CN102680907B - Battery charging stress optical coefficient (SOC) detection method in photovoltaic system - Google Patents
Battery charging stress optical coefficient (SOC) detection method in photovoltaic system Download PDFInfo
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Abstract
The invention discloses a battery charging stress optical coefficient (SOC) detection method in a photovoltaic system. The battery charging SOC detection method includes that charging voltage, charging current, discharging current and environment temperature of batteries are acquired through a charging voltage measurement module, a charging current measurement module, a discharging current measurement module and an environment temperature measurement module; multichannel analog signals are switched by adopting a multichannel analog switch, and the signals are transmitted to a low pass filter module; the low pass filter module filters the received modules, and the signals are transmitted to an analog to digital (A/D) conversion module after interference and sampled noise are removed; and the A/D conversion module converts the signals into digital signals and transmits the digital signals to a data processing unit to be processed. The battery charging SOC detection method is suitable for working environment with continuous fluctuant charging voltage and charging current of the batteries in the photovoltaic system, can be used for evaluating battery charging SOC initial value, leads in battery equivalent circulation times, simplifies a battery aging degree measuring method, compensates influences of battery aging and environment temperature, and enables SOC evaluation to be more accurate.
Description
Technical field
The present invention relates to battery power management technical field, particularly relate to one and utilize genetic algorithm to improve least square method supporting vector machine regression model, and compensate for circulating battery number of times and environment temperature, detect the method for battery charging SOC in photovoltaic system.
Background technology
Along with the exhaustion day by day of traditional energy, sun power has become the one very potential new forms of energy of tool, and photovoltaic generation is the current major way utilizing sun power.Angle of solar battery system is not connected with electrical network, provides electric power directly to load, and the general battery that uses is as energy storage device, and the electrical power storage that photovoltaic panel exports is got up by daytime.Such angle of solar battery system in remote districts, desert, the electrical network such as sentry post, borderland still have very high practical value in unlapped region.
Battery charge state SOC(State of Charge, hereinafter referred to as SOC) accurate estimation be the most basic, the most important aspect of battery management system.Be subject to the impact of the many factors such as light intensity, light angle, temperature due to photovoltaic system, its output charging voltage, charging current are among constantly change.How to utilize battery can survey supplemental characteristic to realize the accurate estimation of present battery charging SOC is the technological difficulties that photovoltaic system battery management is badly in need of solving all the time.At present, conventional battery charging SOC method of estimation mainly contains:
One, charged electrical platen press: under charging current remains unchanged situation, the rule that battery two ends charging voltage changes with SOC and open-circuit voltage quite similar.The advantage of charged electrical platen press can estimate battery SOC in real time, and have good effect when constant-current charge.But in photovoltaic system, charging voltage, electric current can change along with the change of the factors such as light intensity, thus are unfavorable for the realization of charged electrical platen press.The method is generally used as the basis for estimation of battery charge cutoff.
Two, Ah counting method: Ah counting method estimates the SOC of battery, and compensate SOC estimated value according to temperature, rate of charge.It uses the most general SOC method of estimation at present.Use in photovoltaic system during Ah counting method and have the problem in three: method self can not provide battery initial value SOC; In photovoltaic system, charging current change is frequent, and inaccurate current measurement will increase SOC evaluated error, and through accumulating for a long time, this error can become increasing; Battery efficiency coefficient η must be considered during estimation SOC.Although the precision problem of current measurement can solve by using high-performance electric flow sensor, system cost can be made so significantly to increase.Meanwhile, battery efficiency coefficient η problem must set up Temperature affection factor and charge-discharge magnification coefficient experimental formula by lot of experimental data is solved.
Summary of the invention
The technical problem to be solved in the present invention overcomes prior art error comparatively greatly, the deficiency of inapplicable photovoltaic system working environment, provides battery charging SOC detection method in a kind of photovoltaic system.
