CN113191547B - New energy vehicle power battery charging optimization method and system - Google Patents
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Abstract
The invention provides a new energy vehicle power battery charging optimization method and system, relates to the field of vehicle power battery charging optimization, and solves the technical problem of how to improve the accuracy of new energy vehicle power battery charging optimization. The accuracy of the charging optimization of the power battery of the new energy vehicle can be improved by establishing a vehicle power battery charging correlation model.
Description
Technical Field
The invention relates to the field of vehicle power battery charging optimization, in particular to a new energy vehicle power battery charging optimization method and system.
Background
At present, more and more people buy new energy vehicles, the new energy vehicles use a power battery as a unique power source, the power battery needs to be charged when the electric energy of the power battery of the vehicle is insufficient, a charging strategy comprising charging time, charging energy consumption and charging health state is set for the vehicle in the current common use, and the charging strategy is adjusted according to a preset charging strategy table look-up table, so that the charging method cannot be adjusted according to the actual condition of the vehicle.
In order to solve the above problems, chinese patent application No. (CN 201911350993.4) discloses a big data and BMS combined optimal charging method for an electric vehicle, which collects characteristic data of the electric vehicle, trains two machine learning models, and predicts the amount of electricity consumed each day and the allowable charging time, respectively; under different charging modes, two instructions of daily driving and remote driving are combined, and a targeted charging measure is adopted for charging.
According to the method, a power battery charging method is optimized through a training learning model, however, when the existing cloud platform obtains data uploaded by a vehicle, wrong data can be generated, the requirement of the established model on the accuracy of the data is high, once the data with the errors are used, the established model is a wrong model, a correct charging optimization method cannot be obtained, the two models are established through the method, the charging optimization only has two aspects, the two obtained charging optimization methods are not accurate enough, the method is not considered comprehensively, only the charging time and the consumed electric energy are predicted, and the charging energy consumption of the power battery, the charging time of the power battery and the health state of the power battery are not comprehensively considered.
Disclosure of Invention
The invention provides a new energy vehicle power battery charging optimization method and system aiming at the problems in the prior art, and solves the technical problem of how to improve the accuracy of new energy vehicle power battery charging optimization.
The invention is realized by the following technical scheme: a new energy vehicle power battery charging optimization method comprises the following steps:
acquiring power battery charging optimization related data of a vehicle connected with a big data platform;
screening the power battery charging optimization related data to obtain data related to the charging time of the power battery, the charging energy consumption of the power battery and the health state of the power battery, and performing neural network training on the screened data to establish a vehicle power battery charging related model;
and after the model is established, acquiring the current power battery charging optimization related data of the vehicle connected with the big data platform, substituting the current power battery charging optimization related data into the vehicle power battery charging related model for optimal control, and outputting a charging optimization result.
The big data platform obtains relevant charging optimization data of a power battery of a vehicle connected with the big data platform, then screens the relevant charging optimization data to obtain data including relevant charging time of the power battery, relevant charging energy consumption of the power battery and relevant health state of the power battery, carries out neural network training on the screened data to establish a relevant charging model of the power battery of the vehicle, obtains relevant current charging optimization data of the power battery of the vehicle connected with the big data platform after establishing the model, substitutes the relevant charging optimization data of the power battery of the vehicle into the relevant charging model of the power battery of the vehicle for optimal control, and then outputs a charging optimization result of the power battery. According to the method, the charging optimization method of the new energy vehicle power battery is improved by screening the relevant charging optimization data of the power battery and establishing the relevant charging model of the vehicle power battery, the method screens the relevant non-conforming charging optimization data of the power battery so as to reduce the data processing amount of a large data platform, and the accuracy of establishing the relevant charging model of the vehicle power battery is improved by screening the relevant non-conforming charging optimization data of the power battery, so that the accuracy of charging optimization of the vehicle power battery is greatly improved, the relevant charging model of the vehicle power battery is also established, the result which can be accurately obtained by establishing the relevant charging model of the vehicle power battery is obtained, and the driving experience of a driver is improved.
