KR20140067450A - Method of optimizing battery management system algorithm and apparatus for the same - Google Patents

Method of optimizing battery management system algorithm and apparatus for the same Download PDF

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KR20140067450A
KR20140067450A KR1020120134708A KR20120134708A KR20140067450A KR 20140067450 A KR20140067450 A KR 20140067450A KR 1020120134708 A KR1020120134708 A KR 1020120134708A KR 20120134708 A KR20120134708 A KR 20120134708A KR 20140067450 A KR20140067450 A KR 20140067450A
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driving
algorithm
battery
bms
management system
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KR101977729B1 (en
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안성호
김기석
강현우
최은창
이수인
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한국전자통신연구원
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    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/44—Methods for charging or discharging
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/02—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries for charging batteries from ac mains by converters
    • H02J7/04—Regulation of charging current or voltage
    • 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
    • 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
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/16—Information or communication technologies improving the operation of electric vehicles
    • Y02T90/167—Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S30/00—Systems supporting specific end-user applications in the sector of transportation
    • Y04S30/10—Systems supporting the interoperability of electric or hybrid vehicles
    • Y04S30/12—Remote or cooperative charging

Abstract

Disclosed are a method for optimizing the algorithm of a battery management system and an apparatus therefor. The method for optimizing the algorithm of the battery management system according to the present invention includes the steps of: loading a driving feature on a database; performing battery cell balancing to optimize the state of health (SOH) of a battery by performing a simulation using BMS algorithms applying the change of electric energy corresponding to the driving feature; and selecting the BMS algorithm in consideration of the driving feature based on the simulation result.

Description

배터리 관리 시스템 알고리즘 최적화 방법 및 이를 위한 장치 {METHOD OF OPTIMIZING BATTERY MANAGEMENT SYSTEM ALGORITHM AND APPARATUS FOR THE SAME}FIELD OF THE INVENTION [0001] The present invention relates to a method of optimizing a battery management system,

본 발명은 배터리 관리 시스템(Battery Management System; BMS)에 관한 것으로, 보다 상세하게는 운전 특성을 고려하여 배터리 셀 밸런싱을 수행하는 BMS 알고리즘 선택 기술에 관한 것이다.BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a battery management system (BMS), and more particularly, to a BMS algorithm selection technique for performing battery cell balancing in consideration of operational characteristics.

전기자동차가 자동차 업계의 화두로 떠오르면서 전기자동차 등의 배터리를 관리하는 배터리 관리 시스템(BMS; Battery Management System) 기술에 관한 관심이 높아지고 있다. 기존의 배터리 관리 시스템에 대한 알고리즘은 배터리 특성에 맞추어 설계된다. 즉, 종래의 BMS의 알고리즘은 배터리의 전기화학적인 셀의 특성에 따라 설계되어 배터리 잔존용량(SOC; State Of Capacity)을 추정하고 이를 이용하여 배터리 셀 밸런싱을 수행하여, 결과적으로 배터리의 수명(SOH; State Of Health)을 길게 하고자 한다.As electric vehicles have emerged as a hot topic in the automotive industry, there is growing interest in battery management system (BMS) technology that manages batteries for electric vehicles. Algorithms for existing battery management systems are designed to match battery characteristics. That is, the conventional BMS algorithm is designed according to the characteristics of an electrochemical cell of a battery to estimate battery state of charge (SOC) and perform battery cell balancing using the state of charge (SOC) ; State Of Health).

한국공개특허 제2004-0022743호는 차량 주행 패턴을 통해 배터리의 파워의 흐름을 제어하는 기술을 개시하고 있다. 그러나, 차량 주행 패턴만 고려해서는 다양한 운전 특성을 모두 고려하여 최적화된 BMS 알고리즘을 선택하기 어렵다.Korean Patent Publication No. 2004-0022743 discloses a technique for controlling the flow of power of a battery through a vehicle traveling pattern. However, it is difficult to select the optimized BMS algorithm considering all the various driving characteristics considering only the vehicle traveling pattern.

