Nowadays, electric vehicles such as cars and bicycles are increasing their popularity due to the rising environmental consciousness. The autonomy required by these means of transport has marked a significant and steady growth in the development of battery technologies. In this sense, it is crucial to estimate and prognosticate critical parameters of battery packs such as the State of Charge (SOC), the State of Maximum Power Available (SoMPA), and the Failure Time. All these indicators are relevant to determine if both the energy stored in the battery of electric vehicles and power specifications are sufficient to successfully complete a required route, avoiding battery preventive disconnection before arrival. In this regard, this paper presents a novel approach to estimate and prognosticate the SOC and SoMPA of Lithium-Ion batteries in the context of electromobility applications. The proposed method uses the formulation of an optimization problem to find an analytical relationship between the SOC and the SoMPA; whereas the battery pack is modeled in terms of both the polarization resistance and the SOC. Particle filtering algorithms are used to compute online estimates and prognostic results, while the characterization of the usage profile of the battery bank is achieved using probability-based models (Markov chains). The problem of battery monitoring for an electric bicycle is used as a case study to validate the proposed scheme, when driven in flat and sloped routes to generate different usage profiles. It is demonstrated that the proposed methodology allows to successfully prognosticate both SOC and SoMPA when the future discharge current profile is characterized in terms of probability-based models.

中文翻译：

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电动汽车应用中锂离子电池最大可用功率状态的基于粒子过滤的预测

如今，由于环保意识的提高，汽车和自行车等电动汽车越来越受欢迎。这些交通工具所需的自主性标志着电池技术发展的显着而稳定的增长。从这个意义上说，估计和预测电池组的关键参数至关重要，例如充电状态 (SOC)、可用最大功率状态 (SoMPA) 和故障时间。所有这些指标都与确定电动汽车电池中存储的能量和功率规格是否足以成功完成所需路线有关，避免电池在到达前预防性断开连接。在这方面，本文提出了一种在电动汽车应用中估算和预测锂离子电池的 SOC 和 SoMPA 的新方法。所提出的方法使用优化问题的公式来寻找 SOC 和 SoMPA 之间的解析关系；而电池组是根据极化电阻和 SOC 建模的。粒子过滤算法用于计算在线估计和预测结果，而电池组使用情况的特征是使用基于概率的模型（马尔可夫链）来实现的。当在平坦和倾斜的路线上行驶以生成不同的使用情况时，电动自行车的电池监控问题被用作验证所提出方案的案例研究。