In the method for optimizing the charging of the power battery of the new energy vehicle, the screening step comprises the steps of cleaning the charging optimization related data of the power battery, removing abnormal data and null data, performing correlation analysis on the cleaned charging optimization related data of the power battery to obtain data which are related to the charging time of the power battery, the charging energy consumption of the power battery and the health state of the power battery, cleaning the charging optimization related data of the power battery to remove unreadable data which have overlarge deviation with other data and do not accord with a statistical rule and data with null or 0 content, preventing the data from interfering the establishment of a charging related model of the power battery of the vehicle, and if abnormal data or null data exist, reducing the accuracy of the establishment of the model, performing correlation analysis to find data which are related to the charging time of the power battery, the charging energy consumption of the power battery and related to the health state of the power battery, wherein the data can improve the accuracy of training a neural network to establish the charging related model of the power battery of the vehicle.
In the method for optimizing the charging of the power battery of the new energy vehicle, the correlation analysis comprises the step of selecting power battery charging optimization related data with a Pearson correlation coefficient larger than a preset coefficient value, and the Pearson correlation coefficient is a common method for correlation analysis, so that the accuracy of a vehicle power battery charging related model is improved, and the accuracy of the new energy vehicle charging optimization is improved.
In the new energy vehicle power battery charging optimization method, the building of the vehicle power battery charging related model comprises building of a power battery charging time model, a power battery charging energy consumption model and a power battery health state model, and a driver can obtain comprehensive charging related information according to the built vehicle power charging related model, so that the charging optimization accuracy of the new energy vehicle is improved.
In the new energy vehicle power battery charging optimization method, the step of establishing the power battery charging time model includes comparing the charging time output by the power battery charging time related data through a preset algorithm with a reference charging time, establishing the power battery charging time model when the difference value between the charging time and the reference charging time is not greater than a preset error value, adjusting the weight parameter of the hidden layer, the bias of the hidden layer, the weight parameter of the output layer and the bias of the output layer when the difference value between the charging time and the reference charging time is greater than the preset error value, and establishing the power battery charging time model as the power battery charging time model
Wherein y1 is a predicted value of charging time, j =1 \ 8230m, m is the number of nodes of a hidden layer, i =1 \ 8230n, n is the number of input data features, and t is i Charging time-dependent input parameter for a power cell, <' >>
For hiding the layer weight parameter, <' >>
Biased to hide layers, based on the presence of a reference signal>
As output layer weight parameters, b 0 For output layer bias, f H And for activating the function, the accuracy of vehicle charging time detection is improved by referring to the establishment of a power battery charging time model, so that the accuracy of the new energy vehicle power battery charging optimization method is improved.
In the new energy vehicle power battery charging optimization method, the step of establishing the power battery charging energy consumption model comprises the steps of comparing the charging energy consumption output by the power battery charging energy consumption related data through a preset algorithm with a reference charging energy consumption, establishing the power battery charging energy consumption model when the difference value between the charging energy consumption and the reference charging energy consumption is not larger than a preset error value, adjusting the weight parameter of the hidden layer, the bias of the hidden layer, the weight parameter of the output layer and the bias of the output layer when the difference value between the charging energy consumption and the reference charging energy consumption is larger than the preset error value, and establishing the power battery charging energy consumption model which is the power battery charging energy consumption model when the difference value between the charging energy consumption and the reference charging energy consumption is not larger than the preset error value
Wherein y2 is a predicted value of charging energy consumption, j =1 \ 8230m, m is the number of nodes of a hidden layer, i =1 \ 8230n, n and n are characteristic numbers of input data, and p i Input parameters for charging energy consumption of a power battery>
For hiding the layer weight parameter, <' >>
Biased to hide layers, based on the presence of a reference signal>
As output layer weight parameters, b 0 For output layer bias, f H To activateAnd the living function refers to the establishment of a power battery charging energy consumption model to improve the accuracy of vehicle charging energy consumption adjustment, so that the accuracy of the new energy vehicle power battery charging optimization method is improved.
In the method for optimizing charging of the power battery of the new energy vehicle, the step of establishing the power battery state of health model includes comparing the state of health output by the power battery state of health related data through a preset algorithm with a reference state of health, establishing the power battery state of health model when the difference between the state of health and the reference state of health is not greater than a preset error, and adjusting the weight parameter of the hidden layer, the bias of the hidden layer, the weight parameter of the output layer and the bias of the output layer when the difference between the state of health and the reference state of health is greater than the preset error, wherein the power battery state of health model includes the state of health of the hidden layer, the bias of the hidden layer, the weight parameter of the output layer and the bias of the output layer until the difference between the state of health and the reference state of health is not greater than the preset error, and the power battery state of health model includes the state of health and the reference state of health
Wherein y3 is a health state predicted value, j =1 \ 8230m, m is the number of hidden layer nodes, i =1 \ 8230n, n is the number of input data features, c i Entering parameters for the health status of the power cell>
For hiding the layer weight parameter, <' >>
Biased to hide layers, based on the presence of a reference signal>
As output layer weight parameters, b 0 For output layer bias, f H For activating the function, the accuracy of acquiring the health state of the power battery is improved by referring to the establishment of a health state model of the power battery, so that the accuracy of the charging optimization method of the power battery of the new energy vehicle is improved.
