A general analysis of the development of energy management strategies for hybrid electric vehicles is carried out. At present, the influencing factors of energy management strategies for hybrid electric vehicles include: the selection and determination of the objective function, the actual driving condition cycle or the prediction method of the operating conditions (such as The efficiency is different in highway and urban conditions), the degree of hybrid power (the power ratio provided by the motor and the engine), the impact of dynamic changes or transient processes on fuel consumption, the choice of control methods, etc. The limitations of the energy management strategy are mainly reflected in the prediction of the working condition information of the entire driving cycle, the judgment of the driving intention, the large amount of real-time control calculations, and the inability to achieve global optimization. The specific summary is shown in Table 1. In view of the problems and challenges existing in the above hybrid energy management system, the future development and direction of efforts are summarized in the following aspects.
| Classification | Rule based | Based on global optimization | Based on instantaneous optimization |
| Advantage | The algorithm is simple and easy to implement | It has ideal optimization performance, can achieve global optimization, and is often used for performance evaluation of other algorithms | Usually not restricted by cycle conditions, with less computation, it can be used for real-time control, and can achieve optimal instantaneous energy |
| Shortcoming | Relying on experience and static data, it cannot adapt to changes in working conditions and dynamic changes of load, and cannot guarantee optimal control | Usually depends on the working condition cycle, the calculation amount of the algorithm is large, which is not conducive to real-time control, so it has certain limitations | Global optimum cannot be guaranteed |
(1) The regenerative braking control strategy is improved from the perspective of braking energy recovery. HEV regenerative braking contributes about 35% of the total energy efficiency. Therefore, from the perspective of improving regenerative braking control, energy can be further saved, and exploring more optimal energy management control strategies will be more conducive to improving vehicle fuel economy and emissions.
(2) Reduce the energy loss of instantaneous working conditions. The driving cycle conditions of a hybrid electric vehicle have a significant impact on its fuel economy and emission performance. Transient operating conditions account for a high proportion of urban vehicle operating conditions. From the statistical analysis of cyclic operating conditions in European countries, the United States, Japan, and Shanghai, Beijing, Wuhan and other cities in China, the proportion of acceleration time is 20%~45%, most of them are above 30%, the deceleration time is 20%~31%, when the parking frequency is the highest, the average interval between two stops is 33s, improving the instantaneous energy consumption has a greater effect on the fuel economy of the vehicle Space.
(3) Improve the overall efficiency of hybrid electric vehicles from the perspective of vehicle powertrain and parameter matching, so as to achieve the purpose of improving fuel economy. A hybrid electric vehicle is composed of an engine, a motor unit and a transmission system. According to fuel consumption and driving performance, matching and optimizing the specifications and parameters of the powertrain (including motor-generator unit, internal combustion engine and battery, and transmission) can further improve the overall efficiency. Optimize performance.
(4) Acquisition and prediction of real-time road condition information. The impact of energy management control strategies on fuel economy and emission performance and cycle conditions has a strong dependence. The fuel economy under different operating conditions is different, and most algorithms often need to assume that the entire driving cycle conditions are known. This is impossible in practice, so it needs to be combined with some technologies for acquisition and prediction: ① Combined with the intelligent transportation system, using advanced sensors, navigation and global positioning system (Global Positioning System, GPS) and other technologies to obtain real-time 2) Use intelligent control strategies, such as neural networks, machine learning and other methods to predict road conditions and real-time information; 3) Use intelligent transportation systems to obtain road conditions and traffic status, and also achieve coordinated control of fleets.
(5) Improvement of energy management methods. The assumptions, constraints and control methods of the current hybrid electric vehicle energy management scheme are improved to improve the adaptability and practical applicability of the control strategy. For example, dynamic programming algorithms require the assumption that the operating cycle is known and the computational burden is limited: ECMS usually assumes that the equivalence factor is a fixed value, and based on the equivalent fuel consumption, the power compensation is performed at the same operating point of the engine: battery charge and discharge Efficiency equivalence assumption.
Synthesize the advantages of various existing energy management strategies to realize compound control to improve the overall performance of the system. Considering the good optimization performance of the global optimal algorithm, the adaptability and robustness of the fuzzy logic algorithm, the real-time performance of the real-time optimization algorithm, etc., explore the mutual cooperation and integration of different control algorithms, and learn from each other to achieve better control effects.
Develop and explore energy management strategies more applicable to HEVs for better control. Through a more in-depth understanding and mastery of the energy relationship and working characteristics of the various components of HEVs, the problems can be refined from different perspectives and more applicable energy management strategies can be sought.
