CN105021996A - Battery SOH (section of health) estimation method of energy storage power station BMS (battery management system) - Google Patents
Battery SOH (section of health) estimation method of energy storage power station BMS (battery management system) Download PDFInfo
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Abstract
The present invention relates to a battery SOH estimation method of an energy storage power station BMS. The method comprises the following steps: the step S1: electric operation on a system; the step S2: circle and timing collection of battery real time data and extraction of a single voltage, a single internal resistance, a battery temperature, a charge-discharge current and a charge-discharge electric quantity from the real time data; the step S3: respective estimation of a battery SOH through an open-circuit voltage method, an ampere-hour integral method, a Kalman filtering method and a resistance method via the combination of nominal parameter of the battery and collected real time data of the battery; and the step S4: comprehensive calculation of the battery SOH obtained by estimation through a historical data amendment method to work out a model for calculating SOH coefficients, wherein the model for calculating SOH coefficients may be used in subsequent each SOH analytical calculation. The estimation method described herein is strong adaptable to storage batteries under various environments, and has a self-calibration function and high estimation accuracy, wherein the estimated value is approximate to the real value, thereby the real health status of the battery may be relatively accurately reflected.
Description
Technical field
The present invention relates to battery life evaluation method, particularly relate to the battery SOH evaluation method of a kind of energy-accumulating power station BMS.
Background technology
The voltage of existing accumulator system BMS usual monitoring management battery, internal resistance, temperature, part is had to estimate SOC, SOH (section of health, a battery health degree) normally amount ignored is exactly part producer estimation SOH, error is also very large, some errors, more than 50%, are traced it to its cause, mainly unclear to the agine mechaism of battery, to the bad assurance of the use procedure of battery, the computing method of use and algorithm are not studied clear.Secondly, same algorithm is used to the uncertainty of battery.
There is the BMS system of a lot of lithium electricity, plumbic acid, Ni-MH battery in the market, portioned product also has analytical estimating SOH's and SOC, its evaluation method is terminal voltage method, ampere-hour integral method mostly, be exactly judge cell degradation degree according to the terminal voltage of battery, be filled with how many electricity according to battery, release how many electricity to diagnose the health degree of battery.So the cell health state application condition of diagnosis is large.Most importantly bad adaptability, in test battery group, test data adjusts parameter per sample, and the precision of estimation is all right, once test sample changes, work operating mode changes, and parameter is just inapplicable, so error becomes large, precision is just very poor.
Summary of the invention
The object of the invention is to the defect for overcoming prior art, and the battery SOH evaluation method of the energy-accumulating power station BMS providing a kind of adaptability stronger, and improve estimation precision.
For achieving the above object, the present invention is by the following technical solutions:
The battery SOH evaluation method of energy-accumulating power station BMS, comprises the following steps:
Step S1: system electrification is run;
Step S2: circulation timing gathers battery real time data, and extracts monomer voltage, monomer internal resistance, battery temperature, charging and discharging currents and charge/discharge electricity amount from battery real time data;
Step S3: in conjunction with nominal parameter and the battery real time data collected of battery, distinguish estimating battery SOH by open-circuit voltage method, ampere-hour integral method, Kalman filtering method and internal resistance method;
Step S4: by estimating that the battery SOH obtained goes out one by historical data revised law COMPREHENSIVE CALCULATING and calculates SOH Modulus Model, calculating SOH Modulus Model and can be used for follow-up each SOH analytical calculation.
Further, in step s3, open-circuit voltage method specific implementation is: by battery standing a period of time, is in after steady state (SS) until battery open circuit voltage, by comparison open-circuit voltage and SOH mapping table in the past, draws present battery SOH.
Further, in step s3, ampere-hour integral method specific implementation is: record battery charging and discharging electric current, and it is obtained to time integral the electricity that battery bleeds off or be filled with in special time period; Charging operating mode state-of-charge equals the initial state-of-charge of battery and adds the ratio being filled with electric capacity and rated capacitance; Electric discharge operating mode battery charge state equals the initial state-of-charge of battery and deducts the ratio bleeding off electric capacity and rated capacitance.
Further, in step s3, Kalman filtering method specific implementation is: variable SOH being regarded as this dynamic system, by the structure of battery model, draw state equation and the observation equation of battery model, finally draw SOH according to Kalman filter theory.
