Battery Thermal Management and Algorithmic 3D Temperature Prediction

电动汽车正变得越来越普遍，许多制造商正在为新车型转向电动。电池技术是电动汽车发展和普及的瓶颈，因为许多公众担心充电时间、行驶里程短以及温度导致的性能变化。因此，进一步发展这一领域的方法是根据电池温度优化电池性能。锂离子电池有一个最佳温度范围，在该温度范围内它们表现最好(15摄氏度-35摄氏度)。在此温度以下，电池性能会受到影响，电池容量(即练习场)丢失。超过这个温度，电池内部会发生不必要的化学反应，热失控的风险会增加，导致电池的灾难性故障。因此，电池温度由机载电池管理系统监控。然而，这些系统使用的温度读数是在电池外壳的表面获取的，因此不能准确地表示电池过程发生的内部温度。该项目旨在设计和开发一种预测算法，允许使用外部温度测量来预测电池内部温度。然后，该算法将被集成到由项目行业合作伙伴Horiba-Mira开发的电池管理系统中。该算法将使电池管理系统能够更准确地监控电池温度，从而提高电动汽车的安全性和性能。为了实现这一目标，该项目有四个关键目标：1.获得商用锂离子电池的内部温度测量2。根据已知的热量产生和扩散理论，开发一种预测算法。使用通过在不同的驱动周期中循环电池获得的数据验证预测算法4。将预测算法集成到Horiba-Mira电池管理系统中，并将其集成到电动汽车中。使用标准驱动周期和真实驱动周期验证电池管理系统集成本项目的研究方法如下：1.设计一种用于测量商用锂离子电池内部温度传感器的质量保证方法2。使用开发的方法对商用锂离子电池进行了测试，确保了性能和安全性不受影响。使用不同的驱动周期循环电池以获得内部和外部温度读数3。开发一种使用外部测量来计算内部温度的预测算法。将预测算法集成到Horiba-Mira电池管理系统中。通过将该系统放置在电动汽车中并完成各种标准和真实世界的驾驶循环来验证该系统。该项目将调查棱柱型电池中温度传感器的放置情况。这已经在现有的文献中对柱状和囊状细胞进行了研究，但很少有现有的文献研究棱柱状细胞的仪器。它们被广泛应用于电动汽车中，因此，仪器仪表对于进一步发展这项技术非常重要。该项目还将研究电池单元建模的各种方法。这将包括现有的模型，使用各种技术形成混合模型，并可能形成一种新的建模方法，以满足这项工作的特定需求。这项工作与EPSRC的能量储存研究领域一致，并将与行业合作伙伴Horiba-Mira合作完成。

Electric vehicles are becoming more widespread, with many manufacturers moving towards electric power for new vehicle models. Battery technology is a bottleneck in the development and uptake of electric vehicles as many members of the public worry about charging times, low driving range, and performance variations due to temperature. Therefore, a method for furthering development in this field is by optimising battery performance with regards to the battery temperature.Lithium-ion batteries have an optimal range of temperature at which they perform best (15 C - 35 C). Below this temperature, the battery performance suffers and battery capacity (ie. driving range) is lost. Above this temperature, unwanted chemical reactions occur inside the battery and risk of thermal runaway increases, leading to catastrophic failure of the battery. Battery temperature is therefore monitored by an on-board battery management system. However, the temperature readings used with these systems are taken at the surface of the battery casing and therefore do not give an accurate representation of the internal temperature at which the battery processes are taking place. This project aims to design and develop a predictive algorithm which allows internal battery temperatures to be predicted using external temperature measurements. The algorithm would then be integrated in a battery management system developed by the project industry partner, Horiba-MIRA. This algorithm would allow battery management systems to more accurately monitor battery temperatures, therefore, improving safety and performance of electric vehicles.In order to achieve this aim, the project has four key objectives:1. Obtain internal thermal measurements of a commercial lithium-ion cell2. Develop a predictive algorithm based on known theory around heat generation and diffusion3. Validate the predictive algorithm using data obtained by cycling a battery across various drive cycles4. Integrate the predictive algorithm in a Horiba-MIRA battery management system and incorporate this within an electric vehicle5. Validate the battery management system integration using standard and real world drive cyclesThe research methodology for this project is as follows:1. Design a quality assured method for instrumenting an internal temperature sensor inside a commercial lithium-ion battery2. Instrument a commercial lithium-ion battery using the developed method, ensuring performance and safety have not been compromised. Cycle the battery using various drive cycles to obtain internal and external temperature readings3. Develop a predictive algorithm which calculates internal temperature using the external measurement4. Integrate the predictive algorithm in a Horiba-MIRA battery management system. Validate the system by placing it in an electric vehicle and completing various standard and real-world drive cyclesThis project will investigate the placement of temperature sensors in prismatic style battery cells. This has been carried out in existing literature for cylindrical and pouch cells but little existing literature investigates the instrumentation of prismatic cells. These are widely used in electric vehicles and therefore instrumentation is important in developing this technology further. This project will also investigate various methods for modelling battery cells. This will include existing models, using various techniques to form hybrid models, and potentially the formation of a novel modelling method to meet the specific needs of this work.This work aligns with the EPSRC research area of Energy Storage and will be completed in collaboration with the industry partner Horiba-MIRA.