WO2024036959A1 - 一种考虑电池全生命周期的碳排放控制方法和装置 - Google Patents
一种考虑电池全生命周期的碳排放控制方法和装置 Download PDFInfo
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- WO2024036959A1 WO2024036959A1 PCT/CN2023/083315 CN2023083315W WO2024036959A1 WO 2024036959 A1 WO2024036959 A1 WO 2024036959A1 CN 2023083315 W CN2023083315 W CN 2023083315W WO 2024036959 A1 WO2024036959 A1 WO 2024036959A1
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- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 268
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 title claims abstract description 267
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000012549 training Methods 0.000 claims abstract description 15
- 238000011217 control strategy Methods 0.000 claims description 27
- 238000004519 manufacturing process Methods 0.000 claims description 17
- 238000005265 energy consumption Methods 0.000 claims description 16
- 239000002994 raw material Substances 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 6
- 238000004064 recycling Methods 0.000 claims description 6
- 238000010200 validation analysis Methods 0.000 claims description 5
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- 238000005516 engineering process Methods 0.000 description 9
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- 238000012417 linear regression Methods 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32252—Scheduling production, machining, job shop
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- 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
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
- Y02P90/84—Greenhouse gas [GHG] management systems
Definitions
- This application relates to the field of carbon emissions, for example, to a carbon emission control method and device that considers the entire life cycle of a battery.
- Related technology is to obtain the carbon emissions of the battery's full life cycle, mainly by separately obtaining the carbon emissions of multiple links in the battery's full life cycle, such as production, transportation, operation, recycling and other stages, combined with the discharged power of the energy storage battery throughout its life cycle. , calculate the carbon emission coefficient of the entire life cycle. Or, another idea is to calculate the carbon emission equivalent of each stage of the battery and directly add it up. However, these methods all rely on the accuracy of evaluation in multiple links. Considering the different battery models or types, the processes and technologies involved in multiple links may be adjusted accordingly, which will also lead to carbon emissions in multiple links. Emission accounting may produce biases, which will affect the accuracy of full life cycle carbon emission accounting, and the effectiveness of carbon emission control will also be affected.
- This application provides a carbon emission control method and device that considers the entire life cycle of the battery, and solves the technical problem of low effectiveness of related technologies in controlling carbon emissions throughout the battery life cycle.
- embodiments of the present application provide a carbon emission control method that considers the entire life cycle of the battery, including:
- the data fitting basic model is trained to obtain the full life cycle carbon emission accounting model corresponding to the battery type
- embodiments of the present application also provide a carbon emission control device that considers the entire life cycle of the battery, including a data acquisition module, a model building module and a carbon emission control module; wherein,
- the data acquisition module is configured to obtain the battery type of the battery to be evaluated, and obtain historical data of multiple aspects of the entire life cycle of the reference battery corresponding to the battery type within a preset time range;
- the model building module is configured to train the data fitting basic model through the historical data of multiple links of the reference battery to obtain a full life cycle carbon emission accounting model corresponding to the battery type;
- the carbon emission control module is configured to input the measured data of multiple links of the battery to be evaluated into the full life cycle carbon emission accounting model, and based on the output of the full life cycle carbon emission accounting model, combined with the Measured data from multiple links enables carbon emission control of the battery to be evaluated.
- Figure 1 A schematic flowchart of an embodiment of a carbon emission control method that considers the entire life cycle of a battery provided in this application.
- Figure 2 An implementation of the carbon emission control device considering the entire life cycle of the battery provided for this application Structural diagram of the example.
- the full life cycle carbon emissions calculated by the above-mentioned main methods on the market have a high dependence on the accounting accuracy of carbon emissions in multiple links.
- the processes and technologies involved in multiple links may be adjusted accordingly, which may lead to deviations in the carbon emission accounting of multiple links, thereby affecting the full life cycle carbon emission accounting. accuracy, and the effectiveness of carbon emissions control will also be affected.
- different batteries may have different process flow ranges, which may be a combination of at least one of the pre-factory treatment process, transportation process, waste treatment process, physical treatment process, and chemical treatment process.
- the carbon emission accounting boundaries of the corresponding links in different process flow ranges also change accordingly.
- the same method or the same formula cannot be used to calculate the carbon emissions of this link.
- different links may be calculated using different methods.
- the carbon emission equivalent of the battery in the production stage is mainly calculated based on the consumption of raw materials and carbon emission intensity as well as the carbon emissions of each manufacturing process, while the transportation stage is based on transportation. battery weight, fuel consumption of transportation vehicles, etc.
- embodiments of the present application provide a carbon emission control method that considers the entire life cycle of the battery, including steps S1 to S3, where,
- Step S1 Obtain the battery type of the battery to be evaluated, and obtain historical data of multiple aspects of the entire life cycle of the reference battery corresponding to the battery type within a preset time range.
