CN102024999A - Electric car running power management system - Google Patents
Electric car running power management systemDownload PDFInfo
- Publication number
- CN102024999A CN102024999ACN2010105458338ACN201010545833ACN102024999ACN 102024999 ACN102024999 ACN 102024999ACN 2010105458338 ACN2010105458338 ACN 2010105458338ACN 201010545833 ACN201010545833 ACN 201010545833ACN 102024999 ACN102024999 ACN 102024999A
- Authority
- CN
- China
- Prior art keywords
- electric vehicle
- information
- battery
- module
- analysis
- 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
- 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
- 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
- Electric Propulsion And Braking For Vehicles (AREA)
- Secondary Cells (AREA)
Abstract
Translated fromChinese
Description
Translated fromChinese技术领域technical field
本发明涉及的是一种电动车技术领域的系统，具体是一种电动车行动力管理系统。The invention relates to a system in the technical field of electric vehicles, in particular to a power management system for electric vehicles.
背景技术Background technique
电动车的行动力，即指电动车的行驶距离、行驶范围、目的地，主要基于其所载的电池性能及其管理系统。电池管理系统的性能优劣直接决定了对电池/电池组使用寿命预测估计的准确率和电动车行动力的效率，针对电池健康状态研究有效的诊断与预测系统将大大改善电池系统的利用率。电池管理系统对动力电池的工作参数(如电压、电流、温度、剩余电量和健康状态)进行检测，以保证动力电池/电池组安全工作，并对动力电池/电池组的工作状态进行准确估计，以便整车控制系统根据当前的电池状态优化控制策略，提高整车的动力性和行车经济性。目前的电池管理技术所关注的关键点主要有两点：第一，提高电池充放电监控技术，包括充放电监控、充放电安全、充放电平衡技术、容量估算技术。在电池成本一定的情况下，电池的使用寿命决定了电动汽车的运营成本，而电池充放电控制技术直接决定了电池的使用寿命。现在提高电池充放电控制技术的手段，不仅仅在于硬件技术的发展，更在于软件技术的发展，如剩余容量计算、实时充电电流计算、电池健康状态的监控。第二，提高电池管理系统的安全性能，包括电池管理系统抗干扰技术、电池管理系统异常及报警技术和电池组热管理技术。电池管理系统应用在非常恶劣的环境下，环境会对电池管理系统产生极其大的干扰。提高电池管理系统的抗干扰技术，包括硬件抗干扰技术和软件抗干扰技术；同时还需要加强对异常情况的及时处理，如充放电突然增大或电池损坏；还有加强对电池/电池组和电池管理系统的热管理。如一些传统电池健康探测方法用于飞机电池维护，包括放电至一个预定义的电压水平、开放电路电压测试、负载下电压读数、内阻抗测量。传统维护技术的缺点有：电池需要取出，需要专业知识来实现这一探测。此外，内阻抗测量必须要昂贵和笨重的仪器才能实现电动车在线监测和健康分析。许多技术采用了线性模型，如放电率和松弛模型以对电池容量有更好的估计精度。统计经验模型如2步抛物线模型和双S形函数模型建立了在环境控制条件下功率下降和时间事件的关系。上述方法的主要局限在于输出仍以电流、电压或者电容的形式，而不是用户友好的信息，如电池的健康状态和剩余有用寿命。而且所有的模型均仅考虑一种或两种条件(如控制电池荷电状态、放电速率、环境温度)，当在其他工况下这些设定下得到的模型往往失去实际效用。The mobility of an electric vehicle refers to the driving distance, driving range, and destination of an electric vehicle, which are mainly based on the performance of its battery and its management system. The performance of the battery management system directly determines the accuracy of battery/battery pack service life prediction and the efficiency of electric vehicles. Researching effective diagnosis and prediction systems for battery health status will greatly improve the utilization of battery systems. The battery management system detects the working parameters of the power battery (such as voltage, current, temperature, remaining power and health status) to ensure the safe operation of the power battery/battery pack and accurately estimate the working status of the power battery/battery pack. So that the vehicle control system can optimize the control strategy according to the current battery state, and improve the power and driving economy of the vehicle. The current battery management technology focuses on two key points: First, improve battery charge and discharge monitoring technology, including charge and discharge monitoring, charge and discharge safety, charge and discharge balance technology, and capacity estimation technology. In the case of a certain battery cost, the service life of the battery determines the operating cost of the electric vehicle, and the battery charge and discharge control technology directly determines the service life of the battery. The means to improve battery charge and discharge control technology now lies not only in the development of hardware technology, but also in the development of software technology, such as remaining capacity calculation, real-time charging current calculation, and battery health status monitoring. Second, improve the safety performance of the battery management system, including battery management system anti-interference technology, battery management system abnormality and alarm technology, and battery pack thermal management technology. The battery management system is applied in a very harsh environment, and the environment will cause great interference to the battery management system. Improve the anti-jamming technology of the battery management system, including hardware anti-jamming technology and software anti-jamming technology; at the same time, it is necessary to strengthen the timely handling of abnormal situations, such as sudden increase in charge and discharge or battery damage; and strengthen the monitoring of batteries/battery packs and Thermal management of battery management systems. For example, some traditional battery health detection methods are used for aircraft battery maintenance, including discharge to a predefined voltage level, open circuit voltage test, voltage reading under load, and internal impedance measurement. Disadvantages of conventional maintenance techniques are that the battery needs to be removed and expertise is required to achieve this detection. In addition, the measurement of internal impedance must require expensive and bulky instruments to realize online monitoring and health analysis of electric vehicles. Many techniques employ linear models, such as discharge rate and relaxation models, for better estimation accuracy of battery capacity. Statistical empirical models such as the 2-step parabolic model and the double sigmoid model establish the relationship between power dips and time events under environmentally controlled conditions. The main limitation of the above approach is that the output is still in the form of current, voltage, or capacitance, rather than user-friendly information such as the battery's state of health and remaining useful life. Moreover, all models only consider one or two conditions (such as controlling the state of charge of the battery, discharge rate, and ambient temperature), and the models obtained under these settings often lose their practical effectiveness under other operating conditions.
除了以上关注于电池性能状态诊断和建模的研究，一些相关技术涉及预测诊断阶段，不仅以非破坏式地方法揭示电池健康信息为目的，而且也着重解决电池剩余有用寿命的预测，从而使优化电池更换时间。数据驱动模型如自回归滑动平均模型被用来预测电池容量衰退的趋势。状态估计技术如扩展的卡尔曼滤波和粒子滤波算法被用于电荷状态和寿命状态的实时预测。自动化推理方法如模糊逻辑和人工神经网络被用于内部阻抗测量电化学参数和电荷状态、健康状态、电池寿命的估计。虽然许多技术使用电荷状态、健康状态、电池寿命状态作为重要的指标，但现在的电池健康状态着重于电池剩余容量的预测，实际电池使用中的寿命是动态变化的。目前的技术通常仅仅通过电池本身的测量来预测电池剩余电量，缺少动态管理系统，不可能得到准确的结果，与实际使用寿命误差很大。In addition to the above studies focusing on battery performance state diagnosis and modeling, some related technologies involve the predictive diagnosis stage, not only for the purpose of revealing battery health information in a non-destructive way, but also focusing on the prediction of the remaining useful life of the battery, so that the optimized Battery replacement time. Data-driven models such as autoregressive moving average models are used to predict the trend of battery capacity decline. State estimation techniques such as extended Kalman filter and particle filter algorithms are used for real-time prediction of state of charge and lifetime. Automated inference methods such as fuzzy logic and artificial neural networks are used for internal impedance measurements of electrochemical parameters and estimation of state of charge, state of health, and battery life. Although many technologies use state of charge, state of health, and state of battery life as important indicators, the current state of battery health focuses on the prediction of the remaining capacity of the battery, and the life of the actual battery is dynamically changing. The current technology usually only predicts the remaining power of the battery through the measurement of the battery itself. Without a dynamic management system, it is impossible to obtain accurate results, and there is a large error with the actual service life.
