1. The method for predicting and evaluating the service life of the lithium battery system of the electric vehicle is characterized by comprising the following steps of:

s1, counting the working condition data of the specific vehicle, wherein the working condition data at least comprise daily mileage, average speed and operation area as working condition parameters for life prediction, and the total electric quantity and endurance mileage are used as vehicle parameters for life prediction;

s2, carrying out calendar life and cycle life tests on the single battery cell, wherein the test result is used as a battery cell life parameter of the life prediction model;

s3, randomly simulating the specific working conditions of the whole vehicle according to the parameters of the whole vehicle and the working condition parameters and by combining the corresponding working condition distribution probability model, and counting the characteristic distribution of each working condition; the working condition distribution probability model comprises a private car working condition model and an operating car working condition model; the working condition distribution probability model is as follows:

the distribution of the vehicle starting trip time is mainly divided into n time intervals, mu_nRepresents the nth time interval, f (x) represents the probability of a certain moment in the current interval, w_nIs the weight, σ, of the current time interval_nIs as followsProbability parameter of previous time interval.

S4, inputting the battery cell life data and the characteristic distribution of each working condition into the battery cell life model, and obtaining a calendar life curve and a cycle life curve of the battery system under the working condition through a weighting algorithm;

s5, inputting the vehicle parameters and the statistical working condition characteristic distribution into a endurance mileage correction model, and correcting the endurance mileage in three aspects of energy efficiency, accessory energy consumption and load influence;

and S6, iteratively integrating the service life of the battery system with the parameters of the whole vehicle and the working condition parameters, and predicting the service life of the battery system.

2. The method for predicting and evaluating the service life of the lithium battery system of the electric vehicle as claimed in claim 1, wherein the step S3 is implemented by dividing the distribution of the vehicle starting trip time into four time intervals, which are [6, 10 ], [10, 14 ], [14, 20 ], [20, 24 ].

3. The method for predicting and evaluating the service life of the lithium battery system of the electric vehicle as claimed in claim 2, wherein the weight and probability parameters in the private vehicle working condition model and the operating vehicle working condition model are respectively set as:

1) probability parameter sigma of private car working condition distribution probability model corresponding to four time intervals_nRespectively as follows: 1. 1.5, 0.5; weight w corresponding to four time intervals_nRespectively as follows: 0.35, 0.15, 0.45, 0.1.

2) Probability parameter sigma of operation condition distribution probability model corresponding to four time intervals_nRespectively as follows: 2, 2, 2, 2; weight w corresponding to four time intervals_nRespectively as follows: 0.3, 0.2, 0.3, 0.2.

4. The method for predicting and evaluating the service life of the lithium battery system of the electric vehicle as claimed in claim 3, wherein the conditions in the step S3 include charging, discharging, and standing; the working condition characteristic distribution comprises temperature distribution, SOC distribution and current multiplying power distribution; the working condition characteristic distribution specifically comprises the following steps:

wherein cha, dch, and sta respectively represent a charge state, a discharge state, and a rest state. The SOC is divided every 20%, the current multiplying power is divided every 0.2 ℃, and the temperature is divided every 10 ℃; thus, w_{sta/soc_50}Indicates that SOC is [ 40%, 60% ]in the resting state]Time ratio of (a) w_{dch/temp_25}Indicating that the temperature was at 20 ℃ and 30 ℃ in the discharge state]Time ratio of (a) w_{cha/rate_0.5}Indicating that the charging multiplying factor is [0.4C, 0.6C ] in the discharging and charging state]Is a ratio of time.

5. The method for predicting and evaluating the service life of the lithium battery system of the electric vehicle as claimed in any one of claims 1 to 4, wherein the weighting algorithm in the step S4 is as follows:

Cal_sys(τ)＝∑Cal_cell(T，s，τ)*W_{sta/soc_s}*W_{sta/temp_t}

Cyc_sys(n)＝∑Cyc_cell(T，s，n)*w_{cha/rate_r}*w_{cha/temp_t}

wherein, Cal_sys(τ)、Cyc_sys(τ)、Cal_cell(T，s，τ)、Cyc_cell(T, s, tau) respectively represents a calendar life curve and a cycle life curve of the battery system, and a calendar life curve and a cycle life curve of the single battery cell; the calendar life curve of the single battery cell is a decay curve of temperature (T), SOC(s) and resting time (tau); the cycle life curve of the single battery cell is an attenuation curve of temperature (T), charging rate (r) and cycle number (n).

6. The method for predicting and evaluating the service life of the lithium battery system of the electric vehicle as claimed in any one of claims 1 to 4, wherein the correction factors in the step S5 are as follows:

f＝f_e/(f_a*f_m)

f_e＝∑E_t*w_{dch/temp_t}

wherein, f_e、f_a、f_mRespectively representing a total correction factor, an energy efficiency factor, an accessory energy consumption factor and a load influence factor; energy efficiency factor f_eAssociated with the discharge energy efficiency of the individual cells at different temperatures, E_tRepresenting the discharge energy efficiency of the single battery cell at a specific temperature; w is a_tRepresenting the ratio of the temperature interval at the start of the air conditioner, e_tAir conditioning hour energy consumption representing the temperature condition; u and ec respectively represent the average vehicle speed and the hundred kilometers energy consumption of the whole vehicle; load represents the ratio of load to vehicle weight.

