阅读说明：本技术 一种新能源电动汽车续航预警方法 (New energy electric vehicle endurance early warning method ) 是由 张婷婷 杨思文 厉运杰 于 2020-06-18 设计创作，主要内容包括：本发明提出的一种新能源电动汽车续航预警方法,包括：获取新能源汽车在整车运行过程中的BMS数据；统计整车的充电工况数据,并结合整车的充电工况数据识别车辆的使用类型；统计整车的放电工况运行数据,并结合整车的放电工况运行数据识别车辆运行道路环境；结合车辆的使用类型、车辆运行道路环境和车主用车习惯,获得车辆的里程与容量的关系曲线以及里程与能量的关系曲线,结合两种关系曲线实时计算续航里程,并产生续航警报。本发明综合考量了电池系统中的各个单体电芯及其相互关系、电芯使用条件等重要条件,通过分析整车运行工况数据直观的看出电池包的状态,为车辆的行车安全与车主的生命财产安全提供故障预警服务。(The invention provides a new energy electric vehicle endurance early warning method, which comprises the following steps: acquiring BMS data of the new energy automobile in the whole automobile running process; counting charging condition data of the whole vehicle, and identifying the use type of the vehicle by combining the charging condition data of the whole vehicle; counting discharge working condition operation data of the whole vehicle, and identifying a vehicle operation road environment by combining the discharge working condition operation data of the whole vehicle; and obtaining a relation curve of mileage and capacity of the vehicle and a relation curve of mileage and energy by combining the use type of the vehicle, the running road environment of the vehicle and the vehicle using habit of a vehicle owner, calculating the endurance mileage in real time by combining the two relation curves, and generating the endurance alarm. The invention comprehensively considers the important conditions of all single battery cells in the battery system, the interrelation of the single battery cells, the use condition of the battery cells and the like, and visually finds out the state of the battery pack by analyzing the running condition data of the whole vehicle, thereby providing fault early warning service for the driving safety of the vehicle and the life and property safety of the vehicle owner.)

1. The new energy electric vehicle endurance early warning method is characterized by comprising the following steps of:

s1, acquiring BMS data of the new energy automobile in the whole automobile running process and cleaning the data;

s2, counting charging condition data of the whole vehicle according to the BMS data cleaning result, and identifying the use type of the vehicle by combining the charging condition data of the whole vehicle;

s3, counting the discharge working condition operation data of the whole vehicle according to the BMS data cleaning result, and identifying the vehicle operation road environment by combining the discharge working condition operation data of the whole vehicle;

s4, according to the BMS data cleaning result, the charging working condition data of the whole vehicle and the discharging working condition operation data of the whole vehicle are combined, and collected data of the vehicle in a standing state after discharging and charging are screened;

s5, identifying the vehicle usage habit of the vehicle owner by combining the collected data of the whole vehicle in the standing state;

and S6, obtaining a relation curve of mileage and capacity of the vehicle and a relation curve of mileage and energy by combining the use type of the vehicle, the running road environment of the vehicle and the vehicle using habit of a vehicle owner, calculating the endurance mileage in real time by combining the two relation curves, and generating the endurance alarm.

2. The endurance early warning method of the new energy electric vehicle of claim 1, further comprising step S7: and obtaining a battery pack capacity attenuation value by combining the relation curve of the mileage and the capacity and the relation curve of the mileage and the energy, judging the potential fault of the battery pack according to the battery pack capacity attenuation value, and alarming.

3. The new energy electric vehicle endurance early warning method according to claim 1, wherein in step S1, the BMS data includes: battery pack chassis number, CAN message type, reporting time, mileage, vehicle speed, total electric quantity, voltage, current, temperature, battery pack working mode, charging and discharging ampere hours, BMS and battery pack single body voltage.

4. The new energy electric vehicle endurance early warning method according to claim 3, wherein in step S1, the BMS data further includes: the running time, the running mileage, the total electric quantity, the total voltage, the total current, the highest temperature of the single body, the lowest temperature of the single body and the working mode of the battery pack in the charging process of the whole vehicle.

5. The new energy electric vehicle endurance early warning method according to claim 1, wherein in step S2, the charging condition data of the entire vehicle includes: single charge duration, temperature, SOC state, charge current, cell voltage, total charge duration, single charge rate, single charge capacity, and single charge energy.

6. The new energy electric vehicle endurance early warning method according to claim 1, wherein in step S2, the usage type of the vehicle includes: private cars, taxis, operation cars, buses, and logistics cars.

7. The new energy electric vehicle endurance early warning method according to claim 1, wherein in step S3, the discharge condition operation data includes: single discharge duration, temperature, SOC state, mileage, discharge current, cell voltage, total discharge duration, single operating mileage, single discharge rate, vehicle speed, single discharge capacity, single discharge energy, and single energy consumption.

8. The cruising early warning method of claim 1, wherein in step S3, the vehicle operating road environment includes a suburban road environment, a rural road environment, an urban road environment, and an expressway environment.

