电动汽车热系统性能及控制优化研究
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摘要
能源危机、全球变暖、环境污染等因素促使电动汽车成为未来汽车的发展方向。动力电池需要热管理才能保证其使用安全性及较高的性能。另外，汽车动力系统更改后，整车热特性有很大的变化，对空调，特别是对取暖有本质的影响。电动车不仅要提供乘员舱舒适驾乘环境，还要为动力电池及其他电动机及逆变器等电器部件进行热管理。热管理系统的性能优劣及能效比将直接决定了电动车的续航里程、驾乘舒适性及行驶安全。设计开发高能效比、节能和稳定的热系统与控制系统对电动车的开发与推广具有非常高的现实意义。因此，本文在分析比较电动车热管理系统方案的基础上，搭建了电动车热管理系统，并通过理论结合实验的研究手段对空调箱及控制系统进行了设计与节能优化。本文主要内容及成果如下：
一、采用层次分析方法建立了电动车热管理方案的工程评价模型。对常见的电动车热管理可行方案进行了分析，并基于专家的群决策知识开展支持，通过电动车热管理系统功能相对于客户需求总目标优劣次序的排序。模型在定量分析各方案的技术、成本、性能、控制等多方面因素的基础上，获取最优的电动车热管理系统工程应用方案。研究结果表明采用独立的电池空调箱对电池进行热管理具有明显的优势。随后，通过实验对比研究了电池采用风冷与电池采用水冷这两种独立空调的热管理方案。实验表明：两种电池热管理形式都可以满足工程应用，而相对而言，水冷方案具有更好的空调降温和升温特性，且电池SOC消耗也要小于风冷形式。
二、通过数值分析理论与实验验证的方法建立了空调箱空气侧数值分析的CFD模型，模型流量最大误差为3％，最大温度误差为1.4℃。并通过CFD模拟结合实验的研究手段，基于节能目的对电动车乘员舱空调箱进行了空气侧优化设计。在满足空调箱的各项舒适性指标的同时，优化了空调箱内部流道，降低了内部流阻、减少了暖风芯体的漏热、改善了气流在蒸发器表面的分布。优化后制冷模式风量提升11%，制冷性能提升16.6%；制热模式风量提升16.5%，制热性能提升11.1%。大大提升了空调箱的能效比，达到了节能的设计优化目的。
三、提出了人体/电池及电器件的热感知舒适度指标TCI，并基于此建立了前向神经网络，在分析电动车热系统特点的基础上，基于热系统节能的目标，结合模糊神经网络控制理论，建立了电动车热系统控制的模糊神经网络模型。以TCI指标作为热管理系统的控制目标参数，控制对象为电动压缩机、PTC加热器、电动水泵、鼓风机、步进电机、电磁阀等。控制的主体策略为根据输入条件判断，在各个控制对象之间找到一种最匹配的控制方式，使得车厢内、电池包及电器的温度快速达到设定温度，温度波动小、SOC消耗低。
四、基于节能目的，结合微通道蒸发器系统特点，开发了一套更适于电动车微通道蒸发器空调系统的温度控制系统。控制系统具有更好的控制灵敏度和控制反应特性，温控区间可设置在[0,4]。最低蒸发温度达到冷凝水的冰点温度0℃，低于传统设计采用空气温度传感器的[4,5.5]，最低蒸发温度下降2℃，最大极限地挖掘了空调系统的制冷性能。
五、将本文研究得到的基于节能的空调箱优化方案和热管理系统控制策略应用于本文搭建的电动车热管理系统中进行实验研究，并与优化前的方案及传统原型车进行对比。试验结果表明:优化设计后的热系统控制稳定，且可根据用户设定迅速响。电动车空调箱及控制系统优化后，降温性能得到明显提升，降温速率提升，出风口温度下降2.2℃，头部温度下降2.0℃；升温性能速率略有提升，另外由于控制策略，限制PTC加热器及发动机的运行间隔和时常，升温特性优化前后无明显差异。而同时，空调系统对SOC的消耗则得到了明显改善，制冷工况能耗降低SOC17%；制热工况能耗降低SOC18%。
The energy crisis, global warming, environmental pollution and other factorshave contributed to the electric vehicles to become the future of automotivedevelopment. Battery pack needs thermal management to ensure its safety andhigh performance. In addition, after power system changing, the vehicle thermalcharacteristics changed a lot correspondingly, which impacts a lot on the airconditioning, especially for heating. Electric vehicles are not only required toprovide comfortable driving environment in cabin, but also required to thermalmanage the battery pack, motor, inverter and other electrical components in ahappy working condition. The performance of thermal management systems andenergy efficiency will directly determine the electric car's mileage, ridingcomfort and driving safety. Researching and development on thermal systemsand control systems with high energy efficiency, energy saving, stability androbustness will have a high practical significance for the development andpromotion of electric vehicles. Therefore, on the basis of the analysis of currenthigh protential electric vehicle thermal management system options, one electricvehicle thermal management system built and then the performance and energyefficiency optimized by means of theory analysis and experience study. The maincontents and results are as follows.
