CN103176139A - State-of-charge estimation method and system for compensating non-smooth hysteresis in power batteries - Google Patents

Abstract

The present invention is a charge state estimation method and system for power battery non-smooth hysteresis characteristic compensation. The first step of the method is to collect the battery output voltage and current, and obtain the relational expressions of each parameter from the battery equivalent circuit model to construct a neural network OCV(k) prediction Estimating the model, solving its parameters, and estimating the open-circuit terminal voltage OCV(k) online. In the second step, the SDH model and RBF2 are connected in series to form a dynamic hysteresis hybrid model. The SDH model takes the OCV(k) obtained in the first step as input, and the output y(k), OCV(k), and OCV(k-1) are the inputs of RBF2, and RBF2 weighted learning indirectly adjusts the parameters of the SDH model to approach the actual The complex hysteresis relationship, and finally output the online estimated SOC(k). The system is composed of a microprocessor and current and voltage sensors installed in the battery circuit, etc., stores the program for executing the method, and obtains the estimated value of SOC (k). The invention uses the neural network for reference, compensates the complex non-smooth and hysteresis nonlinear characteristics of the power battery, and improves the online estimation accuracy of the SOC(k).

Description

State of charge evaluation method and the system of the non-smooth lagging characteristics compensation of electrokinetic cell

Technical field

The present invention relates to the state of charge estimating techniques field of automobile power cell, be specially state of charge evaluation method and the system of the non-smooth lagging characteristics compensation of a kind of electrokinetic cell.

Background technology

In New-energy electric vehicle, electrokinetic cell is one of its three large gordian technique.Accumulator develops into lithium ion battery from lead-acid battery, Ni-MH battery, and theory and technical research that efficient, the safety of battery and long-life use receive much attention always.Automobile power cell is from fully different for the battery charge and discharge process of other equipment such as mobile phone, electric automobile power battery replaces under the charge and discharge state in stochastic and dynamic, overcharging all may cause the irreversible damage of battery with deep discharge, also relates to safety problem.State of charge SOC(State of Charge in battery operation) be that dynamic charge and discharge process is efficient, one of key parameter in safety management.So, accurate estimation on line or to measure state of charge be the power battery pack safe operation, discharge and recharge effective assurance of optimum management and control.

State of charge is the isoparametric function of electrokinetic cell electric current, voltage, temperature and internal resistance, and state of charge can not directly obtain, and needs measuring by various indirect methods.Existing state of charge assay method and defective are:

1) AH(Ampere Hour) metering method: still have the identification of model parameter and state, the problems such as original state.

2) the AH method SOC evaluation method of being combined with model of mind is considered as the general nonlinearity characteristic with the Hysteresis Nonlinear characteristic of SOC, has carried out approximate processing.

3) impedance method: operand is large, the canbe used on line difficulty.

4) based on the SOC evaluation method of battery equivalent-circuit model, its model can not be described the complicated lagging characteristics that battery is discharging and recharging dynamic changing process and shows fully.

5) based on the SOC evaluation method of intelligent modeling, the model that adopts all fails to consider the complicated lagging characteristics that SOC occurs comprehensively in two processes of charging and discharging.

Capacity is large, energy density is high, service life cycle is long, have the lithium ion battery of wide application prospect and the first-selection that the NiMH battery becomes new-energy automobile power battery.The battery terminal voltage of lithium ion battery charge and discharge process and state of charge SOC show non-smooth complicated Hysteresis Nonlinear characteristic, and as shown in Figure 1, wherein solid line is charging process, and dotted line is discharge process.Current paper showed in 2012, had analyzed its complicated sluggish certainty that exists from the inside battery ion motion.So the compensation of complicated lagging characteristics becomes the factor that the estimation of SOC high precision be can not ignore.For example: for the NiMH battery, if the impact of Hysteresis Nonlinear is not compensated, can cause SOC to measure approximately 40% error.

Electrokinetic cell a plurality of secondary ring phenomenons except main ring occurred discharging and recharging at random alternately dynamic process, be illustrated in figure 2 as the open end voltage OCV(Open Circuit Voltage of ni-mh under charge and discharge process (NiMH) battery) and state of charge SOC main ring and secondary ring lagging characteristics curve, wherein ● line represents discharge process, zero line represents charging process.

Document is just providing, the non-smooth property at flyback lagging characteristics and switching point place, and there are inconsistency in battery terminal voltage and SOC, namely with repeatedly charging, there is drifting problem, battery terminal voltage and SOC lagging characteristics that its 1500th time and the 3000th time cycle charge-discharge shows, there is obvious difference, is illustrated in figure 3 as lithium ion battery terminal voltage and the sluggish graph of a relation of SOC in different serviceable lifes; Wherein solid line is terminal voltage and the sluggish relation of SOC of new battery, dot-and-dash line is through the battery terminal voltage of 1500 charge and discharge cycles and the sluggish relation of SOC, dotted line is through the battery terminal voltage after 3000 charge and discharge cycles and the sluggish relation of SOC, as can be seen from Figure 3, if ignore the compensation of lagging characteristics, directly cause error approximately 3%.

Battery open end voltage OCV and SOC show stable lagging characteristics and have good repetition comformity relation, as shown in Figure 4 ◆ line represent new battery, zero line represent the 1500th cycle charge-discharge, ▲ line represents the OCV of 3000 cycle charge-discharges and the lagging characteristics curve of SOC, visible new battery and show stable consistance at the OCV of the 1500th time, the 3000th time cycle charge-discharge and the lagging characteristics of SOC.

