通过动态规划优化插电式混合动力电动汽车 (PHEV) 能源管理(Matlab、Simulink代码实现)

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通过动态规划优化插电式混合动力电动汽车 (PHEV) 能源管理(Matlab、Simulink代码实现)

2024-07-09 16:31| 来源: 网络整理| 查看: 265

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📋📋📋本文目录如下:🎁🎁🎁

目录

💥1 概述

📚2 运行结果

🎉3 参考文献

🌈4 Matlab代码、Simulink仿真实现

💥1 概述

插电式混合动力电动汽车(PHEV)是一种结合了传统燃油动力和电动动力的先进汽车技术。在PHEV的充电过程中,会产生一定的热量,而本文将重点描述这些热损失的情况。

首先,热损失主要出现在PHEV的逆变器和两个电池模块中。这些部件在工作过程中会产生热量,需要及时进行散热以保证其正常运行。为了解决这一问题,PHEV采用了与冷水流并行排列的冷却板来吸收这些热量。通过这种方式,逆变器和电池模块产生的热量能够被有效地散发出去,保持系统的稳定性和安全性。

另外,这些散热板的设计也考虑到了废热的再利用。在散热板吸收了逆变器和电池模块产生的热量后,这些废热会被导入到一个系统中进行再利用。通过混合三个质量流在一起,废热的能量得以最大程度地利用,从而提高了PHEV的能源利用率和环保性能。

总的来说,PHEV在充电过程中的热损失问题得到了有效的解决和利用。通过合理的散热设计和废热再利用系统,PHEV不仅能够保持稳定的运行状态,还能够最大程度地提高能源利用率,为环境保护和可持续发展做出了积极的贡献。

📚2 运行结果

部分代码:

%% General PEV data %Grid connection P_l=1500;%Power line connection [W]

%Cooling fluid initial conditions m_dot=0.5;%[kg/s] c_p=4200;%[J/kgK] specific heat capacitance H2O T_i=283.15;%Initial cooling liquid temperature at inverter, %heat exchanger inlet, and inverter case [K]  T_ambient=300.15;%Initial ambient temperature;

%% Data inverter/charger

%Assumptions base on Delta-q QuiQ datasheet m_inverter=4;%[kg] E_inverter=0.83;%Inverter efficiency 

%Specific heat capacitance inverter c_Al=897;%Specific heat capacitance aluminum around room temperature [J/kg K] c_Co=385;%Specific heat capacity copper [J/kg K] c_Fe=449;%Specific heat capacity iron [J/kg K] c_Po=50;%Specific heat capacity of typical Polymers in conductor boards c_inverter=0.5*c_Al+0.3*c_Co+0.1*c_Fe+0.1*c_Po;

%% Conductive heat transfer parameters for thermal path between inverter and heat exchanger

k_Al=237;%Thermal conductivity aluminum case around room temperature [W/m 癈] m_case=0.1;%Case weight [kg] c_case=c_Al; w_case=0.05;%Width case [m] A_case=0.246*0.278*0.75;%Assumed surface area of the PEV inverter/charger

w_bond=0.025;%Width bond between case plate and heat exchanger k_bond=296;%Eutectic bond as it is in chip carriers

k_ideal=2*k_Al;%Assumed ideal thermal conductivity [W/m 癈]; A_ideal=2*A_case;%Assumed ideal conductive heat transfer area [m^2];

%% Cooling plate parameters

%The underlying assumption is a custom made turbo tube liquid cold plate %adapted to the geometry of the inverter. Assumptions base on Aavid %Thermalloy Turbo Tube Liquid Cold Plate datasheet

k_Co=401;%Thermal conductivity of copper [W/m 癈] used in the heat exchanger pipes w_pipe=0.0015;%Thickness of copper pipes app. 1.5mm Dm=0.01;%Outer diameter of tubes L=0.75*0.278;%Effective tube length one way [m] n=10;%number of pipes on cooling plate A_pipes=1/3*pi*Dm*L*n; %Assumed relevant area for conductive heat transfer as app.  %1/3 of tube surface is facing towards the inverter case

A_plate=A_case-A_pipes; %Case surface less tube diameter*number of tubes*effective  %tube length adapted from inverter case surface w_plate=0.05;%Assumed thickness extrusion Aluminum plate [m]

V_pipes=pi*(Dm/2)^2*n*L*0.66;%Effective Volume of pipes inside the Aluminum plate profile V_plate=A_case*w_plate-V_pipes;%Volume Aluminum plate less spacetaken by tubes [m^3] rho_Al=2710;%[kg/m^3] Density Aluminum plate m_plate=rho_Al*V_plate;%Mass Aluminum plate  A_bs_bond=2/3*A_pipes/(1/3);%Effective conductive heat transfer surface area of the bond on the backside

A_surface=A_case;%Convective heat transfer area for outside-facing cooling plates under realistic conditions h_surface=25;%Empirical value for free convection of gases. Source: Cengel, Basics of heat transfer, 2002, S. 26

%% Heat exchange process parameters

U=1000;     %[W/m^2 K] Overall heat transfer coefficient steam condenser              %(Cengel, 2002, S. 673)  A_HE=Dm*pi*(L/0.75)*n;%[m^2] Relevant area for convective heat transfer along the whole pipe length UA=U*A_HE; NTU=UA/(m_dot*c_p);%Number of transferred units E=1-exp(-NTU);%Initial heat exchanger efficiency rho_Co=8920;%Density Copper [kg/m^3] m_HE=A_HE*w_pipe*rho_Co; high_efficiency=0.7;%High efficiency option for scenario analysis

%% Energy storage system parameters

E_ess=0.9;%Energy storage system charging efficiency m_ess=40;%Mass of a single energy storage system [kg] c_ess=3482;%Specific heat capacitance Lithium [J/kg K] C_ess=4900;%Energy storage system capacity [Wh] (See Hymotion, L5 Plug-in  %Conversion module for specifications) PHEV=2; EV=11; n_ess=PHEV;%The EV is equipped with 9 conversion modules. Whereas an efficient PHEV %has 2 conversion modules. n_ess adapts the neccessary charging time

%% Charging patterns DOD=0.8;%Depth of discharge  NC_ess=DOD*C_ess*n_ess/(E_ess*E_inverter);%Relevant Net capacity energy storage system for charging D=NC_ess*3600/P_l;%Charging duration [s]

%% Split up heat flow:  m_dot=m_dot/n_ess

🎉3 参考文献

文章中一些内容引自网络,会注明出处或引用为参考文献,难免有未尽之处,如有不妥,请随时联系删除。

[1]刘宝帅.插电式混合动力汽车动力总成控制策略优化研究[D].昆明理工大学[2024-01-29].DOI:CNKI:CDMD:2.1018.715444.

[2]任培林.基于动态规划的PHEV规则控制策略优化设计研究[D].重庆交通大学[2024-01-29].DOI:CNKI:CDMD:2.1018.149776.

🌈4 Matlab代码、Simulink仿真实现


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