CN113687242A - Lithium ion battery SOH estimation method for optimizing and improving GRU neural network based on GA algorithm - Google Patents
Lithium ion battery SOH estimation method for optimizing and improving GRU neural network based on GA algorithm Download PDFInfo
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- 229910001416 lithium ion Inorganic materials 0.000 title claims abstract description 24
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 18
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims abstract description 24
- 229910052744 lithium Inorganic materials 0.000 claims abstract description 24
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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Abstract
The invention discloses a method for estimating SOH of a lithium ion battery based on GA algorithm optimization improvement GRU neural network, which comprises the following steps: 1. acquiring experimental data: setting a lithium battery charging and discharging experiment working condition to charge and discharge the lithium battery, and recording voltage, current and temperature data of the lithium battery and the capacity of the battery which is completely discharged each time in the experiment process; 2. data preprocessing: deleting invalid values of the acquired original data and carrying out data normalization processing; 3. and constructing a network model. 4. And taking the normalized data as the input of a network model for estimating the SOH of the lithium ion battery. The method has the advantages of simple process, accurate estimation result and high precision.
Description
Technical Field
The invention relates to the technical field of power battery management, in particular to a method for estimating SOH of a lithium ion battery based on GA algorithm optimization improvement GRU neural network.
Background
The rapid development of the automotive industry inevitably produces a number of negative effects: large consumption of non-renewable energy sources such as petroleum and the like, generation of automobile exhaust gas, greenhouse gas and the like. In the face of severe challenges such as resource shortage and environmental pollution, automobile enterprises are beginning to vigorously develop electric automobiles using power batteries as new energy sources to reduce dependence on petroleum energy and reduce automobile exhaust emission. Lithium ion batteries are widely used in the field of electric vehicles because of their high energy density, light weight, and long charge-discharge cycle life. The state of health (SOH) of the lithium battery not only represents the attenuation and the deterioration condition of the battery capacity, but also estimates one of important parameters of the endurance mileage of the automobile; secondly, the SOH of the power battery is monitored in real time, so that potential safety hazards can be avoided, and the service life of the battery is prolonged. Accurate estimation of state of health (SOH) of a lithium ion battery is critical to ensure safe and efficient operation of the battery. However, since the state of health of the battery cannot be measured directly by the sensor, it can only be estimated indirectly by external characteristics (voltage, current, temperature, etc.). It is considered that most of the current estimation methods artificially extract the health factor from the external characteristic data of the battery and then estimate the SOH of the battery by using the extracted health factor. The degree to which the health factor correlates with SOH determines the accuracy of the estimation. This not only requires complex data preprocessing of the raw data, but also does not take full advantage of the useful information in the raw data. Therefore, the method has important practical significance for accurately estimating the SOH of the lithium battery.
Disclosure of Invention
The invention aims to provide a method for estimating SOH of a lithium ion battery based on GA algorithm optimization improvement GRU neural network. The method has the advantages of simple process, accurate estimation result and high precision.
The technical scheme of the invention is as follows: a lithium ion battery SOH estimation method for optimizing and improving a GRU neural network based on a GA algorithm comprises the following steps:
step 3, constructing a network model: the network model adopts a time sequence of GRU processing input data, a self-attention layer is used for redistributing weight to output components of the GRU, then a full-connection layer is set for fusing output of the self-attention layer, and finally a genetic algorithm is used for carrying out parameter optimization on the number of GRU layers, the number of GRU neurons, the number of full-connection layers and the number of full-connection neurons, so that the network prediction performance is improved for estimating SOH of the lithium ion battery;
and 4, taking the normalized data as the input of a network model for estimating the SOH of the lithium ion battery.
In the above lithium ion battery SOH estimation method based on GA algorithm optimization improvement GRU neural network, in step 2, a maximum and minimum normalization method is adopted to normalize the original data to between [0,1 ]; the formula of the maximum and minimum normalization method is as follows:
where x represents the raw data observation, min represents the minimum of the data values x, and max represents the maximum of the data values x.
