CN116047339A - Lithium ion battery pack SOC estimation method and device based on thermoelectric coupling model - Google Patents
Lithium ion battery pack SOC estimation method and device based on thermoelectric coupling model Download PDFInfo
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Abstract
The invention discloses a lithium ion battery pack SOC estimation method and device based on a thermoelectric coupling model. Firstly, establishing the characteristics of a battery pack; modeling the battery pack; then, identifying the model parameters of the battery pack; finally, estimating the SOC of the battery pack; the invention provides an average model part which adopts a thermoelectric coupling model structure, so as to consider the influence caused by the change of the working temperature of the battery pack; the average model and the difference model are estimated respectively by adopting double time scales, so that the estimation frequency of the difference model can be reduced, and the calculated amount of the system is further reduced; on the basis, an important battery method is combined, the electric quantity of each single battery in the group is monitored, and when the electric quantity of a certain battery is higher/lower, electric quantity reminding is set, so that the problems of overcharging/discharging of the battery in the group and the like are avoided.
Description
Technical Field
The invention relates to the field of battery state of charge estimation, in particular to a lithium ion battery pack SOC estimation method and device based on a thermoelectric coupling model.
Background
The SOC of the power battery of the electric automobile is equal to the oil meter of the fuel automobile, is an important parameter of a key technology and a battery management system of the electric automobile, and directly influences the energy management control strategy of the automobile and the performance of the electric automobile, thereby influencing the reliability and the cost of the automobile. On the one hand, it can provide the driver with important information about the driving range. On the other hand, it can also prevent the battery from being overcharged and overdischarged, and is convenient for the management and maintenance of the battery pack. However, SOC estimation of a battery pack is mainly affected by three aspects: firstly, the SOC cannot be directly measured, and because complex chemical reaction processes exist in the battery, the chemical characteristics of the battery raw materials also enable the use process of the battery to be highly nonlinear, and the external characteristics such as battery terminal voltage, charge/discharge current and the like can be read through an instrument, but the SOC of the battery cannot be obtained through measurement and can only be obtained through an estimation mode; the second point is inconsistency of the battery pack, because of the limitation of electrode potential and materials, the voltage and capacity of the single batteries cannot fully meet the energy requirement of the electric automobile, a large number of batteries are connected in series and parallel to form a power battery pack, and ideally each single battery leaving the production line has the same performance in the whole service life, however in actual situations, it is difficult to ensure that the initial performance parameters of the single batteries are fully consistent due to factors such as manufacturing process, and the inconsistency further aggravates the difference of the batteries in the use process; the third point is that the SOC of the battery pack is affected by temperature, the battery pack generates heat during operation, and the heat which is not released in time is absorbed by the battery itself, so that the operating temperature is increased, and the temperature further affects the battery parameters. It is difficult to accurately acquire the battery pack SOC.
The battery pack SOC estimation method is not only used for considering the inconsistency of the battery packs, but also cannot bring excessive calculation load to a battery management system. The battery pack SOC estimation methods are mainly classified into three types: large battery method, important battery method, average difference method. The big battery method is used for assuming that all the single batteries in the battery pack have similar characteristics, the battery pack can be simply regarded as a single battery with large capacity, the SOC of the battery pack is calculated based on the estimation method of the single battery SOC, and although the big battery method has lower calculated amount, the inconsistency among the single batteries in the battery pack is ignored, and the SOC of the battery pack cannot be accurately estimated. The important battery method is to use important single batteries in the battery pack to represent the performance of the battery pack, and the important battery generally selects the weakest battery, namely the battery with the lowest voltage in the discharging period and the highest voltage in the charging period, so that the important battery method can effectively protect the operation safety of the power battery pack, but when the SOC works within the range of 30% -80%, the method can reduce the energy utilization rate of the battery pack. The average difference method is to consider a battery pack as an average model and a difference model, and estimate an average model SOC and a difference model delta SOC respectively, wherein delta SOC represents the difference of each single battery compared with the average model.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a lithium ion battery pack SOC estimation method and device based on a thermoelectric coupling model.