经过对现有技术的检索发现，在与电池电力管理领域专利申请的主要企业有：比亚迪、奇瑞汽车、深圳市比克电池有限公司、中兴、天津力神电池股份有限公司和华为公司。比亚迪在电池管理领域的专利申请主要涉及的技术主题包括：电池的充放电及其控制、电池的保护、电池管理系统、防止电池过充或过放的检测及保护、电池短路的检测、电池电压监测、电池性能的测试、电池剩余电量的检测、电池的均衡管理和电池内阻的检测。奇瑞在电池管理领域的专利申请主要涵盖的技术主题有：电池余量检测、分布式电池管理、电池温度检测、电池内阻检测、电池的均衡化管理、电池电压监测、电池性能检测和电池充放电电流的限制保护。深圳市比克电池有限公司在电池管理系统领域的专利主要集中在充电装置(如锂离子电池预充电电路CN200956520)、锂离子电池安全检测(如一种锂离子电池爆炸原因测试方法CN101059553)、充放电均衡和电池保护(如电池组保护装置CN101394080)及电压检测(如一种自动检测电池电压装置及预充柜CN201060259)方面。中兴公司在电池管理系统领域也具有一定的专利申请量，主要集中在电池的充电/放电的方法或装置的技术方向，如蓄电池充放电状态的判断方法CN1389950、一种充电电池的充电方法及其装置CN1885669、一种智能充电方法和装置CN101340011，还具有充电保护方面的专利技术如充电保护方法及装置CN101174770和电池电压检测方面的专利如蓄电池组单节电池的电压采样装置及方法CN101236235。天津力神公司在电池管理系统领域的专利申请主要集中在锂离子电池测试、锂离子电池保护和电池电阻检测方面，如用于方形锂离子二次电池预充电的控制装置CN1937350、多串大功率锂离子电池组过充过放保护电压检测装置CN200947542和锂离子电池单体或电池组低温性能测评方法CN101241170。华为公司在电池管理领域方面的专利申请作为其主要研发方向的辅助技术方向，主要申请集中在电池的充电/放电的方法或装置领域，如一种蓄电池组系统及使用方法CN1738149和电池管理的装置CN201084793。对电池管理系统的技术专利，如清华大学的一种快速评价车用燃料电池使用寿命的方法CN101067646。After searching the existing technologies, it is found that the main companies applying for patents in the field of battery power management are: BYD, Chery Automobile, Shenzhen BAK Battery Co., Ltd., ZTE, Tianjin Lishen Battery Co., Ltd. and Huawei. BYD's patent applications in the field of battery management mainly involve technical topics including: battery charge and discharge and its control, battery protection, battery management system, detection and protection against battery overcharge or overdischarge, battery short circuit detection, battery voltage Monitoring, testing of battery performance, detection of remaining battery power, battery balance management and detection of battery internal resistance. Chery's patent applications in the field of battery management mainly cover the following technical topics: battery remaining detection, distributed battery management, battery temperature detection, battery internal resistance detection, battery equalization management, battery voltage monitoring, battery performance detection and battery charging. Limit protection of discharge current. The patents of Shenzhen BAK Battery Co., Ltd. in the field of battery management systems mainly focus on charging devices (such as lithium-ion battery pre-charging circuit CN200956520), lithium-ion battery safety testing (such as a lithium-ion battery explosion cause test method CN101059553), charging and discharging Balancing and battery protection (such as battery pack protection device CN101394080) and voltage detection (such as a device for automatically detecting battery voltage and pre-charging cabinet CN201060259). ZTE Corporation also has a certain number of patent applications in the field of battery management systems, mainly focusing on the technical direction of battery charging/discharging methods or devices, such as the method for judging the charging and discharging status of batteries CN1389950, a charging method for rechargeable batteries and its The device CN1885669, an intelligent charging method and device CN101340011, also has patented technology on charging protection such as charging protection method and device CN101174770 and patents on battery voltage detection such as the voltage sampling device and method of a single battery in a battery pack CN101236235. Tianjin Lishen Company's patent applications in the field of battery management systems mainly focus on lithium-ion battery testing, lithium-ion battery protection and battery resistance detection, such as CN1937350 control device for pre-charging square lithium-ion secondary batteries, multi-string high-power CN200947542, CN200947542, CN200947542, CN200947542, and CN101241170, CN101241170, CN101241170, CN101241170, CN101241170, CN101241170, CN101241170. Huawei's patent application in the field of battery management is the auxiliary technology direction of its main research and development direction, and the main applications are concentrated in the field of battery charging/discharging methods or devices, such as a battery pack system and its use CN1738149 and battery management device CN201084793 . For technical patents on battery management systems, such as CN101067646, a method for quickly evaluating the service life of vehicle fuel cells issued by Tsinghua University.
上述所申请的专利主要集中在电池制造的材料改进方面，如阳极、阴极、电解质、隔离器，或者电池组批量生产的制造组装与设计。电动车电力管理系统的专利则主要是关于燃料电池寿命或耐久性的定义方法，并且仅局限于对电动车电池电力状态的监测。传统的电池管理系统在电池电量用完后才进行充电或更换电池，其不利之处包括充电时间很长，使用者需耐心待，其对电动车电池剩余电量的估计亦是基于此类充满-放电结束的固定周期实验所得的模型，缺乏动态预测，没有考虑基于不同电动车使用者行为模式特征对电池能耗的影响，没有考虑不同的道路状况和路线对电动车电池能耗的影响，也没有基于对电力及电动车使用者行为习惯动态分析向使用者提出服务建议的职能互动技术与方法。此外，现有技术中所述的电池管理系统中所使用的模型缺乏在线更新能力。The above-mentioned patent applications mainly focus on the improvement of materials for battery manufacturing, such as anodes, cathodes, electrolytes, separators, or manufacturing assembly and design for mass production of battery packs. The patent on electric vehicle power management system is mainly about the definition method of fuel cell life or durability, and is limited to the monitoring of electric vehicle battery power status. The traditional battery management system only charges or replaces the battery after the battery is exhausted. The disadvantages include the long charging time, and the user needs to be patient. Its estimation of the remaining power of the electric vehicle battery is also based on this kind of full- The model obtained from the fixed-cycle experiment at the end of discharge lacks dynamic prediction, does not consider the impact of different electric vehicle user behavior patterns on battery energy consumption, and does not consider the impact of different road conditions and routes on electric vehicle battery energy consumption. There is no functional interaction technology and method for providing service suggestions to users based on dynamic analysis of electric power and electric vehicle user behavior habits. Furthermore, the models used in the battery management systems described in the prior art lack online update capability.
发明内容Contents of the invention
本发明针对现有技术存在的上述不足，提供一种电动车电力管理监控系统，能够准确预测电动车电池的剩余寿命并实现对电动车的行动力情况的动态管理。The present invention aims at the above-mentioned deficiencies in the prior art, and provides an electric vehicle power management and monitoring system, which can accurately predict the remaining life of the battery of the electric vehicle and realize dynamic management of the mobility of the electric vehicle.
本发明是通过以下技术方案实现的，本发明包括：电动车行动力动态管理模块、电池维护系统、远程监控系统、电动车诱导服务系统和智能分析平台，其中：电池维护系统与电动车上的各类传感器相连接并采集数据信息及特征参数，电动车行动力动态管理模块与电池维护系统相连接并由电池维护系统向行动力动态管理模块传输电池状态信息和路况能耗信息，远程监控系统与电池维护系统相连接并从电动车上的电池维护系统取得定位信息，智能分析平台与电动车行动力动态管理模块相连接并传输云计算智能信息分析所得的模型参数信息，电动车诱导服务系统与智能服务平台系统相连接并传输就近充电站预约及正在充电的电动车的服务队列信息、电动车用户在网络分享的服务需求信息。The present invention is realized through the following technical proposals, and the present invention includes: an electric vehicle power dynamic management module, a battery maintenance system, a remote monitoring system, an electric vehicle guidance service system and an intelligent analysis platform, wherein: the battery maintenance system and the electric vehicle Various sensors are connected to collect data information and characteristic parameters. The dynamic management module of electric vehicles is connected to the battery maintenance system and the battery maintenance system transmits battery status information and road condition energy consumption information to the dynamic management module of mobility. The remote monitoring system It is connected with the battery maintenance system and obtains positioning information from the battery maintenance system on the electric vehicle. The intelligent analysis platform is connected with the dynamic management module of the electric vehicle movement and transmits the model parameter information obtained from the intelligent information analysis of cloud computing. The electric vehicle guidance service system It is connected with the intelligent service platform system and transmits the reservation of the nearest charging station, the service queue information of the electric vehicles being charged, and the service demand information shared by electric vehicle users on the network.
所述的数据信息及特征参数包括：电池的电压、电流、电池温度、电动车全球定位信息、行驶速度、加速度、环境温度以及环境湿度。The data information and characteristic parameters include: battery voltage, current, battery temperature, global positioning information of the electric vehicle, driving speed, acceleration, ambient temperature and ambient humidity.