In the new energy vehicle power battery charging optimization method, the optimal control step includes presetting a y1 constraint condition, a y2 constraint condition and a y3 constraint condition, the preset y1 constraint condition is that y1 is within a preset charging time range, the y2 constraint condition is that y2 is within a preset charging energy consumption range, and the y3 constraint condition is that y3 is within a preset health state range, the optimal control step includes selecting the minimum y1, the minimum y2 and the maximum y3 as charging optimization results to output when y1 meets the preset y1 constraint condition, y2 meets the preset y2 constraint condition, and y3 meets the preset constraint condition, and adopting optimal control to comprehensively consider the relationship among vehicle charging time, charging energy consumption and power battery health indexes, and selecting a better scheme to ensure that the battery is charged at a proper temperature and a proper current, so as to ensure the health state of the battery, and prolong the service life of the battery.
In the new energy vehicle power battery charging optimization method, the power battery charging time related data comprises power battery temperature and power battery current data, the power battery charging energy consumption related data comprises power battery current and power battery voltage data, the power battery health state related data comprises power battery voltage and power battery insulation resistance data, and data with high correlation can be selected to obtain a more accurate linear relation, so that the accuracy of power battery charging related model establishment is improved.
The invention also comprises the following scheme: the new energy vehicle power battery charging optimization system further comprises an acquisition module used for acquiring relevant data of power battery charging optimization and a control unit arranged on the vehicle and used for receiving the data output by the acquisition module, the acquisition module is connected with the control unit, the control unit is in wireless connection with the big data platform, the big data platform is used for receiving relevant data of power battery optimization of the vehicle output by the control unit of the vehicle connected with the big data platform, screening the received data to obtain relevant data including relevant data of power battery charging time, relevant power battery charging energy consumption and relevant power battery health state, carrying out neural network training on the screened data to establish a vehicle power battery charging relevant model, and the big data platform is further used for acquiring relevant data of current power battery charging optimization of the vehicle connected with the big data platform after the model is established and substituting the relevant data into the vehicle power battery charging model and outputting an optimization result.
The acquisition module acquires power battery charging optimization related data and outputs the data to the control unit, the control unit receives the power battery charging optimization related data output by the acquisition module and outputs the data to the big data platform, the big data platform receives the vehicle power battery charging optimization related data output by the control unit and then screens the data to obtain data related to power battery charging time, power battery charging energy consumption and power battery health state, the big data platform conducts neural network training on the screened data to establish a vehicle power battery charging related model, the big data platform acquires the current power battery charging optimization related data of a vehicle connected with the big data platform after establishing the model, and the current power battery charging optimization related data is substituted into the vehicle power battery charging related model to conduct optimal control and then outputs a power battery charging optimization result. The system improves a new energy vehicle power battery charging optimization system by presetting a new energy vehicle power battery charging optimization method through a big data platform, screens all vehicle power battery charging optimization related data and establishes a vehicle power battery charging related model to improve the new energy vehicle power battery charging optimization method.
Compared with the prior art, the new energy vehicle power battery charging optimization method and system have the following advantages:
1. according to the method, the result which can be accurately obtained by the vehicle power battery charging correlation model is established, so that the accuracy of the new energy vehicle is improved, and meanwhile, the use experience of a driver and the convenience of maintenance of workers can be improved.
2. The invention adopts optimal control, comprehensively considers the relationship among the vehicle charging time, the charging energy consumption and the power battery health index, selects a better scheme, ensures that the battery is charged at proper temperature and proper current, further ensures the health state of the battery and prolongs the service life of the battery.
Drawings
FIG. 1 is a schematic representation of the process steps of the present invention.
Fig. 2 is a schematic diagram of the system structure of the invention.
In the figure 1, an acquisition module; 2. a control unit; 3. a big data platform.