- the historical data of the multiple links include but are not limited to energy consumption, carbon emission equivalents, battery parameters, carbon emission factors, etc. of the multiple links.
- the acquisition of historical data of multiple aspects of the entire life cycle of the reference battery corresponding to the battery type within a preset time range includes:
- the energy consumption and carbon emission equivalent of multiple links in the entire life cycle of the reference battery within the preset time range (three months) are obtained; wherein the multiple links include raw material processing links, production link, transportation link, use link and recycling link; wherein, the reference battery and the battery to be evaluated are of the same type.
- This embodiment obtains the historical data of the reference battery to form a training set for the full life cycle carbon emission accounting model and The validation set provides data reference and sample reference to It is easy to extract the carbon emission pattern.
- the first database and the second database are enterprise default databases, and they may be the same database or different databases.
- Battery parameters include but are not limited to the rated capacity, internal resistance and cycle life of the battery. You can also consider the dynamic parameters of the battery in multiple aspects, such as the battery's health (state of health, SOH), remaining power (state of charge, SOC), etc. These battery parameters can be preset parameters, or can be obtained through actual measurement during manufacturing and use. Through actual measurement, the parameter collection record table can be constructed, thereby providing data support for the carbon emission control method described in this embodiment.
- Step S2 Use the historical data of multiple links of the reference battery to train the data fitting basic model to obtain a full life cycle carbon emission accounting model corresponding to the battery type.
- the energy consumption of multiple links of the reference battery, the carbon emission equivalent of the multiple links, and the battery parameters of the reference battery are combined to construct the same as the predetermined Assume a data set corresponding to the time range; wherein the data set includes multiple samples. For example, each sample corresponds to a combination of energy consumption, carbon emission equivalent and battery parameters for one day.
- the data set is divided into a training set and a validation set according to a preset ratio, which can be 7:3 or 8:2.
- a preset ratio which can be 7:3 or 8:2.
- the basic data fitting models include but are not limited to data models constructed by data fitting algorithms such as multiple linear regression, kernel methods, or neural networks.
- the convergence conditions can be set according to the loss function.
- the loss function L is, for example:
- N is the number of samples. This embodiment obtains three months of historical data. Therefore, N is the total number of days in three months. y is the output of the basic model fitted to the data, is the desired output. r is a number The dimension of the data. In this embodiment, since each sample corresponds to one day's data, the r-th dimension corresponds to the r-th parameter among energy consumption, carbon emission equivalent, and battery parameters.
- the full life cycle carbon emission accounting model is obtained through historical data training. Through a large number of training sets, the carbon emission rules can be simulated through fitting on the model, effectively reducing the need for calculating the full cycle carbon emission equivalent. Dependence on carbon emission accounting methods for each link.
- the data fitting basic model is trained, iterated, and parameters adjusted through a training set to optimize network settings. And verified through the verification set, the convergence condition can be that the loss function L is less than the preset threshold, or the change of the loss function L is less than the preset threshold (tends to be stable), or the preset number of iterations, at which time the model is judged Convergence, and obtain the full life cycle carbon emission accounting model corresponding to the battery type.
- Step S3 input the measured data of multiple links of the battery to be evaluated into the full life cycle carbon emission accounting model, and combine the measured data of the multiple links based on the output of the full life cycle carbon emission accounting model, Achieve carbon emission control for the battery to be evaluated.
- the carbon emission control of the battery to be evaluated is realized, including:
- the visualization can be displayed to the terminal of managers or technicians, and the display format includes but is not limited to the carbon emission curve, parameter table, tree diagram, timeline, etc.
- the carbon emission curve is the full life cycle curve of the battery.
- a carbon emission control strategy for the battery to be evaluated is generated, and the battery to be evaluated is evaluated according to the control strategy. Carbon emission control.
- generating a carbon emission control strategy for the battery to be evaluated, and performing carbon emission control on the battery to be evaluated according to the control strategy includes:
- the processing method is not limited to standardization. In fact, the processing is to replace the full life cycle carbon emission equivalent and the historical carbon emission equivalent of multiple links to the same coordinates or the same standard, which can improve comparability between each other.
- the carbon emission curve is modified and optimized; the optimization method can be implemented through machine learning, or can be manually verified to improve the robustness of the curve .
- the carbon emission characteristics may be data sample points corresponding to days of carbon emission, or in the form of data segments.
- the carbon emission control strategy includes a daily carbon emission control plan, a weekly carbon emission control plan and a monthly carbon emission control plan;
- the fixed value parameter table includes a plurality of fixed value parameters; the fixed value parameters include energy consumption indicators, carbon emission indicators and battery parameter indicators; each fixed value Parameters include upper and lower limits.
- energy consumption indicators include but are not limited to the quality of raw materials consumed, the types of raw materials consumed, fuel consumption of transportation vehicles, fuel consumption per unit distance, power consumption in the production process, etc.