Existing battery equivalent electrical circuit linear model is described its lagging characteristics roughly with plain mode, fails fully to describe the Dynamic Hysteresis nonlinear characteristic of non-smooth, the many rings of electrokinetic cell.The complicacy of electric automobile power battery electrochemical process, having caused the consistency problem of SOC estimation is drifting problem; The complicated Hysteresis Nonlinear characteristic of SOC and battery open end voltage OCV and compensation problem have appearred; With the sluggish relation estimation of SOC-OCV SOC, in requisition for the online estimation problem fast of the OCV that solves.These three key problems are to need the key issue that breaks through in SOC high precision estimation, are also the bottleneck problems of SOC high precision estimation.

Summary of the invention

The objective of the invention is to design the state of charge evaluation method of the non-smooth lagging characteristics compensation of a kind of electrokinetic cell, this method estimates the state of charge SOC of electrokinetic cell in two steps, and the first step is the On-line Estimation to electrokinetic cell open end voltage OCV; Second step be according to OCV to state of charge Hysteresis Nonlinear compensation of error, estimate current SOC value.

Another object of the present invention is the state of charge estimating system of the non-smooth lagging characteristics compensation of a kind of electrokinetic cell of design, by above-mentioned evaluation method, the signal processing system that designs a calculating machine, realizes showing in real time the electrokinetic cell state of charge of estimation on line.

The state of charge evaluation method of the non-smooth lagging characteristics compensation of design motivation battery of the present invention estimates to the discrete digital amount SOC (k) of the state of charge of electrokinetic cell that in two steps the first step is the discrete digital amount voltage that digital collection obtains electrokinetic cell

And current i (k), the discrete digital amount OCV (k) of battery open end voltage is carried out On-line Estimation; Second step is estimated current discrete digital amount SOC (k) value for according to discrete digital amount OCV (k), state of charge Hysteresis Nonlinear error is compensated.

The first step, to discrete digital amount OCV (k) On-line Estimation of battery open end voltage OCV (t)

Has the stable conforming complicated lagging characteristics that repeats according to the relation of electrokinetic cell (comprising lithium ion battery and Ni-MH battery) SOC and open end voltage OCV, the first step is first carried out On-line Estimation to the discrete digital amount OCV (k) of battery open end voltage, avoids in order to obtain battery open end voltage Holding Problem for a long time;

For discrete digital amount OCV (k) is carried out On-line Estimation, the present invention adopts representative battery equivalent electrical circuit Thevenin model.In this equivalence circuit, resistance R ₂With capacitor C formation in parallel resistance-capacitance circuit, open end voltage OCV (t) the resistance in series R of battery ₁With above-mentioned resistance-capacitance circuit, battery equivalent electrical circuit output voltage is V (t), passes through resistance R ₁Electric current be i (t), the terminal voltage of capacitor C is u _c(t).

Its mathematic(al) representation is as follows:

$\left\{\begin{array}{c}\mathrm{OCV}\left(t\right)=V\left(t\right)+R_{1}i\left(t\right)+u_c\left(t\right)\\ i\left(t\right)=\frac{u_c\left(t\right)}{R_2}+C\frac{{\mathrm{du}}_c\left(t\right)}{\mathrm{dt}}\end{array}\right.---\left(1\right)$

(1) formula is equivalent to:

$\left\{\begin{array}{c}V\left(t\right)=\mathrm{OCV}\left(t\right)+R_{1}i\left(t\right)+u_c\left(t\right)\\ i\left(t\right)=\frac{u_c\left(t\right)}{R_2}+C\frac{{\mathrm{du}}_c\left(t\right)}{\mathrm{dt}}\end{array}\right.---\left(2\right)$

Wherein: OCV (t) is the function of SOC (t).

The charge and discharge process of battery is process more slowly, and OCV (t) is relatively stable within the short time (in several seconds), and OCV (t) can be used as normal value and processes, under metastable state, and OCV (t), resistance R during above-mentioned equation (2) is stable ₁, R ₂Reach capacitor C and exist with determined value, there is stable unique solution in a corresponding dynamic equation of quaternary (2).

The output voltage V (k) and battery beginning voltage OCV (k), current i (k) and capacitance terminal voltage u corresponding according to the above-mentioned math equation that is obtained by the battery equivalent-circuit model (2) _c(k) discrete magnitude relation:

V(k)=OCV(k)-R ₁i(k)-u _c(k) （3）

The capacitance terminal voltage u corresponding according to the above-mentioned math equation that is obtained by the battery equivalent-circuit model (2) _c(k) with the discrete magnitude relation of i (k):

u _c(k)=k ₂u _c(k-1)+k ₁i(k) （4）

Wherein:

${\mathrm{<figure-callout\; id="1"\; label="k"\; filenames="HDA00002897014700011.png,HDA00002897014700042.png"\; state="\{\{state\}\}">k</figure-callout>}}_1=\frac{R_{2}T}{T+R_{2}C},$ ${\mathrm{<figure-callout\; id="2"\; label="K"\; filenames="HDA00002897014700041.png,HDA00002897014700042.png"\; state="\{\{state\}\}">K</figure-callout>}}_2=\frac{R_{2}C}{T+R_{2}C}$

T is the sampling period.

According to (3), (4) formula, corresponding neural network OCV (k) prediction model that builds is used for reference neural network learning, completes finding the solution parameter in the battery equivalent-circuit model.

Neural network OCV(k) discrete digital amount OCV(k in prediction model), V (k), i (k) and u _c(k) analog quantity OCV (t), V (t), i (t) and the u in difference corresponding equation (2) _c(t).