따라서, 차량의 주행 패턴뿐만 아니라 배터리가 장착되는 전동 장치(Electric Vehicle)의 다양한 운전 특성을 팩터(factor)로 사용하여 최적화된 BMS 알고리즘을 찾을 수 있는 새로운 기술의 필요성이 절실하게 대두된다.Therefore, there is an urgent need for a new technology that can find an optimized BMS algorithm using not only a traveling pattern of a vehicle but also various operating characteristics of an electric vehicle in which a battery is mounted as a factor.

본 발명의 목적은 운전 특성을 고려하여 최적화된 BMS 알고리즘을 선택하여 최적의 배터리 충/방전을 수행함으로써 배터리의 수명을 연장시키는 것이다.An object of the present invention is to prolong the life of a battery by selecting an optimized BMS algorithm in consideration of operation characteristics to perform optimal battery charge / discharge.

상기 목적을 달성하기 위한 본 발명에 따른 배터리 관리 시스템 알고리즘 최적화 방법은, 데이터베이스에서 운전 특성을 로드하는 단계; 상기 운전 특성에 상응하는 전기 에너지 변화를 적용한 BMS 알고리즘들을 이용하여 시뮬레이션을 수행하여 배터리의 수명(SOH)이 최적화되도록 배터리 셀 밸런싱을 수행하는 단계; 및 상기 시뮬레이션 결과에 기반하여 운전 특성이 고려된 BMS 알고리즘을 선택하는 단계를 포함한다.According to another aspect of the present invention, there is provided a method of optimizing a battery management system algorithm, the method including: Performing battery cell balancing so as to optimize a life time (SOH) of the battery by performing simulation using BMS algorithms applying an electrical energy change corresponding to the operation characteristics; And selecting a BMS algorithm that considers operational characteristics based on the simulation results.

이 때, 운전 특성은 운전자의 운전 습관(급발진, 급정거 등)을 의미하는 운전 스타일, 출/퇴근 시간이나 단거리/장거리 여행을 의미하는 운전 시간, 교통현황, 온도 및 습도, 충/방전 환경 등을 의미하는 운전 환경, 전동 장치가 전기자동차, 하이브리드 자동차, 기타 전동수송장치인지 여부를 의미하는 운전 장치 중 어느 하나 이상을 포함할 수 있다.In this case, the driving characteristics include driving style which means driver's driving habit (sudden driving, sudden driving, etc.), driving style indicating driving / leaving time, driving time indicating short / long distance travel, traffic condition, temperature and humidity, A driving environment which means, a driving device which means whether the transmission is an electric car, a hybrid car or any other electric transport device.

또한, 본 발명의 실시예에 따른 배터리 관리 시스템 알고리즘 최적화 장치는, 데이터베이스에서 운전 특성을 로드하는 운전 특성 로드부; 상기 운전 특성에 상응하는 전기 에너지 변화를 적용한 BMS 알고리즘을 이용하여 시뮬레이션을 수행하여 배터리의 수명(SOH)이 최적화되도록 배터리 셀 밸런싱을 수행하는 배터리 셀 밸런싱부; 및 상기 시뮬레이션 결과에 기반하여 운전 특성이 고려된 BMS 알고리즘을 선택하는 BMS 알고리즘 선택부를 포함한다.Also, an apparatus for optimizing a battery management system according to an exemplary embodiment of the present invention includes: a driving characteristic load unit for loading a driving characteristic in a database; A battery cell balancing unit that performs battery cell balancing so as to optimize a life time (SOH) of the battery by performing simulation using a BMS algorithm using an electrical energy change corresponding to the operation characteristics; And a BMS algorithm selection unit for selecting a BMS algorithm considering operational characteristics based on the simulation result.

본 발명에 따르면, 운전 특성을 고려하여 최적화된 BMS 알고리즘을 선택하여 최적의 배터리 충/방전을 수행함으로써 배터리의 수명을 연장시킬 수 있다.According to the present invention, the life of the battery can be extended by selecting the optimized BMS algorithm in consideration of the operation characteristics and performing the optimal battery charge / discharge.

도 1은 BMS 알고리즘 최적화 시뮬레이션의 구성도이다.
도 2는 본 발명의 일실시예에 따른 배터리 관리 시스템 알고리즘 최적화 방법을 나타낸 동작 흐름도이다.
도 3은 본 발명의 일실시예에 따른 배터리 관리 시스템 알고리즘 최적화 장치를 나타낸 블록도이다.1 is a block diagram of a BMS algorithm optimization simulation.
2 is a flowchart illustrating a method of optimizing a battery management system algorithm according to an exemplary embodiment of the present invention.
3 is a block diagram illustrating an apparatus for optimizing a battery management system algorithm according to an embodiment of the present invention.

