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Neural networks are widely used in the learning of offline global optimization rules to reduce the fuel consumption and real-time performance of hybrid electric vehicles. Considering that the torque and transmission ratio are direct control variables, online recognition by a neural network of these two parameters is insufficiently accurate. In the...
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... the same time, the DP-A-ECMS algorithm was used to obtain the optimal sequence of equivalent factors offline for test conditions. In Figure 11, the results of the online identification of the equivalent factors of the neural network under a short period of working conditions are selected. Figure 11a is a comparison of the equivalent factor identified by the BP neural network and the median value of the optimal equivalent factors by DP-A-ECMS. ...
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... Figure 11, the results of the online identification of the equivalent factors of the neural network under a short period of working conditions are selected. Figure 11a is a comparison of the equivalent factor identified by the BP neural network and the median value of the optimal equivalent factors by DP-A-ECMS. The mean square error (MSE) is usually used to measure the recognition accuracy of neural networks. ...
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... BP S is the equivalent factor recognized by the neural network, mid S is the median value of optimal equivalent factors obtained by the DP-A-ECMS, and N represents the total time steps. The upper limit and lower limit of optimal equivalent factors obtained by the DP-A-ECMS are shown in Figure 11b. According to the conclusion of the previous analysis, as long as the equivalent factor is within its upper and lower limits, the final output of the vehicle is the same. ...
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... max S and min S are the upper limit and lower limit of the optimal equivalent factors, respectively. It can be seen from Figure 11 that the neural network has a certain error in the recognition, but most of the recognized equivalent factors fall within the upper and lower limits of the optimal equivalent factor. Intuitively, the upper and lower limits of the optimal equivalent factor provide a fault-tolerant space for the online recognition of parameters. ...
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... the upper and lower limits of the optimal equivalent factor provide a fault-tolerant space for the online recognition of parameters. From the mathematical formula, in the updated MSE (Equation (20)), the part where the recognition equivalent factor is within the upper In Figure 11, the results of the online identification of the equivalent factors of the neural network under a short period of working conditions are selected. Figure 11a is a comparison of the equivalent factor identified by the BP neural network and the median value of the optimal equivalent factors by DP-A-ECMS. ...
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... the mathematical formula, in the updated MSE (Equation (20)), the part where the recognition equivalent factor is within the upper In Figure 11, the results of the online identification of the equivalent factors of the neural network under a short period of working conditions are selected. Figure 11a is a comparison of the equivalent factor identified by the BP neural network and the median value of the optimal equivalent factors by DP-A-ECMS. The mean square error (MSE) is usually used to measure the recognition accuracy of neural networks. ...
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... can be seen that the fuel economy of the method proposed in this paper is increased by 2.46%. The upper limit and lower limit of optimal equivalent factors obtained by the DP-A-ECMS are shown in Figure 11b. According to the conclusion of the previous analysis, as long as the equivalent factor is within its upper and lower limits, the final output of the vehicle is the same. ...
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... S max and S min are the upper limit and lower limit of the optimal equivalent factors, respectively. It can be seen from Figure 11 that the neural network has a certain error in the recognition, but most of the recognized equivalent factors fall within the upper and lower limits of the optimal equivalent factor. Intuitively, the upper and lower limits of the optimal equivalent factor provide a fault-tolerant space for the online recognition of parameters. ...
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... There is extensive literature on the HEV controller design [8][9][10][11]. In the literature, two main groups of control strategies are used to develop EMS for HEVs [7]. These are the rule-based (RBCS) and the optimisation-based control strategies (OBCS). ...
... Commonly employed optimisation-based control strategies, such as dynamic programming and equivalent consumption minimisation, seek to minimise a cost function that includes the torque of the internal combustion engine (ICE) and the battery state of charge [19,21,22]. In these control strategies, the inertia effect of the flywheel and crankshaft is either neglected [8,9] or considered within the vehicle's apparent mass [21]. ...
