CN113011082A - Lithium battery SOC prediction method for optimizing particle filtering by improving ant colony algorithm - Google Patents
Lithium battery SOC prediction method for optimizing particle filtering by improving ant colony algorithm Download PDFInfo
- Publication number
- CN113011082A CN113011082A CN202110176036.5A CN202110176036A CN113011082A CN 113011082 A CN113011082 A CN 113011082A CN 202110176036 A CN202110176036 A CN 202110176036A CN 113011082 A CN113011082 A CN 113011082A
- Authority
- CN
- China
- Prior art keywords
- particle
- ant colony
- colony algorithm
- lithium battery
- soc
- 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
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- 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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/25—Design optimisation, verification or simulation using particle-based methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- 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
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Medical Informatics (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Secondary Cells (AREA)
- Tests Of Electric Status Of Batteries (AREA)
Abstract
The invention particularly relates to a lithium battery SOC prediction method for optimizing particle filtering by improving an ant colony algorithm, which comprises the following steps of: carrying out discharge tests on the lithium battery under different working condition currents, and preprocessing experimental data; performing parameter identification according to the preprocessed experimental data, and constructing a state equation according to an ampere-hour integration method and SOC prediction influence factors; establishing a measurement equation of a theoretical prediction model of the battery according to a second-order Thevenin equivalent model; optimizing particle filtering by using an improved ant colony algorithm; and predicting the SOC change of the battery through the optimized particle filter. The prediction method provided by the invention improves the condition that the traditional ant colony algorithm is easy to fall into the local optimal solution; the improved ant colony algorithm is used for optimizing the particle filtering, the problems of low particle diversity and poor particles when the SOC is estimated by the particle filtering algorithm are solved, the problems of complexity and low accuracy of the lithium battery SOC estimation method are solved, and the estimation precision is effectively improved.
Description
Technical Field
The invention belongs to the field of battery energy management systems, relates to a lithium battery state of charge prediction technology, and particularly relates to a lithium battery SOC prediction method for optimizing particle filtering by improving an ant colony algorithm.
Background
With the increasing severity of the problems of energy shortage, environmental pollution and the like, people gradually turn to the technical field of research on low energy consumption and environmental protection, wherein the research and the use of electric vehicles are favored by people and are supported by the strong force of national policies, so that the electric vehicles are rapidly developed. The lithium ion battery has the advantages of high energy ratio, long service life, low self-discharge rate and the like, and is widely applied to the field of electric automobiles. As the lithium battery ages, the capacity and stability of the battery are gradually reduced, and therefore, in order to ensure safe use of the battery, the Battery Management System (BMS) provided in the electric vehicle is required to monitor, predict, control, etc. the battery state in real time.
Battery state of charge (SOC) is an important parameter of the BMS, represents the internal state of the battery, and thus cannot be directly measured. At present, in the existing method, a particle filter algorithm (PF) is used for research on lithium battery SOC prediction, the particle filter algorithm uses a monte carlo method to sample a group of weighted particles to approximate posterior distribution of a system, and no clear assumption exists for a distribution form, so that the method is a high-precision probability-based estimation algorithm. The algorithm has certain advantages when a complex nonlinear battery model is provided under the condition of processing non-Gaussian distribution system noise, but the traditional PF algorithm cannot update the latest observation information, all weights tend to zero except some important weights, the particle diversity is reduced, the particle depletion problem of the PF algorithm is solved, and the accuracy of SOC estimation can be reduced when the PF algorithm is applied to SOC estimation.
Disclosure of Invention
The invention aims to solve the problems in the prior art, provides the lithium battery SOC prediction method for optimizing the particle filter by improving the ant colony algorithm, can overcome the problems of complexity and low accuracy of the lithium battery SOC estimation method, and effectively improves the estimation precision.
The technical scheme of the invention is as follows:
a lithium battery SOC prediction method for optimizing particle filtering by improving an ant colony algorithm comprises the following steps:
and 5, predicting the SOC change of the battery through the optimized particle filter.
