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

能量存储系统（ESS），特别是电池组，或需要跨越广泛的应用。“智能电网”能源存储需要ess，以使间歇性风能和太阳能可再生能源得到更广泛的利用。随着电动汽车（ev）和插电式混合动力汽车（phev）的发展，电池技术变得越来越重要。锂离子电池因其高能量密度和高功率密度被认为是最佳的电池选择。在锂离子电池组中，为了实现安全可靠的运行，必须使用电池管理系统（BMS）。负荷状态（SOC）和健康状态（SOH）是BMS的一个重要组成部分，本文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来估计充电状态。nuinnovation Engineering渴望支持SOC和SOH评估模型的研究，以便在他们的bms上实现，以便在市场上区分他们的产品。目前需要研究如何实现低计算成本、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.