CN114784397A - Automobile charging method and system based on multi-time scale battery fault - Google Patents

Abstract

The invention discloses a vehicle charging method based on multi-time scale battery faults. The charging strategy is used for charging, and the present invention ensures the speed and safety of the electric vehicle in the charging process by formulating different charging strategies for short-term faults and long-term faults in the charging process of the electric vehicle.

Description

A vehicle charging method and system based on multi-time scale battery failure

technical field

The invention belongs to the technical field of electric vehicle charging, and in particular relates to a vehicle charging method and system based on multi-time scale battery faults.

Background technique

In recent years, energy shortage and environmental pollution have become huge challenges for human development. In order to reduce carbon emissions, reduce the threat posed by fossil energy consumption to national energy security, and alleviate the environmental crisis, it has become a global consensus to accelerate the development and promotion of new energy vehicles. As the key technology of new energy vehicles, power batteries and their applications are the technological commanding heights that countries compete to occupy, and are crucial to independently breaking through the technical bottlenecks of new energy vehicles. In order to ensure the safe and stable operation of electric vehicles, ensuring the safety and stability of the power battery is the top priority. In this regard, it is necessary to study the online early warning method of electric vehicle charging process faults to ensure the safety of the charging process and minimize the battery charging time.

Domestic and foreign car companies have achieved certain results in battery safety management. Some scholars have mainly studied the SOH estimation and RUL prediction of electric vehicle power batteries. Some scholars put forward the concept of diagnosing and estimating battery SOH based on the change of OCV curve, using ICA and DVA to quantitatively analyze the change of battery OCV, and diagnose battery SOH from the perspective of battery degradation mechanism. Some scholars have proposed a non-destructive extraction method for the internal health characteristics of lithium-ion batteries, which has realized the quantitative calculation and analysis of the factors causing the capacity loss, overpotential rise and heat generation rate rise of lithium cobalt oxide batteries. Some scholars have studied the battery SOH estimation method based on the voltage curve fitting method and the model method. The biggest disadvantage of the current battery state of health estimation method is that it is difficult to apply online monitoring of real vehicle data, so how to apply scientific research results to real vehicle monitoring is particularly important.

In order to realize the rapid and safe development of electric vehicles and give full play to its potential as an energy-based load, the present invention designs a vehicle charging method based on multi-time-scale battery failures to ensure rapid and safe charging of electric vehicles.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a vehicle charging method based on multi-time scale battery failure, which can ensure the safety during the charging process of the electric vehicle.

To achieve the above object, the present invention provides the following technical solutions:

In a first aspect, a vehicle charging method based on multi-time scale battery failure is provided, including:

Analyze the safety characteristics of electric vehicle power battery charging to determine whether the power battery is faulty;

If there is a fault in the power battery, a corresponding charging strategy is formulated according to the time scale of the fault for charging.

In combination with the first aspect, further, the analysis of the electric vehicle power battery charging safety characteristics to determine whether there is a fault in the power battery is specifically: analyzing the influence of different temperatures, charging rates, and charging voltages on the thermal stability of the battery and the lithium deposition of the negative electrode, And based on the SOC-V curve of the battery cell, the battery voltage change trend in the charging stage is judged whether the battery is faulty.

In combination with the first aspect, further, the formulation of the corresponding charging strategy according to the time scale of the fault is specifically: for short-term faults, real-time detection of relevant parameters and environmental parameters of the lithium battery, and judgment of the failure of the lithium battery according to these parameters , giving a short-term safe and intelligent charging strategy;

For long-term faults, a long-term safe and intelligent charging strategy is generated by shortening the charging time to limit the charging temperature rise as the optimization goal.

In combination with the first aspect, further, the long-term safe and intelligent charging strategy is specifically:

Establish an objective function to limit the charging temperature rise by shortening the charging time as the optimization goal, as shown in the following formula:

Among them, cm represents the charging temperature rise function, T _k represents the battery temperature change during the k-th step of charging, T _k ^st represents the initial battery temperature in the k+1-th step, T _CELL represents the battery temperature per unit time, and t _k represents the k-th step Charging time, U _p represents the voltage across the battery, I _p represents the current flowing through the battery, ΔS represents the change in battery SOC, hA is the heat transfer coefficient, T _p represents the battery temperature at the k-th step of charging, and T _air represents the k-th step The external temperature at the time of charging, R represents the battery resistance.

