Adaptive energy management of a plug-in hybrid electric vehicle based on driving pattern recognition and dynamic programming

Zhang, Shuo; Xiong, Rui

doi:10.1016/j.apenergy.2015.06.003

Cited by 357 publications

(141 citation statements)

References 33 publications

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“…The above problem can be solved with dynamic programming (DP) which is a common method for solving optimal control problems numerically [65][66][67]. To achieve this, the terms on the right-hand side of (6).…”

Section: Optimal Control Statement For Key-off Coolingmentioning

confidence: 99%

Improving the Performance Attributes of Plug-in Hybrid Electric Vehicles in Hot Climates through Key-Off Battery Cooling

et al. 2017

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Ambient conditions can have a significant impact on the average and maximum temperature of the battery of electric and plug-in hybrid electric vehicles. Given the sensitivity of the ageing mechanisms of typical battery cells to temperature, a significant variability in battery lifetime has been reported with geographical location. In addition, high battery temperature and the associated cooling requirements can cause poor passenger thermal comfort, while extreme battery temperatures can negatively impact the power output of the battery, limiting the available electric traction torque. Avoiding such issues requires enabling battery cooling even when the vehicle is parked and not plugged in (key-off), but the associated extra energy requirements make applying key-off cooling a non-trivial decision. In this paper, a representative plug-in parallel hybrid electric vehicle model is used to simulate a typical 24-h duty cycle to quantify the impact of hot ambient conditions on three performance attributes of the vehicle: the battery lifetime, passenger thermal comfort and fuel economy. Key-off cooling is defined as an optimal control problem in view of the duty cycle of the vehicle. The problem is then solved using the dynamic programming method. Controlling key-off cooling through this method leads to significant improvements in the battery lifetime, while benefiting the fuel economy and thermal comfort attributes. To further improve the battery lifetime, partial charging of the battery is considered. An algorithm is developed that determines the optimum combination of key-off cooling and the level of battery charge. Simulation results confirm the benefits of the proposed method.

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Section: Optimal Control Statement For Key-off Coolingmentioning

confidence: 99%

Improving the Performance Attributes of Plug-in Hybrid Electric Vehicles in Hot Climates through Key-Off Battery Cooling

et al. 2017

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“…However, the optimized parameters of HEVs based on a certain driving pattern may not maintain the battery SOC balance in other patterns, not to mention the best fuel consumption [15]. Therefore, energy management strategies based on driving pattern recognition have recently been put forward in the literature [16,17]. To optimize the vehicle performance on a random driving pattern, a multi-mode driving control algorithm using driving pattern recognition is developed for HEVs [18,19].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Control Strategy for Hybrid Electric Vehicles Based on Parameter Optimization for Multiple Driving Cycles and Driving Pattern Recognition

et al. 2017

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Abstract:The driving pattern has an important influence on the parameter optimization of the energy management strategy (EMS) for hybrid electric vehicles (HEVs). A new algorithm using simulated annealing particle swarm optimization (SA-PSO) is proposed for parameter optimization of both the power system and control strategy of HEVs based on multiple driving cycles in order to realize the minimum fuel consumption without impairing the dynamic performance. Furthermore, taking the unknown of the actual driving cycle into consideration, an optimization method of the dynamic EMS based on driving pattern recognition is proposed in this paper. The simulation verifications for the optimized EMS based on multiple driving cycles and driving pattern recognition are carried out using Matlab/Simulink platform. The results show that compared with the original EMS, the former strategy reduces the fuel consumption by 4.36% and the latter one reduces the fuel consumption by 11.68%. A road test on the prototype vehicle is conducted and the effectiveness of the proposed EMS is validated by the test data.

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“…In the process of PHEVs' design and development, sufficient road driving cycle analysis is the basis step related to whether the control strategy could meet the dynamic demand of the PHEV or not, as well as realizing the optimal allocation of energy, improving fuel economy [4].…”

Section: Introductionmentioning

confidence: 99%

Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles

Guo

Zhou

et al. 2017

Energies

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This paper presents a freeway driving cycle (FDC) construction method based on traffic information. A float car collected different type of roads in California and we built a velocity fragment database. We selected a real freeway driving cycle (RFDC) and established the corresponding time traffic information tensor model by using the data in California Department of Transportation performance measure system (PeMS). The correlation of road velocity in the time dimension and spatial dimension are analyzed. According to the average velocity of road sections at different times, the kinematic fragments are stochastically selected in the velocity fragment database to construct a real-time FDC of each section. The comparison between construction freeway driving cycle (CFDC) and real freeway driving cycle (RFDC) show that the CFDC well reflects the RFDC characteristic parameters. Compared to its application in plug-in electric hybrid vehicle (PHEV) optimal energy management based on a dynamic programming (DP) algorithm, CFDC and RFDC fuel consumption are similar within approximately 5.09% error, and non-rush hour fuel economy is better than rush hour 3.51 (L/100 km) at non-rush hour, 4.29 (L/km) at rush hour)). Moreover, the fuel consumption ratio can be up to 13.17% in the same CFDC at non-rush hour.

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Adaptive energy management of a plug-in hybrid electric vehicle based on driving pattern recognition and dynamic programming

Cited by 357 publications

References 33 publications

Improving the Performance Attributes of Plug-in Hybrid Electric Vehicles in Hot Climates through Key-Off Battery Cooling

Improving the Performance Attributes of Plug-in Hybrid Electric Vehicles in Hot Climates through Key-Off Battery Cooling

A Dynamic Control Strategy for Hybrid Electric Vehicles Based on Parameter Optimization for Multiple Driving Cycles and Driving Pattern Recognition

Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles

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