Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles

Sun, Chao; Hu, Xiaosong; Moura, Scott J.; Sun, Fuchun

doi:10.1109/tcst.2014.2359176

Cited by 399 publications

(54 citation statements)

References 29 publications

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“…The results showed non-parametric models consistently outperformed other models for both short and long term predictions. Compared to parametric approaches, non-parametric approaches can result in higher accuracies [31], particularly, neural networks that showed the best prediction results [32]. In another comparative study [33], Back-Propagation Neural Networks (BPNNs) and traditional approaches were compared, and the results showed the BPNN to be superior and more responsive to dynamic conditions.…”

Section: Non-parametric Approachesmentioning

confidence: 99%

Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction

Abduljabbar

Dia

Tsai

et al. 2021

Future Transportation

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Traffic forecasting remains an active area of research in the transport and data science fields. Decision-makers rely on traffic forecasting models for both policy-making and operational management of transport facilities. The wealth of spatial and temporal real-time data increasingly available from traffic sensors on roads provides a valuable source of information for policymakers. This paper adopts the Long Short-Term Memory (LSTM) recurrent neural network to predict speed by considering both the spatial and temporal characteristics of real-time sensor data. A total of 288,653 real-life traffic measurements were collected from detector stations on the Eastern Freeway in Melbourne/Australia. A comparative performance analysis among different models such as the Recurrent Neural Network (RNN) that has an internal memory that is able to remember its inputs and Deep Learning Backpropagation (DLBP) neural network approaches are also reported. The LSTM results showed average accuracies in the outbound direction ranging between 88 and 99 percent over prediction horizons between 5 and 60 min, and average accuracies between 96 and 98 percent in the inbound direction. The models also showed resilience in accuracies as the prediction horizons increased spatially for distances up to 15 km, providing a remarkable performance compared to other models tested. These results demonstrate the superior performance of LSTM models in capturing the spatial and temporal traffic dynamics, providing decision-makers with robust models to plan and manage transport facilities more effectively.

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Section: Non-parametric Approachesmentioning

confidence: 99%

Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction

Abduljabbar

Dia

Tsai

et al. 2021

Future Transportation

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“…where v is the present velocity; + a is the next step acceleration; p ij is the transition probability from v i to a j ; H ij indicates the transition counts from v i to a j ; H i is the total transition counts initiated from v i ; the transition probability matrix ∏ is filled with elements p ij . Motivated by (10), the probability vector of the next state is defined as…”

Section: Interval Fuzzy Predictormentioning

confidence: 99%

“…Because of the capability of achieving high performance in multivariable systems subject to constraints, MPC has attracted considerable interest in the automotive industry (see reviews, [7][8][9] and references therein). However, the performance of MPC is also determined by the precision of future velocity or power forecasts and its prediction model is hard formulated due to strong randomness and uncertainty [10]. The authors have proposed a real-time nonlinear model-based predictive energy management method, which makes it possible to carry on online nonlinear control optimization with advanced predictive models [11].…”

Section: Introductionmentioning

confidence: 99%

Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles

Zhou

et al. 2019

Applied Energy

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“…MPC operates a rolling optimisation process in the vehicle controller, which is based on the prediction of the vehicle's future power demands over an optimisation horizon with mathematic model [24]. However, the performance of MPC is heavily dependent on the prediction of the driving conditions and vehicle states [25]. Recently, the unveiled legislation evaluates the vehicle emissions in realworld driving [26], therefore, the development of learning-based adaptive control is necessary where both rule-based and model-based energy management methods have their limitations.…”

Section: Introductionmentioning

confidence: 99%

Heuristic action execution for energy efficient charge-sustaining control of connected hybrid vehicles with model-free double Q-learning

Shuai

Zhou

et al. 2020

Applied Energy

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Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles

Cited by 399 publications

References 29 publications

Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction

Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction

Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles

Heuristic action execution for energy efficient charge-sustaining control of connected hybrid vehicles with model-free double Q-learning

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