is as follows:
where n is the sample size, p is the dimension of the random variable X, delta is the width coefficient of the Gaussian function, i.e. the smoothing factor, X i And Y i Is the sample observation of random variables x and y.
(1.4) establishing an adaptability function constructed by an average error function of the prediction model, wherein the formula is as follows:
where m is the number of B subset test samples,is the j-th individual actual output value in the B subset.
(2) And (3) optimizing the model by using a DSA algorithm according to the generalized regression neural network prediction model obtained in the step (1).
(2.1) initializing parameters, and inputting an amount affecting the accuracy of a battery power state prediction model: gaussian kernel width coefficient sigma.
(2.2) initializing a population: the population number is set to N, the grouping number is set to D, and the maximum iteration number is Maxiter.
(2.3) creating a group and queuing duck groups, and finding out fitness of all members, wherein the position formula is as follows;
X i ＝L b +(U b -L b )o
wherein Xi represents the spatial position of the ith duck in the duck group, i=1, 2,3, …, N and N are population size numbers; u (U) b And L b Representing the upper and lower limits of the search space, respectively, o is a matrix of random numbers between (0, 1).
(2.4) the formula for the exploration of the self-adaptive mechanism of the duck group is as follows:
K＝sin(2rand)+1
wherein sign (r-0.5) has an effect on the process of searching for food, and can be set to-1 or 1; μ represents a parameter controlling global search; p is the search transition probability of the exploration phase; CF1 and CF2 respectively represent competition and cooperation coefficients between ducks in the searching stage;a historical value representing the optimal duck position in the t-th iteration; />Representing the duck group in the t-th iterationAgents that search around food.
(2.5) selecting the optimal duck position according to the fitness after the exploration of the duck group is finished; if the fitness of one of the ducks is better than the current optimal duck, their positions will be changed according to the following formula:
wherein μ represents a control parameter of global search in the development stage; parameter KF 1 And KF 2 Respectively representing the cooperation and competition coefficients between ducks in the development stage;representing the optimal duck position of the current history value in the t-th iteration; />And->Representing +.f. in foraging of Duck population in t iterations>Surrounding agents, k+.j.
(2.6) if the termination criterion is met, moving to the step (2.7); otherwise, go to step (2.3).
(2.7) outputting the obtained optimal solution.
(3) And (3) obtaining a battery prediction result according to the optimized model in the step (2).
(4) And (3) intelligently regulating and controlling the energy distribution of the vehicle according to the battery power state prediction result obtained in the step (3).
The battery power state prediction system based on the neural network adopts the battery power state prediction method based on the neural network, and is characterized by comprising the following modules:
GRNN neural network prediction model: the method comprises the steps of preprocessing battery information acquired by a real-time monitoring module, inputting the battery information into a GRNN neural network optimized by a DSA algorithm, acquiring a preliminary predicted value through a battery power predicting model, and correcting through a correcting model to acquire an accurate value;
and the real-time monitoring module is used for: monitoring working data of the lithium battery;
vehicle VCU: acquiring the predicted power of the neural network, transmitting the predicted power to a control system to control the power output of a battery, and storing the acquired vehicle information by a transmission information management module;
and (3) a control system: receiving power information sent by a VCU of a vehicle to adjust the power output of a battery pack;
and an information management module: storing important data sent by a vehicle VCU;
battery safety device: and detecting whether the battery pack fails, if so, performing emergency treatment, and transmitting information to the vehicle VCU.
A computer storage medium having stored thereon a computer program which when executed by a processor implements a neural network based battery power state prediction method as described above.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a neural network based battery power state prediction method as described above when executing the computer program.
The beneficial effects are that: compared with the prior art, the invention has the following advantages:
1. according to the invention, the GRNN neural network prediction model is used, so that the battery power state can be predicted through laboratory short-term data, and accurate and reasonable data support is provided for energy distribution of vehicles and the like; compared with the traditional neural network model, the GRNN neural network model has the advantages of better generalization capability, stronger optimizing capability and no sinking into local optimization.
2. The model is optimized by using the duck group search algorithm, so that the model error is smaller; the accurate battery power state prediction data enables the energy distribution adjustment of the vehicle to be more reasonable, and the safety performance of the vehicle is improved.
3. The invention uses the compensation module to simplify huge data required by the traditional neural network, reduces high calculation cost and storage capacity of the neural network, and has better economic benefit.
4. The invention is suitable for new energy vehicles and accords with the design concept of green environmental protection.
Drawings
FIG. 1 is a block diagram of the structure of the present invention;
FIG. 2 is a flow chart of GRNN neural network prediction in accordance with the present invention;
FIG. 3 is a flowchart of the duck group search algorithm optimizing GRNN neural network model of the present invention;
FIG. 4 is a battery power prediction model error map of the present invention;
FIG. 5 is a graph of corrected battery power prediction model errors in accordance with the present invention;
fig. 6 is an error diagram of a neural network prediction model optimized by the duck group algorithm of the invention;
FIG. 7 is a graph showing the comparison of the prediction accuracy of the power prediction model, the modified power prediction model and the algorithm-optimized neural network prediction model.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings.
A battery power state prediction method based on a neural network comprises the following steps:
(1) And establishing a generalized neural network prediction model of the new battery power state prediction and prediction correction coefficient aiming at a small sample of the similar battery historical experimental data, as shown in fig. 2.
And (1.1) dividing the GRNN neural network into a few-sample state prediction model and a compensation model, and combining the two models to establish a neural network battery power state prediction model.
(1.2) establishing a GRNN neural network battery power prediction model, wherein the formula is as follows:
Q(t)＝K(t)Q 1 (t)
wherein Q (t) represents the corrected prediction result, Q 1 (t) represents the prediction result of the less-sample prediction model, and K (t) representsCompensating and correcting the prediction model result.
(1.3) calculating a GRNN neural network battery power prediction model output layer, wherein the formula is as follows:
where n is the sample size, p is the dimension of the random variable X, delta is the width coefficient of the Gaussian function, i.e. the smoothing factor, X i And Y i Is the sample observation of random variables x and y.
(1.4) establishing an adaptability function constructed by an average error function of the prediction model, wherein the formula is as follows:
where m is the number of B subset test samples,is the j-th individual actual output value in the B subset.
(2) And (3) optimizing the model by using a DSA method according to the generalized regression neural network prediction model obtained in the step (1), as shown in figure 3.
(2.1) initializing parameters, and inputting an amount affecting the accuracy of a battery power state prediction model: gaussian kernel width coefficient sigma.
(2.2) initializing a population: the population number is set to N, the grouping number is set to D, and the maximum iteration number is Maxiter.
(2.3) creating a group and queuing duck groups, and finding out fitness of all members, wherein the position formula is as follows;
X i ＝L b +(U b -L b )o
wherein Xi represents that the ith duck is in the duck groupI=1, 2,3, …, N is the population size; u (U) b And L b Representing the upper and lower limits of the search space, respectively, o is a matrix of random numbers between (0, 1).
(2.4) the formula for the exploration of the self-adaptive mechanism of the duck group is as follows:
K＝sin(2rand)+1
wherein sign (r-0.5) has an effect on the process of searching for food, and can be set to-1 or 1; μ represents a parameter controlling global search; p is the search transition probability of the exploration phase; CF1 and CF2 respectively represent competition and cooperation coefficients between ducks in the searching stage;a historical value representing the optimal duck position in the t-th iteration; />Representing the duck group in the t-th iterationAgents that search around food.
(2.5) selecting the optimal duck position according to the fitness after the exploration of the duck group is finished; if the fitness of one of the ducks is better than the current optimal duck, their positions will be changed according to the following formula:
wherein μ represents a control parameter of global search in the development stage; parameter KF 1 And KF 2 Respectively represent developmentThe cooperation and competition coefficients between ducks in the stage;representing the optimal duck position of the current history value in the t-th iteration; />And->Representing +.f. in foraging of Duck population in t iterations>Surrounding agents, k+.j.
(2.6) if the termination criterion is met, moving to the step (2.7); otherwise, go to step (2.3).
(2.7) outputting the obtained optimal solution.
(3) And (3) obtaining a battery prediction result according to the optimized model in the step (2).
(4) And (3) intelligently regulating and controlling the energy distribution of the vehicle according to the battery power state prediction result obtained in the step (3).
As shown in fig. 1, a battery power state prediction system based on a neural network, which adopts the battery power state prediction method based on the neural network, is characterized in that the system comprises the following modules:
GRNN neural network prediction model: the method comprises the steps of preprocessing battery information acquired by a real-time monitoring module, inputting the battery information into a GRNN neural network optimized by a DSA algorithm, acquiring a preliminary predicted value through a battery power predicting model, and correcting through a correcting model to acquire an accurate value;
and the real-time monitoring module is used for: monitoring working data of the lithium battery;
vehicle VCU: acquiring the predicted power of the neural network, transmitting the predicted power to a control system to control the power output of a battery, and storing the acquired important vehicle information by a transmission information management module;
and (3) a control system: receiving power information sent by a VCU of a vehicle to adjust the power output of a battery pack;
and an information management module: storing important data sent by a vehicle VCU;
battery safety device: and detecting whether the battery pack fails, if so, performing emergency treatment, and transmitting information to the vehicle VCU.
As shown in fig. 4-5, the prediction accuracy of the battery power prediction model is significantly improved after the correction of the correction model.
As shown in FIG. 6, after the GRNN neural network prediction model is optimized by the duck group search algorithm, the fitting degree of the model prediction value to the true value is improved, so that the prediction is more accurate.