1) The Analytic Hierarchy Process (AHP) engineering evaluation model was builtfor the analysis of electric vehicle thermal management solutions. Some highpertential electric vehicle thermal management options were analysised in thispaper. With the support of expert group decision making knowledge, to carryout the sort of electric vehicle thermal management system solutions based on customer request and its function. Based on the quantitative analysis oftechnical, cost, performance, control, and other factors for each options, thebest engineering application solution for electric vehicle thermal managementsystem abtained. The analysis results show that the independent battery airconditioning unit for the battery thermal management has obvious advantages.Subsequently, through the experimental comparison of two theraml systemswith air-cooled battery and coolant-cooled battery. We found that two thermalmanagement solutions meet the requirement of engineering applications.Relatively speaking, the Coolant-cooling scheme has better air conditioningcooling and heating features, and better battery SOC consumption thanair-cooled scheme.
2) The CFD model for HVAC module air side performance analysis was found inthis paper by using the theory of numerical analysis and experimentalvalidation. The maximum error of the model for flow rate is3%. And themaximum temperature error is1.4℃. With the help of CFD analysiscombining with experiment study, the HVAC module designed for electricvehicle was optimized on account of energy saving. The airside resistance,heat pick-up reduced, and the KPI of evaporator improved without decreasingthe comfort index. After optimization, airflow enhanced11%and coolingcapacity enhanced16.6%on full cold mode.16.5%and11.1%enhancementrespectively for airflow and heating capacity as well on full heating mode.Which greatlly improved the energy efficiency of HVAC module.
3) The thermal sensing comfort index TCI for passenger, battery and electricalparts established in this paper. And then a feedforward neural networkestablished for forcasting thermal comfort of monitoring components. On thebasis of the characteristics of electric vehicle thermal systems and energysaving goal, combined with the theory of fuzzy neural network control, onefuzzy neural network model for electric vehicle thermal control established inthis paper. TCI index is the control target and HV electric compressor, HV PTC heater, electric water pump, blower, actuator, solenoid valve and etc. arecontrol objects. The main strategy is to find a matching point between allcontrol objects according to TCI index. Which make cabin, battery and HVcomponents reaches set temperature quickly, smaller temperature fluctuations,and lower SOC consumption.
4) Based on the purpose of energy conservation, combined with thecharacteristics of the micro-channel evaporator system, one temperaturecontrol system was developed in this paper for electric vehicle cooling system.This temperature control system is more suitable for the micro-channelevaporator air conditioning system of the electric vehicle. The control systemhas better control sensitivity and control response characteristics, thetemperature interval can be set in [0,4]. Minimum evaporation temperaturereaches the freezing point of the condensate, lower than the traditional designwhich uses air temperature sensor with [4,5.5], the minimum evaporationtemperature decreased by2℃. Which achieved the maximum coolingperformance of air-conditioning system.