Corresponding battery equivalent-circuit model builds neural network OCV(k of the present invention) prediction model, comprise three neuron nodes and the first radial basis function neural network RBF(Radical Basis Function), be expressed as RBF1, three neuron nodes calculate by (3), (4) formula respectively, and output is respectively capacitance terminal voltage u _c(k), resistance R ₁Terminal voltage and equivalent electrical circuit output voltage V (k).The first radial basis function neural network RBF1 completes the On-line Estimation to battery open end voltage OCV (k).Digital collection obtains the discrete digital amount voltage of the actual output of electrokinetic cell And current i (k), as the input value of this model.

The peripheral sensory neuron node is asked capacitance terminal voltage u _c(k).z ^-1For to the back operator, the u of peripheral sensory neuron node output _c(k) pass through z ^-1Obtain corresponding u _c(k-1).The peripheral sensory neuron node is according to formula u _c(k)=k ₂u _c(k-1)+k ₁I (k) is by weighting coefficient k ₁And k ₂Respectively to gathering the discrete digital amount i(k of gained) and u _c(k-1) be weighted summation, obtain exporting u _c(k).

Nervus opticus unit node obtains i (k) and Model Parameter k according to digital collection ₃Obtain R ₁On voltage be k ₃* i (k), k ₃Expression R ₁

Third nerve unit node calculates equivalent electrical circuit output voltage V (k) estimated value, V (k)=OCV (k)-R ₁I (k)-u _c(k).

The output OCV(k of the first radial basis function neural network RBF1) be the kinematic function of current i (k), equivalent electrical circuit output voltage V (k).OCV(k) pass through z ^-1Obtain OCV (k-1) to the back operator, OCV (k-1) is as external feedback, with the last sampling instant value V (k-1) of V (k) and i (k) input signal as the first radial basis function neural network RBF1, the first radial basis function neural network RBF1 is described voltage V (k), current i (k) and kinematic nonlinearity characteristic OCV(k), battery equivalent electrical circuit linear model is replenished and expands, and the first radial basis function neural network RBF1 is output as battery open end voltage OCV (k).

Neural network OCV(k) the weights k of prediction model ₁, k ₂And k ₃Study, estimate resulting cell output voltage V(k with this model) with the discrete digital amount of the battery terminal voltage of actual measurement

Poor, consist of neural network OCV(k) the learning objective function of prediction model, adopt ripe method of steepest descent, i.e. gradient method is by neural network OCV(k) prediction model, parameters R in the electrokinetic cell of identification simultaneously equivalent-circuit model ₁, R ₂And C, and state OCV(k), realize the On-line Estimation of OCV (k).Its neural network OCV(k) the Parameter Self-learning adjustment capability of prediction model can adapt to the otherness of electrokinetic cell on characteristic of different capabilities.

Second step, according to OCV (k) to the complicated Hysteresis Nonlinear error compensation of state of charge, estimate current SOC(k) be worth

The Dynamic Hysteresis mixture model that this step adopts simple dynamic hysteresis model-SDH model (Simple Dynamic Hysteresis) and the second radial basis function neural network RBF2 to be composed in series.

The expression formula of SDH model is as follows:

$\left\{\begin{array}{c}y\left(t\right)=\mathrm{\&eta;}\left(t\right)-\frac{1}{{\mathrm{<figure-callout\; id="4"\; label="k"\; filenames="HDA00002897014700041.png,HDA00002897014700042.png"\; state="\{\{state\}\}">k</figure-callout>}}_4}\mathrm{OCV}\left(t\right)\\ \stackrel{\&CenterDot;}{\mathrm{\&eta;}}\left(t\right)=k_4(\mathrm{OCV}\left(t\right)-\mathrm{\&Delta;}\left(\mathrm{\&eta;}\left(t\right)\right))\end{array}\right.---\left(5\right)$

Wherein

$\mathrm{\&Delta;}\left(\mathrm{\&eta;}\left(t\right)\right)=\left\{\begin{array}{cc}{k}_4(\mathrm{\&eta;}\left(t\right)-1)& \mathrm{\&eta;}\left(t\right)>1\\ 0& \left|\mathrm{\&eta;}\left(t\right)\right|\&le;1\\ k_4(\mathrm{\&eta;}\left(t\right)+1)& \mathrm{\&eta;}\left(t\right)<-1\end{array}\right.---\left(6\right)$

Wherein, OCV (t) is the On-line Estimation value that the first step obtains, and is the input of SDH model, and y (t) is the output of SDH model.

Parameter k in the SDH model ₄Determine the retardant curve of SDH model and actual SOC(t)-OCV(t) sluggish relation curve is at the similarity degree of contour shape, and also decision is connected on the on-line study speed of the learning process of SDH model the second radial basis function neural network RBF2 afterwards.

Can obtain corresponding discrete model by the SDH model.Analog quantity OCV (t) and y (t) be corresponding discrete digital amount OCV (k) and y (k) respectively, and T is the sampling period.

The expression formula of SDH discrete model is as follows:

$\left\{\begin{array}{c}y\left(k\right)=\mathrm{\&eta;}\left(k\right)-\frac{1}{{\mathrm{<figure-callout\; id="4"\; label="k"\; filenames="HDA00002897014700041.png,HDA00002897014700042.png"\; state="\{\{state\}\}">k</figure-callout>}}_4}\mathrm{OCV}\left(k\right)\\ \frac{\mathrm{\&eta;}\left(k\right)-\mathrm{\&eta;}(k-1)}{T}=k_4(\mathrm{OCV}\left(k\right)-\mathrm{\&Delta;}\left(\mathrm{\&eta;}\left(k\right)\right))\end{array}\right.$

After arrangement:

$\left\{\begin{array}{c}y\left(k\right)=\mathrm{\&eta;}\left(k\right)-\frac{1}{{\mathrm{<figure-callout\; id="4"\; label="k"\; filenames="HDA00002897014700041.png,HDA00002897014700042.png"\; state="\{\{state\}\}">k</figure-callout>}}_4}\mathrm{OCV}\left(k\right)\\ \mathrm{\&eta;}\left(k\right)=\mathrm{\&eta;}(k-1)+T\&times;k_4(\mathrm{OCV}\left(k\right)-\mathrm{\&Delta;}\left(\mathrm{\&eta;}\left(k\right)\right))\end{array}\right.---\left(7\right)$

Wherein

$\mathrm{\&Delta;}\left(\mathrm{\&eta;}\left(k\right)\right)=\left\{\begin{array}{cc}{k}_4(\mathrm{\&eta;}\left(k\right)-1)& \mathrm{\&eta;}\left(k\right)>1\\ 0& \left|\mathrm{\&eta;}\left(k\right)\right|\&le;1\\ k_4(\mathrm{\&eta;}\left(k\right)+1)& \mathrm{\&eta;}\left(k\right)<-1\end{array}\right.---\left(8\right)$

The SDH model has the monocycle of description, is the non-smooth lagging characteristics of outer shroud also have the description secondary ring, i.e. many rings lagging characteristics of interior ring.

The present invention with simple for structure, the SDH models of many ring lagging characteristics can be described as the preposition part of Dynamic Hysteresis mixture model.The second radial basis function neural network RBF2 that connects after the SDH model builds the Dynamic Hysteresis mixture model.The OCV (k) that the resulting y of SDH model (k) and the first step obtain, the last sampling instant value OCV (k-1) of OCV (k) are as the input of the second radial basis function neural network RBF2, learn by the weighting in the second radial basis function neural network RBF2, realize the Nonlinear Mapping of any single-valued correspondence, the k of Indirect method SDH model ₄Parameter is to approach actual SOC(k) and OCV(k) complicated sluggish relation, finally export the SOC (k) of estimation on line.

Described Dynamic Hysteresis mixture model not only can represent sluggish outer shroud, and namely main ring, also can describe sluggish a plurality of secondary ring, i.e. interior ring.When electrokinetic cell was used for the electric automobile of dynamic operation, this Dynamic Hysteresis mixture model can be described outer shroud and a plurality of interior ring property that the non-smooth complicated lagging characteristics in the charging, discharging electric batteries Stochastic Dynamic Process shows simultaneously.

The SDH model has non-smooth property, makes SOC(k)-OCV(k) the Dynamic Hysteresis mixture model can be described the non-smooth property of battery.Latter part of the second radial basis function neural network RBF2 of the sluggish mixture model of connecting just realizes Nonlinear Mapping.The non-smooth property of electrokinetic cell by the mode of this Dynamic Hysteresis mixture model, can't directly be processed the problem of non-smooth signal in avoiding dexterously take differential or local derviation as the neural network modeling approach of Fundamentals of Mathematics.

This model adopts ripe steepest decline technology to complete the study of the second radial basis function neural network RBF2 first according to experimental data, adopts the output estimation SOC(k of the Dynamic Hysteresis mixture model that has trained) value.

The state of charge evaluation method of smooth lagging characteristics compensation non-according to the electrokinetic cell of the invention described above, design the state of charge estimating system of the non-smooth lagging characteristics compensation of electrokinetic cell, comprised microprocessor, current sensor, voltage sensor, analog to digital converter, program storage, programmable storage, timer and display.Current sensor and voltage sensor output are through analog to digital converter access microprocessor, microprocessor linker storer, programmable storage, timer and display.

Storage neural network OCV(k in program storage) calculation procedure of prediction model and the SDH model Dynamic Hysteresis mixture model of connecting with the second radial basis function neural network RBF2, store the parameter in OCV (k) prediction model and SDH model in programmable storage, current sensor and voltage sensor are installed in electrokinetic cell and load connecting circuit, the load current of the electrokinetic cell that current sensor and voltage sensor are measured and terminal voltage obtain corresponding load current and the digital quantity of voltage and send into microprocessor by analog to digital conversion.SOC(k in the timer control program storer) operation of estimation program startup and interruption, the current SOC(k of the operation result of microprocessor) estimated value, show in real time by display.

Compared with prior art, state of charge evaluation method and the advantage of system of the non-smooth lagging characteristics compensation of electrokinetic cell of the present invention are: 1, according to battery equivalent-circuit model structure, build neural network OCV (k) prediction model, use for reference the neural network method of solving an equation, realize electrokinetic cell open end voltage OCV(k) On-line Estimation; Utilize the Neural Network Self-learning ability, solve electrokinetic cell different with batch grade because of capacity, the variability issues that causes, On-line Estimation battery open end voltage OCV(k), avoided in order to obtain open end voltage OCV(k) and the problem that waits as long for; 2, utilize SOC(k) and the OCV(k) stable and consistent of relation, built the Dynamic Hysteresis mixture model of SDH model and the second radial basis function neural network RBF2 series connection, complicated non-smooth Hysteresis Nonlinear characteristic to electrokinetic cell realizes compensation, improves the estimation on line precision of SOC (k); 3, first obtain OCV(k), again by Dynamic Hysteresis mixture model estimation SOC(k) two-stage process, solved the complicacy of electrokinetic cell electrochemical reaction and the drifting problem that repeatedly charges and discharge, obtained SOC(k) consistance estimation, effectively improve SOC(k) estimation precision; 4, realize the system of SOC (k) estimation with the active computer software and hardware on this method basis, only need to measure the parameters such as electric current, voltage of electrokinetic cell, can show in real time the state of charge of electrokinetic cell, be convenient to implement to use.

