CN104249736B

CN104249736B - The energy-conservation forecast Control Algorithm of hybrid vehicle based on platoon driving

Info

Publication number: CN104249736B
Application number: CN201410420797.0A
Authority: CN
Inventors: 余开江; 胡治国; 许孝卓; 张宏伟; 王莉; 杨俊起; 荆鹏辉
Original assignee: Henan University of Technology
Current assignee: Henan University of Technology
Priority date: 2014-08-25
Filing date: 2014-08-25
Publication date: 2016-06-22
Anticipated expiration: 2034-08-25
Also published as: CN104249736A

Abstract

The invention discloses an energy-saving predictive control method for hybrid electric vehicles based on platoon driving, which includes obtaining real-time traffic information of the vehicle in front and the vehicle in front from the global positioning system and intelligent traffic system as system input; establishing a mathematical model of hybrid electric vehicles to predict future vehicles State basis; define the optimal control problem of hybrid vehicle platooning, provide the function equation for solving the optimal control quantity; real-time feedback optimal control, solve the optimal control quantity, and meet the safety distance, according to the global positioning system, radar, intelligent transportation The system and the inter-vehicle communication system obtain information to adjust and optimize the energy flow of the hybrid electric vehicle online to obtain the optimal performance of the hybrid electric vehicle system. The planetary gear mechanism is used as an electronic continuously variable transmission to make the engine always work at the best working point. Using road traffic information, forecasting According to the driving state of the vehicle in front, the energy flow of the hybrid electric vehicle can be adjusted online to achieve the goal of energy saving and emission reduction. Different from the traditional fixed headway control method, it provides a new way to improve the performance of the central controller of the hybrid electric vehicle energy management system.

Description

Energy-saving predictive control method for hybrid electric vehicles based on platooning

技术领域technical field

本发明涉及一种基于队列行驶的混合动力汽车节能预测控制方法,特别涉及一种实时最优的混合动力汽车控制方法。The invention relates to an energy-saving predictive control method for a hybrid electric vehicle based on platooning, and in particular to a real-time optimal control method for a hybrid electric vehicle.

背景技术Background technique

全球能源与环境形势的日益严峻，特别是汽车保有量的迅速增长，推动新能源汽车和智能交通系统的发展。为解决交通拥堵，环境恶化和交通事故三大问题，本发明提出了基于队列行驶的混合动力汽车节能预测控制方法。车辆队列行驶技术指多个车辆以较小的车间距离以一个队列行驶的技术。这种技术可以极大改善车辆周围的气动特性，减少其空气阻力，增强交通安全性，并可有效提高车辆的燃油经济性。另一方面，与传统汽车相比，混合动力汽车具有电池和燃油双系统驱动的冗余性，运用这种冗余性可以调节驱动装置工作点到最优位置，从而实现节能减排目标。预计未来汽车的主流将是这种混合动力汽车。由于混合动力汽车可以回收伴随车辆减速产生的再生制动能量；利用驱动系统的冗余性(发动机和电机)优化驱动装置工作点，因此可以极大地发挥节能减排效用。但是最优工作点随发动机的特性，周围车辆的行驶状态，道路交通条件的改变而时刻改变着。而且，旋转系(发动机和电机)具有转速转矩极限，电池具有荷电状态极限，超出这些极限对于车辆关键零部件的性能影响很大。因此，混合动力汽车的节能减排效果很大程度上依赖于其能量管理策略(满足约束条件)。而其关键技术为能量管理中央控制器中的实时最优化，以期实现控制策略的商业化，产业化。The increasingly severe global energy and environmental situation, especially the rapid growth of car ownership, promotes the development of new energy vehicles and intelligent transportation systems. In order to solve the three major problems of traffic congestion, environmental deterioration and traffic accidents, the present invention proposes an energy-saving predictive control method for hybrid electric vehicles based on queue driving. The vehicle platoon driving technology refers to the technology in which multiple vehicles travel in a platoon with a small inter-vehicle distance. This technology can greatly improve the aerodynamic characteristics around the vehicle, reduce its air resistance, enhance traffic safety, and effectively improve the fuel economy of the vehicle. On the other hand, compared with traditional vehicles, hybrid vehicles have the redundancy of battery and fuel dual system drive. Using this redundancy can adjust the operating point of the driving device to the optimal position, so as to achieve the goal of energy saving and emission reduction. It is expected that the mainstream of future cars will be such hybrid cars. Since hybrid electric vehicles can recover the regenerative braking energy generated with vehicle deceleration and optimize the operating point of the drive device by using the redundancy of the drive system (engine and motor), it can greatly play the role of energy saving and emission reduction. However, the optimal operating point changes all the time with the characteristics of the engine, the driving state of the surrounding vehicles, and the change of road traffic conditions. Moreover, the rotating system (engine and electric motor) has speed and torque limits, and the battery has state-of-charge limits, and exceeding these limits has a great impact on the performance of key vehicle components. Therefore, the energy saving and emission reduction effect of HEV largely depends on its energy management strategy (satisfying constraints). And its key technology is the real-time optimization in the energy management central controller, in order to realize the commercialization and industrialization of the control strategy.

混合动力汽车能量管理系统的控制策略是其研发的技术核心和设计难点。目前已经提出的控制策略大致可以分为4类：数值最优控制，解析最优控制，瞬时最优控制和启发式控制。数值最优控制的典型代表是动态规划和模型预测控制。解析最优控制的典型代表是庞特里亚金极小值原理控制策略。瞬时最优控制的典型代表是瞬时等效油耗最低控制策略。启发式控制策略的典型代表是基于规则的控制策略。传统的全局最优控制方法动态规划和庞特里亚金极小值原理控制方法，由于需要事先知道未来全部工况信息，无法实现实时最优。传统的基于规则的控制策略无法实现效率最大化。一般的前馈型控制(假定车辆速度模式一定)无法实现实时最优。传统的瞬时最优控制参数受未来车辆工况变化影响太大，无法满足控制性能。The control strategy of HEMS is the technical core and design difficulty of its research and development. The control strategies that have been proposed so far can be roughly divided into four categories: numerical optimal control, analytical optimal control, instantaneous optimal control and heuristic control. Typical representatives of numerical optimal control are dynamic programming and model predictive control. A typical representative of analytical optimal control is the Pontryagin minimum principle control strategy. The typical representative of instantaneous optimal control is the minimum instantaneous equivalent fuel consumption control strategy. The typical representative of the heuristic control strategy is the rule-based control strategy. The traditional global optimal control methods, dynamic programming and Pontryagin minimum principle control methods, cannot achieve real-time optimization because they need to know all future working conditions in advance. Traditional rule-based control strategies cannot maximize efficiency. The general feed-forward control (assuming that the vehicle speed mode is constant) cannot achieve real-time optimization. The traditional instantaneous optimal control parameters are too affected by changes in future vehicle operating conditions and cannot meet the control performance.