Detailed Description
In order to make the technical problems, technical solutions and advantages solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, the method for optimizing the charging of the power battery of the new energy vehicle comprises the following steps:
the method comprises the steps of obtaining relevant data of power battery charging optimization of a vehicle connected with a big data platform 3, wherein the relevant data of power battery charging optimization comprises data acquisition time, a charging and discharging state, power battery current, power battery voltage, power battery capacity, monomer maximum voltage, power battery temperature and speed, screening the relevant data of power battery charging optimization, cleaning and removing abnormal data and null data, the abnormal data are data which have overlarge deviation with other data and do not accord with statistical rules in the relevant data of power battery charging optimization, the null data are data which do not have input values in the relevant data of power battery charging optimization, the data which do not have input values comprise data which do not have content, cannot determine content and have content of 0, carrying out correlation analysis on the relevant data of power battery charging optimization after data cleaning, selecting the relevant data of power battery charging optimization with a pearson correlation coefficient larger than a preset value, wherein the pearson correlation coefficient can be any number between 0.1 and 0.3, and selecting the relevant data of power battery charging optimization with a pearson correlation coefficient larger than 0.27 in the embodiment to obtain the relevant data of power battery charging optimization including relevant time, relevant data of power battery charging optimization, relevant power battery charging optimization and relevant state of power battery charging and relevant data of power battery charging consumption.
And training the screened data by a neural network to establish a vehicle power battery charging related model, wherein the establishment of the vehicle power battery charging related model comprises the establishment of a power battery charging time model, a power battery charging energy consumption model and a power battery health state model.
The step of establishing the power battery charging time model comprises the steps of comparing the charging time output by the power battery charging time related data through a preset algorithm with a reference charging time, establishing the power battery charging time model when the difference value between the charging time and the reference charging time is not greater than a preset error value, and adjusting the weight parameter of the hidden layer, the bias of the hidden layer, the weight parameter of the output layer and the bias of the output layer when the difference value between the charging time and the reference charging time is greater than the preset error value until the difference value between the charging time and the reference charging time is not greater than the preset error value.
The charging time model of the power battery is
Wherein y1 is a predicted value of charging time, j = 1\8230, m is the number of nodes of a hidden layer, i =1 \8230, n is the number of input data features, t i Charging time-dependent input parameter for a power cell, <' >>
For hiding the layer weight parameter, <' >>
Biased to hide layers, based on the presence of a reference signal>
As output layer weight parameters, b 0 For output layer bias, f H Is an activation function.
Selecting and setting the number of hidden layer nodes, for example, selecting and setting 21 the number of hidden layer nodes in the embodiment, the big data platform 3 obtains model parameters and charging time according to a preset algorithm from the screened power battery charging time related data including power battery temperature and power battery current data, the big data platform compares the charging time with a reference charging time, if the difference between the charging time and the reference charging time is greater than 2%, the parameter selection is unreasonable, the hidden layer weight parameter, the hidden layer bias, the output layer weight parameter and the output layer bias are adjusted, the above processes are repeated, if the difference between the charging time and the reference charging time is less than or equal to 2%, the selected parameter is used as the corresponding parameter of the power battery charging time model to establish a power battery charging time model, the reference charging time is the charging time corresponding to the current vehicle power battery temperature and the power battery current data in the power battery optimization related historical data acquired by the big data platform 3 under the state of the same temperature and the same current data, that is, the corresponding historical actual charging time is obtained by searching the historical data according to the current vehicle power battery temperature and the current data of the power battery, for example, the current power battery temperature is 40 ℃, the current data of the power battery is 1A, the charging time of the power battery temperature is 40 ℃ and the current data of the power battery is 1A under the historical data of the big data platform 3 is checked through the data, the time is the reference charging time, and the reference charging time is selected to prevent the value output by the substitution model from having a large deviation with the current charging time of the vehicle power battery, so that the established model is not accurate enough.
The step of establishing the power battery charging energy consumption model comprises the steps of comparing the charging energy consumption output by the power battery charging energy consumption related data through a preset algorithm with reference charging energy consumption, establishing the power battery charging energy consumption model when the difference value between the charging energy consumption and the reference charging energy consumption is not larger than a preset error value, and adjusting the weight parameter of the hidden layer, the bias of the hidden layer, the weight parameter of the output layer and the bias of the output layer when the difference value between the charging energy consumption and the reference charging energy consumption is larger than the preset error value until the difference value between the charging energy consumption and the reference charging energy consumption is not larger than the preset error value.
