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Application of Deep Neural Networks for Lithium-Ion Battery Surface Temperature Estimation Under Driving and Fast Charge Conditions
IEEE Transactions on Transportation Electrification ( IF 7.2 ) Pub Date : 2022-08-18 , DOI: 10.1109/tte.2022.3200225
Mina Naguib 1 , Phillip Kollmeyer 1 , Ali Emadi 1
Affiliation  

The temperature of lithium-ion batteries (LIBs) is a critical factor that significantly impacts the performance of the battery. One of the essential roles of the battery management system (BMS) is to monitor and control the temperature of the cells in the battery pack. In this article, two deep neural network (DNN) modeling approaches are used to predict the surface temperature of LIBs. The first model type is based on a feedforward neural network (FNN) enhanced with external filters, while the second model is based on a recurrent neural network (RNN) with long short-term memory (LSTM). These models are trained and tested using experimental data from two batteries, one cylindrical cell, and one pouch cell at a range of driving, fast charging, and health conditions. The proposed models are shown to be capable of estimating temperature with less than 2 °C root-mean-square error (RMSE) for challenging low ambient temperature drive cycles and just 0.3 °C for 4 C rate fast charging conditions. In addition, a model which was trained to estimate the temperature of a new battery cell was found to still have a very low error of just 0.8 °C when tested on an aged cell. Both models are deployed to an NXP S32K344 microprocessor to measure their execution time and memory use. The FNN executes significantly faster on the microprocessor than the LSTM, 0.8 ms compared with 2.5 ms for models with around 3000 learnable parameters, and uses less random access memory (RAM), 0.4 kB compared with 1 kB.

中文翻译:

深度神经网络在驱动和快充条件下锂离子电池表面温度估计中的应用

锂离子电池 (LIB) 的温度是显着影响电池性能的关键因素。电池管理系统 (BMS) 的重要作用之一是监测和控制电池组中电芯的温度。在本文中,两种深度神经网络 (DNN) 建模方法用于预测 LIB 的表面温度。第一种模型基于使用外部滤波器增强的前馈神经网络 (FNN),而第二种模型基于具有长短期记忆 (LSTM) 的循环神经网络 (RNN)。这些模型使用来自两个电池、一个圆柱形电池和一个软包电池的实验数据在一系列驾驶、快速充电和健康条件下进行训练和测试。所提出的模型被证明能够以小于 2 °C 的均方根误差 (RMSE) 估算温度,以应对具有挑战性的低环境温度驱动循环,而在 4 C 速率快速充电条件下仅为 0.3 °C。此外,在对老化电池进行测试时,发现经过训练以估计新电池温度的模型的误差仍然非常低,仅为 0.8 °C。两种模型都部署到 NXP S32K344 微处理器,以测量它们的执行时间和内存使用情况。FNN 在微处理器上的执行速度明显快于 LSTM,0.8 毫秒,而具有大约 3000 个可学习参数的模型为 2.5 毫秒,并且使用更少的随机存取存储器 (RAM),0.4 kB 比 1 kB。3 °C 用于 4 C 速率快速充电条件。此外,在对老化电池进行测试时,发现经过训练以估计新电池温度的模型的误差仍然非常低,仅为 0.8 °C。两种模型都部署到 NXP S32K344 微处理器,以测量它们的执行时间和内存使用情况。FNN 在微处理器上的执行速度明显快于 LSTM,0.8 毫秒,而具有大约 3000 个可学习参数的模型为 2.5 毫秒,并且使用更少的随机存取存储器 (RAM),0.4 kB 比 1 kB。3 °C 用于 4 C 速率快速充电条件。此外,在对老化电池进行测试时,发现经过训练以估计新电池温度的模型的误差仍然非常低,仅为 0.8 °C。两种模型都部署到 NXP S32K344 微处理器,以测量它们的执行时间和内存使用情况。FNN 在微处理器上的执行速度明显快于 LSTM,0.8 毫秒,而具有大约 3000 个可学习参数的模型为 2.5 毫秒,并且使用更少的随机存取存储器 (RAM),0.4 kB 比 1 kB。
更新日期:2022-08-18
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