所述的电池维护系统包括：传感器模块、特征提取及编码模块，其中：传感器模块采集与电动车行动力相关的数据，如电池、道路、环境变量，并将数据传至特征提取及编码模块；特征提取及编码模块通过对采集的数据处理得到电池电荷状态、电压、电流用以描述电池剩余电力的特征参数、当前位置行驶路况的电力能耗特征参数、电动车使用者的行为特征参数，经过协议编码后将特征参数及传感数据向电动车行动力动态管理模块传递。The battery maintenance system includes: a sensor module, a feature extraction and encoding module, wherein: the sensor module collects data related to the driving force of the electric vehicle, such as batteries, roads, and environmental variables, and transmits the data to the feature extraction and encoding module; The feature extraction and encoding module processes the collected data to obtain the battery charge state, voltage, and current to describe the characteristic parameters of the remaining power of the battery, the power consumption characteristic parameters of the current location and driving conditions, and the behavior characteristic parameters of the electric vehicle user. After the protocol is encoded, the characteristic parameters and sensing data are transmitted to the power dynamic management module of the electric vehicle.
所述的电动车行动力动态管理模块包括：个人智能通讯装置上的特征提取模块、电动车行动力云计算分析模块以及设置于个人智能通讯装置上的电动车行动力显示模块，其中：电动车行动力动态管理模块中的特征提取模块从电池维护系统取得电池剩余电力特征参数和当前形势路况电力能耗特征，并利用无线网络将数据信息传输至远程的电动车行动力云计算分析模块；电动车行动力动态管理模块中的电动车行动力云计算分析模块对所得的电池剩余电力特征参数、电动车所在区域道路的路况电力能耗特征、电动车使用者的驾驶行为特征进行匹配，经计算得到在电动车可驶及的范围，实现对电动车行动力的预估。The dynamic management module of electric vehicle dynamics includes: a feature extraction module on the personal intelligent communication device, an electric vehicle dynamic cloud computing analysis module and an electric vehicle dynamic display module arranged on the personal intelligent communication device, wherein: the electric vehicle The feature extraction module in the mobility dynamic management module obtains the remaining battery power characteristic parameters and the current road condition power consumption characteristics from the battery maintenance system, and uses the wireless network to transmit the data information to the remote electric vehicle mobility cloud computing analysis module; The electric vehicle dynamic cloud computing analysis module in the vehicle dynamic dynamic management module matches the obtained characteristic parameters of the remaining battery power, the road condition power consumption characteristics of the road where the electric vehicle is located, and the driving behavior characteristics of the electric vehicle user. Get the range that the electric vehicle can drive, and realize the estimation of the driving force of the electric vehicle.
所述的远程监控系统包括：互联网地理信息系统模块和电动车定位信息存储模块，其中：电动车定位信息存储模块用于存储电动车的全球定位数据、速度、加速度历史信息；远程监控系统的互联网地理信息系统模块从电动车行动力动态管理模块取得电动车的定位信息及传感器数据，并将信息存于电动车定位信息存储模块，利用互联网地理信息系统模块，电动车用户可通过互联网查询电动车所在位置和位置历史信息、电力、速度、加速度数据。The remote monitoring system includes: an Internet geographic information system module and an electric vehicle positioning information storage module, wherein: the electric vehicle positioning information storage module is used to store the global positioning data, speed, and acceleration history information of the electric vehicle; the Internet of the remote monitoring system The geographic information system module obtains the positioning information and sensor data of the electric vehicle from the dynamic management module of the electric vehicle, and stores the information in the electric vehicle positioning information storage module. Using the Internet geographic information system module, the electric vehicle user can query the electric vehicle through the Internet Location and location history information, power, speed, acceleration data.
所述的智能分析平台包括：电动车电池特征信息存储模块、道路能耗特征存储模块、电动车用户行为特征存储模块、电动车及其用户信息管理存储模块、分析模型参数存储模块和数据挖掘模块，其中：智能分析平台中的电动车电池特征信息存储模块用于存储电动车行动力动态管理模块和电池维护系统实时电池工况监测各类信号分析所得的特征值；智能分析平台中的道路能耗特征存储模块用于存储电动车行动力动态管理模块中和电池维护系统实时道路能耗监测及在道路上行驶时的监测信号及控制信号分析所得的特征值；电动车用户行为特征存储模块用于存储电动车行动力动态管理模块对用户操作电动车的控制信号分析所得的行为特征信息，电动车及其用户信息管理存储模块用于存储电动车用户的信息和电动车制造信息，分析模型参数存储模块用于存储电动车行动力动态管理模块中电池剩余寿命、电池当前剩余电量使用时间与道路能耗匹配优化算法、电动车用户操作电动车的驾驶行为特征分析算法、电动车用户目的地及偏好分析及诱导建议算法模型的参数信息，数据挖掘模块用以进一步对多个电动车及其用户的信息进行特征信息融合分析，得到更准确的模型参数，所得更新的模型参数存储于分析模型参数存储模块，定期更新电动车行动力动态管理模块和电动车诱导服务系统中的模型分析参数。The intelligent analysis platform includes: electric vehicle battery characteristic information storage module, road energy consumption characteristic storage module, electric vehicle user behavior characteristic storage module, electric vehicle and its user information management storage module, analysis model parameter storage module and data mining module , wherein: the electric vehicle battery feature information storage module in the intelligent analysis platform is used to store the characteristic values obtained from the real-time battery condition monitoring of the electric vehicle dynamic management module and the battery maintenance system; the road energy in the intelligent analysis platform The consumption feature storage module is used to store the characteristic values obtained from the real-time road energy consumption monitoring of the electric vehicle dynamic management module and the battery maintenance system and the monitoring signal and control signal analysis when driving on the road; the electric vehicle user behavior feature storage module is used It is used to store the behavior characteristic information obtained from the analysis of the control signal of the user operating the electric vehicle by the dynamic management module of the electric vehicle action. The electric vehicle and its user information management storage module is used to store the information of the electric vehicle user and the manufacturing information of the electric vehicle, and analyze the model parameters. The storage module is used to store the remaining life of the battery in the dynamic management module of the electric vehicle, the matching optimization algorithm of the current remaining battery power usage time and road energy consumption, the analysis algorithm of the driving behavior characteristics of the electric vehicle user operating the electric vehicle, the destination of the electric vehicle user and The parameter information of the preference analysis and induction suggestion algorithm model, the data mining module is used to further perform feature information fusion analysis on the information of multiple electric vehicles and their users to obtain more accurate model parameters, and the updated model parameters are stored in the analysis model parameters The storage module regularly updates the model analysis parameters in the dynamic management module of electric vehicles and the guidance service system of electric vehicles.
所述的电动车诱导服务系统包括：用户分享评价信息存储模块和评价信息统计及分析模块，其中：用户分享评价信息存储模块用于存储电动车用户群自愿参与在诱导服务系统上对餐馆、充电站、影院、宾馆旅店、健身场所的体验、评价和分享信息，评价信息统计及分析模块对用户分享评价信息存储模块中的评价信息进行智能分析，对具有不同偏好的电动车用户生成定制的诱导性建议。The electric vehicle induction service system includes: user sharing evaluation information storage module and evaluation information statistics and analysis module, wherein: user sharing evaluation information storage module is used to store electric vehicle user groups voluntarily participating in the induction service system for restaurants, charging The evaluation information statistics and analysis module intelligently analyzes the evaluation information shared by users in the evaluation information storage module, and generates customized guidance for electric vehicle users with different preferences. sex advice.
本发明的创新点在于：1电动车上电池状态服务信息均由个人智能通讯设备显示，将电动车行动力管理与可随身携带的个人智能通讯设备紧密结合，该创新点将电动车的制造厂商无需再设计及安装相关仪表装置，个人智能通讯设备就是电动车的虚拟仪表，降低了电动车的制造及设计成本；2基于云计算智能分析电动车及其电池的状况，其智能分析的主要计算负荷由远程云服务器承担，并对众多用户电动车的传感器特征信息进行数据挖掘，智能地更及电动车行动力管理系统和智能诱导服务系统的分析模型参数，该创新点借助云计算不仅提高了远程服务器的计算利用率，也增加了对海量传感数据的分析能力；3所提出的电动车诱导服务将网络社区中用户分享的评价信息与电动车用户目的地历史记录相结合，对电动车使用者具有实用价值，改善用户服务体验和兴趣，产生极大的经济效益。The innovations of the present invention are: 1. The battery status service information on the electric vehicle is displayed by the personal intelligent communication device, and the power management of the electric vehicle is closely combined with the portable personal intelligent communication device. There is no need to design and install related instrumentation devices. The personal intelligent communication equipment is the virtual instrument of the electric vehicle, which reduces the manufacturing and design costs of the electric vehicle; The load is borne by the remote cloud server, and data mining is carried out on the sensor feature information of many users' electric vehicles, and the analysis model parameters of the electric vehicle power management system and intelligent guidance service system are intelligently updated. This innovation not only improves the The computing utilization rate of the remote server also increases the ability to analyze massive sensor data; 3. The proposed electric vehicle guidance service combines the evaluation information shared by users in the online community with the historical records of electric vehicle user destinations. Users have practical value, improve user service experience and interest, and generate great economic benefits.
