Khan Muhammad Kamran, Houran Mohamad Abou, Kauhaniemi Kimmo, Zafar Muhammad Hamza, Mansoor Majad, Rashid Saad
School of Technology and Innovation, University of Vaasa, Finland.
School of Electrical Engineering, Xi'an Jiaotong University, No. 28, West Xianning Road, Xi'an, 710049, China.
Heliyon. 2024 Jul 31;10(15):e35183. doi: 10.1016/j.heliyon.2024.e35183. eCollection 2024 Aug 15.
The battery's performance heavily influences the safety, dependability, and operational efficiency of electric vehicles (EVs). This paper introduces an innovative hybrid deep learning architecture that dramatically enhances the estimation of the state of charge (SoC) of lithium-ion (Li-ion) batteries, crucial for efficient EV operation. Our model uniquely integrates a convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM), optimized through evolutionary intelligence, enabling an advanced level of precision in SoC estimation. A novel aspect of this work is the application of the Group Learning Algorithm (GLA) to tune the hyperparameters of the CNN-Bi-LSTM network meticulously. This approach not only refines the model's accuracy but also significantly enhances its efficiency by optimizing each parameter to best capture and integrate both spatial and temporal information from the battery data. This is in stark contrast to conventional models that typically focus on either spatial or temporal data, but not both effectively. The model's robustness is further demonstrated through its training across six diverse datasets that represent a range of EV discharge profiles, including the Highway Fuel Economy Test (HWFET), the US06 test, the Beijing Dynamic Stress Test (BJDST), the dynamic stress test (DST), the federal urban driving schedule (FUDS), and the urban development driving schedule (UDDS). These tests are crucial for ensuring that the model can perform under various real-world conditions. Experimentally, our hybrid model not only surpasses the performance of existing LSTM and CNN frameworks in tracking SoC estimation but also achieves an impressively quick convergence to true SoC values, maintaining an average root mean square error (RMSE) of less than 1 %. Furthermore, the experimental outcomes suggest that this new deep learning methodology outstrips conventional approaches in both convergence speed and estimation accuracy, thus promising to significantly enhance battery life and overall EV efficiency.
电池性能对电动汽车（EV）的安全性、可靠性和运行效率有重大影响。本文介绍了一种创新的混合深度学习架构，该架构显著提高了锂离子电池充电状态（SoC）的估计，这对于电动汽车的高效运行至关重要。我们的模型独特地将卷积神经网络（CNN）与双向长短期记忆（Bi-LSTM）集成在一起，并通过进化智能进行优化，从而在SoC估计中实现了高精度。这项工作的一个新颖之处在于应用了分组学习算法（GLA）来精心调整CNN-Bi-LSTM网络的超参数。这种方法不仅提高了模型的准确性，还通过优化每个参数以最佳地捕获和整合来自电池数据的空间和时间信息，显著提高了其效率。这与传统模型形成鲜明对比，传统模型通常只关注空间或时间数据，而不能有效地兼顾两者。通过在六个不同的数据集上进行训练，进一步证明了该模型的鲁棒性，这些数据集代表了一系列电动汽车放电曲线，包括高速公路燃油经济性测试（HWFET）、US06测试、北京动态应力测试（BJDST）、动态应力测试（DST）、联邦城市驾驶计划（FUDS）和城市发展驾驶计划（UDDS）。这些测试对于确保模型能够在各种实际条件下运行至关重要。实验表明，我们的混合模型不仅在跟踪SoC估计方面超过了现有LSTM和CNN框架的性能，而且还能以惊人的速度快速收敛到真实的SoC值，平均均方根误差（RMSE）保持在1%以下。此外，实验结果表明，这种新的深度学习方法在收敛速度和估计精度方面都超过了传统方法，有望显著延长电池寿命并提高电动汽车的整体效率。
