Optimized operation of hybrid battery systems for electric vehicles using deterministic and stochastic dynamic programming

Wegmann, Raphael; Döge, Volker; Becker, Jan; Sauer, Dirk Uwe

doi:10.1016/j.est.2017.09.008

Cited by 51 publications

(32 citation statements)

References 33 publications

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“…More focus on some methods of Region-II is in the sequel. In Region-III, global cost minimum is searched by a systematic step-by-step backward calculation in DDP [33], by evolutionary algorithms [36,39,46], or by linear optimization [85]. These methods are used as benchmark for evaluation and analysis of other methods.…”

Section: Integration To Power Management Methodsmentioning

confidence: 99%

“…In the stochastic problem (SDP), the future operating conditions are defined as a probability function P for the transition from current state x k to next state x x+1 [34]. This transition probability model can be described as a normal finite-state Markov model [33] or as a homogeneous one where the future states depend only on the knowledge of the current state x k and not the previous ones [34]. The SDP problem can then be formulated as:…”

Section: Dynamic Programming (Dp)mentioning

confidence: 99%

“…The optimal solution is searched sequentially by evaluating the cost function J π of the states x k backwards in terms of instantaneous cost g k (x k , u k ) and remaining cost J k+1 . The constraints to the optimization function can be considered using a penalty term φ k,F at every step k and final step F respectively [33]. The cost function in discrete form can be written as:…”

Section: Dynamic Programming (Dp)mentioning

confidence: 99%

“…However, the next state x k + 1 should not necessarily coincide with the predefined discrete states. The associated cost function J k+1 should be interpolated to the nearest discrete point [33]. In the stochastic problem (SDP), the future operating conditions are defined as a probability function P for the transition from current state x k to next state x x+1 [34].…”

Section: Dynamic Programming (Dp)mentioning

confidence: 99%

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Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

2018

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Abstract:In light of increasing alerts about limited energy sources and environment degradation, it has become essential to search for alternatives to thermal engine-based vehicles which are a major source of air pollution and fossil fuel depletion. Hybrid electric vehicles (HEVs), encompassing multiple energy sources, are a short-term solution that meets the performance requirements and contributes to fuel saving and emission reduction aims. Power management methods such as regulating efficient energy flow to the vehicle propulsion, are core technologies of HEVs. Intelligent power management methods, capable of acquiring optimal power handling, accommodating system inaccuracies, and suiting real-time applications can significantly improve the powertrain efficiency at different operating conditions. Rule-based methods are simply structured and easily implementable in real-time; however, a limited optimality in power handling decisions can be achieved. Optimization-based methods are more capable of achieving this optimality at the price of augmented computational load. In the last few years, these optimization-based methods have been under development to suit real-time application using more predictive, recognitive, and artificial intelligence tools. This paper presents a review-based discussion about these new trends in real-time optimal power management methods. More focus is given to the adaptation tools used to boost methods optimality in real-time. The contribution of this work can be identified in two points: First, to provide researchers and scholars with an overview of different power management methods. Second, to point out the state-of-the-art trends in real-time optimal methods and to highlight promising approaches for future development.

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Section: Integration To Power Management Methodsmentioning

confidence: 99%

Section: Dynamic Programming (Dp)mentioning

confidence: 99%

Section: Dynamic Programming (Dp)mentioning

confidence: 99%

Section: Dynamic Programming (Dp)mentioning

confidence: 99%

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Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

2018

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“…The real-time application of this method is limited due to high computational costs; usually it is only used to find a reference solution to analyze and adjust. The global optimization strategies can also be divided into: Linear Programming [24], Dynamic Programming [25], Genetic Algorithms [26], Optimal Controllers [27] or based on Particles Swarms Optimization (PSO) [28]. On the other hand, real-time optimization strategies apply instant power-handling decisions in order to minimize an objective function.…”

Section: Introductionmentioning

confidence: 99%

Power Management Control Strategy Based on Artificial Neural Networks for Standalone PV Applications with a Hybrid Energy Storage System

et al. 2019

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Standalone microgrids with photovoltaic (PV) solutions could be a promising solution for powering up off-grid communities. However, this type of application requires the use of energy storage systems (ESS) to manage the intermittency of PV production. The most commonly used ESSs are lithium-ion batteries (Li-ion), but this technology has a low lifespan, mostly caused by the imposed stress. To reduce the stress on Li-ion batteries and extend their lifespan, hybrid energy storage systems (HESS) began to emerge. Although the utilization of HESSs has demonstrated great potential to make up for the limitations of Li-ion batteries, a proper power management strategy is key to achieving the HESS objectives and ensuring a harmonized system operation. This paper proposes a novel power management strategy based on an artificial neural network for a standalone PV system with Li-ion batteries and super-capacitors (SC) HESS. A typical standalone PV system is used to demonstrate and validate the performance of the proposed power management strategy. To demonstrate its effectiveness, computational simulations with short and long duration were performed. The results show a minimization in Li-ion battery dynamic stress and peak current, leading to an increased lifespan of Li-ion batteries. Moreover, the proposed power management strategy increases the level of SC utilization in comparison with other well-established strategies in the literature.

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Electric Vehicle Path Towards Sustainable Transportation

Khare,

Jain,

Bhuiyan

2024

Interconnected Modern Multi‐Energy Networks and Intelligent Transportation Systems

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Optimized operation of hybrid battery systems for electric vehicles using deterministic and stochastic dynamic programming

Cited by 51 publications

References 33 publications

Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

Power Management Control Strategy Based on Artificial Neural Networks for Standalone PV Applications with a Hybrid Energy Storage System

Electric Vehicle Path Towards Sustainable Transportation

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