附图说明Description of drawings
图1为本发明总体结构示意图。Figure 1 is a schematic diagram of the overall structure of the present invention.
图2为电池维护系统实施例示意图。Fig. 2 is a schematic diagram of an embodiment of a battery maintenance system.
图3为电动车行动力动态管理模块实施例示意图。Fig. 3 is a schematic diagram of an embodiment of a power dynamic management module of an electric vehicle.
图4为远程监控系统实施例示意图。Fig. 4 is a schematic diagram of an embodiment of a remote monitoring system.
图5为智能分析平台实施例示意图。Fig. 5 is a schematic diagram of an embodiment of an intelligent analysis platform.
图6为电动车诱导服务系统实施例示意图。Fig. 6 is a schematic diagram of an embodiment of an electric vehicle guidance service system.
具体实施方式Detailed ways
下面对本发明的实施例作详细说明，本实施例在以本发明技术方案为前提下进行实施，给出了详细的实施方式和具体的操作过程，但本发明的保护范围不限于下述的实施例。如图1所示，本实施例包括：电动车行动力动态管理模块1、远程监控系统2、电池维护系统3、电动车诱导服务系统4和智能分析平台5，其中：电池维护系统3与电动车上的各类传感器相连接并采集数据信息及特征参数，电动车行动力动态管理模块1与电池维护系统3相连接并由电池维护系统3向行动力动态管理模块1传输电池状态信息和路况能耗信息，远程监控系统2与电池维护系统3相连接并从电动车上的电池维护系统3取得定位信息，智能分析平台5与电动车行动力动态管理模块1相连接并传输云计算智能信息分析所得的模型参数信息，电动车诱导服务系统4与智能服务平台系统相连接并传输就近充电站预约及正在充电的电动车的服务队列信息、电动车用户在网络分享的服务需求信息。The embodiments of the present invention are described in detail below. This embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation methods and specific operating procedures are provided, but the protection scope of the present invention is not limited to the following implementation example. As shown in Figure 1, this embodiment includes: electric vehicle dynamic
所述的数据信息及特征参数包括：电池的电压、电流、电池温度、电动车全球定位信息、行驶速度、加速度、环境温度以及环境湿度。The data information and characteristic parameters include: battery voltage, current, battery temperature, global positioning information of the electric vehicle, driving speed, acceleration, ambient temperature and ambient humidity.
所述的电池维护系统3包括：传感器模块、特征提取及编码模块，其中：传感器模块采集与电动车行动力相关的数据，如电池、道路、环境变量，并将数据传至特征提取及编码模块；特征提取及编码模块通过对采集的数据处理得到电池电荷状态、电压、电流用以描述电池剩余电力的特征参数、当前位置行驶路况的电力能耗特征参数、电动车使用者的行为特征参数，经过协议编码后将特征参数及传感数据向电动车行动力动态管理模块1传递。The battery maintenance system 3 includes: a sensor module, a feature extraction and encoding module, wherein: the sensor module collects data related to the power of the electric vehicle, such as batteries, roads, and environmental variables, and transmits the data to the feature extraction and encoding module ; The feature extraction and encoding module processes the collected data to obtain the battery charge state, voltage, and current to describe the characteristic parameters of the remaining power of the battery, the power consumption characteristic parameters of the current location and driving conditions, and the behavior characteristic parameters of the electric vehicle user. After protocol encoding, the characteristic parameters and sensing data are transmitted to the electric vehicle dynamic
所述的电动车行动力动态管理模块1包括：个人智能通讯装置上的特征提取模块、电动车行动力云计算分析模块以及设置于个人智能通讯装置上的电动车行动力显示模块，其中：电动车行动力动态管理模块1中的特征提取模块从电池维护系统3取得电池剩余电力特征参数和当前形势路况电力能耗特征，并利用无线网络将数据信息传输至远程的电动车行动力云计算分析模块；电动车行动力动态管理模块1中的电动车行动力云计算分析模块对所得的电池剩余电力特征参数、电动车所在区域道路的路况电力能耗特征、电动车使用者的驾驶行为特征进行匹配，经计算得到在电动车可驶及的范围，实现对电动车行动力的预估。The
所述的远程监控系统2包括：互联网地理信息系统模块和电动车定位信息存储模块，其中：电动车定位信息存储模块用于存储电动车的全球定位数据、速度、加速度历史信息；远程监控系统2的互联网地理信息系统模块从电动车行动力动态管理模块1取得电动车的定位信息及传感器数据，并将信息存于电动车定位信息存储模块，利用互联网地理信息系统模块，电动车用户可通过互联网查询电动车所在位置和位置历史信息、电力、速度、加速度数据。The remote monitoring system 2 includes: an Internet geographic information system module and an electric vehicle positioning information storage module, wherein: the electric vehicle positioning information storage module is used to store the global positioning data, speed, and acceleration history information of the electric vehicle; the remote monitoring system 2 The Internet geographic information system module obtains the positioning information and sensor data of the electric vehicle from the electric vehicle
所述的智能分析平台5包括：电动车电池特征信息存储模块、道路能耗特征存储模块、电动车用户行为特征存储模块、电动车及其用户信息管理存储模块、分析模型参数存储模块和数据挖掘模块，其中：智能分析平台5中的电动车电池特征信息存储模块用于存储电动车行动力动态管理模块1和电池维护系统3实时电池工况监测各类信号分析所得的特征值；智能分析平台5中的道路能耗特征存储模块用于存储电动车行动力动态管理模块1中和电池维护系统3实时道路能耗监测及在道路上行驶时的监测信号及控制信号分析所得的特征值；电动车用户行为特征存储模块用于存储电动车行动力动态管理模块1对用户操作电动车的控制信号分析所得的行为特征信息，电动车及其用户信息管理存储模块用于存储电动车用户的信息和电动车制造信息，分析模型参数存储模块用于存储电动车行动力动态管理模块1中电池剩余寿命、电池当前剩余电量使用时间与道路能耗匹配优化算法、电动车用户操作电动车的驾驶行为特征分析算法、电动车用户目的地及偏好分析及诱导建议算法模型的参数信息，数据挖掘模块用以进一步对多个电动车及其用户的信息进行特征信息融合分析，得到更准确的模型参数，所得更新的模型参数存储于分析模型参数存储模块，定期更新电动车行动力动态管理模块1和电动车诱导服务系统4中的模型分析参数。The intelligent analysis platform 5 includes: electric vehicle battery characteristic information storage module, road energy consumption characteristic storage module, electric vehicle user behavior characteristic storage module, electric vehicle and its user information management storage module, analysis model parameter storage module and data mining module, wherein: the electric vehicle battery characteristic information storage module in the intelligent analysis platform 5 is used to store the characteristic values obtained by the electric vehicle
所述的电动车诱导服务系统4包括：用户分享评价信息存储模块和评价信息统计及分析模块，其中：用户分享评价信息存储模块用于存储电动车用户群自愿参与在诱导服务系统4上对餐馆、充电站、影院、宾馆旅店、健身场所的体验、评价和分享信息，评价信息统计及分析模块对用户分享评价信息存储模块中的评价信息进行智能分析，对具有不同偏好的电动车用户生成定制的诱导性建议。The electric vehicle guidance service system 4 includes: a user sharing evaluation information storage module and an evaluation information statistics and analysis module, wherein: the user sharing evaluation information storage module is used to store electric vehicle user groups voluntarily participating in the guidance service system 4 for restaurants , charging stations, cinemas, hotels, fitness places, experience, evaluation and sharing information, the evaluation information statistics and analysis module intelligently analyzes the evaluation information in the user sharing evaluation information storage module, and generates customization for electric vehicle users with different preferences inductive suggestions.
