CN102951144B - Self-regulating neural network energy managing method based on minimum power loss algorithm - Google Patents
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Abstract
本发明涉及一种基于最小功率损失算法的自调整神经网络能量管理方法，包括以下步骤：1)整车控制器通过CAN总线从汽车动力附件获取能量管理策略计算所需数据，同时采集当前的能量源的实时效率值；2)整车控制器判断是否已接收到完整的数据；3)整车控制器判断是否接收到需更新神经网络的指令，若是，则更新神经网络；4)整车控制器根据接收到的能量管理策略计算所需数据，通过神经网络计算出当前最优功率分配；5)使用功率增益系数对神经网络计算出的最优功率分配进行修正；6)整车控制器通过CAN总线向能量控制器发送功率分配结果。与现有技术相比，本发明具有燃油经济性好、可实现实时调整、扩展性好等优点。
The invention relates to a self-adjusting neural network energy management method based on a minimum power loss algorithm, comprising the following steps: 1) The vehicle controller obtains the data required for energy management strategy calculation from the vehicle power accessories through the CAN bus, and collects the current energy at the same time 2) The vehicle controller judges whether it has received complete data; 3) The vehicle controller judges whether it has received an instruction to update the neural network, and if so, updates the neural network; 4) The vehicle control The controller calculates the required data according to the received energy management strategy, and calculates the current optimal power allocation through the neural network; 5) Uses the power gain coefficient to correct the optimal power allocation calculated by the neural network; 6) The vehicle controller passes The CAN bus sends the power distribution result to the energy controller. Compared with the prior art, the invention has the advantages of good fuel economy, real-time adjustment, good expansibility and the like.
Description
技术领域 technical field
本发明涉及电动车控制技术领域，尤其是涉及一种基于最小功率损失算法的自调整神经网络能量管理方法。 The invention relates to the technical field of electric vehicle control, in particular to a self-adjusting neural network energy management method based on a minimum power loss algorithm. the
背景技术 Background technique
增程式电动车作为混合动力汽车的一个分支，具有混合动力汽车的基本特点。有别于传统汽车和纯电动车，增程式电动车可由两种能量源提供动力。这种双能量源的特征增加了系统设计的灵活性，在整车能量管理系统的协调控制下，双能量源与其他部件相互配合，可以进行多种优化组合，形成不同的动力系统工作模式，以适应不同的行驶工况。 As a branch of hybrid electric vehicles, extended-range electric vehicles have the basic characteristics of hybrid electric vehicles. Unlike conventional cars and pure electric vehicles, range-extended electric vehicles can be powered by two energy sources. The characteristics of this dual energy source increase the flexibility of system design. Under the coordinated control of the vehicle energy management system, the dual energy source cooperates with other components to perform multiple optimized combinations to form different power system working modes. To adapt to different driving conditions. the
同时，双能量源也增加了增程式电动车混合动力系统的复杂性，如何进行多种工作模式的切换，如何实现双能量源间能量流的优化控制，成为混合动力系统研究领域的一个难点。为了解决由混合动力系统双能量源所引起的工作模式切换问题，以及能量流的优化控制问题，不得不增加一个能量管理系统来解决传统内燃机汽车和纯电动汽车所不具有的新问题，即必须通过一定的能量控制策略(Enerhy Control Strategy)来控制双能量源之间能量流的协调和分配。控制策略是能量管理系统的核心，是实现增程式电动车节能、环保等目标的关键所在。 At the same time, dual energy sources also increase the complexity of the hybrid system of extended-range electric vehicles. How to switch between multiple operating modes and how to realize the optimal control of energy flow between dual energy sources has become a difficult point in the field of hybrid system research. In order to solve the problem of working mode switching caused by the dual energy sources of the hybrid system and the problem of optimal control of energy flow, an energy management system has to be added to solve new problems that traditional internal combustion engine vehicles and pure electric vehicles do not have, that is, must The coordination and distribution of energy flow between dual energy sources is controlled through a certain energy control strategy (Enerhy Control Strategy). The control strategy is the core of the energy management system and the key to achieve the goals of energy saving and environmental protection for extended-range electric vehicles. the
