CN211086535U - Lithium ion power battery charge state prediction circuit - Google Patents
Lithium ion power battery charge state prediction circuit Download PDFInfo
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- CN211086535U CN211086535U CN201922017353.3U CN201922017353U CN211086535U CN 211086535 U CN211086535 U CN 211086535U CN 201922017353 U CN201922017353 U CN 201922017353U CN 211086535 U CN211086535 U CN 211086535U
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Abstract
A lithium ion power battery charge state prediction circuit. The lithium ion power battery charge state prediction circuit can be used for electric vehicles, electric tools, secondary charging and energy storage systems, other large-scale power supply systems and the like, and can effectively improve the power lithium ion charge state prediction precision. The lithium ion power battery state of charge prediction circuit comprises a data preprocessing and storage circuit, a battery pack state of charge prediction model circuit, a genetic algorithm solving circuit, a Kalman filtering solving circuit and a battery pack state of charge storage circuit. The utility model provides a lithium ion power battery state of charge prediction circuit adopts genetic algorithm to optimize the coefficient matrix of Kalman filtering model circuit, improves the prediction accuracy of battery state of charge. The genetic algorithm and Kalman filtering algorithm resolving circuit are designed into a special integrated circuit chip, so that the battery charge state prediction precision and the operation speed are improved.
Description
Technical Field
The utility model relates to a new forms of energy and application, in particular to a lithium ion power battery's of charge state prediction circuit that is used for electric automobile, electric tool, secondary charge and energy storage system and other large-scale power electrical power generating system.
Background
The lithium ion battery has the advantages of long service life, stable working voltage, high energy density and charging efficiency, low self-discharge rate, no memory, capability of quick charging and the like, and becomes the mainstream of energy storage technology development in the fields of electric automobiles, electric tools, backup power supplies and the like. In the using process of the lithium battery, how to predict the state of charge of the battery and accurately estimate the residual electric quantity of the battery has important significance for improving the maximum utilization rate of the lithium battery, improving the safety of the battery, reasonably planning the operation and management of electric equipment, giving full play to the best performance of the electric equipment, predicting the driving range of the electric automobile and the like.
Since the state of charge of a lithium ion battery is related to many factors such as the charge and discharge rate of the battery, the aging degree of the battery, the internal resistance of the battery and the like, accurate estimation is difficult. At present, methods for predicting the state of charge of a lithium ion battery mainly include a direct measurement calculation method, a numerical filtering method and a neural network prediction method. The direct measurement calculation method is used for calculating according to external characteristics of the battery, can not correctly process nonlinear parameter change under the condition of dynamic load of the battery, and has a large measurement error; the numerical filtering method mainly adopts a Kalman filtering algorithm, and the acquired data is subjected to real-time operation processing through a computer, so that the numerical filtering method can be used for a nonlinear system. However, the dependence of the numerical filtering method on the battery model is large, the algorithm complexity is high, and the calculated amount is large; the neural network prediction method does not need to establish a specific mathematical model, does not need to consider the complicated chemical change process in the battery, only needs to take the external characteristics of the battery, such as voltage, current and the like, as selected samples and establish a better neural network model, and the more the sample data is, the higher the estimation precision is. However, the method has high requirements on software and hardware of the system, the accuracy of the data sample, the sample capacity, the sample distribution and the training method adopted during training all have great influence on the state of charge prediction of the battery, and the accuracy of the data sample is difficult to guarantee because the charging and discharging of the battery are complex physical and chemical processes.
Therefore, for the state of charge prediction technology of lithium ion power batteries, the existing prediction methods cannot meet the requirements of the development of lithium ion power battery management systems of electric vehicles, electric tools and the like in terms of prediction accuracy, algorithm complexity and the like.
SUMMERY OF THE UTILITY MODEL
The utility model aims at providing a lithium ion power battery state of charge prediction circuit adopts genetic algorithm to optimize the coefficient matrix of Kalman filtering model circuit, improves the prediction accuracy of battery state of charge. The special integrated circuit chip for solving the genetic algorithm and the Kalman filtering algorithm by the single chip is provided, the problems of low precision, complex algorithm, high software and hardware overhead and the like of the traditional algorithm are solved, and the special integrated circuit chip can be used for electric automobiles, electric tools, secondary charging and energy storage systems and other large-scale power supply systems and has good application prospect.
