技术领域technical field
本发明属于车辆能量管理技术领域，特别涉及一种基于缩小SOC可行域的随机动态规划能量管理策略优化方法。The invention belongs to the technical field of vehicle energy management, in particular to a stochastic dynamic programming energy management strategy optimization method based on narrowing the SOC feasible region.
背景技术Background technique
面对全球范围日益严峻的能源形势和环保压力，发展新能源汽车已成为社会可持续发展的重要战略及市场新的增长点。油电型混合动力汽车作为新能源车的一种，其技术相对成熟，且市场发展前景良好，成为传统车向新能源汽车过渡的最好方式。作为新型的多能源交通工具，混合动力汽车(hybrid electric vehicle,HEV)性能与其能量管理控制策略密切相关。如何根据预测路况、汽车功率需求、电池荷电状态(stage of charge,SOC)等信息来确定车辆应处的工作模式以尽可能地减少等效燃油消耗量，成为解决能量管理控制策略的关键。目前，基于动态规划(dynamicprogramming,DP)算法的能量管理控制策略可以得到理论上全局最优的燃油经济性，作为衡量其它控制策略节能效果的标准。由于动态规划控制策略的实现需要依靠已知的驾驶工况信息，且算法计算负荷大，无法实现实时控制，因而开发基于随机动态规划算法的能量管理控制策略。Faced with the increasingly severe energy situation and environmental protection pressure on a global scale, the development of new energy vehicles has become an important strategy for sustainable social development and a new growth point in the market. As a kind of new energy vehicle, the gasoline-electric hybrid vehicle has relatively mature technology and good market development prospects. It has become the best way to transition from traditional vehicles to new energy vehicles. As a new type of multi-energy vehicle, the performance of hybrid electric vehicle (HEV) is closely related to its energy management control strategy. How to determine the working mode of the vehicle according to the predicted road conditions, vehicle power demand, battery state of charge (SOC) and other information to reduce the equivalent fuel consumption as much as possible has become the key to solving the energy management control strategy. At present, the energy management control strategy based on dynamic programming (DP) algorithm can obtain the theoretical global optimal fuel economy, which is used as the standard to measure the energy saving effect of other control strategies. Since the realization of the dynamic programming control strategy needs to rely on the known driving condition information, and the algorithm has a large computational load, real-time control cannot be realized, so an energy management control strategy based on the stochastic dynamic programming algorithm is developed.
基于随机动态规划(stochastic dynamic programming,SDP)的能量管理方法利用现有的标准工况或者车辆历史行驶数据作为随机模型的样本，建立工况的统计模型；基于动态规划算法解决以该统计模型表示的能量管理问题。该方法沿用动态规划多阶段全局寻优思想，引入未来工况变化的统计特性，得到的最优策略是一种平均意义上期望成本最小的策略。随机动态规划方法虽然解决了动态规划需要提前已知车辆全局工况信息的问题，但无论是有限时域SDP还是无限时域SDP控制策略，其实际应用往往受限于算法的计算效率。因此，如何有效减少SDP算法的计算负荷以提高其计算效率成为相关领域的研究热点。SDP控制策略的求解过程可以解释为一种针对具有离散状态量、离散控制量的多阶段代价寻优过程，随着状态量离散精度的增加，算法的计算时间呈指数倍增加。由此可见，状态变量的数量是影响算法计算效率的关键因素，因而，如何减少算法运行过程中所需搜索状态数量是需要解决的关键问题。The energy management method based on stochastic dynamic programming (SDP) uses the existing standard operating conditions or historical vehicle driving data as samples of the stochastic model to establish a statistical model of operating conditions; energy management issues. The method follows the idea of multi-stage global optimization in dynamic programming, and introduces the statistical characteristics of future operating conditions. The optimal strategy obtained is a strategy with the smallest expected cost in the average sense. Although the stochastic dynamic programming method solves the problem that dynamic programming needs to know the vehicle's global operating condition information in advance, its practical application is often limited by the computational efficiency of the algorithm, whether it is a finite time domain SDP or an infinite time domain SDP control strategy. Therefore, how to effectively reduce the computational load of the SDP algorithm to improve its computational efficiency has become a research hotspot in related fields. The solution process of the SDP control strategy can be interpreted as a multi-stage cost optimization process with discrete state quantities and discrete control quantities. With the increase of the discrete precision of the state quantities, the calculation time of the algorithm increases exponentially. It can be seen that the number of state variables is a key factor affecting the computational efficiency of the algorithm. Therefore, how to reduce the number of search states required during the operation of the algorithm is a key issue that needs to be solved.
发明内容SUMMARY OF THE INVENTION
本发明的目的之一是提供一种在有限时域下基于缩小SOC可行域的随机动态规划能量管理策略优化方法，依据不同工况下的SOC最优轨迹域，缩小SOC可行域，以快速实现在有限时域下的SDP控制策略的在线应用，提高整车燃油经济性。One of the objectives of the present invention is to provide a stochastic dynamic programming energy management strategy optimization method based on narrowing the SOC feasible region in a limited time domain. Online application of SDP control strategy in limited time domain to improve vehicle fuel economy.
本发明的目的之二是提供一种在无限时域下基于缩小SOC可行域的随机动态规划能量管理策略优化方法，以DP控制策略得到的SOC最优轨迹为基准，根据不同工况下DP控制策略与SDP控制策略的SOC最优轨迹的距离差，缩小SOC可行域，以快速实现在无限时域下的SDP控制策略的在线应用，提高整车燃油经济性。The second purpose of the present invention is to provide a stochastic dynamic programming energy management strategy optimization method based on narrowing the SOC feasible region in an infinite time domain, taking the SOC optimal trajectory obtained by the DP control strategy as the benchmark, and according to the DP control strategy under different working conditions The distance difference between the SOC optimal trajectory of the strategy and the SDP control strategy reduces the feasible SOC region, so as to quickly realize the online application of the SDP control strategy in the infinite time domain, and improve the fuel economy of the whole vehicle.