The charging energy consumption model of the power battery is
Wherein y2 is a predicted value of charging energy consumption, j =1 \ 8230m, m is the number of nodes of a hidden layer, i =1 \ 8230n, n and n are characteristic numbers of input data, and p i Input parameters for charging energy consumption of the power battery>
For hiding the layer weight parameter, <' >>
Biased to hide layers, based on the presence of a reference signal>
As output layer weight parameters, b 0 For output layer bias, f H Is an activation function.
Selecting and setting hidden layer node number, for example, selecting and setting hidden layer node number 21 in the embodiment, obtaining model parameters and charging energy consumption by large data platform 3 according to a preset algorithm by using screened power battery charging energy consumption related data, comparing the charging energy consumption of the power battery charging energy consumption related data output by the preset algorithm with reference charging energy consumption, when the difference between the charging energy consumption and the reference charging energy consumption is greater than 2%, indicating that the parameter selection is unreasonable, adjusting hidden layer weight parameters, hidden layer offset, output layer weight parameters and output layer offset, repeating the above processes, when the difference between the charging energy consumption and the reference charging energy consumption is not greater than 2%, establishing a power battery charging energy consumption model by using the selected parameters as corresponding parameters of the power battery charging energy consumption model, and when the reference charging energy consumption is greater than 2%, setting the selected parameters as the power battery charging energy consumption data corresponding to the current vehicle battery charging energy consumption data in the power battery optimization related historical data obtained by large data platform 3 as the current vehicle charging energy consumption data, namely, the current battery charging energy consumption data is the current battery charging energy consumption data which is larger than the current charging energy consumption data, namely, the current battery charging energy consumption data is the current charging energy consumption data which is the current charging energy consumption data and is the reference battery charging data which is the current charging data which is detected by the vehicle charging data A, namely, the current power battery charging data which is detected by the reference charging data which is 2, resulting in an inaccurate model being built.
The step of establishing the power battery health state model comprises the steps of comparing the health state output by the power battery health state related data through a preset algorithm with a reference health state, establishing the power battery health state model when the difference value between the health state and the reference health state is not greater than a preset error value, and adjusting the weight parameter of the hidden layer, the bias of the hidden layer, the weight parameter of the output layer and the bias of the output layer when the difference value between the health state and the reference health state is greater than the preset error value until the difference value between the health state and the reference health state is not greater than the preset error value.
The power battery state of health model comprises
Wherein y3 is a health state predicted value, j =1 \ 8230m, m is the number of hidden layer nodes, i =1 \ 8230n, n is the number of input data features, c i Entering parameters for the health status of the power cell>
For hiding the layer weight parameter, <' >>
Biased to hide layers, based on the presence of a reference signal>
As output layer weight parameters, b 0 For output layer bias, f H Is an activation function.
Selecting and setting the number of hidden layer nodes, for example, in the embodiment, selecting and setting 21 the number of hidden layer nodes, the big data platform 3 obtains model parameters and health states from the screened data related to the health state of the power battery, the data related to the health state of the power battery comprises the voltage of the power battery and the insulation resistance data of the power battery, the voltage of the power battery or the insulation resistance data of the power battery according to a preset algorithm, the big data platform 3 compares the health state with a reference health state, when the difference between the health state and the reference health state is greater than 2%, the parameters are unreasonable to select, the hidden layer weight parameter, the hidden layer bias, the output layer weight parameter and the output layer bias are adjusted and then the above process is repeated, when the difference between the health state and the reference health state is not greater than 2%, the selected parameters are used as corresponding parameters of the power battery health state model to establish a power battery health state model, the reference charging health state is the corresponding charging energy consumption in the power battery optimization related historical data acquired by the big data platform 3 under the state that the current vehicle power battery current and the power battery voltage data are the same, namely the corresponding historical actual health state is obtained by searching the historical data through the current vehicle power battery voltage and the power battery insulation resistance data, for example, the current power battery voltage data is 3V, the power battery insulation resistance data is 40 omega, the health state of the big data platform 3 under the power battery voltage data of 3V and the power battery insulation resistance data of 40 omega is checked through the data, the health state is the reference health state, the reference health state is selected to prevent the value output by the substitution model from having larger deviation with the current health state of the vehicle power battery, resulting in an inaccurate model being built.
The health state of the power battery is a measure of the capacity of the power battery to store electric charge, and is usually obtained by a ratio of a reference capacity to a rated capacity of the battery, and is a percentage value, wherein a larger percentage value indicates a stronger capacity to store electric charge, and the battery is generally rejected when the health state of the power battery is equal to a certain value.