7. The method for predicting and evaluating the service life of the lithium battery system of the electric vehicle as claimed in any one of claims 1 to 4, wherein the iterative algorithm in the step S6 is as follows:

Q_cal，sys＝Cal_sys[＞＝Q_loss，sys](Δτ)

Q_cyc，sys＝Cyc_sys[＞＝Q_loss，sys](Δn)

Q_loss，sys+＝Q_cal，sys+Q_cyc，sys

m+＝Δm

wherein, m and Q_loss，sysRespectively representing the accumulated travel mileage and the accumulated capacity attenuation rate in the iteration process, wherein the delta m, the delta tau and the delta n respectively represent the range increment, the shelf time and the cycle number in one iteration period; q_cal，sys、Q_cyc，sysRespectively representing calendar decay and cycle decay in one iteration period.

8. A system for predicting and evaluating the service life of a lithium battery system of an electric vehicle is characterized by comprising:

the parameter setting module is used for counting the working condition data of a specific vehicle, at least comprising daily mileage, average speed and operation area as working condition parameters for life prediction, and taking total electric quantity and endurance mileage as finished vehicle parameters for life prediction;

the battery cell life parameter prediction module is used for carrying out calendar life and cycle life tests on the single battery cells, and the test results are used as the battery cell life parameters of the life prediction model;

the working condition characteristic distribution statistical module is used for randomly simulating the specific working conditions of the whole vehicle according to the whole vehicle parameters and the working condition parameters and combining the corresponding working condition distribution probability model, and counting the characteristic distribution of each working condition; the working condition distribution probability model comprises a private car working condition model and an operating car working condition model; the working condition distribution probability model is as follows:

the distribution of the vehicle starting trip time is mainly divided into n time intervals, mu_nRepresents the nth time interval, f (x) represents the probability of a certain moment in the current interval, w_nIs the weight, σ, of the current time interval_nIs a probability parameter of the current time interval.

The battery system service life calculation module inputs the battery cell service life data and the characteristic distribution of each working condition into the battery cell service life model, and obtains a calendar service life curve and a cycle service life curve of the battery system under the working condition through a weighting algorithm;

the correction module inputs the vehicle parameters and the statistical working condition characteristic distribution into the endurance mileage correction model, and corrects the endurance mileage in three aspects of energy efficiency, accessory energy consumption and load influence;

and the battery system service life prediction module is used for iteratively integrating all parameters and data to predict the service life of the battery system.

9. The system for predicting and evaluating the life of a lithium battery system of an electric vehicle as claimed in claim 8, wherein the statistical module of the distribution of the operating condition characteristics divides the distribution of the vehicle starting trip time into four time intervals, which are [6, 10 ], [10, 14 ], [14, 20 ], [20, 24 ].

10. The system for predicting and evaluating the life of a lithium battery system of an electric vehicle as claimed in claim 9, wherein the weighting and probability parameters in the private vehicle condition model and the operating vehicle condition model are respectively set as:

1) probability parameter sigma of private car working condition distribution probability model corresponding to four time intervals_nRespectively as follows: 1. 1.5, 0.5; weight w corresponding to four time intervals_nRespectively as follows: 0.35, 0.15, 0.45, 0.1.

2) Probability parameter sigma of operation condition distribution probability model corresponding to four time intervals_nRespectively as follows: 2, 2, 2, 2; weight w corresponding to four time intervals_nRespectively as follows: 0.3, 0.2, 0.3, 0.2.

11. The system for predicting and evaluating the life of the lithium battery system of the electric vehicle as claimed in claim 10, wherein the working conditions in the working condition characteristic distribution statistical module comprise charging, discharging and resting; the working condition characteristic distribution comprises temperature distribution, SOC distribution and current multiplying power distribution; the working condition characteristic distribution specifically comprises the following steps:

wherein cha, dch, and sta respectively represent a charge state, a discharge state, and a rest state. The SOC is divided every 20%, the current multiplying power is divided every 0.2 ℃, and the temperature is divided every 10 ℃; thus, W_{sta/soc_50}Indicates that SOC is [ 40%, 60% ]in the resting state]Time ratio of (W)_{dch/temp_25}Indicating that the temperature was at 20 ℃ and 30 ℃ in the discharge state]Time ratio of (W)_{cha/rate_0.5}To representIn the charging and discharging state, the charging multiplying power is set at 0.4C, 0.6C]Is a ratio of time.

12. The system for predicting and evaluating the life of a lithium battery system of an electric vehicle as claimed in any one of claims 8 to 11, wherein the weighting algorithm in the step S4 is as follows:

Cal_sys(τ)＝∑Cal_cell(T，s，τ)*W_{sta/soc_s}*w_{sta/temp_t}

Cyc_sys(n)＝∑Cyc_cell(T，s，n)*w_{cha/rate_r}*w_{cha/temp_t}

wherein, Cal_sys(τ)、Cyc_sys(τ)、Cal_cell(T，s，τ)、Cyc_cell(T, s, tau) respectively represents a calendar life curve and a cycle life curve of the battery system, and a calendar life curve and a cycle life curve of the single battery cell; the calendar life curve of the single battery cell is a decay curve of temperature (T), SOC(s) and resting time (tau); the cycle life curve of the single battery cell is an attenuation curve of temperature (T), charging rate (r) and cycle number (n).