5) The optimized HVAC module and thermal management control system basedon energy efficient strategy were used into the electric vehicle thermal systemwe built in this paper. Then experience performed to study the performanceand control robustness. Compared with the pre-optimized thermal system andtraditional benchmark vehicle, the results showed that: optimum design ofthermal system has better control performance and stability, and can quickrespond to user setting. After optimization, the cooling down performance hasbeen improved significantly and the cooling rate as well, the vent outlettemperature decreased2.2℃, breath temperature dropped2.0℃. There wasno big improvement on heating up performance. Moreover, because of controlstrategy on power limiting of the PTC heater and the engine running intervalsand time duration, there was not no significant difference in heatingcharacteristic before and after optimization. However, the consumption of air conditioning system on battery SOC has been significant improved. Theenergy consumption has been decreased by17%and18%battery SOC oncooling down and heating up conditions respectively.
一、采用层次分析方法建立了电动车热管理方案的工程评价模型。对常见的电动车热管理可行方案进行了分析，并基于专家的群决策知识开展支持，通过电动车热管理系统功能相对于客户需求总目标优劣次序的排序。模型在定量分析各方案的技术、成本、性能、控制等多方面因素的基础上，获取最优的电动车热管理系统工程应用方案。研究结果表明采用独立的电池空调箱对电池进行热管理具有明显的优势。随后，通过实验对比研究了电池采用风冷与电池采用水冷这两种独立空调的热管理方案。实验表明：两种电池热管理形式都可以满足工程应用，而相对而言，水冷方案具有更好的空调降温和升温特性，且电池SOC消耗也要小于风冷形式。
二、通过数值分析理论与实验验证的方法建立了空调箱空气侧数值分析的CFD模型，模型流量最大误差为3％，最大温度误差为1.4℃。并通过CFD模拟结合实验的研究手段，基于节能目的对电动车乘员舱空调箱进行了空气侧优化设计。在满足空调箱的各项舒适性指标的同时，优化了空调箱内部流道，降低了内部流阻、减少了暖风芯体的漏热、改善了气流在蒸发器表面的分布。优化后制冷模式风量提升11%，制冷性能提升16.6%；制热模式风量提升16.5%，制热性能提升11.1%。大大提升了空调箱的能效比，达到了节能的设计优化目的。
三、提出了人体/电池及电器件的热感知舒适度指标TCI，并基于此建立了前向神经网络，在分析电动车热系统特点的基础上，基于热系统节能的目标，结合模糊神经网络控制理论，建立了电动车热系统控制的模糊神经网络模型。以TCI指标作为热管理系统的控制目标参数，控制对象为电动压缩机、PTC加热器、电动水泵、鼓风机、步进电机、电磁阀等。控制的主体策略为根据输入条件判断，在各个控制对象之间找到一种最匹配的控制方式，使得车厢内、电池包及电器的温度快速达到设定温度，温度波动小、SOC消耗低。
四、基于节能目的，结合微通道蒸发器系统特点，开发了一套更适于电动车微通道蒸发器空调系统的温度控制系统。控制系统具有更好的控制灵敏度和控制反应特性，温控区间可设置在[0,4]。最低蒸发温度达到冷凝水的冰点温度0℃，低于传统设计采用空气温度传感器的[4,5.5]，最低蒸发温度下降2℃，最大极限地挖掘了空调系统的制冷性能。
五、将本文研究得到的基于节能的空调箱优化方案和热管理系统控制策略应用于本文搭建的电动车热管理系统中进行实验研究，并与优化前的方案及传统原型车进行对比。试验结果表明:优化设计后的热系统控制稳定，且可根据用户设定迅速响。电动车空调箱及控制系统优化后，降温性能得到明显提升，降温速率提升，出风口温度下降2.2℃，头部温度下降2.0℃；升温性能速率略有提升，另外由于控制策略，限制PTC加热器及发动机的运行间隔和时常，升温特性优化前后无明显差异。而同时，空调系统对SOC的消耗则得到了明显改善，制冷工况能耗降低SOC17%；制热工况能耗降低SOC18%。
The energy crisis, global warming, environmental pollution and other factorshave contributed to the electric vehicles to become the future of automotivedevelopment. Battery pack needs thermal management to ensure its safety andhigh performance. In addition, after power system changing, the vehicle thermalcharacteristics changed a lot correspondingly, which impacts a lot on the airconditioning, especially for heating. Electric vehicles are not only required toprovide comfortable driving environment in cabin, but also required to thermalmanage the battery pack, motor, inverter and other electrical components in ahappy working condition. The performance of thermal management systems andenergy efficiency will directly determine the electric car's mileage, ridingcomfort and driving safety. Researching and development on thermal systemsand control systems with high energy efficiency, energy saving, stability androbustness will have a high practical significance for the development andpromotion of electric vehicles. Therefore, on the basis of the analysis of currenthigh protential electric vehicle thermal management system options, one electricvehicle thermal management system built and then the performance and energyefficiency optimized by means of theory analysis and experience study. The maincontents and results are as follows.
