Estimation and forecasting battery state of health in real-world conditions using a data-driven approach

使用数据驱动的方法估计和预测现实条件下的电池健康状态

• 批准号: 2118158
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2118158
• 关键词: Estimation; forecasting; battery; state; health

Context: Given the increasing drive to reduce CO2 emissions, energy storage has become a dominant topic with the emergence of a larger share of renewable, intermittent generating capacity in the global energy mix. From grid-scale to mobile applications, batteries have become one the most promising alternatives to deliver an efficient energy storage solution. Consequently, addressing the issues that come with the aging of batteries has received commensurate interest - the fade of both storage capacity and ability to provide power over time is perhaps the most significant economic hurdle for battery adoption in applications ranging from grid storage to electric vehicles. The degrading state of health over aging is a problem that affects all rechargeable batteries via specific design- and chemistry-dependent mechanisms. In a simple sense, a battery's state of health may be defined as total energy it can release upon discharge at a given point in time relative to that at the time of manufacture. Estimation and measurement of this quantity has a substantial body of existing research. However, accuracy necessitates invasive, time-consuming tests resulting in almost all analysis having been conducted on data generated in controlled laboratory conditions. For batteries operating in a real-world production environment, estimation becomes considerably more difficult as state of health can only be inferred, rather than measured from data generated under operating conditions. The difficulty is compounded by the noisiness of available data resulting from a variety of factors including sensor inaccuracy and variations in discharge current, time and temperature.Aim: The aim of this project is to develop methods to give an online state of health estimate for batteries operating in real-world conditions, followed by forecasting its evolution over time given the observed pattern of usage. The main challenge is the construction of a mathematical model of battery behaviour. This serves to tie the unobserved state of health to the observed variables, consisting (exclusively) of the voltage across the battery terminals, current drawn from the battery, time and temperature. Complexity of this model is key - the model has to adequately represent reality, in that its outputs have to map to the observed variables to an acceptable degree of accuracy. On the other hand, the model has to be simple enough to be applicable over all batteries manufactured to the same specification. Availability of large datasets is paramount and is often the limiting factor for model complexity and validation accuracy, as inferring parameters from an insufficient quantity of noisy data results in an unacceptable level of uncertainty around the model itself. Novelty:Leveraging the "big data" aspect allows for state-of-the-art machine learning techniques to be applied to infer potentially complex relationships in the data while maintaining model robustness. Applying machine learning to data extracted from multiple batteries across a broad range of real-world operating conditions to estimate and forecast battery state of health is an area unexplored thus far. Additionally, using real-world usage patterns as opposed to laboratory ones, it will highlight the extent to which results from the latter translate to the real operating environment.Value:Reliable state of health estimates and forecasts are critical to battery operators. This issue affects the economics of operating batteries over the whole life cycle of the investment, from the expectation of depreciation, to the variable cost of operation. Of immediate concern for a battery operator would be maintenance planning to minimise down time as well as customer alerts for usage patterns particularly detrimental to battery health. Ultimately, models may be built into automated battery management systems which can optimise usage to maximise economic value. This falls under the EPSRC Energy theme.

背景：鉴于减少二氧化碳排放的动力越来越大，能源存储已成为一个主要的话题，因为在全球能源组合中出现了更大的可再生，间歇性产生能力的份额。从网格尺度到移动应用程序，电池已成为提供有效储能解决方案的最有希望的替代方案之一。因此，解决电池老化所带来的问题已经获得了相应的兴趣 - 存储能力和随着时间的推移提供电力的能力的淡出也许是采用电池从电池存储到电动汽车不等的采用电池的最重要的经济障碍。衰老的健康状况有退化的状态是一个问题，它通过特定的设计和化学依赖性机制影响所有可充电电池。 从简单意义上讲，电池的健康状态可以定义为在给定时间点相对于制造时的总能量时可以释放的总能量。该数量的估计和衡量标准具有大量现有研究。但是，准确性需要侵入性，耗时的测试，从而导致对在受控实验室条件下生成的数据进行了几乎所有分析。对于在实际生产环境中运行的电池，由于只能推断出健康状态，而不是根据在操作条件下产生的数据来衡量的，因此估计变得更加困难。难以通过多种因素引起的可用数据的噪声，包括传感器的不准确性和排放电流，时间和温度的变化。主要的挑战是构建电池行为的数学模型。这有助于将未观察到的健康状态与观察到的变量联系起来，这些变量（仅）是电池端子上电压的电压，电流是从电池，时间和温度中抽出的。该模型的复杂性是关键 - 该模型必须充分代表现实，因为其输出必须映射到可接受的准确性程度。另一方面，该型号必须足够简单，才能适用于所有制造的电池上相同规范的电池。大型数据集的可用性至关重要，通常是模型复杂性和验证精度的限制因素，因为从不足的嘈杂数据中推断出参数会导致模型本身的不确定性不可接受的不确定性水平。新颖性：利用“大数据”方面，可以应用最新的机器学习技术来推断数据中的潜在复杂关系，同时保持模型鲁棒性。迄今为止，将机器学习应用于从多种实际操作条件中从多个电池中提取的数据，以估算和预测电池的健康状况是迄今为止尚未探索的区域。此外，使用现实世界中的使用模式，而不是实验室的使用模式，它将突出显示后者转化为真实操作环境的程度。值：可靠的健康状况估计和预测对电池运营商至关重要。此问题影响了在整个投资生命周期中，从折旧的期望到运营成本的可变成本，都会影响运营电池的经济学。电池操作员的直接关注是维护计划，以最大程度地减少停机时间，以及对使用模式的客户警报，尤其对电池健康有害。最终，模型可以内置在自动电池管理系统中，这些系统可以优化使用以最大化经济价值。这属于EPSRC Energy主题。