One aspect of the invention provides a lithium ion battery pack SOC estimation method based on a thermoelectric coupling model, which comprises the following steps:
step one, establishing battery pack characteristics;
assuming that each battery in the battery pack has consistent open-circuit voltage at the same ambient temperature and the same SOC, normalizing the open-circuit voltage of the battery pack;
and an average open circuit voltage normalization model is adopted to simulate the average open circuit voltage characteristic of the battery pack, and the open circuit voltage difference between each single cell in the battery pack is converted into the difference between each single cell and the open circuit voltage normalization model.
Step two, building a battery pack model;
the thermoelectric coupling model structure serving as the average model part comprises an average equivalent circuit model and an average thermal model, so that the average model can consider the temperature change in the working process of the battery and the influence of the temperature change on the battery parameters.
Step three, identifying parameters of a battery pack model;
based on the HPPC working condition test data, the average model and the difference model parameters are identified.
Estimating the SOC of the battery pack;
estimating the SOC of the battery pack in the charge and discharge processes of the battery pack, measuring the current I of the battery pack connected in series by adopting a current sensor, and measuring the terminal voltage U of each single battery by adopting a voltage sensor t i And the battery pack terminal voltage U pack Measuring average operating temperature of a battery using a limited temperature sensor
Substituting the measured data into ISH-AEKF method to estimate average charge state of battery
Difference Δsoc between average states of battery packs i 。
Another aspect of the present invention provides a lithium ion battery pack SOC estimation apparatus based on a thermoelectric coupling model, the apparatus comprising a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the lithium ion battery pack SOC estimation method.
The invention has the following beneficial effects: aiming at the inconsistency of the batteries in the series battery pack caused by the change of the battery temperature along with the time in the running process of the series battery pack, the invention provides an average model part which adopts a thermoelectric coupling model structure, thereby considering the influence caused by the change of the battery pack working temperature; the average model and the difference model are estimated respectively by adopting double time scales, so that the estimation frequency of the difference model can be reduced, and the calculated amount of the system is further reduced; on the basis, an important battery method is combined, the electric quantity of each single battery in the group is monitored, and when the electric quantity of a certain battery is higher/lower, electric quantity reminding is set, so that the problems of overcharging/discharging of the battery in the group and the like are avoided.
Drawings
FIG. 1 is a modified average difference model;
FIG. 2 is a flow chart of model parameter identification;
FIG. 3 is a schematic diagram of a battery pack SOC estimation;
FIG. 4 is an overcharge and overdischarge monitoring schematic;
FIG. 5 is a two-dimensional differential open circuit voltage curve;
FIG. 6 is a differential open circuit voltage normalization model;
FIG. 7 is a flow chart of the ISH-AEKF algorithm.
Detailed Description
The invention combines the thermoelectric coupling model with the average difference method, so that the invention has the following three characteristics: firstly, an average model part adopts a thermoelectric coupling model structure, so that the influence caused by the change of the working temperature of the battery pack is considered; secondly, the average model and the difference model are estimated respectively by adopting double time scales, so that the estimation frequency of the difference model can be reduced, and the calculated amount of the system is further reduced; thirdly, an important battery method is combined on the basis, the electric quantity of each single battery in the group is monitored, and when the electric quantity of a certain battery is higher/lower, electric quantity reminding is set, so that the problems of overcharging/discharging of the battery in the group and the like are avoided.
In one embodiment of the present application, a method for estimating SOC of a lithium ion battery pack based on a thermoelectric coupling model is provided, including the steps of:
s1: to build a more accurate battery model, the electrical and thermal properties of the battery are explored.
In order to simplify the structure of the battery pack model, the embodiment assumes that each battery in the battery pack has a consistent open-circuit voltage at the same ambient temperature and the same SOC, and normalizes the open-circuit voltage of the battery pack and explores the influence of temperature and SOC. The normalized model can be used to simulate the open circuit voltage characteristics of each cell, but due to the non-uniformity of the battery pack during operation, each cell in the pack is in a different SOC state, resulting in a difference between the open circuit voltages of each cell. Aiming at the problem of variability, an average open circuit voltage normalization model (Mean Open Circuit Voltage, MOCV) is adopted to simulate the average open circuit voltage characteristic of the battery pack, and the open circuit voltage difference among the single cells in the battery pack is converted into the difference between the single cells and the open circuit voltage normalization model.