- Carbon emission indicators mainly include carbon emission equivalents and carbon emission factors in multiple links.
- Battery parameter indicators mainly include the rated capacity, internal resistance and cycle life of the battery.
- the fixed value parameter table includes a total of n fixed value parameters, corresponding to n upper and lower limit values, and the upper and lower limit values are the set values.
- the off-limit days are traced back, and the carbon emission control day plan is constructed based on the traceability results. For example, the corresponding links and processes on the off-limit days are traced back, and the links or processes on the off-limit days are adjusted to Determine a carbon emission control day plan corresponding to the over-limit day;
- the carbon emission control weekly plan is constructed Plan and carbon emission control monthly plan to achieve carbon emission control.
- the control of carbon emissions is a full life cycle control, which can be achieved at the start of the project or halfway through, such as adjusting the project's conceptual design, collaborative design, manufacturing, transportation links, production preparation, etc., thereby optimizing the carbon emissions of the battery's full life cycle. quantity.
- the embodiment of the present application also provides a carbon emission control device that considers the entire life cycle of the battery, including a data acquisition module 101, a model building module 102 and a carbon emission control module 103; wherein,
- the data acquisition module 101 is configured to obtain the battery type of the battery to be evaluated, and obtain historical data of multiple aspects of the entire life cycle of the reference battery corresponding to the battery type within a preset time range;
- the model building module 102 is configured to train the data fitting basic model through the historical data of multiple links of the reference battery to obtain a full life cycle carbon emission accounting model corresponding to the battery type;
- the carbon emission control module 103 is configured to input the measured data of multiple links of the battery to be evaluated into the full life cycle carbon emission accounting model, and based on the output of the full life cycle carbon emission accounting model, combined with the The measured data of the above multiple links are used to achieve carbon emission control of the battery to be evaluated.
- the carbon emission control module 103 is configured to implement the evaluation of the battery to be evaluated based on the output of the full life cycle carbon emission accounting model and the measured data of multiple links in the following manner: Carbon emission control:
- a carbon emission control strategy for the battery to be evaluated is generated, and the battery to be evaluated is evaluated according to the control strategy. Carbon emission control.
- the measured data of the multiple links include measured carbon emission equivalents
- the historical data includes the historical carbon emission equivalent of the reference battery
- the carbon emission control module 103 is configured to generate a carbon emission control strategy for the battery to be evaluated in the following manner, and to perform carbon emission control on the battery to be evaluated according to the control strategy:
- the carbon emission control strategy includes a carbon emission control daily plan , carbon emission control weekly plan and carbon emission control monthly plan.
- the historical data includes energy consumption, carbon emission equivalent and battery parameters
- the data acquisition module 101 is configured to acquire historical data of multiple aspects of the entire life cycle of the reference battery corresponding to the battery type within a preset time range in the following manner:
- the energy consumption and carbon emission equivalent of multiple links in the entire life cycle of the reference battery within the preset time range are obtained; wherein the multiple links include raw material processing links, production links, transportation links, Use stage and recycling stage;
- the battery parameters of the reference battery within the preset time range are obtained.
- the model building module 102 is configured to train the data fitting basic model in the following manner to obtain a full life cycle carbon emission accounting model:
- the data fitting basic model is trained until the data fitting basic model converges, and the full life cycle carbon emission accounting model corresponding to the battery type is obtained.
- Embodiments of the present application provide a method and device for carbon emission control that considers the entire life cycle of a battery.
- the method includes: obtaining the battery type of the battery to be evaluated, and obtaining the reference battery corresponding to the battery type within a preset time range. Historical data of multiple links in the entire life cycle; through the historical data of multiple links of the reference battery, the data fitting basic model is trained to obtain a full life cycle carbon emission accounting model corresponding to the battery type; the The measured data of multiple links of the battery to be evaluated is input into the full life cycle carbon emission accounting model, and based on the output of the full life cycle carbon emission accounting model, combined with the measured data of the multiple links, the evaluation of the battery to be evaluated is realized. Battery carbon emission control.