In the foregoing method for estimating SOH of a lithium ion battery based on GA algorithm optimization and improvement of a GRU neural network, in step 3, a mathematical formula of an information transfer process in the GRU is as follows:
Rt＝σ(XtWxr+Ht-1Whr+br)；
Zt＝σ(XtWxz+Ht-1Whz+bz)；
in the formula: rtTo reset the gate, ZtIn order to update the door,
as candidate hidden states, HtIs a hidden state of time step t; wxr，Whr，Wxz，Whz，Wxh，WhhWeights representing reset gate, refresh gate and hidden state respectively; br，bz，bhBiases representing reset gate, refresh gate and hidden state, respectively; sigma represents an activation function of an update gate and a reset gate, and a sigmoid function is adopted; tanh represents the activation function of the candidate hidden state for the current time step, and an element multiplication is indicated by a hyperbolic tangent function, for example.
In the foregoing method for estimating SOH of a lithium ion battery based on GA algorithm optimization and improvement of a GRU neural network, in step 3, the step of using a self-attention layer to reassign weights to outputs of GRU hidden units includes the following steps:
Q＝WqI；
K＝WkI；
V＝WvI；
wherein I represents an input from the attention layer, I ═ a1，a2，...，at}，atIs the t-th component of vector I; wq、Wk、WvQ, K and V, Q, K and V, respectively, represent Query vector Query, Key vector Key, and Value vector Value; kTRepresents the transpose of matrix K;
represents the softmax activation function;
representing a newly generated attention weight matrix; o denotes the output from the attention layer.
The lithium ion battery SOH estimation method based on GA algorithm optimization improvement GRU neural network is characterized in that: in the step 3, the number of GRU layers, the number of GRU neurons, the number of fully-connected layers, and the number of fully-connected neurons are optimized by a genetic algorithm, specifically:
step 3.1, encoding the GRU layer number, the GRU neuron number, the full-connection layer number and the full-connection neuron number into an initial population;
step 3.2, selecting a fitness function, taking the mean square error between the predicted value and the true value as the fitness function of the genetic algorithm, and selecting a certain number of better individuals through the fitness function;
3.3, carrying out selection, crossing and mutation operations on newly generated individuals to generate a new population;
and 3.4, after iteration of the designated population algebra, selecting the optimal individual from the population algebra, and searching the optimal solution.
Compared with the prior art, the method takes the discharge working condition of the battery in actual use into consideration, the lithium battery is charged and discharged by setting the charge and discharge experimental working condition of the lithium battery, the voltage, current and temperature data of the lithium battery and the capacity of the battery discharging completely each time in the experimental process are recorded, so that the original data of the lithium battery are obtained, and the data set for estimating the SOH of the lithium battery is constructed by using the original data. The GRU neural network is optimized and improved based on the GA algorithm, and the number of the GRU layers, the number of the GRU neurons, the number of the full-link layers and the number of the full-link neurons are optimized through the genetic algorithm in consideration of the fact that the number of the network layers and the number of the neurons have great influence on the prediction precision of the established model, so that the precision of the model is improved. Verification shows that the method has good prediction precision and robustness. The method has the advantages of simple process, accurate estimation result and high precision.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a diagram of a GRU structure in the process of the present invention;
FIG. 3 is a schematic diagram of a self-attention layer in the method of the present invention;
FIG. 4 is a flow chart of a genetic algorithm in the method of the present invention;
FIG. 5 is a graph of the NASA battery RW20 prediction result in the method of the present invention;
FIG. 6 is a graph of the error of the prediction result of the NASA battery RW20 in the method of the present invention;
FIG. 7 is a graph of the NASA battery RW24 prediction result in the method of the present invention;
FIG. 8 is a graph of the error of the prediction result of the NASA battery RW24 in the method of the present invention.
Detailed Description
The invention is further illustrated by the following figures and examples, which are not to be construed as limiting the invention.
