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首页> 外文期刊>IEEE transactions on industrial informatics >A Data-Driven Power Management Strategy for Plug-In Hybrid Electric Vehicles Including Optimal Battery Depth of Discharging
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A Data-Driven Power Management Strategy for Plug-In Hybrid Electric Vehicles Including Optimal Battery Depth of Discharging

机译：用于插入式混合动力电动汽车的数据驱动电源管理策略，包括最佳电池放电深度

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摘要

For hybrid electric vehicles, higher depth of discharge (DOD) indicates more use of battery energy, which can supply more inexpensive propulsions than the fossil fuel but accelerate the battery aging, thus leading to the increase in the equivalent battery life loss cost (EBLLC) related to battery aging. While developing an energy management strategy considering the battery aging effect for plug-in hybrid electric vehicles (PHEVs), a tradeoff between energy consumption cost (ECC) and EBLLC should be made to identify the optimal DOD and minimize the total cost (TC). Furthermore, the optimal DOD is changeable with the initial state of charge (SOC) level. To develop a robust controller to deal with varying initial SOCs for PHEVs, this paper proposes a data-driven method, namely, a three-layer artificial neural network (ANN) to realize real-time power distribution including battery life model. Real-world speed profiles and Pontryagin's minimum principle (PMP) are leveraged to identify the optimal DODs and generate the neural network training data based on cases with a range of initial SOCs. The results clearly demonstrate the robustness of the proposed ANN and also indicate that the data-driven method can effectively reduce the total of ECC and EBLLC compared to typical optimization algorithms without a battery aging model, including the dynamic programming, PMP, and the rule-based strategy. In particular, the ANN can reduce the TC by 19.99%, 25.97%, and 33.13%, respectively, for cases with the initial SOC of 0.95, 0.85, and 0.65, compared to the rule-based method. And the TC of the ANN is comparable to the PMP including the battery degradation model. Moreover, the training sample scale on forecasting accuracy and computational efficiency of the ANN is evaluated. Finally, the computational time of these methods is comprehensively discussed to evaluate the time efficiency of the proposed method.
机译：对于混合动力电动车辆，更高的放电深度（DOD）表示更多使用电池能量，这可以提供比化石燃料更便宜的推进，但加速电池老化，从而导致等效电池寿命损失成本的增加（EBLLC）与电池老化有关。在考虑用于插入式混合动力电动车（PHEV）的电池老化效果（PHEV）的电池老化效果的情况下，应制作能耗成本（ECC）和EBLLC之间的权衡来识别最佳国防部并最小化总成本（TC）。此外，最佳DOD可通过初始充电状态（SOC）水平而变化。为了开发强大的控制器来处理用于PHEV的变化的初始SOC，提出了一种数据驱动方法，即三层人工神经网络（ANN），以实现包括电池寿命模型的实时功率分布。真实世界的速度简档和Pontryagin的最低原则（PMP）可以利用来识别最佳DOD，并根据具有一系列初始SOC的情况生成神经网络培训数据。结果清楚地展示了所提出的ANN的稳健性，并且还表明，与没有电池老化模型的典型优化算法相比，数据驱动方法可以有效地减少ECC和EBLLC的总量，包括动态编程，PMP和规则 - 基于策略。特别是，与基于规则的方法相比，ANN可以将TC减少19.99％，25.97％和33.13％，初始SOC为0.95,0.85和0.65的情况。 ANN的TC与包括电池劣化模型的PMP相当。此外，评估了对ANN的预测精度和计算效率的训练样本规模。最后，综合地讨论了这些方法的计算时间以评估所提出的方法的时间效率。

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