The object of the invention is to be achieved through the following technical solutions: battery charging SOC detection method in a kind of photovoltaic system, photovoltaic system battery charging SOC detection module realizes, and photovoltaic system battery charging SOC detection module comprises charging voltage measurement module, charging current measurement module, discharge current measurement module, ambient temperature measurement module, multiway analog switch, low-pass filtering module, A/D modular converter and data processing unit etc.; Charging voltage measurement module, charging current measurement module, discharge current measurement module are all connected with mesuring battary with one end of ambient temperature measurement module, the other end is all connected with multiway analog switch, and multiway analog switch, low-pass filtering module, A/D modular converter are connected successively with data processing unit;
The method comprises the following steps:
(1) charging voltage measurement module, charging current measurement module, discharge current measurement module and ambient temperature measurement module gather battery charging voltage, charging current, discharge current and environment temperature respectively;
(2) multiway analog switch switches above-mentioned multichannel analog signals, and transmits signals to low-pass filtering module;
(3) low-pass filtering module filters to the received signal, is resent to A/D modular converter after removing interference and sampling noiset;
(4) A/D modular converter is sent to data processing unit after the signal of reception is transformed into digital signal;
(5) data processing unit receives battery charging voltage
, charging current
, discharge current
and environment temperature
after data, calculate battery accumulated discharge electricity
with battery equivalent cycle number of times;
Accumulated discharge electricity:
;
Equivalent cycle number of times:
;
Wherein,
for battery accumulated discharge electricity;
for what calculate when the last time samples
;
battery is discharge current;
for sample frequency;
for equivalent cycle number of times, reflection cell degradation degree;
for battery standard capacity;
Wherein, battery standard capacity is determined
concrete steps as follows: in room temperature 25
under condition, with 0.2C electric current, constant-current discharge is carried out to the battery being full of electricity, and to discharge current integration; Be discharged to cut-off voltage to stop, now gained current integration values and battery standard capacity
;
(6) the least square method supporting vector machine regression model that improves based on genetic algorithm of data processing unit, calculates the electricity of present battery:
Battery electric quantity model:
;
Model kernel function:
;
Mode input:
;
Wherein,
for battery current electric quantity;
for model sample number;
for mode input;
for the input of model sample;
with
for the intra coeff of trying to achieve during Modling model;
for model kernel function;
for kernel functional parameter;
for battery charging voltage;
for battery charge;
(7) data processing unit to battery capacity in addition environment temperature and cell degradation compensate, and calculate the SOC of present battery; By to battery capacity in addition environment temperature and compensation of ageing, effectively improve the estimated accuracy of battery charging SOC:
State-of-charge:
;
Wherein:
for battery charge state, span 0 ~ 1, value is 0 expression battery electric quantity is sky, and value is that 1 expression battery is full of electricity;
for battery current electric quantity;
for battery standard capacity;
for battery capacity temperature compensation coefficient, reflection environment temperature is on the impact of battery capacity;
for battery capacity compensation of ageing coefficient, reflection circulating battery number of times is on the impact of capacity.
The invention has the beneficial effects as follows: the working environment that the present invention is applicable to battery charging voltage in photovoltaic system, charging current constantly fluctuates; Battery charging SOC initial value can be estimated; Battery charging SOC evaluated error can not cumulative rises; Introduce battery equivalent cycle number of times, simplify the method weighing cell degradation degree, and balancing battery is aging and the impact of environment temperature, SOC is estimated more accurate; The battery charging SOC model setting up same model only need calculate once, can be used for battery of the same type SOC On-line Estimation afterwards; Be applicable to various battery.
Accompanying drawing explanation
Fig. 1 is the structure diagram of photovoltaic system battery charging SOC detection module.
Embodiment
In photovoltaic system of the present invention, battery charging SOC detection method realizes on photovoltaic system battery charging SOC detection module, as shown in Figure 1, photovoltaic system battery charging SOC detection module comprises charging voltage measurement module, charging current measurement module, discharge current measurement module, ambient temperature measurement module, multiway analog switch, low-pass filtering module, A/D modular converter and data processing unit.Charging voltage measurement module, charging current measurement module, discharge current measurement module are all connected with mesuring battary with one end of ambient temperature measurement module, the other end is all connected with multiway analog switch, and multiway analog switch, low-pass filtering module, A/D modular converter are connected successively with data processing unit.
Charging voltage measurement module, charging current measurement module, discharge current measurement module and ambient temperature measurement module gather battery charging voltage, charging current, discharge current and environment temperature respectively, use multiway analog switch to switch above-mentioned multichannel analog signals, and transmit signals to low-pass filtering module.Low-pass filtering module filters to the received signal, is resent to A/D modular converter, is sent to data processing unit after being transformed into digital signal by A/D modular converter after removing interference and sampling noiset.
Battery charging SOC detection method in photovoltaic system of the present invention, comprises the following steps:
1, charging voltage measurement module, charging current measurement module, discharge current measurement module and ambient temperature measurement module gather battery charging voltage, charging current, discharge current and environment temperature respectively.