The preset algorithm comprises a Bayesian regularization algorithm, belongs to the prior art, and in the embodiment, the Bayesian regularization algorithm is used for obtaining data related to the charging time of the power battery, the charging energy consumption of the power battery and the health state of the power battery and parameters of the model according to input power battery charging optimization related data of a vehicle after selecting the number of nodes of a hidden layer, obtaining the charging time and parameters of the power battery charging time model according to input power battery temperature or power battery current in establishing the power battery charging time model, obtaining the charging energy consumption and parameters of the power battery charging energy consumption model according to input power battery current or power battery voltage data in establishing the power battery charging energy consumption model, and obtaining the health state and parameters of the power battery health state model according to input power battery voltage or power battery insulation resistance data in establishing the power battery health state model.
The method comprises the steps that when y1 meets the preset y1 constraint condition, y2 meets the preset y2 constraint condition and y3 meets the preset constraint condition, the y2 meets the preset y2 constraint condition and the y3 meets the preset constraint condition, the minimum y1, the minimum y2 and the maximum y3 are selected as charging optimization results to be output, the power battery has no optimal condition, and the y1, the minimum y2 and the maximum y3 need to be comprehensively considered, so that an optimal charging scheme suitable for vehicle charging is obtained.
After the model is established, the big data platform 3 collects charging optimization related data of one or more vehicle power batteries and then screens out charging time related data, charging energy consumption related data and health state related data of the power batteries, and the big data platform 3 screens the charging time related data of the power batteriesInput parameter t as power battery temperature model i Substituting the data into a power battery charging correlation model to obtain a charging time predicted value y1, and using the power battery charging energy consumption data as an input parameter p of the power battery charging energy consumption model by the big data platform 3 i Substituting the data into a power battery charging energy consumption model to obtain a charging energy consumption predicted value y2, and using the power battery health state data as an input parameter c of the power battery health state model by the big data platform 3 i And substituting the estimated value into a power battery state of health model to obtain a state of health predicted value y3, and when y1 is within the range of 3-5 hours, y2 is within the range of 100-120kwh, y3 is within 70% -80%, selecting y1 as 3 hours, y2 as 100kwh and y3 as 80% as the charging optimization result to be output, wherein the ranges of y1, y2 and y3 can be selected according to the actual condition of the power battery.
Alternatively, when y1, y2 and y3 are obtained, one or two or three of y1, y2 or y3 are output as the power battery optimization result. The adjustment of the charging time of the power battery, the charging energy consumption of the power battery and the health state of the power battery are all the conventional technologies.
The big data platform 3 can establish one or two or three models of a power battery charging time model, a power battery charging energy consumption model and a power battery state of health model when the vehicle has a demand for the power battery.
The big data platform 3 can optimize the charging of a power battery of the vehicle which is in data communication with the big data platform, and can optimize the charging of any one vehicle and all vehicles.
And when the related data of the power battery charging optimization is updated, updating the vehicle power battery charging related model to obtain a new vehicle power battery charging related model.
The existing neural network comprises an input layer, a hidden layer and an output layer, data input by the input layer is output at the output layer through calculation of the hidden layer and the output layer, the neural network training is a process of establishing a model and is also an existing method for finding parameters of the hidden layer and the output layer of the neural network, and the existing neural network training is usually carried out in a mode of obtaining an error function through forward calculation and reducing a reverse derivative gradient.
The method improves the new energy vehicle power battery charging optimization method by screening relevant data of power battery charging optimization and establishing a vehicle power battery charging relevant model, and the method screens the relevant data of power battery charging optimization which is not in conformity so as to reduce the data processing amount of a large data platform, and screens the relevant data of power battery charging optimization which is not in conformity so as to improve the accuracy of establishing the vehicle power battery charging relevant model.
As shown in fig. 2, the new energy vehicle power battery charging optimization system comprises an acquisition module 1, a control unit 2 and a big data platform 3, wherein the acquisition module 1 is connected with the control unit 2, and the control unit 2 is wirelessly connected with the big data platform 3.