13. The system for predicting and evaluating the life of the lithium battery system of the electric vehicle as recited in any one of claims 8 to 11, wherein the correction factor in the correction module is as follows:

f＝f_e/(f_a*f_m)

f_e＝∑E_t*w_{dch/temp_t}

wherein, f_e、f_a、f_mRespectively representing a total correction factor, an energy efficiency factor, an accessory energy consumption factor and a carrierA double image noise factor; energy efficiency factor f_eAssociated with the discharge energy efficiency of the individual cells at different temperatures, E_tRepresenting the discharge energy efficiency of the single battery cell at a specific temperature; w is a_tRepresenting the ratio of the temperature interval at the start of the air conditioner, e_tAir conditioning hour energy consumption representing the temperature condition; u and ec respectively represent the average vehicle speed and the hundred kilometers energy consumption of the whole vehicle; load represents the ratio of load to vehicle weight.

14. The system for predicting and evaluating the life of the lithium battery system of the electric vehicle as recited in any one of claims 8 to 11, wherein the iterative algorithm in the battery system life prediction module is:

Q_cal，sys＝Cal_sys[＞＝Q_loss，sys](Δτ)

Q_cyc，sys＝Cyc_sys[＞＝Q_loss，sys](Δn)

Q_loss，sys+＝Q_cal，sys+Q_cyc，sys

m+＝Δm

wherein, m and Q_loss，sysRespectively representing the accumulated travel mileage and the accumulated capacity attenuation rate in the iteration process, wherein the delta m, the delta tau and the delta n respectively represent the range increment, the shelf time and the cycle number in one iteration period; q_cal，sys、Q_cyc，sysRespectively representing calendar decay and cycle decay in one iteration period.

Background

The lithium ion battery is used as a new generation of green energy, and has the advantages of high energy density, high working voltage, long cycle life and the like, so that the lithium ion battery is widely applied to the fields of electric automobiles and energy storage systems. As a core component of an electric vehicle, the performance of a lithium battery system plays a crucial role in the use of the vehicle, and sufficient research is required to ensure the economic life and reliability of the battery pack during the life. The most significant concern with battery systems is degradation of battery capacity.

The attenuation of the lithium battery can be divided into calendar attenuation and cycle attenuation. Calendar decay is affected by ambient temperature and resting SOC state, and cycle decay is affected by ambient temperature, charge-discharge rate, depth of discharge, and other factors. At present, the service life evaluation of a lithium battery system is only to simply add the calendar attenuation and the cycle attenuation of a single battery cell to obtain the attenuation condition of the system. And the influence of the coupling action of the whole vehicle working condition, the calendar attenuation and the cycle attenuation is not considered. Therefore, the life trend of the lithium battery system cannot be evaluated more accurately. According to the invention, the service life data of the single battery cell is combined in the whole vehicle service life prediction, the influence of the whole vehicle operation condition on the system service life is considered, the endurance mileage is corrected, the coupling calculation is carried out on the calendar attenuation and the cycle attenuation by adopting an iterative algorithm, and the prediction evaluation of the service life of the lithium battery system is realized.

The method includes the steps of firstly establishing a first capacity attenuation model representing the storage life of a battery, solving unknown parameters of the first capacity attenuation model according to storage experiment data of a storage experiment, then establishing a second capacity attenuation model according to the determined first capacity attenuation model and combining the charge and discharge cycle times of the battery within set time, and solving the unknown parameters of the second capacity attenuation model according to the cycle experiment data of the charge and discharge cycle experiment, so that the second capacity attenuation model representing the storage life and the cycle life of the battery is finally determined. And finally, substituting the actual working condition information of the battery into the second capacity attenuation model so as to obtain the predicted service life of the battery. Compared with the method for simply predicting the storage life or the cycle life in the prior art, the method is more suitable for the actual service condition of the battery, and can quickly predict the actual service life of the battery. The method aims at predicting the service life of the single battery cell, does not consider the whole vehicle to be public, and cannot directly obtain the service life prediction result of the battery system.

Disclosure of Invention

The invention aims to provide a lithium battery system service life prediction and evaluation method considering the influence of the running condition of a whole vehicle on the service life of a battery system.

The invention solves the technical problems through the following technical means:

a method for predicting and evaluating the service life of a lithium battery system of an electric vehicle comprises the following steps:

s1, counting the working condition data of the specific vehicle, wherein the working condition data at least comprise daily mileage, average speed and operation area as working condition parameters for life prediction, and the total electric quantity and endurance mileage are used as vehicle parameters for life prediction;

s2, carrying out calendar life and cycle life tests on the single battery cell, wherein the test result is used as a battery cell life parameter of the life prediction model;

s3, randomly simulating the specific working conditions of the whole vehicle according to the parameters of the whole vehicle and the working condition parameters and by combining the corresponding working condition distribution probability model, and counting the characteristic distribution of each working condition; the working condition distribution probability model comprises a private car working condition model and an operating car working condition model; the working condition distribution probability model is as follows:

the distribution of the vehicle starting trip time is mainly divided into n time intervals, mu_nRepresents the nth time interval, f (x) represents the probability of a certain moment in the current interval, w_nIs the weight, σ, of the current time interval_nIs a probability parameter of the current time interval.