自20世纪90年代初以来，世界各国对混合动力汽车和智能交通系统的研发给予了高度重视，并取得了一些重大的成果和进展。1997年，在由美国交通部主办的智能交通系统展示会上，展示了由8辆车组成的队列行驶技术。日本丰田汽车公司于1997年实现了混合动力汽车的量产化，2012年实现了插电式混合动力汽车的量产化。美国总统奥巴马2009年宣布了下一代先进蓄电池和插电式混合动力汽车计划。在国内，国家“十一五”863计划设立了节能与新能源汽车重大项目。本发明人在日本九州大学攻读博士学位期间，掌握了日本企业和大学普遍采用的模型预测控制法以及日本学者大塚敏之提出的C/GMRES快速解法。这两种方法的结合解决了模型预测控制这种先进方法的实际应用问题。Since the early 1990s, countries around the world have paid great attention to the research and development of hybrid electric vehicles and intelligent transportation systems, and have achieved some significant results and progress. In 1997, at the intelligent transportation system exhibition sponsored by the US Department of Transportation, the platoon driving technology composed of 8 vehicles was demonstrated. Japan's Toyota Motor Corporation realized the mass production of hybrid electric vehicles in 1997, and realized the mass production of plug-in hybrid electric vehicles in 2012. In 2009, US President Barack Obama announced plans for the next generation of advanced batteries and plug-in hybrid vehicles. In China, the national "Eleventh Five-Year" 863 plan has set up major projects for energy saving and new energy vehicles. The inventor mastered the model predictive control method commonly used by Japanese enterprises and universities and the C/GMRES fast solution method proposed by Japanese scholar Otsuka Toshiyuki during his PhD study at Kyushu University in Japan. The combination of these two methods solves the practical application problem of the advanced method of model predictive control.

在此背景下，提高能源利用效率，减少汽车对环境的污染和增强交通安全已成为当今汽车工业发展的首要任务。同时，利用道路交通信息，进一步提高驱动装置效率也成为当今社会发展的现实需要。为了解决上述问题，需要开发出一种基于队列行驶的可产业化的混合动力汽车模型预测控制方法，从而实现节能减排目标。In this context, improving energy utilization efficiency, reducing automobile pollution to the environment and enhancing traffic safety have become the primary tasks for the development of today's automobile industry. At the same time, using road traffic information to further improve the efficiency of driving devices has become a realistic need for the development of today's society. In order to solve the above problems, it is necessary to develop an industrialized model predictive control method for hybrid electric vehicles based on platoon driving, so as to achieve the goal of energy saving and emission reduction.

发明内容Contents of the invention

针对上述问题，本发明的目的在于提供一种能够对未来车辆工况进行实时预测的基于队列行驶的混合动力汽车模型预测方法，以达到最大限度地节能减排，使之成为产业化混合动力汽车能量管理中央控制器。In view of the problems referred to above, the object of the present invention is to provide a hybrid electric vehicle model prediction method based on platooning that can carry out real-time prediction of future vehicle operating conditions, so as to achieve maximum energy saving and emission reduction, making it an industrialized hybrid electric vehicle. Energy management central controller.

为实现上述目的，本发明采取以下技术方案：一种基于队列行驶的混合动力汽车节能预测控制方法，第一步为信息采集，第二步为车辆建模，第三步为公式化控制策略，第四步为在线最优控制；其特征在于：包括以下步骤：In order to achieve the above object, the present invention adopts the following technical solutions: an energy-saving predictive control method for hybrid electric vehicles based on platoon driving, the first step is information collection, the second step is vehicle modeling, the third step is a formula control strategy, and the second step is The four steps are online optimal control; it is characterized in that it includes the following steps:

1)信息采集：1) Information collection:

由全球定位系统采集前车和自车的位置信息，作为实时车辆状态反馈；由车载雷达测速装置采集前方车辆速度，用于跟踪控制；由智能交通系统和车间通信系统采集交通信号信息，实时路况信息以及自车和前车速度，加速度信息，用于智能交通控制；由卡尔曼滤波器利用采集的蓄电池信息对蓄电池荷电状态进行估计。The position information of the vehicle in front and the vehicle is collected by the global positioning system as real-time vehicle status feedback; the speed of the vehicle in front is collected by the vehicle-mounted radar speed measuring device for tracking control; traffic signal information is collected by the intelligent transportation system and the inter-vehicle communication system, and real-time road conditions Information, as well as the speed and acceleration information of the ego vehicle and the vehicle in front, are used for intelligent traffic control; the Kalman filter uses the collected battery information to estimate the state of charge of the battery.

2)车辆建模：2) Vehicle modeling:

行星齿轮式混联混合动力汽车包含5大动态部件，它们是发动机，蓄电池，2个发电电动一体机和车轮。行星齿轮作为动力分配装置既有速度耦合器的作用，又有电子无极变速器作用。根据车辆机械耦合和电子耦合关系，列写系统动力学方程，对动力学方程解耦，最终获得系统的状态空间模型，如式(1)所示：The planetary gear type hybrid hybrid vehicle contains 5 major dynamic components, which are the engine, the storage battery, 2 electric generators and wheels. As a power distribution device, the planetary gear not only functions as a speed coupling, but also as an electronic continuously variable transmission. According to the vehicle mechanical coupling and electronic coupling relationship, write the system dynamic equation, decouple the dynamic equation, and finally obtain the state space model of the system, as shown in formula (1):

$\begin{matrix} x x = = [[{p p}_{11} {v v}_{11} {w w}_{11} {SOC SOC}_{11} {p p}_{22} {v v}_{22} {w w}_{22} {SOC SOC}_{22}]] \\ u u = = [[{u u}_{11} {u u}_{22} {P P}_{batt batt 11} {P P}_{batt batt 11}]] \\ x x = = f f ((x x,, u u)) \\ f f ((x x,, u u)) = = [\begin{matrix} {v v}_{11} \\ {w w}_{11} - - \frac{11}{22} ρ ρ {C C}_{D D. 11} {A A}_{11} {v v}_{11}^{22} / / {m m}_{11} - - 9.8 9.8 μ μ - - 9.8 9.8 sin sin {θ θ}_{11} \\ kp kp * * (({u u}_{11} - - {w w}_{11})) \\ - - \frac{{V V}_{max max 11} - - \sqrt{{V V}_{max max 11}^{22} - - 44 {P P}_{batt batt 11} {R R}_{batt batt 11}}}{22 {R R}_{batt batt 11} {Q Q}_{batt batt 11}} \\ {v v}_{22} \\ {w w}_{22} - - \frac{11}{22} ρ ρ {C C}_{D D. 22} {A A}_{22} {v v}_{22}^{22} / / {m m}_{22} - - 9.8 9.8 μ μ - - 9.8 9.8 sin sin {θ θ}_{22} \\ kp kp * * (({u u}_{22} - - {w w}_{22})) \\ - - \frac{{V V}_{max max 22} - - \sqrt{{V V}_{max max 22}^{22} - - 44 {P P}_{batt batt 22} {R R}_{batt batt 22}}}{22 {R R}_{batt batt 22} {Q Q}_{batt batt 22}} \end{matrix}] \end{matrix} - - - - - - ((11))$