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Abstract
提供了一种考虑电池全生命周期的碳排放控制方法和装置，方法包括：获取待评估电池的电池类型，并获取对应的参考电池的历史数据（S1）；通过历史数据，对数据拟合基础模型进行训练，获得全生命周期碳排放核算模型（S2）；将待评估电池的多个环节实测数据输入至模型，并基于全生命周期碳排放核算模型的输出，结合多个环节实测数据，实现对待评估电池的碳排放控制（S3）。
Description
本申请要求在2022年8月19日提交中国专利局、申请号为202210998473.X的中国专利申请的优先权，该申请的全部内容通过引用结合在本申请中。
本申请涉及碳排放领域，例如涉及一种考虑电池全生命周期的碳排放控制方法和装置。
在全球气候问题及国内相关政策压力下，相关方均在积极推进经济社会绿色转型，低碳与可持续发展成为当下经济社会发展的热点与趋势。而材料或零部件的生产、制造等环节都可能存在一定的碳排放量。而对电池全生命周期多个环节进行碳排放的评估和控制，可以有效提高电池及衍生产品的质量和竞争力，也能有助于提高多个环节的信息化、智能化。
相关技术为获得电池的全生命周期的碳排放量，主要通过分别获取电池全生命周期多个环节譬如生产、运输、运行、回收等阶段的碳排放量，结合储能电池全生命周期的释放电量，计算得到全生命周期的碳排放系数。或者，另一种思路是计算电池各阶段的碳排放当量，直接进行加总。但是，这些方法都依赖于多个环节评估的准确性，考虑到电池型号或者类型的不同，多个环节的工艺、涉及的技术都可能会发生相应的调整，也就会导致多个环节的碳排放核算可能会产生偏差，进而影响全生命周期碳排放核算的准确性，而碳排放控制的有效性也会受到影响。
发明内容
本申请提供了一种考虑电池全生命周期的碳排放控制方法和装置，解决了相关技术对电池全生命周期碳排放控制有效性低的技术问题。
为了解决上述技术问题，本申请实施例提供了一种考虑电池全生命周期的碳排放控制方法，包括：
获取待评估电池的电池类型，并获取与所述电池类型对应的参考电池在预设时间范围内全生命周期多个环节的历史数据；
通过所述参考电池多个环节的历史数据，对数据拟合基础模型进行训练，获得所述电池类型对应的全生命周期碳排放核算模型；
将所述待评估电池的多个环节实测数据输入至所述全生命周期碳排放核算模型，并基于所述全生命周期碳排放核算模型的输出，结合所述多个环节实测数据，实现对所述待评估电池的碳排放控制。
相应的，本申请实施例还提供了一种考虑电池全生命周期的碳排放控制装置，包括数据获取模块、模型构建模块和碳排放控制模块；其中，
所述数据获取模块，设置为获取待评估电池的电池类型，并获取与所述电池类型对应的参考电池在预设时间范围内全生命周期多个环节的历史数据；
所述模型构建模块，设置为通过所述参考电池多个环节的历史数据，对数据拟合基础模型进行训练，获得所述电池类型对应的全生命周期碳排放核算模型；
所述碳排放控制模块，设置为将所述待评估电池的多个环节实测数据输入至所述全生命周期碳排放核算模型，并基于所述全生命周期碳排放核算模型的输出，结合所述多个环节实测数据，实现对所述待评估电池的碳排放控制。
图1：为本申请提供的考虑电池全生命周期的碳排放控制方法的一种实施例的流程示意图。
图2：为本申请提供的考虑电池全生命周期的碳排放控制装置的一种实施
例的结构示意图。
根据相关技术记载，为获得电池的全生命周期碳排放量，市面上主要的方法包括以下两种：
(1)分别确定电池在生产、运输、运行、回收阶段的碳排放量；基于多个阶段的碳排放量，结合电池的全生命周期的释放电量，计算得到电池全生命周期的系数，进而得到碳排放当量。
(2)确定电池在多个阶段的碳排放当量，直接加总，或者通过加权求和，计算得到碳排放当量。
但是上述的市面主要的方法计算得到的全生命周期碳排放量都对多个环节的碳排放量的核算准确性有较高的依赖性。考虑到电池型号或者类型的不同，多个环节的工艺、涉及的技术都可能会发生相应的调整，也就会导致多个环节的碳排放核算可能会产生偏差，进而影响全生命周期碳排放核算的准确性，而碳排放控制的有效性也会受到影响。
譬如，不同电池可能会有不同的工艺流动范围，该范围可能是厂前处理流程、运输流程、废物处理流程、物理处理流程、化学处理流程中的至少一种的组合。
所以当工艺流动范围发送变化时，不同工艺流动范围对应环节的碳排放核算边界也相应发生变化，此时，就不能用同一种方式或者同一种公式去计算该环节的碳排放量。另外，不同的环节可能也会用不同的方法进行计算，譬如电池在生产阶段的碳排放当量主要依靠原材料的消耗和碳排放强度以及各制造工序的碳排放进行计算，而运输阶段则是基于运输的电池重量、运输工具的油耗等。
