Implementation of Radial Basis Function Artificial Neural Network into an Adaptive Equivalent Consumption Minimization Strategy for Optimized Control of a Hybrid Electric Vehicle

Continued increases in the emission of greenhouse gases by passenger vehicles have accelerated the production of hybrid electric vehicles. With this increase in production, there has been a parallel demand for continuously improving strategies of hybrid electric vehicle control. The goal of an ideal control strategy is to maximize fuel economy while minimizing emissions. Methods exist by which the globally optimal control strategy may be found. However, these methods are not applicable in real-world driving applications since these methods require a priori knowledge of the upcoming drive cycle. Real-time control strategies use the global optimal as a benchmark against which performance can be evaluated. The goal of this work is to use a previously defined strategy that has been shown to closely approximate the global optimal and implement a radial basis function (RBF) artificial neural network (ANN) that dynamically adapts the strategy based on past driving conditions. The strategy used is the Equivalent Consumption Minimization Strategy (ECMS), which uses an equivalence factor to define the control strategy and the power train component torque split. An equivalence factor that is optimal for a single drive cycle can be found offline with a priori knowledge of the drive cycle. The RBF-ANN is used to dynamically update the equivalence factor by examining a past time window of driving characteristics. A total of 30 sets of training data (drive cycles) are used to train the RBF-ANN. For the majority of drive cycles examined, the RBF-ANN implementation is shown to produce fuel economy values that are within ±2.5% of the fuel economy obtained with the optimal equivalence factor. The advantage of the RBF-ANN is that it does not require a priori drive cycle knowledge and is able to be implemented in real-time while meeting or exceeding the performance of the optimal ECMS. Recommendations are made on how the RBF-ANN could be improved to produce better results across a greater array of driving conditions.

A Novel Method for the Application of the ECMS(Equivalent Consumption Minimization Strategy)to Reduce Hydrogen Consumption in Fuel Cell Hybrid Electric Vehicles

Fuel cell hybrid electric vehicles are currently being considered as ideal means to solve the energy crisis and global warming in today’s society.In this context,this paper proposes a method to solve the problem related to the dependence of the so-called optimal equivalent factor(determined in the framework of the equivalent consumption minimum strategy-ECMS)on the working conditions.The simulation results show that under typical conditions(some representative cities being considered),the proposed strategy can maintain the power balance;for different initial battery’s states of charge(SOC),after the SOC stabilizes,the fuel consumption is 5.25 L/100 km.

A Predictive Energy Management Strategies for Mining Dump Trucks

The plug-in hybrid vehicles(PHEV)technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks.Meanwhile,plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies(EMS).Therefore,a series hybrid system is constructed based on a 100-ton mining dump truck in this paper.And inspired by the dynamic programming(DP)algorithm,a predictive equivalent consumption minimization strategy(P-ECMS)based on the DP optimization result is proposed.Based on the optimal control manifold and the SOC reference trajectory obtained by the DP algorithm,the P-ECMS strategy performs real-time stage parameter optimization to obtain the optimal equivalent factor(EF).Finally,applying the equivalent consumption minimization strategy(ECMS)realizes real-time control.The simulation results show that the equivalent fuel consumption of the P-ECMS strategy under the experimentally collected mining cycle conditions is 150.8 L/100 km,which is 10.9%less than that of the common CDCS strategy(169.3 L/100 km),and achieves 99.47%of the fuel saving effect of the DP strategy(150 L/100 km).

Design and optimization of equivalent consumption minimization strategy for 4WD hybrid electric vehicles incorporating vehicle connectivity

This paper presents an optimized equivalent consumption minimization strategy(ECMS) for four-wheel-drive(4 WD) hybrid electric vehicles(HEVs) incorporating vehicle connectivity. In order to be applicable to the 4 WD architecture, the ECMS is designed based on a rule-based strategy and used under the condition that a certain propulsion mode is activated. Assuming that a group of 4 WD HEVs are connected and position information can be shared with each other, we formulate a decentralized model predictive control(MPC) framework that compromises fuel efficiency, mobility, and inter-vehicle distance to optimize the velocity profile of each individual vehicle. Based on the optimized velocity profile, an optimization problem considering both fuel economy and battery state of charge(SOC) sustainability is formulated to optimize the equivalent factors(EFs) of the ECMS for HEVs over an appropriate time window. MATLAB User Datagram Protocol(UDP) is used in the codes run on multiple computers to simulate the wireless communication among vehicles, which share position information via UDP-based communication, and dSPACE is used as a software-in-the-loop platform for the simulation of the optimized ECMS. Simulation results validate the control effectiveness of the proposed method.