S4, inputting the battery cell life data and the characteristic distribution of each working condition into the battery cell life model, and obtaining a calendar life curve and a cycle life curve of the battery system under the working condition through a weighting algorithm;

s5, inputting the vehicle parameters and the statistical working condition characteristic distribution into a endurance mileage correction model, and correcting the endurance mileage in three aspects of energy efficiency, accessory energy consumption and load influence;

and S6, iteratively integrating the service life of the battery system with the parameters of the whole vehicle and the working condition parameters, and predicting the service life of the battery system.

According to the invention, the service life data of the single battery cell is combined in the whole vehicle service life prediction, the influence of the whole vehicle operation condition on the system service life is considered, the endurance mileage is corrected, the coupling calculation is carried out on the calendar attenuation and the cycle attenuation by adopting an iterative algorithm, and the prediction evaluation of the service life of the lithium battery system is realized.

Further, in the step S3, the distribution of the vehicle starting travel time is mainly divided into four time intervals, which are [6, 10 ], [10, 14 ], [14, 20 ], and [20, 24 ].

Further, the weight and probability parameters in the private vehicle condition model and the operating vehicle condition model are respectively set as:

1) probability parameter sigma of private car working condition distribution probability model corresponding to four time intervals_nRespectively as follows: 1. 1.5, 0.5; weight w corresponding to four time intervals_nRespectively as follows: 0.35, 0.15, 0.45, 0.1.

2) Probability parameter sigma of operation condition distribution probability model corresponding to four time intervals_nRespectively as follows: 2, 2, 2, 2; weight w corresponding to four time intervals_nRespectively as follows: 0.3, 0.2, 0.3, 0.2.

Further, the conditions in the step S3 include charging, discharging, and standing; the working condition characteristic distribution comprises temperature distribution, SOC distribution and current multiplying power distribution; the working condition characteristic distribution specifically comprises the following steps:

wherein cha, dch, and sta respectively represent a charge state, a discharge state, and a rest state. The SOC is divided every 20%, the current multiplying power is divided every 0.2 ℃, and the temperature is divided every 10 ℃; thus, w_{sta/soc_50}Indicates that SOC is [ 40%, 60% ]in the resting state]Time ratio of (a) w_{dch/temp_25}Indicating that the temperature was at 20 ℃ and 30 ℃ in the discharge state]Time ratio of (a) w_{cha/rate_0.5}Indicating that the charging multiplying factor is [0.4C, 0.6C ] in the discharging and charging state]Is a ratio of time.

Further, the weighting algorithm in step S4 is as follows:

Cal_sys(τ)＝∑Cal_cell(T，s，τ)*w_{sta/soc_s}*w_{sta/temp_t}

Cyc_sys(n)＝∑Cyc_cell(T，s，n)*w_{cha/rate_r}*w_{cha/temp_t}

wherein, Cal_sys(τ)、Cyc_sys(τ)、Cal_cell(T，s，τ)、Cyc_cell(T, s, tau) respectively represents a calendar life curve and a cycle life curve of the battery system, and a calendar life curve and a cycle life curve of the single battery cell; the calendar life curve of the single battery cell is a decay curve of temperature (T), SOC(s) and resting time (tau); the cycle life curve of the single battery cell is an attenuation curve of temperature (T), charging rate (r) and cycle number (n).

Further, the correction factor in step S5 is as follows:

f＝f_e/(f_a*f_m)

f_e＝∑E_t*w_{dch/temp_t}

wherein, f_e、f_a、f_mRespectively represent the total repairPositive factors, energy efficiency factors, accessory energy consumption factors and load influence factors; energy efficiency factor f_eAssociated with the discharge energy efficiency of the individual cells at different temperatures, E_tRepresenting the discharge energy efficiency of the single battery cell at a specific temperature; w is a_tRepresenting the ratio of the temperature interval at the start of the air conditioner, e_tAir conditioning hour energy consumption representing the temperature condition; u and ec respectively represent the average vehicle speed and the hundred kilometers energy consumption of the whole vehicle; load represents the ratio of load to vehicle weight.

Further, the iterative algorithm in step S6 is:

Q_cal，sys＝Cal_sys[＞＝Q_loss，sys](Δτ)

Q_cyc，sys＝Cyc_sys[＞＝Q_loss，sys](Δn)

Q_loss，sys+＝Q_cal，sys+Q_cyc，sys

m+＝Δm

wherein, m and Q_loss，sysRespectively representing the accumulated travel mileage and the accumulated capacity attenuation rate in the iteration process, wherein the delta m, the delta tau and the delta n respectively represent the range increment, the shelf time and the cycle number in one iteration period; q_cal，sys、Q_cyc，sysRespectively representing calendar decay and cycle decay in one iteration period.

Corresponding to the method, the invention also provides a system for predicting and evaluating the service life of the lithium battery system of the electric vehicle, which comprises the following steps:

the parameter setting module is used for counting the working condition data of a specific vehicle, at least comprising daily mileage, average speed and operation area as working condition parameters for life prediction, and taking total electric quantity and endurance mileage as finished vehicle parameters for life prediction;

the battery cell life parameter prediction module is used for carrying out calendar life and cycle life tests on the single battery cells, and the test results are used as the battery cell life parameters of the life prediction model;

the working condition characteristic distribution statistical module is used for randomly simulating the specific working conditions of the whole vehicle according to the whole vehicle parameters and the working condition parameters and combining the corresponding working condition distribution probability model, and counting the characteristic distribution of each working condition; the working condition distribution probability model comprises a private car working condition model and an operating car working condition model;

the battery system service life calculation module inputs the battery cell service life data and the characteristic distribution of each working condition into the battery cell service life model, and obtains a calendar service life curve and a cycle service life curve of the battery system under the working condition through a weighting algorithm;

the correction module inputs the vehicle parameters and the statistical working condition characteristic distribution into the endurance mileage correction model, and corrects the endurance mileage in three aspects of energy efficiency, accessory energy consumption and load influence;

and the battery system service life prediction module is used for iteratively integrating all parameters and data to predict the service life of the battery system.