式中，x为状态量，u为控制量。参数p₁，v₁，w₁和SOC₁为自车的位置，速度，考虑延迟的驱动加速度和蓄电池荷电状态。参数p₂，v₂，w₂和SOC₂为前车的位置，速度，考虑延迟的驱动加速度和蓄电池荷电状态。参数u₁，u₂，P_batt1和P_batt2为自车的驱动加速度，前车的驱动加速度，自车蓄电池的充放电功率和前车蓄电池的充放电功率。参数ρ，C_D1，C_D2，A₁，A₂，m₁，m₂，g，μ，θ₁和θ₂是空气密度，自车空气阻力系数，前车空气阻力系数，自车迎风面积，前车迎风面积，自车质量，前车质量，重力加速度，滚动阻力系数，自车道路坡度和前车道路坡度。V_OC，R_batt和Q_batt是蓄电池开路电压，内阻和容量。In the formula, x is the state quantity, and u is the control quantity. The parameters p ₁ , v ₁ , w ₁ and SOC ₁ are the position, speed, driving acceleration and battery state of charge of the ego vehicle considering the delay. The parameters p ₂ , v ₂ , w ₂ and SOC ₂ are the position, speed, driving acceleration and battery state of charge of the preceding vehicle taking into account the delay. The parameters u ₁ , u ₂ , P _batt1 and P _batt2 are the driving acceleration of the own vehicle, the driving acceleration of the preceding vehicle, the charge and discharge power of the battery of the own vehicle and the charge and discharge power of the battery of the preceding vehicle. The parameters ρ, C _D1 , C _D2 , A ₁ , A ₂ , m ₁ , m ₂ , g, μ, θ ₁ and θ ₂ are the air density, the air resistance coefficient of the own vehicle, the air resistance coefficient of the front vehicle, and the frontal area of the own vehicle , the frontal area of the front vehicle, the mass of the self-vehicle, the mass of the front vehicle, the acceleration of gravity, the rolling resistance coefficient, the road gradient of the self-vehicle and the road gradient of the front vehicle. V _oc , R _batt and Q _batt are battery open circuit voltage, internal resistance and capacity.

车辆的燃油经济性评价采用威兰氏线性模型,如式(2)所示：The fuel economy evaluation of the vehicle adopts the Weilan linear model, as shown in formula (2):

.m_f(t)＝.m_f(P_req(t)-P_batt(t))≈c_f(P_req(t)-P_batt(t))(2).m _f (t)＝.m _f (P _req (t)-P _batt (t))≈c _f (P _req (t)-P _batt (t))(2)

式中m_f为燃油消耗率。参数P_req为车辆需求功率。c_f为常数参数。Where m _f is the fuel consumption rate. The parameter P _req is the required power of the vehicle. c _f is a constant parameter.

3)公式化控制策略：3) Formulated control strategy:

基于队列行驶的混合动力汽车能量管理模型预测最优控制策略的步骤为：首先检测自车和前车状态，包括位置，速度和加速度信息，其次运用所建立的数学模型和公式化控制策略求解最优控制问题，最后应用所求得的最优控制序列的第一个控制量于系统；由于模型预测控制为区间最优控制，所以其求得的最优控制量是数量为预测区间除以采样间隔的序列。最优控制序列的第一个控制量与实际状态最接近，所以采用它来作为实际的控制量。The steps of predicting the optimal control strategy based on the HEV energy management model of platoon driving are as follows: firstly detect the state of the ego vehicle and the vehicle in front, including position, velocity and acceleration information, and then use the established mathematical model and formulaic control strategy to solve the optimal control strategy. For the control problem, finally apply the first control quantity of the obtained optimal control sequence to the system; since the model predictive control is an interval optimal control, the optimal control quantity obtained is the prediction interval divided by the sampling interval the sequence of. The first control quantity of the optimal control sequence is the closest to the actual state, so it is used as the actual control quantity.

模型预测控制的基本原理为：在每一个采样时刻，根据预测模型对系统未来代价函数进行预测，通过对未来预测区间内的性能指标进行优化，并根据实测对象的输出进行反馈校正，将控制策略设计转化为优化过程，通过求解相应预测区间的优化问题得到控制序列，并将序列的第一个控制量作用于系统，实现反馈控制，之后在下一个采样时刻，将预测区间向前推进一步，不断重复该过程。总结来说其包括三部分：预测模型，滚动优化和反馈控制。通过对未来系统输入的预测可以实现对系统的实时最优控制。The basic principle of model predictive control is: at each sampling moment, predict the future cost function of the system according to the prediction model, optimize the performance index in the future prediction interval, and perform feedback correction according to the output of the measured object, and the control strategy The design is transformed into an optimization process. The control sequence is obtained by solving the optimization problem of the corresponding prediction interval, and the first control quantity of the sequence is applied to the system to realize feedback control. Then, at the next sampling time, the prediction interval is pushed forward one step, continuously Repeat the process. In summary, it includes three parts: predictive model, rolling optimization and feedback control. The real-time optimal control of the system can be realized by predicting the future system input.

本控制策略的特色有两点。第一，随着汽车导航，数字化地图，车间通信技术和智能交通系统的发展，利用道路交通状况，对混合动力汽车速度模式进行最优化。第二，前方有车辆的情况下，传统的固定车间距的控制方法现在还是主流，车间距离在最小值以上浮动的控制策略，提高了车辆速度变化的自由度，使混合动力汽车燃油经济性的提高有了可能。上述两大特色在控制策略设计中评价函数里有相应体现，为混合动力汽车系统性能提高提供了更大可能性。There are two characteristics of this control strategy. First, with the development of car navigation, digital map, vehicle-to-vehicle communication technology and intelligent transportation system, the speed pattern of hybrid electric vehicles is optimized by using road traffic conditions. Second, when there is a vehicle in front, the traditional control method of fixed inter-vehicle distance is still the mainstream, and the control strategy of inter-vehicle distance floating above the minimum value improves the freedom of vehicle speed change and improves the fuel economy of hybrid vehicles. Improvement is possible. The above two features are reflected in the evaluation function in the design of the control strategy, which provides a greater possibility for the performance improvement of the hybrid electric vehicle system.

预测模型在已在上部分论述。Predictive models have been discussed in the previous section.

最优控制问题定义如式(3)所示：The optimal control problem is defined as formula (3):

$\begin{matrix} min min imize imize & J J = = {&Integral; &Integral;}_{t t}^{t t + + T T} L L ((x x ((τ τ | | t t)),, u u ((τ τ | | t t)))) dτ dτ \\ subject to subject to & {P P}_{batt batt 11 min min} \leq \leq {P P}_{batt batt 11} ((τ τ | | t t)) \leq \leq {P P}_{batt batt 11 max max} \\ {u u}_{11 min min} \leq \leq {u u}_{11} ((τ τ | | t t)) \leq \leq {u u}_{11 max max} \\ {P P}_{batt batt 22 min min} \leq \leq {P P}_{batt batt 22} ((τ τ | | t t)) \leq \leq {P P}_{batt batt 22 max max} \\ {u u}_{22 min min} \leq \leq {u u}_{22} ((τ τ | | t t)) \leq \leq {u u}_{22 max max} \end{matrix} - - - - - - ((33))$

式中T为预测区间。参数P_batt1min，P_batt1max，P_batt2min，P_batt2max，u_1max，u_1min，u_2max和u_2min为控制量约束。where T is the prediction interval. The parameters P _batt1min , P _batt1max , P _batt2min , P _batt2max , u _1max , u _1min , u _2max and u _2min are control quantity constraints.