The energy management system (EMS) of a hybrid electric vehicle (HEV) is an algorithm that determines the power split between the electrical and thermal paths. It defines the operating state of the power sources, i.e., the electric motor (EM) and the internal combustion engine (ICE). It is therefore one of the main factors that can significantly influence the fuel consumption and performance of hybrid vehicles. In the transmission path, the power generated by the ICE is in part employed to accelerate the rotating components of the powertrain, such as the crankshaft, flywheel, gears, and shafts. The main inertial components are the crankshaft and the flywheel. This additional power is significant during high-intensity acceleration. Therefore, the actual engine operation is different from that required by the power split unit. This study focuses on exploring the influence of engine inertia on HEV fuel consumption by developing a controller based on an equivalent consumption minimisation strategy (ECMS) that considers crankshaft and flywheel inertia. The optimal solution obtained by the ECMS controller is refined by incorporating the inertia effect of the main rotating components of the engine into the cost function. This reduces the engine operation during high inertial torque transient phases, resulting in a decrease in vehicle CO2 emissions by 2.34, 2.22, and 1.13 g/km for the UDDS, US06, and WLTC driving cycles, respectively.
... Overall, an ANN-based power management strategy is a promising approach to improving the performance and efficiency of electric vehicles, and it has the potential to further improve as machine learning algorithms continue to evolve [41]. Efficient energy management strives to maximize the efficiency of powertrain components while preserving a sufficient quantity of energy in storage devices. ...
Integrating solar energy in electric vehicles (EV) is expected to play a dominant role in the decarbonization of the transportation sector as well as reducing the charging costs. These integrated photovoltaic automobiles are particularly adapted for urban driving that is to say the driving range is limited. On that account, the use of a feasible energy storage system is necessary to boost the driving mileage. This paper uses a hybrid energy system, containing a battery as the main storage device and a supercapacitor (SC) as a backup. This combination was suggested in order to negate the former's deficiencies. The use of a hybrid energy source leads to the necessity of introducing a robust power management strategy to guarantee the optimal power flow in electric vehicle components. An artificial neural network is then trained with model calculation for the power management approach for the traction chain. The results of the simulation indicate that the proposed system is efficient in improving the energy management of the vehicle. Moreover, the system's simplicity could potentially make it easier to implement in real-time using a DSP or a DSPACE platform.
... According to the previous studies, the factors affecting EF in ECMS mainly include driving mileage, working condition and initial SOC [49,50]. As shown in Fig. 7 (a) and (b), under the same initial SOC, the optimal EF and varies approximately proportionally with the driving mileage. ...
In this paper, a hierarchical energy management control strategy is investigated for autonomous plug-in hybrid electric vehicle in vehicle-following environment. With the target of safety and comfort, the designed algorithm is divided into two layers. The grey neural network is leveraged in the upper layer controller to predict the future speed trend of preceding vehicle, and the target speed of ego vehicle is planned by fuzzy adaptive control algorithm. By combining with the planned state of charge reference trajectory, genetic algorithm is exploited in the adaptive equivalent consumption minimization strategy-based lower layer controller to determine the initial equivalent factor map by offline iterative calculation, and the fuzzy logic algorithm is employed to update the equivalent factor in real time according to the state of charge difference. Finally, the simulation and hardware-in-the-loop experiment are conducted to validate the performance of the proposed strategy. The simulation results highlight the capability of the proposed strategy in solving multi-objective optimization for autonomous plug-in hybrid electric vehicle in vehicle-following environment, and the experiment results validate that the energy consumption economy of the proposed strategy reaches 95.43% optimality of the results derived by dynamic programming while ensuring the satisfied driving comfort and safety.
In order to prevent the aggravation of global environmental problems, all industries are facing the challenge of green development. In the automotive field, the development of “new-energy vehicles” (plug-in electric vehicles) is particularly necessary. Hybrid electric vehicles (HEVs) have been proven to be an efficient way of solving environmental and energy problems. As the core of HEVs, the energy management strategy (EMS) plays an important role in fuel economy, power performance, and drivability. However, considering the randomness of actual driving conditions, there are great challenges involved in the establishment
of an EMS. Therefore, it is critical to develop an efficient and adaptable EMS. This paper presents a systematic review of EMSs for HEVs. First, different
issues that can affect the performance of EMSs are summarized. Second, recent studies on EMSs for HEVs are reviewed. Third, the advantages and disadvantages of
different categories of EMSs are compared in detail. Finally, promising EMS research topics for future study are put forward.