本发明提供的技术方案为：The technical scheme provided by the present invention is:
一种基于缩小SOC可行域的随机动态规划能量管理策略优化方法，包括如下步骤：A stochastic dynamic programming energy management strategy optimization method based on narrowing the SOC feasible region, comprising the following steps:
步骤一、基于动态规划算法，分别得到多种工况下的第一SOC最优轨迹和多种工况下表征全局最优燃油经济性的SOC最优轨迹域；以及在有限时域下，基于随机动态规划算法，得到多种工况下的第二SOC最优轨迹；Step 1. Based on the dynamic programming algorithm, the first SOC optimal trajectory under various operating conditions and the SOC optimal trajectory domain representing the global optimal fuel economy under various operating conditions are obtained respectively; and in the limited time domain, based on Stochastic dynamic programming algorithm to obtain the optimal trajectory of the second SOC under various working conditions;
步骤二、分别计算多种工况下所述第一SOC最优轨迹和所述第二SOC最优轨迹上的同一时刻对应的状态点的距离差值；以及分别计算多种工况下的所述SOC最优轨迹域的宽度值；Step 2: Calculate the distance difference between the state points corresponding to the same moment on the first SOC optimal trajectory and the second SOC optimal trajectory under various operating conditions; and separately calculate all the operating conditions. the width value of the SOC optimal trajectory domain;
步骤三、根据所述宽度值、所述距离差值及所述第一SOC最优轨迹，得到SOC可行域。Step 3: Obtain an SOC feasible region according to the width value, the distance difference value and the first SOC optimal trajectory.
优选的是，所述的基于缩小SOC可行域的随机动态规划能量管理策略优化方法，还包括：Preferably, the stochastic dynamic programming energy management strategy optimization method based on narrowing the SOC feasible region further includes:
分别以起始SOC和终止SOC为临界点，通过电池最大充电电流和电池最大放电电流确定临界区域，得到修正的SOC可行域。Taking the starting SOC and the ending SOC as the critical points, respectively, the critical region is determined by the maximum charging current and the maximum discharging current of the battery, and the feasible region of the modified SOC is obtained.
优选的是，所述临界区域的确定过程包括：Preferably, the process of determining the critical region includes:
分别以起始SOC、终止SOC为基点，得到分别表征电池最大充电电流的直线和电池最大放电电流的直线，与所述SOC可行域上下边界相交，得到修正的SOC可行域；其中，Taking the starting SOC and ending SOC as the base points, respectively, a straight line representing the maximum charging current of the battery and a straight line representing the maximum discharging current of the battery are obtained, which intersect with the upper and lower boundaries of the SOC feasible region to obtain the modified SOC feasible region; among them,
所述表征电池最大充电电流的直线斜率为：The slope of the straight line characterizing the maximum charging current of the battery is:
以及表征电池最大充电电流的直线斜率为：And the slope of the straight line characterizing the maximum charging current of the battery is:
其中，Icharge，Idischarge分别为电池最大充电电流和最大放电电流，当电池充电时，Icharge＜0，电池放电时，Idischarge＞0；Qmax为电池容量。Among them, Icharge and Idischarge are the maximum charging current and maximum discharging current of the battery, respectively. When the battery is charging, Icharge <0, and when the battery is discharging, Idischarge >0; Qmax is the battery capacity.
优选的是，在所述步骤二中，得到多种工况下对应全局最优燃油经济性的SOC最优轨迹域的方法，包括如下步骤：Preferably, in the second step, the method for obtaining the SOC optimal trajectory domain corresponding to the global optimal fuel economy under various operating conditions includes the following steps:
步骤1、基于多阶段寻优的动态规划算法，得到多条第三SOC最优轨迹，记录所有第三SOC最优轨迹所经过的状态点，形成最优SOC点的集合域；Step 1. Based on a multi-stage optimization dynamic programming algorithm, a plurality of third SOC optimal trajectories are obtained, and the state points passed by all the third SOC optimal trajectories are recorded to form a set domain of optimal SOC points;
步骤2、将原始SOC可行域内的所有SOC状态点编号，并按编号顺序求解每一时刻对应的所有SOC离散点至起点的最优值函数，并记录并存当前时刻的离散点所在的最优轨迹上的前一时刻SOC状态点，直至顺序运行至终点；Step 2. Number all SOC state points in the original SOC feasible region, and solve the optimal value function from all SOC discrete points corresponding to each moment to the starting point in the order of numbering, and record the optimal trajectory where the discrete points at the current moment are located. SOC state point at the previous moment on the SOC, until the sequence runs to the end point;
步骤3、从终点出发，逆序依次查找并存储当前时刻所有最优轨迹上的SOC状态点，直至逆序运行至起点，形成SOC最优轨迹域。Step 3. Starting from the end point, search and store the SOC state points on all optimal trajectories at the current moment in reverse order, and run to the starting point in reverse order to form the SOC optimal trajectory domain.
优选的是，在所述步骤三中，得到所述SOC可行域的方法为：Preferably, in the step 3, the method for obtaining the SOC feasible region is:
分别获取多工况下的所述SOC最优轨迹域的最大宽度值L1、L2、……Ln，并分别计算S1、S2、……Sn与L1、L2、……Ln的偏差率D1、D2、……Dn；统计得到D1、D2、……Dn中的最大值Dmax，并且根据最大值Dmax得到修正偏差率Damend；Obtain the maximum widthvaluesL1 ,L2,...... The deviation rate D1 , D2 , ... Dn of ... Ln ; the maximum value Dmax among D1 , D2 , ... Dn is obtained by statistics, and the corrected deviation rate Damend is obtained according to the maximum value Dmax ;
在第i种工况下，以所述第一SOC最优轨迹为基准线，在所述基准线的两侧分别增加半径ri的宽度，得到SOC可行域；Under thei -th operating condition, the first SOC optimal trajectory is used as the reference line, and the width of the radius ri is respectively increased on both sides of the reference line to obtain the SOC feasible region;
其中，ri＝Li×(1+Damend)，i＝1、2……n；in, ri =Li ×(1+Damend ), i=1, 2...n;
并且Damend>Dmax；andDamend >Dmax;
n表示工况总数，S1、S2、……Sn分别表示多种工况下第一SOC最优轨迹和所述第二SOC最优轨迹最大距离差值。n represents the total number of operating conditions, and S1 , S2 , ... Sn represent the maximum distance difference between the first SOC optimal trajectory and the second SOC optimal trajectory under multiple operating conditions, respectively.