Further, the working condition distribution probability model in the working condition characteristic distribution statistical module is as follows:

the distribution of the starting trip time of the vehicle is mainly divided into four time intervals, namely [6, 10 ], [10, 14 ], [14, 20 ], [20, 24]，μ_nRepresents the nth time interval, f (x) represents the probability of a certain moment in the current interval, w_nIs the weight, σ, of the current time interval_nIs a probability parameter of the current time interval.

Further, the weight and probability parameters in the private vehicle condition model and the operating vehicle condition model are respectively set as:

1) probability parameter sigma of private car working condition distribution probability model corresponding to four time intervals_nRespectively as follows: 1. 1.5, 0.5; weight w corresponding to four time intervals_nRespectively as follows: 0.35, 0.15, 0.45, 0.1.

2) Probability parameter sigma of operation condition distribution probability model corresponding to four time intervals_nRespectively as follows: 2, 2, 2, 2; weight w corresponding to four time intervals_nRespectively as follows: 0.3, 0.2, 0.3, 0.2.

Further, the working conditions in the working condition characteristic distribution statistical module comprise charging, discharging and shelving; the working condition characteristic distribution comprises temperature distribution, SOC distribution and current multiplying power distribution; the working condition characteristic distribution specifically comprises the following steps:

wherein cha, dch, and sta respectively represent a charge state, a discharge state, and a rest state. The SOC is divided every 20%, the current multiplying power is divided every 0.2 ℃, and the temperature is divided every 10 ℃; thus, w_{sta/soc_50}Indicates that SOC is [ 40%, 60% ]in the resting state]Time ratio of (a) w_{dch/temp_25}Indicating that the temperature was at 20 ℃ and 30 ℃ in the discharge state]Time ratio of (a) w_{cha/rate_0.5}Indicating that the charging multiplying factor is [0.4C, 0.6C ] in the discharging and charging state]Is a ratio of time.

Further, the weighting algorithm in step S4 is as follows:

Cal_sys(τ)＝∑Cal_cell(T，s，τ)*w_{sta/soc_s}*w_{sta/temp_t}

Cyc_sys(n)＝∑Cyc_cell(T，s，n)*w_{cha/rate_r}*w_{cha/temp_t}

wherein, Cal_sys(τ)、Cyc_sys(τ)、Cal_cell(T，s，τ)、Cyc_cell(T, s, tau) respectively represents a calendar life curve and a cycle life curve of the battery system, and a calendar life curve and a cycle life curve of the single battery cell; the calendar life curve of the single battery cell is a decay curve of temperature (T), SOC(s) and resting time (tau); the cycle life curve of the single battery cell is an attenuation curve of temperature (T), charging rate (r) and cycle number (n).

Further, the correction factor in the correction module is as follows:

f＝f_e/(f_a*f_m)

f_e＝∑E_t*w_{dch/temp_t}

wherein, f_e、f_a、f_mRespectively representing a total correction factor, an energy efficiency factor, an accessory energy consumption factor and a load influence factor; energy efficiency factor f_eAssociated with the discharge energy efficiency of the individual cells at different temperatures, E_tRepresenting the discharge energy efficiency of the single battery cell at a specific temperature; w is a_tRepresenting the ratio of the temperature interval at the start of the air conditioner, e_tAir conditioning hour energy consumption representing the temperature condition; u and ec respectively represent the average vehicle speed and the hundred kilometers energy consumption of the whole vehicle; load represents the ratio of load to vehicle weight.

Further, the iterative algorithm in the battery system life prediction module is as follows:

Q_cal，sys＝Cal_sys[＞＝Q_loss，sys](Δτ)

Q_cyc，sys＝Cyc_sys[＞＝Q_loss，sys](Δn)

Q_loss，sys+＝Q_cal，sys+Q_cyc，sys

m+＝Δm

wherein, m and Q_loss，sysRespectively representing the accumulated travel mileage and the accumulated capacity attenuation rate in the iteration process, wherein the delta m, the delta tau and the delta n respectively represent the range increment, the shelf time and the cycle number in one iteration period; q_cal，sys、Q_cyc，sysRespectively representing calendar decay and cycle decay in one iteration period.

The invention has the advantages that:

the invention randomly simulates the specific working condition of the whole vehicle according to the parameters of the whole vehicle and the working condition parameters and in combination with a set working condition distribution probability model, and counts the characteristic (SOC, temperature and current multiplying power) distribution of each working condition (discharging working condition, charging working condition and shelving working condition). And inputting the service life data of the battery cell and the statistical working condition distribution into the service life model of the battery cell, and obtaining a calendar service life curve and a cycle service life curve of the system under the working condition through a weighting algorithm. And inputting the parameters of the whole vehicle and the statistical working condition distribution into the endurance mileage correction model, and correcting the endurance mileage in three aspects of energy efficiency, accessory energy consumption and load influence. And finally, integrating all parameters and data by using a system life iterative model, and predicting the life of the system. According to the invention, the service life data of the single battery cell is combined in the whole vehicle service life prediction, the influence of the whole vehicle operation condition on the system service life is considered, the endurance mileage is corrected, the coupling calculation is carried out on the calendar attenuation and the cycle attenuation by adopting an iterative algorithm, and the prediction evaluation of the service life of the lithium battery system is realized.