在实际应用中，电池技术在不断迭代优化，随着技术水平的提高，会出现越来越多的工艺。为满足实际应用需求，也可能会对相关技术中不同类型的工
艺进行组合。此时，传统的全生命周期碳排放量计算方法就不能够满足需求。由此，针对现状，需要赋予全生命周期碳排放量计算方法一定程度的提取和识别多个环节碳排放规律的能力，譬如采用人工智能、机器学习技术等。
示例性地，在全球气候问题、中国相关的政策压力下，企业需要积极推动经济社会的绿色转型。碳排放可能会在产品全生命周期的多个环节产生，每个环节都可能存在一定的碳排放量。企业的运营和管理都趋向于信息化，而产品的全生命周期管理是企业信息化的关键技术之一，产品的全生命周期管理可以涉及产品全生命周期的协同设计、制造、概念设计、工程设计、生产准备等多个方面，提高产品的质量和竞争力。
而针对电池的全生命周期的碳排放控制这一问题上，计算全生命周期的碳排放量是重要基础。同时，通过电池全生命周期的碳排放量，可以有助于逆向追溯环节中存在的异常碳排放，识别异常碳排放特征，从而支持碳排放的控制。
实施例一：
针对上述的至少一个技术问题，参照图1，本申请实施例提供了一种考虑电池全生命周期的碳排放控制方法，包括步骤S1至步骤S3，其中，
步骤S1，获取待评估电池的电池类型，并获取与所述电池类型对应的参考电池在预设时间范围内全生命周期多个环节的历史数据。
在本实施例中，所述多个环节的历史数据包括但不限于多个环节的能量消耗、碳排放当量、电池参数和碳排放因子等。
所述获取与所述电池类型对应的参考电池在预设时间范围内全生命周期多个环节的历史数据，包括：
通过调用第一数据库，获得所述参考电池在预设时间范围(三个月)内全生命周期的多个环节的能量消耗和碳排放当量；其中，所述多个环节包括原材料处理环节、生产环节、运输环节、使用环节和回收环节；其中，所述参考电池与所述待评估电池为同一类型，本实施例通过获取参考电池的历史数据，为全生命周期碳排放核算模型的训练集以及验证集提供数据参考和样本参考，以
便于提取出碳排放规律。
通过调用第二数据库，结合参数采集记录表，获得所述参考电池在预设时间范围(三个月)内的电池参数。所述第一数据库和所述第二数据库为企业预设数据库，两者可以为同一数据库也可以为不同数据库。
电池参数包括但不限于电池的额定容量、内阻和循环寿命等。也可以考虑电池在多个环节的动态参数，譬如电池的健康度(state of health，SOH)，剩余电量(state of charge，SOC)等。这些电池参数可以为预设参数，也可以在生产制造、使用过程中实测获得，通过实测可以构建所述参数采集记录表，从而为本实施例所述的碳排放控制方法提供数据支撑。
步骤S2，通过所述参考电池多个环节的历史数据，对数据拟合基础模型进行训练，获得所述电池类型对应的全生命周期碳排放核算模型。
可选地，作为本实施例的一种举例，将所述参考电池多个环节的能量消耗、所述多个环节的碳排放当量和所述参考电池的电池参数进行组合，构建与所述预设时间范围对应的数据集；其中，数据集中包括多个样本，示例性地，每一样本对应一天的能量消耗、碳排放当量和电池参数的组合。
将所述数据集按照预设比例划分为训练集和验证集，可选为7:3或8:2。通过所述训练集，对所述数据拟合基础模型进行训练，直到所述数据拟合基础模型收敛，获得与所述电池类型对应的所述全生命周期碳排放核算模型。
在本步骤中，数据拟合基础模型包括但不限于多元线性回归、核方法或神经网络等数据拟合算法构建的数据模型，收敛的条件可以根据损失函数进行设定，损失函数L例如：
其中，N为样本的个数，本实施例获取的是三个月的历史数据，因此，N为三个月的总天数。y为所述数据拟合基础模型的输出，为期望输出。r为数
据的维数，在本实施例中，由于每个样本对应一天的数据，因此第r维即对应能量消耗、碳排放当量和电池参数中第r个参数。实施本申请实施例，通过历史数据训练得到所述全生命周期碳排放核算模型，通过大量的训练集可以在模型上通过拟合，模拟出碳排放规律，有效降低计算全周期碳排放当量对多个环节碳排放核算方法的依赖性。
示例性地，通过训练集对所述数据拟合基础模型进行训练、迭代、调整参数，以优化网络的设置。并通过验证集进行验证，收敛条件可以为所述损失函数L小于预设的阈值，或损失函数L的变化小于预设的阈值(趋于稳定)，或预设的迭代次数，此时判断模型收敛，获得与所述电池类型对应的所述全生命周期碳排放核算模型。