The utility model provides a lithium ion power battery state of charge prediction circuit comprises data preprocessing and storage circuit, group battery state of charge prediction model circuit, genetic algorithm solution circuit, Kalman filtering solution circuit and group battery state of charge storage circuit.
The data preprocessing and storing circuit is provided with three input ends and two output ends, the three input ends are respectively connected with external signals V, T and I, one output end of the data preprocessing and storing circuit is connected with one input end of the genetic algorithm solving circuit, and the other output end of the data preprocessing and storing circuit is connected with the input end of the battery pack state-of-charge prediction model circuit; the genetic algorithm solving circuit is provided with three input ends and an output end, wherein one input end is connected with the output end of the data preprocessing and storage circuit, the other input end is connected with the output end of the battery pack state-of-charge prediction model circuit, the third input end is connected with the outside, and the output end of the third input end is connected with the input end of the Kalman filtering solving circuit; the battery pack charge state prediction model circuit is provided with an input end and an output end, wherein the input end of the battery pack charge state prediction model circuit is connected with one output end of the data preprocessing and storage circuit, and the output end of the battery pack charge state prediction model circuit is connected with one input end of the genetic algorithm solving circuit; the Kalman filtering resolving circuit is provided with two input ends and an output end, wherein one input end of the Kalman filtering resolving circuit is connected with the output end of the genetic algorithm resolving circuit, the other input end of the Kalman filtering resolving circuit is connected with one output end of the battery pack charge state storage circuit, and the output end of the Kalman filtering resolving circuit is connected with the output end of the battery pack charge state storage circuit; the battery pack charge state storage circuit is provided with an input end and two output ends, the input end of the battery pack charge state storage circuit is connected with the output end of the Kalman filtering resolving circuit, one output end of the battery pack charge state storage circuit is connected with one input end of the Kalman filtering resolving circuit, and the other output end of the battery pack charge state storage circuit is connected with an external signal.
The data preprocessing and storing circuit consists of a first low noise amplifier, a second low noise amplifier, a charge and discharge amount calculating circuit, a symbol detecting circuit, a data normalization processing circuit, a first memory and an alarm signal generating circuit; the input end of the first low-noise amplifier is connected with an external input signal, and the output end of the first low-noise amplifier is connected with one input end of the data normalization processing circuit; the input end of the second low-noise amplifier is connected with an external input signal, and the output end of the second low-noise amplifier is connected with one input end of the data normalization processing circuit; the input end of the symbol detection circuit is connected with an external input signal, one output end of the symbol detection circuit is connected with one input end of the data normalization processing circuit, and the other output end of the symbol detection circuit is connected with the input end of the charge and discharge amount calculation circuit; the input end of the charge and discharge quantity calculating circuit is connected with the output end of the symbol detection circuit, and the output end of the charge and discharge quantity calculating circuit is connected with one input end of the data normalization processing circuit; the four input ends of the data normalization processing circuit are respectively connected with the output ends of the first low-noise amplifier, the second low-noise amplifier and the charge and discharge quantity calculating circuit and one output end of the symbol detection circuit, and the output ends of the data normalization processing circuit are respectively connected with the first memory and one input end of the alarm signal generating circuit; the first memory has an input end and two output ends, the input end is connected with an output end of the data normalization processing circuit, and the two output ends are respectively connected with the external signal (OUTA) and the external signal (OUTB); the input end of the alarm signal generating circuit is connected with one output end of the data normalization processing circuit, and the output end of the alarm signal generating circuit is connected with the outside.