Drawings

FIG. 1 is a logic structure diagram of a method for predicting and evaluating the service life of a lithium battery system of an electric vehicle according to an embodiment of the invention;

FIG. 2 is an iterative flowchart of a method for predicting and evaluating the life of a lithium battery system of an electric vehicle according to an embodiment of the invention;

FIG. 3 is a calendar life curve and a cycle life curve obtained by road test vehicle fitting in an embodiment of the invention.

Fig. 4 is a life prediction result of the road test vehicle in the embodiment of the present invention.

Fig. 5 is a comparison of the life prediction and actual measurement results of the road test vehicle in the embodiment of the invention.

FIG. 6 is a comparison of market operated vehicle capacity fade and model prediction in an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The embodiment discloses a method for predicting and evaluating the service life of a lithium battery system of an electric vehicle, and as shown in fig. 1, the method comprises three dimensional parameter inputs, four levels of evaluation models and 1 index result output. The three dimensional parameter inputs include: system parameters (total electricity and endurance mileage), operating condition parameters (daily mileage, average speed and operating area), and cell life data (calendar life and cycle life data). The four-level evaluation model includes: the system comprises a working condition simulation model, a battery cell life model, a endurance mileage correction model and a system life iteration model. The 1 index result output comprises: and (5) predicting the service life.

The method comprises the following steps:

step 1, counting working condition data of a specific vehicle, wherein the working condition data at least comprises daily mileage, average speed and operation area as working condition parameters for life prediction, and total electric quantity and endurance mileage are used as finished vehicle parameters for life prediction;

step 2, carrying out calendar life and cycle life tests on the single battery cell, wherein the test result is used as a battery cell life parameter of a life prediction model;

step 3, according to the parameters of the whole vehicle and the working condition parameters, combining with the corresponding working condition distribution probability model, randomly simulating the specific working condition of the whole vehicle, and counting the characteristic distribution of each working condition; the working condition distribution probability model comprises a private car working condition model and an operating car working condition model;

the working condition distribution probability model is as follows:

the distribution of the starting trip time of the vehicle is mainly divided into four time intervals, namely [6, 10 ], [10, 14 ], [14, 20 ], [20, 24]，μ_nRepresents the nth time interval, f (x) represents the probability of a certain moment in the current interval, w_nIs the weight, σ, of the current time interval_nIs a probability parameter of the current time interval.

The weight and probability parameters in the private car working condition model and the operating car working condition model are respectively set as follows:

1) private carProbability parameter sigma of working condition distribution probability model corresponding to four time intervals_nRespectively as follows: 1. 1.5, 0.5; weight w corresponding to four time intervals_nRespectively as follows: 0.35, 0.15, 0.45, 0.1.

2) Probability parameter sigma of operation condition distribution probability model corresponding to four time intervals_nRespectively as follows: 2, 2, 2, 2; weight w corresponding to four time intervals_nRespectively as follows: 0.3, 0.2, 0.3, 0.2.

The working conditions comprise charging, discharging and holding; the working condition characteristic distribution comprises temperature distribution, SOC distribution and current multiplying power distribution; the working condition characteristic distribution specifically comprises the following steps:

wherein cha, dch, and sta respectively represent a charge state, a discharge state, and a rest state. The SOC is divided every 20%, the current multiplying power is divided every 0.2 ℃, and the temperature is divided every 10 ℃; thus, W_{sta/soc_50}Indicates that SOC is [ 40%, 60% ]in the resting state]Time ratio of (a) w_{dch/temp_25}Indicating that the temperature was at 20 ℃ and 30 ℃ in the discharge state]Time ratio of (a) w_{cha/rate_0.5}Indicating that the charging multiplying factor is [0.4C, 0.6C ] in the discharging and charging state]Is a ratio of time.

Step 4, inputting the service life data of the battery cell and the characteristic distribution of each working condition into a battery cell service life model, and obtaining a calendar service life curve and a cycle service life curve of the battery system under the working condition through a weighting algorithm;

the weight algorithm is as follows:

Cal_sys(τ)＝∑Cal_cell(T，s，τ)*w_{sta/soc_s}*w_{sta/temp_t}

Cyc_sys(n)＝∑Cyc_cell(T，s，n)*w_{cha/rate_r}*w_{cha/temp_t}

wherein, Cal_sys(τ)、Cyc_sys(τ)、Cal_cell(T，s，τ)、CyC_cell(T, s, τ) represent the calendar life curve and the cycle of the battery system, respectivelyA ring life curve, a calendar life curve and a cycle life curve of the single battery cell; the calendar life curve of the single battery cell is a decay curve of temperature (T), SOC(s) and resting time (tau); the cycle life curve of the single battery cell is an attenuation curve of temperature (T), charging rate (r) and cycle number (n).