Description of drawings

Fig. 1 is the non-smooth complicated Hysteresis Nonlinear performance diagram of electrokinetic cell terminal voltage and state of charge SOC (t) under charge and discharge process;

Fig. 2 is the open end voltage OCV (t) and state of charge SOC (t) main ring and secondary ring lagging characteristics curve map of ni-mh under charge and discharge process (NiMH) battery;

Fig. 3 is lithium ion battery terminal voltage and the SOC(t in different serviceable lifes) sluggish graph of a relation;

Fig. 4 is the electrokinetic cell open end voltage OCV (t) in different serviceable lifes and SOC(t) sluggish graph of a relation;

Fig. 5 carries out the model schematic diagram of state of charge SOC (k) estimation of electrokinetic cell in two steps for the state of charge evaluation method embodiment of the non-smooth lagging characteristics compensation of this electrokinetic cell;

Fig. 6 is electrokinetic cell equivalent-circuit model schematic diagram used in the state of charge evaluation method embodiment of the non-smooth lagging characteristics compensation of this electrokinetic cell;

Fig. 7 is the neural network model schematic diagram of open end voltage OCV (k) On-line Estimation used in the state of charge evaluation method embodiment of the non-smooth lagging characteristics compensation of this electrokinetic cell;

Fig. 8 is the Dynamic Hysteresis mixture model schematic diagram of SDH model series connection the second radial basis function neural network RBF2 used in the state of charge evaluation method embodiment of the non-smooth lagging characteristics compensation of this electrokinetic cell;

Fig. 9 is the monocycle lagging characteristics curve map of SDH model in the state of charge evaluation method embodiment of the non-smooth lagging characteristics compensation of this electrokinetic cell;

Figure 10 is many rings lagging characteristics curve map of SDH model in the state of charge evaluation method embodiment of the non-smooth lagging characteristics compensation of this electrokinetic cell;

Figure 11 is the state of charge estimating system example structure schematic diagram of the non-smooth lagging characteristics compensation of this electrokinetic cell.

Embodiment

The state of charge evaluation method embodiment of the non-smooth lagging characteristics compensation of electrokinetic cell

This example estimates the state of charge SOC of electrokinetic cell in two steps, and as shown in Figure 5, the first step is the digital collection electrokinetic cell, obtains the discrete digital amount of the battery terminal voltage of actual measurement

With discrete digital amount current i (k), the discrete digital amount OCV (k) of battery open end voltage OCV (t) is carried out On-line Estimation; Second step is estimated current SOC (k) value for according to OCV (k), state of charge Hysteresis Nonlinear error is compensated.

The first step, to battery open end voltage OCV(t) discrete digital amount OCV (k) On-line Estimation

Battery equivalent electrical circuit Thevenin model as shown in Figure 6, in equivalent electrical circuit, resistance R ₂With capacitor C formation in parallel resistance-capacitance circuit, the OCV of battery (t) is contact resistance R successively ₁With above-mentioned resistance-capacitance circuit, battery equivalent electrical circuit output voltage is V (t), passes through resistance R ₁Electric current be i (t), the terminal voltage of capacitor C is u _c(t).

Its mathematic(al) representation is as follows:

$\left\{\begin{array}{c}\mathrm{OCV}\left(t\right)=V\left(t\right)+R_{1}i\left(t\right)+u_c\left(t\right)\\ i\left(t\right)=\frac{u_c\left(t\right)}{{\mathrm{<figure-callout\; id="2"\; label="R"\; filenames="HDA00002897014700041.png,HDA00002897014700042.png"\; state="\{\{state\}\}">R</figure-callout>}}_2}+C\frac{{\mathrm{du}}_c\left(t\right)}{\mathrm{dt}}\end{array}\right.---\left(1\right)$

(1) formula is equivalent to:

$\left\{\begin{array}{c}V\left(t\right)=\mathrm{OCV}\left(t\right)+R_{1}i\left(t\right)+u_c\left(t\right)\\ i\left(t\right)=\frac{u_c\left(t\right)}{{\mathrm{<figure-callout\; id="2"\; label="R"\; filenames="HDA00002897014700041.png,HDA00002897014700042.png"\; state="\{\{state\}\}">R</figure-callout>}}_2}+C\frac{{\mathrm{du}}_c\left(t\right)}{\mathrm{dt}}\end{array}\right.---\left(2\right)$

Wherein: OCV (t) is the function of SOC (t).

According to the above-mentioned math equation that is obtained by the battery equivalent-circuit model (2), can obtain corresponding output voltage V (k) and capacitance terminal voltage u _c(k) discrete magnitude relational expression:

V(k)=OCV(k)-R ₁i(k)-u _c(k) （3）

u _c(k)=k ₂u _c(k-1)+k ₁i(k) （4）

Wherein:

${\mathrm{<figure-callout\; id="1"\; label="k"\; filenames="HDA00002897014700011.png,HDA00002897014700042.png"\; state="\{\{state\}\}">k</figure-callout>}}_1=\frac{R_{2}T}{T+R_{2}C},$ ${\mathrm{<figure-callout\; id="2"\; label="K"\; filenames="HDA00002897014700041.png,HDA00002897014700042.png"\; state="\{\{state\}\}">K</figure-callout>}}_2=\frac{R_{2}C}{T+R_{2}C}$

T is the sampling period.

Neural network OCV(k) prediction model as shown in Figure 7, comprises three neuron node J ₁, J ₂And J ₃, and the first radial basis function neural network RBF1.Discrete digital amount OCV(k in this model), V (k), i (k) and u _c(k) analog quantity OCV (t), V (t), i (t) and the u in difference corresponding equation (2) _c(t).The output of three neuron nodes is respectively capacitance terminal voltage u _c(k), resistance R ₁Terminal voltage and equivalent electrical circuit output voltage V (k).The first radial basis function neural network RBF1 completes the On-line Estimation to battery open end voltage OCV (k).The digital collection electrokinetic cell obtains the discrete digital amount of the battery terminal voltage of actual measurement

With discrete digital amount current i (k), as the input value of this model.