本装置通过以下方式进行工作：使用者在个人智能移动通讯设备上的电动车行动力动态管理模块1中输入所要去的目的地，并将目的地信息传输至云服务器上的电动车行动力动态管理模块；电动车行动力动态管理模块同时提取远程监控系统2中的当前位置，初步分析计算得到最佳路径，并下载到个人智能移动通讯设备上；电动车上电启动后，电池维护系统3对电池和电动车上的其他传感器开始监测，所得信息不断传至云服务器上的电动车行动力动态管理模块；电动车在行驶过程中通过加速度传感器传感器间接地取得道路精细信息、通过电动车上的电子控制单元取得使用者的操作信息、通过对电池电压、电流信息的监测得到不同转弯、摩擦阻力条件下的电池能耗及当前电池状态，云服务器上的电动车行动力动态管理模块对所得到的信息中分析得到电池电量的消耗模式及剩余电量所能行驶的范围，更新最佳路径和电动车行动力估计信息，更准确地估计当前电动车的行动力范围，并传回至个人智能移动通讯装置上显示；电动车智能诱导服务系统4对网络社区用户群分享的评价信息、电动车用户目的地及驾驶行为的历史信息进行智能分析和匹配，当用户给出新的目的地时，给出诱导性建议相关地点和路线，并在电动车行动力动态管理系统中显示；云服务器上的智能分析平台5将电动车上传感器数据特征、用户目的地历史和驾驶行为历史信息进行综合分析及数据挖掘，得到更佳的电动车行动力估计精确度和用户体验满意度的模型参数，从而更新电动车行动力动态管理模块和电动车智能诱导服务系统。The device works in the following way: the user inputs the desired destination in the
如图2所示，电池维护系统中，电压传感器、电流传感器、分布在电池包上的温度传感器、3轴加速度传感器、全球定位系统传感器、车外温度传感器、湿度传感器各传感器，及电动车电子操控系统与电池维护系统的数据采集系统相连接并传输至特征提取及编码模块，通过对采集数据的分析得到如电池电荷状态、电压、电流用以描述电池剩余电力的特征参数、当前位置行驶路况的电力能耗特征参数、电动车使用者的行为特征参数，经过协议编码后将特征参数及传感数据向电动车行动力动态管理模块传递。As shown in Figure 2, in the battery maintenance system, voltage sensors, current sensors, temperature sensors distributed on the battery pack, 3-axis acceleration sensors, global positioning system sensors, outside temperature sensors, humidity sensors, and electric vehicle electronics The control system is connected with the data acquisition system of the battery maintenance system and transmitted to the feature extraction and encoding module. Through the analysis of the collected data, the characteristic parameters such as the battery charge state, voltage, and current used to describe the remaining power of the battery, and the current location and driving conditions are obtained. The characteristic parameters of electric power consumption and the behavior characteristic parameters of electric vehicle users, after protocol encoding, the characteristic parameters and sensing data are transmitted to the dynamic management module of electric vehicle behavior.
如图3所示，电动车行动力动态管理模块中的特征提取模块从电池维护系统取得电池剩余电力特征参数和当前形势路况电力能耗特征后，利用无线网络将数据信息传输至远程的电动车行动力云计算分析模块，由电动车行动力云计算分析模块对所得的电池剩余电力特征参数、电动车所在区域道路的路况电力能耗特征、电动车使用者的驾驶行为特征进行匹配，经计算得到在电动车可驶及的范围，即实现对电动车行动力的预估，精准地计算电池未来充电时间，有效地安排电池更换计划，所得到的电动车当前电力剩余使用时间、可行动范围信息，再经过无线移动网络传回电动车剩余使用时间和可行动范围信息，在个人智能通讯装置模块上显示上述信息，此外，电动车的定位信息亦由该行动力动态管理模块传至远程监控系统；并从智能分析平台定期更新云服务器计算模块的分析模型参数。As shown in Figure 3, after the feature extraction module in the dynamic management module of electric vehicles obtains the characteristic parameters of battery remaining power and the characteristics of current road conditions and power consumption from the battery maintenance system, it uses the wireless network to transmit the data information to the remote electric vehicle Mobility cloud computing analysis module, the electric vehicle mobility cloud computing analysis module matches the obtained battery remaining power characteristic parameters, the road condition power consumption characteristics of the road where the electric vehicle is located, and the driving behavior characteristics of the electric vehicle user. Obtain the driving range of the electric vehicle, that is, realize the estimation of the driving force of the electric vehicle, accurately calculate the future charging time of the battery, and effectively arrange the battery replacement plan. Information, and then through the wireless mobile network to send back the information of the remaining use time and the range of action of the electric vehicle, and display the above information on the personal intelligent communication device module. In addition, the positioning information of the electric vehicle is also transmitted to the remote monitoring by the dynamic management module of the mobility. system; and regularly update the analysis model parameters of the cloud server computing module from the intelligent analysis platform.
如图4所示，远程监控系统从电动车行动力动态管理模块取得电动车的定位信息及传感器数据，并将信息存于电动车定位信息存储模块，利用互联网地理信息系统模块，电动车用户可通过互联网查询电动车所在位置和位置历史信息、电力、速度、加速度数据。As shown in Figure 4, the remote monitoring system obtains the positioning information and sensor data of the electric vehicle from the dynamic management module of the electric vehicle, and stores the information in the storage module of the electric vehicle positioning information. Using the Internet geographic information system module, the electric vehicle user can Query the location and historical information of the electric vehicle, power, speed, and acceleration data through the Internet.
如图5所示，智能分析平台中，用户使用的电动车信息被存储于电动车及其用户信息管理模块，电动车电池状态传感器数据及道路能耗数据经电动车行动力动态管理模块的智能分析后，其特征信息分别存储于电动车电池特征信息存储模块、道路能耗特征存储模块，基于云计算技术的数据挖掘模块进一步对多用户的数据进行特征信息融合分析、并更新模型参数，所得更新的模型参数存储于分析模型参数存储模块，由电动车行动力动态管理模块定期从智能分析平台的模型参数存储模块调用更新的参数，以便对电动车上传感数据智能分析时调用；电动车诱导服务系统亦定期从智能分析平台的模型参数存储模块调用更新的分析参数，以便诱导服务信息分析生成时调用。As shown in Figure 5, in the intelligent analysis platform, the electric vehicle information used by the user is stored in the electric vehicle and its user information management module. After analysis, its feature information is stored in the electric vehicle battery feature information storage module and road energy consumption feature storage module respectively. The data mining module based on cloud computing technology further performs feature information fusion analysis on multi-user data and updates model parameters. The updated model parameters are stored in the analysis model parameter storage module, and the dynamic management module of the electric vehicle dynamics calls the updated parameters from the model parameter storage module of the intelligent analysis platform on a regular basis, so as to call when intelligently analyzing the sensing data on the electric vehicle; The service system also regularly calls updated analysis parameters from the model parameter storage module of the intelligent analysis platform, so as to induce service information to be called when analysis is generated.
如图6所示，通过电动车诱导服务系统，电动车用户自愿参与在诱导服务系统的网络上餐馆、充电站、影院、宾馆旅店、健身场所体验评价和分享，这些分享的评价信息存储于用户分享评价信息存储模块，通过信息智能挖掘模块从远程监控系统取得电动车前往不同类型目的地的频率，并调用用户分享评价信息存储模块中的评价信息进行智能分析，生成诱导性的建议，以满足和服务电动车用户在吃穿住行生活各方面的需求，这些服务可由电动车智能诱导服务系统通过移动服务网络与用户的个人智能移动通讯设备如iPad，iPhone及iPod Touch、安装有Android系统的智能手机传输建议并实现。As shown in Figure 6, through the electric vehicle guidance service system, electric vehicle users voluntarily participate in the experience evaluation and sharing of restaurants, charging stations, theaters, hotels, and fitness venues on the network of the guidance service system, and the shared evaluation information is stored in the user The shared evaluation information storage module obtains the frequency of electric vehicles going to different types of destinations from the remote monitoring system through the information intelligent mining module, and calls the evaluation information in the user shared evaluation information storage module to conduct intelligent analysis and generate inductive suggestions to meet And serve the needs of electric vehicle users in all aspects of food, clothing, housing and transportation. These services can be provided by the electric vehicle intelligent guidance service system through the mobile service network and the user's personal intelligent mobile communication devices such as iPad, iPhone and iPod Touch, Android system installed Smartphone transfer advice and implementation.
