Neural network controller for hybrid energy management system applied to electric vehicles

IF 8.9 2区 工程技术 Q1 ENERGY & FUELS
Alex N. Ribeiro , Daniel M. Muñoz
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引用次数: 0

Abstract

Hybrid energy storage systems based on batteries and supercapacitors can mitigate the aging of electric vehicle batteries aging by avoiding high currents and rapid discharge cycles. This system requires energy management systems that efficiently split the power during real driving cycles. This paper outlines a design methodology for creating a Multilayer Perceptron neural controller that governs the power distribution between the storage system components. In parallel, a Proportional-Integral-Derivative controller was implemented to ensure voltage regulation in the primary DC bus, ensuring stable operation of the entire system and providing flexibility to the neural design. The controller was tuned using particle swarm optimization, hippopotamus optimization, and differential evolution algorithms, designed to minimize the battery root-mean-square current in a comprehensive vehicle simulation. The training was structured into layers to approach the physical problem and controller optimization facets. The controller’s primary goal is to minimize the battery strain, mitigating stress events and prolonging its lifespan. The energy management neural controller was designed using a simple drive cycle and later validated with a realistic cycle. The findings demonstrate the feasibility of achieving a significant reduction in current, both in peak value and on average, especially when compared to basic energy management strategies. The process uncovered a strategy that prioritizes the use of the supercapacitor in power balance, with this effect being more pronounced during critical load events. The main bus voltage remained stable, with no deviations exceeding 5%, owing to the voltage regulator’s stability. Additionally, the particle swarm optimization and the hippopotamus optimization techniques exhibited notable performance compared to the differential evolution algorithm for this problem. Subsequently, the design was evaluated in a more complex cycle, revealing a slight decrease in average battery current performance. However, it still maintained a significant reduction in peak battery current compared to traditional controllers. Nevertheless, this methodology demonstrated power management optimization efficacy and can be extended to more complex scenarios.
应用于电动汽车的混合能源管理系统神经网络控制器
基于电池和超级电容器的混合储能系统可避免大电流和快速放电循环,从而缓解电动汽车电池的老化问题。该系统需要在实际驾驶周期中有效分配电能的能量管理系统。本文概述了创建多层感知器神经控制器的设计方法,该控制器可控制存储系统组件之间的功率分配。同时,还实施了一个比例-积分-派生控制器,以确保主要直流母线的电压调节,从而保证整个系统的稳定运行,并为神经设计提供灵活性。使用粒子群优化、河马优化和差分进化算法对控制器进行了调整,目的是在综合车辆模拟中使电池均方根电流最小化。训练分层进行,以解决物理问题和控制器优化问题。控制器的主要目标是将电池应变最小化,减轻应力事件并延长其使用寿命。能量管理神经控制器的设计使用了一个简单的驱动循环,随后使用一个现实的循环进行了验证。研究结果表明,无论是峰值还是平均值,大幅降低电流都是可行的,尤其是与基本的能量管理策略相比。该过程发现了一种在功率平衡中优先使用超级电容器的策略,这种效果在关键负载事件中更为明显。由于电压调节器的稳定性,主母线电压保持稳定,偏差未超过 5%。此外,与微分进化算法相比,粒子群优化和河马优化技术在该问题上表现出了显著的性能。随后,该设计在一个更复杂的循环中进行了评估,结果显示平均电池电流性能略有下降。不过,与传统控制器相比,它仍能显著降低电池峰值电流。尽管如此,该方法还是展示了电源管理优化的功效,并可扩展到更复杂的场景中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of energy storage
Journal of energy storage Energy-Renewable Energy, Sustainability and the Environment
CiteScore
11.80
自引率
24.50%
发文量
2262
审稿时长
69 days
期刊介绍: Journal of energy storage focusses on all aspects of energy storage, in particular systems integration, electric grid integration, modelling and analysis, novel energy storage technologies, sizing and management strategies, business models for operation of storage systems and energy storage developments worldwide.
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