Estimation Techniques for State of Charge in Battery Management Systems on Board of Hybrid Electric Vehicles Implemented in a Real-Time MATLAB/SIMULINK Environment

Dynamic programming is a very powerful algorithmic paradigm which solves a problem by identifying subproblems and tackling them one by one. First the smallest are solved, and then using their answers, it can be figured out larger ones, until the whole lot of them is solved. This paper presents a control strategy for hybrid electric vehicles, based on the dynamic programming, applied in MATLAB, Simulink environment, using ADVISOR. It was tried this method due to the calculation speed of the suitable torque and speed required from the engine, considering the driver power request (torque and speed), and the state of charge (SOC) of the batteries. Using the fuel converter (FC) fuel map, and the remaining SOC of the battery pack, it was designed an algorithm that will chose at each time the required torque and speed from the first and second source of power.

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Robust proportional ECMS control of a parallel hybrid electric vehicle

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407016659198 ◽

2016 ◽

Vol 231 (1) ◽

pp. 99-119 ◽

Cited By ~ 7

Author(s):

Wisdom Enang ◽

Chris Bannister

Keyword(s):

Real Time ◽

Energy Management ◽

Electric Vehicles ◽

Hybrid Electric Vehicles ◽

Driving Cycle ◽

State Of Charge ◽

Fuel Savings ◽

Equivalence Factor ◽

Battery State Of Charge ◽

Hybrid Electric

Improved fuel efficiency in hybrid electric vehicles requires a delicate balance between the internal combustion engine usage and battery energy, using a carefully designed energy management control algorithm. Numerous energy management strategies for hybrid electric vehicles have been proposed in literature, with many of these centered on the equivalent consumption minimisation strategy (ECMS) owing to its potential for online implementation. The key challenge with the equivalent consumption minimisation strategy lies in estimating or adapting the equivalence factor in real-time so that reasonable fuel savings are achieved without over-depleting the battery state of charge at the end of the defined driving cycle. To address the challenge, this paper proposes a novel state of charge feedback ECMS controller which simultaneously optimises and selects the adaption factors (proportional controller gain and initial equivalence factor) as single parameters which can be applied in real time, over any driving cycle. Unlike other existing state of charge feedback methods, this approach solves a conflicting multiple-objective optimisation control problem, thus ensuring that the obtained adaptation factors are optimised for robustness, charge sustenance and fuel reduction. The potential of the proposed approach was thoroughly explored over a number of legislative and real-world driving cycles with varying vehicle power requirements. The results showed that, whilst achieving fuel savings in the range of 8.40 −19.68% depending on the cycle, final battery state of charge can be optimally controlled to within ±5% of the target battery state of charge.

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An intelligent predictive controller for power and battery management in plug-in hybrid electric vehicles

Journal of Energy Resources Technology ◽

10.1115/1.4050577 ◽

2021 ◽

pp. 1-18

Author(s):

Mojtaba Hassanzadeh ◽

Zahra Rahmani

Keyword(s):

Real Time ◽

Electric Vehicles ◽

Hybrid Electric Vehicles ◽

Fuzzy Inference ◽

Battery Management ◽

State Variables ◽

Energy Management Strategy ◽

Inference System ◽

The Real ◽

Hybrid Electric

Abstract This paper presents a novel real-time energy management strategy (EMS) for plug-in hybrid electric vehicles (PHEVs), which combines the adaptive neuro-fuzzy inference system (ANFIS) and the model predictive control (MPC). A two-objective EMS with two state variables is defined by integrating the battery aging and fuel economy in the objective function. First, the dynamic programming (DP) approach is applied offline to obtain the globally optimal solutions. Then a real-time predictive EMS is proposed, in which DP carries out a moving-horizon optimization. Contrary to the charge-sustaining HEVs, the optimal trajectory of the battery state-of-charge (SOC) in PHEVs does not fluctuate around a constant level. Thus, determining the desired value of SOC for the real-time moving-horizon optimization is a challenging issue. Unlike the EMSs with a pre-determined reference for SOC, a trained ANFIS model constructs the real-time sub-optimal SOC trajectory in advance. Finally, the effectiveness of the proposed approach is shown through simulation. The proposed EMS is examined over multiple real-time driving cycles, and the results indicate that the total cost is increased compared to those unaware of battery aging. The real-time EMS is then compared to different approaches. While suboptimal, the proposed EMS is real-time implementable, and the results are found to be close enough to those of optimal controller, compared to the two other tested approaches.

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