CN113391225A - Lithium battery state-of-charge estimation method considering capacity degradation - Google Patents
Lithium battery state-of-charge estimation method considering capacity degradation Download PDFInfo
- Publication number
- CN113391225A CN113391225A CN202110545488.6A CN202110545488A CN113391225A CN 113391225 A CN113391225 A CN 113391225A CN 202110545488 A CN202110545488 A CN 202110545488A CN 113391225 A CN113391225 A CN 113391225A
- Authority
- CN
- China
- Prior art keywords
- lithium battery
- state
- charge
- neural network
- battery
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
- G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Secondary Cells (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention discloses a lithium battery state-of-charge estimation method considering capacity degradation, which comprises the steps of firstly, acquiring a lithium battery experimental data set, and calculating battery state-of-charge and health state data to obtain a complete data set; then constructing a hybrid neural network with two input ends, one output end, one or more circulating network layers and one or more full connection layers; and after the established hybrid neural network is trained by the complete data set, the hybrid neural network is used for estimating the state of charge of the battery to be estimated. The method is suitable for the state of charge estimation of the lithium battery considering capacity degradation, namely different health states, and has high estimation precision and high practical application value.
Description
Technical Field
The invention relates to the technical field of lithium batteries and artificial neural networks, provides a lithium battery state of charge estimation method, and particularly relates to a lithium battery state of charge estimation method considering capacity degradation.
Background
Accurate lithium battery state of charge estimation is of great importance to a lithium battery management system, and the method can help the battery management system to predict the remaining driving mileage and avoid overcharge or overdischarge of the battery and the like, so that the safety and reliability of the lithium battery are guaranteed. However, accurately estimating the state of charge of a lithium battery at the time of capacity degradation remains quite challenging.
Common lithium battery state-of-charge estimation methods mainly include an ampere-hour integration method, an open-circuit voltage method, a model-based method, a data driving method and the like. The ampere-hour integration method is simple, but has a large accumulated error; the open circuit voltage method requires the battery to be kept still for a long time to obtain a stable voltage value, and after the capacity of the battery is degraded, the change curve of the state of charge of the battery along with the open circuit voltage also changes, so that the open circuit voltage method cannot ensure the accuracy of the estimated value of the state of charge when the capacity degradation is considered; the model-based method usually simulates the dynamic characteristics of the interior of the battery by constructing an equivalent circuit model, an electrochemical model and the like of the battery, and estimates the state quantity in the battery model based on various filter algorithms such as extended kalman filter, particle filter and various observers such as a slip film observer, an extended state observer and the like, thereby realizing the estimation of the state of charge of the battery. The accuracy of the state of charge estimation result of the method highly depends on the accuracy of the constructed battery model, deep research and test on the battery are required for constructing the accurate battery model, and an unavoidable contradiction exists between the complexity of the model and the accuracy of the model, so that the applicability and the generalization capability of the model-based method are limited. Different from a model-based method, the data driving method does not care about the internal dynamic characteristics of the battery, but considers the battery as a black box, and directly learns the corresponding relation between some measured values such as voltage, current and the like and the charge state of the battery from experimental data, and the commonly used data driving method specifically comprises a support vector machine, a neural network and the like. Although the estimation results of the data-driven type method do not perform well in consideration of the dynamic discharge current or the dynamic ambient temperature, currently, there is less research on the estimation of the state of charge of the battery in consideration of the capacity degradation.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a lithium battery state of charge estimation method considering capacity degradation. The method is suitable for estimating the state of charge of the lithium battery in different degradation states, and has great practical application value.
Step 1: acquiring a lithium battery experiment data set, wherein the data set comprises the rated capacity of a lithium battery and the voltage, current and time data of the lithium battery in the experiment process;
step 2: calculating the state of charge and the state of health of the lithium battery based on the lithium battery experimental data set to obtain a complete data set;
and step 3: building a neural network comprising two input ends, one output end, one or more than one circulation network layer and one or more than one full connection layer;
and 4, step 4: and training the neural network by using the complete data set, and using the trained neural network for estimating the state of charge of the lithium battery.
Optionally, the step 3 further comprises the following steps: one input end of the neural network takes the voltage and the current of the battery as input data, and the input end directly inputs the data to a circulation network layer, the other input end of the neural network takes the health state of the battery as the input data, and the input end directly inputs the data to a full connection layer, and the output end of the neural network outputs the state of charge value of the lithium battery.
The invention has the beneficial effects that: the invention adopts the neural network with double input ends and combined with the circulating network layer and the full connection layer, and makes full use of the voltage data, the current data and the health state data of the lithium battery, thereby being capable of carrying out accurate state of charge estimation on the lithium battery with capacity degradation, namely under different health states.