评价函数定义如式(4)所示：The definition of the evaluation function is shown in formula (4):

$\begin{matrix} L L = = {w w}_{x x} {L L}_{x x} + + {w w}_{y the y} {L L}_{y the y} + + {w w}_{z z} {L L}_{z z} + + {w w}_{d d} {L L}_{d d} + + {w w}_{e e} {L L}_{e e} + + {w w}_{f f} {L L}_{f f} + + {w w}_{r r} {L L}_{r r} \\ {L L}_{x x} = = {(({w w}_{11} - - \frac{11}{22} ρ ρ {C C}_{D D. 11} {A A}_{11} {v v}_{11}^{22} / / {m m}_{11} - - 9.8 9.8 * * μ μ))}^{22} / / 22 + + {(({w w}_{22} - - \frac{11}{22} ρ ρ {C C}_{D D. 22} {A A}_{22} {v v}_{22}^{22} / / {m m}_{11} - - 9.8 9.8 * * μ μ))}^{22} / / 22 \\ {L L}_{y the y} = = {(({v v}_{11} - - {v v}_{d d}))}^{22} / / 22 + + {(({v v}_{22} - - {v v}_{d d}))}^{22} / / 22 \\ {L L}_{z z} = = 0.0874 0.0874 * * (({m m}_{11} * * {w w}_{11} * * {v v}_{11} / / 10001000 - - {P P}_{batt batt 11})) / / ((11 + + {e e}^{- - 0.5 0.5 * * (({m m}_{11} * * {w w}_{11} * * {v v}_{11} / / 10001000 - - {P P}_{batt batt 11}))})) \\ + + 0.0874 0.0874 * * (({m m}_{22} * * {w w}_{22} * * {v v}_{22} / / 10001000 - - {P P}_{batt batt 22})) / / ((11 + + {e e}^{- - 0.5 0.5 * * (({m m}_{22} * * {w w}_{22} * * {v v}_{22} / / 10001000 - - {P P}_{batt batt 22}))})) \\ {L L}_{d d} = = {(({SOC SOC}_{11} - - {SOC SOC}_{d d}))}^{22} + + {(({SOC SOC}_{22} - - {SOC SOC}_{d d}))}^{22} \\ {L L}_{e e} = = {(({m m}_{11} * * {w w}_{11} * * {v v}_{11} / / 10001000 - - {P P}_{batt batt 11}))}^{22} / / 22 + + {(({m m}_{22} * * {w w}_{22} * * {v v}_{22} / / 10001000 - - {P P}_{batt batt 22}))}^{22} / / 22 \\ {L L}_{f f} = = ((- - ln ln [[{SOC SOC}_{11} - - 0.6 0.6]] - - ln ln [[0.8 0.8 - - {SOC SOC}_{11}]])) + + ((- - ln ln [[{SOC SOC}_{22} - - 0.6 0.6]] - - ln ln [[0.8 0.8 - - {SOC SOC}_{22}]])) \\ {L L}_{r r} = = - - ln ln ((d d - - {d d}_{d d})) \end{matrix} - - - - - - ((44))$

式中SOC_d是目标蓄电池荷电状态。v_d是车辆目标速度，它取值为车辆最优等速燃油经济性速度。w_x，w_y，w_z，w_d，w_e，w_f，和w_r是权重系数。d_d为最低车辆间距，评价函数设置使其在最低车辆间距以上浮动，从而增加控制自由度，提高车辆燃油经济性。障碍函数用于处理系统状态约束。where SOC _d is the target battery state of charge. v _d is the target speed of the vehicle, and its value is the optimal constant speed fuel economy speed of the vehicle. w _x , w _y , w _z , w _d , w _e , w _f , and w _r are weight coefficients. d _d is the minimum distance between vehicles, and the evaluation function is set to make it float above the minimum distance between vehicles, so as to increase the degree of freedom of control and improve the fuel economy of vehicles. Barrier functions are used to deal with system state constraints.

4)在线最优控制：4) Online optimal control:

为保证系统的实时最优性能，运用基于哈密顿方程的数值快速求解方法来求解上述最优控制问题。由于其只需有限几次迭代就可以计算出数值方程的最优解，这种方法的在线性能很好。而且由于其基于哈密顿方程，这种解法的稳定性可以得到保证。解法具体来说，运用极小值原理将最优控制问题转化为两点边值问题，在处理哈密顿函数相关的微分方程组和代数方程组时采用部分空间法求解，这是一种GMRES解法。In order to ensure the real-time optimal performance of the system, a fast numerical solution method based on the Hamiltonian equation is used to solve the above optimal control problems. Since it only needs a limited number of iterations to calculate the optimal solution of the numerical equation, the online performance of this method is very good. And because it is based on the Hamiltonian equation, the stability of this solution can be guaranteed. Solution Specifically, the optimal control problem is transformed into a two-point boundary value problem using the minimum value principle, and the partial space method is used to solve the differential equations and algebraic equations related to the Hamiltonian function. This is a GMRES solution. .

在每个采样时刻，首先，测取前车位置，自车位置，前车速度，自车速度，前车加速度，自车加速度，前车蓄电池荷电状态和自车蓄电池荷电状态等实时状态信号，其次，利用全球定位系统和智能交通系统预测未来一定区间车辆及周围环境的状态，再次，根据建立的车辆模型和最优控制问题，利用上述数值快速解法求解预测区间内的最优控制序列。应用预测区间内的最优控制序列的第一个控制量于车辆。之后在下一个采样时刻，将预测区间向前推进一步，如此循环往复，实现在线最优控制。At each sampling moment, firstly, measure the real-time status of the vehicle in front, the position of the vehicle in front, the speed of the vehicle in front, the vehicle in front, the acceleration of the vehicle in front, the acceleration of the vehicle in front, the charge state of the battery in front and the charge state of the battery in the vehicle Signal, secondly, use the global positioning system and intelligent transportation system to predict the state of the vehicle and the surrounding environment in a certain interval in the future, and thirdly, according to the established vehicle model and optimal control problem, use the above numerical fast solution method to solve the optimal control sequence in the prediction interval . Apply the first control quantity of the optimal control sequence within the prediction interval to the vehicle. Then at the next sampling time, the prediction interval is pushed forward one step, and this cycle repeats to realize online optimal control.

本发明由于采取以上技术方案，其具有以下优点：The present invention has the following advantages due to the adoption of the above technical scheme:

1)随着汽车导航，数字化地图的发展，利用道路交通状况，对混合动力汽车速度模式进行最优化。不同于传统方法中假定速度模式给定已知的情况。1) With the development of car navigation and digital maps, the speed mode of hybrid electric vehicles is optimized by using road traffic conditions. It is different from the situation in which the speed mode is assumed to be known in the traditional method.

2)前方有车辆的情况下，传统的固定车间距的控制方法现在还是主流。申请者提出了车间距离在最小值以上浮动的控制策略，提高了车辆速度变化的自由度，使混合动力汽车燃油经济性的提高有了可能。2) When there is a vehicle ahead, the traditional control method of fixed inter-vehicle distance is still the mainstream. The applicant proposed a control strategy in which the inter-vehicle distance fluctuates above the minimum value, which improves the freedom of vehicle speed changes and makes it possible to improve the fuel economy of hybrid electric vehicles.

3)提出了基于队列行驶的混合动力汽车集中控制模型，为混合动力汽车队列行驶的模型化提供了一般的通用方法论指导。3) A centralized control model of hybrid electric vehicles based on platooning is proposed, which provides general methodological guidance for the modeling of platooning of hybrid electric vehicles.

运用本方法能够大幅度提高混合动力汽车燃油经济性，排放性能和安全性能。By using the method, the fuel economy, emission performance and safety performance of the hybrid electric vehicle can be greatly improved.

附图说明Description of drawings

图1是本发明行星齿轮式混联混合动力汽车驱动系统结构示意图。Fig. 1 is a schematic structural diagram of a drive system of a planetary gear hybrid hybrid vehicle according to the present invention.

图1中：1、发动机；2、动力分配器；3、发电机；4、蓄电池；5、逆变器；6电动机；7、主减速器。In Fig. 1: 1. Engine; 2. Power splitter; 3. Generator; 4. Storage battery; 5. Inverter; 6. Electric motor; 7. Final reducer.

图2是基于跟车模型的混合动力汽车节能预测控制方法流程图。Fig. 2 is a flowchart of a method for energy-saving predictive control of a hybrid electric vehicle based on a car-following model.

图3是基于跟车模型的混合动力汽车节能预测控制器结构图。Figure 3 is a structural diagram of a hybrid electric vehicle energy-saving predictive controller based on the car-following model.

具体实施方式detailed description

以下结合技术方案和附图详细叙述本发明的具体实施方式。The specific embodiments of the present invention will be described in detail below in conjunction with the technical solutions and accompanying drawings.

如图所示，本发明公开了一种基于队列行驶的混合动力汽车节能预测控制方法，包括以下步骤：从全球定位系统和智能交通系统获取实时自车和前车交通信息作为系统输入；建立混合动力汽车数学模型作为预测未来车辆状态的依据；定义混合动力汽车队列行驶最优控制问题，提供求解最优控制量的函数方程；实时反馈最优控制，求解最优控制量。本发明在满足安全间距的情况下，采用一种基于队列行驶的混合动力汽车节能预测控制方法，根据全球定位系统，雷达，智能交通系统和车间通信系统获得的信息在线调整优化混合动力汽车能量流动，进而可以获得混合动力汽车系统最优性能。该方法运用行星齿轮机构作为电子无极变速器，使发动机始终工作于其最佳工作点。同时，运用道路交通信息，预测前车行驶状态，在线调整混合动力汽车能量流动，达到节能减排的目标。另外，本发明不同于传统的固定车头时距控制方法,可应用于实际车辆的实时控制，为混合动力汽车能量管理系统中央控制器性能提高提供了一种新途径。As shown in the figure, the present invention discloses an energy-saving predictive control method for hybrid electric vehicles based on platooning, which includes the following steps: obtaining real-time traffic information of the own vehicle and the vehicle in front from the global positioning system and the intelligent transportation system as system input; establishing a hybrid The mathematical model of the electric vehicle is used as the basis for predicting the future vehicle state; the optimal control problem of hybrid vehicle platooning is defined, and the function equation for solving the optimal control quantity is provided; the optimal control is fed back in real time, and the optimal control quantity is solved. In the case of satisfying the safety distance, the present invention adopts an energy-saving predictive control method for hybrid electric vehicles based on queue driving, and adjusts and optimizes the energy flow of hybrid electric vehicles online according to the information obtained from the global positioning system, radar, intelligent transportation system and inter-vehicle communication system , and then the optimal performance of the hybrid electric vehicle system can be obtained. This method uses the planetary gear mechanism as an electronic continuously variable transmission to make the engine always work at its optimum working point. At the same time, it uses road traffic information to predict the driving status of the vehicle in front, and adjusts the energy flow of hybrid electric vehicles online to achieve the goal of energy saving and emission reduction. In addition, the present invention is different from the traditional fixed headway control method, can be applied to the real-time control of actual vehicles, and provides a new approach for improving the performance of the central controller of the energy management system of the hybrid electric vehicle.

图1是本发明行星齿轮式混联混合动力驱动系统结构示意图，主要包括：发动机1；动力分配器2；发电机3；蓄电池4；逆变器5；电动机6；主减速器7。图1为本专利控制方法的研究对象的结构图。在车辆建模过程中使用本结构图分析系统机械和电气耦合关系。结构图中包含混合动力汽车包含5大动态部件。它们是发动机，蓄电池，2个发电电动一体机和车轮。电动机通过主减速器与车轮相连，传递系统动力。行星齿轮作为动力分配装置既有速度耦合器的作用，又有电子无极变速器作用。行星齿轮机械耦合发动机和2个发电电动一体机。逆变器电气耦合蓄电池和2个发电电动一体机。通过对系统机械耦合和电气耦合解耦获得独立的3自由度系统模型。本发明控制方法为系统软件，图1所示为系统硬件。1 is a schematic structural diagram of a planetary gear type hybrid drive system of the present invention, which mainly includes: an engine 1; a power splitter 2; a generator 3; a storage battery 4; an inverter 5; an electric motor 6; Fig. 1 is a structural diagram of the research object of the patent control method. Use this block diagram during vehicle modeling to analyze system mechanical and electrical coupling relationships. The structure diagram contains five dynamic components of the hybrid vehicle. They are the engine, battery, 2 electric generators and wheels. The electric motor is connected to the wheels through the final reducer to transmit the power of the system. As a power distribution device, the planetary gear not only functions as a speed coupling, but also as an electronic continuously variable transmission. The planetary gear is mechanically coupled to the engine and 2 electric generators. The inverter is electrically coupled to the storage battery and 2 electric generators. An independent 3-DOF system model is obtained by decoupling the mechanical coupling and electrical coupling of the system. The control method of the present invention is system software, and Fig. 1 shows system hardware.

图2是揭示了整个控制方法的过程。采集的信息作为系统模型的输入。由车载雷达测速装置采集前方车辆速度，用于跟踪控制。由智能交通系统采集交通信号信息以及实时路况信息，用于智能交通控制。由卡尔曼滤波器利用采集的蓄电池信息对蓄电池荷电状态进行估计。车辆建模为公式化模型预测控制策略提供预测未来车辆状态所需要的模型。公式化控制策略为在线最优控制提供需要求解的函数方程。Figure 2 is a process that reveals the entire control method. The collected information is used as the input of the system model. The speed of the vehicle in front is collected by the on-board radar speed measuring device for tracking control. The intelligent traffic system collects traffic signal information and real-time road condition information for intelligent traffic control. The battery state of charge is estimated by the Kalman filter using the collected battery information. Vehicle modeling provides the models needed to predict future vehicle states for formulating model predictive control strategies. The formulated control strategy provides the functional equations to be solved for online optimal control.

图3为本发明具体控制方法的整个过程。由全球定位系统通过车辆位置查询得到车辆所在位置的道路坡度。目标蓄电池荷电状态发生器根据道路坡度产生目标蓄电池荷电状态。由智能交通系统获得前方车辆位置，速度和交通信息。测取的车辆状态，道路坡度信息，前方车辆位置和速度以及交通信息，目标蓄电池荷电状态，目标车辆速度输入模型预测控制器，模型预测控制器根据车辆系统模型，求解最优控制问题，得到最优控制量，并作用于车辆。Fig. 3 is the whole process of the specific control method of the present invention. The road gradient of the vehicle location is obtained by the global positioning system through the vehicle location query. The target battery state of charge generator generates the target battery state of charge according to the road gradient. The position, speed and traffic information of the vehicle in front is obtained by the intelligent traffic system. The measured vehicle state, road slope information, the position and speed of the vehicle in front and traffic information, the target battery charge state, and the target vehicle speed are input into the model predictive controller, and the model predictive controller solves the optimal control problem according to the vehicle system model, and obtains The optimal control amount and acts on the vehicle.

实施例：Example:

以行星齿轮式混联混合动力驱动系统为例进行说明，如图1所示；本发明方法第一步为信息采集，第二步为车辆建模，第三步为公式化控制策略，第四步为在线最优控制。Taking the planetary gear hybrid drive system as an example, as shown in Figure 1; the first step of the method of the present invention is information collection, the second step is vehicle modeling, the third step is a formula control strategy, and the fourth step For online optimal control.

该方法的原理如图2所示，具体控制方法包括以下步骤：The principle of the method is shown in Figure 2, and the specific control method includes the following steps:

1)信息采集：1) Information collection:

由全球定位系统采集前车和自车的位置信息，作为实时车辆状态反馈。由车载雷达测速装置采集前方车辆速度，用于跟踪控制。由智能交通系统和车间通信系统采集交通信号信息，实时路况信息以及自车和前车速度，加速度信息，用于智能交通控制。由卡尔曼滤波器利用采集的蓄电池4信息对蓄电池荷电状态进行估计。The global positioning system collects the position information of the vehicle in front and the vehicle as real-time vehicle status feedback. The speed of the vehicle in front is collected by the on-board radar speed measuring device for tracking control. The intelligent traffic system and the inter-vehicle communication system collect traffic signal information, real-time road condition information, and the speed and acceleration information of the ego vehicle and the vehicle in front for intelligent traffic control. The Kalman filter utilizes the collected battery 4 information to estimate the state of charge of the battery.

2)车辆建模：2) Vehicle modeling:

行星齿轮式混联混合动力汽车包含5大动态部件。它们是发动机1，蓄电池4，发电机3，电动机6和车轮。动力分配器2作为动力分配装置既有速度耦合器的作用，又有电子无极变速器作用。根据车辆机械耦合和电子耦合关系，可以列写系统动力学方程。对动力学方程解耦，最终可以获得系统的状态空间模型，如式(1)所示。A planetary gear hybrid hybrid vehicle contains five dynamic components. They are engine 1, battery 4, generator 3, electric motor 6 and wheels. As a power distribution device, the power splitter 2 not only functions as a speed coupling, but also as an electronic continuously variable transmission. According to the vehicle mechanical coupling and electronic coupling relationship, the system dynamic equation can be written. By decoupling the dynamic equations, the state space model of the system can be finally obtained, as shown in formula (1).

式中，x为状态量，u为控制量。参数p₁，v₁，w₁和SOC₁为自车的位置，速度，考虑延迟的驱动加速度和蓄电池4荷电状态。参数p₂，v₂，w₂和SOC₂为前车的位置，速度，考虑延迟的驱动加速度和蓄电池4荷电状态。参数u₁，u₂，P_batt1和P_batt2为自车的驱动加速度，前车的驱动加速度，自车蓄电池4的充放电功率和前车蓄电池4的充放电功率。参数ρ，C_D1，C_D2，A₁，A₂，m₁，m₂，g，μ，θ₁和θ₂是空气密度，自车空气阻力系数，前车空气阻力系数，自车迎风面积，前车迎风面积，自车质量，前车质量，重力加速度，滚动阻力系数，自车道路坡度和前车道路坡度。V_OC，R_batt和Q_batt是蓄电池4开路电压，内阻和容量。In the formula, x is the state quantity, and u is the control quantity. The parameters p ₁ , v ₁ , w ₁ and SOC ₁ are the position, speed, driving acceleration taking into account the delay and the state of charge of the battery 4 of the ego vehicle. The parameters p ₂ , v ₂ , w ₂ and SOC ₂ are the position, speed, driving acceleration taking into account the delay and the battery 4 state of charge of the preceding vehicle. The parameters u ₁ , u ₂ , P _batt1 and P _batt2 are the driving acceleration of the own vehicle, the driving acceleration of the preceding vehicle, the charge and discharge power of the battery 4 of the own vehicle and the charge and discharge power of the battery 4 of the lead vehicle. The parameters ρ, C _D1 , C _D2 , A ₁ , A ₂ , m ₁ , m ₂ , g, μ, θ ₁ and θ ₂ are the air density, the air resistance coefficient of the own vehicle, the air resistance coefficient of the front vehicle, and the frontal area of the own vehicle , the frontal area of the front vehicle, the mass of the self-vehicle, the mass of the front vehicle, the acceleration of gravity, the rolling resistance coefficient, the road gradient of the self-vehicle and the road gradient of the front vehicle. V _oc , R _batt and Q _batt are battery 4 open circuit voltage, internal resistance and capacity.

3)公式化控制策略：3) Formulated control strategy:

基于队列行驶的混合动力汽车能量管理模型预测最优控制策略的步骤为：首先检测自车和前车状态，包括位置，速度和加速度信息，其次运用所建立的数学模型和公式化控制策略求解最优控制问题，最后应用所求得的最优控制序列的第一个控制量于系统。由于模型预测控制为区间最优控制，所以其求得的最优控制量是数量为预测区间除以采样间隔的序列。最优控制序列的第一个控制量与实际状态最接近，所以一般采用它来作为实际的控制量。The steps of predicting the optimal control strategy based on the HEV energy management model of platoon driving are as follows: firstly detect the state of the ego vehicle and the vehicle in front, including position, velocity and acceleration information, and then use the established mathematical model and formulaic control strategy to solve the optimal control strategy. Control problem, and finally apply the first control quantity of the obtained optimal control sequence to the system. Since the model predictive control is an interval optimal control, the optimal control quantity obtained by it is a sequence whose quantity is the prediction interval divided by the sampling interval. The first control quantity of the optimal control sequence is the closest to the actual state, so it is generally used as the actual control quantity.

本控制策略的特色有两点。第一，随着汽车导航，数字化地图，车间通信技术和智能交通系统的发展，利用道路交通状况，对混合动力汽车速度模式进行最优化。第二，前方有车辆的情况下，传统的固定车间距的控制算法现在还是主流，车间距离在最小值以上浮动的控制策略，提高了车辆速度变化的自由度，使混合动力汽车燃油经济性的提高有了可能。上述两大特色在控制策略设计中评价函数里有相应体现，为混合动力汽车系统性能提高提供了更大可能性。There are two characteristics of this control strategy. First, with the development of car navigation, digital map, vehicle-to-vehicle communication technology and intelligent transportation system, the speed pattern of hybrid electric vehicles is optimized by using road traffic conditions. Second, when there is a vehicle ahead, the traditional control algorithm of fixed inter-vehicle distance is still the mainstream, and the control strategy of inter-vehicle distance floating above the minimum value improves the freedom of vehicle speed change and improves the fuel economy of hybrid vehicles. Improvement is possible. The above two characteristics are reflected in the evaluation function in the design of the control strategy, which provides a greater possibility for the performance improvement of the hybrid electric vehicle system.

评价函数定义如式(4)所示。The definition of evaluation function is shown in formula (4).

式中SOC_d是目标蓄电池4荷电状态。v_d是车辆目标速度，它取值为车辆最优等速燃油经济性速度。w_x，w_y，w_z，w_d，w_e，w_f，和w_r是权重系数。d_d为最低车辆间距，评价函数设置使其在最低车辆间距以上浮动，从而增加控制自由度，提高车辆燃油经济性。障碍函数用于处理系统状态约束等。In the formula, SOC _d is the state of charge of the target battery 4 . v _d is the target speed of the vehicle, and its value is the optimal constant speed fuel economy speed of the vehicle. w _x , w _y , w _z , w _d , w _e , w _f , and w _r are weight coefficients. d _d is the minimum distance between vehicles, and the evaluation function is set to make it float above the minimum distance between vehicles, so as to increase the degree of freedom of control and improve the fuel economy of vehicles. Barrier functions are used to deal with system state constraints, etc.

4)在线最优控制：4) Online optimal control:

在每个采样时刻，首先，测取前车位置，自车位置，前车速度，自车速度，前车加速度，自车加速度，前车蓄电池4荷电状态和自车蓄电池4荷电状态等实时状态信号，其次，利用全球定位系统和智能交通系统预测未来一定区间车辆及周围环境的状态，再次，根据建立的车辆模型和最优控制问题，利用上述数值快速解法求解预测区间内的最优控制序列。应用预测区间内的最优控制序列的第一个控制量于车辆。之后在下一个采样时刻，将预测区间向前推进一步，如此循环往复，实现在线最优控制。At each sampling moment, firstly, measure the position of the vehicle in front, the position of the vehicle in front, the speed of the vehicle in front, the speed of the vehicle in front, the acceleration of the vehicle in front, the acceleration of the vehicle in front, the state of charge of the battery 4 of the front vehicle and the state of charge of the battery 4 of the own vehicle, etc. Real-time state signal, secondly, use the global positioning system and intelligent transportation system to predict the state of the vehicle and the surrounding environment in a certain interval in the future, thirdly, according to the established vehicle model and optimal control problem, use the above numerical fast solution method to solve the optimal control sequence. Apply the first control quantity of the optimal control sequence within the prediction interval to the vehicle. Then at the next sampling time, the prediction interval is pushed forward one step, and this cycle repeats to realize online optimal control.

本发明同样适用于其他形式混合动力汽车驱动系统，具体建模方法与控制过程与行星齿轮式混联混合动力汽车驱动系统一致，在此不再赘述。The present invention is also applicable to drive systems of other forms of hybrid electric vehicles, and the specific modeling method and control process are consistent with the drive systems of planetary gear hybrid hybrid electric vehicles, which will not be repeated here.

Claims

1. A hybrid electric vehicle energy-saving predictive control method based on platoon travel, is characterized in that:

The first step is information collection, the second step is vehicle modeling, the third step is the formula control strategy, and the fourth step is online optimal control; the specific steps are as follows:

1) Information collection: the global positioning system collects the position information of the vehicle in front and the vehicle as real-time vehicle status feedback; the vehicle-mounted radar speed measuring device collects the speed of the vehicle in front for tracking control; the intelligent traffic system and the vehicle-to-vehicle communication system collect traffic Signal information, real-time road condition information, self-vehicle speed, self-vehicle acceleration and front vehicle acceleration information are used for intelligent traffic control; Kalman filter uses the collected battery information to estimate the state of charge of the battery;

2) Vehicle modeling: The planetary gear type hybrid electric vehicle contains 5 major dynamic components, which are the engine, battery, two electric generators and wheels. As a power distribution device, the planetary gear not only functions as a speed coupling, It has the function of electronic continuously variable transmission; according to the vehicle mechanical coupling and electronic coupling relationship, write the system dynamic equation, decouple the dynamic equation, and finally obtain the state space model of the system, as shown in formula (1):

x＝[p ₁ v ₁ w ₁ SOC ₁ p ₂ v ₂ w ₂ SOC ₂ ]

u＝[u ₁ u ₂ P _batt1 P _batt1 ]

\overset{\cdot \cdot}{x x} = = f f ((x x,, u u))

f f ((x x,, u u)) = = [\begin{matrix} {v v}_{11} \\ {w w}_{11} - - \frac{11}{22} {ρC ρC}_{D D. 11} {A A}_{11} {v v}_{11}^{22} / / {m m}_{11} - - 9.8 9.8 μ μ - - 9.8 9.8 {sinθ sinθ}_{11} \\ {k k}_{p p} * * (({u u}_{11} - - {w w}_{11})) \\ - - \frac{{V V}_{max max 11} - - \sqrt{{V V}_{m m a a x x 11}^{22} - - 44 {P P}_{b b a a t t t t 11} {R R}_{b b a a t t t t 11}}}{22 {R R}_{b b a a t t t t 11} {Q Q}_{b b a a t t t t 11}} \\ {v v}_{22} \\ {w w}_{22} - - \frac{11}{22} {ρC ρC}_{D D. 22} {A A}_{22} {v v}_{22}^{22} / / {m m}_{22} - - 9.8 9.8 μ μ - - 9.8 9.8 {sinθ sinθ}_{22} \\ {k k}_{p p} * * (({u u}_{22} - - {w w}_{22})) \\ - - \frac{{V V}_{max max 22} - - \sqrt{{V V}_{max max 22}^{22} - - 44 {P P}_{b b a a t t t t 22} {R R}_{b b a a t t t t 22}}}{22 {R R}_{b b a a t t t t 22} {Q Q}_{b b a a t t t t 22}} \end{matrix}] - - - - - - ((11))

In the formula, x is the state quantity, u is the control quantity; the parameters p ₁ , v ₁ , w ₁ and SOC ₁ are the position, speed, acceleration considering the delay and the state of charge of the battery; the parameters p ₂ , v ₂ , w ₂ and SOC ₂ are the position and speed of the vehicle in front, the acceleration considering the delay and the state of charge of the battery; the parameters u ₁ , u ₂ , P _batt1 and P _batt2 are the acceleration of the vehicle in front, the acceleration of the vehicle in front, and the battery charge of the vehicle Charge and discharge power and the charge and discharge power of the front car battery; parameters ρ, C _D1 , C _D2 , A ₁ , A ₂ , m ₁ , m ₂ , g, μ, θ ₁ and θ ₂ are the air density, the air resistance of the self-vehicle coefficient, front vehicle air resistance coefficient, self-vehicle frontal area, front vehicle frontal area, self-vehicle mass, front vehicle mass, acceleration of gravity, rolling resistance coefficient, self-vehicle road gradient and front vehicle road gradient; V _OC , R _batt and Q _batt is the battery open circuit voltage, internal resistance and capacity;

The fuel economy evaluation of the vehicle adopts the Weilan linear model, as shown in formula (2):

{\overset{\cdot &Center Dot;}{m m}}_{f f} ((t t)) = = {\overset{\cdot &Center Dot;}{m m}}_{f f} (({P P}_{r r e e q q} ((t t)) - - {P P}_{b b a a t t t t} ((t t)))) \approx \approx {c c}_{f f} (({P P}_{r r e e q q} ((t t)) - - {P P}_{b b a a t t t t} ((t t)))) - - - - - - ((22))