Further, in order to explore the difference between the cells and MOCV in the battery pack, a two-dimensional curve is drawn as shown in fig. 5, wherein the curve represents a normalized model of open circuit voltage at a certain ambient temperature, and the average state of the battery pack is assumed to be at the point (m, n) in the normalized model, i.e. the average SOC of the battery pack is
Average open circuit voltage->
The state of the ith battery in the battery pack is at point P, and the difference SOC between the state of the ith battery in the battery pack and the average state of the battery pack is delta SOC i =a, differential open circuit voltage Δu oc i When =b, the SOC of the battery is +.>
Open circuit voltage is +.>
In the figure, the battery state is lower than the average state of the battery pack, so the values of a and b are negative; similarly, the state of the jth battery in the group is at the point Q, the battery state is higher than the average state of the battery group, and the values of c and d are positive.
Differential open circuit voltage DeltaU of each single battery in group oc i And delta SOC i The same correspondence exists, and a differential open circuit voltage normalization model (Differential Open Circuit Voltage, DOCV) can be also explored. In this embodiment, the i-th cell in the group is taken as an example to explore the correspondence, and analysis is performed with respect to two limit values of the point (0, x) and the point (100, y). When the point (m, n) moves to (0, x), the i-th battery cannot be lower than the average state of the battery pack, otherwise, the SOC of the i-th battery is less than 0%, i.e.
Such a case is meaningless. Similarly, when the point (m, n) moves to (100, y), the i-th battery can no longer be higher than the average state of the battery pack, otherwise, the SOC of the i-th battery will be greater than 100%, i.e. +.>
Therefore, ΔU should be explored on the premise that the SOC of each battery in the group is significant oc i And delta SOC i 、
The relation between the two is that the SOC is simultaneously 0 percent or less i Less than or equal to 100 percent and%>
Also taking 25 ℃ as an example, a three-dimensional curved surface of a DOCV is shown in fig. 6.
The temperature of each single battery in the battery pack is increased due to heat absorption in the working process, and when the battery stands still, the battery does not generate heat any more, and the temperature can be slowly reduced. Because of the difference of the battery positions and the temperature change, the temperature of the middle battery is slightly higher than that of the batteries distributed on the outer side, and because the heat generated by the batteries at the middle position cannot be timely taken away, the heat absorption of the middle battery is slightly higher, but the difference is small, and the overall temperature change trend of the batteries in the battery pack is consistent, so that the embodiment only considers the overall temperature change of the battery pack when estimating the SOC of the battery pack.
S2: the thermoelectric coupling model structure serving as the average model part comprises an average equivalent circuit model and an average thermal model, so that the model can consider the temperature change in the working process of the battery and the influence of the temperature change on the battery parameters, and the model structure is shown in figure 1.
The average equivalent circuit model is shown in the upper left half of figure 1,
is a group ofThe average terminal voltage of all the series batteries in the battery pack can be calculated by the measured terminal voltage of the battery pack and the number of the batteries in the battery pack, and the expression is as follows: />
Wherein U is pack Measuring terminal voltage for the series battery; u (U) t i The voltage of the measuring terminal of the ith single battery in the series battery pack; n is the total number of battery cells in the battery string.
The parameters in the average equivalent circuit model have the following relationship:
wherein I is the current of the series battery pack;
is the average open circuit voltage, which can be derived from MOCV;
For average ohmic resistance, U 1 、U 2 Characterization of R 1 C 1 、R 2 C 2 Polarization voltage of two loops.
As shown in the lower left average thermal model of fig. 1, T f Representing the ambient temperature of the battery pack, the temperature variation of the battery pack is considered in the average model because the overall variation trend of the battery pack temperature is the same and the temperature difference among the monomers is not large,
represents the average surface temperature in the battery, +.>
Represents the average core temperature of the battery pack,/-, and>
indicating the average heat generation during operation of the battery. The relational expression of the average thermal model is:
wherein C is c For the average core heat capacity of the battery pack, C s R is the average surface heat capacity c R is the average heat conduction resistance u Is the average convective thermal resistance.
As can be seen from the difference model on the right side of fig. 1, the difference model terminal voltage Δu of the ith unit cell t i From differential open circuit voltage DeltaU oc i And a differential internal resistance DeltaR i The terminal voltage expression of the differential model is:
ΔU t i ＝ΔU oc i -IΔR i (4)
the relational expression between each parameter and the average model is:
wherein U is oc i Open circuit voltage of the ith single battery in the series battery pack; r is R 0 i Is the ohmic resistance of the i-th unit cell.