目前研究最为广泛的四类混合动力汽车能量管理策略：基于规则的控制策略、瞬时优化控制策略、全局优化控制策略和基于优化算法的自适应控制策略。 The four most widely studied energy management strategies for HEVs are rule-based control strategies, instantaneous optimal control strategies, global optimal control strategies, and adaptive control strategies based on optimization algorithms. the
根据部件性能特性和工程经验选择控制参数，在控制参数的变化范围内使用一组阈值(逻辑门限值)将其划分成不同的区域，在不同的控制参数区域，混合动力汽车按照不同的状态和能量供给模式工作，从而形成一组控制规则。控制参数通常有车速、汽车行驶的需求功率、发动机的功率、电池的荷电状态(SOC)等。根据控制参数的阈值确定与否，又可以分成静态逻辑门限能量管理策略和模糊规则能量管理策略，前者以精确的阈值来划分系统的能量供应状态，后者对阈值进行模糊化 处理后来决定系统的能量供应状态。这类策略的最大的优点是易于工程实现。但是，基于规则的能量管理策略，无论是否进行过控制参数优化，其在燃油经济性的提高方面还是存在一定的局限性。 Control parameters are selected according to component performance characteristics and engineering experience, and a set of thresholds (logic thresholds) are used within the range of control parameters to divide them into different areas. In different control parameter areas, hybrid vehicles operate according to different states. Work with the energy supply mode to form a set of control rules. The control parameters usually include the vehicle speed, the required power of the vehicle, the power of the engine, the state of charge (SOC) of the battery, and the like. According to whether the threshold value of the control parameter is determined or not, it can be divided into static logic threshold energy management strategy and fuzzy rule energy management strategy. The former divides the energy supply state of the system with precise threshold value, and the latter determines the energy supply state of the system by fuzzifying the threshold value. Energy supply status. The biggest advantage of this type of strategy is that it is easy to implement in engineering. However, the rule-based energy management strategy, no matter whether the control parameters have been optimized or not, still has certain limitations in improving fuel economy. the
瞬时优化策略一般是采用“等效燃油消耗最少”法或“功率损失最小”法，二者原理类似，都是将两个能量源的能量消耗用特定方法进行量化统一，以便于计算出最小整体消耗。瞬时优化策略在每一步长内是最优的，但无法保证在整个运行区间内最优，而且需要大量的浮点运算和比较精确的车辆模型，计算量大，实现困难。这类能量管理策略目前主要应用在内燃机-蓄电池混合动力系统上，在计算机仿真上取得了很好的燃料经济性，在实车应用上也取得了一些成果。但是这类策略在实车上应用得并不广泛，因为其对于车辆实时行驶状态参数的采集、处理要求较高。 The instantaneous optimization strategy generally adopts the "minimum equivalent fuel consumption" method or the "minimum power loss" method. The principles of the two are similar, and the energy consumption of the two energy sources is quantified and unified by a specific method, so as to calculate the minimum overall consume. The instantaneous optimization strategy is optimal in each step, but it cannot be guaranteed to be optimal in the entire operating range, and requires a large number of floating-point operations and a relatively accurate vehicle model, which requires a large amount of calculation and is difficult to implement. This type of energy management strategy is currently mainly used in internal combustion engine-battery hybrid power systems, and has achieved good fuel economy in computer simulations, and has also achieved some results in real vehicle applications. However, this type of strategy is not widely used in real vehicles, because it has high requirements for the collection and processing of vehicle real-time driving state parameters. the