Peripheral sensory neuron node J ₁Ask for capacitance terminal voltage u _c(k).z ^-1For to the back operator, the u of peripheral sensory neuron node output _c(k) pass through z ^-1Obtain corresponding u _c(k-1).The peripheral sensory neuron node is according to formula u _c(k)=k ₂u _c(k-1)+k ₁I (k) is by weighting coefficient k ₁And k ₂Respectively to gathering the i(k of gained) and u _c(k-1) be weighted summation, obtain exporting u _c(k).

The node J of nervus opticus unit ₂According to collecting i (k) and Model Parameter k ₃Obtain R ₁On voltage be k ₃* i (k), k ₃Expression R ₁

The node J of third nerve unit ₃Calculate equivalent electrical circuit output voltage V (k) estimated value, V (k)=OCV (k)-R ₁I (k)-u _c(k), namely the weighting coefficient of this node summation operation is respectively 1, and-1 and-1.

The output OCV(k of the first radial basis function neural network RBF1) be the kinematic function of current i (k), equivalent electrical circuit output voltage V (k).OCV(k) pass through z ^-1Obtain OCV (k-1) to the back operator, OCV (k-1) is as external feedback, with the last sampling instant value V (k-1) of V (k) and i (k) input signal as the first radial basis function neural network RBF1, the first radial basis function neural network RBF1 is described voltage V (k), current i (k) and kinematic nonlinearity characteristic OCV(k), the battery equivalent-circuit model is replenished and expands, and the first radial basis function neural network RBF1 is output as battery open end voltage OCV (k).

Neural network OCV(k) the weights k of prediction model ₁, k ₂And k ₃Study, estimate resulting cell output voltage V(k with this model) with the battery terminal voltage of actual measurement

Poor, consist of neural network OCV(k) the learning objective function of prediction model.Adopt ripe method of steepest descent, i.e. gradient method is by neural network OCV(k) prediction model, parameters R in the electrokinetic cell of identification simultaneously equivalent-circuit model ₁, R ₂With C and state OCV(k), realize the On-line Estimation of OCV (k).Its neural network OCV(k) the Parameter Self-learning adjustment capability of prediction model can adapt to the otherness of electrokinetic cell on characteristic of different capabilities.

Second step, according to OCV(k) to the complicated Hysteresis Nonlinear error compensation of state of charge, estimate current SOC(k) be worth

This step has designed the Dynamic Hysteresis mixture model that simple dynamic hysteresis model-SDH model and the second radial basis function neural network RBF2 are composed in series, as shown in Figure 8.

The expression formula of SDH model is as follows:

$\left\{\begin{array}{c}y\left(t\right)=\mathrm{\&eta;}\left(t\right)-\frac{1}{{\mathrm{<figure-callout\; id="4"\; label="k"\; filenames="HDA00002897014700041.png,HDA00002897014700042.png"\; state="\{\{state\}\}">k</figure-callout>}}_4}\mathrm{OCV}\left(t\right)\\ \stackrel{\&CenterDot;}{\mathrm{\&eta;}}\left(t\right)=k_4(\mathrm{OCV}\left(t\right)-\mathrm{\&Delta;}\left(\mathrm{\&eta;}\left(t\right)\right))\end{array}\right.---\left(5\right)$

Wherein

$\mathrm{\&Delta;}\left(\mathrm{\&eta;}\left(t\right)\right)=\left\{\begin{array}{cc}{k}_4(\mathrm{\&eta;}\left(t\right)-1)& \mathrm{\&eta;}\left(t\right)>1\\ 0& \left|\mathrm{\&eta;}\left(t\right)\right|\&le;1\\ k_4(\mathrm{\&eta;}\left(t\right)+1)& \mathrm{\&eta;}\left(t\right)<-1\end{array}\right.---\left(6\right)$

Wherein, OCV (t) is the On-line Estimation value that the first step obtains, and is the input of SDH model, and y (t) is the output of SDH model.

Parameter K 4 in the SDH model determines the retardant curve of SDH model and actual SOC(t)-OCV(t) sluggish relation curve is at the similarity degree of contour shape, and also decision is connected on the on-line study speed of the learning process of SDH model the second radial basis function neural network RBF2 afterwards.

Can obtain corresponding discrete model by the SDH model.Analog quantity OCV (t) and y (t) be corresponding discrete digital amount OCV (k) and y (k) respectively, and T is the sampling period.

The expression formula of SDH discrete model is as follows:

$\left\{\begin{array}{c}y\left(k\right)=\mathrm{\&eta;}\left(k\right)-\frac{1}{{\mathrm{<figure-callout\; id="4"\; label="k"\; filenames="HDA00002897014700041.png,HDA00002897014700042.png"\; state="\{\{state\}\}">k</figure-callout>}}_4}\mathrm{OCV}\left(k\right)\\ \frac{\mathrm{\&eta;}\left(k\right)-\mathrm{\&eta;}(k-1)}{T}=k_4(\mathrm{OCV}\left(k\right)-\mathrm{\&Delta;}\left(\mathrm{\&eta;}\left(k\right)\right))\end{array}\right.$

After arrangement:

$\left\{\begin{array}{c}y\left(k\right)=\mathrm{\&eta;}\left(k\right)-\frac{1}{{\mathrm{<figure-callout\; id="4"\; label="k"\; filenames="HDA00002897014700041.png,HDA00002897014700042.png"\; state="\{\{state\}\}">k</figure-callout>}}_4}\mathrm{OCV}\left(k\right)\\ \mathrm{\&eta;}\left(k\right)=\mathrm{\&eta;}(k-1)+T\&times;k_4(\mathrm{OCV}\left(k\right)-\mathrm{\&Delta;}\left(\mathrm{\&eta;}\left(k\right)\right))\end{array}\right.---\left(7\right)$

