A Comprehensive Review on Hybrid and Electric Vehicle Energy Control and Management Strategies

Abebe Debella, Hailu; Mekbib Atnaw, Samson; Ramayya Ancha, Venkata · 2023 · Crossref

DOI: 10.5772/intechopen.111421

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Summary

This paper provides a comprehensive review of energy control and management strategies for hybrid electric vehicles (HEVs) and electric vehicles (EVs), motivated by the need to improve fuel economy and reduce emissions amidst declining fossil fuel reserves and strict environmental regulations. The authors analyze the state-of-the-art in power management, focusing on regenerative braking, modeling approaches, and control algorithms. The review categorizes strategies into offline (non-causal) and online (causal, real-time) methods, aiming to identify research gaps and evaluate the effectiveness of various techniques in optimizing energy distribution between internal combustion engines and electric motors. The study examines three primary stages of HEV computational modeling: detailed modeling, software-in-the-loop (SIL), and hardware-in-the-loop (HIL). Within these, it details kinematic (backward), quasi-static (forward), and dynamic modeling approaches, noting that kinematic models are simple but ignore transient behaviors, while dynamic models capture engine transients but are computationally expensive. The review then analyzes power split configurations and control strategies. Offline strategies, such as linear programming, dynamic programming, genetic algorithms, and particle swarm optimization, are described as global optimization methods that require a priori knowledge of driving cycles, making them suitable for benchmarking rather than real-time application. Online strategies, which are implementable in real time, are divided into rule-based and optimization-based approaches. Rule-based strategies include deterministic methods, which use look-up tables to maintain engine efficiency, and fuzzy logic controllers, which handle nonlinear systems through linguistic variables. Variants such as adaptive and predictive fuzzy control are discussed, with the latter utilizing GPS data to anticipate driving conditions. Online optimization strategies include Pontryagin’s Minimum Principle (PMP), Equivalent Consumption Minimization Strategy (ECMS), stochastic model predictive control, and artificial neural networks. The paper also reviews regenerative braking systems, highlighting hydraulic regenerative braking and blended brake control strategies that synchronize friction and electric braking to maximize energy recovery. The findings indicate that while rule-based strategies dominate production vehicles due to their simplicity and low computational demand, they often yield sub-optimal fuel consumption compared to optimization methods. Global optimization techniques like dynamic programming provide the best performance benchmarks but are not causal. Online optimization methods like ECMS and PMP offer near-optimal real-time performance. Regarding regenerative braking, the review cites studies showing that blended brake control can improve energy efficiency by up to 11.18% and driving range by 12.58% under specific cycles, with some advanced strategies achieving up to 41.9% improvement in energy transfer efficiency. The paper concludes by identifying gaps in research, particularly in the robustness of rule-based systems, the real-time implementation of complex optimization algorithms, and the need for further evaluation of regenerative braking contributions across diverse driving conditions and vehicle types.

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