基于驾驶意图云模型识别的混合动力汽车A-ECMS的构建

基于驾驶意图云模型识别的自适应等效燃油消耗最小策略(A-ECMS),构建了一种混合动力汽车能量管理策略,并对该策略进行了仿真研究。采用云模型算法,识别驾驶意图,获取了典型行驶工况下的意图识别结果;以加权平均的方法,求得各意图下对应的等效因子;建立了等效因子查询表。结果表明:在新欧洲行驶循环(NEDC)工况下,相比于基于充电状态(SOC)反馈的A-ECMS策略,本策略的燃油经济性提高了1.3%;在自定义组合工况下也能取得近似ECMS策略的燃油经济性控制效果,且具有更好的SOC稳定性控制效果。因而,该A-ECMS策略提高了原来的等效燃油消耗最小策略(ECMS)的实用性,体现驾驶意图。

考虑驾驶风格的混合动力汽车强化学习能量管理策略

为了提升混合动力汽车能量管理策略对不同风格驾驶员的适应性,基于深度强化学习和等效燃油消耗最小策略(equivalent consumption minimization strategy,ECMS),提出一种考虑驾驶风格的混合动力汽车能量管理策略。通过实车试验采集驾驶员行驶数据,基于采集数据进行驾驶员驾驶风格的聚类分析,建立驾驶风格识别模型;构建基于强化学习和ECMS的能量管理策略,将驾驶风格系数作为强化学习状态变量,利用多种驾驶风格的组合工况训练深度确定性策略梯度智能体,获取不同工况和驾驶风格下ECMS等效因子,采用ECMS求解最优发动机、电机转矩分配以及变速箱挡位;搭建硬件在环测试平台,并基于实际采集的不同驾驶员驾驶数据构建测试工况,验证所提出控制策略的有效性。研究结果表明,相较于基于规则策略、基于等效因子比例修正的自适应ECMS以及DRL-SAC策略,提出的考虑驾驶风格的强化学习能量管理策略使整车能量消耗分别降低16.35%、11.11%和7.56%,所提控制策略的有效性得到了验证。

基于充电行为电量规划的自适应能量管理策略

为提高插电式混合动力汽车(PHEV)的燃油经济性,以并联式PHEV为研究对象,考虑驾驶员充电行为,提出基于充电行为电量规划和等效燃油消耗最小策略(ECMS)的自适应能量管理策略。构建了包含不同类型工况的组合工况。根据驾驶员的充电行为特征(充电频率、充电上限、放电下限)进行充电行为分析,并进行4类工况电量消耗与燃油消耗ECMS策略下的离线仿真数据获取。根据驾驶员充电行为以及离线数据进行SOC轨迹的规划。结果表明:在电池SOC初始值分别为0.9、0.6时,提出的能量管理策略相较于基于电量消耗维持的等效燃油消耗策略,整车燃油经济性分别提升7.87%和13.1%。

复合储能式装载机能量控制优化与仿真

针对工程车辆在机群作业下的能量管理问题,以复合储能式装载机为研究对象,利用Recurdyn-Edem联合仿真建立连续作业环境获取工况数据;分别以V型工况及集群作业下装载机单次循环工况油耗最少为目标,设计了基于等效油耗最小策略与规则相结合的控制策略,为了能够进一步提高全局工况的燃油经济性,使用遗传算法对等效油耗最小关键参数进行寻优,最后带入整车模型进行仿真验证。结果表明,V型工况与集群作业工况下,该控制策略相较于自适应神经模糊控制,燃油经济性分别提升了3.23%和4.26%;通过dSPACE进行硬件在环试验,试验与仿真结果基本一致,进而验证了优化结果的有效性。

