Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook

Abstract

Hybrid Electric Vehicles (HEVs) have been proven to be a promising solution to environmental pollution and fuel savings. The benefit of the solution is generally realized as the amount of fuel consumption saved, which by itself represents a challenge to develop the right energy management strategies (EMSs) for HEVs. Moreover, meeting the design requirements are essential for optimal power distribution at the price of conflicting objectives. To this end, a significant number of EMSs have been proposed in the literature, which require a categorization method to better classify the design and control contributions, with an emphasis on fuel economy, providing power demand, and real-time applicability. The presented review targets two main headlines: (a) offline EMSs wherein global optimization-based EMSs and rule-based EMSs are presented; and (b) online EMSs, under which instantaneous optimization-based EMSs, predictive EMSs, and learning-based EMSs are put forward. Numerous methods are introduced, given the main focus on the presented scheme, and the basic principle of each approach is elaborated and compared along with its advantages and disadvantages in all aspects. In this sequel, a comprehensive literature review is provided. Finally, research gaps requiring more attention are identified and future important trends are discussed from different perspectives. The main contributions of this work are twofold. Firstly, state-of-the-art methods are introduced under a unified framework for the first time, with an extensive overview of existing EMSs for HEVs. Secondly, this paper aims to guide researchers and scholars to better choose the right EMS method to fill in the gaps for the development of future-generation HEVs.

Computationally Efficient Energy Management in Hybrid Electric Vehicles Based on Approximate Pontryagin’s Minimum Principle

“…It is well known that the EMSs are generally realized as rule-based EMS and optimization-based EMSs. The rule-based EMSs can be implemented by predefining a set of logical rules according to HEV system characteristics and operation mode [3]. Thus, the rule-based EMSs are widely employed in the commercial application of vehicle controllers.…”

“…The engine output torque is formulated as Equation (2). The engine maximum torque curve is fitted as a function of speed, shown in Equation (3), and the fitting coefficients are obtained by the MATLAB ′ s curve-fitting tool.…”

“…where k is the time instant, x(k) is the state variable at time instant k, SOC(k) is the battery state of charge at current instant, g(k) is the gear number at time instant k, g(k) ∈ [1,2,3,4,5], u(k) is the control variable, r(k) is defined as the ratio of the motor torque to the total torque demand, d(k) is the gearshift command, and d(k) ∈ [−1, 0, 1] is defined as follows.…”

Analytical Solution to Equivalent Consumption Minimization Strategy for Series Hybrid Electric Vehicles

“…The methodology provides globally optimal solution to energy management problem by finding optimal trajectories through backward induction, but suffers from high computational complexity and the requirement that the entire drive cycles should be precisely known in advance. Due to its non-causal nature, DP cannot be implemented in real-time as it is impossible to know the exact future driving cycle conditions [6]. Hence, DP is usually regarded as an offline optimization method whose results can be used as benchmark to assess performance of other methods.…”

“…Stochastic dynamic programming (SDP) has been employed to tackle the prior knowledge requirement of DP for real-time implementation by estimating future power demands using probabilistic framework [7], [8]. SDP achieves near-optimal results but has high computational burden that limits its usage in real-time [6], [9].…”