S3: based on HPPC working condition test data, identifying parameters of an average model and a difference model, wherein the parameters to be identified of the average model have average open-circuit voltage
Average ohmic resistance->
Average internal polarization resistance R 1 And R is R 2 Average polarization capacitance C 1 And C 2 Average core heat capacity C c Average surface heat capacity C s Average heat conduction resistance R c Average convection thermal resistance R u The parameters to be identified by the differential model have differential open circuit voltage delta U oc i Differential ohmic resistance DeltaR i 。
In a preferred embodiment of the present application, the identification process is shown in fig. 2, and includes:
(1) Performing experiments on the battery pack, performing HPPC working condition tests at different ambient temperatures, and measuring the current I of the battery pack and the terminal voltage U of each single battery t i And the battery pack terminal voltage U pack Average operating temperature of battery pack
(2) Identify in FIG. 2
Obtaining an average model at each temperature by fitting>
And->
MOCV in state, i.e. +.>
The expression is:
(3) Calculating the average terminal voltage of the battery pack by using (1)
(4) On the basis of MOCV, ΔU was investigated according to the procedure one oc i And delta SOC i 、
Performing surface fitting on the relationship of each single battery, and identifying to obtain the DOCV of each single battery, namely +.>
The expression is:
(5) Measuring voltage U by each cell t i Terminal voltage of average model
The terminal voltage delta U of each single battery difference model can be calculated t i . In addition, the average terminal voltage is +.>
Average open circuit voltage->
The average heat generation amount of the battery pack can be calculated by the current I of the battery pack connected in series>
For identifying the average thermal model parameters.
(6) Identifying the difference Europe of each single battery difference model by adopting intelligent genetic algorithm or recursive least square method and other algorithmsThe resistance DeltaR i . For differential resistance DeltaR i In other words, it exists to reduce the influence of the difference in internal resistance on the model accuracy with little change in a short time, so it is assumed that the difference internal resistance Δr of the ith battery i The value in the working process is fixed, and the voltage delta U is controlled by the difference end t i Differential open circuit voltage DeltaU oc i And identifying the current I of the series battery pack, wherein the expression is as follows:
(7) According to average terminal voltage
Average open circuit voltage->
With the current I of the series battery pack, an intelligent genetic algorithm or a recursive least square method and other algorithms are adopted to identify the average ohmic resistance in the average model>
Polarization internal resistance R 1 And R is R 2 Polarization capacitor C 1 And C 2 。
(8) Average kernel heat capacity C in average model c Average surface heat capacity C s Average heat conduction resistance R c Average convection thermal resistance R u According to the ambient temperature T of the battery pack f Average temperature with battery pack
And (5) identifying.
S4: estimating the SOC of the battery pack in the charge and discharge processes of the battery pack, measuring the current I of the battery pack connected in series by adopting a current sensor, and measuring the terminal voltage U of each single battery by adopting a voltage sensor t i And the battery pack terminal voltage U pack Measuring average operating temperature of a battery using a limited temperature sensor
Substituting the measured data into the ISH-AEKF method to estimate +.>
ΔSOC i The estimation flow is shown in fig. 3.
The ISH-AEKF algorithm is improved on the basis of the adaptive extended Kalman filtering algorithm, and by simplifying the covariance of process noise and the covariance of measurement noise, a part of the covariance is reserved, and the expression is:
R k ＝(1-d k-1 )R k-1 +d k-1 (eke k T ) (12)
Q k ＝(1-d k-1 )Q k-1 +d k-1 (K k e k e k T K k T ) (13)
the ISH-AEKF method not only has high accuracy, but also has estimation errors completely falling in an error reference line, and an estimation flow chart is shown in figure 7.
In some embodiments of the present application, the average model is estimated by using an ISH-AEKF method, and the polarization voltage U is selected 1 Polarization voltage U 2 Average state of charge of battery
Average core temperature of battery->
And average surface temperature of the battery>
As state variable x. The average state of charge of the battery is an indispensable basic parameter as the estimated subject, and the average core temperature and the average surface temperature of the battery are the sources of the parameter changes of the battery related to the temperature. Selecting average terminal voltage of battery pack>
And average surface temperature>
As the system output quantity, the battery pack SOC state may be estimated by measuring the output quantity.
And (3) making:
model output y is known from equivalent circuit model structure and thermal model structure k ：
Wherein:
wherein: Δt is the sampling period of the average model.
Step 4.3: delta SOC of difference model i The estimation adopts an ISH-AEKF method, and the state equation is as follows:
wherein: delta T d Is the difference ofSampling period of different model.
As an embodiment, because of the inconsistency of the battery packs, the battery in the battery packs has a risk of overcharge and overdischarge, when the battery packs are in a low/high state of charge, two single batteries with the lowest/high residual charge in the battery packs should be more concerned, and therefore, a monitoring module is provided to remind the state of the important battery, and a schematic diagram is shown in fig. 4.
Delta SOC in differential model by ISH-AEKF method based on state equation i An estimation is made. As shown in the estimation step flow block in fig. 3, the present embodiment proposes a method of performing the estimation step flow block
Calculating battery pack SOC pack And pass through SOC i And detecting the batteries in the group in real time. Because of the inconsistency of the battery packs, there is a risk of overcharging and overdischarging of the battery cells in the battery packs, when the battery packs are in a low/high state of charge, more attention should be paid to the two battery cells having the lowest/high remaining charge in the battery packs. Because the open circuit voltage of the battery has a higher slope and a quicker change when the residual electric quantity is higher than 80% and lower than 20%, the embodiment proposes to set the monitoring module to remind the state of the important battery. When the battery with the lowest/high remaining capacity is lower/higher than 20%/80% during the discharging/charging process of the battery pack, i.e., min (SOC i ) Less than or equal to 20% or max (SOC) i ) And when the number of the battery is more than or equal to 80%, the monitoring module can remind a user and reduce the step length estimated by the difference model, so that the problem of overdischarge in the battery process is avoided.
In this embodiment, for the problem of temperature change and the influence of temperature change on model parameters in the working process of the battery pack, the lithium ion battery pack is taken as a research object, the thermoelectric coupling model is combined with the traditional average difference model according to the characteristics of the battery pack, the operating temperature of the battery pack is estimated while the battery pack is estimated by adopting an ISH-AEKF method with higher accuracy and robustness, and model parameters are updated according to the operating temperature of the battery.
The application also discloses a lithium ion battery pack SOC estimation device based on the thermoelectric coupling model, which comprises a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the thermoelectric coupling model-based lithium ion battery pack SOC estimation method.
At the hardware level, the electronic device comprises a processor, optionally an internal bus, a network interface, a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least 1 disk Memory. Of course, the electronic device may also include hardware processors, network interfaces, and memory required by other services may be interconnected by an internal bus, which may be an ISA (Industry Standard Architecture ) bus, PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be divided into address buses, data buses, control buses, etc.
And the memory is used for storing programs. In particular, the program may include program code including computer-operating instructions. The memory may include memory and non-volatile memory and provide instructions and data to the processor
The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form the lithium ion battery pack SOC estimation device based on the thermoelectric coupling model on a logic level. And a processor executing a program stored in the memory, the program being configured to perform the above-described lithium ion battery pack SOC estimation method based on the thermoelectric coupling model.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing is merely a specific embodiment of the invention and other modifications and variations can be made by those skilled in the art in light of the above teachings. It is to be understood by persons skilled in the art that the foregoing detailed description is provided for the purpose of illustrating the invention more fully, and that the scope of the invention is defined by the appended claims.
It is to be understood that the above examples are for clarity of illustration of the workflow of the invention and are not limiting of the embodiments. Various changes and modifications may be made by one skilled in the art in light of the above-described embodiments. It is not necessary for all embodiments to be exhaustive. And obvious variations or modifications thereof are contemplated as falling within the scope of the present invention.
Claims (8)
1. The lithium ion battery pack SOC estimation method based on the thermoelectric coupling model is characterized by comprising the following steps of: the method comprises the following steps:
step one, establishing battery pack characteristics;
assuming that each battery in the battery pack has consistent open-circuit voltage at the same ambient temperature and the same SOC, normalizing the open-circuit voltage of the battery pack;
the average open circuit voltage normalization model is used for simulating the average open circuit voltage characteristic of the battery pack, and the open circuit voltage difference between each single cell in the battery pack is converted into the difference between each single cell and the open circuit voltage normalization model;
step two, building a battery pack model;
the thermoelectric coupling model structure serving as the average model part comprises an average equivalent circuit model and an average thermal model, so that the average model can consider the temperature change in the working process of the battery and the influence of the temperature change on the battery parameters;
step three, identifying parameters of a battery pack model;
based on HPPC working condition test data, identifying average model and difference model parameters;
estimating the SOC of the battery pack;
estimating the SOC of the battery pack in the charge and discharge processes of the battery pack, measuring the current I of the battery pack connected in series by adopting a current sensor, and measuring the terminal voltage U of each single battery by adopting a voltage sensor t i And the battery pack terminal voltage U pack Measuring average operating temperature of a battery using a limited temperature sensor
Substituting the measured data into ISH-AEKF method to estimate average charge state of battery
Difference Δsoc between average states of battery packs i 。
2. The lithium ion battery pack SOC estimation method based on the thermoelectric coupling model as set forth in claim 1, wherein: in step one, only the temperature change of the entire battery pack is considered in estimating the battery pack SOC.
3. The lithium ion battery pack SOC estimation method based on the thermoelectric coupling model as set forth in claim 1, wherein: the second step comprises:
in the average equivalent circuit model, set
For the average terminal voltage of all series cells in the group, then:
wherein U is pack Measuring terminal voltage for the series battery; u (U) t i The voltage of the measuring terminal of the ith single battery in the series battery pack; n is the total number of battery cells in series;
the parameters in the average equivalent circuit model have the following relationship:
wherein I is the current of the series battery pack;
is the average open circuit voltage;
For average ohmic resistance, U 1 、U 2 Characterization of R 1 C 1 、R 2 C 2 Polarization voltages of the two loops;
let T be f Indicating the ambient temperature at which the battery pack is located,
represents the average surface temperature in the battery, +.>
Represents the average core temperature of the battery pack,/-, and>
representing the average heat generation amount during the operation of the battery pack, the relationship expression of the average heat generation model is:
wherein C is c Average kernel for battery packHeat capacity, C s R is the average surface heat capacity c R is the average heat conduction resistance u Is the average convective thermal resistance.
4. The lithium ion battery pack SOC estimation method based on the thermoelectric coupling model as set forth in claim 3, wherein: in the differential model, the differential model terminal voltage delta U of the ith single battery t i From differential open circuit voltage DeltaU oc i And a differential internal resistance DeltaR i The terminal voltage expression of the differential model is:
ΔU t i ＝ΔU oc i -IΔR i (4)
the relational expression between each parameter and the average model is:
wherein U is oc i Open circuit voltage of the ith single battery in the series battery pack; r is R 0 i Is the ohmic resistance of the i-th unit cell.
5. The lithium ion battery pack SOC estimation method based on the thermoelectric coupling model as set forth in claim 1, wherein: in the third step, the parameters to be identified by the average thermal model have average open-circuit voltage
Average ohmic resistance->
Average internal polarization resistance R 1 And R is R 2 Average polarization capacitance C 1 And C 2 Average core heat capacity C c Average surface heat capacity C s Average heat conduction resistance R c Average convection thermal resistance R u The method comprises the steps of carrying out a first treatment on the surface of the The parameters to be identified by the differential model have differential open circuit voltage delta U oc i Differential ohmic resistance DeltaR i 。
6. The lithium ion battery pack SOC estimation method based on the thermoelectric coupling model as set forth in claim 1, wherein: step five, battery state reminding; and reminding the state of the important battery by adopting a monitoring module.
7. The lithium ion battery pack SOC estimation method based on the thermoelectric coupling model as set forth in claim 6, wherein:
when the battery with the lowest residual electric quantity in the discharging process of the battery pack is lower than 20%, the monitoring module reminds a user and reduces the step length estimated by the difference model;
when the battery with the highest residual electric quantity in the battery pack charging process is higher than 280%, the monitoring module reminds a user and reduces the step length estimated by the difference model.
8. The lithium ion battery pack SOC estimation device based on the thermoelectric coupling model is characterized in that: the apparatus includes a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the lithium ion battery pack SOC estimation method of any of claims 1-7.
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