全局优化控制策略，在事先知道汽车行驶的所有过程中所有工况参数的条件下，可以实现能量管理的全局优化，有多种控制算法应用到这种能量管理策略中，如动态规划算法、离散动态规划算法、遗传算法等。全局优化模式实现了真正意义上的最优化，但实现这种策略的算法往往都比较复杂，计算量也很大，并且需要预先获得所有的道路信息，在实际车辆的实时控制中很难得到应用。 The global optimization control strategy can realize the global optimization of energy management under the condition of knowing all the parameters of all working conditions in the driving process of the car in advance. There are many control algorithms applied to this energy management strategy, such as dynamic programming algorithm, discrete Dynamic programming algorithm, genetic algorithm, etc. The global optimization mode realizes optimization in the true sense, but the algorithm to realize this strategy is often complex, the calculation amount is also large, and all road information needs to be obtained in advance, which is difficult to be applied in the real-time control of actual vehicles . the
基于优化算法的自适应控制策略，可以根据当前的行驶条件和路况自动的预测未来一段时间内的自动调整控制参数以适应行驶工况的变化。所谓自适应，就是在每一时间步，根据当前的行驶条件和路况要求来调整部件工作方式，通过优化算法，在保证目标函数最优化的前提下，将扭矩需求合理地分配给发动机和电机。虽然自适应控制策略的目标函数模型、优化算法等各不相同，但由于自适应控制要实时采集大量的发动机运行数据，计算燃油油耗和排放点，优化过程复杂，计算量大，导致其目前无法在实际中得到应用。 The adaptive control strategy based on the optimization algorithm can automatically predict the automatic adjustment of control parameters in the future according to the current driving conditions and road conditions to adapt to changes in driving conditions. The so-called self-adaptation means that at each time step, the working mode of the components is adjusted according to the current driving conditions and road conditions. Through the optimization algorithm, the torque demand is reasonably allocated to the engine and the motor under the premise of ensuring the optimization of the objective function. Although the objective function models and optimization algorithms of the adaptive control strategy are different, the optimization process is complicated and the calculation amount is large because the adaptive control needs to collect a large amount of engine operating data in real time and calculate the fuel consumption and emission points, so it cannot be implemented at present. be applied in practice. the
发明内容 Contents of the invention
本发明的目的就是为了克服上述现有技术存在的缺陷而提供一种燃油经济性好、可实现实时调整的基于最小功率损失算法的自调整神经网络能量管理方法。 The object of the present invention is to provide a self-adjusting neural network energy management method based on a minimum power loss algorithm with good fuel economy and real-time adjustment in order to overcome the above-mentioned defects in the prior art. the
本发明的目的可以通过以下技术方案来实现： The purpose of the present invention can be achieved through the following technical solutions:
一种基于最小功率损失算法的自调整神经网络能量管理方法，该方法用于增程式电动汽车的能量管理，所述的增程式电动汽车包括整车控制器、CAN总线、能量源、能量控制器和汽车动力附件，所述的整车控制器分别通过CAN总线连接能 量控制器和汽车动力附件，所述的能量源与能量控制器连接，所述的方法包括以下步骤： A self-adjusting neural network energy management method based on a minimum power loss algorithm, the method is used for energy management of an extended-range electric vehicle, and the extended-range electric vehicle includes a vehicle controller, a CAN bus, an energy source, and an energy controller And automobile power accessory, described whole vehicle controller is connected energy controller and automobile power accessory by CAN bus respectively, and described energy source is connected with energy controller, and described method comprises the following steps:
1)整车控制器通过CAN总线向汽车动力附件发送访问信号，从汽车动力附件获取能量管理策略计算所需数据，所述的数据包括整车需求功率和蓄电池SOC，整车控制器同时采集当前的能量源的实时效率值； 1) The vehicle controller sends an access signal to the vehicle power accessories through the CAN bus, and obtains the data required for energy management strategy calculation from the vehicle power accessories. The data includes the vehicle demand power and battery SOC, and the vehicle controller collects the current The real-time efficiency value of the energy source;
2)整车控制器判断是否已接收到完整的数据，若是，则执行步骤3)，若否，则返回步骤1)； 2) The vehicle controller judges whether the complete data has been received, if so, execute step 3), if not, return to step 1);