Claims (6)
Translated fromChinesePriority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2010105458338ACN102024999B (en) | 2010-11-16 | 2010-11-16 | Electric car running power management system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2010105458338ACN102024999B (en) | 2010-11-16 | 2010-11-16 | Electric car running power management system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102024999Atrue CN102024999A (en) | 2011-04-20 |
| CN102024999B CN102024999B (en) | 2013-05-01 |
Family
ID=43866033
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2010105458338AExpired - Fee RelatedCN102024999B (en) | 2010-11-16 | 2010-11-16 | Electric car running power management system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102024999B (en) |
Cited By (57)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102169168A (en)* | 2011-05-17 | 2011-08-31 | 杭州电子科技大学 | Battery dump energy estimation method based on particle filtering |
| CN102520365A (en)* | 2011-12-23 | 2012-06-27 | 上海交通大学 | Quick estimating system of remaining battery power and method thereof |
| CN102520366A (en)* | 2011-12-23 | 2012-06-27 | 上海交通大学 | Electric car cell safety and health assessment system and method thereof |
| CN102567488A (en)* | 2011-12-21 | 2012-07-11 | 江苏远为科技有限公司 | System and method for mining data of electric vehicle based on cloud computer framework |
| CN102592330A (en)* | 2012-01-19 | 2012-07-18 | 上海交通大学 | Method for extracting road vehicle running status based on battery data of electric vehicle |
| CN102752380A (en)* | 2012-06-29 | 2012-10-24 | 山东电力集团公司电力科学研究院 | Electric vehicle-mounted terminal based on cloud computing and using method thereof |
| CN102837697A (en)* | 2011-06-24 | 2012-12-26 | 北汽福田汽车股份有限公司 | Management system and working method for endurance mileage of electric vehicle |
| CN102930395A (en)* | 2012-10-31 | 2013-02-13 | 北京中电普华信息技术有限公司 | Power battery assets management method and system of electric automobiles |
| CN103369030A (en)* | 2013-05-17 | 2013-10-23 | 洪承洋 | Electric bicycle wireless driving communication system |
| CN103455020A (en)* | 2012-05-28 | 2013-12-18 | 哈尔滨工业大学深圳研究生院 | Intelligent cloud-detection service system and intelligent cloud-detection service method for vehicle conditions |
| CN103630840A (en)* | 2012-08-20 | 2014-03-12 | 低碳动能开发股份有限公司 | Active cloud power management system for secondary battery |
| CN103682484A (en)* | 2013-12-09 | 2014-03-26 | 惠州市亿能电子有限公司 | Method using vehicle controller for battery management of electric vehicle |
| CN104145224A (en)* | 2012-01-09 | 2014-11-12 | 爱尔比奎特公司 | Electric vehicle charging network service |
| TWI489401B (en)* | 2012-03-02 | 2015-06-21 | Ind Tech Res Inst | Electric vehicle dispatching method and system with power-on planning function |
| CN104751271A (en)* | 2015-03-04 | 2015-07-01 | 径圆（上海）信息技术有限公司 | Intelligent order scheduling method and server, electric vehicle, mobile terminal and system |
| CN104751272A (en)* | 2015-03-04 | 2015-07-01 | 径圆（上海）信息技术有限公司 | Intelligent order scheduling method and server, electric vehicle, mobile terminal and system |
| CN104986160A (en)* | 2015-06-16 | 2015-10-21 | 北京亿利智慧能源科技有限公司 | Electric automobile driving behavior guide instrument |
| CN103620658B (en)* | 2011-07-12 | 2015-12-02 | 日产自动车株式会社 | Information of vehicles generator and vehicle information management system |
| CN105446223A (en)* | 2015-12-22 | 2016-03-30 | 镇江恒驰科技有限公司 | System and method for optimizing new energy vehicle whole vehicle control parameters based on Internet |
| CN106114252A (en)* | 2016-06-21 | 2016-11-16 | 观致汽车有限公司 | The method and system that battery uses are managed service life based on battery contactor |
| CN106779905A (en)* | 2016-11-18 | 2017-05-31 | 合肥工业大学 | Method and device for recommending battery configuration based on user behavior |
| CN106740134A (en)* | 2016-11-22 | 2017-05-31 | 天津韶乐科技有限公司 | A kind of electric quantity of batteries of electric vehicle management system and its management method |
| CN106936915A (en)* | 2017-03-23 | 2017-07-07 | 南京越博动力系统股份有限公司 | A kind of electric automobile remote monitoring data cloud analysis system |
| CN107264303A (en)* | 2017-05-08 | 2017-10-20 | 广东硅岳能源科技有限公司 | Method and system for converting vehicle driving conditions into power battery operating conditions |
| CN107526040A (en)* | 2017-07-28 | 2017-12-29 | 广州亿程交通信息有限公司 | New-energy automobile management method and system |
| CN107533724A (en)* | 2016-02-24 | 2018-01-02 | 松下知识产权经营株式会社 | Server unit and its control method |
| CN107968840A (en)* | 2017-12-15 | 2018-04-27 | 华北电力大学（保定） | A kind of extensive power equipment monitoring, alarming Real-time Data Processing Method and system |
| CN108382208A (en)* | 2018-02-26 | 2018-08-10 | 杭州翼兔网络科技有限公司 | An electric vehicle terminal for an electric vehicle comprehensive management and control system |
| CN108899599A (en)* | 2018-07-16 | 2018-11-27 | 矩电新能源（苏州）有限公司 | Battery cloud early warning system and its method for early warning |
| CN109109784A (en)* | 2017-06-22 | 2019-01-01 | 吴俊逸 | System for providing remaining driving information of vehicle according to user behavior and method thereof |
| CN109214521A (en)* | 2017-07-11 | 2019-01-15 | 孟贞生 | Intelligent management apapratus, system and method based on battery pack or battery PACK packet |
| CN109263602A (en)* | 2018-08-31 | 2019-01-25 | 金华安靠电源科技有限公司 | A kind of vehicle-mounted service system |
| CN110382339A (en)* | 2017-03-07 | 2019-10-25 | 泽哈斯股份公司 | The system and method for the road quality in bicyclist's behavior and/or the path followed along the bicycle on estimation bicycle |
| WO2020029727A1 (en)* | 2018-08-08 | 2020-02-13 | 上海交通大学 | Fault monitoring and diagnosis system for port freight electric agv |
| CN111274713A (en)* | 2020-03-09 | 2020-06-12 | 西南交通大学 | Method for controlling consistency of remaining service life of multi-pile fuel cell system of motor train unit |
| CN111381166A (en)* | 2018-12-29 | 2020-07-07 | 北京骑胜科技有限公司 | Method and system for monitoring battery state |
| CN111775711A (en)* | 2020-07-08 | 2020-10-16 | 深圳技术大学 | Method and system for state monitoring of pure electric vehicle |
| CN111966111A (en)* | 2020-10-23 | 2020-11-20 | 北京国新智电新能源科技有限责任公司 | Automatic power distribution based mobile charging equipment formation control method, system and device |
| CN112020687A (en)* | 2018-03-22 | 2020-12-01 | 美光科技公司 | Power management, dynamic route planning, and memory management for autonomous driving vehicles |
| CN112396177A (en)* | 2019-08-12 | 2021-02-23 | 美光科技公司 | Predictive repair of vehicles |
| CN112490516A (en)* | 2019-08-23 | 2021-03-12 | 上海汽车集团股份有限公司 | A power battery maintenance mode generation system and method |
| CN113049964A (en)* | 2013-02-12 | 2021-06-29 | Cps 科技控股有限公司 | battery monitoring network |
| CN113340315A (en)* | 2020-03-02 | 2021-09-03 | 北京百度网讯科技有限公司 | Vehicle charging method and device, electronic equipment and storage medium |
| CN114966415A (en)* | 2022-07-11 | 2022-08-30 | 北京电满满科技有限公司 | Method and system for detecting aging performance of battery in use |
| CN115042640A (en)* | 2017-01-25 | 2022-09-13 | Sea汽车有限公司 | Management system for commercial electric vehicle |
| CN115063938A (en)* | 2022-08-16 | 2022-09-16 | 山东电工时代能源科技有限公司 | Vehicle energy storage cabin on-line monitoring system |
| CN115593273A (en)* | 2022-12-12 | 2023-01-13 | 成都壹为新能源汽车有限公司（Cn） | Electric automobile chassis system and control method |
| CN116148585A (en)* | 2023-04-21 | 2023-05-23 | 山东世拓房车集团有限公司 | Electric power monitoring system and method for caravan based on artificial intelligence |
| CN116562405A (en)* | 2023-05-25 | 2023-08-08 | 东风汽车集团股份有限公司 | An intelligent method, device and computer system for reserved charging of an extended-range plug-in hybrid electric vehicle |