In the formula, m _f is the fuel consumption rate, the parameter P _req is the required power of the vehicle, and c _f is a constant parameter;

3) Formulated control strategy: The steps of predicting the optimal control strategy based on the HEV energy management model of platoon driving are as follows: first, detect the state of the ego vehicle and the vehicle in front, including position, speed and acceleration information, and then use the established mathematical model and Formulate the control strategy to solve the optimal control problem, and finally apply the first control quantity of the obtained optimal control sequence to the system; since the model predictive control is an interval optimal control, the obtained optimal control sequence is The prediction interval is divided by the control sequence of the sampling interval. The first control quantity of the optimal control sequence is the closest to the actual state, so it is used as the actual control quantity;

At each sampling moment, predict the future cost function of the system according to the prediction model, optimize the performance index in the future prediction interval, and perform feedback correction according to the output of the measured object, transform the control strategy design into an optimization process, and solve The optimization problem of the corresponding prediction interval obtains the optimal control sequence, and the first control quantity of the optimal control sequence is applied to the system to realize feedback control, and then at the next sampling time, the prediction interval is pushed forward one step, and the process is repeated continuously ;

In summary, it includes three parts: predictive model, rolling optimization and feedback control, through the prediction of future system input to achieve real-time optimal control of the system;

The optimal control problem is defined as formula (3):

\begin{matrix} min min i i m m i i z z e e & J J = = {&Integral; &Integral;}_{t t}^{t t + + T T} L L ((x x ((τ τ | | t t)),, u u ((τ τ | | t t)))) d d τ τ \\ s the s u u b b j j e e c c t t t t o o & {P P}_{b b a a t t t t 11 min min} \leq \leq {P P}_{b b a a t t t t 11} ((τ τ | | t t)) \leq \leq {P P}_{b b a a t t t t 11 max max} \\ {u u}_{11 min min} \leq \leq {u u}_{11} ((τ τ | | t t)) \leq \leq {u u}_{11 max max} \\ {P P}_{b b a a t t t t 22 min min} \leq \leq {P P}_{b b a a t t t t 22} ((τ τ | | t t)) \leq \leq {P P}_{b b a a t t t t 22 max max} \\ {u u}_{22 min min} \leq \leq {u u}_{22} ((τ τ | | t t)) \leq \leq {u u}_{22 max max} \end{matrix} - - - - - - ((33))

In the formula, T is the prediction interval, and the parameters P _batt1min , P _batt1max , P _batt2min , P _batt2max , u _1max , u _1min , u _2max and u _2min are control quantity constraints,

The definition of the evaluation function is shown in formula (4):

\begin{matrix} L L = = {w w}_{x x} {L L}_{x x} + + {w w}_{y the y} {L L}_{y the y} + + {w w}_{z z} {L L}_{z z} + + {w w}_{d d} {L L}_{d d} + + {w w}_{e e} {L L}_{e e} + + {w w}_{f f} {L L}_{f f} + + {w w}_{r r} {L L}_{r r} \\ {L L}_{x x} = = {(({w w}_{11} - - \frac{11}{22} {ρC ρC}_{D D. 11} {A A}_{11} {v v}_{11}^{22} / / {m m}_{11} - - 9.8 9.8 * * μ μ))}^{22} / / 22 + + {(({w w}_{22} - - \frac{11}{22} {ρC ρC}_{D D. 22} {A A}_{22} {v v}_{22}^{22} / / {m m}_{11} - - 9.8 9.8 * * μ μ))}^{22} / / 22 \\ {L L}_{y the y} = = {(({v v}_{11} - - {v v}_{d d}))}^{22} / / 22 + + {(({v v}_{22} - - {v v}_{d d}))}^{22} / / 22 \\ {L L}_{z z} = = 0.0874 0.0874 * * (({m m}_{11} * * {w w}_{11} * * {v v}_{11} / / 10001000 - - {P P}_{b b a a t t t t 11})) / / ((11 + + {e e}^{- - 0.5 0.5 * * (({m m}_{11} * * {w w}_{11} * * {v v}_{11} / / 10001000 - - {P P}_{b b a a t t t t 11}))})) \\ + + 0.0874 0.0874 * * (({m m}_{22} * * {w w}_{22} * * {v v}_{22} / / 10001000 - - {P P}_{b b a a t t t t 22})) / / ((11 + + {e e}^{- - 0.5 0.5 * * (({m m}_{22} * * {w w}_{22} * * {v v}_{22} / / 10001000 - - {P P}_{b b a a t t t t 22}))})) \\ {L L}_{d d} = = {(({SOC SOC}_{11} - - {SOC SOC}_{d d}))}^{22} + + {(({SOC SOC}_{22} - - {SOC SOC}_{d d}))}^{22} \\ {L L}_{e e} = = {(({m m}_{11} * * {w w}_{11} * * {v v}_{11} / / 10001000 - - {P P}_{b b a a t t t t 11}))}^{22} / / 22 + + {(({m m}_{22} * * {w w}_{22} * * {v v}_{22} / / 10001000 - - {P P}_{b b a a t t t t 22}))}^{22} / / 22 \\ {L L}_{f f} = = ((- - ln ln [[{SOC SOC}_{11} - - 0.6 0.6]] - - ln ln [[0.8 0.8 - - {SOC SOC}_{11}]])) + + ((- - ln ln [[{SOC SOC}_{22} - - 0.6 0.6]] - - ln ln [[0.8 0.8 - - {SOC SOC}_{22}]])) \\ {L L}_{r r} = = - - ln ln ((d d - - {d d}_{d d})) \end{matrix} - - - - - - ((44))

where SOC _d is the target state of charge of the battery, v _d is the target speed of the vehicle, and its value is the optimal constant fuel economy speed of the vehicle, w _x , w _y , w _z , w _d , w _e , w _f , and w _r is the weight coefficient, d _d is the minimum vehicle distance, and the evaluation function is set to float above the minimum vehicle distance, thereby increasing the control degree of freedom and improving vehicle fuel economy. The barrier function is used to deal with system state constraints;

4) Online optimal control: In order to ensure the real-time optimal performance of the system, the numerical fast solution method based on the Hamiltonian equation is used to solve the above optimal control problem, because it only needs a limited number of iterations to calculate the optimal value of the numerical equation Solution, solution Specifically, the optimal control problem is transformed into a two-point boundary value problem by using the minimum value principle, and the partial space method is used to solve the differential equations and algebraic equations related to the Hamiltonian function, which is a kind of GMRES solution;

At each sampling moment, firstly, measure the front vehicle position, the self-vehicle position, the front vehicle speed, the self-vehicle speed, the acceleration of the front vehicle, the acceleration of the self-vehicle, the state of charge of the battery of the front vehicle and the real-time state signal of the state of charge of the self-vehicle battery , secondly, use the global positioning system and intelligent transportation system to predict the state of the vehicle and the surrounding environment in a certain interval in the future, and thirdly, according to the established vehicle model and optimal control problem, use the above numerical fast solution method to solve the optimal control sequence in the prediction interval; Apply the first control quantity of the optimal control sequence in the prediction interval to the vehicle; then at the next sampling time, advance the prediction interval one step forward, and so on, and realize the online optimal control.