As shown in FIG. 7, after the GRNN neural network prediction model is optimized by the duck swarm algorithm, the maximum error value is lower than 0.02, and the matching degree of the prediction value is high.
Claims (5)
1. The battery power state prediction method based on the neural network is characterized by comprising the following steps of:
(1) Establishing a generalized neural network prediction model of a new battery power state prediction and prediction correction coefficient aiming at a few samples of similar battery historical experimental data;
dividing the GRNN neural network into a few-sample state prediction model and a correction prediction model, and establishing a GRNN neural network battery power state prediction model by combining the two models;
(1.2) establishing a GRNN neural network battery power prediction model, wherein the formula is as follows:
Q(t)＝K(t)Q 1 (t)
wherein Q (t) represents the corrected prediction result, Q 1 (t) represents the prediction result of the low-sample prediction model, and K (t) represents the result of the compensation correction prediction model;
(1.3) calculating a GRNN neural network battery power prediction model output layer, wherein the formula is as follows:
where n is the sample size, p is the dimension of the random variable X, delta is the width coefficient of the Gaussian function, i.e. the smoothing factor, X i And Y i Sample observations for random variables x and y;
(1.4) establishing an adaptability function constructed by an average error function of the prediction model, wherein the formula is as follows:
where m is the number of B subset test samples,is the actual output value of the jth individual in the B subset;
(2) Optimizing the model by using a DSA algorithm according to the generalized regression neural network prediction model obtained in the step (1);
(3) Obtaining a battery prediction result according to the optimized model in the step (2);
(4) And (3) intelligently regulating and controlling the energy distribution of the vehicle according to the battery power state prediction result obtained in the step (3).
2. The method for predicting a battery power state based on a neural network according to claim 1, wherein the step (2) specifically comprises:
(2.1) initializing parameters, and inputting an amount affecting the accuracy of a battery power state prediction model: gaussian kernel width coefficient sigma;
(2.2) initializing a population: setting the population number as N, the grouping number as D and the maximum iteration number as Maxiter;
(2.3) creating a group and queuing duck groups, and finding out fitness of all members, wherein the position formula is as follows;
X i ＝L b +(U b -L b )o
wherein Xi represents the spatial position of the ith duck in the duck group, i=1, 2,3, …, N and N are population size numbers; u (U) b And L b Representing the upper and lower limits of the search space, respectively, o being a matrix of random numbers between (0, 1);
(2.4) the self-adaptive mechanism exploration formula of the duck group is as follows;
K＝sin(2rand)+1
wherein sign (r-0.5) has an effect on the process of searching for food, and can be set to-1 or 1; μ represents a parameter controlling global search; p is the search transition probability of the exploration phase; CF1 and CF2 respectively represent competition and cooperation coefficients between ducks in the searching stage;a historical value representing the optimal duck position in the t-th iteration; />Representing the duck group in t iteration +.>Agents that search around food;
(2.5) selecting the optimal duck position according to the fitness after the exploration of the duck group is finished; if the fitness of one of the ducks is better than the current optimal duck, their positions will be changed according to the following formula:
wherein μ represents a control parameter of global search in the development stage; parameter KF 1 And KF 2 Respectively representing the cooperation and competition coefficients between ducks in the development stage;representing the optimal duck position of the current history value in the t-th iteration; />And->Representing +.f. in foraging of Duck population in t iterations>Surrounding agents, k+.j;
(2.6) if the termination criterion is met, moving to the step (2.7); otherwise, go to step (2.3);
(2.7) outputting the obtained optimal solution.
3. A neural network-based battery power state prediction system employing a neural network-based battery power state prediction method according to any one of claims 1-2, the system comprising:
GRNN neural network prediction model: the method comprises the steps of preprocessing battery information acquired by a real-time monitoring module, inputting the battery information into a GRNN neural network optimized by a DSA algorithm, acquiring a preliminary predicted value through a battery power predicting model, and correcting through a correcting model to acquire an accurate value;
and the real-time monitoring module is used for: monitoring working data of the lithium battery;
vehicle VCU: acquiring the predicted power of the neural network, transmitting the predicted power to a control system to control the power output of a battery, and storing the acquired vehicle information by a transmission information management module;
and (3) a control system: receiving power information sent by a VCU of a vehicle to adjust the power output of a battery pack;
and an information management module: storing important data sent by a vehicle VCU;
battery safety device: and detecting whether the battery pack fails, if so, performing emergency treatment, and transmitting information to the vehicle VCU.
4. A computer storage medium having stored thereon a computer program which, when executed by a processor, implements a neural network based battery power state prediction method as claimed in any one of claims 1-2.
5. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements a neural network based battery power state prediction method as claimed in any one of claims 1-2 when the computer program is executed.
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