3)整车控制器判断是否接收到需更新神经网络的指令，若是，则更新神经网络后执行步骤4)，若否，则执行步骤4)； 3) The vehicle controller judges whether an instruction to update the neural network is received, if so, execute step 4) after updating the neural network, if not, execute step 4);
4)整车控制器根据接收到的能量管理策略计算所需数据，通过神经网络计算出当前最优功率分配； 4) The vehicle controller calculates the required data according to the received energy management strategy, and calculates the current optimal power distribution through the neural network;
5)使用功率增益系数对神经网络计算出的最优功率分配进行修正； 5) Use the power gain coefficient to correct the optimal power distribution calculated by the neural network;
6)整车控制器通过CAN总线向能量控制器发送功率分配结果，完成整车控制器对混合动力系统中各能量源输出功率的分配控制。 6) The vehicle controller sends the power distribution result to the energy controller through the CAN bus, and completes the distribution control of the vehicle controller to the output power of each energy source in the hybrid system. the
所述的汽车动力附件包括散热子系统和空调子系统。 The vehicle power accessory includes a cooling subsystem and an air conditioning subsystem. the
所述的步骤3)中的指令包括驾驶员指定更新指令和程序自动更新指令。 The instructions in step 3) include driver-specified update instructions and program automatic update instructions. the
所述的步骤3)中更新神经网络的具体步骤包括： The specific steps of updating the neural network in the described step 3) include:
a)通过CAN总线获取相关数据，根据如下动力系统瞬时功率损失公式计算出不同功率分配下的功率损失， a) Obtain relevant data through the CAN bus, and calculate the power loss under different power allocations according to the following power system instantaneous power loss formula,
式中，PRE为增程器输出功率，ηPE_max为增程器效率最高值，ηRE为增程器当前功率下效率值， 为增程器平均效率值，Pb为蓄电池输出功率，ηdis为蓄电池放电效率，ηchr为蓄电池充电效率， 为蓄电池平均放电效率， 为蓄电池平均充电效率，ηDC-DC为DC-DC转换器效率； In the formula, P RE is the output power of the range extender, η PE_max is the highest efficiency value of the range extender, and η RE is the efficiency value of the range extender at the current power, is the average efficiency value of the range extender, P b is the output power of the battery, ηdis is the discharge efficiency of the battery, ηchr is the charging efficiency of the battery, is the average discharge efficiency of the battery, is the average charging efficiency of the storage battery, and η DC-DC is the efficiency of the DC-DC converter;
b)根据在线计算的结果，对神经网络进行训练； b) According to the result of online calculation, train the neural network;
c)用训练所得的新神经网络替代原神经网络。 c) Replace the original neural network with the new neural network obtained from training. the
所述的能量源包括增程器和蓄电池，所述的增程器通过DC-DC转换器与蓄电池连接。 The energy source includes a range extender and a battery, and the range extender is connected to the battery through a DC-DC converter. the
所述的步骤4)中通过神经网络计算出当前最优功率分配具体包括以下步骤： Calculating the current optimal power distribution through the neural network in the described step 4) specifically includes the following steps:
a)将通过CAN总线获得的数据传送给神经网络； a) Send the data obtained through the CAN bus to the neural network;
b)通过神经网络计算出当前最优功率分配，包括增程器输出功率。 b) Calculate the current optimal power allocation through the neural network, including the output power of the range extender. the
所述的步骤5)中的修正具体是：将神经网络计算出的增程器输出功率与增程器功率增益系数K相乘得到调节后的增程器输出功率；所述的K通过以下公式计算： The correction in said step 5) is specifically: the output power of the range extender calculated by the neural network is multiplied by the power gain coefficient K of the range extender to obtain the adjusted output power of the range extender; the K is obtained by the following formula calculate:
Pdis为增程器与蓄电池一起放电时的最小功率，Padj为系数调节功率，REmax为增程器能量最大储量，REcons为增程器能量消耗量。 P dis is the minimum power when the range extender and the battery are discharged together, P adj is the coefficient to adjust the power, RE max is the maximum energy reserve of the range extender, and RE cons is the energy consumption of the range extender.