Step 5, inputting the parameters of the whole vehicle and the statistical working condition characteristic distribution into a endurance mileage correction model, and correcting the endurance mileage in three aspects of energy efficiency, accessory energy consumption and load influence;

the correction factors are as follows:

f＝f_e/(f_a*f_m)

f_e＝∑E_t*w_{dch/temp_t}

wherein, f_e、f_a、f_mRespectively representing a total correction factor, an energy efficiency factor, an accessory energy consumption factor and a load influence factor; energy efficiency factor f_eAssociated with the discharge energy efficiency of the individual cells at different temperatures, E_tRepresenting the discharge energy efficiency of the single battery cell at a specific temperature; w is a_tRepresenting the ratio of the temperature interval at the start of the air conditioner, e_tAir conditioning hour energy consumption representing the temperature condition; u and ec respectively represent the average vehicle speed and the hundred kilometers energy consumption of the whole vehicle; load represents the ratio of load to vehicle weight.

And 6, iteratively integrating the service life of the battery system with the parameters of the whole vehicle and the working conditions, and predicting the service life of the battery system.

The generation algorithm is as follows:

Q_cal，sys＝Cal_sys[＞＝Q_loss，sys](Δτ)

Q_cyc，sys＝Cyc_sys[＞＝Q_loss，sys](Δn)

Q_loss，sys+＝Q_cal，sys+Q_cyc，sys

m+＝Δm

the iterative process is shown in fig. 2, where m, day, Qloss, sys respectively represent the accumulated traveled mileage, the accumulated traveled days, and the accumulated capacity fade rate during the iterative process. d represents an iteration period, and Δ m, Δ τ and Δ n respectively represent the increment of the inner range, the resting time and the cycle number in one iteration period. Qcal, sys, Qcyc, sys represent calendar decay and cycle decay, respectively, over one iteration period. M, Day, and Qloss represent the set iteration cutoff conditions: total mileage, total time and total attenuation.

According to the method, the specific working conditions of the whole vehicle are randomly simulated by combining a set working condition distribution probability model according to the parameters and the working conditions of the whole vehicle, and the characteristic (SOC, temperature and current multiplying power) distribution of each working condition (a discharging working condition, a charging working condition and a shelving working condition) is counted. And inputting the service life data of the battery cell and the statistical working condition distribution into the service life model of the battery cell, and obtaining a calendar service life curve and a cycle service life curve of the system under the working condition through a weighting algorithm. And inputting the parameters of the whole vehicle and the statistical working condition distribution into the endurance mileage correction model, and correcting the endurance mileage in three aspects of energy efficiency, accessory energy consumption and load influence. And finally, integrating all parameters and data by using a system life iterative model, and predicting the life of the system. In the embodiment, the service life data of the single battery cell is combined in the whole vehicle service life prediction, the influence of the whole vehicle operation condition on the system service life is considered, the endurance mileage is corrected, the calendar attenuation and the cycle attenuation are subjected to coupling calculation by adopting an iterative algorithm, and the prediction and evaluation of the service life of the lithium battery system are realized.

Next, a long-distance road test is performed on a certain electric vehicle equipped with a lithium battery, and 100 electric vehicles are randomly selected from the vehicles operated in the market and analyzed. The data analysis processing steps are as follows:

(1) and counting the working condition data of the road test vehicles or 100 market operating vehicles, wherein the daily mileage, the average speed and the operating area in the statistical data are used as the working condition parameters of the service life prediction model. In addition, the total electric quantity and the endurance mileage are used as system parameters of the life prediction model;

(2) carrying out calendar life and cycle life tests on the single battery cell, wherein the test result is used as a battery cell life parameter of a life prediction model;

(3) according to the vehicle parameters and the working condition parameters, the specific working condition of the vehicle is simulated at random by combining a specific working condition distribution probability model, and the characteristic (SOC, temperature and current multiplying power) distribution of each working condition (discharging working condition, charging working condition and shelving working condition) is counted. The specific working condition distribution probability model comprises a private car working condition model and an operating car working condition model;

(4) and inputting the service life data of the battery cell and the statistical working condition distribution into a battery cell service life model, and obtaining a calendar service life curve and a cycle service life curve of the system under the working condition through a weighting algorithm.

As shown in fig. 2, a calendar life curve and a cycle life curve obtained by fitting the road test vehicle are obtained. The calendar life curve and the cycle life curve in fig. 3 show the amount of decay of the calendar life with the shelf life and the amount of decay of the cycle life with the number of cycles, respectively.

(5) And inputting the parameters of the whole vehicle and the statistical working condition distribution into the endurance mileage correction model, and correcting the endurance mileage in three aspects of energy efficiency, accessory energy consumption and load influence.

(6) And integrating all parameters and data by the system life iterative model, predicting the life of the system and obtaining a confidence interval. As shown in fig. 4, the life prediction result of the road test is shown. The calendar decay curve and the cycle decay curve in fig. 4 represent the decay amounts of the calendar life and the cycle life, respectively, with time under the entire vehicle condition.

The life prediction result of the road test run is compared with the actual measurement data, and as shown in fig. 5, the prediction result of the road test run is basically identical with the actual measurement result.

(7) 100 vehicles are randomly selected from the market operated vehicles for analysis, and the comparison between the capacity attenuation of the market operated vehicles and the model prediction shows that the measured capacity attenuation of the market operated vehicles fluctuates around the prediction curve as shown in fig. 6, and the error is caused by the difference of working conditions.

The embodiment also provides a system for predicting and evaluating the service life of a lithium battery system of an electric vehicle, which comprises:

the parameter setting module is used for counting the working condition data of a specific vehicle, at least comprising daily mileage, average speed and operation area as working condition parameters for life prediction, and taking total electric quantity and endurance mileage as finished vehicle parameters for life prediction; the above step 1 is specifically performed.

