Heavy-duty electric powertrains provide a potential solution to the high emissions and low fuel economy of trucks, buses, and other heavy-duty vehicles. However, the cost, weight, and lifespan of electric vehicle batteries limit the implementation of such vehicles. This paper proposes supplementing the battery with on-board photovoltaic modules. In this paper, a bus model is created to analyze the impact of on-board photovoltaics on electric bus range and battery lifespan. Photovoltaic systems that cover the bus roof and bus sides are considered. The bus model is simulated on a suburban bus drive cycle on a bus route in Davis, CA, USA for a representative sample of yearly weather conditions. Roof-mounted panels increased vehicle driving range by 4.7% on average annually, while roof and side modules together increased driving range by 8.9%. However, variations in weather conditions meant that this additional range was not reliably available. For constant vehicle range, rooftop photovoltaic modules extended battery cycle life by up to 10% while modules on both the roof and sides extended battery cycle life by up to 19%. Although side-mounted photovoltaics increased cycle life and range, they were less weight-and cost-effective compared to the roof-mounted panels.

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Hybrid electric vehicle (HEV) control strategies are often designed around specific driving conditions. However, when driving conditions differ from the designed conditions, HEV performance can suffer. This paper develops a novel HEV energy management strategy (EMS) that is robust to uncertain driving conditions by augmenting a stochastic dynamic programming (SDP) controller with minimax dynamic programming (MDP). This combination of MDP and SDP has not previously been studied in the literature. The stochastic element uses a Markov chain model to represent driver behavior and is used to optimize the control for expected future driver behavior. The minimax element instead optimizes against potential worst-case (maximal cost) future driver behavior. The resulting EMS can be directly implemented on a vehicle. This method is demonstrated on a series hybrid electric bus model. Robustness to uncertain driving conditions is tested by simulating on a variety of heavy-duty vehicle drive cycles that differ from the drive cycle on which the EMS was trained. A single tuning parameter is used to balance the stochastic and minimax elements of the EMS, and a parametric study shows the effects of this tuning parameter. It was found that using minimax control could increase the vehicle fuel economy on multiple uncertain driving conditions, with a trade-off of decreased fuel economy when the driving conditions match the designed conditions. That is, it offers an exchange of performance on the nominal driving conditions for performance on uncertain driving conditions.

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During the lifetime of an energy storage system, its health deteriorates from use due to irreversible internal changes to the system. This degradation results in decreased capacity and efficiency of the battery or capacitor. This chapter reviews empirical aging models for lithium-ion battery and ultracapacitor energy storage systems. It will explore how operating conditions like large currents, high temperature, or deep discharge cycles impact the health of the energy storage system. After reviewing aging models, this chapter will then show how these models can be used in vehicle energy management control systems to reduce energy storage system aging. This includes both aging-aware control and control of hybrid energy storage systems (systems that include both a battery and an ultracapacitor).