The genetic algorithm solving circuit consists of a Kalman filtering model coefficient matrix extracting circuit, a fitness evaluating circuit, a selection calculating circuit, a cross calculating circuit, a variation calculating circuit, a second memory and a control circuit; one input end of the Kalman filtering model coefficient matrix extraction circuit is connected with the outside, the other input end of the Kalman filtering model coefficient matrix extraction circuit is connected with one output end of the control circuit, and the output end of the Kalman filtering model coefficient matrix extraction circuit is connected with one input end of the fitness evaluation circuit; one input end of the fitness evaluation circuit is connected with the outside, the other input end of the fitness evaluation circuit is connected with the output end of the Kalman filtering model coefficient matrix extraction circuit, the third input end of the fitness evaluation circuit is connected with one output end of the variation calculation circuit, the fourth input end of the fitness evaluation circuit is connected with one output end of the control circuit, and the output end of the fitness evaluation circuit is connected with one input end of the selection calculation circuit; one input end of the selection calculation circuit is connected with the output end of the fitness evaluation circuit, the other input end of the selection calculation circuit is connected with one output end of the control circuit, and the output end of the selection calculation circuit is connected with one input end of the cross calculation circuit; one input end of the cross calculation circuit is connected with the output end of the selection calculation circuit, the other input end of the cross calculation circuit is connected with one output end of the control circuit, and the output end of the cross calculation circuit is connected with the input end of the variation calculation circuit; one input end of the variation calculation circuit is connected with the output end of the cross calculation circuit, the other input end of the variation calculation circuit is connected with one output end of the control circuit, and the output end of the variation calculation circuit is connected with one input end of the fitness evaluation circuit; one input end of the second memory is connected with one output end of the fitness evaluation circuit, the other input end of the second memory is connected with one output end of the control circuit, and the output end of the second memory is connected with the outside.
The lithium ion power battery charge state prediction circuit is characterized in that each module circuit is designed into a special integrated circuit chip and manufactured by adopting a general CMOS (complementary metal oxide semiconductor) process.
The utility model has the advantages that:
the utility model provides a lithium ion power battery state of charge prediction circuit has adopted genetic algorithm to optimize the coefficient matrix of Kalman filtering model circuit, has effectively improved lithium ion power battery state of charge's prediction accuracy and processing speed. Because the algorithm resolving circuit is designed into a special integrated circuit chip, and compared with a software resolving method based on a microprocessor, the processing process efficiency is higher. The power supply system is used for power supply systems of electric automobiles, electric tools, secondary charging, energy storage and the like, and has the characteristics of high precision, high speed, low cost and the like.
Drawings
FIG. 1 is a schematic diagram of a lithium ion power battery state of charge prediction circuit;
FIG. 2 is a functional block diagram of a data preprocessing and storage circuit;
FIG. 3 is a schematic block diagram of a genetic algorithm solving circuit;
FIG. 4 is a Kalman filter solution circuit flow diagram.
Detailed Description
As shown in figure 1, the utility model provides a pair of lithium ion power battery state of charge prediction circuit comprises data preprocessing and storage circuit 1, group battery state of charge prediction model circuit 5, genetic algorithm solution circuit 2, kalman filter solution circuit 3 and group battery state of charge storage circuit 4.
The data preprocessing and storage circuit 1 is provided with three input ends and two output ends, wherein the three input ends are respectively connected with external signals V (battery pack voltage signal), T (battery pack temperature) and I (battery pack charging and discharging current, wherein I is a negative value and represents discharging current, and I is a positive value and represents charging current), one output end of the data preprocessing and storage circuit is connected with one input end of the genetic algorithm solving circuit 2, and the other output end of the data preprocessing and storage circuit is connected with the input end of the battery pack state-of-charge prediction model circuit 5; the genetic algorithm solving circuit 2 is provided with three input ends and an output end, wherein one input end is connected with one output end of the data preprocessing and storage circuit 1, the other input end is connected with the output end of the battery pack state of charge prediction model circuit 5, the third input end is an external enabling signal (EN) of the genetic algorithm control circuit 2 is connected with the outside, when the enabling signal (EN) is in a high level, the genetic algorithm solving circuit 2 works normally, and the output end of the genetic algorithm solving circuit is connected with the input end of the Kalman filter solving circuit 3; the battery pack charge state prediction model circuit 5 is provided with an input end and an output end, wherein the input end is connected with an output end of the data preprocessing and storage circuit 1, and the output end is connected with an input end of the genetic algorithm solving circuit 2; the Kalman filter resolving circuit 3 is provided with two input ends and an output end, wherein one input end is connected with the output end of the genetic algorithm resolving circuit 2, the other input end is connected with one output end of the battery pack charge state storage circuit 4, and the output end is connected with the input end of the battery pack charge state storage circuit 4; the battery pack charge state storage circuit 4 is provided with an input end and two output ends, the input end of the battery pack charge state storage circuit is connected with the output end of the Kalman filtering resolving circuit 3, one output end of the battery pack charge state storage circuit is connected with one input end of the Kalman filtering resolving circuit 3, and the other output end of the battery pack charge state storage circuit is connected with an external signal.