| CN116774058A (en)* | 2023-08-18 | 2023-09-19 | 深圳凌奈智控有限公司 | Battery life prediction method, device, equipment and storage medium |
| US11977121B2 (en) | 2020-09-15 | 2024-05-07 | Analog Devices International Unlimited Company | Autonomous battery monitoring system |
| US12039318B2 (en) | 2020-10-26 | 2024-07-16 | Micron Technology, Inc. | Endpoint customization via online firmware store |
| US12061240B2 (en) | 2019-06-28 | 2024-08-13 | Analog Devices International Unlimited Company | Battery fleet monitoring systems |
| US12061971B2 (en) | 2019-08-12 | 2024-08-13 | Micron Technology, Inc. | Predictive maintenance of automotive engines |
| US12210401B2 (en) | 2019-09-05 | 2025-01-28 | Micron Technology, Inc. | Temperature based optimization of data storage operations |
| US12229060B2 (en) | 2019-12-13 | 2025-02-18 | Micron Technology, Inc. | Memory module with computation capability |
| US12249189B2 (en) | 2019-08-12 | 2025-03-11 | Micron Technology, Inc. | Predictive maintenance of automotive lighting |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101067646A (en)* | 2007-06-05 | 2007-11-07 | 清华大学 | A method for quickly evaluating the service life of vehicle fuel cells |
| CN101624017A (en)* | 2009-08-13 | 2010-01-13 | 顾金华 | System for managing continuous status of batteries of electric motorcar |
- 2010
- 2010-11-16CNCN2010105458338Apatent/CN102024999B/ennot_activeExpired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101067646A (en)* | 2007-06-05 | 2007-11-07 | 清华大学 | A method for quickly evaluating the service life of vehicle fuel cells |
| CN101624017A (en)* | 2009-08-13 | 2010-01-13 | 顾金华 | System for managing continuous status of batteries of electric motorcar |
Cited By (78)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102169168A (en)* | 2011-05-17 | 2011-08-31 | 杭州电子科技大学 | Battery dump energy estimation method based on particle filtering |
| CN102837697A (en)* | 2011-06-24 | 2012-12-26 | 北汽福田汽车股份有限公司 | Management system and working method for endurance mileage of electric vehicle |
| CN102837697B (en)* | 2011-06-24 | 2015-10-28 | 北汽福田汽车股份有限公司 | A kind of electronlmobil course continuation mileage management system and method for work |
| CN103620658B (en)* | 2011-07-12 | 2015-12-02 | 日产自动车株式会社 | Information of vehicles generator and vehicle information management system |
| CN102567488A (en)* | 2011-12-21 | 2012-07-11 | 江苏远为科技有限公司 | System and method for mining data of electric vehicle based on cloud computer framework |
| CN102520365B (en)* | 2011-12-23 | 2015-10-14 | 上海交通大学 | Fast battery remaining capacity estimation system and method thereof |
| CN102520366A (en)* | 2011-12-23 | 2012-06-27 | 上海交通大学 | Electric car cell safety and health assessment system and method thereof |
| CN102520365A (en)* | 2011-12-23 | 2012-06-27 | 上海交通大学 | Quick estimating system of remaining battery power and method thereof |
| CN102520366B (en)* | 2011-12-23 | 2014-11-12 | 上海交通大学 | Electric car cell safety and health assessment system and method thereof |
| US10859390B2 (en) | 2012-01-09 | 2020-12-08 | Airbiquity Inc. | Electric vehicle charging network services |
| US10161759B2 (en) | 2012-01-09 | 2018-12-25 | Airbiquity Inc. | Electric vehicle charging network services |
| CN104145224A (en)* | 2012-01-09 | 2014-11-12 | 爱尔比奎特公司 | Electric vehicle charging network service |
| US9476725B2 (en) | 2012-01-09 | 2016-10-25 | Airbiquity Inc. | Electric vehicle charging network services |
| CN104145224B (en)* | 2012-01-09 | 2016-08-24 | 爱尔比奎特公司 | Electric vehicle charging network service method |
| CN102592330A (en)* | 2012-01-19 | 2012-07-18 | 上海交通大学 | Method for extracting road vehicle running status based on battery data of electric vehicle |
| CN102592330B (en)* | 2012-01-19 | 2014-08-06 | 上海交通大学 | Method for extracting road vehicle running status based on battery data of electric vehicle |
| TWI489401B (en)* | 2012-03-02 | 2015-06-21 | Ind Tech Res Inst | Electric vehicle dispatching method and system with power-on planning function |
| CN103455020B (en)* | 2012-05-28 | 2016-08-03 | 哈尔滨工业大学深圳研究生院 | A kind of intelligence vehicle condition cloud detection service system and method |
| CN103455020A (en)* | 2012-05-28 | 2013-12-18 | 哈尔滨工业大学深圳研究生院 | Intelligent cloud-detection service system and intelligent cloud-detection service method for vehicle conditions |
| CN102752380B (en)* | 2012-06-29 | 2015-04-22 | 山东电力集团公司电力科学研究院 | Electric vehicle-mounted terminal based on cloud computing and using method thereof |
| CN102752380A (en)* | 2012-06-29 | 2012-10-24 | 山东电力集团公司电力科学研究院 | Electric vehicle-mounted terminal based on cloud computing and using method thereof |
| CN103630840A (en)* | 2012-08-20 | 2014-03-12 | 低碳动能开发股份有限公司 | Active cloud power management system for secondary battery |
| CN102930395B (en)* | 2012-10-31 | 2016-10-19 | 北京中电普华信息技术有限公司 | A method and system for managing electric vehicle power battery assets |
| CN102930395A (en)* | 2012-10-31 | 2013-02-13 | 北京中电普华信息技术有限公司 | Power battery assets management method and system of electric automobiles |
| CN113049964A (en)* | 2013-02-12 | 2021-06-29 | Cps 科技控股有限公司 | battery monitoring network |
| CN103369030A (en)* | 2013-05-17 | 2013-10-23 | 洪承洋 | Electric bicycle wireless driving communication system |
| CN103682484A (en)* | 2013-12-09 | 2014-03-26 | 惠州市亿能电子有限公司 | Method using vehicle controller for battery management of electric vehicle |
| CN104751271A (en)* | 2015-03-04 | 2015-07-01 | 径圆（上海）信息技术有限公司 | Intelligent order scheduling method and server, electric vehicle, mobile terminal and system |
| CN104751272A (en)* | 2015-03-04 | 2015-07-01 | 径圆（上海）信息技术有限公司 | Intelligent order scheduling method and server, electric vehicle, mobile terminal and system |
| CN104986160A (en)* | 2015-06-16 | 2015-10-21 | 北京亿利智慧能源科技有限公司 | Electric automobile driving behavior guide instrument |
| CN105446223A (en)* | 2015-12-22 | 2016-03-30 | 镇江恒驰科技有限公司 | System and method for optimizing new energy vehicle whole vehicle control parameters based on Internet |
| CN105446223B (en)* | 2015-12-22 | 2018-11-02 | 镇江恒驰科技有限公司 | A kind of system and method optimizing entire new energy automobile control parameter based on internet |
| CN107533724B (en)* | 2016-02-24 | 2022-03-08 | 松下知识产权经营株式会社 | Server device and control method thereof |
| CN107533724A (en)* | 2016-02-24 | 2018-01-02 | 松下知识产权经营株式会社 | Server unit and its control method |
| CN106114252A (en)* | 2016-06-21 | 2016-11-16 | 观致汽车有限公司 | The method and system that battery uses are managed service life based on battery contactor |
| CN106779905A (en)* | 2016-11-18 | 2017-05-31 | 合肥工业大学 | Method and device for recommending battery configuration based on user behavior |
| CN106779905B (en)* | 2016-11-18 | 2018-07-10 | 合肥工业大学 | Recommend the method and device of battery configuration based on user behavior |
| CN106740134A (en)* | 2016-11-22 | 2017-05-31 | 天津韶乐科技有限公司 | A kind of electric quantity of batteries of electric vehicle management system and its management method |
| CN115042640A (en)* | 2017-01-25 | 2022-09-13 | Sea汽车有限公司 | Management system for commercial electric vehicle |
| CN110382339A (en)* | 2017-03-07 | 2019-10-25 | 泽哈斯股份公司 | The system and method for the road quality in bicyclist's behavior and/or the path followed along the bicycle on estimation bicycle |
| CN106936915A (en)* | 2017-03-23 | 2017-07-07 | 南京越博动力系统股份有限公司 | A kind of electric automobile remote monitoring data cloud analysis system |
| CN107264303A (en)* | 2017-05-08 | 2017-10-20 | 广东硅岳能源科技有限公司 | Method and system for converting vehicle driving conditions into power battery operating conditions |
| CN107264303B (en)* | 2017-05-08 | 2020-03-17 | 广东硅岳能源科技有限公司 | Method and system for converting vehicle running condition into power battery running condition |