Example (b): the lithium ion battery SOH estimation method for optimizing and improving the GRU neural network based on the GA algorithm comprises the following steps as shown in figure 1:
step 1: acquiring experimental data: setting a lithium battery charging and discharging experiment working condition to charge and discharge the lithium battery, and recording voltage, current and temperature data of the lithium battery and the capacity of the battery which is completely discharged each time in the experiment process; the temperature of the lithium battery charging and discharging experiment environment is set to be 0 ℃, 25 ℃ and 45 ℃, and low temperature, normal temperature and high temperature in actual driving are simulated respectively; the lithium battery charging and discharging experiment comprises a charging experiment and a discharging experiment, wherein the charging experiment is to perform constant current charging to upper cut-off voltage by 1C multiplying power, then perform constant voltage charging to current reduction of 1/20C by the upper cut-off voltage, and finish charging; the discharge experiment simulates the discharge condition in the actual driving process by three different discharge multiplying factors of 1C, 3C and 5C; data are acquired through a sensor, and mainly battery end voltage, battery current and battery temperature are acquired, and capacity change is obtained through an ampere-hour integration method.
where x represents the raw data observation, min represents the minimum of the data values x, and max represents the maximum of the data values x.
Step 3, constructing a network model: the network model adopts a time sequence of GRU processing input data, a self-attention layer is used for redistributing weight to output components of the GRU, then a full-connection layer is set for fusing output of the self-attention layer, and finally a genetic algorithm is used for carrying out parameter optimization on the GRU layer number, the GRU neuron number, the full-connection layer number and the full-connection neuron number, so that the network prediction performance is improved for estimating SOH of the lithium ion battery. In particular, the method comprises the following steps of,
as shown in fig. 2, the GRU network is used to perform feature extraction on input data of a training data set (i.e., the GRU is used to process a time series of input data), and a mathematical formula of an information transfer process in the GRU is as follows:
Rt＝σ(XtWxr+Ht-1Whr+br)；
Zt＝σ(XtWxz+Ht-1Whz+bz)；
in the formula: rtTo reset the gate, ZtIn order to update the door,
as candidate hidden states, HtIs a hidden state of time step t; wxr，Whr，Wxz，Whz，Wxh，WhhWeights representing reset gate, refresh gate and hidden state respectively; br，bz，bhBiases representing reset gate, refresh gate and hidden state, respectively; sigma represents an activation function of an update gate and a reset gate, and a sigmoid function is adopted; tanh represents the activation function of the candidate hidden state for the current time step, and an element multiplication is indicated by a hyperbolic tangent function, for example.
As shown in fig. 3, using the self-attention layer to re-assign weights to the outputs of the GRU concealment units comprises the following steps:
Q＝WqI；
K＝WkI；
V＝WvI；
wherein I represents an input from the attention layer, I ═ a1，a2，...，at}，atIs the t-th component of vector I; wq、Wk、WvQ, K and V, Q, K and V, respectively, represent Query vector Query, Key vector Key, and Value vector Value; kTRepresents the transpose of matrix K;
represents the softmax activation function;
representing a newly generated attention weight matrix; o denotes the output from the attention layer.
As shown in fig. 4, the genetic algorithm is used to perform parameter optimization on the GRU level, the GRU neuron number, the total ligation level and the total ligation neuron number, specifically:
step 3.1, encoding the GRU layer number, the GRU neuron number, the full-connection layer number and the full-connection neuron number into an initial population;
step 3.2, selecting a fitness function, taking the mean square error between the predicted value and the true value as the fitness function of the genetic algorithm, and selecting a certain number of better individuals through the fitness function;
3.3, carrying out selection, crossing and mutation operations on newly generated individuals to generate a new population;
and 3.4, after iteration of the designated population algebra, selecting the optimal individual from the population algebra, and searching the optimal solution.
And 4, taking the test data set as the input of the network model for estimating the SOH of the lithium ion battery.
In order to verify the superiority of the invention, the predictive performance of the algorithm of the invention is compared with the three algorithms, namely, the FCNN algorithm, the SVR algorithm and the GRU algorithm. Fig. 5 and fig. 7 show the predicted results of the four algorithms on RW20 and RW24 batteries on NASA random battery data sets, and it can be seen that the estimation of the SOH of the lithium battery by using the method of the present invention is very close to the true value, which indicates that the present invention has the characteristic of accurate estimation result. Fig. 6 and 8 are corresponding prediction error maps. Through the error analysis of fig. 6 and fig. 8, it can be found that the algorithm provided by the present invention can control the prediction error within 2%, and has higher prediction accuracy compared with the other three algorithms.