2, multiway analog switch switches above-mentioned multichannel analog signals, and transmits signals to low-pass filtering module.3, low-pass filtering module filters to the received signal, is resent to A/D modular converter after removing interference and sampling noiset.
4, A/D modular converter is sent to data processing unit after the signal of reception is transformed into digital signal.
5, data processing unit receives battery charging voltage
, charging current
, discharge current
and environment temperature
after data, calculate battery accumulated discharge electricity
with battery equivalent cycle number of times
.By introducing the concept of battery equivalent cycle number of times, simplify the method weighing cell degradation degree.
Accumulated discharge electricity:
;
Equivalent cycle number of times:
;
In aforesaid equation:
for battery accumulated discharge electricity;
for what calculate when the last time samples
;
battery is discharge current;
for sample frequency;
for equivalent cycle number of times, reflection cell degradation degree;
for battery standard capacity.
Wherein, battery standard capacity is determined
concrete steps as follows: in room temperature 25
under condition, with 0.2C electric current, constant-current discharge is carried out to the battery being full of electricity, and to discharge current integration; Be discharged to cut-off voltage to stop, now gained current integration values and battery standard capacity
.
Herein,
only need measure once same size battery, can be used as known constant after determining and estimate for the SOC of this size battery.
6, the least square method supporting vector machine regression model that improves based on genetic algorithm of data processing unit, calculates the electricity of present battery.By adopting least square method supporting vector machine regression model, avoiding error accumulation and increasing, effectively reduce the required quantity measuring sample, decrease computing time, and reduce the possibility that study crossed by sample.By adopting genetic algorithm improved model, trying to achieve optimized parameter, effectively improve the precision that electricity is estimated.To same size battery, model only need calculate once, can be used as known function and estimate for the SOC of this size battery after determining.
Battery electric quantity model:
;
Model kernel function:
;
Mode input:
;
In aforesaid equation:
for battery current electric quantity;
for model sample number;
for mode input;
for the input of model sample;
with
for the intra coeff of trying to achieve during Modling model;
for model kernel function;
for kernel functional parameter;
for battery charging voltage;
for battery charge.
Set up battery electric quantity
concrete steps based on least square method supporting vector machine regression model are as follows:
The 6.1 full spectrum lamp simulated solar irradiations utilizing adjustable light intensity, irradiate the photovoltaic panel of photovoltaic system, stop after carrying out charging a period of time to battery, measure and charging voltage before recording stopping
, charging current
.Note:
；
In aforesaid equation:
for the input of model sample;
for sample charging voltage;
for sample charging current.
Sample needed for Modling model is obtained by conversion intensity of light and battery charge time.
Battery after charging is placed in room temperature 25 by 6.2
under condition, carry out constant-current discharge with 0.2C electric current, measure battery electric quantity
.
6.3 foundation set up battery charge based on least square method supporting vector machine
with charging voltage, electric current
regression model:
；
In formula:
for battery electric quantity is about the function of charging voltage, electric current;
for battery charging voltage, electric current;
for
to the mapping function of higher dimensional space;
for the weight vector in model;
for the constant that model is tried to achieve.
The quadratic power norm of Select Error is loss function, and its optimization problem is:
In formula:
for loss function, less then model accuracy is higher;
for penalty coefficient, for regulating error, can make to get between training error and model complexity-individual compromise, to make required function have good generalization ability,
be worth larger, the regression error of model is less;
for the weight vector in model;
for the constant that model is tried to achieve;
for error;
for battery charge;
for battery charging voltage, electric current;
for
to the mapping function of higher dimensional space.
Introduce Lagrange multiplier
, above-mentioned optimization problem is converted into:
Above formula is right respectively
ask local derviation:
Can obtain:
Eliminate
with
, obtain system of linear equations:
Wherein,
,
,
,
.
In aforesaid equation:
for optimization aim, less then model accuracy is higher;
for loss function;
for penalty coefficient;
for the weight vector in model;
for the constant that model is tried to achieve;
for Lagrange multiplier;
for error;
for battery charge;
for battery charging voltage, electric current;
for
to the mapping function of higher dimensional space.
Introducing gaussian kernel function substitutes
, solve higher-dimension computational problem:
；
In formula:
for model kernel function;
for battery charging voltage, electric current;
for battery charging voltage, electric current;
for kernel functional parameter.
Final acquisition regression function is:
；
In formula,
be the battery charge of trying to achieve;
for battery charging voltage, electric current;
for model sample charging voltage, electric current;
with
for the intra coeff of trying to achieve during Modling model.