与现有技术相比，本发明具有以下优点： Compared with prior art, the present invention has the following advantages:
1)本发明基于瞬时功率最小算法并结合自调整神经网络控制增程式电动汽车中各能量源的输出功率，具有燃油经济性好、易于实车实现等特点； 1) The present invention is based on the instantaneous power minimum algorithm combined with the self-adjusting neural network to control the output power of each energy source in the extended-range electric vehicle, which has the characteristics of good fuel economy and easy realization in real vehicles;
2)本发明能根据动力系统效率在使用过程中的改变，做出相应调整； 2) The present invention can make corresponding adjustments according to changes in power system efficiency during use;
3)本发明所采用的能量管理方法可以方便的应用于内燃机——蓄电池，燃料电池发动机——蓄电池等混合动力系统，具有良好的扩展性。 3) The energy management method adopted in the present invention can be conveniently applied to hybrid power systems such as internal combustion engine-battery, fuel cell engine-battery, etc., and has good scalability. the
附图说明 Description of drawings
图1为本发明方法的流程示意图； Fig. 1 is a schematic flow sheet of the inventive method;
图2为实施例中增程式电动汽车的结构示意图。 Fig. 2 is a schematic structural diagram of the extended-range electric vehicle in the embodiment. the
具体实施方式 Detailed ways
下面结合附图和具体实施例对本发明进行详细说明。 The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. the
实施例 Example
如图1所示，一种基于最小功率损失算法的自调整神经网络能量管理方法，该方法用于增程式电动汽车的能量管理。该方法基于瞬时优化能量管理策略中的瞬时功率最小算法，推导出针对目标混合动力系统的数学公式，确定了影响能量分配的两个主要影响因素：整车需求功率和蓄电池SOC，再配合实车采集到两个能量源的实时效率值，采用在线计算的方法，将能量管理策略提炼为一组双输入、单输出的能量管理规则。采用BP神经网络对所提炼出的能量管理规则进行拟合，再使用训练出的神经网络建立用于能量管理的神经网络控制器。此外，根据两个能量源实 时效率值的不断改变，控制器将根据指令采用实时计算并训练神经网络的方法，不断更新控制器内部的神经网络以反映实车的真实的能量源效率状态。所述指令包括驾驶员指定更新指令和程序设定的自动更新指令。该控制器根据混合动力系统的需求转矩和蓄电池SOC等参数实时控制混合动力系统的功率输出分配，以提高混合动力系统的燃料经济性。 As shown in Figure 1, a self-adjusting neural network energy management method based on the minimum power loss algorithm is used for energy management of extended-range electric vehicles. This method is based on the instantaneous power minimum algorithm in the instantaneous optimal energy management strategy, deduces the mathematical formula for the target hybrid system, and determines two main factors that affect energy distribution: the vehicle demand power and the battery SOC, and then cooperates with the actual vehicle The real-time efficiency values of two energy sources are collected, and the energy management strategy is refined into a set of energy management rules with double input and single output by using online calculation method. The BP neural network is used to fit the extracted energy management rules, and then the trained neural network is used to establish a neural network controller for energy management. In addition, according to the continuous change of the real-time efficiency values of the two energy sources, the controller will adopt the method of real-time calculation and training of the neural network according to the instructions, and continuously update the neural network inside the controller to reflect the real energy source efficiency status of the actual vehicle. The instructions include a driver-specified update instruction and an automatic update instruction for program settings. The controller controls the power output distribution of the hybrid system in real time according to the required torque of the hybrid system and battery SOC and other parameters, so as to improve the fuel economy of the hybrid system. the