Further, the equation of state in step 2 is xk+1=fk(xk,ωk) Wherein, x in the formulak+1Is the battery SOC state value at the time of system k +1, fk(xk,ωk) Is a state transition model; the state transition model formula is as follows:wherein, ω iskProcess random noise; x is the number ofkIs the SOC state value of the battery at the moment k of the system, eta is the coulombic efficiency, the discharging condition is generally 1, delta t is the sampling interval, C is the rated capacity of the battery, i is the rated capacity of the batterykIs a discharge current.
Further, the measurement equation in step 3 is: y isk=hk(xk,vk),ykIs the output voltage value at the moment k of the system, hk(xk,vk) For the measurement model, the formula of the measurement model is as follows: h isk(xk,vk)=uOC(xk)-u1k-u2k-r0ik+vk,vkTo observe random noise, uOC(xk) Is a relation of open circuit voltage and SOC, u1k,u2kTerminal voltages r of two RC links0Is the internal resistance of the battery.
Further, the optimization in step 4 includes the following steps:
4.1 initializing an algorithm, and setting initial relevant parameters;
4.2 initializing particles, and optimizing particle state distribution according to an ant colony algorithm;
4.3 resampling the particle filter and outputting the state.
Further, the initial relevant parameters in step 4.1 include the number of particles, the number of iterations, and a threshold.
Further, the step 4.2 comprises the following steps:
4.2.1 Ant substituted particle, transition probability PijRepresenting the probability of selecting a particle j as a moving direction for a particle i, wherein the particle j is the N-1 particles left by dividing i;
4.2.2 improving transfer probability parameters in the ant colony algorithm and updating the pheromone;
4.2.3 setting the probability threshold λPAnd a distance threshold λLWhen the distance to the target particle is not in lambdaLWithin, and without exceeding the maximum number of iterations, is selected at λLAccording to transition probability PijSelecting a moving object when Pij<λPWhen P is equal, the particle position remains unchangedij>λPThen, the particles are transferred to the target particles, and then the parameters are updated;
4.2.4 outputting the updated particle state.
Further, the step 2 comprises: the pheromone and heuristic interaction parameters and the pheromone evaporation rate in pheromone updating are improved, and the ant colony algorithm is prevented from falling into local optimization.
The invention has the beneficial effects that:
(1) according to the lithium battery SOC prediction method for optimizing the particle filter by the improved ant colony algorithm, the particle filter is optimized by the improved ant colony algorithm, and the problems of low particle diversity and poor particles when the SOC is estimated by the particle filter algorithm are solved; the method is improved aiming at the ant colony algorithm, and the situation that the traditional ant colony algorithm is easy to fall into the local optimal solution is improved.
(2) The prediction method provided by the invention has good prediction accuracy on the non-Gaussian and non-linear system of the battery in the actual working condition operation process of the vehicle; the problems of complexity and low accuracy of the lithium battery SOC estimation method can be solved, and the estimation precision is effectively improved.
Drawings
FIG. 1 is a schematic diagram of a lithium battery SOC prediction model building process provided by the invention;
FIG. 2 is a graph of the terminal voltage of the present invention at a pulse current of 1.5A;
FIG. 3 is a graph of the open circuit voltage of the present invention at a pulse current of 1.5A;
FIG. 4 is a lithium battery SOC prediction curve diagram under a pulse discharge condition according to the present invention;
FIG. 5 is a diagram of a lithium battery SOC prediction error curve under a pulse discharge condition according to the present invention;
FIG. 6 is a DST condition current curve of the present invention;
FIG. 7 is a diagram of a lithium battery SOC prediction curve under a DST condition according to the present invention;
FIG. 8 is a diagram of a lithium battery SOC prediction error curve under the DST condition according to the present invention;
fig. 9 is a diagram of a second-order Thevenin equivalent model.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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.
For a further understanding of the invention, reference will now be made to the following description taken in conjunction with the accompanying drawings and examples.