The working principle of the circuit is as follows: the data preprocessing and storage circuit 1 acquires, processes and stores voltage V, current I and temperature T signals of the power lithium ion battery pack in real time; the battery pack charge state prediction model circuit 5 establishes and revises a battery pack charge state prediction circuit model according to the data output by the data preprocessing and storage circuit 1; the genetic algorithm solving circuit 2 calculates a coefficient matrix of a Kalman filtering equation by utilizing a genetic algorithm according to real-time data measured by the data preprocessing and storage circuit 1 and a circuit model provided by the battery pack charge state prediction model circuit 5; the Kalman filtering resolving circuit 3 predicts the value of the battery pack charge state at the current moment according to the battery pack charge state at the last moment stored by the battery pack charge state storage circuit 4 and the battery pack charge state actually measured and calculated by the data preprocessing and storage circuit 1, and updates the data stored in the battery pack charge state storage circuit 4.
EXAMPLE 2 implementation of data preprocessing and storage Circuit
As shown in FIG. 2, the data preprocessing and storage circuit 1 comprises a first low noise amplifier 7, a second low noise amplifier 6, a charge/discharge amount calculation circuit 11, a symbol detection circuit 12, a data normalization circuit 9, a first memory 10 and an alarm signal generation circuit 8, wherein an input end of the first low noise amplifier 7 is connected with an external battery pack voltage input signal V, an output end of the first low noise amplifier is connected with an input end of the data normalization circuit 9, an input end of the second low noise amplifier 6 is connected with an external battery pack temperature input signal T, an output end of the second low noise amplifier is connected with an input end of the data normalization circuit 9, an input end of the symbol detection circuit 12 is connected with an input end of the data normalization circuit 12, an input end of the symbol detection circuit 12 is connected with an input end of the data normalization circuit 9, an output end of the symbol detection circuit 9 is connected with an input end of the data normalization circuit 9, four input ends of the data normalization circuit 9 are respectively connected with the first low noise amplifier 7, the second low noise amplifier 6, an input end of the symbol detection circuit 12 and an output end of the charge detection circuit 11, an output end of the data normalization circuit 9 is connected with an input end of the data normalization circuit 9, an output end of the charge normalization circuit 9 is connected with an output end of the data normalization circuit 10, and an alarm signal generation circuit 9, an output end of the charge normalization circuit 9 is connected with an output end of the charge normalization circuit 9, and an alarm signal generation circuit 9, and an output end of the charge prediction circuit 9, a prediction circuit 10, when a prediction circuit 5 is connected with an output end of the charge prediction circuit, a prediction circuit 5 is connected with an output end of the charge prediction circuit, a prediction circuit 5, a prediction.
The working principle of the circuit is as follows: the voltage and the temperature of the battery pack are acquired in real time by using a first low noise amplifier 7 and a second low noise amplifier 6; the symbol detection circuit 12 and the charge and discharge quantity calculation circuit 11 are used for collecting and calculating the charge and discharge electric quantity of the battery pack; the sign detection circuit 12 is used for judging the direction of the current of the battery pack; the data normalization processing circuit 9 normalizes the acquired real-time data, stores the normalized data in the first memory 10 and provides subsequent circuits for further processing; the alarm signal generating circuit 8 analyzes the acquired voltage, temperature and current signals in real time, and the alarm signal generating circuit 8 outputs alarm signals to an external circuit under the conditions of over-temperature, over-voltage, under-voltage, over-current, insufficient battery pack charge state and the like of the battery.
Example 3 implementation of genetic Algorithm solving Circuit
As shown in fig. 3, the genetic algorithm solving circuit 2 is composed of a kalman filter model coefficient matrix extracting circuit 13, a fitness evaluating circuit 14, a selection calculating circuit 15, a cross calculating circuit 16, a variance calculating circuit 17, a second memory 18, and a control circuit 19. Wherein, an input end signal (M) of the kalman filter model coefficient matrix extraction circuit 13 is connected with the output end of the battery pack state-of-charge prediction model circuit, the other input end is connected with an output end of the control circuit 19, and the output end is connected with an input end of the fitness evaluation circuit 14; an input signal (TD) of the fitness evaluation circuit 14 is connected with one output end of the data preprocessing and storage circuit 1, the other input end of the fitness evaluation circuit is connected with the output end of the Kalman filtering model coefficient matrix extraction circuit 13, the third input end of the fitness evaluation circuit is connected with the output end of the variation calculation circuit 17, the fourth input end of the fitness evaluation circuit is connected with one output end of the control circuit 19, one output end of the fourth input end of the fitness evaluation circuit is connected with one input end of the selection calculation circuit 15, and the other output end of the fourth input end of the fitness evaluation circuit is connected with one; one input terminal of the selection calculation circuit 15 is connected to the output terminal of the fitness evaluation circuit 14, the other input terminal thereof is connected to one output terminal of the control circuit 19, and the output terminal thereof is connected to one input terminal of the intersection calculation circuit 16; one input end of the cross calculation circuit 16 is connected with the output end of the selection calculation circuit 15, the other input end is connected with one output end of the control circuit 19, and the output end is connected with the input end of the variation calculation circuit 17; one input end of the variation calculating circuit 17 is connected with the output end of the intersection calculating circuit 16, the other input end is connected with one output end of the control circuit 19, and the output end is connected with one input end of the fitness evaluating circuit 14; one input of the second memory 18 is connected to one output of the fitness evaluation circuit 14, the other input is connected to one output of the control circuit 19, and the output is connected to one input of the kalman filter solution circuit 3. An input terminal of the control circuit 19 is connected to an external enable signal (EN).
The working principle of the circuit is as follows: a Kalman filtering model coefficient matrix extraction circuit 13 receives the prediction model characteristic data output by the battery pack charge state prediction model circuit 5 and determines a coefficient matrix of a Kalman filtering calculation formula; the fitness evaluation circuit 14 acquires data from the first storage circuit 10, forms coefficient matrix codes, and determines a fitness evaluation function and an evaluation method; through the cyclic optimization process of selection calculation, cross calculation and variation calculation, the fitness evaluation circuit 14 obtains the optimal quality of the Kalman filtering model coefficient matrix, and then stores the optimal quality into the second memory 18; the Kalman calculation circuit 3 reads the optimized initial value of the coefficient matrix from the second memory, and the calculation precision of the Kalman calculation circuit 3 is improved.
Example 4 implementation of Kalman Filter solution Circuit
The working flow chart of the implementation of the kalman filter solution circuit is shown in fig. 4, the current battery pack state of charge is predicted according to the stored battery pack state of charge, and the current battery pack state of charge data is calculated by using the state parameters of the battery pack. And then according to a Kalman filtering principle, finishing repeated iterative operations of error estimation, Kalman gain calculation and error covariance calculation according to the predicted value and the calculated value of the battery pack state of charge, finally obtaining an optimal estimated value, and updating and storing the current battery pack state of charge data.
Claims (3)
1. A lithium ion power battery state of charge prediction circuit which characterized in that: the circuit comprises a data preprocessing and storage circuit, a battery pack state of charge prediction model circuit, a genetic algorithm solving circuit, a Kalman filtering resolving circuit and a battery pack state of charge storage circuit;
the data preprocessing and storing circuit is provided with three input ends and two output ends, the three input ends are respectively connected with external signals V, T and I, one output end of the data preprocessing and storing circuit is connected with one input end of the genetic algorithm solving circuit, and the other output end of the data preprocessing and storing circuit is connected with the input end of the battery pack state-of-charge prediction model circuit; the genetic algorithm solving circuit is provided with three input ends and an output end, wherein one input end is connected with the output end of the data preprocessing and storage circuit, the other input end is connected with the output end of the battery pack state-of-charge prediction model circuit, the third input end is connected with the outside, and the output end of the third input end is connected with the input end of the Kalman filtering solving circuit; the battery pack charge state prediction model circuit is provided with an input end and an output end, wherein the input end of the battery pack charge state prediction model circuit is connected with one output end of the data preprocessing and storage circuit, and the output end of the battery pack charge state prediction model circuit is connected with one input end of the genetic algorithm solving circuit; the Kalman filtering resolving circuit is provided with two input ends and an output end, wherein one input end of the Kalman filtering resolving circuit is connected with the output end of the genetic algorithm resolving circuit, the other input end of the Kalman filtering resolving circuit is connected with one output end of the battery pack charge state storage circuit, and the output end of the Kalman filtering resolving circuit is connected with the output end of the battery pack charge state storage circuit; the battery pack charge state storage circuit is provided with an input end and two output ends, the input end of the battery pack charge state storage circuit is connected with the output end of the Kalman filtering resolving circuit, one output end of the battery pack charge state storage circuit is connected with one input end of the Kalman filtering resolving circuit, and the other output end of the battery pack charge state storage circuit is connected with an external signal.