优选的是，所述的基于缩小SOC可行域的随机动态规划能量管理策略优化方法，还包括：在所述SOC可行域中，以各时刻的各离散点为基点，以电池最大放电电流为斜率画直线，得到下一时刻的离散点。Preferably, the stochastic dynamic programming energy management strategy optimization method based on narrowing the SOC feasible region further includes: in the SOC feasible region, taking each discrete point at each moment as the base point, and taking the maximum discharge current of the battery as the slope Draw a straight line to get the discrete points at the next moment.
优选的是，所述多种工况包括：NEDC、UDDS、Japan 1015、FTP72和HWEET工况。Preferably, the multiple working conditions include: NEDC, UDDS, Japan 1015, FTP72 and HWEET working conditions.
优选的是，Damend＝20％。Preferably,Damend = 20%.
一种基于缩小SOC可行域的随机动态规划能量管理策略优化方法，在无限时域下，基于动态规划算法，得到多种工况下的第一SOC最优轨迹；以及基于随机动态规划算法，得到多种工况下的第二SOC最优轨迹；分别计算多种工况下所述第一SOC最优轨迹和所述第二SOC最优轨迹上的同一时刻对应的状态点的距离差值；并且根据所述距离差值及所述第一SOC最优轨迹，得到多种工况下的随机动态规划算法的SOC可行域。A stochastic dynamic programming energy management strategy optimization method based on narrowing the SOC feasible region, in the infinite time domain, based on the dynamic programming algorithm, the first SOC optimal trajectory under various working conditions is obtained; and based on the stochastic dynamic programming algorithm, the obtained the second SOC optimal trajectory under various operating conditions; separately calculating the distance difference between the first SOC optimal trajectory and the state point corresponding to the same moment on the second SOC optimal trajectory under various operating conditions; And according to the distance difference and the first SOC optimal trajectory, the SOC feasible region of the stochastic dynamic programming algorithm under various working conditions is obtained.
优选的是，分别获取多种工况下的所述第一SOC最优轨迹和所述第二SOC最优轨迹最大距离差值S1、S2、……Sn，统计得到S1、S2、……Sn中的最大值Smax，并且根据最大值Smax得到SOC搜索区域的半径R；在每种工况下，以所述第一SOC最优轨迹为基准线，在所述基准线的两侧分别增加半径R的宽度，得到随机动态规划算法的SOC可行域；Preferably, the maximum distance differences S1 , S2 , ... Sn of the first SOC optimal trajectory and the second SOC optimal trajectory under multiple operating conditions are obtained respectively, and S1 , S are obtained by statistics.2 , the maximum value Smax inSn , and the radius R of the SOC search area is obtained according to the maximum value Smax ; Increase the width of the radius R on both sides of the baseline to obtain the SOC feasible region of the stochastic dynamic programming algorithm;
其中，R>Smax。where R>Smax .
本发明的有益效果是：The beneficial effects of the present invention are:
本发明提供的基于缩小SOC可行域的随机动态规划能量管理策略优化方法，在有限时域下，基于全局寻域算法得到DP控制策略下SOC最优轨迹域，并在此基础上形成条带状SOC可行域，缩小有限时域SDP状态搜索范围，减少算法运行过程中所需搜索状态点数量，以提高算法的计算效率；同时基于DP控制策略所得SOC最优轨迹域形成用于SDP求解的SOC可行域，保证了混合动力汽车能量管理控制策略的全局次优性，改善全局范围内的燃油经济性；此外，该方法可以依据不同工况离线获取其最优域及用于SDP的SOC可行域范围，以快速实现SDP控制策略的在线应用。The stochastic dynamic programming energy management strategy optimization method based on narrowing the SOC feasible region provided by the present invention obtains the SOC optimal trajectory domain under the DP control strategy based on the global domain search algorithm in the finite time domain, and forms a stripe shape on this basis. The SOC feasible region narrows the limited time domain SDP state search range and reduces the number of search state points required during the operation of the algorithm to improve the computational efficiency of the algorithm; at the same time, the SOC optimal trajectory domain obtained based on the DP control strategy forms the SOC for SDP solution. Feasible domain, which ensures the global suboptimal of the hybrid electric vehicle energy management control strategy and improves the fuel economy in the global scope; in addition, this method can obtain its optimal domain offline and the SOC feasible domain for SDP according to different operating conditions. range to quickly realize the online application of the SDP control strategy.
本发明提供的基于缩小SOC可行域的随机动态规划能量管理策略优化方法，在无限时域下，以DP控制策略得到的SOC最优轨迹为基准，根据不同工况下DP控制策略与SDP距离的SOC最优轨迹的距离差，缩小SOC可行域，能够快速实现在无限时域下的SDP控制策略的在线应用，提高整车燃油经济性。The stochastic dynamic programming energy management strategy optimization method based on narrowing the SOC feasible region provided by the present invention, in the infinite time domain, takes the SOC optimal trajectory obtained by the DP control strategy as the benchmark, and according to the difference between the DP control strategy and the SDP distance under different working conditions The distance difference of the SOC optimal trajectory reduces the SOC feasible region, which can quickly realize the online application of the SDP control strategy in the infinite time domain, and improve the fuel economy of the whole vehicle.