步骤S3，将所述待评估电池的多个环节实测数据输入至所述全生命周期碳排放核算模型，并基于所述全生命周期碳排放核算模型的输出，结合所述多个环节实测数据，实现对所述待评估电池的碳排放控制。
作为可选方案，所述基于所述全生命周期碳排放核算模型的输出，结合所述多个环节实测数据，实现对所述待评估电池的碳排放控制，包括：
对所述全生命周期碳排放核算模型的输出进行可视化展示，并通过所述全生命周期碳排放核算模型输出所述待评估电池的碳排放曲线和全生命周期碳排放当量；其中，所述可视化展示可以展示到管理人员或技术人员的终端，展示的形式包括但不限于所述碳排放曲线、参数表格、树状图、时间轴等。作为一种举例，所述碳排放曲线为电池的全生命周期曲线。
根据所述碳排放曲线和所述全生命周期碳排放当量，结合所述多个环节实测数据，生成所述待评估电池的碳排放控制策略，并根据所述控制策略对所述待评估电池进行碳排放控制。
示例性地，所述生成所述待评估电池的碳排放控制策略，并根据所述控制策略对所述待评估电池进行碳排放控制，包括：
通过所述全生命周期碳排放当量和所述历史碳排放当量，对所述实测碳排
放当量进行标幺化处理，获得调整数据。需要说明的是，处理的方式并不局限于标幺化，实际上，该处理是为了将全生命周期碳排放当量和多个环节的历史碳排放当量置换到同一坐标或同一标准下，可以提高互相之间的可比较性。
基于所述调整数据和所述全生命周期碳排放当量，对所述碳排放曲线进行修正优化；所述优化的方法可以通过机器学习实现，也可以人工进行校验，以提高曲线的鲁棒性。
从优化后的碳排放曲线中提取出多个全生命周期碳排放特征。所述碳排放特征可以为碳排放的与天对应的数据样本点，或者以数据段的形式。
根据所述多个全生命周期碳排放特征生成碳排放控制策略，并根据所述碳排放控制策略对所述待评估电池进行碳排放控制，示例性地：
所述碳排放控制策略包括碳排放控制日计划、碳排放控制周计划和碳排放控制月计划；
根据所述历史数据构建定值参数表；其中，所述定值参数表包括多个定值参数；所述定值参数包括能量消耗指标、碳排放指标和电池参数指标；每一所述定值参数均包括上下限值。其中，能量消耗指标包括但不限于原材料消耗的质量、消耗的原材料的种类、运输工具的油耗、单位距离的油耗、生产过程中的耗电量等。碳排放指标主要为多个环节的碳排放当量和碳排放因子。电池参数指标主要为电池的额定容量、内阻和循环寿命等。所述定值参数表包括共n个定值参数，则对应有n个上下限值，上下限值为设定值。
将所述多个全生命周期碳排放特征与所述碳排放指标的上下限值进行比对。当存在越限的碳排放特征时，标记比对结果中越限的碳排放特征，并确定对应的越限天；
对所述越限天进行追溯，基于追溯结果构建所述碳排放控制日计划，示例性地，追溯到越限天中对应的环节和工艺，对越限天中的环节或工艺进行调整，以确定对应于所述越限天的碳排放控制日计划；
基于越限天以及未越限天的碳排放控制日计划，构建所述碳排放控制周计
划和碳排放控制月计划，从而实现碳排放控制。
碳排放的控制为全生命周期的控制，可以在项目启动时或半途实现，譬如对项目的概念设计、协同设计、制造、运输环节、生产准备等进行调整，从而优化电池全生命周期的碳排放量。
相应的，参照图2，本申请实施例还提供了一种考虑电池全生命周期的碳排放控制装置，包括数据获取模块101、模型构建模块102和碳排放控制模块103；其中，
所述数据获取模块101，设置为获取待评估电池的电池类型，并获取与所述电池类型对应的参考电池在预设时间范围内全生命周期多个环节的历史数据；
所述模型构建模块102，设置为通过所述参考电池多个环节的历史数据，对数据拟合基础模型进行训练，获得所述电池类型对应的全生命周期碳排放核算模型；
所述碳排放控制模块103，设置为将所述待评估电池的多个环节实测数据输入至所述全生命周期碳排放核算模型，并基于所述全生命周期碳排放核算模型的输出，结合所述多个环节实测数据，实现对所述待评估电池的碳排放控制。
在一种可能实现的方式中，所述碳排放控制模块103设置为通过以下方式基于所述全生命周期碳排放核算模型的输出，结合所述多个环节实测数据，实现对所述待评估电池的碳排放控制：
对所述全生命周期碳排放核算模型的输出进行可视化展示，并通过所述全生命周期碳排放核算模型输出所述待评估电池的碳排放曲线和全生命周期碳排放当量；
根据所述碳排放曲线和所述全生命周期碳排放当量，结合所述多个环节实测数据，生成所述待评估电池的碳排放控制策略，并根据所述控制策略对所述待评估电池进行碳排放控制。
在一种可能实现的方式中，所述多个环节实测数据包括实测碳排放当量；
所述历史数据包括所述参考电池的历史碳排放当量；