氢能无人机等效消耗最小化策略设计

【目的】为了提高氢能混合动力固定翼无人机飞行过程中能源系统功率变化的稳定性以及燃料经济性。【方法】在传统等效消耗最小化策略理论的基础上引入一种考虑蓄电池组功率变化幅度对能量消耗影响的变等效因子。将此因子与原有等效因子加权相加,形成复合等效因子。建立了适用于复杂飞行工况的无人机混合动力仿真平台,研究了复合等效因子对等效消耗最小化策略控制效果的影响。【结果】结果表明,使用复合等效因子的等效消耗最小化策略可以有效降低能源系统的功率波动幅度,巡航飞行时氢燃料电池的功率波动幅度比传统等效消耗最小化策略降低了94.7%,飞行过程中蓄电池组SOC累计变化量降低了46%;在3.5 h的飞行任务中可节省193标准升氢气,提高了燃料的经济性。当蓄电池组SOC接近平衡值时,可以大幅减缓充电功率的变化率。改进后的等效消耗最小化策略有利于延长能源系统的使用寿命,提高燃料电池和蓄电池组的安全性。

混合动力汽车深度强化学习分层能量管理策略

为了提高混合动力汽车的燃油经济性和控制策略的稳定性,以第三代普锐斯混联式混合动力汽车作为研究对象,提出了一种等效燃油消耗最小策略(equivalent fuel consumption minimization strategy,ECMS)与深度强化学习方法(deep feinforcement learning,DRL)结合的分层能量管理策略。仿真结果证明,该分层控制策略不仅可以让强化学习中的智能体在无模型的情况下实现自适应节能控制,而且能保证混合动力汽车在所有工况下的SOC都满足约束限制。与基于规则的能量管理策略相比,此分层控制策略可以将燃油经济性提高20.83%~32.66%;增加智能体对车速的预测信息,可进一步降低5.12%的燃油消耗;与没有分层的深度强化学习策略相比,此策略可将燃油经济性提高8.04%;与使用SOC偏移惩罚的自适应等效燃油消耗最小策略(A-ECMS)相比,此策略下的燃油经济性将提高5.81%~16.18%。

燃料电池混合动力汽车深度强化学习能量管理优化

针对配备有锂电池与超级电容的燃料电池混合动力汽车,为降低车辆总体运行成本,延长能量源寿命,本文提出一种基于深度强化学习的能量管理策略.首先,依据超级电容高功率密度特性,建立基于模糊自适应滤波的功率分层结构,并依据燃料电池与锂电池的经验退化模型,建立能量源退化的成本函数,采用等效消耗最小策略平衡氢耗成本与能量源退化成本,以最小化总体运行成本为目标来优化能量源功率分配;然后,引入优先经验回放与软更新以提高深度强化学习的离线训练效率;最后,在多种工况下进行仿真,结果表明,与未考虑退化的策略相比,本文所提出策略在全球统一轻型车辆测试循环下可使氢耗量降低11.8%,并可有效减缓燃料电池与锂电池的退化速率,降低燃料电池混合动力汽车的总体运行成本.

Research on ECMS Based on Segmented Path Braking Energy Recovery in a Fuel Cell Vehicle

Proton exchange membrane fuel cells are widely regarded as having the potential to replace internal combustion engines in vehicles.Since fuel cells cannot recover energy and have a slow dynamic response,they need to be used with different power sources.Developing efficient energy management strategies to achieve excellent fuel economy is the goal of research.This paper proposes an adaptive equivalent fuel minimum consumption strategy(AECMS)to solve the problem of the poor economy of the whole vehicle caused by the wrong selection of equivalent factors(EF)in traditional ECMS.In this method,the kinematics interval is used to update the equivalent factor by considering the penalty term of energy recovery on SOC changes.Finally,the optimized equivalent factor is substituted into the optimization objective function to achieve efficient energy regulation.Simulation results under the New European Driving Cycle show that compared with the traditional ECMS based on fixed SOC benchmarks,the proposed method improves fuel economy by 1.7%while ensuring vehicle power and increases SOC by 30%.