The acquisition module 1 acquires power battery charging optimization related data and outputs the data to the control unit 2, the control unit 2 receives the power battery charging optimization related data output by the acquisition module and outputs the data to the big data platform 3, the big data platform 3 receives the vehicle power battery charging optimization related data output by the control unit 2 and then screens the data to obtain data related to power battery charging time, power battery charging energy consumption and power battery health state, the big data platform 3 conducts neural network training on the screened data to establish a vehicle power battery charging related model, the big data platform 3 acquires the current power battery charging optimization related data of a vehicle connected with the big data platform after establishing the model, and the current power battery charging optimization related data is substituted into the vehicle power battery charging related model to conduct optimal control and then outputs a power battery charging optimization result.
The system improves a new energy vehicle power battery charging optimization system by setting a new energy vehicle power battery charging optimization method through a big data platform 3, screens all vehicle power battery charging optimization related data and establishes a vehicle power battery charging related model to improve the new energy vehicle power battery charging optimization method, screens non-conforming power battery charging optimization related data so as to reduce data processing amount of the big data platform, and screens the non-conforming power battery charging optimization related data so as to improve accuracy of establishing the vehicle power battery charging related model.
The acquisition module 1 comprises a timer for acquiring charging time, an energy consumption sensor for acquiring vehicle charging energy consumption and a storage battery sensor for acquiring vehicle health status.
The big data platform 3 is a background server and can be connected with the control unit 2 of the vehicle, namely a vehicle-mounted computer, which can be a T-box, to acquire charging optimization related data of the power battery of the vehicle.
The specific embodiments described herein are merely illustrative of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (7)
1. A new energy vehicle power battery charging optimization method is characterized by comprising the following steps:
acquiring power battery charging optimization related data of a vehicle connected with the big data platform (3);
screening the power battery charging optimization related data to obtain data related to power battery charging time, power battery charging energy consumption and power battery health state, wherein the data related to power battery charging time comprises power battery temperature and power battery current data, the data related to power battery charging energy consumption comprises power battery current and power battery voltage data, and the data related to power battery health state comprises power battery voltage and power battery insulation resistance data; carrying out neural network training on the screened data to establish a vehicle power battery charging relevant model, wherein the model comprises a power battery charging time model, a power battery charging energy consumption model and a power battery health state model;
after the model is established, the current power battery charging optimization related data of the vehicle connected with the big data platform (3) is obtained and substituted into the vehicle power battery charging related model for optimal control, and then the charging optimization result is output, and the method specifically comprises the following operations: the big data platform (3) substitutes the charging time related data of the power battery into the charging related model of the power battery to obtain a charging time predicted value y1, substitutes the charging energy consumption related data of the power battery into the charging energy consumption model of the power battery to obtain a charging energy consumption predicted value y2, substitutes the health state related data of the power battery into the health state model of the power battery to obtain a health state predicted value y3;
presetting a y1 constraint condition, a y2 constraint condition and a y3 constraint condition, wherein the preset y1 constraint condition is that y1 is in a preset charging time range, the y2 constraint condition is that y2 is in a preset charging energy consumption range, and the y3 constraint condition is that y3 is in a preset health state range, and the optimal control step comprises the steps of selecting the minimum y1, the minimum y2 and the maximum y3 as charging optimization results to be output when y1 meets the preset y1 constraint condition, y2 meets the preset y2 constraint condition and y3 meets the preset constraint condition.
2. The method for optimizing the charging of the power battery of the new energy vehicle according to claim 1, wherein the screening step comprises the steps of cleaning the relevant data for optimizing the charging of the power battery, removing abnormal data and null data, and performing correlation analysis on the relevant data for optimizing the charging of the power battery of a plurality of cleaned vehicles to obtain the relevant data including the relevant data of the charging time of the power battery, the relevant data of the charging energy consumption of the power battery and the relevant data of the health state of the power battery.
3. The new energy vehicle power battery charging optimization method according to claim 2, wherein the correlation analysis comprises selecting power battery charging optimization related data with a pearson correlation coefficient greater than a preset coefficient value.
4. The method as claimed in claim 3, wherein the step of establishing the charging time model of the power battery comprises comparing the charging time outputted from the data related to the charging time of the power battery through a predetermined algorithm with a reference charging time, establishing the charging time model of the power battery when the difference between the charging time and the reference charging time is not greater than a predetermined error value, and adjusting the weight parameter of the hidden layer, the bias of the hidden layer, the weight parameter of the output layer, and the bias of the output layer when the difference between the charging time and the reference charging time is greater than the predetermined error value, wherein the charging time model of the power battery is determined as follows
Wherein y1 is a predicted value of charging time, j =1 \ 8230m, m is the number of nodes of a hidden layer, i =1 \ 8230n, n is the number of input data features, and t is i For the input parameters related to the charging time of the power battery,
in order to hide the layer weight parameters,
to hideThe layers are biased in such a way that,
as output layer weight parameters, b 0 For output layer bias, f H Is an activation function.
