Architectures and algorithms of charge management and thermal control for energy storage systems and mobile devices

This dissertation is dedicated to address several thermal-related problems in a multiple systems, including energy storage systems and mobile devices. ❧ The first part of this dissertation introduces hybrid electrical energy storage (HEES) systems by explaining the motivation, proposing basic architectures, defining key HEES operations, and presenting the near-optimal charge management policies. Key HEES operations, including the charge allocation, charge replacement, charge migration, and state-of-health-aware charge management are all formulated as mathematical optimization problems. Near-optimal charge management control algorithms based on certain approximations and iterative solving convex optimization problems are also presented separately for each key HEES operation. Simulation results show significant improvements in energy efficiency and battery lifespan. A HEES prototype is built and implemented with the presented charge management policies to further demonstrate the energy efficiency improvements brought by the hybrid use of different types of energy storage elements and the efficacy of presented policies. In addition, this dissertation also points out that elevated battery temperature can significantly speed up the aging process of batteries, which necessitates a proper thermal management policy for the HEES. This thesis later formulates a joint thermal and charge management problem for batteries in the HEES with a forced air-convection cooling technique and presents a hierarchical algorithm that combines reinforcement learning method and dynamic programming method to derive the optimal joint management policy, which determines charging/discharging profiles of the power source and settings of the cooling device. Simulation results show that presented policy significant improves the battery lifespan, resulting in completion of much more workload before the battery expires. ❧ The second part of the dissertation focuses on mobile devices. The dissertation points out that maintaining the skin temperature (surface temperature on the exterior case of mobile devices) at an appropriate level is a new design challenge in mobile devices because most of them are directly touched by users. A compact thermal modeling-based approach is presented and implemented as Therminator, which is capable of producing temperature maps of all important components, from the application processor (AP) to the skin of the device itself, in an accurate and efficient manner. Therminator have been validated against the practical temperature measurements and commercial computational fluid dynamics simulation tools. A case study on the thermal path design and skin temperature management policy is carried out for a state-of-art smartphone by using Therminator. Next, it is observed in smartphones that there exists a thermal coupling effect, which is referring to the phenomenon that the major heat generation components, such as the AP and the battery, thermally affect each other. Theoretical analysis show that ignoring this thermal coupling effect results in an underestimation of the chip temperature. Practical experiments are presented to quantitatively characterize and model this effect. A thermal coupling-aware dynamic thermal management policy based on an accurate RC-thermal model considering the thermal coupling effect is presented to reduce the maximal temperature violations in mobile devices.

本论文致力于解决多系统中的若干热相关问题，涵盖储能系统与移动设备两大场景。
论文第一部分围绕混合式电化学储能（hybrid electrical energy storage, HEES）系统展开，首先阐述了该系统的研究动机、提出了基础架构、明确了核心HEES运行环节，并给出了近最优充电管理策略。核心HEES运行环节包括充电分配、充电替换、充电迁移以及健康状态感知充电管理，均被建模为数学优化问题。针对每一类核心HEES运行环节，本文还分别提出了基于近似处理与凸优化迭代求解的近最优充电管理控制算法。仿真结果表明，该类算法可显著提升能源利用效率与电池使用寿命。为进一步验证不同储能元件混合使用带来的能效提升效果，以及所提策略的有效性，本文搭建了搭载该充电管理策略的HEES原型系统。此外，本文还指出电池温度升高会显著加速电池老化过程，因此HEES系统需要配套合理的热管理策略。随后，本文针对采用强制对流冷却技术的HEES系统中的电池，建立了热管理与充电管理的联合优化问题，并提出了一种结合强化学习与动态规划的分层算法，以求解最优联合管理策略——该策略可同时确定电源的充放电曲线与冷却设备的运行参数。仿真结果表明，所提策略可显著延长电池使用寿命，使电池失效前可完成更多工作负载。
论文第二部分聚焦移动设备场景。本文指出，由于多数移动设备会被用户直接握持，将机身外壳温度（即设备外表面温度）维持在合理区间已成为移动设备设计的全新挑战。本文提出了一种基于紧凑热建模的方法，并将其实现为Therminator工具；该工具可精准且高效地生成从应用处理器（application processor, AP）到设备机身外壳在内的所有关键组件的温度分布图。Therminator已通过实际温度测量数据与商用计算流体动力学仿真工具进行了验证。本文利用Therminator对一款旗舰智能手机开展了热通路设计与机身温度管理策略的案例研究。随后，本文在智能手机中发现了热耦合效应——即主要发热组件（如AP与电池）之间会互相产生热影响的现象。理论分析表明，忽略热耦合效应会导致对芯片温度的低估。本文通过实际实验对该效应进行了定量表征与建模。为降低移动设备的最高温度超限问题，本文提出了一种基于考虑热耦合效应的精准RC热模型的热耦合感知动态热管理策略。