基于粒子群优化的模糊自适应等效油耗最小能量管理策略

为提高等效燃油消耗最小能量管理策略(ECMS)对不同工况的适应能力,提出一种基于粒子群算法优化的模糊自适应等效燃油消耗最小的能量管理策略(PSO-fuzzy A-ECMS)。以基于SOC反馈原理对等效因子进行比例调节方法为基础,针对仅依靠SOC反馈的等效因子调节方式存在一定局限性的问题,引入模糊控制以需求功率和SOC为输入对比例系数进行调节,利用粒子群算法优化模糊控制器中的隶属度函数,减少模糊控制在能量管理应用中对专家经验的依赖,最后在标准循环工况下进行仿真。仿真结果显示:相较于基于SOC反馈的等效因子修正方法,本文提出的PSO-fuzzy A-ECMS策略能够维持SOC在合理区间内变化,在燃油经济性和电池充放电平衡方面能够取得更好的控制效果。

混合动力重卡自适应等效燃油消耗最小化能量管理策略

为解决等效燃油消耗最小化策略的关键参数在不同工况下的动态调整,提出一种适用于混合动力重型汽车的自适应等效燃油消耗最小化策略(adaptive equivalent consumption minimization strategy,A-ECMS)。该策略以层次聚类算法得到的6种典型行驶工况为例,提出了一种基于神经网络工况识别算法。应用改进的混沌粒子群优化(chaosparticle swarm optimization,CPSO)算法优化特定驾驶循环下等效燃油消耗最小化策略(equivalent consumption minimization strategy,ECMS)的3个关键参数:等效因子、惩罚函数的比例因子和发动机起动车速阈值。提出了一种基于工况识别的新型自适应能量管理策略对关键参数进行优化,根据该重型卡车的传动系统构型,建立了车辆的纵向动力学模型,并通过仿真进行了验证。仿真结果表明复合工况驱动循环下的CPSO-ECMS和A-ECMS控制策略与传统等效燃油消耗策略相比,油耗分别降低了5.9%和8.9%。

融合工况预测的混合动力拖拉机自适应能量管理策略

针对传统等效燃油消耗最小策略(ECMS)下等效因子取值固定和工况适应性差的问题,提出了一种融合拖拉机工况预测的自适应等效燃油消耗最小策略(PA-ECMS)。以搭载混合动力液压机械无级变速动力总成的大马力拖拉机为研究对象,将ECMS策略应用于混合动力拖拉机的动力分配。首先,基于径向基(RBF)神经网络,建立了拖拉机工况预测模型,通过历史工况预测未来一段时间的工况信息;接着,结合电池荷电状态(SOC)反馈和预测的工况信息,对等效因子进行自适应调整;最后,在PA-ECMS策略框架下,对混合动力拖拉机的功率进行优化分配。仿真结果表明:与固定等效因子的ECMS策略和仅基于SOC反馈的自适应等效燃油消耗最小策略(A-ECMS)相比,采用所提策略时拖拉机在犁耕工况下的油耗分别降低了6.30%和2.55%,且具有更好的电量维持性能。

混合动力船舶等效油耗最小能量管理策略

为了降低混合动力船舶的燃油消耗,同时解决传统等效油耗最小能量管理策略(ECMS)易导致动力设备处于恶劣工况的缺点,提出一种利用逻辑门限值规则对混合动力系统工作模式进行预识别的改进ECMS,并采用改进的蚁群算法对充放电等效因子进行离线优化。在船舶典型航行工况下,通过MATLAB/Simulink建立的船舶仿真模型进行逻辑门限值能量管理策略、等效因子分别取经验值和优化值的改进ECMS的仿真分析。结果表明:等效因子寻优后的改进ECMS有更好的燃油经济性,同时也利于保证电池荷电状态(SOC)平衡和动力设备的效率。