Further, in step s3, internal resistance method estimates SOH by the relation set up between internal resistance and SOH.
The present invention's beneficial effect is compared with prior art:
SOH evaluation method of the present invention is adapted to lithium battery, lead-acid battery, Ni-MH battery, utilize the monomer voltage of Real-time Collection, monomer internal resistance, battery temperature, charging and discharging currents, charge/discharge electricity amount, in conjunction with the nominal parameter of battery, use open-circuit voltage method, ampere-hour integral method, internal resistance method, Kalman filtering method, the state of the many algorithms analytical calculation batteries such as historical data revised law, method adapts to the accumulator under various environment, strong adaptability, and there is self-calibration function, and estimated value is close to actual value, estimation precision is high, more accurately can reflect the real health status of battery.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the battery SOH evaluation method of energy-accumulating power station BMS.
Embodiment
In order to more fully understand technology contents of the present invention, below in conjunction with specific embodiment technical scheme of the present invention being introduced further and illustrating.
The process flow diagram of the embodiment of the present invention as shown in Figure 1.
The battery SOH evaluation method of the energy-accumulating power station BMS of the present embodiment comprises the following steps:
Step S1: system electrification is run;
Step S2: circulation timing gathers battery real time data, and extracts monomer voltage, monomer internal resistance, battery temperature, charging and discharging currents and charge/discharge electricity amount from battery real time data;
Step S3: in conjunction with nominal parameter and the battery real time data collected of battery, distinguish estimating battery SOH by open-circuit voltage method, ampere-hour integral method, Kalman filtering method and internal resistance method;
Step S4: by estimating that the battery SOH obtained goes out one by historical data revised law COMPREHENSIVE CALCULATING and calculates SOH Modulus Model, calculating SOH Modulus Model and can be used for follow-up each SOH analytical calculation.
Open-circuit voltage method is better simply SOH evaluation method.Under concrete applying working condition, there is specific corresponding relation in the open-circuit voltage of battery charge state and lithium battery.In step s3, open-circuit voltage method specific implementation is: by battery standing a period of time, is in after steady state (SS) until battery open circuit voltage, by comparison open-circuit voltage and SOH mapping table in the past, draws present battery SOH.The advantage of open-circuit voltage method is, simple to operate, only need table look-up and can determine battery SOH, and have suitable precision; Its weak point is, can only carry out, and battery reaches the time that steady state (SS) generally needs a few hours, be therefore also not suitable with the performance requirement of lithium battery monitoring system for electric automobile at battery idle state.For lead-acid battery, open-circuit voltage method but can not draw above-mentioned conclusion, only shows certain relation when cell degradation is serious.
Ampere-hour integral method is applied the most general in SOH estimating algorithm.In step s3, ampere-hour integral method specific implementation is: record battery charging and discharging electric current, and it is obtained to time integral the electricity that battery bleeds off or be filled with in special time period; Charging operating mode state-of-charge equals the initial state-of-charge of battery and adds the ratio being filled with electric capacity and rated capacitance; Electric discharge operating mode battery charge state equals the initial state-of-charge of battery and deducts the ratio bleeding off electric capacity and rated capacitance.Ampere-hour integral method advantage is that principle is simple, and meets battery on-line measurement, is therefore widely used.The deficiency of ampere-hour integral method is, its initial SOH cannot determine, and the measuring accuracy such as current signal exist error, and the accumulated error along with the time becomes increasing gradually, causes SOH estimated value to depart from actual value.
Ampere-hour integral method calculates battery electric quantity C, and during charging, charging current is multiplied by the duration of charging exactly, is filled with the electricity of battery exactly.Battery electric quantity C:C=C0 ± ∫ Idt during discharge and recharge; Wherein, C0 is the battery electric quantity before discharge and recharge, and I is battery charging and discharging electric current, and the time is accurate to second.The electricity that can be filled with according to battery and the electricity relation that can release diagnose the SOH of battery.
Kalman filtering method is the SOH evaluation method based on battery model.Lithium battery model is a dynamic system, and Changing Pattern is non-linear.In step s3, Kalman filtering method specific implementation is: variable SOH being regarded as this dynamic system, by the structure of battery model, draws state equation and the observation equation of battery model, finally draws SOH according to Kalman filter theory.Kalman filtering method precision is high, and the impact by initial error is minimum, and antijamming capability is strong.The weak point of Kalman filtering method is, its SOH estimation precision depends on the precision of Li-ion battery model, and battery model is the core of algorithm accurately.Lithium battery model is the system of a dynamic change, and building process is complicated, and the method needs a large amount of computings simultaneously, and difficulty is larger.
