对电动无人机 (UAV) 的电池测试和放电模型验证.
Attilio Di Nisio1, Giulio Avanzini2, Daniel Lotano1
1Department of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, 70125 Bari, Italy.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
这项研究引入了一种新的电池模型,用于准确预测电动汽车的能量水平. 该模型有效地描述了电池组,这些电池组对于优化性能和防止电动和飞行汽车的意外功率损失至关重要.
科学领域:
- 电气工程 电气工程
- 储能系统 储能系统 储能系统
- 电池技术 电池技术
背景情况:
- 越来越多的电动汽车需要精确的电池充电状态 (SoC) 估计.
- 不准确的SoC预测会导致运营效率低下或意想不到的能源耗尽,这对于像飞行器这样的能源受限应用特别重要.
- 现有的电池表征模型可能很复杂,很难适应各种电池组配置.
研究的目的:
- 开发一种新的,简化的实证模型,用于预测不同细胞数量的电池组的放电曲线.
- 创建一个需要最小调节参数的模型,以便轻松适应特定的电池组.
- 为了能够根据恒定功率的特征来准确预测可变功率负载下的电池性能.
主要方法:
- 开发和描述用于电池测试的测量设置,包括电压和电流传感器.
- 使用恒定和可变功率负载进行电池放电测试,以模拟现实世界的使用场景.
- 设计用于电池组表征和SoC预测的新实证模型的验证.
主要成果:
- 开发的经验模型证明了能够准确预测在变量功率负载下电池放电行为的能力.
- 在恒定功率下进行的模型初步表征有效地为可变功率条件的预测提供了信息.
- 对于预测放电容量的正常化误差低于0.7%,这表明模型的准确性很高.
结论:
- 这种新的经验模型提供了一种简化但准确的方法来表征电池组.
- 该模型能够从恒定功率数据中预测可变功率放电,这使得它能够快速调整和广泛应用.
- 这项研究有助于改进电池管理系统,提高电动汽车的可靠性和效率,特别是飞行汽车.
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