附图说明Description of drawings
图1为本发明所述的随机动态规划能量管理策略优化方法的结构关联图。FIG. 1 is a structural correlation diagram of the stochastic dynamic programming energy management strategy optimization method according to the present invention.
图2为NEDC工况下DP与无限时域SDP所得SOC轨迹间距计算的示意图。Figure 2 is a schematic diagram of the calculation of the distance between SOC traces obtained by DP and infinite time domain SDP under NEDC conditions.
图3为UDDS工况下DP与无限时域SDP所得SOC轨迹间距计算的示意图。Figure 3 is a schematic diagram of the calculation of the distance between SOC traces obtained by DP and infinite time domain SDP under UDDS conditions.
图4为NEDC工况下SDP控制策略状态搜索域的示意图。FIG. 4 is a schematic diagram of the state search domain of the SDP control strategy under the NEDC condition.
图5为UDDS工况下SDP控制策略状态搜索域的示意图。FIG. 5 is a schematic diagram of the state search domain of the SDP control strategy under the UDDS working condition.
图6为UDDS工况下DP与有限时域SDP所得SOC轨迹间距计算的示意图。Figure 6 is a schematic diagram of the calculation of the distance between SOC traces obtained by DP and finite time domain SDP under UDDS conditions.
图7为UDDS工况下SDP控制策略状态搜索域的示意图。FIG. 7 is a schematic diagram of the state search domain of the SDP control strategy under the UDDS working condition.
图8为全局寻域算法程序说明图。FIG. 8 is an explanatory diagram of a global search algorithm program.
图9为NEDC工况下基于改进DP形成的SOC最优轨迹域示意图。Figure 9 is a schematic diagram of the optimal trajectory domain of SOC formed based on the improved DP under NEDC conditions.
图10为UDDS工况下基于改进DP形成的SOC最优轨迹域示意图。Figure 10 is a schematic diagram of the optimal trajectory domain of SOC formed based on the improved DP under UDDS conditions.
图11为UDDS工况下SOC最优域宽度计算的示意图。Figure 11 is a schematic diagram of the calculation of the optimal SOC domain width under UDDS conditions.
图12为UDDS工况下基于SOC最优轨迹域形成的条带状SOC可行域示意图。FIG. 12 is a schematic diagram of a strip-shaped SOC feasible region formed based on the SOC optimal trajectory domain under UDDS conditions.
图13为SOC可行域范围优化的示意图。FIG. 13 is a schematic diagram of SOC feasible region range optimization.
图14为人为离散SOC网格造成误差的示意图。FIG. 14 is a schematic diagram of errors caused by artificially discrete SOC grids.
图15为改进SOC离散域的离散示意图。Figure 15 is a discrete schematic diagram of the improved SOC discrete domain.
具体实施方式Detailed ways
下面结合附图对本发明做进一步的详细说明，以令本领域技术人员参照说明书文字能够据以实施。The present invention will be further described in detail below with reference to the accompanying drawings, so that those skilled in the art can implement it with reference to the description.
如图1所示，本发明提供了一种基于缩小SOC可行域的随机动态规划能量管理策略优化方法，具体实现过程如下：As shown in FIG. 1 , the present invention provides a stochastic dynamic programming energy management strategy optimization method based on narrowing the SOC feasible region, and the specific implementation process is as follows:
一、基于条带状SOC可行域的无限时域SDP优化1. Infinite time-domain SDP optimization based on the feasible region of strip SOC
在无限时域下，基于动态规划(DP)和随机动态规划(SDP)算法，分别得到NEDC、UDDS、Japan 1015、FTP72、HWEET等多组工况下的SOC最优轨迹，计算同一时刻两轨迹间SOC状态点的距离。统计多组工况下基于DP与无限时域SDP所得SOC最优轨迹上状态点间距离的最大值，如表1所示。其中，以NEDC工况及UDDS工况为例，如图2所示，为NEDC工况下基于DP与无限时域SDP所得SOC最优轨迹的最大间距计算示意图；如图3所示，为UDDS工况下基于DP与无限时域SDP所得SOC最优轨迹的最大间距计算示意图。In the infinite time domain, based on the dynamic programming (DP) and stochastic dynamic programming (SDP) algorithms, the optimal SOC trajectories under multiple sets of operating conditions such as NEDC, UDDS, Japan 1015, FTP72, and HWEET were obtained respectively, and the two trajectories at the same time were calculated. The distance between the SOC state points. The maximum distance between state points on the optimal SOC trajectory obtained based on DP and infinite time domain SDP under multiple sets of operating conditions is calculated, as shown in Table 1. Among them, taking NEDC and UDDS conditions as examples, as shown in Figure 2, it is a schematic diagram of the calculation of the maximum distance of the optimal SOC trajectory based on DP and infinite time domain SDP under NEDC conditions; as shown in Figure 3, for UDDS Schematic diagram of the maximum distance calculation of the optimal SOC trajectory obtained based on DP and infinite time domain SDP under working conditions.