| CN109109784A (en)* | 2017-06-22 | 2019-01-01 | 吴俊逸 | System for providing remaining driving information of vehicle according to user behavior and method thereof |
| CN109214521A (en)* | 2017-07-11 | 2019-01-15 | 孟贞生 | Intelligent management apapratus, system and method based on battery pack or battery PACK packet |
| CN107526040A (en)* | 2017-07-28 | 2017-12-29 | 广州亿程交通信息有限公司 | New-energy automobile management method and system |
| CN107968840A (en)* | 2017-12-15 | 2018-04-27 | 华北电力大学（保定） | A kind of extensive power equipment monitoring, alarming Real-time Data Processing Method and system |
| CN107968840B (en)* | 2017-12-15 | 2020-10-09 | 华北电力大学（保定） | Real-time processing method and system for monitoring alarm data of large-scale power equipment |
| CN108382208A (en)* | 2018-02-26 | 2018-08-10 | 杭州翼兔网络科技有限公司 | An electric vehicle terminal for an electric vehicle comprehensive management and control system |
| CN112020687A (en)* | 2018-03-22 | 2020-12-01 | 美光科技公司 | Power management, dynamic route planning, and memory management for autonomous driving vehicles |
| US11880206B2 (en) | 2018-03-22 | 2024-01-23 | Lodestar Licensing Group Llc | Power management, dynamic routing and memory management for autonomous driving vehicles |
| CN108899599A (en)* | 2018-07-16 | 2018-11-27 | 矩电新能源（苏州）有限公司 | Battery cloud early warning system and its method for early warning |
| WO2020029727A1 (en)* | 2018-08-08 | 2020-02-13 | 上海交通大学 | Fault monitoring and diagnosis system for port freight electric agv |
| CN109263602A (en)* | 2018-08-31 | 2019-01-25 | 金华安靠电源科技有限公司 | A kind of vehicle-mounted service system |
| CN111381166A (en)* | 2018-12-29 | 2020-07-07 | 北京骑胜科技有限公司 | Method and system for monitoring battery state |
| US12061240B2 (en) | 2019-06-28 | 2024-08-13 | Analog Devices International Unlimited Company | Battery fleet monitoring systems |
| CN112396177A (en)* | 2019-08-12 | 2021-02-23 | 美光科技公司 | Predictive repair of vehicles |
| US12249189B2 (en) | 2019-08-12 | 2025-03-11 | Micron Technology, Inc. | Predictive maintenance of automotive lighting |
| US12061971B2 (en) | 2019-08-12 | 2024-08-13 | Micron Technology, Inc. | Predictive maintenance of automotive engines |
| CN112490516A (en)* | 2019-08-23 | 2021-03-12 | 上海汽车集团股份有限公司 | A power battery maintenance mode generation system and method |
| CN112490516B (en)* | 2019-08-23 | 2021-12-07 | 上海汽车集团股份有限公司 | Power battery maintenance mode generation system and method |
| US12210401B2 (en) | 2019-09-05 | 2025-01-28 | Micron Technology, Inc. | Temperature based optimization of data storage operations |
| US12229060B2 (en) | 2019-12-13 | 2025-02-18 | Micron Technology, Inc. | Memory module with computation capability |
| CN113340315A (en)* | 2020-03-02 | 2021-09-03 | 北京百度网讯科技有限公司 | Vehicle charging method and device, electronic equipment and storage medium |
| CN111274713A (en)* | 2020-03-09 | 2020-06-12 | 西南交通大学 | Method for controlling consistency of remaining service life of multi-pile fuel cell system of motor train unit |
| CN111775711A (en)* | 2020-07-08 | 2020-10-16 | 深圳技术大学 | Method and system for state monitoring of pure electric vehicle |
| CN111775711B (en)* | 2020-07-08 | 2022-05-31 | 深圳技术大学 | Method and system for state monitoring of pure electric vehicle |
| US11977121B2 (en) | 2020-09-15 | 2024-05-07 | Analog Devices International Unlimited Company | Autonomous battery monitoring system |
| CN111966111A (en)* | 2020-10-23 | 2020-11-20 | 北京国新智电新能源科技有限责任公司 | Automatic power distribution based mobile charging equipment formation control method, system and device |
| US12039318B2 (en) | 2020-10-26 | 2024-07-16 | Micron Technology, Inc. | Endpoint customization via online firmware store |
| CN114966415A (en)* | 2022-07-11 | 2022-08-30 | 北京电满满科技有限公司 | Method and system for detecting aging performance of battery in use |
| CN115063938A (en)* | 2022-08-16 | 2022-09-16 | 山东电工时代能源科技有限公司 | Vehicle energy storage cabin on-line monitoring system |
| CN115593273B (en)* | 2022-12-12 | 2023-04-07 | 成都壹为新能源汽车有限公司 | An electric vehicle chassis system and control method |
| CN115593273A (en)* | 2022-12-12 | 2023-01-13 | 成都壹为新能源汽车有限公司（Cn） | Electric automobile chassis system and control method |
| CN116148585A (en)* | 2023-04-21 | 2023-05-23 | 山东世拓房车集团有限公司 | Electric power monitoring system and method for caravan based on artificial intelligence |
| CN116562405A (en)* | 2023-05-25 | 2023-08-08 | 东风汽车集团股份有限公司 | An intelligent method, device and computer system for reserved charging of an extended-range plug-in hybrid electric vehicle |
| CN116774058A (en)* | 2023-08-18 | 2023-09-19 | 深圳凌奈智控有限公司 | Battery life prediction method, device, equipment and storage medium |
| CN116774058B (en)* | 2023-08-18 | 2023-10-20 | 深圳凌奈智控有限公司 | Battery life prediction method, device, equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102024999B (en) | 2013-05-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102024999A (en) | Electric car running power management system | |
| Guo et al. | Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network | |
| CN111027165B (en) | A power battery pack management system and method based on digital twin | |
| CN110492186B (en) | A management method of power battery module based on cloud control technology | |
| CN111612204B (en) | Systems, methods and storage media for optimizing battery pack performance | |
| CN110324383B (en) | Cloud server, electric automobile and management system and method of power battery in electric automobile | |
| US20120296512A1 (en) | Method and system for electric vehicle battery prognostics and health management | |
| CN110549902A (en) | Cloud server, electric automobile and management system and method of power battery in electric automobile | |
| CN108749607A (en) | A kind of electric automobile power battery management and monitoring system based on cloud computing | |
| US20110264318A1 (en) | Remote monitoring of a plurality of vehicles | |
| CN109143085A (en) | A kind of method and system carrying out early warning to lithium ion battery based on intelligent algorithm | |
| Singh et al. | Driving electric vehicles into the future with battery management systems | |
| CN116609676A (en) | Method and system for monitoring state of hybrid energy storage battery based on big data processing | |
| US20210129706A1 (en) | Management system with supervisory control for rechargeable energy storage device in electric vehicle | |
| Sheikhan et al. | State of charge neural computational models for high energy density batteries in electric vehicles | |
| Qahtan et al. | Charging Station of Electric Vehicle Based on IoT: A Review | |
| Mukherjee et al. | State of charge estimation techniques for battery management system used in electric vehicles: A review | |
| CN107492688A (en) | A kind of battery maintenance method, apparatus and vehicle | |
| Bhupathi et al. | AI-Powered Efficiency Machine Learning Techniques for EV Battery Charging | |
| Wei et al. | Intelligent EV Charging Control and Management: From Microscale Battery Cell to Macroscale Grid Synergy | |
| CN111308350A (en) | Integral model frame for rail transit lithium battery performance evaluation through big data | |
| Selvam et al. | Synergizing Digital Twins for Enhanced Maintenance of Integrated Chargers in Electric Vehicles: A State-of-the-Art Analysis and Future Implications | |
| CN108957341A (en) | Battery capacity collects processing method, apparatus and system | |
| Gu | Health State Estimation of Electric Vehicle Power Battery Based on K-means Algorithm | |
| Kumar et al. | An Efficient Battery Management System for Electric Vehicles Using IoT & Blockchain |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee | Granted publication date:20130501 |