Hierarchical Model Predictive Control for Optimization of Vehicle Speed and Battery Thermal Using Vehicle Connectivity

Piao, Xinyu; Wang, Xiangfei; Han, Kyoungseok

doi:10.1109/access.2021.3120406

Cited by 10 publications

(3 citation statements)

References 33 publications

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“…Consequently, the operational range of the refrigerant system is constrained within the prediction horizon of the MPC. Piao et al [19] proposed a hierarchical MPC approach for an EV battery TMS. The algorithm uses the rate of heat transfer by the refrigerant system as the sole control variable while neglecting the refrigerant dynamics.…”

Section: B Literature Reviewmentioning

confidence: 99%

Integrated Thermal Management of Electric Vehicles Based on Model Predictive Control With Approximated Value Function

Chen,

Kwak,

Kim

et al. 2024

IEEE Access

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The thermal management system (TMS) in electric vehicles (EVs), including climate control and battery thermal regulation, consumes more energy than any other auxiliary components. Therefore, optimizing TMS control is crucial for enhancing EV driving range. However, the complexity of the TMS, described by a differential algebraic system, poses challenges for real-time optimal control. This study proposes model predictive control (MPC)-based solutions for integrated TMS operation in EVs. An optimal thermal management problem is formulated using economic nonlinear MPC (NMPC), and its performance is evaluated. To reduce computational load, an approximated value function (VF) is introduced based on the economic NMPC results. A linear-time-varying MPC (LTV-MPC) with the approximated VF is proposed for real-time implementation using quadratic programming, and through simulations it is compared with the baseline NMPC controller and a rule-based (RB) controller. Results reveal that the LTV-MPC with an approximated VF performs similarly to NMPC while offering slightly compromised cooling performance. It also significantly reduces the computational time by a factor of 10 4 compared with NMPC owing to the short prediction horizon enabled by the approximated VF. Furthermore, when compared with the RB controller, the proposed LTV-MPC achieves energy savings in the range of 22.3% to 29.8%.

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Section: B Literature Reviewmentioning

confidence: 99%

Integrated Thermal Management of Electric Vehicles Based on Model Predictive Control With Approximated Value Function

Chen,

Kwak,

Kim

et al. 2024

IEEE Access

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“…Predictive thermal management techniques recently started gaining attention as an alternative to state-of-the-art reactive controllers in the domain of electrified vehicles [24], [25]. They exploit forecasts of future operating conditions (e.g.…”

Section: A Literature Review On Thermal Management Of Fcevsmentioning

confidence: 99%

Dynamic Programming for Thermal Management of Automotive Fuel Cell Systems: Investigating Hydrogen Saving Potential

2023

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Thermal management strategies implemented on-board fuel cell electrified vehicles (FCEVs) are currently based on heuristic reactive approaches. In this framework, developing predictive thermal management approaches that anticipate the travel needs of FCEV users could lead to improved hydrogen savings. However, the theoretical hydrogen saving achievable by predictive thermal management needs assessment first to quantify the technical and economical viability of the technology proposal. This paper lays the foundations in this domain by analyzing the a priori optimal thermal management of a fuel cell system in an FCEV. Initially, an electrochemical and thermal modeling technique for the fuel cell system is described. A reactive rule-based approach is then selected as the baseline controller for the coolant rate and the instantaneous radiator fan state of the FCEV. Then, the optimal control problem formulation for the thermal management of fuel cell systems in FCEVs is discussed and solved using a dynamic programming (DP) based optimization approach that makes use of a priori information about the entire driving mission. The fuel cell system is evaluated while the FCEV performs one to ten repetitions of the Worldwide Harmonized Light Vehicle Test Cycle (WLTC) at different ambient temperatures ranging from −20 • C to 40 • C. When compared to the baseline reactive control technique, the offline optimal benchmark can save up to 10.2% hydrogen. Results presented in this paper demonstrate the potential of hydrogen saving achievable by improving the thermal management of automotive fuel cell systems. Moreover, they may be used to develop and benchmark real-time capable predictive thermal management strategies for fuel cell systems in FCEVs.INDEX TERMS Dynamic programming, electrified vehicle, fuel cell system, hydrogen economy, optimal control, thermal management.Open Access funding provided by 'Politecnico di Torino' within the CRUI CARE Agreement

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“…Among them, many automakers emphasized the improvement of energy efficiency of EVs, and combining the connected-and-automated vehicle (CAV) technologies and electrified powertrain has been recently highlighted [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Speed Planner for Connected and Automated Electric Vehicles on Sloped Roads

2022

Self Cite

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This paper proposes an energy-efficient speed planning strategy for a connected and automated vehicle (CAV) considering the upcoming traffic and road gradient information, which can be provided by the vehicle-to-everything communication systems. Unlike human drivers, CAV that receives long and short sighted traffic and road geometry information can optimize their speed profile to increase energy efficiency, depending on the powertrain types. In particular, the developed speed planner reducing the battery output power through the energy-efficiency improvement systems in electrified vehicles. Consequently, the CAV that is aware of the existence of the upcoming road gradient increases the speed on the uphill, and decreases the speed on the downhill to minimize the battery output power, which is different from the natural behaviors of human-driven vehicles on sloped roads. To consider the constraints, the model predictive control-based speed planner is developed, and its effectiveness is verified under various driving conditions. Simulation results show that our approach significantly outperforms the alternative speed profiles in terms of battery energy-saving, achieving about 27.21% of the energy efficiency improvement.INDEX TERMS Connected and automated vehicle, model predictive control, electric vehicle, energyefficiency improvement, energy-efficient driving.

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Hierarchical Model Predictive Control for Optimization of Vehicle Speed and Battery Thermal Using Vehicle Connectivity

Cited by 10 publications

References 33 publications

Integrated Thermal Management of Electric Vehicles Based on Model Predictive Control With Approximated Value Function

Integrated Thermal Management of Electric Vehicles Based on Model Predictive Control With Approximated Value Function

Dynamic Programming for Thermal Management of Automotive Fuel Cell Systems: Investigating Hydrogen Saving Potential

Energy-Efficient Speed Planner for Connected and Automated Electric Vehicles on Sloped Roads

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