Minimizing Battery Stress during Hybrid Electric Vehicle Control Design: Real World Considerations for Model-Based Control Development

Vagg, Christopher; Brace, Chris J.; Akehurst, Sam; Ash, Lloyd · 2013 · Crossref

DOI: 10.1109/vppc.2013.6671713

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Summary

This paper addresses the challenge of minimizing battery stress and electric machine (EM) thermal load in mild hybrid electric vehicles (HEVs) without compromising fuel economy. While aggressive use of the electrical powertrain maximizes fuel savings, it accelerates battery capacity fade and risks EM overheating. The authors argue that these practical constraints are often neglected in simulation-based control design. The study proposes a modified control strategy cost function that penalizes high-power operation, thereby reducing battery State of Charge (SOC) imbalances and thermal stress, validated on a retrofit HEV system developed by Ashwoods Automotive. The methodology employs Dynamic Programming (DP) to optimize the control strategy over a known drive cycle. A bespoke vehicle model was developed in Matlab/Simulink and validated against chassis dynamometer test data, achieving high accuracy in predicting fuel consumption. The DP algorithm utilizes a state vector comprising vehicle speed, acceleration, gear, and SOC. Crucially, the instantaneous cost function was modified to include a term proportional to the square of the battery C-rate ($L = F + \alpha C^2$), where $F$ is fuel consumption and $\alpha$ is a weighting factor. This approach targets ohmic heating ($I^2R$ losses), the primary cause of thermal stress in both battery cells and the EM, without requiring detailed thermal or battery degradation models. Results demonstrate that increasing the weighting factor $\alpha$ significantly reduces the average battery C-rate from 0.35 to approximately 0.25, a 32% reduction, while sacrificing only about 10% of the potential fuel consumption benefit. The relationship between C-rate reduction and fuel penalty is non-linear, indicating diminishing returns in fuel savings at higher C-rates. The study also highlights implementation nuances of DP, noting that discretization and interpolation introduce inherent errors (typically <0.15% in the final implementation) and that control bifurcations can occur, requiring specific logic to resolve. The significance of this work lies in demonstrating that battery and motor stress can be mitigated through simple modifications to the control cost function, eliminating the need for complex, computationally expensive thermal or degradation models. This approach extends the effective life of the battery and reduces the likelihood of thermal cutbacks, making it particularly valuable for retrofit HEV applications where system robustness and simplicity are paramount. The paper also contributes to the field by detailing often-overlooked limitations and trade-offs in DP implementation, such as interpolation errors and sensitivity to model fidelity.

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