5. The method for optimizing charging of a power battery of a new energy vehicle according to claim 4, wherein the step of establishing the power battery charging energy consumption model comprises comparing the charging energy consumption output by the power battery charging energy consumption related data through a preset algorithm with a reference charging energy consumption, establishing the power battery charging energy consumption model when the difference between the charging energy consumption and the reference charging energy consumption is not greater than a preset error value, and adjusting the weight parameter of the hidden layer, the bias of the hidden layer, the weight parameter of the output layer and the bias of the output layer when the difference between the charging energy consumption and the reference charging energy consumption is greater than the preset error value until the difference between the charging energy consumption and the reference charging energy consumption is not greater than the preset error value, wherein the power battery charging energy consumption model is the power battery charging energy consumption model
Wherein y2 is a charging energy consumption predicted value, j = 1\8230, m and m are hidden layer node numbers, i =1 \8230, n and n are input data characteristic numbers, and p i Inputting parameters for charging energy consumption of the power battery,
in order to hide the layer weight parameters,
in order to hide the layer bias from view,
as output layer weight parameters, b 0 For output layer bias, f H Is an activation function.
6. The method for optimizing charging of a power battery of a new energy vehicle according to claim 5, wherein the method comprisesThe step of establishing the power battery health state model comprises the steps of comparing the health state output by the power battery health state related data through a preset algorithm with a reference health state, establishing the power battery health state model when the difference value between the health state and the reference health state is not larger than a preset error value, adjusting the weight parameter of the hidden layer, the bias of the hidden layer, the weight parameter of the output layer and the bias of the output layer when the difference value between the health state and the reference health state is larger than the preset error value until the difference value between the health state and the reference health state is not larger than the preset error value, wherein the power battery health state model comprises the steps of
Wherein y3 is a health state predicted value, j =1 \ 8230m, m is the number of hidden layer nodes, i =1 \ 8230n, n is the number of input data features, c i The parameters are input for the health state of the power battery,
in order to hide the layer weight parameters,
in order to hide the layer bias from view,
as output layer weight parameters, b 0 For output layer bias, f H Is an activation function.
7. The new energy vehicle power battery charging optimization system comprises a big data platform (3) which is connected with a vehicle and can acquire data on the vehicle, and is characterized by further comprising an acquisition module (1) used for acquiring power battery charging optimization related data and a control unit (2) arranged on the vehicle and used for receiving the data output by the acquisition module (1), wherein the acquisition module (1) is connected with the control unit (2), the control unit (2) is wirelessly connected with the big data platform (3), the big data platform (3) is used for receiving the power battery optimization related data of the vehicle output by the control unit (2) of the vehicle connected with the big data platform, and screening the received data to obtain data related to power battery charging time, power battery charging energy consumption and power battery health state, wherein the power battery charging time related data comprises power battery temperature and power battery voltage data, and the power battery health state related data comprises power battery voltage and power battery insulation resistance data; carrying out neural network training on the screened data to establish a vehicle power battery charging relevant model, wherein the model comprises a power battery charging time model, a power battery charging energy consumption model and a power battery health state model;
the big data platform (3) is also used for acquiring current power battery charging optimization related data of a vehicle connected with the big data platform (3) after the model is established, and outputting a charging optimization result after the model is substituted into the vehicle power battery charging related model, and the specific operation is as follows: the big data platform (3) substitutes the charging time related data of the power battery into the charging related model of the power battery to obtain a charging time predicted value y1, substitutes the charging energy consumption related data of the power battery into the charging energy consumption model of the power battery to obtain a charging energy consumption predicted value y2, substitutes the health state related data of the power battery into the health state model of the power battery to obtain a health state predicted value y3;
presetting a y1 constraint condition, a y2 constraint condition and a y3 constraint condition, wherein the preset y1 constraint condition is that y1 is in a preset charging time range, the y2 constraint condition is that y2 is in a preset charging energy consumption range, and the y3 constraint condition is that y3 is in a preset health state range, and the optimal control step comprises the steps of selecting the minimum y1, the minimum y2 and the maximum y3 as charging optimization results to be output when y1 meets the preset y1 constraint condition, y2 meets the preset y2 constraint condition and y3 meets the preset constraint condition.
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