所述碳排放控制模块103设置为通过以下方式生成所述待评估电池的碳排放控制策略，并根据所述控制策略对所述待评估电池进行碳排放控制：
通过所述全生命周期碳排放当量和所述历史碳排放当量，对所述实测碳排放当量进行标幺化处理，获得调整数据；
基于所述调整数据和所述全生命周期碳排放当量，对所述碳排放曲线进行修正优化；
从优化后的碳排放曲线中提取出多个全生命周期碳排放特征；
根据所述多个全生命周期碳排放特征生成碳排放控制策略，并根据所述碳排放控制策略对所述待评估电池进行碳排放控制；其中，所述碳排放控制策略包括碳排放控制日计划、碳排放控制周计划和碳排放控制月计划。
在一种可能实现的方式中，所述历史数据包括能量消耗、碳排放当量和电池参数；
所述数据获取模块101设置为通过以下方式获取与所述电池类型对应的参考电池在预设时间范围内全生命周期多个环节的历史数据：
通过调用第一数据库，获得所述参考电池在预设时间范围内全生命周期的多个环节的能量消耗和碳排放当量；其中，所述多个环节包括原材料处理环节、生产环节、运输环节、使用环节和回收环节；
通过调用第二数据库，结合参数采集记录表，获得所述参考电池在预设时间范围内的电池参数。
在一种可能实现的方式中，所述模型构建模块102通过设置为通过以下方式对数据拟合基础模型进行训练，获得全生命周期碳排放核算模型：
将所述参考电池多个环节的能量消耗、所述多个环节的碳排放当量和所述参考电池的电池参数进行组合，构建与所述预设时间范围对应的数据集；
将所述数据集按照预设比例划分为训练集和验证集；
通过所述训练集，对所述数据拟合基础模型进行训练，直到所述数据拟合基础模型收敛，获得与所述电池类型对应的所述全生命周期碳排放核算模型。
本申请实施例提供了一种考虑电池全生命周期的碳排放控制方法和装置，所述方法包括：获取待评估电池的电池类型，并获取与所述电池类型对应的参考电池在预设时间范围内全生命周期多个环节的历史数据；通过所述参考电池多个环节的历史数据，对数据拟合基础模型进行训练，获得所述电池类型对应的全生命周期碳排放核算模型；将所述待评估电池的多个环节实测数据输入至所述全生命周期碳排放核算模型，并基于所述全生命周期碳排放核算模型的输出，结合所述多个环节实测数据，实现对所述待评估电池的碳排放控制。相比于相关技术，通过获取待评估电池同类型电池的历史数据，基于历史数据对基础模型进行训练，获得全生命周期碳排放核算模型，当待评估电池的类型或制作生产工艺发生变化时，在预设时间范围内的历史数据也会相应发生变化，进而通过变化的历史数据训练得到全生命周期碳排放核算模型，使得全生命周期碳排放核算模型的输出结果能够贴合待评估电池全生命周期的多个环节，进而获得更准确的全生命周期碳排放核算结果；准确的核算结果能够对待评估电池的碳排放控制提供数据支持以及稳定的参考基础。
Claims (10)
- 一种考虑电池全生命周期的碳排放控制方法，包括：获取待评估电池的电池类型，并获取与所述电池类型对应的参考电池在预设时间范围内全生命周期多个环节的历史数据；通过所述参考电池多个环节的历史数据，对数据拟合基础模型进行训练，获得所述电池类型对应的全生命周期碳排放核算模型；将所述待评估电池的多个环节实测数据输入至所述全生命周期碳排放核算模型，并基于所述全生命周期碳排放核算模型的输出，结合所述多个环节实测数据，实现对所述待评估电池的碳排放控制。
- 如权利要求1所述的一种考虑电池全生命周期的碳排放控制方法，其中，所述基于所述全生命周期碳排放核算模型的输出，结合所述多个环节实测数据，实现对所述待评估电池的碳排放控制，包括：对所述全生命周期碳排放核算模型的输出进行可视化展示，并通过所述全生命周期碳排放核算模型输出所述待评估电池的碳排放曲线和全生命周期碳排放当量；根据所述碳排放曲线和所述全生命周期碳排放当量，结合所述多个环节实测数据，生成所述待评估电池的碳排放控制策略，并根据所述控制策略对所述待评估电池进行碳排放控制。
- 如权利要求2所述的一种考虑电池全生命周期的碳排放控制方法，其中，所述多个环节实测数据包括实测碳排放当量；所述历史数据包括所述参考电池的历史碳排放当量；所述生成所述待评估电池的碳排放控制策略，并根据所述控制策略对所述待评估电池进行碳排放控制，包括：通过所述全生命周期碳排放当量和所述历史碳排放当量，对所述实测碳排放当量进行标幺化处理，获得调整数据；基于所述调整数据和所述全生命周期碳排放当量，对所述碳排放曲线进行修正优化；从优化后的碳排放曲线中提取出多个全生命周期碳排放特征；根据所述多个全生命周期碳排放特征生成碳排放控制策略，并根据所述碳排放控制策略对所述待评估电池进行碳排放控制。
