Stochastic dynamic programming (SDP) is applied to the optimal control of a hybrid electric vehicle in a concerted attempt to deploy and evaluate such a controller in the real world. Practical considerations for robust implementation of the SDP algorithm are addressed, such as the choice of discount factor used and how charge sustaining characteristics of the SDP controller can be examined and adjusted. A novel cost function is used incorporating the square of battery charge (C-rate) as an indicator of electrical powertrain stress, with the aim of lessening the affliction of real-world concerns such as battery health and motor temperature, while allowing short spells of operation toward the system peak power limits where advantageous. This paper presents the simulation and chassis dynamometer results over the LA92 drive cycle, as well as the results of testing on open roads. The hybrid system is operated at several levels of aggressivity, allowing the tradeoff between fuel savings and electrical powertrain stress to be evaluated. In dynamometer testing, this approach yielded a 13% reduction in electrical powertrain stress without sacrificing any fuel savings, compared with a controller that does not consider aggressivity in its optimization.

Driver training schemes and eco-driving techniques can reduce fuel consumption by 10%, but their effectiveness depends on the willingness of drivers to change their behavior, and changes may be short lived. Onboard driver assistance systems have been proposed, which encourage driving style improvement. Such systems, when fitted in commercial vehicles, can assume some authority since uneconomical driving styles can be reported to a fleet manager. A driver assistance system has been developed and tried in the field with commercial vehicle drivers. The system aims to reduce fuel consumption by encouraging two behaviors: reduced rates of acceleration, and early upshifting through the gears. Visual feedback is reinforced with audible warnings when the driver makes uneconomical power demands of the engine. Field trials of the system were undertaken in the U.K. using 15 light commercial vehicles, driven by their professional drivers from a range of commercial applications. The trials consisted of two-week baseline data collection, which drivers were not aware of, followed by two weeks of data collection with the system being active. During the trials a total of 39 300 km of trip data were collected, which demonstrated fuel savings of up to 12% and average fuel savings of 7.6%.

Life cycle assessment (LCA) quantifies the whole‐life environmental impacts of products and is essential for helping policymakers and manufacturers transition toward sustainable practices. However, typical LCA estimates future recycling benefits as if it happens today. For long‐lived products such as lithium‐ion batteries, this may be misleading since there is a considerable time gap between production and recycling. To explore this temporal mismatch problem, we apply future electricity scenarios from an integrated assessment model—IMAGE—using “premise” in Brightway2 to conduct a prospective LCA (pLCA) on the global warming potential of six battery chemistries and four recycling routes. We find that by 2050, electricity decarbonization under an RCP2.6 scenario mitigates production impacts by 57%, so to reach zero‐carbon batteries it is important to decarbonize upstream heat, fuels, and direct emissions. For the best battery recycling case, data for 2020 gives a net recycling benefit of −22 kg CO ₂ e kWh ⁻¹ which reduces the net impact of production and recycling from 71 to 49 kg CO ₂ e kWh ⁻¹ . However, for recycling in 2040 with decarbonized electricity, net recycling benefits would be nearly 75% lower (−6 kg CO ₂ e kWh ⁻¹ ), giving a net impact of 65 kg CO ₂ e kWh ⁻¹ . This is because materials recycled in the future substitute lower‐impact processes due to expected electricity decarbonization. Hence, more focus should be placed on mitigating production impacts today instead of relying on future recycling. These findings demonstrate the importance of pLCA in tackling problems such as temporal mismatch that are difficult to capture in typical LCA.

Passenger car carbon footprints are highly sensitive to future energy systems, a factor often overlooked in life cycle assessment. We use a time-dependent prospective life cycle assessment to enhance carbon footprints under four 1.5–3.0 °C decarbonisation pathways for electricity, fuel, and hydrogen from an energy-based integrated assessment model. Across 5000 comparative cases, battery electric vehicles consistently have the lowest carbon footprints compared to hybrid, plug-in hybrid, and fuel-cell vehicles. For example, battery electric vehicles show an average 32 to 47% lower footprint than hybrid combustion in 3.0 °C and 1.5 °C climate-compatible futures, respectively. This is driven by greater projected decarbonisation of electricity compared to fossil-dominated fuels and hydrogen. Battery electric vehicles meaningfully retain their advantage for mileages over 100,000 km, even in regions with carbon-intensive electricity since these are anticipated to decarbonise the most. Although our study supports battery electric vehicles as the most reliable climate-mitigation option for passenger cars, reducing their high manufacturing footprint remains important.

Hybrid electric vehicles offer the potential for fuel consumption improvements when compared with conventional vehicle powertrains. The fuel consumption benefits which can be realised when utilising the hybrid electric vehicle architecture are dependent on how much braking energy is regenerated, and how well the regenerated energy is utilised. A number of power management strategies have been proposed in literature. Owing to the prospect of real-time implementation, many of these proposals have centred on the use of heuristics. Despite the research advances made, the key challenge with heuristic strategies remains achieving reasonable fuel savings without over-depleting the battery’s state of charge at the end of the trip. In view of this challenge, this paper offers two main contributions to existing energy management literature. The first is a novel, simple but effective heuristic control strategy which employs a tuneable parameter (the percentage of the maximum motor tractive power) to decide the control sequence, such that impressive fuel savings are achieved without over-depleting the final state of charge of the battery (the battery energy). The second is the quantitative exploration of braking patterns and its impact on kinetic energy regeneration. The potential of the proposed heuristic control strategy was explored over a range of driving cycles which reflect different driving scenarios. The results from this analysis show that fuel savings of as much as 19.07% can be achieved over the Japan 10–15 driving cycle. In comparison with a suboptimal controller whose control signals were derived from dynamic programming optimal control, our proposed strategy was found to be outperforming, in that it achieved impressive real-time fuel savings without much penalty to the final state of charge of the battery. Gentle braking patterns were also found to significantly improve brake energy regeneration by the electric motor.