9. The new energy electric vehicle endurance early warning method according to claim 1, wherein in step S4, the data acquisition in the static state includes: resting temperature, resting SOC state and resting time.

10. The endurance early warning method of the new energy electric vehicle of any one of claims 1 to 9, wherein in step S6, when the obtained endurance mileage is less than or equal to a preset endurance threshold value, an endurance alarm is generated.

Technical Field

The invention relates to the technical field of new energy electric automobiles, in particular to a new energy electric automobile endurance early warning method.

Background

In recent years, new energy automobiles have been rapidly developed. The new energy automobile meets the daily travel requirements of people, is pollution-free and beneficial to environmental protection, and is considered to be one of effective ways for realizing energy conservation and emission reduction. Among all parts of the new energy automobile, a battery system is the most core part, and the service life and the safety of the new energy automobile are directly determined. The service life of the battery system depends on the service life of the single battery cell to a great extent and is influenced by the service conditions such as the service temperature of the whole vehicle and the SOC state.

Disclosure of Invention

Based on the technical problems in the background art, the invention provides a new energy electric vehicle endurance early warning method.

The invention provides a new energy electric vehicle endurance early warning method, which comprises the following steps:

s1, acquiring BMS data of the new energy automobile in the whole automobile running process and cleaning the data;

s2, counting charging condition data of the whole vehicle according to the BMS data cleaning result, and identifying the use type of the vehicle by combining the charging condition data of the whole vehicle;

s3, counting the discharge working condition operation data of the whole vehicle according to the BMS data cleaning result, and identifying the vehicle operation road environment by combining the discharge working condition operation data of the whole vehicle;

s4, according to the BMS data cleaning result, the charging working condition data of the whole vehicle and the discharging working condition operation data of the whole vehicle are combined, and collected data of the vehicle in a standing state after discharging and charging are screened;

s5, identifying the vehicle usage habit of the vehicle owner by combining the collected data of the whole vehicle in the standing state;

and S6, obtaining a relation curve of mileage and capacity of the vehicle and a relation curve of mileage and energy by combining the use type of the vehicle, the running road environment of the vehicle and the vehicle using habit of a vehicle owner, calculating the endurance mileage in real time by combining the two relation curves, and generating the endurance alarm.

Preferably, the method further comprises step S7: and obtaining a battery pack capacity attenuation value by combining the relation curve of the mileage and the capacity and the relation curve of the mileage and the energy, judging the potential fault of the battery pack according to the battery pack capacity attenuation value, and alarming.

Preferably, in step S1, the BMS data includes: battery pack chassis number, CAN message type, reporting time, mileage, vehicle speed, total electric quantity, voltage, current, temperature, battery pack working mode, charging and discharging ampere hours, BMS and battery pack single body voltage.

Preferably, in step S1, the BMS data further includes: the running time, the running mileage, the total electric quantity, the total voltage, the total current, the highest temperature of the single body, the lowest temperature of the single body and the working mode of the battery pack in the charging process of the whole vehicle.

Preferably, in step S2, the charging condition data of the entire vehicle includes: single charge duration, temperature, SOC state, charge current, cell voltage, total charge duration, single charge rate, single charge capacity, and single charge energy.

Preferably, in step S2, the usage type of the vehicle includes: private cars, taxis, operation cars, buses, and logistics cars.

Preferably, in step S3, the discharge condition operation data includes: single discharge duration, temperature, SOC state, mileage, discharge current, cell voltage, total discharge duration, single operating mileage, single discharge rate, vehicle speed, single discharge capacity, single discharge energy, and single energy consumption.

Preferably, in step S3, the vehicle-running road environment includes a suburban road environment, a rural road environment, an urban road environment, and an expressway environment.

Preferably, in step S4, the data collected in the resting state includes: resting temperature, resting SOC state and resting time.

Preferably, in step S6, when the obtained cruising range is less than or equal to the preset cruising threshold value, a cruising alarm is generated.

The invention provides a new energy electric vehicle endurance early warning method which is based on statistical data of vehicle running, comprehensively considers the use state under each working condition, and counts the occupation ratio under each condition, thereby accurately obtaining the running state of a battery system.

The invention comprehensively considers the important conditions of each single battery cell, the interrelation of the battery cells, the use condition of the battery cells and the like in the battery system, and visually finds out the state of the battery pack by analyzing the running condition data of the whole vehicle, thereby providing fault early warning service for the driving safety of the vehicle and the life and property safety of the vehicle owner; and the use type of the whole vehicle, the travel characteristic of the vehicle owner and the use habit of the vehicle owner are identified so as to obtain the online real-time battery capacity attenuation value and the real-time endurance mileage of the vehicle, thereby providing a more optimized scheme for the use of the vehicle owner.

Drawings

Fig. 1 is a flow chart of a new energy electric vehicle endurance early warning method provided by the invention.

Detailed Description

Referring to fig. 1, the invention provides a new energy electric vehicle endurance early warning method, which comprises the following steps.

And S1, acquiring BMS data of the new energy automobile in the whole automobile running process and cleaning the data. Specifically, in this embodiment, the BMS data includes: battery pack chassis number, CAN message type, reporting time, mileage, vehicle speed, total electric quantity, voltage, current, temperature, battery pack working mode, charging and discharging ampere hours, BMS and battery pack single body voltage. The BMS data further includes: the running time, the running mileage, the total electric quantity, the total voltage, the total current, the highest temperature of the single body, the lowest temperature of the single body and the working mode of the battery pack in the charging process of the whole vehicle.

And S2, counting the charging condition data of the whole vehicle according to the BMS data cleaning result, and identifying the use type of the vehicle by combining the charging condition data of the whole vehicle. The types of use of the vehicle include: private cars, taxis, operation cars, buses, logistics cars, and the like.

In this embodiment, the charging condition data of the entire vehicle includes: single charge duration, temperature, SOC state, charge current, cell voltage, total charge duration, single charge rate, single charge capacity, and single charge energy. Wherein, the single charging time length is the charging end time-charging start time; the total charging time length is the accumulated value of the single charging time length of the vehicle;

single charge rate is charge current/rated capacity;

single charge capacity is charge current × single charge time;

single charging energy is charging voltage multiplied by charging current multiplied by charging time; the charging time is the time from the beginning of charging to the end of charging, and the charging current can specifically adopt the charging current at the acquisition moment.

Specifically, in this embodiment, corresponding charging reference condition data is set for different vehicle types, and then the usage type of the vehicle is determined according to the comparison result between the charging condition data of the entire vehicle and the charging reference condition data. For example, recharging slowly once a day is a feature of use in a private car. The charging is carried out for a plurality of times in one day, the charging is carried out quickly in the daytime, and the slow charging is a use characteristic of the taxi at night. Therefore, the use type of the vehicle can be judged according to the charging speed and the charging frequency.

And S3, counting the discharge working condition operation data of the whole vehicle according to the BMS data cleaning result, and identifying the vehicle operation road environment by combining the discharge working condition operation data of the whole vehicle. Vehicle operating road environments include suburban road environments, rural road environments, urban road environments, and highway environments.

In this embodiment, the discharge condition operation data includes: single discharge duration, temperature, SOC state, mileage, discharge current, cell voltage, total discharge duration, single operating mileage, single discharge rate, vehicle speed, single discharge capacity, single discharge energy, and single energy consumption.

The single discharge duration is the time length from the start of vehicle operation to the stop of the vehicle operation;

the total discharge time length is the sum of the single discharge time lengths of the vehicles;

single discharge multiplying factor is discharge current/rated capacity;

the vehicle speed is the single operation mileage/single operation duration;

single discharge capacity is discharge current x discharge time; the discharge time is a time from the vehicle running to the vehicle stop.

Single discharge energy is discharge voltage x discharge current x time;

single energy consumption is equal to single discharge energy/single operating mileage.

Specifically, the discharge current is the discharge current at the time of collection.

Specifically, in step S3, the trip characteristics of the vehicle owner, including the vehicle type and the driving road, can be identified from the discharge duration and the daily mileage distribution. Generally, the discharge time is short, 1-2 hours, the daily operating mileage is distributed within 0-75Km, and the vehicles moving on duty instead of walking or private cars can be identified, otherwise; the discharging time is between 8 and 12 hours, and the daily operating mileage is between 200 and 400Km, so that the use characteristics of the taxi can be distinguished.

And the road environment of the vehicle can be deduced from the discharge multiplying power, the vehicle speed, the single discharge capacity and the single energy consumption distribution. The average discharge multiplying power is 0.1-0.2C, the average running speed per hour is 10-30Km/h, and the energy consumption is 20Kwh/100Km, so that the running of the vehicle on an urban road can be identified; the average discharge multiplying power is 0.3-0.4C, the average running speed per hour is 30-50Km/h, and the running of the vehicle on a suburb road can be identified; the average discharge multiplying factor is more than 0.4C, the average running speed per hour is 80-110Km/h, and the vehicle can be identified to run on an expressway.

And S4, according to the BMS data cleaning result, combining the charging working condition data of the whole vehicle and the discharging working condition operation data of the whole vehicle, and screening the collected data of the vehicle in the standing state after discharging and charging. The data acquisition in the standing state comprises the following steps: resting temperature, resting SOC state and resting time. Specifically, the standing temperature is an average value of the discharge ending temperature and the charge starting temperature and an average value of the charge ending temperature and the discharge starting temperature; the standing time period is the total running time-total charging time-total discharging time.

And S5, identifying the vehicle usage habit of the vehicle owner by combining the collected data of the whole vehicle in the standing state. In specific implementation, the occupation ratio of the standing time, the charging time and the discharging time can be calculated, and the travel characteristics of the vehicle owner can be deduced from the standing SOC state and the standing time of the vehicle. For example, a private car or a working vehicle has a certain daily running route, the running time is small, the standing time is large, and a taxi is opposite.

Therefore, in the embodiment, the judgment of the vehicle type can be performed by combining the charging working condition data, the discharging operation working condition data and the collected data in the standing state for mutual verification.