- 如权利要求1所述的一种考虑电池全生命周期的碳排放控制方法，其中，所述历史数据包括能量消耗、碳排放当量和电池参数；所述获取与所述电池类型对应的参考电池在预设时间范围内全生命周期多个环节的历史数据，包括：通过调用第一数据库，获得所述参考电池在预设时间范围内全生命周期的多个环节的能量消耗和碳排放当量；其中，所述多个环节包括原材料处理环节、生产环节、运输环节、使用环节和回收环节；通过调用第二数据库，结合参数采集记录表，获得所述参考电池在预设时间范围内的电池参数。
- 如权利要求4所述的一种考虑电池全生命周期的碳排放控制方法，其中，所述通过所述参考电池多个环节的历史数据，对数据拟合基础模型进行训练，获得所述电池类型对应的全生命周期碳排放核算模型，包括：将所述参考电池多个环节的能量消耗、所述多个环节的碳排放当量和所述参考电池的电池参数进行组合，构建与所述预设时间范围对应的数据集；将所述数据集按照预设比例划分为训练集和验证集；通过所述训练集，对所述数据拟合基础模型进行训练，直到所述数据拟合基础模型收敛，获得与所述电池类型对应的所述全生命周期碳排放核算模型。
- 一种考虑电池全生命周期的碳排放控制装置，包括数据获取模块(101)、模型构建模块(102)和碳排放控制模块(103)；其中，所述数据获取模块(101)，设置为获取待评估电池的电池类型，并获取与所述电池类型对应的参考电池在预设时间范围内全生命周期多个环节的历史数据；所述模型构建模块(102)，设置为通过所述参考电池多个环节的历史数据， 对数据拟合基础模型进行训练，获得所述电池类型对应的全生命周期碳排放核算模型；所述碳排放控制模块(103)，设置为将所述待评估电池的多个环节实测数据输入至所述全生命周期碳排放核算模型，并基于所述全生命周期碳排放核算模型的输出，结合所述多个环节实测数据，实现对所述待评估电池的碳排放控制。
- 如权利要求6所述的一种考虑电池全生命周期的碳排放控制装置，其中，所述碳排放控制模块(103)设置为通过以下方式基于所述全生命周期碳排放核算模型的输出，结合所述多个环节实测数据，实现对所述待评估电池的碳排放控制：对所述全生命周期碳排放核算模型的输出进行可视化展示，并通过所述全生命周期碳排放核算模型输出所述待评估电池的碳排放曲线和全生命周期碳排放当量；根据所述碳排放曲线和所述全生命周期碳排放当量，结合所述多个环节实测数据，生成所述待评估电池的碳排放控制策略，并根据所述控制策略对所述待评估电池进行碳排放控制。
- 如权利要求7所述的一种考虑电池全生命周期的碳排放控制装置，其中，所述多个环节实测数据包括实测碳排放当量；所述历史数据包括所述参考电池的历史碳排放当量；所述碳排放控制模块(103)设置为通过以下方式生成所述待评估电池的碳排放控制策略，并根据所述控制策略对所述待评估电池进行碳排放控制：通过所述全生命周期碳排放当量和所述历史碳排放当量，对所述实测碳排放当量进行标幺化处理，获得调整数据；基于所述调整数据和所述全生命周期碳排放当量，对所述碳排放曲线进行修正优化；从优化后的碳排放曲线中提取出多个全生命周期碳排放特征；根据所述多个全生命周期碳排放特征生成碳排放控制策略，并根据所述碳排放控制策略对所述待评估电池进行碳排放控制；其中，所述碳排放控制策略包括碳排放控制日计划、碳排放控制周计划和碳排放控制月计划。
- 如权利要求6所述的一种考虑电池全生命周期的碳排放控制装置，其中，所述历史数据包括能量消耗、碳排放当量和电池参数；所述数据获取模块(101)设置为通过以下方式获取与所述电池类型对应的参考电池在预设时间范围内全生命周期多个环节的历史数据：通过调用第一数据库，获得所述参考电池在预设时间范围内全生命周期的多个环节的能量消耗和碳排放当量；其中，所述多个环节包括原材料处理环节、生产环节、运输环节、使用环节和回收环节；通过调用第二数据库，结合参数采集记录表，获得所述参考电池在预设时间范围内的电池参数。
- 如权利要求9所述的一种考虑电池全生命周期的碳排放控制装置，其中，所述模型构建模块(102)设置为通过以下方式对数据拟合基础模型进行训练，获得全生命周期碳排放核算模型：将所述参考电池多个环节的能量消耗、所述多个环节的碳排放当量和所述参考电池的电池参数进行组合，构建与所述预设时间范围对应的数据集；将所述数据集按照预设比例划分为训练集和验证集；通过所述训练集，对所述数据拟合基础模型进行训练，直到所述数据拟合基础模型收敛，获得与所述电池类型对应的所述全生命周期碳排放核算模型。
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