如图2所示，本实施例中的增程式电动汽车包括整车控制器(VMS)5、CAN总线、增程器1、蓄电池2、增程器控制器3、蓄电池控制器4和汽车动力附件6，所述的整车控制器分别通过CAN总线连接增程器控制器3、蓄电池控制器4和汽车动力附件6，所述的增程器1与增程器控制器3连接，所述的蓄电池2与蓄电池控制器4连接，所述的增程器1通过DC-DC转换器与蓄电池2连接。能量管理策略的控制参数通过CAN总线在VMS与能量源的控制器之间完成数据交互。VMS从CAN总线获得能量管理策略计算所需数据后，通过内部的神经网络计算出最优功率分配，并使用功率增益系数k对神经网络计算出的最优功率分配进行修正以获得最终的功率分配，再通过CAN总线将这一分配发送给能量源的控制器，以完成对能量源的输出功率控制。 As shown in Figure 2, the range-extender electric vehicle in this embodiment includes a vehicle controller (VMS) 5, a CAN bus, a range extender 1, a battery 2, a range extender controller 3, a battery controller 4 and a vehicle power Attachment 6, the vehicle controller is respectively connected to the range extender controller 3, the battery controller 4 and the vehicle power accessory 6 through the CAN bus, the range extender 1 is connected to the range extender controller 3, and the The battery 2 is connected to the battery controller 4, and the range extender 1 is connected to the battery 2 through a DC-DC converter. The control parameters of the energy management strategy complete the data exchange between the VMS and the controller of the energy source through the CAN bus. After VMS obtains the data required for energy management strategy calculation from the CAN bus, it calculates the optimal power allocation through the internal neural network, and uses the power gain coefficient k to correct the optimal power allocation calculated by the neural network to obtain the final power allocation , and then send this assignment to the controller of the energy source through the CAN bus to complete the output power control of the energy source. the
上述基于最小功率损失算法的自调整神经网络能量管理方法具体包括以下步骤： The above self-adjusting neural network energy management method based on the minimum power loss algorithm specifically includes the following steps:
在步骤401中，整车控制器(VMS)向CAN总线询问能量管理策略计算所需数据，包括需求功率、蓄电池SOC等，所述的能量管理策略计算所需数据根据基于最小功率损失算法获得。然后执行步骤402。 In step 401, the vehicle controller (VMS) asks the CAN bus for data required for energy management strategy calculation, including required power, battery SOC, etc., and the data required for energy management strategy calculation is obtained based on an algorithm based on minimum power loss. Then step 402 is executed. the
在步骤402中，从各汽车附件向CAN总线传输的数据中选取能量管理策略计算所需数据发送给VMS，整车控制器同时通过CAN总线采集当前的能量源的实时效率值。然后执行步骤403。 In step 402, the data required for energy management strategy calculation is selected from the data transmitted from each car accessory to the CAN bus and sent to the VMS, and the vehicle controller collects the real-time efficiency value of the current energy source through the CAN bus at the same time. Then step 403 is executed. the
在步骤403中，VMS判断是否已接接收到完整的数据，如果完整，执行步骤404；如果不完整，返回步骤402。 In step 403, the VMS judges whether complete data has been received, if complete, execute step 404; if incomplete, return to step 402. the
在步骤404中，VMS判断是否接收到需更新神经网络的指令，若是，则执行步骤405，若否，则执行步骤406。 In step 404 , the VMS judges whether an instruction to update the neural network is received, if yes, execute step 405 , if not, execute step 406 . the
在步骤405中，更新神经网络，然后执行步骤406。 In step 405, the neural network is updated, and then step 406 is performed. the
所述的更新神经网络的具体步骤包括： The concrete steps of described update neural network include:
a)通过CAN总线获取相关数据，根据如下动力系统瞬时功率损失公式计算 出不同功率分配下的功率损失， a) Obtain relevant data through the CAN bus, and calculate the power loss under different power allocations according to the following power system instantaneous power loss formula,