Drawings
Fig. 1 is a flowchart illustrating steps of a lithium battery state of charge estimation method considering capacity degradation according to an embodiment of the present invention.
Fig. 2 is a diagram of a neural network structure constructed according to an embodiment of the present invention.
Fig. 3 shows the estimation result of the state of charge of the battery according to the embodiment of the invention.
Detailed Description
The invention is further described with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, a lithium battery state of charge estimation method considering capacity degradation includes the following steps:
s1, acquiring a lithium battery experiment data set, wherein the data set comprises the rated capacity of a lithium battery and voltage, current and time data in the experiment process. In the embodiment, standard charge-discharge cycle experimental data of lithium batteries with the rated capacities of 1.1Ah, which are provided by calice center of university of maryland and are numbered CS2_33, CS2_34, CS2_35, CS2_36 and CS2_37, are used as data sources, wherein the experimental data of CS2_33, CS2_34, CS2_35 and CS2_36 batteries are used as an experimental data set of the lithium batteries, and the CS2_37 battery is used as a battery to be estimated in the state of charge. In this experiment, five cells were all subjected to the same standard charging process at room temperature with a constant current rate of 0.5C until the voltage reached 4.2V, and then the charging voltage was maintained at 4.2V until the charging current dropped below 0.05A. Meanwhile, the discharge cutoff voltages of the four batteries are all 2.7V, wherein the discharge current multiplying powers of CS2_33, CS2_34 are 0.5C, and the discharge current multiplying powers of CS2_35, CS2_36 and CS2_37 are all 1C.
And S2, calculating the state of charge and the health state of the lithium battery based on the lithium battery experimental data set to obtain a complete data set. In this example, the ampere-hour integral method was used to calculate the state of charge of the lithium battery:
therein, SOC0Is the initial state of charge, SOC, of the lithium batterytThe charge state value of the lithium battery at the time t, i is the discharge current of the lithium battery, and C is the current capacity of the lithium battery;
in this embodiment, the lithium battery health status is calculated using the following formula:
wherein C is the current capacity of the lithium battery, C0The lithium battery rated capacity is adopted, and the SOH is the lithium battery health state.
And S3, establishing a neural network comprising two input ends, one output end, more than one circulation network layer and more than one full-connection layer, wherein one input end takes the voltage and the current of the battery as input data, the input end directly inputs the data to one circulation network layer, the other input end takes the health state of the battery as input data, the input end directly inputs the data to one full-connection layer, and the output end outputs the charge state value of the lithium battery. In this embodiment, the built neural network comprises 4 gated cyclic network layers and 2 fully-connected layers, and the specific structure is as shown in fig. 2, wherein layer 1, layer 2, layer 5 and layer 6 are gated cyclic network layers, layer 1, layer 2 and layer 5 are all composed of 80 gated cyclic units, layer 6 is composed of 1 gated cyclic unit, layer 3 and layer 4 are fully-connected layers, each layer is composed of 80 fully-connected units, layer 1 receives the input I of input end 11And I1＝[V，Cur]V, Cur are voltage and current values respectively, and layer 3 receives input I from input terminal 22And I2＝[SOH]。
And S4, training the neural network by using the complete data set, and using the trained neural network for estimating the state of charge of the lithium battery. In this embodiment, a complete data set is normalized, and the processed voltage, current, battery state of health, and corresponding battery state of charge data are used as a processed experimental data set, according to 7: a scale of 3 divides the complete data set into a training set and a validation set and trains the neural network in conjunction with the adam optimizer.
And S5, estimating the state of charge of the lithium battery by using the trained neural network, wherein in the embodiment, the trained neural network is used for estimating the state of charge of the CS2_37 battery when the capacity is degraded to a state of health (SOH) of 1 and 0.8 respectively, and the estimation result of the state of charge and the experimental result based on an ampere-hour integration method are shown in FIG. 3. In two estimated discharge cycles, the root mean square errors between the estimated value of the state of charge of the battery and the experimental value based on the ampere-hour integration method are respectively 2.38% and 2.44%, and the feasibility and the effectiveness of the method in the state of charge estimation of the capacity-degraded lithium battery are proved.
Claims (2)
1. A lithium battery state-of-charge estimation method considering capacity degradation is characterized by comprising the following steps:
step 1: acquiring a lithium battery experiment data set, wherein the data set comprises the rated capacity of a lithium battery and the voltage, current and time data of the lithium battery in the experiment process;
step 2: calculating the state of charge and the state of health of the lithium battery based on the lithium battery experimental data set to obtain a complete data set;
and step 3: building a neural network comprising two input ends, one output end, one or more than one circulation network layer and one or more than one full connection layer;
and 4, step 4: and training the neural network by using the complete data set, and using the trained neural network for estimating the state of charge of the lithium battery.
