Energy Management Strategies in Electric and Hybrid Vehicles

Energy management strategies in electric and hybrid vehicles encompass a suite of control algorithms designed to optimise the distribution of power between energy storage devices, prime movers and auxiliary loads. Approaches range from rule-based heuristics that switch between electric and combustion propulsion, to optimisation-driven methods that seek global minima of fuel consumption or battery degradation. Predictive strategies use future driving information to adjust control actions in real time, while learning-based methods adapt to driving patterns without a priori models. Advances in power-electronics integration and machine-learning-enabled controllers have yielded significant improvements in driving range, component longevity and operational robustness, with global trials demonstrating reductions in energy costs and greenhouse-gas emissions across diverse urban and highway conditions.

Research from Nature Portfolio

Recent studies have introduced real-time predictive energy management frameworks that employ digital-twin models and short-term traffic forecasting to optimise battery usage and thermal management under variable driving conditions, achieving up to 8 % improvement in overall efficiency. Another investigation has demonstrated an integrated management system for hybrid battery–fuel-cell drivetrains using neural-network-based estimators to balance hydrogen consumption against stack degradation, extending fuel-cell lifetime by over 20 % without sacrificing vehicle performance. Emerging work also explores decentralised control architectures that coordinate multiple energy sources in modular electric platforms, enabling scalable designs for heavy-duty applications.

Energy Management Strategies in Electric and Hybrid Vehicles publication trend

The graph below shows the total number of articles in energy management strategies in electric and hybrid vehicles across all publications each year (not limited to Nature Index journals).

Technical terms

Energy Management Strategy (EMS): algorithmic framework to allocate power between storage and propulsion systems to optimise fuel efficiency, emissions and component life.

State of Charge (SOC): measure of a battery’s remaining capacity, expressed as a percentage of its full charge.

Dynamic Programming (DP): global optimisation method that derives control policies by discretising time and state variables for minimum-cost trajectories.

Reinforcement Learning (RL): machine-learning approach where control policies are iteratively improved through reward-based interaction with the driving environment.

Equivalent Consumption Minimisation Strategy (ECMS): online optimisation technique that balances electrical energy and fuel consumption by converting electrical energy use into an equivalent fuel cost.

References

1. Q-learning based control for energy management of series-parallel hybrid vehicles with balanced fuel consumption and battery life. Energy and AI (2023).
2. Fuel Cell Electric Vehicles—A Brief Review of Current Topologies and Energy Management Strategies. Energies (2021).
3. Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook. Energies (2020).