The optimization of the battery pack in an off-grid Photovoltaic application must consider the minimum sizing that assures the availability of the system under the worst environmental conditions. Thus, it is necessary to predict the evolution of the state of charge of the battery under incomplete daily charging and discharging processes and fluctuating temperatures over day-night cycles.

Much of previous development work has been carried out in order to model the short term evolution of battery variables. Many works focus on the on-line parameter estimation of available charge, using standard or advanced estimators, but they are not focused on the development of a model with predictive capabilities. Moreover, normally stable environmental conditions and standard charge-discharge patterns are considered. As the actual cycle-patterns differ from the manufacturer's tests, batteries fail to perform as expected.

This paper proposes a novel methodology to model these issues, with predictive capabilities to estimate the remaining charge in a battery after several solar cycles. A new non-linear state space model is proposed as a basis, and the methodology to feed and train the model is introduced. The new methodology is validated using experimental data, providing only 5% of error at higher temperatures than the nominal one.

离网光伏应用中电池组的优化必须考虑最小尺寸，以确保在最恶劣的环境条件下系统的可用性。因此，有必要预测在不完整的日常充电和放电过程以及昼夜循环中温度波动的情况下电池充电状态的演变。

为了建模电池变量的短期演变，已经进行了许多先前的开发工作。许多工作着重于使用标准或高级估计器对可用电量进行在线参数估计，但它们并未致力于具有预测能力的模型的开发。而且，考虑了通常稳定的环境条件和标准的充放电模式。由于实际的循环模式与制造商的测试不同，因此电池无法达到预期的性能。

本文提出了一种新颖的方法来对这些问题进行建模，并具有预测能力，可以估算多个太阳循环后电池中的剩余电荷。提出了一种新的非线性状态空间模型作为基础，并介绍了模型的馈入和训练方法。新方法已通过实验数据进行了验证，在比标称温度更高的温度下，仅能提供5％的误差。