Wherein

$\mathrm{\&Delta;}\left(\mathrm{\&eta;}\left(k\right)\right)=\left\{\begin{array}{cc}{k}_4(\mathrm{\&eta;}\left(k\right)-1)& \mathrm{\&eta;}\left(k\right)>1\\ 0& \left|\mathrm{\&eta;}\left(k\right)\right|\&le;1\\ k_4(\mathrm{\&eta;}\left(k\right)+1)& \mathrm{\&eta;}\left(k\right)<-1\end{array}\right.---\left(8\right)$

The SDH model has the monocycle of description, is the non-smooth lagging characteristics of outer shroud, as shown in Figure 9, its figure below horizontal ordinate is time t, its ordinate is the amplitude of SDH mode input signal OCV (t), the big or small time dependent curve of the input signal OCV (t) of expression SDH model, upper figure horizontal ordinate is the amplitude of SDH mode input signal OCV (t), and its ordinate is SDH model output signal y (t), the input of expression SDH model, the relation curve of output signal.The SDH model also has the description secondary ring, namely in many rings lagging characteristics of ring, as shown in figure 10, the parameter that represents of coordinate is identical with Fig. 9 in length and breadth for upper figure below of Figure 10.

The SDH model is as the preposition part of Dynamic Hysteresis mixture model, and the second radial basis function neural network RBF2 that connects after the SDH model builds the Dynamic Hysteresis mixture model.The OCV (k) that the resulting y of SDH model (k) and the first step obtain, the previous moment value OCV (k-1) of OCV (k) are as the input of the second radial basis function neural network RBF2, learn by the weighting in RBF2, realize the Nonlinear Mapping of any single-valued correspondence, the k of Indirect method SDH model ₄Parameter is to approach actual SOC(k) and OCV(k) complicated sluggish relation, finally export the SOC (k) of estimation on line.

Described Dynamic Hysteresis mixture model not only can represent sluggish outer shroud, and namely main ring, also can describe sluggish a plurality of secondary ring, i.e. interior ring.When electrokinetic cell was used for the electric automobile of dynamic operation, this Dynamic Hysteresis mixture model can be described outer shroud and a plurality of interior ring property that the non-smooth complicated lagging characteristics in the charging, discharging electric batteries Stochastic Dynamic Process shows simultaneously.

This routine Dynamic Hysteresis mixture model adopts ripe steepest decline technology to complete the study of the second radial basis function neural network RBF2 first according to experimental data, adopts the output estimation SOC(k of the Dynamic Hysteresis mixture model that has trained) value.

The state of charge estimating system of the non-smooth lagging characteristics compensation of electrokinetic cell

The state of charge evaluation method of smooth lagging characteristics compensation non-according to upper routine electrokinetic cell, set up the state of charge estimating system embodiment of the non-smooth lagging characteristics compensation of this electrokinetic cell, system architecture comprises microprocessor, current sensor, voltage sensor, analog to digital converter (AD converter), program storage, programmable storage, timer and display as shown in figure 11.Current sensor and voltage sensor output are through analog to digital converter access microprocessor, microprocessor linker storer, programmable storage, timer and display.This routine display is LCD display.

Storage neural network OCV(k in program storage) calculation procedure of prediction model, the SDH model Dynamic Hysteresis mixture model of connecting with the second radial basis function neural network RBF2, store the parameter in OCV (k) prediction model and SDH model in programmable storage, current sensor and voltage sensor are installed in electrokinetic cell and load connecting circuit, the load current of the electrokinetic cell that current sensor and voltage sensor are measured and terminal voltage obtain corresponding load current and the digital quantity of voltage and send into microprocessor by analog to digital conversion.With the SOC(k in the timer control program storer) the estimation program operation that starts and interrupt, the current SOC(k of the operation result of microprocessor) estimated value, show in real time by display.

Above-described embodiment is only the specific case that purpose of the present invention, technical scheme and beneficial effect are further described, and the present invention is defined in this.All any modifications of making, be equal to replacement, improvement etc., within all being included in protection scope of the present invention within scope of disclosure of the present invention.

Claims (5)

1. The method of estimating the state of charge of the non-smooth hysteresis characteristic compensation of the power battery is to estimate the state of charge SOC(k) of the power battery in two steps. The first step is to collect the power battery to obtain the discrete digital value of the actually measured battery terminal voltage

and the discrete digital current i(k), to estimate the discrete digital OCV(k) of the battery open-circuit terminal voltage online; the second step is to compensate the state of charge hysteresis nonlinear error according to OCV(k), and estimate the current SOC( k) value; The first step, the online estimation of the discrete digital quantity OCV(k) of the open circuit terminal voltage of the battery According to the battery equivalent circuit model, the discrete relationship between output voltage V(k) and battery open terminal voltage OCV(k), current i(k) and capacitor terminal voltage _uc (k), and the relationship between capacitor terminal voltage _uc (k) and Discrete relationship of i(k): V(k)=OCV(k)-R ₁ i(k)-u _c (k) u _c (k)=k ₂ u _c (k-1)+k ₁ i(k) in: ${\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; T</span>}}{\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; C</span>}}<span\; class="notranslate">\; ,</span>$ ${\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; C</span>}}{\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; C</span>}}$ T is the sampling period; Construct a neural network OCV(k) estimation model corresponding to the above formula, including three neuron nodes (J ₁ , J ₂ , J ₃ ) and the first radial basis function neural network (RBF1), digitally collect power batteries, and actually measure A discrete digital quantity of the battery terminal voltage

