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The key to improving the fuel economy of plug-in hybrid electric vehicles (PHEVs) lies in the energy management strategy (EMS). Existing EMS often neglects engine operating conditions, leading to frequent start–stop events, which affect fuel economy and engine lifespan. This paper proposes an Integrated Engine Start–Stop Dynamic Programming (IESS-D...
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Context 1
... not only enhances the engine system's lifespan and reliabil also improves fuel efficiency by keeping the engine within its optimal operating ran longer periods. Table 4 shows that the fuel consumption per 100 kilometers under the RB stra 3.97L. Fuel consumption decreases to 3.49L under the DP strategy and further to under the IESS-DP strategy, representing a reduction of approximately 6.15%. ...Context 2
... not only enhances the engine system's lifespan and reliability but also improves fuel efficiency by keeping the engine within its optimal operating range for longer periods. Table 4 shows that the fuel consumption per 100 kilometers under the RB strategy is 3.97L. Fuel consumption decreases to 3.49L under the DP strategy and further to 3.36L under the IESS-DP strategy, representing a reduction of approximately 6.15%. ...Context 3
... not only enhances the engine system's lifespan and reliability but also improves fuel efficiency by keeping the engine within its optimal operating range for longer periods. Table 4 shows that the fuel consumption per 100 km under the RB strategy is 3.97 L. Fuel consumption decreases to 3.49 L under the DP strategy and further to 3.36 L under the IESS-DP strategy, representing a reduction of approximately 6.15%. Additionally, the IESS-DP strategy reduces fuel consumption by 5.35% compared to the DDRL/SS strategy. ...Similar publications
Regenerative braking technology is essential for reducing energy consumption in electric vehicles (EVs). This study introduces a method for optimizing the distribution of deceleration forces in front-wheel-drive electric vehicles that complies with the distribution range outlined by ECE-R13 braking regulations and aligns with an ideal braking distr...
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Over a period of four years, the influences of partially automated driving functions (SAE Level 2) on forensic accident reconstruction were investigated as part of the first author's dissertation. The core results are summarized in this article. Based on experimental analyses, various scenarios were identified in which dangerous driving situations...
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Plug-in Hybrid Electric Vehicles (PHEVs) represent an innovative breed of transportation, harnessing diverse power sources for enhanced performance. Energy management strategies (EMSs) that coordinate and control different energy sources is a critical component of PHEV control technology, directly impacting overall vehicle performance. This study p...
This paper presents an energy management algorithm based on an extended proportional integral derivative (PID) controller. To validate the proposed algorithm, comprehensive simulation models were developed, including a longitudinal dynamics-based vehicle model, an ampere–hour integration-based power battery model, a fuel cell model based on the Ner...
The traffic environment of mountainous highways is more complex than that of nonmountainous highways, with higher driving loads, which increases the risk in overtaking. The changes in the driver’s pupils, eye gaze behavior, and heart rate can be used to evaluate the level of driving tension and safety. To analyze the driving load while overtaking o...
Citations
... belongs to a set of variables in system space. When the algorithm performs control at different stages, the objective function size of the current stage can be obtained by solving the state function of the algorithm, as shown in equation (9) Gnanaprakasam et al., 2023;Chen et al., 2024). ...
This study mainly analyses the fuel consumption of plug-in hybrid vehicles during operation. A new control method for automobiles based on energy management strategy and dynamic programming algorithm is proposed. The new method plans and analyses the minimum electricity consumption, and then uses dynamic programming algorithms to analyse this parameter. The research results indicated that the vehicle state was constantly changing with the variation of SOC value during driving. The energy mobilization of the vehicle was more obvious after adding dynamic programming strategy. The efficiency of the vehicle was relatively high in driving state 1, with a minimum value of 70%, which was about 20% higher than in driving state 4. The average fuel consumption in driving state 2 was 1.8L higher than in other driving states. The overall efficiency of automobiles after incorporating dynamic programming was improved, with a shorter time to reach the lowest efficiency point compared with not incorporating dynamic programming algorithms. The highest efficiency value was 7.86% higher than that of not incorporating dynamic programming models. The new control method can reduce energy consumption and improve the energy management and control effect. The study provides a better research direction for energy management and control of hybrid electric vehicles in the future.
