Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles

Sun, Chao; Sun, Fuchun; He, Haibo

doi:10.1016/j.apenergy.2016.02.026

Cited by 282 publications

(149 citation statements)

References 20 publications

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“…Moreover, pattern/route recognition is also applied to update cost equivalent factors of PMP and ECMS [59,65]. Adaptation of the computing rate is widely applied to MPC [72] and adaptive/stochastic DP [34,73].…”

Section: Integration To Power Management Methodsmentioning

confidence: 99%

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Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

2018

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Abstract:In light of increasing alerts about limited energy sources and environment degradation, it has become essential to search for alternatives to thermal engine-based vehicles which are a major source of air pollution and fossil fuel depletion. Hybrid electric vehicles (HEVs), encompassing multiple energy sources, are a short-term solution that meets the performance requirements and contributes to fuel saving and emission reduction aims. Power management methods such as regulating efficient energy flow to the vehicle propulsion, are core technologies of HEVs. Intelligent power management methods, capable of acquiring optimal power handling, accommodating system inaccuracies, and suiting real-time applications can significantly improve the powertrain efficiency at different operating conditions. Rule-based methods are simply structured and easily implementable in real-time; however, a limited optimality in power handling decisions can be achieved. Optimization-based methods are more capable of achieving this optimality at the price of augmented computational load. In the last few years, these optimization-based methods have been under development to suit real-time application using more predictive, recognitive, and artificial intelligence tools. This paper presents a review-based discussion about these new trends in real-time optimal power management methods. More focus is given to the adaptation tools used to boost methods optimality in real-time. The contribution of this work can be identified in two points: First, to provide researchers and scholars with an overview of different power management methods. Second, to point out the state-of-the-art trends in real-time optimal methods and to highlight promising approaches for future development.

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Section: Integration To Power Management Methodsmentioning

confidence: 99%

“…Second, considering an equivalent factor function in terms of two optimized values for charging/discharging cases and a probability factor based on current and expected electric energy depletion [56]. Third, by implementing a multi-dimensional LUT using the current information of Vehicle load [57], position [58], speed [59], or trip length [60].…”

Section: Ecmsmentioning

confidence: 99%

Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

2018

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“…Some researchers have studied the model predictive control in hybrid electric vehicles. [30][31][32] In literature, 30 stochastic Markov chain and neural network-based velocity prediction approaches were described depending on basic principles of exponential variation, and results demonstrate that NN-based velocity predictors provide the best overall performance across a range of certification and real-world drive cycles. 30 A supervisory state of charge (SoC) planning level generating an SoC trajectory from traffic data for the terminal SoC constraints in the MPC level and a power balance PHEV model were developed in Sun et al 31 Results showed that the predictive energy management strategy with dynamic traffic data can achieve 94%-96% fuel optimality of the deterministic DP benchmark in a highway driving scenario.…”

Section: Battery System Modelingmentioning

confidence: 99%

Electric vehicle energy predictive optimal control by V2I communication

Zhu

Zheng

et al. 2019

Advances in Mechanical Engineering

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To achieve a better energy-efficient and comfortable vehicle driving, electric vehicle energy predictive optimal control was discussed in this article. It used Vehicle to Infrastructure (V2I) communication of traffic light distance and time gap to make velocity decision in order to improve whole driving performance. Based on the data of standard New European Driving Cycle, this article put forward a real-time cycle including traffic light position and time information at first. Then, a preview and optimal decision controller was built. Four evaluating indexes were set for optimal decision making to decide the optimal expected speed, according to the pass time that the forward traffic lights allowed and the distance or the forward vehicle speed and distance. After that AMESim and Simulink co-simulation platform was used to build the vehicle dynamics model and to exploit the control algorithm. The simulation results indicated that vehicle with V2I can effectively improve the operating condition of the whole vehicle and reduce the frequency of vehicle starting and stopping, which makes the vehicle driving smoother and steady. Moreover, it can reduce vehicle energy consumption and improve endurance mileage. Meanwhile, the vehicle acceleration and jerk have decreased dramatically with better comfortable performance.

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“…Peng et al [78] explored a novel way to calibrate the existed heuristic control strategy with the global optimization result through advanced intelligent algorithms in order to improve the performance of the rule-based energy management for the engine in plug-in hybrid electric vehicles. Sun et al [79] constructed a neural network based velocity predictor to forecast the short-term future driving behaviors by learning from history data, which is able to achieve better fuel economy and more stable battery state of charge trajectory, with a fuel consumption reduction by over 3%. The performance of plug-in hybrid electric vehicles largely depends on the energy management strategy.…”

Section: Electric Vehiclesmentioning

confidence: 99%