多旋翼混合动力无人机自适应能量管理策略仿真

为提高多旋翼混合动力无人机的运行稳定性、输出动力性和能量利用率,利用GT-Power和Simulink进行模型联合搭建,对比基于规则的能量管理策略及等效燃油最小消耗能量管理策略(Equivalent consumption minimization strategy,ECMS),设计开发了基于BP(Back propagation)神经网络优化的自适应ECMS(Adaptive-ECMS,A-ECMS)。仿真研究表明:A-ECMS在运行稳定性上,整体工况转速波动率为7.74%,较基于规则策略和ECMS都有明显降低;A-ECMS在复合扰动下和随机紊流下转速波动率分别为8.32%、7.18%,与基于规则策略和ECMS相比在突发工况下运行更为稳定。A-ECMS能有效提高混合动力系统动力性能,使发动机处于经济工况10 kW;可根据荷电状态(State of charge,SOC)变化实时对电池功率进行调整。A-ECMS平均燃油消耗率为297.585 g/(kW⋅h),整体燃油消耗量为3755.31 g,与基于规则策略和ECMS相比明显较低,在各工况下运行时发动机工况点集中于燃油经济区,有效提高了系统经济性。

基于驾驶风格识别的混合动力汽车能量管理策略

为了进一步提高混合动力汽车的燃油经济性,针对驾驶员的驾驶风格对混合动力汽车的燃油经济性有较大影响的问题。通过对驾驶操作引起的汽车行驶过程的车体冲击度分析,确定不同类型行驶工况下的驾驶风格区分方法,应用该方法在所采集的大量行驶工况数据上,得到不同驾驶风格的车速信息。将行驶工况识别算法、不同驾驶风格的车速信息、等效燃油最小能量管理策略相结合,获取不同驾驶风格下的最优需求功率分配方式,从而建立基于驾驶风格识别的能量管理策略。对一段随机工况应用所制定的能量管理策略,仿真结果表明,所提出的控制策略比不考虑驾驶风格的等效燃油最小能量管理策略燃油经济性提高了8.47%,发动机工作点更好地运行在其最佳效率曲线附近,电池SOC更稳定且更好地维持在高效区域。

基于运行模式和动态混合度的燃料电池混合动力有轨电车等效氢耗最小化能量管理方法研究

清洁环保的燃料电池混合动力有轨电车近年来受到极大关注,其高效的能量管理方法对混合动力系统性能起到至关重要的作用。传统等效氢耗最小化方法的荷电状态(state of charge,SOC)平衡系数通常采用恒定值,而有轨电车在大功率峰值需求和减速制动过程中,恒定的SOC平衡系数不能满足瞬时等效氢耗最小的指标要求,并且在未知有轨电车工况条件下最优SOC平衡系数无法确定。针对上述问题,建立基于燃料电池/锂电池的混合动力有轨电车动力系统模型,并通过分析SOC平衡系数与氢耗特性,提出一种基于运行模式和动态混合度的等效氢耗最小化能量管理控制方法。该方法通过划分有轨电车运行模式,分析不同运行模式下SOC平衡系数与瞬时氢耗的关系,在此基础上提出基于运行模式和动态混合度的等效氢耗瞬时优化方法。结合有轨电车典型工况,搭建RT-LAB实时仿真平台,开展有轨电车能量管理系统实时仿真,并与传统等效氢耗最小化方法进行对比分析。结果表明,所提出的能量管理方法能够根据有轨电车工况的实时变化而自动分配需求功率,并在不同初始SOC的情况下,满足等效氢耗量最小的性能指标要求,提高整车燃料经济性。

混合动力汽车工况识别自适应能量管理策略

为改善传统等效燃油消耗最低策略(ECMS)在真实复杂路况下的控制效果,以并联混合动力汽车为研究对象,提出了一种依据工况变化在线调整等效因子的自适应等效燃油消耗最低(A-ECMS)控制策略。首先,提取差异化显著的工况特征参数,采用聚类分析方法来完成工况分类,构建典型工况库,计算出各典型工况对应的最优等效因子;然后,采用学习向量量化(LVQ)神经网络设计了工况识别器,经充分训练后识别器准确率达到98.8%;最后,在线采集选定的车辆行驶特征参数,将当前实际工况识别为典型工况库中某一种,采用对应典型工况下的最优等效因子作为当前优化输入,建立了基于工况识别的A-ECMS控制策略。仿真结果表明:与ECMS相比,在单一给定工况下,A-ECMS燃油经济性降低了0.8%,而电池组荷电状态(SOC)提高了0.13%,能取得近似优化效果;在多工况联合工况下,燃油经济性提高了4.18%,且SOC波动减小了43.26%,证明了A-ECMS控制策略的优越性。