2. The lithium battery state of charge estimation method of claim 1, wherein the step 3 further comprises the following:
one input end of the neural network takes the voltage and the current of the battery as input data, and the input end directly inputs the data to the circulating network layer, the other input end of the neural network takes the health state of the battery as the input data, and the input end directly inputs the data to the full connection layer, and the output end of the neural network outputs the charge state value of the lithium battery.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110545488.6A CN113391225B (en) | 2021-05-19 | 2021-05-19 | Lithium battery state-of-charge estimation method considering capacity degradation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110545488.6A CN113391225B (en) | 2021-05-19 | 2021-05-19 | Lithium battery state-of-charge estimation method considering capacity degradation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113391225A true CN113391225A (en) | 2021-09-14 |
| CN113391225B CN113391225B (en) | 2022-06-03 |
Family
ID=77618050
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110545488.6A Active CN113391225B (en) | 2021-05-19 | 2021-05-19 | Lithium battery state-of-charge estimation method considering capacity degradation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113391225B (en) |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105319515A (en) * | 2015-11-18 | 2016-02-10 | 吉林大学 | A combined estimation method for the state of charge and the state of health of lithium ion batteries |
| KR101595956B1 (en) * | 2014-11-12 | 2016-02-22 | 충북대학교 산학협력단 | Apparatus and method for measuring state of charge(soc) for lithium ion battery |
| US20180136285A1 (en) * | 2016-11-16 | 2018-05-17 | Samsung Electronics Co., Ltd. | Method and apparatus for estimating state of battery |
| CN108519556A (en) * | 2018-04-13 | 2018-09-11 | 重庆邮电大学 | A kind of lithium ion battery SOC prediction techniques based on Recognition with Recurrent Neural Network |
| CN109031147A (en) * | 2018-08-21 | 2018-12-18 | 湖南兴业绿色电力科技有限公司 | A kind of SOC estimation method of ferric phosphate lithium cell group |
| CN110082682A (en) * | 2019-03-12 | 2019-08-02 | 浙江大学 | A kind of lithium battery charge state estimation method |
| CN110658459A (en) * | 2019-09-12 | 2020-01-07 | 北京航空航天大学 | Lithium ion battery state of charge estimation method based on bidirectional cyclic neural network |
| CN110703101A (en) * | 2019-09-12 | 2020-01-17 | 北京交通大学 | Lithium ion battery inter-partition cycle capacity decline prediction method |
| CN111832220A (en) * | 2020-06-16 | 2020-10-27 | 天津大学 | Lithium ion battery health state estimation method based on codec model |
| CN112067998A (en) * | 2020-09-10 | 2020-12-11 | 昆明理工大学 | Lithium ion battery state of charge estimation method based on deep neural network |
| CN112269134A (en) * | 2020-09-10 | 2021-01-26 | 杭州电子科技大学 | Battery SOC and SOH joint estimation method based on deep learning |
| CN112557907A (en) * | 2020-12-17 | 2021-03-26 | 杭州六纪科技有限公司 | SOC estimation method of electric vehicle lithium ion battery based on GRU-RNN |
-
2021
- 2021-05-19 CN CN202110545488.6A patent/CN113391225B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101595956B1 (en) * | 2014-11-12 | 2016-02-22 | 충북대학교 산학협력단 | Apparatus and method for measuring state of charge(soc) for lithium ion battery |
| CN105319515A (en) * | 2015-11-18 | 2016-02-10 | 吉林大学 | A combined estimation method for the state of charge and the state of health of lithium ion batteries |
| US20180136285A1 (en) * | 2016-11-16 | 2018-05-17 | Samsung Electronics Co., Ltd. | Method and apparatus for estimating state of battery |
| CN108519556A (en) * | 2018-04-13 | 2018-09-11 | 重庆邮电大学 | A kind of lithium ion battery SOC prediction techniques based on Recognition with Recurrent Neural Network |
| CN109031147A (en) * | 2018-08-21 | 2018-12-18 | 湖南兴业绿色电力科技有限公司 | A kind of SOC estimation method of ferric phosphate lithium cell group |
| CN110082682A (en) * | 2019-03-12 | 2019-08-02 | 浙江大学 | A kind of lithium battery charge state estimation method |
| CN110658459A (en) * | 2019-09-12 | 2020-01-07 | 北京航空航天大学 | Lithium ion battery state of charge estimation method based on bidirectional cyclic neural network |
| CN110703101A (en) * | 2019-09-12 | 2020-01-17 | 北京交通大学 | Lithium ion battery inter-partition cycle capacity decline prediction method |
| CN111832220A (en) * | 2020-06-16 | 2020-10-27 | 天津大学 | Lithium ion battery health state estimation method based on codec model |
| CN112067998A (en) * | 2020-09-10 | 2020-12-11 | 昆明理工大学 | Lithium ion battery state of charge estimation method based on deep neural network |