表1不同工况下基于DP与无限时域SDP所得SOC最优轨迹的最大间距统计表Table 1 Statistics of the maximum spacing of SOC optimal trajectories based on DP and infinite time domain SDP under different operating conditions
由表1可知，不同工况下，基于DP及无限时域SDP控制策略得到的SOC最优轨迹上状态点间最大距离基本在0.12范围内。由此可知，无限时域SDP控制策略所得SOC最优轨迹大致位于以DP控制策略SOC最优轨迹为基准，以0.12为半径形成的条带状区域内。若以该SOC可行域作为SDP控制策略状态搜索范围，排除掉多余的SOC状态点，可使SDP在新定义的搜索域内进行寻优，进而降低SDP算法的计算负荷，提高其计算效率。同时，DP控制策略所求SOC最优轨迹代表此工况对应的全局能耗最优解，为SDP能量管理策略提供准确参考，保证SDP能量管理控制策略的全局次优性，改善全局燃油经济性。It can be seen from Table 1 that under different working conditions, the maximum distance between state points on the optimal SOC trajectory obtained based on the DP and infinite time domain SDP control strategies is basically within the range of 0.12. From this, it can be seen that the optimal SOC trajectory obtained by the infinite time domain SDP control strategy is roughly located in the strip-shaped region formed with the radius of 0.12 based on the optimal SOC trajectory of the DP control strategy. If the SOC feasible region is used as the state search range of the SDP control strategy and the redundant SOC state points are excluded, the SDP can be optimized in the newly defined search region, thereby reducing the computational load of the SDP algorithm and improving its computational efficiency. At the same time, the optimal SOC trajectory obtained by the DP control strategy represents the optimal solution of the global energy consumption corresponding to this working condition, which provides an accurate reference for the SDP energy management strategy, ensures the global suboptimality of the SDP energy management control strategy, and improves the global fuel economy. .
因此，基于DP控制策略所得的SOC最优轨迹，以此轨迹为核心，针对该轨迹上的每一状态点，在该轨迹上下宽度为0.12范围内形成的条带状SDP控制策略状态搜索域，作为无限时域SDP求解的全新状态空间。通过建立此条带状状态搜索域，限制了SOC范围，极大地缩小了SDP算法求解过程中所需搜索的状态数量，有助于快速得到SDP控制结果，进而有效改善算法计算效率。以NEDC工况及UDDS工况为例，如图4所示，为NEDC工况下SDP控制策略状态搜索域；如图5所示，为UDDS工况下SDP控制策略状态搜索域。Therefore, based on the SOC optimal trajectory obtained by the DP control strategy, with this trajectory as the core, for each state point on the trajectory, the strip-shaped SDP control strategy state search domain formed within the range of the upper and lower width of the trajectory is 0.12, A completely new state space as an infinite time domain SDP solution. By establishing this strip-shaped state search domain, the SOC range is limited, and the number of states required to be searched in the process of solving the SDP algorithm is greatly reduced, which is helpful to obtain the SDP control results quickly, thereby effectively improving the computational efficiency of the algorithm. Taking NEDC and UDDS conditions as examples, as shown in Figure 4, it is the SDP control strategy state search domain under NEDC condition; as shown in Figure 5, it is the SDP control strategy state search domain under UDDS condition.
二、基于改进SOC可行域的有限时域SDP优化2. Finite time domain SDP optimization based on improved SOC feasible region
步骤一、得到有限时域下条带状SDP控制策略状态搜索域Step 1. Obtain the state search domain of the striped SDP control strategy in the limited time domain
参照无限时域下条带状SDP控制策略状态搜索域的形成过程，对比有限时域SDP控制策略及DP控制策略所得的SOC最优轨迹，计算不同工况各时刻下SOC点间最大距离，选取一个上限值，形成针对有限时域SDP控制策略的状态搜索域。统计多组不同工况下DP控制策略与无限时域SDP控制策略所得SOC轨迹上状态点间距离的最大值，如表2所示。其中，以UDDS工况为例，如图6所示，为UDDS工况下DP与有限时域SDP所得SOC轨迹间距计算示意图。Referring to the formation process of the state search domain of the strip-shaped SDP control strategy in the infinite time domain, comparing the optimal SOC trajectory obtained by the finite time domain SDP control strategy and the DP control strategy, calculate the maximum distance between SOC points under different operating conditions and various moments, and select An upper limit value that forms the state search domain for the finite-time-domain SDP control strategy. The maximum value of the distance between state points on the SOC trajectory obtained by the DP control strategy and the infinite time domain SDP control strategy under multiple groups of different operating conditions is shown in Table 2. Among them, taking the UDDS working condition as an example, as shown in Figure 6, it is a schematic diagram of the calculation of the SOC trajectory distance obtained by the DP and the finite time domain SDP under the UDDS working condition.
表2有限时域SDP与DP所得SOC轨迹最大间距统计表Table 2 Statistical table of maximum distance between SOC traces obtained by SDP and DP in finite time domain
由表2可知，不同工况下，有限时域SDP控制策略与DP控制策略所得SOC轨迹间距最大值基本保证在0.07范围内。由此，可将0.07作为有限时域SDP控制策略状态搜索域的宽度。与条带状无限时域SDP控制策略状态搜索域的形成过程类似，以DP控制策略所得SOC最优轨迹为核心，以0.07为上下宽度形成条带状SDP控制策略状态搜索域。以UDDS工况为例，如图7所示，为UDDS工况下SDP控制策略状态搜索域。It can be seen from Table 2 that under different working conditions, the maximum value of the SOC trajectory distance obtained by the finite time domain SDP control strategy and the DP control strategy is basically guaranteed to be within the range of 0.07. Therefore, 0.07 can be taken as the width of the finite-time-domain SDP control strategy state search domain. Similar to the formation process of the strip-shaped infinite time-domain SDP control strategy state search domain, the strip-shaped SDP control strategy state search domain is formed with the SOC optimal trajectory obtained by the DP control strategy as the core and 0.07 as the upper and lower width. Taking the UDDS working condition as an example, as shown in Figure 7, it is the state search domain of the SDP control strategy under the UDDS working condition.
步骤二、基于全局寻域算法，形成DP控制策略下SOC最优轨迹域Step 2. Based on the global domain search algorithm, form the optimal trajectory domain of SOC under the DP control strategy
基于传统动态规划控制策略，可以得到全局意义上整车燃油经济性最优的一条SOC轨迹、对应的最优控制序列及最低油耗值。阶段油耗计算过程中，由于阶段油耗的计算与当前阶段的需求功率、控制量及SOC变化有关，且SOC可行域离散均匀，因而同一阶段存在许多相同变化量的SOC转移。同时，基于多阶段寻优的DP求解过程可视为不同阶段代价的累积计算，因而基于全局寻优算法进行最低油耗查找时，最终得到的SOC最优轨迹不止一条。因此，开发全局寻域算法得到所有表征DP控制策略最优控制结果的SOC轨迹，其算法程序说明如图8所示。Based on the traditional dynamic programming control strategy, an SOC trajectory with the best overall vehicle fuel economy, the corresponding optimal control sequence and the minimum fuel consumption value can be obtained. During the calculation of stage fuel consumption, since the calculation of stage fuel consumption is related to the required power, control quantity and SOC changes in the current stage, and the SOC feasible region is discrete and uniform, there are many SOC transitions with the same change in the same stage. At the same time, the DP solution process based on multi-stage optimization can be regarded as the cumulative calculation of costs in different stages. Therefore, when searching for the lowest fuel consumption based on the global optimization algorithm, more than one SOC optimal trajectory is finally obtained. Therefore, a global search algorithm is developed to obtain all the SOC trajectories that characterize the optimal control results of the DP control strategy. The description of the algorithm program is shown in Figure 8.
基于全局寻优算法得到所有SOC最优轨迹需要较长时间，且随输出轨迹的增加，算法计算时间成倍增加，因此难以实现短时间内输出全部满足要求的SOC轨迹。本发明提出一种新的最优解输出方式，即“SOC最优轨迹域”。该方法通过记录所有SOC最优轨迹所经过的状态点，形成最优SOC点的集合域。求取“SOC最优轨迹域”的过程主要包括两部分：①将所有SOC状态点重新编号，顺序求解每一时刻各SOC离散点至起点的最优值函数，并记录该SOC离散点所有最优轨迹所经前一时刻SOC状态点，并对应存储在矩阵中，直至顺序运行至终点；②从终点出发，逆序查找并存储每一时刻下的所有最优轨迹点(即首先查找终点至前一时刻的最优轨迹对应的状态点，之后查找该状态点至其前一时刻的最优轨迹对应的状态点，依次类推)，直至逆序运行至起点，所有最优点SOC状态点形成的区域即为SOC最优轨迹域(图8中阴影区域)。以NEDC工况及UDDS工况为例，如图9所示，为NEDC工况下SOC最优轨迹域；如图10所示，为UDDS工况下SOC最优轨迹域。It takes a long time to obtain all SOC optimal trajectories based on the global optimization algorithm, and with the increase of output trajectories, the calculation time of the algorithm increases exponentially, so it is difficult to output all SOC trajectories that meet the requirements in a short time. The present invention proposes a new optimal solution output mode, namely "SOC optimal trajectory domain". This method forms a set domain of optimal SOC points by recording the state points passed by all SOC optimal trajectories. The process of obtaining the "SOC optimal trajectory domain" mainly includes two parts: ① Renumber all SOC state points, sequentially solve the optimal value function from each SOC discrete point to the starting point at each moment, and record all the most optimal SOC discrete points. The SOC state point at the moment before the optimal trajectory passes through, and correspondingly stored in the matrix until the sequence runs to the end point; ② Starting from the end point, search and store all optimal trajectory points at each moment in reverse order (that is, first search the end point to the previous point The state point corresponding to the optimal trajectory at one moment, then find the state point corresponding to the optimal trajectory at the previous moment, and so on), until the reverse order runs to the starting point, the area formed by all the optimal SOC state points is is the SOC optimal trajectory domain (shaded area in Figure 8). Taking NEDC and UDDS conditions as examples, as shown in Figure 9, it is the optimal trajectory domain of SOC under NEDC condition; as shown in Figure 10, it is the optimal trajectory domain of SOC under UDDS condition.
由图9和图10可知，基于全局寻域算法所求的“SOC最优轨迹域”分布于传统DP控制策略所得SOC最优轨迹(即步骤一中DP控制策略所得的SOC最优轨迹)下方，即传统DP所求SOC的最优轨迹可视为“SOC最优轨迹域”的上轮廓线。原因如下：传统动态规划求解过程中，对任意时刻自上而下顺序计算每一SOC状态点至终点最优能耗SOC轨迹，当计算结果中出现与最优能耗值相等的SOC轨迹时，最优SOC轨迹不更新；而全局寻域算法则记录此后出现的所有最优轨迹所经过的SOC状态点。It can be seen from Figure 9 and Figure 10 that the "SOC optimal trajectory domain" obtained based on the global area search algorithm is distributed below the SOC optimal trajectory obtained by the traditional DP control strategy (that is, the SOC optimal trajectory obtained by the DP control strategy in step 1). , that is, the optimal trajectory of SOC obtained by traditional DP can be regarded as the upper contour of the “SOC optimal trajectory domain”. The reasons are as follows: in the traditional dynamic programming process, the optimal energy consumption SOC trajectory from each SOC state point to the end point is sequentially calculated from top to bottom at any time. When the SOC trajectory equal to the optimal energy consumption value appears in the calculation result, The optimal SOC trajectory is not updated; while the global search algorithm records the SOC state points passed by all optimal trajectories that appear thereafter.
步骤三、基于“SOC最优轨迹域”，形成条带状SOC可行域Step 3. Based on the "SOC optimal trajectory domain", form a strip-shaped SOC feasible domain
通过全局寻域算法，得到了反映全局最优燃油经济性的“SOC最优轨迹域”。计算每一时刻该区域在垂直方向所占宽度，并将区域所占宽度最大值作为该工况下最优域宽度，以量化该区域范围。对比有限时域与无限时域下所得条带状SDP控制策略状态搜索域的宽度，可知，有限时域SDP控制策略状态搜索域范围进一步缩小。以UDDS工况为例，最优域宽度计算如图11所示。Through the global search algorithm, the "SOC optimal trajectory domain" reflecting the global optimal fuel economy is obtained. The width occupied by the region in the vertical direction is calculated at each moment, and the maximum value of the width occupied by the region is taken as the optimal domain width under the working condition to quantify the scope of the region. Comparing the width of the state search domain of the strip-shaped SDP control strategy obtained in the finite time domain and the infinite time domain, it can be seen that the range of the state search domain of the finite time domain SDP control strategy is further reduced. Taking the UDDS working condition as an example, the optimal domain width calculation is shown in Figure 11.
基于全局寻域算法完成多组工况的仿真运行，将其最优域宽度与传统DP及有限时域SDP两种控制策略SOC轨迹最大间距值进行对比，统计结果如表3所示。Based on the global domain search algorithm, the simulation operation of multiple groups of working conditions is completed, and the optimal domain width is compared with the maximum distance of the SOC trajectory of the traditional DP and finite time domain SDP control strategies. The statistical results are shown in Table 3.
表3不同工况下SOC最优域宽度与两种策略SOC轨迹间距对比表Table 3 Comparison of SOC optimal domain width and SOC trajectory spacing of two strategies under different operating conditions
由表3可知，相比于传统DP控制策略与SDP控制策略所求SOC轨迹实际宽度，SOC最优域宽度范围与上述两者存在一定范围的比例关系，偏差程度在20％范围内。将SOC最优域与统计规则进行有机结合，形成条带状SOC可行域，作为有限时域SDP能量管理控制策略的全新状态空间。具体做法为：首先基于全局寻域算法得到对应工况的SOC最优轨迹域及其宽度值，在SOC最优轨迹基础上，以最优域宽度值为上下宽度形成初步SOC可行域，并在此范围基础上增加近20％，以形成用于求解SDP控制策略的条带状SOC可行域。以UDDS工况为例，如图12所示，为基于SOC最优轨迹域形成的条带状SOC可行域。It can be seen from Table 3 that compared with the actual width of the SOC trajectory obtained by the traditional DP control strategy and the SDP control strategy, the SOC optimal domain width has a proportional relationship with the above two, and the degree of deviation is within 20%. The SOC optimal domain and statistical rules are organically combined to form a strip-shaped SOC feasible domain, which is a new state space for the finite-time-domain SDP energy management control strategy. The specific method is as follows: first, based on the global search algorithm, the optimal SOC trajectory domain and its width value of the corresponding operating conditions are obtained. This range is increased by nearly 20% to form a strip-shaped SOC feasible region for solving the SDP control strategy. Taking the UDDS working condition as an example, as shown in Figure 12, it is a strip-shaped SOC feasible region formed based on the SOC optimal trajectory domain.
不同工况下，对比基于统计方法得到初步建立的SOC可行域及基于“SOC最优域”方法得到的改进SOC可行域范围，结果如表4所示。Under different working conditions, the SOC feasible region initially established based on the statistical method and the improved SOC feasible region based on the "SOC optimal region" method were compared. The results are shown in Table 4.
表4不同工况下两种方法获取的SOC可行域范围对比表Table 4 Comparison of SOC feasible range obtained by two methods under different working conditions
由表4可知，以NEDC与UDDS工况为例，选用统计方法，形成0.07宽度范围的SOC可行域；而选用“DP最优域”方法形成宽度分别为0.054与0.020的SOC可行域，依然满足SDP控制策略与DP控制策略所得SOC轨迹的距离范围。因此，基于统计规则获取的SOC可行域从大范围角度上优化了状态搜索范围，而基于SOC最优轨迹域获得的SOC可行域对工况的针对性更强，可依据不同工况获得不同范围且更加准确的SOC可行域。It can be seen from Table 4 that, taking the NEDC and UDDS working conditions as examples, the statistical method is used to form a SOC feasible region with a width of 0.07; while the "DP optimal region" method is used to form a SOC feasible region with a width of 0.054 and 0.020, which still meets the requirements. The distance range of the SOC trajectory obtained by the SDP control strategy and the DP control strategy. Therefore, the SOC feasible region obtained based on statistical rules optimizes the state search range from a large-scale perspective, while the SOC feasible region obtained based on the SOC optimal trajectory domain is more pertinent to the working conditions, and different ranges can be obtained according to different working conditions. And more accurate SOC feasible region.
由于动态规划方法的求解所需存储空间较大，且计算时间长，导致算法计算效率低，通常离线完成，并将所得控制结果存于控制器内。因此，结合以上所提更细致、准确的SOC可行域形成方法，在实际应用中可依据不同工况离线获取其最优域及用于SDP控制策略的SOC可行域范围，有助于快速实现SDP控制策略的在线应用。Due to the large storage space required for the solution of the dynamic programming method and the long calculation time, the algorithm calculation efficiency is low, and the algorithm is usually completed offline, and the obtained control results are stored in the controller. Therefore, in combination with the more detailed and accurate SOC feasible region formation method mentioned above, in practical applications, the optimal region and the SOC feasible region range for the SDP control strategy can be obtained offline according to different working conditions, which is helpful for the rapid realization of SDP Online application of control strategies.
同时，由全局寻域算法的求解过程可知，SOC最优轨迹域的形成不会对DP控制算法的计算时间产生影响，即对计算设备不会造成严重的计算负荷。因此，该方法可为进一步优化有限时域SDP状态搜索范围，改善控制算法计算效率提供有力支持。At the same time, it can be seen from the solution process of the global domain search algorithm that the formation of the SOC optimal trajectory domain will not affect the calculation time of the DP control algorithm, that is, it will not cause serious computational load to the computing equipment. Therefore, this method can provide strong support for further optimizing the limited time domain SDP state search range and improving the computational efficiency of the control algorithm.
步骤四、SOC可行域的进一步优化，形成改进SOC可行域以优化SDP控制策略Step 4. Further optimization of the SOC feasible region to form an improved SOC feasible region to optimize the SDP control strategy
形成条带状SOC可行域后，为了便于控制算法的应用实施，需要对状态空间进行离散，得到离散化的SOC可行域。对有限时域SDP来说，状态的离散方式及离散精度对控制策略效果有着至关重要的作用。因此，从以下两方面进行对有限时域SDP相关SOC可行域进行优化：After the strip-shaped SOC feasible region is formed, in order to facilitate the application and implementation of the control algorithm, it is necessary to discretize the state space to obtain the discrete SOC feasible region. For finite-time-domain SDP, the discrete mode and discrete precision of the state play a crucial role in the effect of the control strategy. Therefore, the finite-time-domain SDP-related SOC feasible region is optimized from the following two aspects:
(1)优化SOC可行域范围(1) Optimizing the SOC feasible range
初步建立的SOC可行域为一个以DP控制策略所求最优SOC轨迹为准线的条带状区域。由于混合动力汽车的电池容量有限，为了延长电池的使用寿命，需要引入电池所允许的最大充放电电流限制。基于此，分别以起始SOC、终止SOC为基点，画出分别代表电池最大充/放电电流的直线，与条带状SOC可行域上下边界相交，形成新的SOC可行域以作为有限时域SDP控制策略的状态搜索域，如图13所示。其中，表征电池最大充放电电流的直线斜率的计算如下：The preliminarily established SOC feasible region is a strip-shaped region based on the optimal SOC trajectory obtained by the DP control strategy. Due to the limited battery capacity of hybrid vehicles, in order to prolong the service life of the battery, it is necessary to introduce a limit on the maximum charge and discharge current allowed by the battery. Based on this, take the starting SOC and the ending SOC as the base points, respectively, draw a straight line representing the maximum charge/discharge current of the battery, and intersect the upper and lower boundaries of the strip-shaped SOC feasible region to form a new SOC feasible region as a finite time domain SDP The state search domain of the control strategy is shown in Figure 13. Among them, the calculation of the slope of the straight line characterizing the maximum charge and discharge current of the battery is as follows:
其中，Icharge，Idischarge分别为电池最大充/放电电流(A)，当电池充电时，Icharge＜0，电池放电时，Idischarge＞0；Qmax为电池容量(Ah)。Among them, Icharge and Idischarge are respectively the maximum charge/discharge current (A) of the battery. When the battery is charging, Icharge <0, and when the battery is discharging, Idischarge >0; Qmax is the battery capacity (Ah).
形成的新的SOC可行域可进一步缩小SOC可行域范围，减少SDP算法运行过程中所需搜索状态数量，以提高算法计算效率。The new SOC feasible region formed can further narrow the scope of the SOC feasible region and reduce the number of search states required during the operation of the SDP algorithm, so as to improve the computational efficiency of the algorithm.
(2)优化SOC可行域离散方式(2) Optimizing the discrete method of SOC feasible region
随机动态规划的核心是一种基于贝尔曼原理的多阶段决策问题，通常将状态变量进行均匀离散。此离散方式忽略了电池最大放电限制(车辆工作在纯电动模式)所能达到的SOC点，因而存在一定的缺陷。如图14所示，纯电动模式下电池SOC转移所能到达的位置，与人为离散SOC网格存在误差δSOC，导致阶段油耗的计算存在误差，甚至导致有限域全局燃油经济性的计算有误。基于此，本发明选用一种新的SOC可行域离散方式，即要求需要存在能表征车辆工作在纯电动驱动模式的SOC状态点，如图15所示。以各时刻的各离散点为基点，以电池最大放电电流为斜率，得到下一时刻的离散点。当相邻时刻为1s时，δSOCreal的计算如下：The core of stochastic dynamic programming is a multi-stage decision-making problem based on Bellman's principle, which usually uniformly discretizes state variables. This discrete method ignores the SOC point that can be reached by the maximum discharge limit of the battery (the vehicle works in pure electric mode), so it has certain defects. As shown in Figure 14, the position that the battery SOC transfer can reach in pure electric mode has an error δSOC with the artificial discrete SOC grid, which leads to errors in the calculation of stage fuel consumption, and even leads to errors in the calculation of global fuel economy in the finite domain. Based on this, the present invention selects a new SOC feasible region discrete method, that is, it requires that there be an SOC state point that can characterize the vehicle operating in the pure electric drive mode, as shown in FIG. 15 . Taking each discrete point at each moment as the base point, and taking the maximum discharge current of the battery as the slope, the discrete point at the next moment is obtained. When the adjacent time is 1s, the calculation of δSOCreal is as follows:
基于SOC可行域范围及离散优化得到改进SOC可行域，作为有限时域SDP能量管理控制策略的状态搜索范围，简化了传统SDP控制策略求解过程中所需搜索状态点数量，尽可能改善SDP算法的计算效率以快速实现SDP能量管理控制策略的在线应用。Based on the SOC feasible region range and discrete optimization, the improved SOC feasible region is used as the state search range of the finite time domain SDP energy management control strategy, which simplifies the number of search state points required in the traditional SDP control strategy solution process, and improves the performance of the SDP algorithm as much as possible. Computational efficiency to quickly realize online application of SDP energy management control strategies.
尽管本发明的实施方案已公开如上，但其并不仅仅限于说明书和实施方式中所列运用，它完全可以被适用于各种适合本发明的领域，对于熟悉本领域的人员而言，可容易地实现另外的修改，因此在不背离权利要求及等同范围所限定的一般概念下，本发明并不限于特定的细节和这里示出与描述的图例。Although the embodiment of the present invention has been disclosed as above, it is not limited to the application listed in the description and the embodiment, and it can be applied to various fields suitable for the present invention. For those skilled in the art, it can be easily Therefore, the invention is not limited to the specific details and illustrations shown and described herein without departing from the general concept defined by the appended claims and the scope of equivalents.
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