In order to mitigate the power density shortage of current energy storage systems (ESSs) in pure electric vehicles (PEVs or EVs), a hybrid ESS (HESS), which consists of a battery and a supercapacitor, is considered in this research. Due to the use of the two ESSs, an energy management should be carried out for the HESS. An optimal energy management strategy is proposed based on the Pontryagin's minimum principle in this research, which instantaneously distributes the required propulsion power to the two ESSs during the vehicle's propulsion and also instantaneously allocates the regenerative braking energy to the two ESSs during the vehicle's braking. The objective of the proposed energy management strategy is to minimize the electricity usage of the EV and meanwhile to maximize the battery lifetime. A simulation study is conducted for the proposed energy management strategy and also for a rule-based energy management strategy. The simulation results show that the proposed strategy saves electricity compared to the rule-based strategy and the single ESS case for the three typical driving cycles studied in this research. Meantime, the proposed strategy has the effect of prolonging the battery lifetime compared to the other two cases.

An Energy Management Strategy of Hybrid Energy Storage Systems for Electric Vehicle Applications

The key challenge in energy management of plug-in hybrid electric vehicles is how to macroscopically plan the battery energy considering trip information while microscopically distributing the torque between power sources to improve the energy efficiency of the hybrid propulsion system. Nowadays, future partial trip information can be obtained from an intelligent transportation system and a navigation system when traveling starts. But how to use these trip information sufficiently remains to be further studied. Moreover, the online energy management strategy should not take up too many resources of micro-controller. Based on these conditions, we proposed a novel energy management strategy for plug-in hybrid electric city buses. First, Pontryagin’s minimum principle was used to obtain the optimal results of different driving cycles. Then, a neural network module was designed and trained to learn the mechanisms of optimal state-of-charge (SOC) curves. When trip starts, the neural network module can be used to generate a reasonable battery SOC reference curve according to the partial trip information available. Finally, an adaptive fuzzy logic controller was applied to follow the trend of the SOC reference curve. The main contribution of this paper is that the effectiveness of battery energy planning by comprehensively considering the future partial trip information and historical optimal SOC curves is verified, which provides a new perspective for real vehicle energy management. Simulation results show 4.61%–13.49% fuel savings on the trained and untrained driving cycles as compared with the charge-depleting and charge-sustaining strategy.

Adaptive Fuzzy Logic Energy Management Strategy Based on Reasonable SOC Reference Curve for Online Control of Plug-in Hybrid Electric City Bus

Energy management systems (EMSs), implemented in the electronic control unit (ECU) of an actual vehicle with electrified powertrain, are a much simpler version of the theoretically developed EMS. Such simplification is done to accommodate the EMS within the given memory constraint and computational capacity of the ECU. The simplification should ensure reasonable performance compared to theoretical EMS under real-life driving scenarios. The process of simplification must be effective to create a versatile and utilitarian EMS. Hence, it is comprised of rigorous analysis of results obtained from theoretical EMS under various driving scenarios. This review paper broadly categorizes most of the reported utilitarian EMSs into three major categories and discusses the processes of simplification associated with each category. The utilitarian EMSs are classified based on their dependence on either online computation or offline pre-computation or even both for spewing control decisions. The paper delineates the chronological steps of a utilitarian EMS development, starting from theoretical background, process of simplification, validation through model-in-the-loop, software-in-the-loop, hardware-in-the-loop simulation, dynamometer test, and on-road performance validation. Future trends of on-board EMSs are also discussed before the conclusion.

Energy Management Systems for Electrified Powertrains: State-of-the-Art Review and Future Trends

The aim of this paper is to document 15 years of hybrid electric vehicle energy management research at The Ohio State University Center for Automotive Research (OSUCAR). Hybrid Electric Vehicle (HEV) technology encompasses many diverse aspects. In this paper we focus exclusively on the evolution of supervisory control strategies for on-board energy management in HEV. We present a series of control algorithms that have been developed in simulation and implemented in prototype vehicles for charge-sustaining HEVs at OSU-CAR. These solutions span from fuzzy-logic control algorithms to more sophisticated model-based optimal control methods. Finally, methods developed for plug-in HEVs energy management are also discussed

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Energy Management of Hybrid Electric Vehicles: 15 years of development at the Ohio State University

A length ratio based neural network energy management strategy for online control of plug-in hybrid electric city bus

This paper covers the design and implementation of a rule-based energy management strategy for a medium duty hybrid truck. In this paper, a procedure for the design of a near-optimal energy management strategy is presented. The procedure utilises the dynamic programming (DP) algorithm to find the optimal control strategy that minimises the fuel consumption over a given driving mission. Through the analysis of the behaviour of DP control actions, near-optimal rules are extracted and tuned to design a rule-based strategy for charge sustaining operation which, unlike DP control signals, is implementable on-board of the vehicle. Drivability metrics such as frequent clutching and engine on/off behaviour are also included in the control design based on the implementation of the DP under different drivability scenarios.

The performance of the proposed energy management control strategy is studied by using a proposed longitudinal vehicle model of a pre-transmission parallel medium duty hybrid truck with a clutch. The proposed near-optimal rule-based strategy, benchmarked against the optimal DP solution, shows performance within 3% of the global optimal one.

Riccardo Biasini

Papers

A near–optimal rule–based energy management strategy for medium duty hybrid truck