본 발명을 첨부된 도면을 참조하여 상세히 설명하면 다음과 같다. 여기서, 반복되는 설명, 본 발명의 요지를 불필요하게 흐릴 수 있는 공지 기능, 및 구성에 대한 상세한 설명은 생략한다. 본 발명의 실시형태는 당 업계에서 평균적인 지식을 가진 자에게 본 발명을 보다 완전하게 설명하기 위해서 제공되는 것이다. 따라서, 도면에서의 요소들의 형상 및 크기 등은 보다 명확한 설명을 위해 과장될 수 있다.The present invention will now be described in detail with reference to the accompanying drawings. Hereinafter, a repeated description, a known function that may obscure the gist of the present invention, and a detailed description of the configuration will be omitted. Embodiments of the present invention are provided to more fully describe the present invention to those skilled in the art. Accordingly, the shapes and sizes of the elements in the drawings and the like can be exaggerated for clarity.

본 발명에서 설명하는 배터리가 장착된 전동 장치는 전기에너지를 바탕으로 하는 자동차나 전동 수송장치 등을 의미하나 반드시 이에 제한되는 것은 아니다.The power transmission device equipped with the battery described in the present invention means an automobile or an electric transport device based on electric energy, but is not necessarily limited thereto.

이하, 본 발명에 따른 바람직한 실시예를 첨부된 도면을 참조하여 상세하게 설명한다.Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the accompanying drawings.

도 1은 BMS 알고리즘 최적화 시뮬레이션의 구성도이다.1 is a block diagram of a BMS algorithm optimization simulation.

도 1에서, 운전 특성을 데이터베이스화하여 프로파일(profile) 형태로 가지고 있고, PC 기반의 시뮬레이션을 통해 저장, 수행, 학습, 편집 등을 할 수 있다. 그리고, 이에 최적화된 BMS 알고리즘을 선택하거나, 프로그래밍하여 최종적인 알고리즘을 찾을 수 있다.In FIG. 1, the operating characteristics are stored in a database in a profile form, and can be stored, executed, learned, and edited through a PC-based simulation. Then, the optimized BMS algorithm can be selected or programmed to find the final algorithm.

프로파일 정보에는 운전 특성이 담겨 있을 수 있다. 운전 특성은 운전 스타일, 운전 시간, 운전 환경 및 운전 장치 중 어느 하나 이상을 포함할 수 있다. 이와 같은 운전 특성을 고려함으로서 최적의 BMS 알고리즘을 찾을 수 있다.The profile information may include driving characteristics. The driving characteristic may include at least one of a driving style, a driving time, an operating environment, and a driving device. The optimal BMS algorithm can be found by considering such operating characteristics.

예를 들어, 운전 스타일은 운전자의 운전 습관(급발진, 급정거 등)을 주로 의미하고, 운전 시간은 출/퇴근 시간, 단거리나 장거리 여행 등을 의미하며, 운전 환경은 교통 현황, 온도 및 습도, 충/방전 환경 등을 의미하고, 운전 장치는 전동 장치가 전기자동차냐, 하이브리드 자동차냐, 그 외 전동 수송장치냐에 따라 달라질 수 있다.For example, driving style refers to driver's driving habits (sudden driving, sudden driving, etc.), driving time means driving time / departure time, short distance or long distance travel, and driving environment includes traffic condition, temperature and humidity, / Discharge environment, etc., and the operating device may vary depending on whether the transmission is an electric car, a hybrid car or other electric transport device.