| CN112269134A (en) * | 2020-09-10 | 2021-01-26 | 杭州电子科技大学 | Battery SOC and SOH joint estimation method based on deep learning |
| CN112557907A (en) * | 2020-12-17 | 2021-03-26 | 杭州六纪科技有限公司 | SOC estimation method of electric vehicle lithium ion battery based on GRU-RNN |
Non-Patent Citations (8)
| Title |
|---|
| HICHAM CHAOUI 等: "State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks", 《IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY》 * |
| HICHAM CHAOUI 等: "State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks", 《IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY》, vol. 66, no. 10, 14 June 2017 (2017-06-14), pages 8773 - 8783, XP055831934, DOI: 10.1109/TVT.2017.2715333 * |
| JONG-HYUN LEE 等: "State of Charge Estimation and State of Health Diagnostic Method Using Multilayer Neural Networks", 《2021 INTERNATIONAL CONFERENCE ON ELECTRONICS, INFORMATION, AND COMMUNICATION (ICEIC)》 * |
| JONG-HYUN LEE 等: "State of Charge Estimation and State of Health Diagnostic Method Using Multilayer Neural Networks", 《2021 INTERNATIONAL CONFERENCE ON ELECTRONICS, INFORMATION, AND COMMUNICATION (ICEIC)》, 10 March 2021 (2021-03-10) * |
| XU J. 等: "Coupling Effect of State-of-Health and State-of-Charge on the Mechanical Integrity of Lithium-Ion Batteries", 《EXPERIMENTAL MECHANICS》 * |
| XU J. 等: "Coupling Effect of State-of-Health and State-of-Charge on the Mechanical Integrity of Lithium-Ion Batteries", 《EXPERIMENTAL MECHANICS》, vol. 58, no. 4, 31 December 2018 (2018-12-31), pages 633 - 643, XP036463010, DOI: 10.1007/s11340-018-0380-9 * |
| 李超然 等: "基于深度学习的锂离子电池SOC和SOH联合估算", 《中国电机工程学报》 * |
| 李超然 等: "基于深度学习的锂离子电池SOC和SOH联合估算", 《中国电机工程学报》, 23 October 2020 (2020-10-23), pages 681 - 691 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113391225B (en) | 2022-06-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chemali et al. | Long short-term memory networks for accurate state-of-charge estimation of Li-ion batteries | |
| Chaoui et al. | Lyapunov-based adaptive state of charge and state of health estimation for lithium-ion batteries | |
| CN104535934B (en) | The electrokinetic cell state of charge method of estimation and system of online feedforward compensation | |
| CN105759213A (en) | Method for measuring storage battery residual capacity SOC | |
| Azis et al. | State of charge (SoC) and state of health (SoH) estimation of lithium-ion battery using dual extended kalman filter based on polynomial battery model | |
| CN112881930B (en) | Energy storage battery health management prediction method and system based on Internet of things | |
| CN110658459A (en) | Lithium ion battery state of charge estimation method based on bidirectional cyclic neural network | |
| Qiuting et al. | State of health estimation for lithium-ion battery based on D-UKF | |
| CN107769335A (en) | A kind of multi-mode lithium battery intelligent charging management method and device | |
| CN110474400A (en) | A kind of battery pack equilibrium method and device | |
| CN110462412A (en) | Device and method for estimating the SOC of battery | |
| CN106772097B (en) | Method for correcting SOC (State of Charge) by using charger | |
| CN112946480B (en) | Lithium battery circuit model simplification method for improving SOC estimation real-time performance | |
| CN106004481A (en) | SOH value estimation method for battery pack of hybrid electric vehicle | |
| CN113420444A (en) | Lithium ion battery SOC estimation method based on parameter online identification | |
| CN113391225B (en) | Lithium battery state-of-charge estimation method considering capacity degradation | |
| CN117169723A (en) | Battery evaluation system based on multi-model fusion | |
| CN117074955A (en) | Cloud-end correction OCV-based lithium battery state joint estimation method | |
| Chen et al. | Online estimation of state of power for lithium-ion battery considering the battery aging | |
| Wang et al. | Impact of sensor accuracy of battery management system on SOC estimation of electric vehicle based on EKF algorithm | |
| CN114487844A (en) | Lithium ion battery SOC estimation method based on battery capacity | |
| Kustiman et al. | Battery state of charge estimation based on coulomb counting combined with recursive least square and pi controller | |
| Wang et al. | A new state of charge estimation method for lithium-ion battery based on sliding mode observer | |
| Chang et al. | An SOC estimation method based on sliding mode observer and the Nernst Equation | |
| CN108445396A (en) | The evaluation method of the online state-of-charge of lithium manganate battery group based on rebound voltage |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |