Cyber-physical energy optimization control system and control method for hybrid electric vehicle

Abstract

A cyber-physical energy optimization control system for a hybrid electric vehicle includes an information layer which is configured to realize vehicle and road condition information collection, hybrid control unit (HCU) threshold optimization and threshold wireless update loading, and an optimized object plug-in hybrid electric bus (PHEB) as a physical layer. A cyber-physical energy optimization control method for a hybrid electric vehicle includes steps of collecting a real-time position of an optimized HEV and road slope information of the real-time position, collecting speed information which reflects traffic conditions on a road section to be optimized, constructing a vehicle model virtual operating platform for threshold optimization through the collected information, quickly optimizing related parameters with a help of efficient optimization algorithms, obtaining best results, and finally sending and loading corresponding parameters to a hybrid control unit (HCU) before the optimized vehicle is about to arrive at the optimized road section.

Claims

1. A cyber-physical energy optimization control system for a hybrid electric vehicle (HEV), the system comprising an information layer which is configured to realize vehicle and road condition information collection, hybrid control unit (HCU) threshold optimization and threshold wireless update loading, and an optimized object plug-in hybrid electric bus (PHEB) as a physical layer, wherein: the information layer comprises: a global positioning system (GPS) and a geographic information system (GIS) configured to detect a real-time position of a vehicle and a road slope of the real-time position; a traffic flow condition acquisition device which comprises multiple roadside speed detection cameras and multiple vehicles with a same route as an optimized vehicle, wherein the traffic flow condition acquisition device is configured to collect vehicle speed information which reflects traffic conditions; and a remote monitoring center which is configured to collect information from the GPS/GIS and the traffic flow condition acquisition device on a road section to be optimized for constructing a vehicle model virtual operating platform for threshold optimization, and then quickly optimize HCU thresholds with the help of efficient optimization algorithms, and then obtain the best results, and then send and load the optimized HCU thresholds to a HCU before the optimized vehicle is about to arrive at the optimized road section.

2. A cyber-physical energy optimization control method for a hybrid electric vehicle (HEV) comprises steps of: collecting a real-time position of an optimized HEV and road slope information of the real-time position, collecting speed information which reflects traffic conditions on a road section to be optimized, constructing a vehicle model virtual operating platform for threshold optimization through the collected information, quickly optimizing hybrid control unit (HCU) thresholds with a help of efficient optimization algorithms, obtaining best results, and finally sending and loading the optimized HCU thresholds to a HCU before the optimized vehicle is about to arrive at the optimized road section.

3. The cyber-physical energy optimization control method for the HEV according to claim 2, wherein the threshold optimization is achieved by firework algorithm.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0034] FIG. 1 shows an existing energy management strategy optimization process.

[0035] FIG. 2 shows a cyber-physical energy optimization control system provided by the present invention.

[0036] FIG. 3 is a structural diagram of a powertrain of a plug-in hybrid electric bus (PHEB) provided by the present invention.

[0037] FIG. 4 shows a control logic of a hybrid control unit (HCU) provided by the present invention.

[0038] FIG. 5 shows a HCU threshold optimization process based on firework algorithm provided by the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

[0039] The specific technical scheme of the present invention is explained in combination with the embodiment as follows.

[0040] The present embodiment intends to propose an efficient cyber-physical energy optimization control method for a plug-in hybrid electric bus (PHEB) with the help of the currently rapidly developing intelligent network technologies including vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). As shown in FIG. 2, a cyber-physical energy optimization control system for a hybrid electric vehicle (HEV) comprises an information layer which is able to realize vehicle and road condition information collection, hybrid control unit (HCU) threshold optimization and threshold wireless update loading, and an optimized object PHEB as a physical layer, wherein main components and functions of the information layer are as follows:

[0041] a global positioning system (GPS) and a geographic information system (GIS) configured to detect a real-time position of a vehicle and a road slope of the real-time position;

[0042] a traffic flow condition acquisition device which comprises multiple roadside speed detection cameras and multiple vehicles with a same route as an optimized vehicle (namely, multiple buses with a same route as the optimized PHEB in FIG. 2), wherein the traffic flow condition acquisition device is configured to collect vehicle speed information which reflects traffic conditions; and

[0043] a remote monitoring center which is configured to collect the above two types of information on a road section to be optimized for constructing a vehicle model virtual operating platform for threshold optimization, and then quickly optimize related parameters with the help of efficient optimization algorithms, and then obtain the best results, and then send and load HCU thresholds to a HCU before the optimized vehicle is about to arrive at the optimized road section.

[0044] As shown in FIG. 3, a powertrain of the PHEB is illustrated, which comprises an engine 1, a clutch 2, a motor 3, a gearbox 4, a differential mechanism 5 and a battery 6, wherein the engine 1, the clutch 2, the motor 3, the gearbox 4 and the differential mechanism 5 are connected with each other in sequence, the motor 3 is connected with the battery 6. A control logic of the HCU inside the HEV and a corresponding specific rule base are shown in FIG. 4 and Table 1, respectively. It is able to be known from Table 1 that there are four important thresholds SOC_h, SOC_l, Pe_h and Pe_l that need to be optimized to improve fuel economy of the HEV. Moreover, combined with specific rules in Table 1, it is able to be seen that the prerequisite for improving the fuel economy of the HEV is to ensure the dynamic performance of the HEV and the safety of its own hardware.

TABLE-US-00001 TABLE 1 Rule-controlled rule base Input 1 Input 2 P.sub.e P.sub.m SOC > SOC_h P.sub.dem ≤ 0 0 0 P.sub.dem < P.sub.m.sub.—.sub.max 0 min (P.sub.dem − P.sub.e, P.sub.m.sub.—.sub.max) P.sub.dem ≥ P.sub.dem < P.sub.e.sub.—.sub.h min (P.sub.e.sub.—.sub.h, min (P.sub.dem − P.sub.e, P.sub.m.sub.—.sub.max P.sub.e.sub.—.sub.max) P.sub.m.sub.—.sub.max) P.sub.e.sub.—.sub.l ≤ P.sub.dem ≤ min (P.sub.dem, P.sub.dem − P.sub.e P.sub.e.sub.—.sub.h P.sub.e.sub.—.sub.max) P.sub.dem < P.sub.e.sub.—.sub.l min (P.sub.e.sub.—.sub.l, P.sub.e.sub.—.sub.max) max (P.sub.dem − P.sub.e, P.sub.m.sub.—.sub.min) SOC_l ≤ SOC ≤ P.sub.dem ≤ 0 0 max (P.sub.dem, SOC_h P.sub.m.sub.—.sub.min) P.sub.dem P.sub.e.sub.—.sub.h min (P.sub.e.sub.—.sub.h, min (P.sub.dem − P.sub.e, P.sub.e.sub.—.sub.max) P.sub.m.sub.—.sub.max) P.sub.e.sub.—.sub.l ≤ P.sub.dem ≤ P.sub.e.sub.—.sub.h min (P.sub.dem, P.sub.dem − P.sub.e P.sub.e.sub.—.sub.max) P.sub.dem < P.sub.e.sub.—.sub.l 0 min (P.sub.dem, P.sub.m.sub.—.sub.max) SOC < SOC_l P.sub.dem ≤ 0 0 max (P.sub.dem, P.sub.m.sub.—.sub.min) P.sub.dem > P.sub.e.sub.—.sub.h min (P.sub.dem, min (P.sub.dem − P.sub.e, P.sub.e.sub.—.sub.max) P.sub.m.sub.—.sub.max) P.sub.e.sub.—.sub.l ≤ P.sub.dem ≤ P.sub.e.sub.—.sub.h min (P.sub.dem, P.sub.dem − P.sub.e P.sub.e.sub.—.sub.max) 0.8 × P.sub.e.sub.—.sub.l ≤ P.sub.dem ≤ P.sub.e.sub.—.sub.l min (P.sub.e.sub.—.sub.l, P.sub.e.sub.—.sub.max) min (P.sub.e.sub.—.sub.l, P.sub.e.sub.—.sub.max) P.sub.dem < P.sub.e.sub.—.sub.l 0 min (P.sub.dem, P.sub.m.sub.—.sub.max)

[0045] Of course, in the process of threshold optimization, it is also very important to adopt an efficient optimization algorithm to improve optimization efficiency. In the present invention, it is proposed to adopt the firework algorithm as the threshold optimization method in the HCU, which is a new intelligent optimization algorithm that has emerged in recent years. The firework algorithm simulates the process of firework display in real life, regards a certain display space as the parameter range, and takes the multi-dimensional position coordinates of randomly generated sparks as candidate values for the optimized parameter sequence. For example, the four-dimensional firework position coordinates (0.7, 0.5, 150, 98) are able to be understood that the thresholds SOC_h, SOC_l, Pe_h, and Pe_l are 0.7, 0.5, 150, 98, respectively. The firework algorithm has the characteristics of distributed and diffuse optimization, careful search in high-probability areas, and fast search in low-probability areas. FIG. 5 shows a specific optimization process for HCU thresholds. After the verification by related simulation experiments, it is proved that the firework algorithm is able to better fit the HCU parameter optimization work under the cyber-physical energy optimization control system.

[0046] In the process of parameter optimization of the firework algorithm, the following points need to be noted.

[0047] (1) The fitness value in FIG. 5 is the key to link the optimization algorithm with the vehicle control problem. Firstly, in the firework algorithm, the fitness value directly judges the quality of HCU parameters. The higher the fitness, the better the parameter is suitable for the current control work, and the easier it is to be preserved in the iterative evolution process. For vehicle control problems, the fitness value is directly related to the fuel consumption of the vehicle on a specific road section under the control of the HCU thresholds, and the two are in a negative correlation.

[0048] (2) Firework position (more than one firework) and spark position have the same status for finding the best position (multi-dimensional parameters). The difference is that the spark is usually randomly generated with the firework position as the center and a certain distance as the radius, so as to check whether there are better coordinate points around the firework position. The generation of sparks follows the principle that better fireworks produce more sparks in a smaller radius, while poor fireworks produce less sparks in a larger radius. The basis of this operation is that there is a certain continuity in the performance of the parameters, and the best parameters have a high probability of appearing near the better parameters. Therefore, this action is tantamount to rationally using the limited operation ability to quickly expand the global optimization.

[0049] (3) When selecting the position of the next generation of fireworks, the principle of the elite retention strategy will be followed, and the best firework or spark position of this generation will be reserved as one of the next generation of fireworks. At the same time, in order to effectively avoid the optimization falling into the local optimum, other fireworks are randomly generated in the current known positions.