여기서, 운전 특성에 해당하는 사항에 대해 전기 에너지 관점에서 어떻게 변화하는지를 추정하여, 이를 BMS 알고리즘에 적용하기 위해 DB화된 형태로 시뮬레이션 하게 되어 있다. 즉, 프로파일 정보에는 운전 특성의 고려되는 사항들과 배터리의 충/방전 성태의 상호작용이 전기적 에너지로 추정되어 있을 수 있고 이를 기존의 BMS 알고리즘에 추가함으로써, 배터리의 잔존용량(SOC; State Of Capacity)을 추정하고, 이를 기반으로 배터리의 수명(SOH; State Of Health)이 최적화되도록 셀 밸런싱을 수행하여 최종적으로 선택된 전동장치의 운전 특성이 고려된 BMS 알고리즘을 선택할 수 있다.
Here, it is estimated that how to change from the viewpoint of electric energy to the operation characteristic is simulated in DB form so as to apply it to the BMS algorithm. That is, in the profile information, the interaction between charge / discharge characteristics of the battery and the factors considered in operation characteristics may be estimated as electrical energy, and by adding this to the existing BMS algorithm, the remaining capacity (SOC) ), And cell balancing is performed so that the lifetime (SOH) of the battery is optimized based on the BMS algorithm. Thus, the BMS algorithm considering the operation characteristics of the finally selected transmission device can be selected.

도 2는 본 발명의 일실시예에 따른 배터리 관리 시스템 알고리즘 최적화 방법을 나타낸 동작 흐름도이다.2 is a flowchart illustrating a method of optimizing a battery management system algorithm according to an exemplary embodiment of the present invention.

도 2를 참조하면, 본 발명의 일실시예에 따른 배터리 관리 시스템 알고리즘 최적화 방법은 먼저 데이터베이스에서 운전 특성을 로드한다(S210).Referring to FIG. 2, a method of optimizing a battery management system algorithm according to an embodiment of the present invention loads operation characteristics in a database (S210).

이 때, 운전 특성은 운전자의 운전 습관(급발진, 급정거 등)을 의미하는 운전 스타일, 출/퇴근 시간이나 단거리/장거리 여행을 의미하는 운전 시간, 교통현황, 온도 및 습도, 충/방전 환경 등을 의미하는 운전 환경, 전동 장치가 전기자동차, 하이브리드 자동차, 기타 전동수송장치인지 여부를 의미하는 운전 장치 중 어느 하나 이상을 포함할 수 있다.In this case, the driving characteristics include driving style which means driver's driving habit (sudden driving, sudden driving, etc.), driving style indicating driving / leaving time, driving time indicating short / long distance travel, traffic condition, temperature and humidity, A driving environment which means, a driving device which means whether the transmission is an electric car, a hybrid car or any other electric transport device.

또한, 본 발명의 일실시예에 따른 배터리 관리 시스템 알고리즘 최적화 방법은 상기 운전 특성에 상응하는 전기에너지 변화를 적용한 BMS 알고리즘들을 이용하여 시뮬레이션을 수행하여 배터리의 수명(SOH)이 최적화되도록 배터리 셀 밸런싱을 수행한다(S220).Also, the method of optimizing the battery management system according to an embodiment of the present invention performs simulation using BMS algorithms applying the electrical energy change corresponding to the operation characteristics to optimize the battery life (SOH) (S220).

또한, 본 발명의 일실시예에 따른 배터리 관리 시스템 알고리즘 최적화 방법은 상기 시뮬레이션 결과에 기반하여 운전 특성이 고려된 BMS 알고리즘을 선택한다(S230).In addition, the algorithm optimization method of the battery management system according to an embodiment of the present invention selects a BMS algorithm considering operation characteristics based on the simulation result (S230).

도 3은 본 발명의 일실시예에 따른 배터리 관리 시스템 알고리즘 최적화 장치를 나타낸 블록도이다.3 is a block diagram illustrating a battery management system algorithm optimizing apparatus according to an embodiment of the present invention.

도 3을 참조하면, 본 발명의 일실시예에 따른 배터리 관리 시스템 알고리즘 최적화 장치는 운전 특성 로드부(310), 배터리 셀 밸런싱부(320) 및 BMS 알고리즘 선택부(330)를 포함한다.Referring to FIG. 3, the apparatus for optimizing a battery management system algorithm according to an embodiment of the present invention includes an operation characteristic load unit 310, a battery cell balancing unit 320, and a BMS algorithm selection unit 330.

운전 특성 로드부(310)는 데이터베이스에서 프로파일 형태의 운전 특성을 로드한다.The operation characteristic load unit 310 loads the profile-type operation characteristic from the database.