In summary, compared with most lithium battery SOH estimation methods for extracting health factors, the method provided by the invention has the advantages that the voltage, the current and the temperature of the lithium battery are used as the input of the network model, so that the complicated process of artificially extracting the health factors is omitted, the sampling data is directly utilized, and the useful information in the original data can be retained to the greatest extent. The method is based on GA algorithm optimization and improvement of GRU neural network, adopts optimized data driving algorithm, and has good prediction precision and robustness through verification. The method has the advantages of simple process, accurate estimation result and high precision.
Claims (5)
1. A lithium ion battery SOH estimation method for optimizing and improving a GRU neural network based on a GA algorithm is characterized by comprising the following steps: the method comprises the following steps:
step 1, acquiring experimental data: setting a lithium battery charging and discharging experiment working condition to charge and discharge the lithium battery, and recording voltage, current and temperature data of the lithium battery and the capacity of the battery which is completely discharged each time in the experiment process;
step 2, data preprocessing: deleting invalid values of the acquired original data and carrying out data normalization processing;
step 3, constructing a network model: the network model adopts a time sequence of GRU processing input data, redistributes weight to GRU output components by a self-attention layer, then sets a full-connection layer to fuse the output of the self-attention layer, and finally carries out parameter optimization on the GRU layer number, the GRU neuron number, the full-connection layer number and the full-connection neuron number by a genetic algorithm;
and 4, taking the normalized data as the input of a network model for estimating the SOH of the lithium ion battery.
2. The method for estimating SOH of lithium ion battery based on GA algorithm optimization improvement GRU neural network of claim 1, wherein: in the step 2, a maximum and minimum normalization method is adopted to normalize the original data to be between [0 and 1 ]; the formula of the maximum and minimum normalization method is as follows:
where x represents the raw data observation, min represents the minimum of the data values x, and max represents the maximum of the data values x.
3. The method for estimating SOH of lithium ion battery based on GA algorithm optimization improvement GRU neural network of claim 1, wherein: in step 3, the mathematical formula of the information transfer process in the GRU is as follows:
Rt＝σ(XtWxr+Ht-1Whr+br)；
Zt＝σ(XtWxz+Ht-1Whz+bz)；
in the formula: rtTo reset the gate, ZtIn order to update the door,
as candidate hidden states, HtIs a hidden state of time step t; wxr，Whr，Wxz，Whz，Wxh，WhhWeights representing reset gate, refresh gate and hidden state respectively; br，bz，bhBiases representing reset gate, refresh gate and hidden state, respectively; sigma represents an activation function of an update gate and a reset gate, and a sigmoid function is adopted; tanh represents the activation function of the candidate hidden state for the current time step, and an element multiplication is indicated by a hyperbolic tangent function, for example.
4. The method for estimating SOH of lithium ion battery based on GA algorithm optimization improvement GRU neural network of claim 1, wherein: in the step 3, the step of using the self-attention layer to reassign the weight to the output of the GRU hiding unit includes the following steps:
Q＝WqI；
K＝WkI；
V＝WvI；
wherein I represents an input from the attention layer, I ═ a1，a2，...，at}，atIs the t-th component of vector I; wq、Wk、WvQ, K and V, Q, K and V, respectively, represent Query vector Query, Key vector Key, and Value vector Value; kTRepresents the transpose of matrix K;
represents softmax activation function;
representing a newly generated attention weight matrix; o denotes the output from the attention layer.
5. The method for estimating SOH of lithium ion battery based on GA algorithm optimization improvement GRU neural network of claim 1, wherein: in the step 3, the number of GRU layers, the number of GRU neurons, the number of fully-connected layers, and the number of fully-connected neurons are optimized by a genetic algorithm, specifically:
step 3.1, encoding the GRU layer number, the GRU neuron number, the full-connection layer number and the full-connection neuron number into an initial population;
step 3.2, selecting a fitness function, taking the mean square error between the predicted value and the true value as the fitness function of the genetic algorithm, and selecting a certain number of better individuals through the fitness function;
3.3, carrying out selection, crossing and mutation operations on newly generated individuals to generate a new population;
and 3.4, after iteration of the designated population algebra, selecting the optimal individual from the population algebra, and searching the optimal solution.
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