As shown in fig. 1, the invention relates to a lithium battery SOC prediction method for optimizing particle filtering by improving ant colony algorithm, comprising the following steps:
TABLE 1 parameter identification results
In Table 1 above, R0Is the internal resistance of the battery; r1C1And R2C2Corresponding to parameters in two RC links; see in particular the second order Thevenin equivalent model diagram shown in fig. 9.
4.1, initializing an algorithm, setting initial relevant parameters, and setting relevant initial parameters such as particle number, iteration times, threshold values and the like;
4.2 initializing particles, and optimizing particle state distribution according to an ant colony algorithm; the method specifically comprises the following steps:
4.2.1 Ant substituted particles, transition probabilityExpressed as the probability of selecting particle j (N-1 particles left by dividing i) as the moving direction for particle i, and updating the formula according to the pheromoneUpdating parameters;
4.2.2 improving transfer probability parameters in the ant colony algorithm and updating the pheromone; the method comprises the following specific steps:
(1) the improved pheromone and heuristic interaction parameters alpha and beta are as follows: α ═ 1+ e-0.1N_c,Wherein N _ c is the current iteration number;
(2) improving pheromone evaporation rate in pheromone updates: ρ ═ ρ0+(0.9-ρ0)×(N_c/N_cMAX)2Where ρ is0For the initial definition of the pheromone volatilization rate, N _ cMAXIs the maximum number of iterations.
4.2.3 setting the probability threshold λPAnd a distance threshold λLWhen the distance to the target particle is not in lambdaLWithin, and without exceeding the maximum number of iterations, is selected at λLAccording to transition probability PijSelecting a moving object when Pij<λPWhen P is equal, the particle position remains unchangedij>λPThen, the particles are transferred to the target particles, and then the parameters are updated;
4.2.4 outputting the updated particle state.
And 5, predicting the SOC change of the battery through the optimized particle filter.
As shown in FIG. 4 and FIG. 5, the present invention can predict the SOC value well under the pulse discharge condition, and the error is controlled within 1%.
As shown in FIG. 7 and FIG. 8, the present invention can predict the SOC value well under the DST condition, and the error is controlled within 1.5%.
Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art will understand that various changes, modifications and substitutions can be made without departing from the spirit and scope of the present invention. Any modification, equivalent replacement, or modification made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A lithium battery SOC prediction method for optimizing particle filtering by improving an ant colony algorithm is characterized by comprising the following steps:
step 1, carrying out discharge tests on lithium batteries under currents of different working conditions, and preprocessing experimental data;
step 2, performing parameter identification according to the preprocessed experimental data, and constructing a state equation according to an ampere-hour integral method;
step 3, establishing a measurement equation of a theoretical prediction model of the battery according to a second-order Thevenin equivalent model;
step 4, optimizing particle filtering by using an improved ant colony algorithm;
and 5, predicting the SOC change of the battery through the optimized particle filter.
2. The lithium battery SOC prediction method for improving ant colony algorithm optimized particle filtering as claimed in claim 1, wherein the state equation in the step 2 is xk+1=fk(xk,ωk) Wherein, x in the formulak+1Is the battery SOC state value at the time of system k +1, fk(xk,ωk) Is a state transition model; the state transition model formula is as follows:wherein, ω iskIs process random noise, xkIs the SOC state value of the battery at the moment k of the system, eta is the coulombic efficiency, delta t is the sampling interval, C is the rated capacity of the battery, ikIs a discharge current.
3. The lithium battery SOC prediction method for improving ant colony algorithm optimized particle filtering according to claim 1, wherein the measurement equation in the step 3 is as follows: y isk=hk(xk,vk),ykIs the output voltage value at the moment k of the system, hk(xk,vk) For the measurement model, the formula of the measurement model is as follows: h isk(xk,vk)=uOC(xk)-u1k-u2k-r0ik+vk,vkTo observe random noise, uOC(xk) Is a relation of open circuit voltage and SOC, u1k,u2kTerminal voltages r of two RC links0Is the internal resistance of the battery.
4. The lithium battery SOC prediction method based on improved ant colony optimization algorithm particle filtering as claimed in claim 1, wherein the optimization in step 4 includes the following steps:
4.1 initializing an algorithm, and setting initial relevant parameters;
4.2 initializing particles, and optimizing particle state distribution according to an ant colony algorithm;
4.3 resampling the particle filter and outputting the state.
5. The lithium battery SOC prediction method based on improved ant colony optimization algorithm particle filtering as claimed in claim 4, wherein the initial relevant parameters in step 4.1 include particle number, iteration number, and threshold value.
6. The lithium battery SOC prediction method based on improved ant colony algorithm optimized particle filtering as claimed in claim 4, wherein the step 4.2 comprises the following steps:
4.2.1 Ant substituted particle, transition probability PijRepresenting the probability of selecting a particle j as a moving direction for a particle i, wherein the particle j is the N-1 particles left by dividing i;
4.2.2 improving transfer probability parameters in the ant colony algorithm and updating the pheromone;
4.2.3 setting the probability threshold λPAnd a distance threshold λLWhen the distance to the target particle is not in lambdaLWithin, and without exceeding the maximum number of iterations, is selected at λLAccording to transition probability PijSelecting a moving object when Pij<λPWhen P is equal, the particle position remains unchangedij>λPThen, the particles are transferred to the target particles, and then the parameters are updated;
4.2.4 outputting the updated particle state.
7. The lithium battery SOC prediction method for improving ant colony algorithm optimized particle filtering according to claim 6, wherein the step 2 comprises: the pheromone and heuristic interaction parameters and the pheromone evaporation rate in pheromone updating are improved, and the ant colony algorithm is prevented from falling into local optimization.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110176036.5A CN113011082B (en) | 2021-02-07 | 2021-02-07 | Lithium battery SOC prediction method for optimizing particle filtering by improving ant colony algorithm |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110176036.5A CN113011082B (en) | 2021-02-07 | 2021-02-07 | Lithium battery SOC prediction method for optimizing particle filtering by improving ant colony algorithm |
Publications (2)
Publication Number | Publication Date |
---|---|
CN113011082A true CN113011082A (en) | 2021-06-22 |
CN113011082B CN113011082B (en) | 2022-07-12 |
Family
ID=76384279
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202110176036.5A Active CN113011082B (en) | 2021-02-07 | 2021-02-07 | Lithium battery SOC prediction method for optimizing particle filtering by improving ant colony algorithm |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN113011082B (en) |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113625177A (en) * | 2021-08-06 | 2021-11-09 | 国网安徽省电力有限公司滁州供电公司 | SOC estimation method based on particle swarm optimization particle filter algorithm |
CN113671378A (en) * | 2021-07-12 | 2021-11-19 | 南通大学 | Fractional order theory-based lithium ion battery modeling and parameter identification method |
CN115102200A (en) * | 2022-07-19 | 2022-09-23 | 贵州大学 | Energy storage system model identification method and system |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108872870A (en) * | 2018-06-21 | 2018-11-23 | 浙江工业大学 | A kind of lithium battery SOC estimation method based on particle group optimizing expanded Kalman filtration algorithm |
US20190178945A1 (en) * | 2017-12-13 | 2019-06-13 | Beijing Chuangyu Technology Co., Ltd. | Battery state of charge prediction method and system |
CN110598830A (en) * | 2019-04-03 | 2019-12-20 | 常熟理工学院 | Joint multi-cell tracking method based on label ant colony |
-
2021
- 2021-02-07 CN CN202110176036.5A patent/CN113011082B/en active Active
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20190178945A1 (en) * | 2017-12-13 | 2019-06-13 | Beijing Chuangyu Technology Co., Ltd. | Battery state of charge prediction method and system |
CN108872870A (en) * | 2018-06-21 | 2018-11-23 | 浙江工业大学 | A kind of lithium battery SOC estimation method based on particle group optimizing expanded Kalman filtration algorithm |
