Assessing the Potential of Predictive Control for Hybrid Vehicle Powertrains Using Stochastic Dynamic Programming

By Lars Johannesson Mårdh, Mattias Asbogard, Bo Egardt. IEEE transactions on intelligent transportation systems (Print), 2005

338 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/f2600c56cde8ccf589eeb8ec016571057202cf52

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Xiaosong Hu
2. Scott Jason Moura
3. Fengchun Sun
4. Shaobo Xie
5. Zongke Xin
6. James Brighton
7. Duncan S. Callaway
8. J. Bagnell

In Electrical and Electronic Engineering

1. Xiaosong Hu
2. Scott Jason Moura
3. Shaobo Xie
4. Zongke Xin
5. James Brighton
6. Fengchun Sun
7. Anind K. Dey
8. Duncan S. Callaway

Citations in the corpus

• Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles (2015, methodology): “A. Control-oriented Power-split HEV Model Power-split HEVs are dominant in the current HEV market [14].”
• Pontryagin’s Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus (2019, background): “To achieve global optimization, stochastic DP has been proposed [17-18].”
• Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior (2008, background): “They will also alert drivers of unanticipated hazards well in advance [25] – even when the driver does not specify the intended destination, and better optimize vehicle energy consumption using short-term turn prediction [10].”
• Stochastic MPC With Learning for Driver-Predictive Vehicle Control and its Application to HEV Energy Management (2014, background): “For instance, [20]–[23] apply stochastic dynamic programming (SDP) to optimize fuel economy and emissions, and [24] applies linear stochastic optimal control to chassis control.”
• A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles (2011, background): “…programming (SDP) methods are particularly appealing in this context, despite their well-recognized computational complexity [20], because of their ability to optimize a power split map for a probabilistic distribution of many drive cycles, rather than a single cycle [21] [22] [23] [24] [25].”