이 때, 운전 특성은 운전자의 운전 습관(급발진, 급정거 등)을 의미하는 운전 스타일, 출/퇴근 시간이나 단거리/장거리 여행을 의미하는 운전 시간, 교통현황, 온도 및 습도, 충/방전 환경 등을 의미하는 운전 환경, 전동 장치가 전기자동차, 하이브리드 자동차, 기타 전동수송장치인지 여부를 의미하는 운전 장치 중 어느 하나 이상을 포함할 수 있다.In this case, the driving characteristics include driving style which means driver's driving habit (sudden driving, sudden driving, etc.), driving style indicating driving / leaving time, driving time indicating short / long distance travel, traffic condition, temperature and humidity, A driving environment which means, a driving device which means whether the transmission is an electric car, a hybrid car or any other electric transport device.

배터리 셀 밸런싱부(320)는 상기 운전 특성에 상응하는 전기 에너지 변화를 적용한 BMS 알고리즘을 이용하여 시뮬레이션을 수행하여 배터리의 수명(SOH)이 최적화되도록 배터리 셀 밸런싱을 수행한다.The battery cell balancing unit 320 performs battery cell balancing so as to optimize the lifetime (SOH) of the battery by performing a simulation using a BMS algorithm using an electrical energy change corresponding to the operation characteristics.

BMS 알고리즘 선택부(330)는 상기 시뮬레이션 결과에 기반하여 운전 특성이 고려된 BMS 알고리즘을 선택한다.The BMS algorithm selection unit 330 selects a BMS algorithm considering operation characteristics based on the simulation result.

이상에서와 같이 본 발명에 따른 배터리 관리 시스템 알고리즘 최적화 방법 및 이를 위한 장치는 상기한 바와 같이 설명된 실시예들의 구성과 방법이 한정되게 적용될 수 있는 것이 아니라, 상기 실시예들은 다양한 변형이 이루어질 수 있도록 각 실시예들의 전부 또는 일부가 선택적으로 조합되어 구성될 수도 있다.As described above, the method and apparatus for optimizing the algorithm of the battery management system according to the present invention are not limited to the configuration and method of the embodiments described above, but the embodiments may be modified in various ways All or some of the embodiments may be selectively combined.

310: 운전 특성 로드부
320: 배터리 셀 밸런싱부
330: BMS 알고리즘 선택부310: Operation characteristic load section
320: battery cell balancing unit
330: BMS algorithm selection unit

Claims (1)

데이터베이스에서 운전 특성을 로드하는 단계;
상기 운전 특성에 상응하는 전기 에너지 변화를 적용한 BMS 알고리즘들을 이용하여 시뮬레이션을 수행하여 배터리의 수명(SOH)이 최적화되도록 배터리 셀 밸런싱을 수행하는 단계; 및
상기 시뮬레이션 결과에 기반하여 운전 특성이 고려된 BMS 알고리즘을 선택하는 단계
를 포함하는 것을 특징으로 하는 배터리 관리 시스템 알고리즘 최적화 방법.Loading operating characteristics in the database;
Performing battery cell balancing so as to optimize a life time (SOH) of the battery by performing simulation using BMS algorithms applying an electrical energy change corresponding to the operation characteristics; And
Selecting a BMS algorithm considering operation characteristics based on the simulation result
Wherein the battery management system algorithm optimization method comprises:
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Cited By (2)

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Publication number Priority date Publication date Assignee Title
KR20190134876A (en) 2018-05-02 2019-12-05 주식회사 에스.제이테크 a BMS optimizing system using a cloud system and big data
US11754630B2 (en) 2019-08-29 2023-09-12 Lg Energy Solution, Ltd. Method and device for determining temperature estimating model, and battery management system to which the temperature estimating model is applied

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20090064774A (en) * 2007-12-17 2009-06-22 에이치케이산업(주) The module of battery management system

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20090064774A (en) * 2007-12-17 2009-06-22 에이치케이산업(주) The module of battery management system

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20190134876A (en) 2018-05-02 2019-12-05 주식회사 에스.제이테크 a BMS optimizing system using a cloud system and big data
US11754630B2 (en) 2019-08-29 2023-09-12 Lg Energy Solution, Ltd. Method and device for determining temperature estimating model, and battery management system to which the temperature estimating model is applied

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