In combination with the first aspect, the method further includes verifying the charging strategy, and the verification of the charging strategy is specifically: verifying the charging strategy by using the charging status information data provided by the charging pile monitoring platform.

In a second aspect, a vehicle charging system based on multi-time scale battery failure is provided, including:

Fault judgment module: used to analyze the charging safety characteristics of electric vehicle power battery to determine whether the power battery is faulty;

Strategy formulation module: used to formulate a corresponding charging strategy for charging according to the time scale of the failure if the power battery is faulty.

Beneficial technical effect: the present invention ensures the speed and safety of the electric vehicle in the charging process by formulating different charging strategies for short-term faults and long-term faults in the charging process of the electric vehicle.

Description of drawings

Fig. 1 is the flow chart of the short-term charging safety intelligent protection strategy of the present invention;

2 is a schematic diagram of a lithium iron phosphate battery capacity test result diagram in the present invention;

3 is a schematic diagram of the capacity test result of a ternary battery in the present invention;

Fig. 4 is a schematic diagram of the change of open circuit voltage and internal resistance of lithium iron phosphate battery with SOC in the present invention;,

FIG. 5 is a schematic diagram of the change of open circuit voltage and internal resistance of ternary battery with SOC in the present invention;

Fig. 6 is the normalization curve diagram of the voltage difference in the present invention;

7 is a schematic diagram of a polarization-limited acceptable charging current curve and a capacity-temperature-rise charging strategy in the present invention;

FIG. 8 is a schematic diagram of the optimized calculation result when the coefficient α=0.5 and β=0.5 in the present invention;

FIG. 9 is a schematic diagram of the optimized calculation result when the coefficient α=0.3 and β=0.7 in the present invention;

FIG. 10 is a schematic diagram of the optimized calculation result when the coefficient α=0.7β=0.3 in the present invention;

11 is a schematic diagram showing the comparison between the SOC calculation data of the battery model in the present invention and the SOC data provided by the BMS;

12 is a schematic diagram showing the comparison between the voltage calculation data of the battery model in the present invention and the voltage data provided by the BMS;

13 is a schematic diagram showing the comparison between the SOC calculation data of the battery model in the present invention and the SOC data provided by the BMS;

FIG. 14 is a schematic diagram showing the comparison between the voltage data provided by the charger and the voltage data provided by the BMS in the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

Example 1

As shown in Fig. 1-14, the present invention provides a vehicle charging method based on multi-time scale battery failure, including the following steps:

Step 1: Analyze the charging safety characteristics of the electric vehicle power battery to determine whether the power battery is faulty;

(1) Research on basic performance of single battery

Aiming at the problem of charging safety, a battery test platform was built, and typical lithium-ion batteries (ternary batteries and lithium iron phosphate batteries) were taken as the research objects to analyze the effects of different temperatures, charging rates, and charging voltages on the thermal stability of the battery and the lithium evolution of the negative electrode. .

The capacity test experiment and the mixed pulse power characteristic experiment are carried out on the single battery to analyze the basic characteristics of the single battery. The capacity test results of the two batteries are shown in Figure 2-3. Figure 4-5 shows the relationship between battery open circuit voltage and internal resistance with SOC.

(2) Analysis of charging safety characteristics of power battery pack

There are various reasons for the voltage difference of different battery cells, among which the main influencing factors are the capacity, internal resistance and discharge interval of the battery.

The invention discriminates the variation trend of the battery voltage in the charging stage based on the SOC-V curve of the battery cell. However, due to the existence of a voltage plateau in lithium-ion batteries, when the battery is in the plateau area, the difference in battery voltage may be small, and it is difficult to judge only by using the SOC-V curve. In order to accurately and clearly characterize the changing trend of the battery voltage, the "voltage difference normalized curve" is used for analysis. Taking the SOC during the charging and discharging process as the abscissa, the formula for forming the curve is:

Wherein, V(SOC), _Vmin (SOC), and _Vmax (SOC) represent the voltage, the lowest cell voltage, and the highest cell voltage of the selected cell corresponding to the SOC, respectively. For a complete charge-discharge process, taking any single battery, the curve varies between 0 and 100%. The voltage variation trend of the selected single battery can be analyzed by comparing the reference curve. The reference curve selects the "voltage difference normalization curve" of the median voltage of the battery, namely:

where V _mid (SOC) is the median of all cell voltages corresponding to SOC. The median is selected as the reference standard to prevent abnormally high and low batteries in the battery pack from affecting the reference standard. Figure 6 shows the comparison of the "normalized voltage difference curve" of a single battery with the reference curve during one charge.

