融合工况预测的燃料电池汽车里程自适应等效氢耗最小控制策略.pdfVIP

融合工况预测的燃料电池汽车里程自适应等效氢耗最

小控制策略

摘

要

为有效地提高插电式燃料电池汽车的经济性，实现燃料电池和动力电池的功率最优分配，考虑到行驶工况、电池荷

电状态（State

of

charge,

SOC）、等效因子与氢气消耗之间的密切联系，制定融合工况预测的里程自适应等效氢耗最小策略.

通

过基于误差反向传播的神经网络来实现未来短期车速的预测，分析未来车辆需求功率变化，同时借助全球定位系统规划一条

通往目的地的路径，智能交通系统便可获取整个行程的交通流量信息，利用行驶里程和SOC实时动态修正等效消耗最小策

略中的等效因子，实现能量管理策略的自适应性.

基于MATLAB/Simulink软件，搭建整车仿真模型与传统的能量管理策略进

行仿真对比验证.

仿真结果表明，采用基于神经网络的工况预测算法能够较好地预测未来短期工况，其预测精度相较于马尔

可夫方法提高12.5%，所提出的能量管理策略在城市道路循环工况(UDDS)下的氢气消耗比电量消耗维持(CD/CS)策略下降

55.6%.

硬件在环试验表明，在市郊循环工况

(EUDC)下的氢气消耗比CD/CS策略下降26.8%，仿真验证结果表明了所提出的

策略相比于CD/CS策略在氢气消耗方面的优越性能，并通过硬件在环实验验证了所提策略的有效性.

关键词

燃料电池汽车；能量管理策略；等效消耗最小策略；工况预测；反向传播神经网络

分类号

U461.8

Trip

distance

adaptive

equivalent

hydrogen

consumption

minimization

strategy

for

fuel-

cell

electric

vehicles

integrating

driving

cycle

prediction

ABSTRACT

The

environment

pollution

and

petroleum

problems,

which

are

increasingly

becoming

serious,

have

caused

the

vehicle

industry

to

transition

into

a

low-carbon

and

energy-saving

industry.

During

processes,

plug-in

fuel-cell

electric

vehicles

(PFCEVs)

play

an

important

role

due

to

their

advantages

of

rapid

fueling,

high

energy

density

and

efficiency,

low

operating

temperature,

and

zero

onboard

emissions.

PFCEVs

use

high-capacity

rechargeable

batteries

to

avoid

working

in

low-efficiency

areas.

However,

a

robust

energy

management

strategy

that

can

achieve

reliable

energy

distribution

by

regulating

the

output

power

of

the

fuel

cell

and

battery

within

the

hybrid

powertrain

merits

further

investigation.

Considering

the

close

relationship

between

the

driving

cycle,

state

of

charge

(SOC),

equivalent

factor,

and

hydrogen

consumption,

a

trip

distance

adaptive

equivalent

consumption

minimum

strategy

integrating

driving

cycle

prediction

is

proposed.

A

backpropagation-based

neural

network

is

used

to

predict

short-term

vehicle

velocity

and

analyze

future

changes

in

vehicle

demand

power.

Planning

a

path

to

the

destination

with

the

help

of

the

global

positioning

system,

the

intelligent

trans