In above model solution process, introduce penalty factor
with kernel functional parameter
.This method adopts genetic algorithm to be optimized these two model parameters.
Utilize the concrete steps of genetic algorithm improvement least square method supporting vector machine regression model as follows:
(a) given penalty factor
span (as 1 to 1000) and kernel functional parameter
span (as 0.1 to 10) is the item chromosome of certain length (as 20) by binary coding.Random generation some chromosome as initial population, and sets maximum evolutionary generation.
B current population is decoded by (), for training battery electric quantity regression model, and a kth individual fitness function:
；
In formula,
for fitness function, larger then parameter is more excellent;
for prevent denominator be of 0 setting indivisible (as
);
for model regression error, N is sample number.
If c () Evolution of Population algebraically reaches setting value, then export in population
maximum chromosome, decoding is the optimized parameter of model.Otherwise enter step (d).
D () adopts spinning roller method to produce population of future generation.A kth individual selected probability:
In formula,
for the probability that individuality is selected;
,
for fitness function, N is sample number.
Single point of contact is adopted to intersect: two chromosomes intercourse code segment; Crossover probability can be set to 0.8.
Adopt binary variation to compile: two chromosome carry out with or, XOR; Mutation probability can be set to 0.1.
Step (b) is returned after obtaining population of new generation.
Herein, to same size battery, genetic algorithm and least square method supporting vector machine regression model only need calculate once, can be used as known function and estimate for the SOC of this size battery after determining.
7, data processing unit to battery capacity in addition environment temperature and cell degradation compensate, and calculate the SOC of present battery.By to battery capacity in addition environment temperature and compensation of ageing, effectively improve the estimated accuracy of battery charging SOC.
State-of-charge:
;
In formula:
for battery charge state, span 0 ~ 1, value is 0 expression battery electric quantity is sky, and value is that 1 expression battery is full of electricity;
for battery current electric quantity;
for battery standard capacity;
for battery capacity temperature compensation coefficient, reflection environment temperature is on the impact of battery capacity;
for battery capacity compensation of ageing coefficient, reflection circulating battery number of times is on the impact of capacity.
Determine battery capacity temperature compensation coefficient
concrete steps as follows:
(A) battery being full of electricity is placed in different environment temperatures
under, carry out constant-current discharge with 0.2C electric current, measure the battery capacity under relevant temperature
.
(B) by least square fitting, try to achieve
with
cubic polynomial curve relation:
；
In formula:
for battery capacity;
for environment temperature;
for
about
function;
,
,
,
for multinomial coefficient.
(C) environment temperature is
time, corresponding battery capacity temperature compensation coefficient
for:
；
In formula:
for battery capacity temperature compensation coefficient;
for environment temperature;
for battery capacity is about environment temperature
function;
for the battery capacity under standard conditions.
Herein,
only need measure once same size battery, can be used as known function after determining and estimate for the SOC of this size battery.
Determine battery capacity compensation of ageing coefficient
concrete steps as follows:
A () will use
the battery that secondary cycle index is full of electricity is placed in room temperature 25
under condition, carry out constant-current discharge with 0.2C electric current, measure corresponding
under battery capacity
.
B (), by least square fitting, is tried to achieve
with
cubic polynomial curve relation:
；
In formula:
for battery capacity;
for circulating battery number of times;
for
about
function;
,
,
,
for multinomial coefficient.
C () equivalent cycle number of times is
time, corresponding battery capacity compensation of ageing coefficient
for:
；
In formula:
for battery capacity compensation of ageing coefficient;
for battery equivalent cycle number of times;
for battery capacity is about equivalent cycle number of times
function;
for the battery capacity under standard conditions.
Herein,
only need measure once same size battery, can be used as known function after determining and estimate for the SOC of this size battery.
Just battery charging SOC in photovoltaic system can be obtained, span 0 ~ 1 by above-mentioned steps.If SOC value is 0, then represent that battery electric quantity is empty; If SOC value is 1, then represent that battery is full of electricity.