1) The Analytic Hierarchy Process (AHP) engineering evaluation model was builtfor the analysis of electric vehicle thermal management solutions. Some highpertential electric vehicle thermal management options were analysised in thispaper. With the support of expert group decision making knowledge, to carryout the sort of electric vehicle thermal management system solutions based on customer request and its function. Based on the quantitative analysis oftechnical, cost, performance, control, and other factors for each options, thebest engineering application solution for electric vehicle thermal managementsystem abtained. The analysis results show that the independent battery airconditioning unit for the battery thermal management has obvious advantages.Subsequently, through the experimental comparison of two theraml systemswith air-cooled battery and coolant-cooled battery. We found that two thermalmanagement solutions meet the requirement of engineering applications.Relatively speaking, the Coolant-cooling scheme has better air conditioningcooling and heating features, and better battery SOC consumption thanair-cooled scheme.
2) The CFD model for HVAC module air side performance analysis was found inthis paper by using the theory of numerical analysis and experimentalvalidation. The maximum error of the model for flow rate is3%. And themaximum temperature error is1.4℃. With the help of CFD analysiscombining with experiment study, the HVAC module designed for electricvehicle was optimized on account of energy saving. The airside resistance,heat pick-up reduced, and the KPI of evaporator improved without decreasingthe comfort index. After optimization, airflow enhanced11%and coolingcapacity enhanced16.6%on full cold mode.16.5%and11.1%enhancementrespectively for airflow and heating capacity as well on full heating mode.Which greatlly improved the energy efficiency of HVAC module.
3) The thermal sensing comfort index TCI for passenger, battery and electricalparts established in this paper. And then a feedforward neural networkestablished for forcasting thermal comfort of monitoring components. On thebasis of the characteristics of electric vehicle thermal systems and energysaving goal, combined with the theory of fuzzy neural network control, onefuzzy neural network model for electric vehicle thermal control established inthis paper. TCI index is the control target and HV electric compressor, HV PTC heater, electric water pump, blower, actuator, solenoid valve and etc. arecontrol objects. The main strategy is to find a matching point between allcontrol objects according to TCI index. Which make cabin, battery and HVcomponents reaches set temperature quickly, smaller temperature fluctuations,and lower SOC consumption.
4) Based on the purpose of energy conservation, combined with thecharacteristics of the micro-channel evaporator system, one temperaturecontrol system was developed in this paper for electric vehicle cooling system.This temperature control system is more suitable for the micro-channelevaporator air conditioning system of the electric vehicle. The control systemhas better control sensitivity and control response characteristics, thetemperature interval can be set in [0,4]. Minimum evaporation temperaturereaches the freezing point of the condensate, lower than the traditional designwhich uses air temperature sensor with [4,5.5], the minimum evaporationtemperature decreased by2℃. Which achieved the maximum coolingperformance of air-conditioning system.
5) The optimized HVAC module and thermal management control system basedon energy efficient strategy were used into the electric vehicle thermal systemwe built in this paper. Then experience performed to study the performanceand control robustness. Compared with the pre-optimized thermal system andtraditional benchmark vehicle, the results showed that: optimum design ofthermal system has better control performance and stability, and can quickrespond to user setting. After optimization, the cooling down performance hasbeen improved significantly and the cooling rate as well, the vent outlettemperature decreased2.2℃, breath temperature dropped2.0℃. There wasno big improvement on heating up performance. Moreover, because of controlstrategy on power limiting of the PTC heater and the engine running intervalsand time duration, there was not no significant difference in heatingcharacteristic before and after optimization. However, the consumption of air conditioning system on battery SOC has been significant improved. Theenergy consumption has been decreased by17%and18%battery SOC oncooling down and heating up conditions respectively.
引文
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