scholarly journals A High-Efficiency Bidirectional Active Balance for Electric Vehicle Battery Packs Based on Model Predictive Control

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3220 ◽  
Author(s):  
Shixin Song ◽  
Feng Xiao ◽  
Silun Peng ◽  
Chuanxue Song ◽  
Yulong Shao
Keyword(s):  
Linear Programming ◽  
Model Predictive Control ◽  
Predictive Control ◽  
High Efficiency ◽  
Experimental Result ◽  
Equilibrium Time ◽  
Equilibrium Control ◽  
Battery Packs ◽  
Electric Vehicle Battery ◽  
Study Designs

This study designs an active equilibrium control strategy based on model predictive control (MPC) for series battery packs. To shorten equalisation time and reduce unnecessary energy consumption, bidirectional active equalisation is modelled and analysed, and the model predictive control algorithm is then applied to the established state space equation. The optimisation problem that minimises the equilibrium time is transformed to a linear programming form in each cycle. By solving the linear programming problem online, a group of control optimal solutions is found and the series equalisation problem is decoupled. The equalisation time is shortened by dynamically adjusting the equalisation current. Simulation results show that the MPC algorithm can avoid unnecessary energy transfer and shorten equalisation time. The bench experimental result shows that the equilibrium time is reduced by 31%, verifying the rationality of the MPC strategy.

Module-based Active Equalization for Battery Packs: A Two-Layer Model Predictive Control Strategy

2021 ◽  
pp. 1-1
Author(s):  
Quan Ouyang ◽  
Youmin Zhang ◽  
Nourallah Ghaeminezhad ◽  
Jian Chen ◽  
Zhisheng Wang ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
Layer Model ◽  
Battery Packs ◽  
Two Layer Model

Electric Vehicle Battery Thermal and Cabin Climate Management Based on Model Predictive Control

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Yuanzhi Liu ◽  
Jie Zhang
Keyword(s):  
Energy Efficiency ◽  
Model Predictive Control ◽  
Electric Vehicle ◽  
Energy Management ◽  
Predictive Control ◽  
Critical Role ◽  
Peak Load ◽  
Energy Management Strategy ◽  
Battery Thermal Management System ◽  
Electric Vehicle Battery

Abstract Energy management plays a critical role in electric vehicle (EV) operations. To improve EV energy efficiency, this paper proposes an effective model predictive control (MPC)-based energy management strategy to simultaneously control the battery thermal management system (BTMS) and the cabin air conditioning (AC) system. We aim to improve the overall energy efficiency and battery cycle-life, while retaining soft constraints from both BTMS and AC systems. The MPC-based strategy is implemented by optimizing the battery operations and discharging schedules to avoid a peak load and by directly utilizing the regenerative power instead of recharging the battery. Compared with the benchmark system without any control coordination between BTMS and AC, the proposed MPC-based energy management has shown a 4.3% reduction in the recharging energy and a 6.5% improvement for the overall energy consumption. Overall, the MPC-based energy management is a promising solution to enhance the battery efficiency for EVs.

Model predictive control of a high efficiency solar thermal cooling system with thermal storage

2019 ◽  
Vol 196 ◽  
pp. 214-226 ◽  
Author(s):  
Sergio Pintaldi ◽  
Jiaming Li ◽  
Subbu Sethuvenkatraman ◽  
Stephen White ◽  
Gary Rosengarten
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
High Efficiency ◽  
Cooling System ◽  
Thermal Storage ◽  
Solar Thermal ◽  
Solar Thermal Cooling ◽  
Thermal Cooling

scholarly journals A Stochastic Model Predictive Control Approach for Joint Operational Scheduling and Hourly Reconfiguration of Distribution Systems

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1884 ◽  
Author(s):  
Saeid Esmaeili ◽  
Amjad Anvari-Moghaddam ◽  
Shahram Jadid ◽  
Josep Guerrero
Keyword(s):  
Linear Programming ◽  
Model Predictive Control ◽  
Stochastic Model ◽  
Predictive Control ◽  
Distribution System ◽  
Distribution Systems ◽  
Mixed Integer ◽  
Practical Implementation ◽  
Wholesale Market ◽  
Stochastic Model Predictive Control

Due to the recent developments in the practical implementation of remotely controlled switches (RCSs) in the smart distribution system infrastructure, distribution system operators face operational challenges in the hourly reconfigurable environment. This paper develops a stochastic Model Predictive Control (MPC) framework for operational scheduling of distribution systems with dynamic and adaptive hourly reconfiguration. The effect of coordinated integration of energy storage systems and demand response programs through hourly reconfiguration on the total costs (including cost of total loss, switching cost, cost of bilateral contract with power generation owners and responsive loads, and cost of exchanging power with the wholesale market) is investigated. A novel Switching Index (SI) based on the RCS ages and critical points in the network along with the maximum number of switching actions is introduced. Due to nonlinear nature of the problem and several existing binary variables, it is basically considered as a Mixed Integer Non-Linear Programming (MINLP) problem, which is transformed into a Mixed Integer Linear Programming (MILP) problem. The satisfactory performance of the proposed model is demonstrated through its application on a modified IEEE 33-bus distribution system.

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