CN110598830A (en) * | 2019-04-03 | 2019-12-20 | 常熟理工学院 | Joint multi-cell tracking method based on label ant colony |
Non-Patent Citations (3)
Title |
---|
曹义亲等: "一种改进的基于蚁群优化的粒子滤波算法", 《计算机应用研究》 * |
田冬冬等: "基于IBA-PF的锂电池SOC估算", 《储能科学与技术》 * |
赵又群等: "基于扩展卡尔曼粒子滤波算法的锂电池SOC估计", 《中国机械工程》 * |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113671378A (en) * | 2021-07-12 | 2021-11-19 | 南通大学 | Fractional order theory-based lithium ion battery modeling and parameter identification method |
CN113671378B (en) * | 2021-07-12 | 2022-04-08 | 南通大学 | Fractional order theory-based lithium ion battery modeling and parameter identification method |
CN113625177A (en) * | 2021-08-06 | 2021-11-09 | 国网安徽省电力有限公司滁州供电公司 | SOC estimation method based on particle swarm optimization particle filter algorithm |
CN115102200A (en) * | 2022-07-19 | 2022-09-23 | 贵州大学 | Energy storage system model identification method and system |
Also Published As
Publication number | Publication date |
---|---|
CN113011082B (en) | 2022-07-12 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN113011082B (en) | Lithium battery SOC prediction method for optimizing particle filtering by improving ant colony algorithm | |
Bi et al. | State-of-health estimation of lithium-ion battery packs in electric vehicles based on genetic resampling particle filter | |
Li et al. | A novel state of charge approach of lithium ion battery using least squares support vector machine | |
CN113702843B (en) | Lithium battery parameter identification and SOC estimation method based on suburb optimization algorithm | |
CN112989690B (en) | Multi-time-scale state-of-charge estimation method for lithium battery of hybrid electric vehicle | |
CN111753416A (en) | Lithium ion battery RUL prediction method based on two-stage Wiener process | |
CN111426957A (en) | SOC estimation optimization method for power battery under simulated vehicle working condition | |
Qiao et al. | A chaotic firefly-Particle filtering method of dynamic migration modeling for the state-of-charge and state-of-health co-estimation of a lithium-ion battery performance | |
Chung et al. | State-of-charge estimation of lithium-ion batteries using LSTM deep learning method | |
Li et al. | Multi-state joint estimation for a lithium-ion hybrid capacitor over a wide temperature range | |
CN115494400B (en) | Lithium battery lithium separation state online monitoring method based on ensemble learning | |
CN112946481A (en) | Based on federation H∞Filtering sliding-mode observer lithium ion battery SOC estimation method and battery management system | |
CN114839550B (en) | UKF-AUKF-based lithium battery SOC joint estimation method | |
CN116125278A (en) | Lithium battery SOC estimation method and system based on LSTM-EKF algorithm | |
AU2021100545A4 (en) | A method for estimating the state of health of lithium ion power batteries based on machine learning | |
Xiong et al. | A novel joint estimation method of state of charge and state of health based on the strong tracking-dual adaptive extended Kalman filter algorithm for the electric vehicle lithium-ion batteries | |
Zhang et al. | State of charge estimation of lithium battery based on FFRLS-SRUKF algorithm | |
CN116718920B (en) | Lithium battery SOC estimation method based on RNN (RNN-based optimized extended Kalman filter) | |
CN113013509A (en) | Lithium ion battery rapid charging method and system based on dynamic programming | |
Zhou et al. | Online estimation of state of charge of Li-ion battery using an iterated extended Kalman particle filter | |
Zhang et al. | SOC Estimation of Lithium-ion Battery Based on RLS-EKF for Unmanned Aerial Vehicle | |
CN115113053A (en) | Lithium battery soc estimation method based on high-adaptivity filtering algorithm | |
CN115327416A (en) | Lithium ion battery SOC estimation method based on group intelligent optimization and particle filtering | |
CN115308623A (en) | Battery state of charge estimation method based on particle resampling and searcher optimization algorithm | |
CN115436810A (en) | Hybrid electric vehicle lithium battery SOC and SOH combined estimation method based on double-adaptive Kalman filtering algorithm |
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 |