As an extension and supplement of ordinary statistical methods, "voltage difference normalization curve" not only enhances the accuracy of abnormal battery diagnosis, but also this method is simple and easy to implement, and can be applied to each battery cell, making the location of abnormal batteries more intuitive .

Step 2: If the power battery has a fault, formulate a corresponding charging strategy for charging according to the time scale of the fault.

(1) Short-term safe and intelligent charging strategy

Common fault phenomena include: battery pack capacity reduction, high charging voltage, battery pack not charging, low discharge voltage; large battery self-discharge; local high temperature, poor cell voltage consistency, battery arc breakdown, etc.

Figure 1 shows the flow chart of the short-term charging safety intelligent protection strategy. This strategy detects the charge and discharge current, cell voltage, total voltage of the battery pack, and ambient temperature of the lithium-ion battery in real time, and judges the possible faults of the power lithium battery according to these data, and gives corresponding treatment measures to avoid Lithium batteries have serious failures and accidents, and the data is uploaded to the cloud server for long-term safety warning. The battery data that needs to be collected are:

1) The voltage of the single battery;

2) The tab temperature of the single battery;

3) The total voltage of the battery pack;

4) Total charge and discharge current;

(2) Medium and long-term safe and intelligent charging strategy

The acceptable charging current curve of the lithium battery based on the charging polarization voltage characteristics is shown in Figure 7. As the battery SOC increases, the acceptable charging current gradually decreases. As the SOC of the battery increases, the ohmic internal resistance, polarization internal resistance and entropy change coefficient will change accordingly, resulting in different charging temperature rise rates in different SOC intervals. Therefore, under the limit of acceptable charging current based on polarization characteristics , increase the charging current in the range with a small temperature rise rate, and reduce the charging current in the range with a large temperature rise rate, so as to achieve a balance between the whole charging time and the charging temperature rise, and prolong the battery life on the premise of ensuring the charging speed. Therefore, this chapter proposes an optimized charging strategy (time-temperature rise strategy) with shortening the charging time and limiting the charging temperature rise as the optimization goals: the charging SOC interval is divided into N steps with △SOC as the interval. Under the constraint of the charging current curve, the charging current of each step is selected reasonably and optimally, so that the optimized charging objective function can obtain the optimal value. Because the temperature rise is related to the battery life, the objective function comprehensively considers the charging time and the life, and will already be composed of the charging time and the charging temperature rise.

The charging current is changed every certain ΔSOC, and it is assumed that N steps of changing the current are required to charge from 0% SOC to charging 100% SOC. Without considering precharge, the relationship between N and SOC is equation (3).

The charging time t _k of the k-th step is:

The battery charging capacity Q is based on the 0.05C charging capacity.

The total charging time t is:

The total charging capacity Q _ch is:

Q _ch =ΔS·Q·(N-1)+Q _N (6)

The N-step charging capacity Q _N and charging time are discussed separately here, because the N-step charging capacity may not necessarily reach the theoretical value ΔS·Q. In most cases, the N-step charging current is greater than 0.05C, and the total charging capacity is less than 0.05 Charge capacity at C. Next, calculate the charging capacity and charging time in the final step. Take the ternary material battery charging upper limit voltage of 4.2V as an example. When the upper limit voltage of charging is reached, it is related:

I _N R+kI _N +b+OCV(N)=4.2V (7)

OCV(N) represents the OCV at the end of the Nth step of charging.

Then OCV(N) is:

OCV( _N )=4.2V-(IN R+ _kIN +b) (8)

According to the OCV-SOC curve, the SOC _N at the end of the Nth step of charging can be solved as:

SOC _N =f ^-1 (4.2-I _N R-kI _N -b) (9)

Then the capacity flushed in the Nth step is:

Q _N =Q·[SOC _N -ΔS(N-1)] (10)

The charging time of the Nth step is also available.

The functional relationship of the charging temperature rise is as follows:

Among them, cm represents the charging temperature rise function, T _k represents the battery temperature change during the k-th step of charging, T _k ^st represents the initial battery temperature in the k+1-th step, T _CELL represents the battery temperature per unit time, and t _k represents the k-th step Charging time, U _p represents the voltage across the battery, I _p represents the current flowing through the battery, ΔS represents the change in battery SOC, hA is the heat transfer coefficient, T _p represents the battery temperature at the k-th step of charging, and T _air represents the k-th step The external temperature at the time of charging, R is the battery resistance, F is the Faraday constant, and b is the reserved battery charging capacity. The initial temperature of the battery in step k+1 is equal to the end temperature of the battery in step k

The temperature rise is the maximum temperature during the charging process - the initial temperature:

The objective function normalized linear scoring system, with the maximum allowable temperature rise as 60 points and the 1/20C charging temperature rise as 100 points. The maximum allowable temperature rise is the maximum temperature rise artificially set during battery production or use to ensure battery safety; 1/20C charging temperature rise is the minimum temperature rise, because this rate is usually used to measure the battery OCV-SOC curve. The maximum allowable charging time is 60 minutes, and the polarizing boundary current charging time is 100 minutes. The maximum allowable charging time is customized from the user's point of view, because if the charging time exceeds a certain length, the user will be restless and unacceptable. The final objective function is formula (14), which adopts the form of linear weighting. The weighting coefficient α is called the time weighting coefficient, which represents the importance of the charging time, and the weighting coefficient β is called the temperature rise weighting coefficient, which represents the importance of the charging temperature rise. Satisfaction α+β=1.

Run the matlab genetic algorithm program, set the initial population number to 10, the generation gap to 0.9, and the generation number to 40, and set different charging time and charging temperature rise weight coefficient. The optimization results are shown in Figure 8-Figure 10:

It can be seen from the matlab simulation optimization results of different weight coefficients that when the genetic algorithm is carried out for 40 generations, the maximum fitness value of each generation of individuals has approximately stabilized, which indicates that the fitness function, that is, the objective function, is convergent. Since the charging current takes the acceptable charging current limited by the polarization voltage as the boundary condition, and this current boundary condition gradually decreases with the increase of SOC, the overall change trend of the optimal charging current obtained by optimization is gradually decreasing with the increase of SOC. , at the same time, in some SOC intervals, the charging current will increase slightly, this is because in these SOC intervals, the battery has a small internal resistance or polarization, and the heat generation rate is relatively low, so it can be charged with a large current to speed up charging speed. When the weight coefficient α=0.3, β=0.7, the corresponding charging time corresponding to the optimal charging current is 1.99h, and the charging temperature rise is 1.9℃; when the weighting coefficient α=0.5, β=0.5, the corresponding optimal charging current corresponds to The charging time is 1.34h, and the charging temperature rise is 2.87℃; when the weight coefficient α=0.7, β=0.3, the corresponding charging time corresponding to the corresponding optimized charging current is 1.07h, and the charging temperature rise is 3.8℃.

Step 3: Verify the charging strategy, and use the charging status information data provided by the charging pile monitoring platform to verify the charging strategy.

Using the charging status information data provided by the charging pile monitoring platform, the proposed online warning of charging facility charging data based on the battery model is verified. Build a power battery model according to the type, specification and parameter information of the electric vehicle power battery, such as battery type, rated capacity, initial state of charge and other information. According to the proposed method of simulating the charging response of the power battery and the battery model, the charging response of the simulated power battery is calculated. Using CAN bus monitoring technology, analyze the CAN communication messages of the charger and the battery management system during the charging process, obtain the charging status information of the charger and the battery and the charging demand information of the battery in real time, and compare the charging response information simulated by the battery model with the charging status of the battery. The information is compared, and the charging status information of the charger is compared with the battery charging demand information to judge whether the charging process is normal. If the difference in the comparison information is within the allowable range, it means that the charging process is normal. If there is a significant difference in the comparison information, it means that the charging process is wrong. The specific analysis of the difference information can clarify the charging fault information, and then realize the charging fault warning.

(1) Normal charging process

Select a complete set of battery status information data in the normal charging process, including charging time, charging current, charging voltage, state of charge and other information, and carry out program design in the MATLAB simulation environment to verify the feasibility of the proposed fault monitoring method . The comparison result between the SOC calculation data of the battery model and the SOC data provided by the BMS is shown in Figure 11. The maximum relative error between the calculation data of the battery model and the data provided by the BMS is less than 2%. Figure 12 shows the comparison result between the voltage calculation data of the battery model and the voltage data provided by the BMS. The maximum relative error between the calculation data of the battery model and the data provided by the BMS is less than 0.5%. The above comparison results show that the electric vehicle is charged normally. The charging results show that the initial SOC of the battery is 60%, the initial voltage is 552V, and the battery is fully charged in 92 minutes, and the voltage reaches 570V.

(2) Abnormal charging process

The charging data of electric vehicle charging accident cases are selected for simulation to verify the application of the proposed fault monitoring method in the abnormal charging process. Figure 13 shows the comparison results between the SOC calculation data of the battery model and the SOC data provided by the BMS. It can be seen from Figure 13 that the initial SOC of the battery is 62%. When the battery is fully charged after 88 minutes of charging, the BMS does not send a stop charging command to the charger, and the battery continues to charge. Under normal circumstances, the charging process should be stopped. , so it means that the charging process is abnormal, and a charging fault warning signal should be issued. The comparison result between the charging voltage information provided by the charger and the charging voltage data provided by the BMS is shown in Figure 14. It can be seen from Figure 14 that when the battery is fully charged after 88 minutes of charging, the charging process continues, and the battery voltage data provided by the BMS does not Update according to the actual charging process. There is a big difference between the battery charging demand and the charging voltage of the charger. The difference value exceeds the allowable range. It can be judged that the charging process is abnormal or faulty due to the failure of the BMS module function. The fault warning signal was issued in time, and the charging process was not terminated in time.

Example 2

A vehicle charging system based on multiple time scale battery failures is provided, including:

Fault judgment module: used to analyze the charging safety characteristics of electric vehicle power battery to determine whether the power battery is faulty;

Strategy formulation module: used to formulate a corresponding charging strategy for charging according to the time scale of the failure if the power battery is faulty.

The above are only the preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, without departing from the technical principle of the present invention, several improvements and modifications can also be made. These improvements and modifications It should also be regarded as the protection scope of the present invention.

Claims (6)

1. a vehicle charging method based on multi-time scale battery failure, is characterized in that, comprises: Analyze the safety characteristics of electric vehicle power battery charging to determine whether the power battery is faulty; If there is a fault in the power battery, a corresponding charging strategy is formulated according to the time scale of the fault for charging. 2. The vehicle charging method based on multi-time scale battery faults according to claim 1, characterized in that: the analysis of the electric vehicle power battery charging safety characteristics to determine whether the power battery has a fault is specifically: analyzing different temperatures, charging The influence of rate and charging voltage on the thermal stability of the battery and the lithium deposition of the negative electrode, and based on the SOC-V curve of the battery cell, the battery voltage change trend during the charging phase was determined to determine whether the battery was faulty. 3. The vehicle charging method based on multi-time scale battery faults according to claim 2, wherein the formulating a corresponding charging strategy according to the time scale of the fault is specifically: for short-term faults, real-time detection of the relevant parameters of the lithium battery and environmental parameters, and according to these parameters to judge the failure of the lithium battery, and provide a short-term safe and intelligent charging strategy; For long-term faults, a long-term safe and intelligent charging strategy is generated by shortening the charging time to limit the charging temperature rise as the optimization goal. 4. The vehicle charging method based on multi-time scale battery failures according to claim 3, wherein the long-term safe and intelligent charging strategy is specifically: Establish an objective function to limit the charging temperature rise by shortening the charging time as the optimization goal, as shown in the following formula:

Among them, cm represents the charging temperature rise function, T _k represents the battery temperature change during the k-th charging process,

Represents the initial temperature of the battery in the k+1 step, T _CELL represents the battery temperature per unit time, t _k represents the charging time of the k step, U _p represents the voltage across the battery, I _p represents the current flowing through the battery, ΔS represents the battery SOC , hA is the heat transfer coefficient, T _p is the battery temperature at the k-th step of charging, T _air is the external temperature at the k-th step of charging, and R is the battery resistance. 5 . The vehicle charging method based on multi-time scale battery failures according to claim 1 , further comprising verifying the charging strategy, and the verification of the charging strategy is specifically: using a charging pile to monitor the charging provided by the platform. 6 . Status information data to verify the charging strategy. 6. A vehicle charging system based on multi-time scale battery failure, characterized in that it comprises: Fault judgment module: used to analyze the charging safety characteristics of electric vehicle power battery to determine whether the power battery is faulty; Strategy formulation module: used to formulate a corresponding charging strategy for charging according to the time scale of the failure if the power battery is faulty.

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