Claims (1)
1. battery charging SOC detection method in a photovoltaic system, photovoltaic system battery charging SOC detection module realizes, and photovoltaic system battery charging SOC detection module comprises charging voltage measurement module, charging current measurement module, discharge current measurement module, ambient temperature measurement module, multiway analog switch, low-pass filtering module, A/D modular converter and data processing unit; Charging voltage measurement module, charging current measurement module, discharge current measurement module are all connected with mesuring battary with one end of ambient temperature measurement module, the other end is all connected with multiway analog switch, and multiway analog switch, low-pass filtering module, A/D modular converter are connected successively with data processing unit; It is characterized in that, the method comprises the following steps:
(1) charging voltage measurement module, charging current measurement module, discharge current measurement module and ambient temperature measurement module gather battery charging voltage, charging current, discharge current and environment temperature respectively;
(2) multiway analog switch switches multichannel analog signals, and transmits signals to low-pass filtering module;
(3) low-pass filtering module filters to the received signal, is resent to A/D modular converter after removing interference and sampling noiset;
(4) A/D modular converter is sent to data processing unit after the signal of reception is transformed into digital signal;
(5) data processing unit receives battery charging voltage u, charging current i, discharge current i
outafter environment temperature T data, calculate battery accumulated discharge electricity Q
outwith battery equivalent cycle number of times;
Accumulated discharge electricity:
Equivalent cycle number of times:
Wherein, Q
outfor battery accumulated discharge electricity; Q
prevfor the Q calculated when the last time samples
out; i
outfor battery discharge current; F is sample frequency; N is equivalent cycle number of times, reflection cell degradation degree; Q
0for battery standard capacity;
Wherein, battery standard capacity Q is determined
0concrete steps as follows: under room temperature 25 DEG C of conditions, with 0.2C electric current, constant-current discharge is carried out, and to discharge current integration to the battery being full of electricity; Be discharged to cut-off voltage to stop, now gained current integration values and battery standard capacity Q
0;
(6) the least square method supporting vector machine regression model that improves based on genetic algorithm of data processing unit, calculates the electricity of present battery:
Battery electric quantity model:
Model kernel function:
Mode input: x=[u i]
t;
Wherein, Q is battery current electric quantity; N is model sample number; X is mode input; x
kfor the input of model sample; a
kthe intra coeff of trying to achieve when being Modling model with b; K (x, x
k) be model kernel function; σ is kernel functional parameter; U is battery charging voltage; I is battery charge;
(7) data processing unit to battery standard capacity in addition environment temperature and cell degradation compensate, and calculate the SOC of present battery; By to battery capacity in addition environment temperature and compensation of ageing, effectively improve the estimated accuracy of battery charging SOC:
State-of-charge:
In formula: SOC is battery charge state, span 0-1, value is 0 expression battery electric quantity is sky, and value is that 1 expression battery is full of electricity; Q is battery current electric quantity; Q
0for battery standard capacity; η
tfor battery capacity temperature compensation coefficient, reflection environment temperature is on the impact of battery capacity; η
nfor battery capacity compensation of ageing coefficient, reflection circulating battery number of times is on the impact of capacity;
In described step (7), described battery capacity temperature compensation coefficient η
tdetermine as follows:
(A) battery being full of electricity is placed in different environment temperature T
kunder, carry out constant-current discharge with 0.2C electric current, measure the battery capacity Q under relevant temperature
k;
(B) by least square fitting, Q is tried to achieve
kwith T
kcubic polynomial curve relation:
Q
k＝f
T(T
k)＝a
TT
k 3+b
TT
k 2+c
TT
k+d
T；
In formula: Q
kfor battery capacity; T
kfor environment temperature; f
t(T
k) be Q
kabout T
kfunction; a
t, b
t, c
t, d
tfor multinomial coefficient;
(C) when environment temperature is T, corresponding battery capacity temperature compensation coefficient η
tfor:
In formula: η
tfor battery capacity temperature compensation coefficient; T is environment temperature; f
t(T) for battery capacity is about the function of environment temperature T;
In described step (7), described battery capacity compensation of ageing coefficient η
ndetermine as follows:
A () will use n
ksecondary cycle index be full of electricity battery be placed in room temperature 25 DEG C of conditions under, carry out constant-current discharge with 0.2C electric current, measure corresponding n
kunder battery capacity Q
k;
B (), by least square fitting, tries to achieve Q
kwith n
kcubic polynomial curve relation:
Q
k＝f
n(n
k)＝a
nn
k 3+b
nn
k 2+c
nn
k+d
n；
In formula: Q
kfor battery capacity; n
kfor circulating battery number of times; f
n(n
k) be Q
kabout n
kfunction; a
n, b
n, c
n, d
nfor multinomial coefficient;
When () equivalent cycle number of times is n c, corresponding battery capacity compensation of ageing coefficient η
nfor:
In formula: η
nfor battery capacity compensation of ageing coefficient; N is battery equivalent cycle number of times; f
tn () is for battery capacity is about the function of equivalent cycle frequency n.
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