Implementation of state of charge and state of health estimation techniques in lithium-ion battery management systems

储能系统（ESS），特别是电池组，或广泛应用所需。“智能电网”储能需要ESS，以便更广泛地使用间歇性风能和太阳能可再生能源。电动汽车（EV）和插电式混合动力电动汽车（PHEV）也在增长，电池技术变得越来越重要。锂离子电池由于其高能量密度和高功率密度而被认为是最佳的电池选择。在锂离子电池组中，必须使用电池管理系统（BMS）以实现安全可靠的操作。BMS的一个组成部分是估计充电状态（SOC）和健康状态（SOH），拟议的研究将集中在这两个方面。锂离子电池的放电曲线非常平坦，因此很难用传统的电池方法估计充电状态。Nuvation Engineering渴望支持SOC和SOH估计模型的研究，以便在其BMS上实施，从而使其产品在市场上脱颖而出。目前需要研究实现具有低计算成本和高精度的SOC和SOH估计算法。

Energy storage systems (ESS), specifically battery packs, or needed across a wide range of applications. ESSs are needed for the 'Smart Grid' energy storage to enable the wider use of intermittent wind and solar renewable energy. Also electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) grows, battery technology has become increasingly important. Lithium-ion batteries are considered the best battery choice due to their high energy density and high power density. In Li-ion battery packs, a battery management system (BMS) must be utilized in order to achieve safe and reliable operation. An integral part of the BMS is estimating state of charge (SOC) and state of health (SOH) and the proposed research will focus on these two aspects. The discharge profiles of Li-ion cells are very flat and as such it is very difficult to estimate state of charge with traditional battery approaches. Nuvation Engineering is eager to support the investigation of SOC and SOH estimation models for implementation on their BMSs in order to differentiate their product in the marketplace. Research is currently required in implementing SOC and SOH estimation algorithms that have low computational cost and high accuracy.