In step s3, internal resistance method estimates SOH by the relation set up between internal resistance and SOH.Internal resistance is an amount that can reflect cell health state, and when battery dispatches from the factory, internal resistance value is in a desirable scope, and along with the working time of a Battery pack extends, the internal resistance value having percentage of batteries increases, and the Trend value of increase and relation can diagnose SOH.
In step s3, historical data revised law sums up a calculating SOH Modulus Model according to front SOH result of testing several times, and each analytical calculation SOH with reference to this Modulus Model, will calibrate this with reference to Modulus Model under given conditions later.With this model for foundation, battery, when state changes, can estimate SOH more exactly, can improve algorithm and have same precision to different electric battery.Historical data revised law can according to the present SOH of the parameter diagnosises such as the voltage limit of historical record, capacity limit value, SOH ultimate value, to reduce error.Historical data revised law can adapt to different electric battery, improves precision.
More than state only with embodiment to further illustrate technology contents of the present invention so that reader is easier to understand, but does not represent embodiments of the present invention and be only limitted to this, any technology done according to the present invention extends or recreation, all by protection of the present invention.
Claims (5)
1. the battery SOH evaluation method of energy-accumulating power station BMS, is characterized in that, comprise the following steps:
Step S1: system electrification is run;
Step S2: circulation timing gathers battery real time data, and extracts monomer voltage, monomer internal resistance, battery temperature, charging and discharging currents and charge/discharge electricity amount from battery real time data;
Step S3: in conjunction with nominal parameter and the battery real time data collected of battery, distinguish estimating battery SOH by open-circuit voltage method, ampere-hour integral method, Kalman filtering method and internal resistance method;
Step S4: by estimating that the battery SOH obtained goes out one by historical data revised law COMPREHENSIVE CALCULATING and calculates SOH Modulus Model, calculating SOH Modulus Model and can be used for follow-up each SOH analytical calculation.
2. the method for claim 1, is characterized in that, in step s3, open-circuit voltage method specific implementation is: by battery standing a period of time, be in after steady state (SS) until battery open circuit voltage, by comparison open-circuit voltage and SOH mapping table in the past, draw present battery SOH.
3. the method for claim 1, is characterized in that, in step s3, ampere-hour integral method specific implementation is: record battery charging and discharging electric current, and it is obtained to time integral the electricity that battery bleeds off or be filled with in special time period; Charging operating mode state-of-charge equals the initial state-of-charge of battery and adds the ratio being filled with electric capacity and rated capacitance; Electric discharge operating mode battery charge state equals the initial state-of-charge of battery and deducts the ratio bleeding off electric capacity and rated capacitance.
4. the method for claim 1, it is characterized in that, in step s3, Kalman filtering method specific implementation is: variable SOH being regarded as this dynamic system, by the structure of battery model, draw state equation and the observation equation of battery model, finally draw SOH according to Kalman filter theory.
5. the method for claim 1, is characterized in that, in step s3, internal resistance method estimates SOH by the relation set up between internal resistance and SOH.
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| CN105487016A (en) * | 2016-01-21 | 2016-04-13 | 深圳市沃特玛电池有限公司 | SOC value estimation method and system thereof |
| CN105607011A (en) * | 2016-03-23 | 2016-05-25 | 北京经纬恒润科技有限公司 | Method and device for estimating state of health (SOH) of battery |
| CN105717459A (en) * | 2016-02-05 | 2016-06-29 | 北京长城华冠汽车科技股份有限公司 | Power battery charge state distributed computing method and system |
| CN105717457A (en) * | 2016-02-03 | 2016-06-29 | 惠州市蓝微新源技术有限公司 | Method for utilizing big database analysis to carry out battery pack health state estimation |
| CN106249173A (en) * | 2016-10-10 | 2016-12-21 | 哈尔滨理工大学 | A kind of battery health degree SOH evaluation method |
| CN107121642A (en) * | 2017-06-28 | 2017-09-01 | 北京新能源汽车股份有限公司 | The SOH coefficient updating methods and device of electrokinetic cell |
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