2. The lithium ion power battery state of charge prediction circuit of claim 1, wherein: the data preprocessing and storing circuit consists of a first low noise amplifier, a second low noise amplifier, a charge and discharge amount calculating circuit, a symbol detecting circuit, a data normalization processing circuit, a first memory and an alarm signal generating circuit; the input end of the first low-noise amplifier is connected with an external input signal, and the output end of the first low-noise amplifier is connected with one input end of the data normalization processing circuit; the input end of the second low-noise amplifier is connected with an external input signal, and the output end of the second low-noise amplifier is connected with one input end of the data normalization processing circuit; the input end of the symbol detection circuit is connected with an external input signal, one output end of the symbol detection circuit is connected with one input end of the data normalization processing circuit, and the other output end of the symbol detection circuit is connected with the input end of the charge and discharge amount calculation circuit; the input end of the charge and discharge quantity calculating circuit is connected with the output end of the symbol detection circuit, and the output end of the charge and discharge quantity calculating circuit is connected with one input end of the data normalization processing circuit; the four input ends of the data normalization processing circuit are respectively connected with the output ends of the first low-noise amplifier, the second low-noise amplifier and the charge and discharge quantity calculating circuit and one output end of the symbol detection circuit, and the output ends of the data normalization processing circuit are respectively connected with the first memory and one input end of the alarm signal generating circuit; the first memory has an input end and two output ends, the input end is connected with one output end of the data normalization processing circuit, and the two output ends are respectively connected with an external signal OUTA) and an external signal OUTB); the input end of the alarm signal generating circuit is connected with one output end of the data normalization processing circuit, and the output end of the alarm signal generating circuit is connected with the outside.
3. The lithium ion power battery state of charge prediction circuit of claim 1, wherein: the genetic algorithm solving circuit consists of a Kalman filtering model coefficient matrix extracting circuit, a fitness evaluating circuit, a selection calculating circuit, a cross calculating circuit, a variation calculating circuit, a second memory and a control circuit; one input end of the Kalman filtering model coefficient matrix extraction circuit is connected with the outside, the other input end of the Kalman filtering model coefficient matrix extraction circuit is connected with one output end of the control circuit, and the output end of the Kalman filtering model coefficient matrix extraction circuit is connected with one input end of the fitness evaluation circuit; one input end of the fitness evaluation circuit is connected with the outside, the other input end of the fitness evaluation circuit is connected with the output end of the Kalman filtering model coefficient matrix extraction circuit, the third input end of the fitness evaluation circuit is connected with one output end of the variation calculation circuit, the fourth input end of the fitness evaluation circuit is connected with one output end of the control circuit, and the output end of the fitness evaluation circuit is connected with one input end of the selection calculation circuit; one input end of the selection calculation circuit is connected with the output end of the fitness evaluation circuit, the other input end of the selection calculation circuit is connected with one output end of the control circuit, and the output end of the selection calculation circuit is connected with one input end of the cross calculation circuit; one input end of the cross calculation circuit is connected with the output end of the selection calculation circuit, the other input end of the cross calculation circuit is connected with one output end of the control circuit, and the output end of the cross calculation circuit is connected with the input end of the variation calculation circuit; one input end of the variation calculation circuit is connected with the output end of the cross calculation circuit, the other input end of the variation calculation circuit is connected with one output end of the control circuit, and the output end of the variation calculation circuit is connected with one input end of the fitness evaluation circuit; one input end of the second memory is connected with one output end of the fitness evaluation circuit, the other input end of the second memory is connected with one output end of the control circuit, and the output end of the second memory is connected with the outside.
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