and discrete digital current i(k), as the input value of this model; The first neuron node (J ₁ ) calculates the capacitive terminal voltage u _c (k); z ^-1 is the forward step operator, and the output u _c (k) of the first neuron node (J ₁ ) is obtained through z ^-1 Corresponding to u _c (k-1), the first neuron node (J ₁ ) passes the weighting coefficient k ₁ and k according to the formula u _c (k)=k ₂ u _c (k-1)+k ₁ i(k) ₂ Carry out weighted summation on i(k) and u _c (k-1) obtained by digital acquisition respectively, and obtain the output u _c (k); The second neuron node (J ₂ ) obtains i(k) according to the digital acquisition and the parameter k ₃ in the model to obtain the voltage on R ₁ as k ₃ ×i(k), and k ₃ represents R ₁ ; The third neuron node (J ₃ ) calculates the estimated value of the equivalent circuit output voltage V(k), V(k)=OCV(k)-R ₁ i(k)-u _c (k), that is, the node sums The weighting coefficients of the operation are 1, -1, and -1 respectively; The output OCV(k) of the first radial basis function neural network (RBF1) is a dynamic function of the current i(k) and the output voltage V(k) of the equivalent circuit; OCV(k) is a step forward operator through z ^-1 Get OCV(k-1), OCV(k-1) is used as external feedback, and V(k-1) and i(k) at the previous sampling time of V(k) are used as the first radial basis function neural network (RBF1) input signal, the first radial basis function neural network (RBF1) describes the dynamic nonlinear characteristics of voltage V(k), current i(k) and OCV(k), and supplements the battery equivalent circuit model And expanded, the output of the first radial basis function neural network (RBF1) is the battery open-circuit terminal voltage OCV(k); The second step is to estimate the current SOC (k) value based on the OCV (k) compensation for the complex hysteresis nonlinear error of the state of charge In this step, a simple dynamic hysteresis model—SDH model and the second radial basis function neural network (RBF2) are used in series to form a dynamic hysteresis hybrid model; The expression of the SDH discrete model is as follows: $\left\{\begin{array}{c}\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; \&eta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}}\mathrm{<span\; class="notranslate">\; OCV</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span>\\ \mathrm{<span\; class="notranslate">\; \&eta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; \&eta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; OCV</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&eta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>\end{array}\right.$ $\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&eta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&eta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>& \mathrm{<span\; class="notranslate">\; \&eta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ></span>\mathrm{<span\; class="notranslate">\; 1</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& <span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; \&eta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 1</span>}\\ {\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&eta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>& \mathrm{<span\; class="notranslate">\; \&eta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; <</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}\end{array}\right.$ Among them, OCV(k) is the online estimated value obtained in the first step, which is the input of the SDH model, y(k) is the output of the SDH discrete model, and T is the sampling period; The y(k) obtained by the SDH model and the OCV(k) obtained in the first step, and the value OCV(k-1) of the previous sampling time of OCV(k) are used as the input of the second radial basis function neural network (RBF2) , through the weighted learning in the second radial basis function neural network (RBF2), realize the nonlinear mapping corresponding to any single value, and indirectly adjust the k ₄ parameters of the SDH model to approximate the actual SOC (k) and OCV (k) Complex hysteresis relationship, and finally output the online estimated SOC(k). 2. The method for estimating the state of charge of the power battery non-smooth hysteresis characteristic compensation according to claim 1, characterized in that: In the first step, the learning of the weights k ₁ , k ₂ and k ₃ of the neural network OCV(k) estimation model is used to estimate the obtained battery output voltage V(k) and the actual measured battery terminal voltage with this model

The difference constitutes the learning objective function of the neural network OCV(k) prediction model; using the mature steepest descent method, the neural network OCV(k) prediction model is used to identify the parameters R ₁ , R in the equivalent circuit model of the power battery ₂ and C, and state OCV(k), realize online estimation of OCV(k). 3. The method for estimating the state of charge of the power battery non-smooth hysteresis characteristic compensation according to claim 1, characterized in that: The SDH model in the second step has the non-smooth hysteresis characteristic describing a single loop, namely the outer loop, and also has the multi-loop hysteresis characteristic describing the secondary loop, namely the inner loop. 4. The method for estimating the state of charge of power battery non-smooth hysteresis characteristic compensation according to claim 1, characterized in that: In the second step, the second radial basis function neural network (RBF2) first uses the mature steepest descent technique to complete the learning based on the experimental data, and uses the output of the trained dynamic hysteresis hybrid model to estimate the SOC(k) value. 5. According to the method for estimating the state of charge of the non-smooth hysteresis characteristic compensation of the power battery according to any one of claims 1 to 5, the designed state of charge estimation system for the non-smooth hysteresis characteristic compensation of the power battery is characterized in that: Including microprocessor, current sensor, voltage sensor, analog-to-digital converter, program memory, programmable memory, timer and display; the output of current sensor and voltage sensor is connected to the microprocessor through the analog-to-digital converter, and the microprocessor is connected to the program memory, programmable memory, timer and display; Store the neural network OCV(k) prediction model in the program memory, the calculation program of the dynamic hysteresis hybrid model in series with the SDH model and the second radial basis function neural network (RBF2), and store the OCV(k) prediction model in the programmable memory And the parameters in the SDH model, the current sensor and the voltage sensor are installed in the connection circuit between the power battery and the load, and the load current and terminal voltage of the power battery measured by the current sensor and the voltage sensor are obtained through analog-to-digital conversion to obtain the corresponding load current and voltage The digital quantity is sent to the microprocessor; the timer controls the SOC (k) estimation program in the program memory to start and interrupt the operation, and the current SOC (k) estimation value of the microprocessor operation result is displayed in real time through the display.

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