The battery cell life parameter prediction module is used for carrying out calendar life and cycle life tests on the single battery cells, and the test results are used as the battery cell life parameters of the life prediction model; specifically, the above step 2 is performed.

The working condition characteristic distribution statistical module is used for randomly simulating the specific working conditions of the whole vehicle according to the whole vehicle parameters and the working condition parameters and combining the corresponding working condition distribution probability model, and counting the characteristic distribution of each working condition; the working condition distribution probability model comprises a private car working condition model and an operating car working condition model; the working condition distribution probability model is as follows:

the distribution of the starting trip time of the vehicle is mainly divided into four time intervals, namely [6, 10 ], [10, 14 ], [14, 20 ], [20, 24]，μ_nRepresents the nth time interval, f (x) represents the probability of a certain moment in the current interval, w_nIs the weight, σ, of the current time interval_nIs a probability parameter of the current time interval.

The weight and probability parameters in the private car working condition model and the operating car working condition model are respectively set as follows:

1) probability parameter sigma of private car working condition distribution probability model corresponding to four time intervals_nRespectively as follows: 1. 1.5, 0.5; weight w corresponding to four time intervals_nRespectively as follows: 0.35, 0.15, 0.45, 0.1.

2) Probability parameter sigma of operation condition distribution probability model corresponding to four time intervals_nRespectively as follows: 2, 2, 2, 2; weight w corresponding to four time intervals_nRespectively as follows: 0.3, 0.2,0.3、0.2。

The working conditions comprise charging, discharging and holding; the working condition characteristic distribution comprises temperature distribution, SOC distribution and current multiplying power distribution; the working condition characteristic distribution specifically comprises the following steps:

wherein cha, dch, and sta respectively represent a charge state, a discharge state, and a rest state. The SOC is divided every 20%, the current multiplying power is divided every 0.2 ℃, and the temperature is divided every 10 ℃; thus, w_{sta/soc_50}Indicates that SOC is [ 40%, 60% ]in the resting state]Time ratio of (a) w_{dch/temp_25}Indicating that the temperature was at 20 ℃ and 30 ℃ in the discharge state]Time ratio of (a) w_{cha/rate_0.5}Indicating that the charging multiplying factor is [0.4C, 0.6C ] in the discharging and charging state]Is a ratio of time.

The battery system service life calculation module inputs the battery cell service life data and the characteristic distribution of each working condition into the battery cell service life model, and obtains a calendar service life curve and a cycle service life curve of the battery system under the working condition through a weighting algorithm; the weight algorithm is as follows:

Cal_sys(τ)＝∑Cal_cell(T，s，τ)*w_{sta/soc_s}*w_{sta/temp_t}

Cyc_sys(n)＝∑Cyc_cell(T，s，n)*w_{cha/rate_r}*w_{cha/temp_t}

wherein, Cal_sys(τ)、Cyc_sys(τ)、Cal_cell(T，s，τ)、Cyc_cell(T, s, tau) respectively represents a calendar life curve and a cycle life curve of the battery system, and a calendar life curve and a cycle life curve of the single battery cell; the calendar life curve of the single battery cell is a decay curve of temperature (T), SOC(s) and resting time (tau); the cycle life curve of the single battery cell is an attenuation curve of temperature (T), charging rate (r) and cycle number (n).

The correction module inputs the vehicle parameters and the statistical working condition characteristic distribution into the endurance mileage correction model, and corrects the endurance mileage in three aspects of energy efficiency, accessory energy consumption and load influence; the correction factors are as follows:

f＝f_e/(f_a*f_m)

f_e＝∑E_t*w_{dch/temp_t}

wherein, f_e、f_a、f_mRespectively representing a total correction factor, an energy efficiency factor, an accessory energy consumption factor and a load influence factor; energy efficiency factor f_eAssociated with the discharge energy efficiency of the individual cells at different temperatures, E_tRepresenting the discharge energy efficiency of the single battery cell at a specific temperature; w is a_tRepresenting the ratio of the temperature interval at the start of the air conditioner, e_tAir conditioning hour energy consumption representing the temperature condition; u and ec respectively represent the average vehicle speed and the hundred kilometers energy consumption of the whole vehicle; load represents the ratio of load to vehicle weight.

And the battery system service life prediction module is used for iteratively integrating all parameters and data to predict the service life of the battery system. The generation algorithm is as follows:

Q_cal，sys＝Cal_sys[＞＝Q_loss，sys](Δτ)

Q_cyc，sys＝Cyc_sys[＞＝Q_loss，sys](Δn)

Q_loss，sys+＝Q_cal，sys+Q_cyc，sys

m+＝Δm

the iterative process is shown in fig. 2, where m, day, Qloss, sys respectively represent the accumulated traveled mileage, the accumulated traveled days, and the accumulated capacity fade rate during the iterative process. d represents an iteration period, the iteration period in the embodiment is 1-30 days, and Δ m, Δ τ and Δ n respectively represent the length increase, the shelf life and the cycle number in one iteration period. Qcal, sys, Qcyc, sys represent calendar decay and cycle decay, respectively, over one iteration period. M, Day, and Qloss represent the set iteration cutoff conditions: total mileage, total time and total attenuation.

The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.