式中，PRE为增程器输出功率，ηRE_max为增程器效率最高值，ηRE为增程器当前功率下效率值， 为增程器平均效率值，Pb为蓄电池输出功率，ηdis为蓄电池放电效率，ηchr为蓄电池充电效率， 为蓄电池平均放电效率， 为蓄电池平均充电效率，ηDC-DC为DC-DC转换器效率； In the formula, P RE is the output power of the range extender, η RE_max is the highest efficiency value of the range extender, and η RE is the efficiency value of the range extender at the current power, is the average efficiency value of the range extender, P b is the output power of the battery, ηdis is the discharge efficiency of the battery, ηchr is the charging efficiency of the battery, is the average discharge efficiency of the battery, is the average charging efficiency of the storage battery, and η DC-DC is the efficiency of the DC-DC converter;
b)根据在线计算的结果，对神经网络进行训练； b) According to the result of online calculation, train the neural network;
c)用训练所得的新神经网络替代原神经网络。 c) Replace the original neural network with the new neural network obtained from training. the
在步骤406中，VMS根据所收到的能量管理策略计算所需数据，通过当前的神经网络计算出最优功率分配。然后进入步骤407。 In step 406, the VMS calculates the required data according to the received energy management strategy, and calculates the optimal power allocation through the current neural network. Then go to step 407. the
通过神经网络计算出当前最优功率分配具体包括以下步骤： Calculating the current optimal power allocation through the neural network specifically includes the following steps:
a)将通过CAN总线获得的数据传送给神经网络； a) Send the data obtained through the CAN bus to the neural network;
b)通过神经网络计算出当前最优功率分配，包括增程器输出功率。 b) Calculate the current optimal power allocation through the neural network, including the output power of the range extender. the
在步骤407中，使用功率增益系数K对神经网络计算出的最优功率分配进行修正；然后进入步骤408。 In step 407, the optimal power allocation calculated by the neural network is corrected by using the power gain coefficient K; and then enters step 408. the
为调节增程器能量剩余量与电能剩余量之间的平衡，引入增程器功率增益系数K，将神经网络计算出的增程器输出功率与K相乘得到调节后的增程器输出功率。系数K要能使增程器能量剩余量时，放出较大功率；增程器能量剩余量较少时，放出原始功率。为实现动态调整，将增程器功率增益系数设计为与增程器能量剩余量有关的幂函数，K通过以下公式计算： In order to adjust the balance between the remaining energy of the range extender and the remaining electric energy, the power gain coefficient K of the range extender is introduced, and the output power of the range extender calculated by the neural network is multiplied by K to obtain the adjusted output power of the range extender . The coefficient K should be able to make the range extender release more power when the remaining energy of the range extender; when the remaining energy of the range extender is small, release the original power. In order to realize dynamic adjustment, the power gain coefficient of the range extender is designed as a power function related to the remaining energy of the range extender, and K is calculated by the following formula:
Pdis为增程器与蓄电池一起放电时的最小功率，Padj为系数调节功率，REmax为增程器能量最大储量，REcons为增程器能量消耗量。 P dis is the minimum power when the range extender and the battery are discharged together, P adj is the coefficient to adjust the power, RE max is the maximum energy reserve of the range extender, and RE cons is the energy consumption of the range extender.
在步骤408中，VMS通过CAN总线向各能量源的控制器发送功率分配结果，完成控制端VMS对混合动力系统各能量源输出功率的分配。 In step 408, the VMS sends the power distribution results to the controllers of each energy source through the CAN bus, and completes the distribution of the output power of each energy source of the hybrid power system by the control terminal VMS. the
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